system

Abstract

The system according to the embodiment aims to analyze the situation during a match in real time and propose appropriate tactical changes and player substitutions. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects video of a match. The analysis unit analyzes the video collected by the collection unit to understand the situation of the match. The suggestion unit suggests tactical changes or player substitutions based on the analysis results obtained by the analysis unit. The provision unit provides the advice generated by the suggestion unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology had the problem of making it difficult to analyze the situation during a match in real time and suggest appropriate tactical changes or player substitutions.

[0005] The system according to the embodiment aims to analyze the situation during a match in real time and propose appropriate tactical changes and player substitutions. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a provision unit. The collection unit collects video of the match. The analysis unit analyzes the video collected by the collection unit to understand the situation of the match. The suggestion unit suggests tactical changes or player substitutions based on the analysis results obtained by the analysis unit. The provision unit provides the advice generated by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the situation during a match in real time and suggest appropriate tactical changes and player substitutions. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A match analysis system according to an embodiment of the present invention analyzes game footage in real time and provides advice such as tactical changes and player substitutions. The match analysis system collects game footage, analyzes it using AI, and grasps the situation of the game. For example, the match analysis system analyzes player positions and ball movement to evaluate team tactics and player performance. Next, the match analysis system generates advice such as tactical changes and player substitutions based on the analysis results. For example, if a specific player is fatigued, the system provides advice to substitute that player. The system also suggests tactical changes to counter the opposing team's tactics. Finally, the match analysis system provides the generated advice to a coach or manager. For example, the advice can be displayed in real time using a tablet or smartphone. This allows tactical changes and player substitutions to be made in real time during the game. This allows the match analysis system to make tactical changes and player substitutions in real time during the game. For example, in a sports game, the system can suggest tactical changes to counter the opposing team's tactics, thereby changing the flow of the game. The system can also grasp players' fatigue levels and substitute players at the appropriate time. This maximizes team performance and contributes to winning the game.

[0029] A match analysis system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects video of a match. The video of a match may include, but is not limited to, live video, recorded video, and video from a specific camera angle. The collection unit may, for example, use multiple cameras to collect an overall image of the match. The collection unit may also collect aerial video of the match using a drone. For example, the collection unit may collect bird's-eye video of the entire match. The analysis unit analyzes the video collected by the collection unit to understand the situation of the match. The analysis may be performed using, but is not limited to, image analysis, motion analysis, pattern recognition, or other methods. For example, the analysis unit may analyze player positions and ball movement to evaluate team tactics and player performance. The analysis unit may also analyze the opposing team's tactics and propose tactical changes to counter them. The proposal unit generates advice, such as tactical changes and player substitutions, based on the analysis results obtained by the analysis unit. The suggestions may include, but are not limited to, changes in formation or playing style. For example, the suggestion unit evaluates a player's fatigue level and suggests substituting a player at an appropriate time. The suggestion unit may also generate suggestions for quickly responding to tactical changes by the opposing team. The providing unit provides the advice generated by the suggestion unit. The advice may be provided in real time using, for example, a tablet device or a smartphone, but is not limited to, for example. For example, the providing unit may provide the advice by voice, allowing a coach or manager to respond immediately. The providing unit may also store a history of the advice so that it can be reviewed later. In this way, the match analysis system according to the embodiment can analyze game footage in real time and provide advice such as tactical changes and player substitutions.

[0030] The collection unit can collect video of the entire game using multiple cameras. The collection unit collects video of the entire game using, for example, fixed cameras, mobile cameras, drone cameras, etc. For example, the collection unit can use fixed cameras to collect video covering the entire field. The collection unit can also use mobile cameras to collect video capturing player movements in detail. The collection unit can also collect aerial video of the game using drone cameras. For example, the collection unit can use a drone to collect bird's-eye video of the entire game. In this way, by using multiple cameras, the overall picture of the game can be accurately grasped.

[0031] The analysis unit can analyze the positions of players and the movement of the ball to evaluate team tactics or player performance. The analysis unit, for example, identifies the positions of players using image analysis technology. For example, the analysis unit identifies the positions of players using image analysis technology and tracks the movements of players. The analysis unit can also analyze the movement of the ball using tracking technology. For example, the analysis unit can analyze the movement of the ball using tracking technology to evaluate the speed and trajectory of the ball. The analysis unit can also analyze the movements of players to evaluate player performance. For example, the analysis unit analyzes the movements of players to evaluate player performance. In this way, the analysis of the positions of players and the movement of the ball can evaluate team tactics and player performance.

[0032] The suggestion unit can evaluate a player's fatigue level and suggest a player substitution based on the player's fatigue level. The suggestion unit, for example, evaluates a player's heart rate and determines the player's fatigue level. For example, the suggestion unit monitors a player's heart rate in real time and evaluates the player's fatigue level. The suggestion unit can also evaluate a player's body temperature and determine the player's fatigue level. For example, the suggestion unit monitors a player's body temperature in real time and evaluates the player's fatigue level. The suggestion unit can also analyze a player's movement and determine the player's fatigue level. For example, the suggestion unit analyzes a player's movement and evaluates the player's fatigue level. In this way, by evaluating a player's fatigue level, it is possible to suggest a player substitution at an appropriate time.

[0033] The suggestion unit can analyze the specific tactics of the opposing team and propose tactical changes to counter them. The suggestion unit can, for example, analyze the playing patterns of the opposing team and propose tactical changes to counter them. For example, the suggestion unit can analyze the playing patterns of the opposing team and propose a change in formation. The suggestion unit can also analyze the movements of players on the opposing team and propose tactical changes to counter them. For example, the suggestion unit can analyze the movements of players on the opposing team and propose a change in playing style. The suggestion unit can also analyze the tactics of the opposing team and propose tactical changes to counter them. For example, the suggestion unit can analyze the tactics of the opposing team and propose strengthening marking of a specific player. In this way, by analyzing the tactics of the opposing team, it is possible to propose tactical changes to counter them.

[0034] The providing unit can display advice instantly using a tablet device or a smartphone. The providing unit displays the advice using, for example, a tablet device. For example, the providing unit displays the advice on a tablet device in real time, allowing a coach or manager to respond immediately. The providing unit can also display the advice using a smartphone. For example, the providing unit displays the advice on a smartphone in real time, allowing a coach or manager to respond immediately. The providing unit can also provide the advice by voice. For example, the providing unit provides the advice by voice, allowing a coach or manager to obtain information without using their hands. This makes it possible to display advice in real time by using a tablet device or a smartphone.

[0035] The collection unit can automatically detect specific moments in a game and collect those moments in high resolution. The collection unit, for example, automatically detects goal scenes and scoring scenes and collects those moments in high resolution. For example, the collection unit automatically detects goal scenes and collects them in high resolution. The collection unit can also detect important plays by players (for example, sliding tackles and dunk shots) and collect those moments in high resolution. For example, the collection unit automatically detects the moment of a sliding tackle and collects them in high resolution. The collection unit can also detect the moment of an important tactical change that changes the flow of the game and collects those moments in high resolution. For example, the collection unit automatically detects the moment of a tactical change and collects them in high resolution. In this way, by collecting important moments in a game in high resolution, detailed footage can be provided.

[0036] The collection unit can automatically adjust camera settings according to weather or lighting conditions. For example, the collection unit detects weather using a weather sensor and automatically adjusts camera settings. For example, the collection unit uses a waterproof camera when it is raining and automatically adjusts settings to prevent lens fogging. The collection unit can also detect lighting conditions using a lighting sensor and automatically adjust camera settings. For example, the collection unit automatically adjusts settings by increasing the camera's sensitivity at night or in dark environments to enable brighter images. The collection unit can also automatically adjust camera exposure under strong sunlight to prevent excessive brightness. For example, the collection unit adjusts the camera's exposure under strong sunlight to enable images to be captured at an appropriate brightness. In this way, optimal video can be collected by adjusting camera settings according to weather and lighting conditions.

[0037] The collection unit can dynamically change the zoom and focus of the camera according to the progress of the match. For example, the collection unit analyzes the progress of the match in real time and dynamically changes the zoom and focus of the camera. For example, when the attack and defense in front of the goal becomes intense, the collection unit automatically zooms in the camera to provide detailed footage. Furthermore, when the flow of the match speeds up, the collection unit can automatically adjust the focus of the camera to clearly capture the movements of the players. Furthermore, the collection unit can automatically zoom in the camera to coincide with important moments of the match (e.g., free kicks and corner kicks) to provide detailed footage. For example, the collection unit automatically zooms in the camera at the moment of a free kick to provide detailed footage. In this way, detailed footage can be provided by dynamically changing the zoom and focus of the camera according to the progress of the match.

[0038] The collection unit can use a drone to collect aerial footage of a game. For example, the collection unit uses a drone of a specific brand or model to collect aerial footage of a game. For example, the collection unit uses a drone to collect bird's-eye footage of the entire game. The collection unit can also use a drone to collect aerial footage of specific players or plays. For example, the collection unit uses a drone to collect footage capturing player movements from above. The collection unit can also dynamically change the camera angle depending on the progress of the game to collect realistic footage. For example, the collection unit uses a drone to change the camera angle depending on the progress of the game to collect realistic footage. In this way, by using a drone, it is possible to collect a bird's-eye view of the entire game.

[0039] The collection unit can collect vital data of players and synchronize it with the video. For example, the collection unit collects the heart rates of players in real time and displays them in synchronization with the video. For example, the collection unit monitors the heart rates of players in real time and displays them in synchronization with the video. The collection unit can also collect body temperatures of players in real time and display them in synchronization with the video. For example, the collection unit monitors the body temperatures of players in real time and displays them in synchronization with the video. The collection unit can also collect vital data of players and dynamically display them according to the progress of the game. For example, the collection unit collects vital data of players in real time and dynamically displays them according to the progress of the game. In this way, by collecting vital data of players and synchronizing it with the video, the condition of players can be grasped in real time.

[0040] The collection unit can collect spectator reactions and evaluate the excitement of a match. The collection unit, for example, collects the volume of spectator cheers and applause and evaluates the excitement of a match. For example, the collection unit collects the volume of spectator cheers and applause using a microphone and evaluates the excitement of a match. The collection unit can also capture spectator facial expressions with a camera and evaluate the excitement of a match. For example, the collection unit can capture spectator facial expressions with a camera and evaluate the excitement of a match using facial expression analysis technology. The collection unit can also detect spectator movements with a sensor and evaluate the excitement of a match. For example, the collection unit can detect spectator movements with a sensor and evaluate the excitement of a match. In this way, the excitement of a match can be evaluated by collecting spectator reactions.

[0041] The analysis unit can analyze a player's movement patterns and identify areas for improvement in performance. The analysis unit can, for example, analyze a player's running distance or the number of sprints and identify areas for improvement in performance. For example, the analysis unit can analyze a player's running distance and identify areas for improvement in performance. The analysis unit can also analyze a player's pass success rate and shot accuracy and identify areas for improvement in performance. For example, the analysis unit can analyze a player's pass success rate and identify areas for improvement in performance. The analysis unit can also analyze a player's positioning and movement patterns and identify areas for improvement in performance. For example, the analysis unit can analyze a player's positioning and identify areas for improvement in performance. In this way, by analyzing a player's movement patterns, it is possible to identify areas for improvement in performance.

[0042] The analysis unit can analyze the rotation and speed of the ball and evaluate the quality of the play. The analysis unit, for example, analyzes the rotation speed of the ball and evaluates the quality of a shot or a pass. For example, the analysis unit analyzes the rotation speed of the ball and evaluates the quality of a shot. The analysis unit can also analyze the speed of the ball and evaluate the quality of a shot or a pass. For example, the analysis unit analyzes the speed of the ball and evaluates the quality of a pass. The analysis unit can also analyze the trajectory of the ball and evaluate the quality of the play. For example, the analysis unit analyzes the trajectory of the ball and evaluates the quality of the play. In this way, the quality of the play can be evaluated by analyzing the rotation and speed of the ball.

[0043] The analysis unit can predict the flow of the game and present the possibility of the next play. The analysis unit predicts the possibility of the next play based on, for example, past game data. For example, the analysis unit analyzes past game data and predicts the possibility of the next play. The analysis unit can also predict the possibility of the next play based on the current game situation. For example, the analysis unit analyzes the current game situation and predicts the possibility of the next play. The analysis unit can also analyze the opposing team's tactics and predict the possibility of the next play. For example, the analysis unit analyzes the opposing team's tactics and predict the possibility of the next play. In this way, the flow of the game can be predicted and the possibility of the next play can be presented.

[0044] The analysis unit can evaluate the current performance by comparing it with the player's past match data. The analysis unit, for example, evaluates the current performance based on the player's past match data. For example, the analysis unit analyzes the player's past match data and evaluates the current performance. The analysis unit can also compare the player's past performance with the current performance to identify areas for improvement. For example, the analysis unit analyzes the player's past performance and evaluates the current performance. The analysis unit can also analyze the player's past match data and evaluate the current performance. For example, the analysis unit evaluates the player's past match data and evaluates the current performance. In this way, the current performance can be evaluated by comparing it with the player's past match data.

[0045] The analysis unit can analyze the movements of players on the opposing team and identify weak points. The analysis unit, for example, analyzes the movement patterns of players on the opposing team and identifies weak points. For example, the analysis unit analyzes the movement patterns of players on the opposing team and identifies weak points. The analysis unit can also analyze performance data of players on the opposing team and identify weak points. For example, the analysis unit analyzes performance data of players on the opposing team and identifies weak points. The analysis unit can also analyze the positioning of players on the opposing team and identify weak points. For example, the analysis unit analyzes the positioning of players on the opposing team and identifies weak points. In this way, weak points can be identified by analyzing the movements of players on the opposing team.

[0046] The analysis unit can analyze statistical data of a game in real time. The analysis unit, for example, analyzes score data during a game in real time to evaluate team performance. For example, the analysis unit analyzes score data during a game in real time to evaluate team performance. The analysis unit can also analyze assist data during a game in real time to evaluate player performance. For example, the analysis unit analyzes assist data during a game in real time to evaluate player performance. The analysis unit can also analyze foul data during a game in real time to evaluate the flow of the game. For example, the analysis unit analyzes foul data during a game in real time to evaluate the flow of the game. In this way, the flow of the game and team performance can be evaluated by analyzing statistical data of a game in real time.

[0047] The suggestion unit can evaluate a player's fatigue level in real time and optimize the timing of substitutions. The suggestion unit, for example, evaluates a player's heart rate and body temperature in real time to determine the player's fatigue level. For example, the suggestion unit monitors a player's heart rate in real time to evaluate the player's fatigue level. The suggestion unit can also evaluate a player's running distance and number of sprints in real time to determine the player's fatigue level. For example, the suggestion unit monitors a player's running distance in real time to evaluate the player's fatigue level. The suggestion unit can also evaluate a player's performance data in real time to determine the player's fatigue level. For example, the suggestion unit monitors a player's performance data in real time to evaluate the player's fatigue level. In this way, the timing of substitutions can be optimized by evaluating a player's fatigue level in real time.

[0048] The suggestion unit can generate a suggestion for immediately responding to a tactical change by the opposing team. For example, the suggestion unit detects a tactical change by the opposing team in real time and generates a suggestion for countering the change. For example, the suggestion unit detects a tactical change by the opposing team in real time and proposes a change in formation. The suggestion unit can also detect a player substitution by the opposing team in real time and generate a suggestion for countering the change. For example, the suggestion unit detects a player substitution by the opposing team in real time and proposes a change in playing style. The suggestion unit can also detect a formation change by the opposing team in real time and generate a suggestion for countering the change. For example, the suggestion unit detects a formation change by the opposing team in real time and proposes to strengthen marking of a specific player. In this way, by generating a suggestion for immediately responding to a tactical change by the opposing team, it is possible to quickly respond to tactical changes during a match.

[0049] The suggestion unit can present multiple tactical options depending on the situation of the game. For example, the suggestion unit presents an offensive tactical option depending on the progress of the game. For example, the suggestion unit analyzes the progress of the game and presents an offensive tactical option. The suggestion unit can also present a defensive tactical option depending on the progress of the game. For example, the suggestion unit analyzes the progress of the game and presents a defensive tactical option. The suggestion unit can also present a balanced tactical option depending on the progress of the game. For example, the suggestion unit analyzes the progress of the game and presents a balanced tactical option. This allows for more flexible tactical changes by presenting multiple tactical options depending on the situation of the game.

[0050] The suggestion unit can evaluate the psychological state of the player and suggest mental support. For example, if the player is nervous, the suggestion unit suggests mental support to help the player relax. For example, if the player is nervous, the suggestion unit suggests mental support to help the player relax. Furthermore, if the player is depressed, the suggestion unit can also suggest mental support to help the player increase their motivation. For example, if the player is depressed, the suggestion unit suggests mental support to help the player increase their motivation. Furthermore, if the player is excited, the suggestion unit can also suggest mental support to help the player stay calm. For example, if the player is excited, the suggestion unit suggests mental support to help the player stay calm. In this way, by evaluating the psychological state of the player, appropriate mental support can be suggested.

[0051] The suggestion unit can suggest the timing of a tactical change in accordance with the progress of the match. The suggestion unit can suggest the timing of a tactical change, for example, when the flow of the match changes. For example, the suggestion unit can suggest the timing of a tactical change when the flow of the match changes. The suggestion unit can also suggest the timing of a tactical change in accordance with a change in the tactical of the opposing team. For example, the suggestion unit can suggest the timing of a tactical change in accordance with a change in the tactical of the opposing team. The suggestion unit can also suggest the timing of a tactical change in accordance with the fatigue level of the players. For example, the suggestion unit can suggest the timing of a tactical change in accordance with the fatigue level of the players. In this way, by suggesting the timing of a tactical change in accordance with the progress of the match, more effective tactical changes can be made.

[0052] The suggestion unit can improve the accuracy of the suggestion by referring to the past decisions of the coach or manager. The suggestion unit, for example, improves the accuracy of the suggestion based on data of past tactical changes made by the coach or manager. For example, the suggestion unit improves the accuracy of the suggestion based on data of past tactical changes made by the coach or manager. The suggestion unit can also improve the accuracy of the suggestion based on data of past player substitutions made by the coach or manager. For example, the suggestion unit improves the accuracy of the suggestion based on data of past player substitutions made by the coach or manager. The suggestion unit can also improve the accuracy of the suggestion based on data of past matches made by the coach or manager. For example, the suggestion unit improves the accuracy of the suggestion based on data of past matches made by the coach or manager. In this way, the accuracy of the suggestion can be improved by referring to the past decisions of the coach or manager.

[0053] The providing unit can provide advice by voice, allowing the coach or manager to respond immediately. The providing unit, for example, can provide advice by voice in real time, allowing the coach or manager to respond immediately. For example, the providing unit can provide advice by voice in real time, allowing the coach or manager to respond immediately. The providing unit can also provide advice by voice, allowing the coach or manager to obtain information without using their hands. For example, the providing unit can provide advice by voice, allowing the coach or manager to obtain information without using their hands. The providing unit can also provide advice by voice, making it easy to hear even in a noisy environment during a game. For example, the providing unit can provide advice by voice, making it easy to hear even in a noisy environment during a game. In this way, by providing advice by voice, the coach or manager can respond immediately.

[0054] The providing unit can save the history of advice so that it can be reviewed later. The providing unit, for example, automatically saves the history of advice so that it can be reviewed later. For example, the providing unit automatically saves the history of advice so that it can be reviewed later. The providing unit can also save the history of advice so that it can be used for post-match analysis. For example, the providing unit can save the history of advice so that it can be used for post-match analysis. The providing unit can also save the history of advice so that it can be used to find areas for improvement for the next match. For example, the providing unit can save the history of advice so that it can be used to find areas for improvement for the next match. In this way, saving the history of advice makes it possible to look back on it later.

[0055] The providing unit can provide the content of advice in multiple languages. The providing unit, for example, provides the content of advice in multiple languages, such as English, Japanese, and Spanish. For example, the providing unit provides the content of advice in multiple languages, such as English, Japanese, and Spanish. The providing unit can also automatically switch the content of advice based on a user's language setting. For example, the providing unit automatically switches the content of advice based on a user's language setting. The providing unit can also provide the content of advice in multiple languages ​​so that it can be used in international matches. For example, the providing unit can provide the content of advice in multiple languages ​​so that it can be used in international matches. In this way, by providing the content of advice in multiple languages, it can be used in international matches.

[0056] The providing unit can visualize the advice and display it on the tactics board. For example, the providing unit visualizes the advice and displays it on the tactics board to make it visually easier to understand. For example, the providing unit visualizes the advice and displays it on the tactics board to make it visually easier to understand. The providing unit can also visualize the advice and clarify the tactics by showing the positions and movements of the players. For example, the providing unit visualizes the advice and clarifies the tactics by showing the positions and movements of the players. The providing unit can also visualize the advice and display it on the tactics board in real time to enable a quick response. For example, the providing unit visualizes the advice and displays it on the tactics board in real time to enable a quick response. In this way, the advice can be visualized and displayed on the tactics board to make it visually easier to understand.

[0057] The providing unit can share advice in real time, and share information with the entire team. The providing unit, for example, shares advice in real time, and provides information not only to coaches and managers but also to players. For example, the providing unit can share advice in real time, and provide information not only to coaches and managers but also to players. The providing unit can also share advice in real time, and unify tactics with the entire team. For example, the providing unit can share advice in real time, and unify tactics with the entire team. The providing unit can also share advice in real time, and facilitate communication during a match. For example, the providing unit can share advice in real time, and facilitate communication during a match. In this way, by sharing advice in real time, information is shared with the entire team, and tactics are unified.

[0058] The providing unit can evaluate the effectiveness of the advice and suggest areas for improvement for the next match. The providing unit, for example, evaluates the effectiveness of the advice after a match and suggests areas for improvement for the next match. For example, the providing unit evaluates the effectiveness of the advice after a match and suggests areas for improvement for the next match. The providing unit can also evaluate the effectiveness of the advice in real time and suggest areas for improvement during a match. For example, the providing unit can evaluate the effectiveness of the advice in real time and suggest areas for improvement during a match. The providing unit can also evaluate the effectiveness of the advice and use it to improve the team's performance in the long term. For example, the providing unit can evaluate the effectiveness of the advice and use it to improve the team's performance in the long term. In this way, by evaluating the effectiveness of the advice, it is possible to suggest areas for improvement for the next match.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The match analysis system can further include a spectator reaction collection unit that collects spectator reactions. The spectator reaction collection unit collects the volume of spectator cheers and applause to evaluate the excitement of the match. For example, the spectator reaction collection unit collects the volume of spectator cheers and applause using a microphone to evaluate the excitement of the match. It can also capture spectator facial expressions with a camera to evaluate the excitement of the match. Furthermore, it can detect spectator movements with a sensor to evaluate the excitement of the match. In this way, by collecting spectator reactions, it is possible to evaluate the excitement of the match and grasp the atmosphere of the match.

[0061] The match analysis system can further include a vital data synchronization unit that collects players' vital data and synchronizes it with the video. The vital data synchronization unit collects players' heart rates and body temperatures in real time and displays them in synchronization with the video. For example, the player's heart rate can be monitored in real time and displayed in synchronization with the video. The player's body temperature can also be collected in real time and displayed in synchronization with the video. Furthermore, the player's vital data can be collected and dynamically displayed according to the progress of the match. In this way, by collecting players' vital data and synchronizing it with the video, the player's condition can be grasped in real time.

[0062] The match analysis system can further include a moment detection unit that automatically detects specific moments in a match and collects those moments in high resolution. The moment detection unit automatically detects goal scenes and scoring scenes and collects those moments in high resolution. For example, it can automatically detect goal scenes and collect them in high resolution. It can also detect important player plays (for example, sliding tackles and dunk shots) and collect those moments in high resolution. It can also detect moments of important tactical changes that change the flow of the match and collect those moments in high resolution. In this way, by collecting important moments in a match in high resolution, it is possible to provide detailed footage.

[0063] The match analysis system may further include an environmental adaptation unit that automatically adjusts camera settings according to weather and lighting conditions. The environmental adaptation unit detects weather using a weather sensor and automatically adjusts camera settings. For example, in rainy weather, a waterproof camera is used and settings are automatically adjusted to prevent lens fogging. It may also detect lighting conditions using a lighting sensor and automatically adjust camera settings. For example, at night or in dark environments, the camera's sensitivity may be increased to automatically adjust settings to capture brighter images. Furthermore, under strong sunlight, the camera's exposure may be adjusted automatically to prevent excessive brightness. This allows optimal video to be collected by adjusting camera settings according to weather and lighting conditions.

[0064] The match analysis system can further include a dynamic camera adjustment unit that dynamically changes the zoom and focus of the camera according to the progress of the match. The dynamic camera adjustment unit analyzes the progress of the match in real time and dynamically changes the zoom and focus of the camera. For example, as the attack and defense in front of the goal becomes more intense, the camera automatically zooms in to provide more detailed images. Also, as the pace of the match quickens, the camera can automatically adjust its focus to clearly capture the movements of the players. Furthermore, the camera can automatically zoom in to provide more detailed images at important moments in the match (e.g., free kicks and corner kicks). This makes it possible to provide more detailed images by dynamically changing the zoom and focus of the camera according to the progress of the match.

[0065] The match analysis system may further include a past comparison unit that evaluates current performance by comparing with a player's past match data. The past comparison unit evaluates current performance based on the player's past match data. For example, it may analyze a player's past match data to evaluate current performance. It may also compare a player's past performance with their current performance to identify areas for improvement. It may also analyze a player's past match data to evaluate their current performance. This allows current performance to be evaluated by comparing with the player's past match data.

[0066] The match analysis system may further include an opponent analysis unit that analyzes the movements of players on the opposing team and identifies their weak points. The opponent analysis unit analyzes the movement patterns of players on the opposing team and identifies their weak points. For example, it analyzes the movement patterns of players on the opposing team and identifies their weak points. It may also analyze performance data of players on the opposing team and identify their weak points. It may also analyze the positioning of players on the opposing team and identify their weak points. In this way, it is possible to identify weak points by analyzing the movements of players on the opposing team.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects game footage. Game footage can include live footage, recorded footage, and specific camera angles. The collection unit can use multiple cameras or drones to collect overviews of the game or aerial footage. Step 2: The analysis unit analyzes the footage collected by the collection unit to understand the situation of the game. Analysis is carried out using methods such as image analysis, motion analysis, and pattern recognition to analyze player positions and ball movement, and evaluate team tactics and player performance. It can also analyze the opposing team's tactics and propose tactical changes to counter them. Step 3: The suggestion unit generates advice such as tactical changes and player substitutions based on the analysis results obtained by the analysis unit. Suggestions include changes in formation, changes in playing style, substitution suggestions based on player fatigue assessments, and suggestions for responding quickly to tactical changes by the opposing team. Step 4: The advice provider provides the advice generated by the suggestion provider. The advice provider provides the advice in real time using a tablet or smartphone, and can also provide the advice by voice. The advice history can also be saved so that it can be reviewed later.

[0069] (Example 2) A match analysis system according to an embodiment of the present invention analyzes game footage in real time and provides advice such as tactical changes and player substitutions. The match analysis system collects game footage, analyzes it using AI, and grasps the situation of the game. For example, the match analysis system analyzes player positions and ball movement to evaluate team tactics and player performance. Next, the match analysis system generates advice such as tactical changes and player substitutions based on the analysis results. For example, if a specific player is fatigued, the system provides advice to substitute that player. The system also suggests tactical changes to counter the opposing team's tactics. Finally, the match analysis system provides the generated advice to a coach or manager. For example, the advice can be displayed in real time using a tablet or smartphone. This allows tactical changes and player substitutions to be made in real time during the game. This allows the match analysis system to make tactical changes and player substitutions in real time during the game. For example, in a sports game, the system can suggest tactical changes to counter the opposing team's tactics, thereby changing the flow of the game. The system can also grasp players' fatigue levels and substitute players at the appropriate time. This maximizes team performance and contributes to winning the game.

[0070] A match analysis system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects video of a match. The video of a match may include, but is not limited to, live video, recorded video, and video from a specific camera angle. The collection unit may, for example, use multiple cameras to collect an overall image of the match. The collection unit may also collect aerial video of the match using a drone. For example, the collection unit may collect bird's-eye video of the entire match. The analysis unit analyzes the video collected by the collection unit to understand the situation of the match. The analysis may be performed using, but is not limited to, image analysis, motion analysis, pattern recognition, or other methods. For example, the analysis unit may analyze player positions and ball movement to evaluate team tactics and player performance. The analysis unit may also analyze the opposing team's tactics and propose tactical changes to counter them. The proposal unit generates advice, such as tactical changes and player substitutions, based on the analysis results obtained by the analysis unit. The suggestions may include, but are not limited to, changes in formation or playing style. For example, the suggestion unit evaluates a player's fatigue level and suggests substituting a player at an appropriate time. The suggestion unit may also generate suggestions for quickly responding to tactical changes by the opposing team. The providing unit provides the advice generated by the suggestion unit. The advice may be provided in real time using, for example, a tablet device or a smartphone, but is not limited to, for example. For example, the providing unit may provide the advice by voice, allowing a coach or manager to respond immediately. The providing unit may also store a history of the advice so that it can be reviewed later. In this way, the match analysis system according to the embodiment can analyze game footage in real time and provide advice such as tactical changes and player substitutions.

[0071] The collection unit can collect video of the entire game using multiple cameras. The collection unit collects video of the entire game using, for example, fixed cameras, mobile cameras, drone cameras, etc. For example, the collection unit can use fixed cameras to collect video covering the entire field. The collection unit can also use mobile cameras to collect video capturing player movements in detail. The collection unit can also collect aerial video of the game using drone cameras. For example, the collection unit can use a drone to collect bird's-eye video of the entire game. In this way, by using multiple cameras, the overall picture of the game can be accurately grasped.

[0072] The analysis unit can analyze the positions of players and the movement of the ball to evaluate team tactics or player performance. The analysis unit, for example, identifies the positions of players using image analysis technology. For example, the analysis unit identifies the positions of players using image analysis technology and tracks the movements of players. The analysis unit can also analyze the movement of the ball using tracking technology. For example, the analysis unit can analyze the movement of the ball using tracking technology to evaluate the speed and trajectory of the ball. The analysis unit can also analyze the movements of players to evaluate player performance. For example, the analysis unit analyzes the movements of players to evaluate player performance. In this way, the analysis of the positions of players and the movement of the ball can evaluate team tactics and player performance.

[0073] The suggestion unit can evaluate a player's fatigue level and suggest a player substitution based on the player's fatigue level. The suggestion unit, for example, evaluates a player's heart rate and determines the player's fatigue level. For example, the suggestion unit monitors a player's heart rate in real time and evaluates the player's fatigue level. The suggestion unit can also evaluate a player's body temperature and determine the player's fatigue level. For example, the suggestion unit monitors a player's body temperature in real time and evaluates the player's fatigue level. The suggestion unit can also analyze a player's movement and determine the player's fatigue level. For example, the suggestion unit analyzes a player's movement and evaluates the player's fatigue level. In this way, by evaluating a player's fatigue level, it is possible to suggest a player substitution at an appropriate time.

[0074] The suggestion unit can analyze the specific tactics of the opposing team and propose tactical changes to counter them. The suggestion unit can, for example, analyze the playing patterns of the opposing team and propose tactical changes to counter them. For example, the suggestion unit can analyze the playing patterns of the opposing team and propose a change in formation. The suggestion unit can also analyze the movements of players on the opposing team and propose tactical changes to counter them. For example, the suggestion unit can analyze the movements of players on the opposing team and propose a change in playing style. The suggestion unit can also analyze the tactics of the opposing team and propose tactical changes to counter them. For example, the suggestion unit can analyze the tactics of the opposing team and propose strengthening marking of a specific player. In this way, by analyzing the tactics of the opposing team, it is possible to propose tactical changes to counter them.

[0075] The providing unit can display advice instantly using a tablet device or a smartphone. The providing unit displays the advice using, for example, a tablet device. For example, the providing unit displays the advice on a tablet device in real time, allowing a coach or manager to respond immediately. The providing unit can also display the advice using a smartphone. For example, the providing unit displays the advice on a smartphone in real time, allowing a coach or manager to respond immediately. The providing unit can also provide the advice by voice. For example, the providing unit provides the advice by voice, allowing a coach or manager to obtain information without using their hands. This makes it possible to display advice in real time by using a tablet device or a smartphone.

[0076] The collection unit can estimate the user's emotions and adjust the camera placement and shooting angle based on the user's emotions. The collection unit, for example, analyzes the user's facial expressions to estimate the user's emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using facial expression analysis technology. The collection unit can also analyze the user's voice to estimate the user's emotions. For example, the collection unit records the user's voice and estimates the emotions using voice analysis technology. The collection unit can also analyze the user's vital signs to estimate the user's emotions. For example, the collection unit monitors the user's heart rate and body temperature to estimate the emotions. This makes it possible to collect more appropriate footage by adjusting the camera placement and shooting angle based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The collection unit can automatically detect specific moments in a game and collect those moments in high resolution. The collection unit, for example, automatically detects goal scenes and scoring scenes and collects those moments in high resolution. For example, the collection unit automatically detects goal scenes and collects them in high resolution. The collection unit can also detect important plays by players (for example, sliding tackles and dunk shots) and collect those moments in high resolution. For example, the collection unit automatically detects the moment of a sliding tackle and collects them in high resolution. The collection unit can also detect the moment of an important tactical change that changes the flow of the game and collects those moments in high resolution. For example, the collection unit automatically detects the moment of a tactical change and collects them in high resolution. In this way, by collecting important moments in a game in high resolution, detailed footage can be provided.

[0078] The collection unit can automatically adjust camera settings according to weather or lighting conditions. For example, the collection unit detects weather using a weather sensor and automatically adjusts camera settings. For example, the collection unit uses a waterproof camera when it is raining and automatically adjusts settings to prevent lens fogging. The collection unit can also detect lighting conditions using a lighting sensor and automatically adjust camera settings. For example, the collection unit automatically adjusts settings by increasing the camera's sensitivity at night or in dark environments to enable brighter images. The collection unit can also automatically adjust camera exposure under strong sunlight to prevent excessive brightness. For example, the collection unit adjusts the camera's exposure under strong sunlight to enable images to be captured at an appropriate brightness. In this way, optimal video can be collected by adjusting camera settings according to weather and lighting conditions.

[0079] The collection unit can dynamically change the zoom and focus of the camera according to the progress of the match. For example, the collection unit analyzes the progress of the match in real time and dynamically changes the zoom and focus of the camera. For example, when the attack and defense in front of the goal becomes intense, the collection unit automatically zooms in the camera to provide detailed footage. Furthermore, when the flow of the match speeds up, the collection unit can automatically adjust the focus of the camera to clearly capture the movements of the players. Furthermore, the collection unit can automatically zoom in the camera to coincide with important moments of the match (e.g., free kicks and corner kicks) to provide detailed footage. For example, the collection unit automatically zooms in the camera at the moment of a free kick to provide detailed footage. In this way, detailed footage can be provided by dynamically changing the zoom and focus of the camera according to the progress of the match.

[0080] The collection unit can estimate the user's emotions and determine the priority of videos to be collected based on the estimated user emotions. The collection unit, for example, analyzes the user's facial expressions to estimate the user's emotions. For example, the collection unit captures the user's facial expressions with a camera and estimates the emotions using facial expression analysis technology. The collection unit can also analyze the user's voice to estimate the user's emotions. For example, the collection unit records the user's voice and estimates the emotions using voice analysis technology. The collection unit can also analyze the user's vital signs to estimate the user's emotions. For example, the collection unit monitors the user's heart rate and body temperature to estimate the emotions. This allows the system to provide more appropriate videos by determining the priority of videos to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The collection unit can use a drone to collect aerial footage of a game. For example, the collection unit uses a drone of a specific brand or model to collect aerial footage of a game. For example, the collection unit uses a drone to collect bird's-eye footage of the entire game. The collection unit can also use a drone to collect aerial footage of specific players or plays. For example, the collection unit uses a drone to collect footage capturing player movements from above. The collection unit can also dynamically change the camera angle depending on the progress of the game to collect realistic footage. For example, the collection unit uses a drone to change the camera angle depending on the progress of the game to collect realistic footage. In this way, by using a drone, it is possible to collect a bird's-eye view of the entire game.

[0082] The collection unit can collect vital data of players and synchronize it with the video. For example, the collection unit collects the heart rates of players in real time and displays them in synchronization with the video. For example, the collection unit monitors the heart rates of players in real time and displays them in synchronization with the video. The collection unit can also collect body temperatures of players in real time and display them in synchronization with the video. For example, the collection unit monitors the body temperatures of players in real time and displays them in synchronization with the video. The collection unit can also collect vital data of players and dynamically display them according to the progress of the game. For example, the collection unit collects vital data of players in real time and dynamically displays them according to the progress of the game. In this way, by collecting vital data of players and synchronizing it with the video, the condition of players can be grasped in real time.

[0083] The collection unit can collect spectator reactions and evaluate the excitement of a match. The collection unit, for example, collects the volume of spectator cheers and applause and evaluates the excitement of a match. For example, the collection unit collects the volume of spectator cheers and applause using a microphone and evaluates the excitement of a match. The collection unit can also capture spectator facial expressions with a camera and evaluate the excitement of a match. For example, the collection unit can capture spectator facial expressions with a camera and evaluate the excitement of a match using facial expression analysis technology. The collection unit can also detect spectator movements with a sensor and evaluate the excitement of a match. For example, the collection unit can detect spectator movements with a sensor and evaluate the excitement of a match. In this way, the excitement of a match can be evaluated by collecting spectator reactions.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, analyzes the user's facial expressions to estimate the user's emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using facial expression analysis technology. The analysis unit can also analyze the user's voice to estimate the user's emotions. For example, the analysis unit records the user's voice and estimates the emotions using voice analysis technology. The analysis unit can also analyze the user's vital signs to estimate the user's emotions. For example, the analysis unit monitors the user's heart rate and body temperature to estimate the emotions. This allows for adjusting the display method of the analysis results based on the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] The analysis unit can analyze a player's movement patterns and identify areas for improvement in performance. The analysis unit can, for example, analyze a player's running distance or the number of sprints and identify areas for improvement in performance. For example, the analysis unit can analyze a player's running distance and identify areas for improvement in performance. The analysis unit can also analyze a player's pass success rate and shot accuracy and identify areas for improvement in performance. For example, the analysis unit can analyze a player's pass success rate and identify areas for improvement in performance. The analysis unit can also analyze a player's positioning and movement patterns and identify areas for improvement in performance. For example, the analysis unit can analyze a player's positioning and identify areas for improvement in performance. In this way, by analyzing a player's movement patterns, it is possible to identify areas for improvement in performance.

[0086] The analysis unit can analyze the rotation and speed of the ball and evaluate the quality of the play. The analysis unit, for example, analyzes the rotation speed of the ball and evaluates the quality of a shot or a pass. For example, the analysis unit analyzes the rotation speed of the ball and evaluates the quality of a shot. The analysis unit can also analyze the speed of the ball and evaluate the quality of a shot or a pass. For example, the analysis unit analyzes the speed of the ball and evaluates the quality of a pass. The analysis unit can also analyze the trajectory of the ball and evaluate the quality of the play. For example, the analysis unit analyzes the trajectory of the ball and evaluates the quality of the play. In this way, the quality of the play can be evaluated by analyzing the rotation and speed of the ball.

[0087] The analysis unit can predict the flow of the game and present the possibility of the next play. The analysis unit predicts the possibility of the next play based on, for example, past game data. For example, the analysis unit analyzes past game data and predicts the possibility of the next play. The analysis unit can also predict the possibility of the next play based on the current game situation. For example, the analysis unit analyzes the current game situation and predicts the possibility of the next play. The analysis unit can also analyze the opposing team's tactics and predict the possibility of the next play. For example, the analysis unit analyzes the opposing team's tactics and predict the possibility of the next play. In this way, the flow of the game can be predicted and the possibility of the next play can be presented.

[0088] The analysis unit can estimate the user's emotions and adjust the importance of the analysis results based on the estimated user emotions. The analysis unit, for example, analyzes the user's facial expressions to estimate the user's emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using facial expression analysis technology. The analysis unit can also analyze the user's voice to estimate the user's emotions. For example, the analysis unit records the user's voice and estimates the emotions using voice analysis technology. The analysis unit can also analyze the user's vital signs to estimate the user's emotions. For example, the analysis unit monitors the user's heart rate and body temperature to estimate the emotions. This allows the analysis unit to adjust the importance of the analysis results based on the user's emotions, thereby providing more important information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The analysis unit can evaluate the current performance by comparing it with the player's past match data. The analysis unit, for example, evaluates the current performance based on the player's past match data. For example, the analysis unit analyzes the player's past match data and evaluates the current performance. The analysis unit can also compare the player's past performance with the current performance to identify areas for improvement. For example, the analysis unit analyzes the player's past performance and evaluates the current performance. The analysis unit can also analyze the player's past match data and evaluate the current performance. For example, the analysis unit evaluates the player's past match data and evaluates the current performance. In this way, the current performance can be evaluated by comparing it with the player's past match data.

[0090] The analysis unit can analyze the movements of players on the opposing team and identify weak points. The analysis unit, for example, analyzes the movement patterns of players on the opposing team and identifies weak points. For example, the analysis unit analyzes the movement patterns of players on the opposing team and identifies weak points. The analysis unit can also analyze performance data of players on the opposing team and identify weak points. For example, the analysis unit analyzes performance data of players on the opposing team and identifies weak points. The analysis unit can also analyze the positioning of players on the opposing team and identify weak points. For example, the analysis unit analyzes the positioning of players on the opposing team and identifies weak points. In this way, weak points can be identified by analyzing the movements of players on the opposing team.

[0091] The analysis unit can analyze statistical data of a game in real time. The analysis unit, for example, analyzes score data during a game in real time to evaluate team performance. For example, the analysis unit analyzes score data during a game in real time to evaluate team performance. The analysis unit can also analyze assist data during a game in real time to evaluate player performance. For example, the analysis unit analyzes assist data during a game in real time to evaluate player performance. The analysis unit can also analyze foul data during a game in real time to evaluate the flow of the game. For example, the analysis unit analyzes foul data during a game in real time to evaluate the flow of the game. In this way, the flow of the game and team performance can be evaluated by analyzing statistical data of a game in real time.

[0092] The suggestion unit can estimate the user's emotion and adjust the way in which suggestions are expressed based on the estimated user's emotion. The suggestion unit, for example, analyzes the user's facial expression to estimate the user's emotion. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using facial expression analysis technology. The suggestion unit can also analyze the user's voice to estimate the user's emotion. For example, the suggestion unit records the user's voice and estimates the emotion using voice analysis technology. The suggestion unit can also analyze the user's vital sign data to estimate the user's emotion. For example, the suggestion unit monitors the user's heart rate and body temperature to estimate the emotion. This allows the suggestion unit to adjust the way in which suggestions are expressed based on the user's emotion, thereby providing more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0093] The suggestion unit can evaluate a player's fatigue level in real time and optimize the timing of substitutions. The suggestion unit, for example, evaluates a player's heart rate and body temperature in real time to determine the player's fatigue level. For example, the suggestion unit monitors a player's heart rate in real time to evaluate the player's fatigue level. The suggestion unit can also evaluate a player's running distance and number of sprints in real time to determine the player's fatigue level. For example, the suggestion unit monitors a player's running distance in real time to evaluate the player's fatigue level. The suggestion unit can also evaluate a player's performance data in real time to determine the player's fatigue level. For example, the suggestion unit monitors a player's performance data in real time to evaluate the player's fatigue level. In this way, the timing of substitutions can be optimized by evaluating a player's fatigue level in real time.

[0094] The suggestion unit can generate a suggestion for immediately responding to a tactical change by the opposing team. For example, the suggestion unit detects a tactical change by the opposing team in real time and generates a suggestion for countering the change. For example, the suggestion unit detects a tactical change by the opposing team in real time and proposes a change in formation. The suggestion unit can also detect a player substitution by the opposing team in real time and generate a suggestion for countering the change. For example, the suggestion unit detects a player substitution by the opposing team in real time and proposes a change in playing style. The suggestion unit can also detect a formation change by the opposing team in real time and generate a suggestion for countering the change. For example, the suggestion unit detects a formation change by the opposing team in real time and proposes to strengthen marking of a specific player. In this way, by generating a suggestion for immediately responding to a tactical change by the opposing team, it is possible to quickly respond to tactical changes during a match.

[0095] The suggestion unit can present multiple tactical options depending on the situation of the game. For example, the suggestion unit presents an offensive tactical option depending on the progress of the game. For example, the suggestion unit analyzes the progress of the game and presents an offensive tactical option. The suggestion unit can also present a defensive tactical option depending on the progress of the game. For example, the suggestion unit analyzes the progress of the game and presents a defensive tactical option. The suggestion unit can also present a balanced tactical option depending on the progress of the game. For example, the suggestion unit analyzes the progress of the game and presents a balanced tactical option. This allows for more flexible tactical changes by presenting multiple tactical options depending on the situation of the game.

[0096] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. The suggestion unit, for example, analyzes the user's facial expression to estimate the user's emotions. For example, the suggestion unit captures the user's facial expression with a camera and estimates the emotion using facial expression analysis technology. The suggestion unit can also analyze the user's voice to estimate the user's emotions. For example, the suggestion unit records the user's voice and estimates the emotion using voice analysis technology. The suggestion unit can also analyze the user's vital sign data to estimate the user's emotions. For example, the suggestion unit monitors the user's heart rate and body temperature to estimate the emotion. This allows the suggestion unit to prioritize suggestions based on the user's emotions, thereby providing more important suggestions first. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The suggestion unit can evaluate the psychological state of the player and suggest mental support. For example, if the player is nervous, the suggestion unit suggests mental support to help the player relax. For example, if the player is nervous, the suggestion unit suggests mental support to help the player relax. Furthermore, if the player is depressed, the suggestion unit can also suggest mental support to help the player increase their motivation. For example, if the player is depressed, the suggestion unit suggests mental support to help the player increase their motivation. Furthermore, if the player is excited, the suggestion unit can also suggest mental support to help the player stay calm. For example, if the player is excited, the suggestion unit suggests mental support to help the player stay calm. In this way, by evaluating the psychological state of the player, appropriate mental support can be suggested.

[0098] The suggestion unit can suggest the timing of a tactical change in accordance with the progress of the match. The suggestion unit can suggest the timing of a tactical change, for example, when the flow of the match changes. For example, the suggestion unit can suggest the timing of a tactical change when the flow of the match changes. The suggestion unit can also suggest the timing of a tactical change in accordance with a change in the tactical of the opposing team. For example, the suggestion unit can suggest the timing of a tactical change in accordance with a change in the tactical of the opposing team. The suggestion unit can also suggest the timing of a tactical change in accordance with the fatigue level of the players. For example, the suggestion unit can suggest the timing of a tactical change in accordance with the fatigue level of the players. In this way, by suggesting the timing of a tactical change in accordance with the progress of the match, more effective tactical changes can be made.

[0099] The suggestion unit can improve the accuracy of the suggestion by referring to the past decisions of the coach or manager. The suggestion unit, for example, improves the accuracy of the suggestion based on data of past tactical changes made by the coach or manager. For example, the suggestion unit improves the accuracy of the suggestion based on data of past tactical changes made by the coach or manager. The suggestion unit can also improve the accuracy of the suggestion based on data of past player substitutions made by the coach or manager. For example, the suggestion unit improves the accuracy of the suggestion based on data of past player substitutions made by the coach or manager. The suggestion unit can also improve the accuracy of the suggestion based on data of past matches made by the coach or manager. For example, the suggestion unit improves the accuracy of the suggestion based on data of past matches made by the coach or manager. In this way, the accuracy of the suggestion can be improved by referring to the past decisions of the coach or manager.

[0100] The providing unit can estimate the user's emotion and adjust the display method of the advice based on the estimated user's emotion. The providing unit, for example, analyzes the user's facial expression to estimate the user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using facial expression analysis technology. The providing unit can also analyze the user's voice to estimate the user's emotion. For example, the providing unit records the user's voice and estimates the emotion using voice analysis technology. The providing unit can also analyze the user's vital sign data to estimate the user's emotion. For example, the providing unit monitors the user's heart rate and body temperature to estimate the emotion. This allows the system to adjust the display method of the advice based on the user's emotion, thereby providing more appropriate advice. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The providing unit can provide advice by voice, allowing the coach or manager to respond immediately. The providing unit, for example, can provide advice by voice in real time, allowing the coach or manager to respond immediately. For example, the providing unit can provide advice by voice in real time, allowing the coach or manager to respond immediately. The providing unit can also provide advice by voice, allowing the coach or manager to obtain information without using their hands. For example, the providing unit can provide advice by voice, allowing the coach or manager to obtain information without using their hands. The providing unit can also provide advice by voice, making it easy to hear even in a noisy environment during a game. For example, the providing unit can provide advice by voice, making it easy to hear even in a noisy environment during a game. In this way, by providing advice by voice, the coach or manager can respond immediately.

[0102] The providing unit can save the history of advice so that it can be reviewed later. The providing unit, for example, automatically saves the history of advice so that it can be reviewed later. For example, the providing unit automatically saves the history of advice so that it can be reviewed later. The providing unit can also save the history of advice so that it can be used for post-match analysis. For example, the providing unit can save the history of advice so that it can be used for post-match analysis. The providing unit can also save the history of advice so that it can be used to find areas for improvement for the next match. For example, the providing unit can save the history of advice so that it can be used to find areas for improvement for the next match. In this way, saving the history of advice makes it possible to look back on it later.

[0103] The providing unit can provide the content of advice in multiple languages. The providing unit, for example, provides the content of advice in multiple languages, such as English, Japanese, and Spanish. For example, the providing unit provides the content of advice in multiple languages, such as English, Japanese, and Spanish. The providing unit can also automatically switch the content of advice based on a user's language setting. For example, the providing unit automatically switches the content of advice based on a user's language setting. The providing unit can also provide the content of advice in multiple languages ​​so that it can be used in international matches. For example, the providing unit can provide the content of advice in multiple languages ​​so that it can be used in international matches. In this way, by providing the content of advice in multiple languages, it can be used in international matches.

[0104] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. The providing unit, for example, analyzes the user's facial expression to estimate the user's emotions. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using facial expression analysis technology. The providing unit can also analyze the user's voice to estimate the user's emotions. For example, the providing unit records the user's voice and estimates the emotion using voice analysis technology. The providing unit can also analyze the user's vital sign data to estimate the user's emotions. For example, the providing unit monitors the user's heart rate and body temperature to estimate the emotion. This allows the priority of advice to be determined based on the user's emotions, thereby enabling more important advice to be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] The providing unit can visualize the advice and display it on the tactics board. For example, the providing unit visualizes the advice and displays it on the tactics board to make it visually easier to understand. For example, the providing unit visualizes the advice and displays it on the tactics board to make it visually easier to understand. The providing unit can also visualize the advice and clarify the tactics by showing the positions and movements of the players. For example, the providing unit visualizes the advice and clarifies the tactics by showing the positions and movements of the players. The providing unit can also visualize the advice and display it on the tactics board in real time to enable a quick response. For example, the providing unit visualizes the advice and displays it on the tactics board in real time to enable a quick response. In this way, the advice can be visualized and displayed on the tactics board to make it visually easier to understand.

[0106] The providing unit can share advice in real time, and share information with the entire team. The providing unit, for example, shares advice in real time, and provides information not only to coaches and managers but also to players. For example, the providing unit can share advice in real time, and provide information not only to coaches and managers but also to players. The providing unit can also share advice in real time, and unify tactics with the entire team. For example, the providing unit can share advice in real time, and unify tactics with the entire team. The providing unit can also share advice in real time, and facilitate communication during a match. For example, the providing unit can share advice in real time, and facilitate communication during a match. In this way, by sharing advice in real time, information is shared with the entire team, and tactics are unified.

[0107] The providing unit can evaluate the effectiveness of the advice and suggest areas for improvement for the next match. The providing unit, for example, evaluates the effectiveness of the advice after a match and suggests areas for improvement for the next match. For example, the providing unit evaluates the effectiveness of the advice after a match and suggests areas for improvement for the next match. The providing unit can also evaluate the effectiveness of the advice in real time and suggest areas for improvement during a match. For example, the providing unit can evaluate the effectiveness of the advice in real time and suggest areas for improvement during a match. The providing unit can also evaluate the effectiveness of the advice and use it to improve the team's performance in the long term. For example, the providing unit can evaluate the effectiveness of the advice and use it to improve the team's performance in the long term. In this way, by evaluating the effectiveness of the advice, it is possible to suggest areas for improvement for the next match. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects video of the game using the camera 42 of the smart device 14 or a drone. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected video and understands the situation of the game. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates advice such as tactical changes and player substitutions based on the analysis results. The provision unit, realized, for example, by the control unit 46A of the smart device 14, provides the generated advice in real time using a tablet terminal or smartphone. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects game footage using the camera 42 of the smart glasses 214 or a drone. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected footage and grasps the game situation. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates advice such as tactical changes and player substitutions based on the analysis results. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides the generated advice in real time using a tablet terminal or smartphone. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects video of the game using the camera 42 of the headset-type terminal 314 or a drone. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected video to understand the situation of the game. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates advice such as tactical changes and player substitutions based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides the generated advice in real time using a tablet terminal or smartphone. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects video of the game using the camera 42 of the robot 414 or a drone. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected video to understand the situation of the game. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates advice such as tactical changes and player substitutions based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated advice in real time using a tablet terminal or smartphone.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The match analysis system can further include a psychological evaluation unit that evaluates the psychological state of a player. The psychological evaluation unit analyzes the player's facial expressions, voice, and vital data to evaluate the player's psychological state. For example, if the player is nervous, the psychological evaluation unit can suggest mental support to help him relax. Also, if the player is depressed, the psychological evaluation unit can suggest mental support to increase his motivation. Furthermore, if the player is excited, the psychological evaluation unit can suggest mental support to help him stay calm. In this way, by evaluating the player's psychological state and providing appropriate mental support, it is possible to maximize the player's performance.

[0110] The match analysis system can further include a spectator reaction collection unit that collects spectator reactions. The spectator reaction collection unit collects the volume of spectator cheers and applause to evaluate the excitement of the match. For example, the spectator reaction collection unit collects the volume of spectator cheers and applause using a microphone to evaluate the excitement of the match. It can also capture spectator facial expressions with a camera to evaluate the excitement of the match. Furthermore, it can detect spectator movements with a sensor to evaluate the excitement of the match. In this way, by collecting spectator reactions, it is possible to evaluate the excitement of the match and grasp the atmosphere of the match.

[0111] The match analysis system can further include a vital data synchronization unit that collects players' vital data and synchronizes it with the video. The vital data synchronization unit collects players' heart rates and body temperatures in real time and displays them in synchronization with the video. For example, the player's heart rate can be monitored in real time and displayed in synchronization with the video. The player's body temperature can also be collected in real time and displayed in synchronization with the video. Furthermore, the player's vital data can be collected and dynamically displayed according to the progress of the match. In this way, by collecting players' vital data and synchronizing it with the video, the player's condition can be grasped in real time.

[0112] The match analysis system can further include a moment detection unit that automatically detects specific moments in a match and collects those moments in high resolution. The moment detection unit automatically detects goal scenes and scoring scenes and collects those moments in high resolution. For example, it can automatically detect goal scenes and collect them in high resolution. It can also detect important player plays (for example, sliding tackles and dunk shots) and collect those moments in high resolution. It can also detect moments of important tactical changes that change the flow of the match and collect those moments in high resolution. In this way, by collecting important moments in a match in high resolution, it is possible to provide detailed footage.

[0113] The match analysis system may further include an environmental adaptation unit that automatically adjusts camera settings according to weather and lighting conditions. The environmental adaptation unit detects weather using a weather sensor and automatically adjusts camera settings. For example, in rainy weather, a waterproof camera is used and settings are automatically adjusted to prevent lens fogging. It may also detect lighting conditions using a lighting sensor and automatically adjust camera settings. For example, at night or in dark environments, the camera's sensitivity may be increased to automatically adjust settings to capture brighter images. Furthermore, under strong sunlight, the camera's exposure may be adjusted automatically to prevent excessive brightness. This allows optimal video to be collected by adjusting camera settings according to weather and lighting conditions.

[0114] The match analysis system can further include an emotion adaptation unit that estimates the user's emotions and adjusts the camera placement and shooting angle based on the user's emotions. The emotion adaptation unit analyzes the user's facial expressions to estimate the user's emotions. For example, the user's facial expressions are captured with a camera and emotion is estimated using facial expression analysis technology. The user's voice can also be analyzed to estimate the user's emotions. Furthermore, the user's vital signs data can be analyzed to estimate the user's emotions. This makes it possible to collect more appropriate footage by adjusting the camera placement and shooting angle based on the user's emotions.

[0115] The match analysis system can further include a dynamic camera adjustment unit that dynamically changes the zoom and focus of the camera according to the progress of the match. The dynamic camera adjustment unit analyzes the progress of the match in real time and dynamically changes the zoom and focus of the camera. For example, as the attack and defense in front of the goal becomes more intense, the camera automatically zooms in to provide more detailed images. Also, as the pace of the match quickens, the camera can automatically adjust its focus to clearly capture the movements of the players. Furthermore, the camera can automatically zoom in to provide more detailed images at important moments in the match (e.g., free kicks and corner kicks). This makes it possible to provide more detailed images by dynamically changing the zoom and focus of the camera according to the progress of the match.

[0116] The match analysis system can further include an emotion prioritization unit that estimates the user's emotion and determines the priority of the video to be collected based on the estimated user emotion. The emotion prioritization unit analyzes the user's facial expression to estimate the user's emotion. For example, the user's facial expression can be captured with a camera and the emotion can be estimated using facial expression analysis technology. The user's voice can also be analyzed to estimate the user's emotion. Furthermore, the user's vital data can be analyzed to estimate the user's emotion. This allows the priority of the video to be collected based on the user's emotion, making it possible to provide more appropriate video.

[0117] The match analysis system may further include a past comparison unit that evaluates current performance by comparing with a player's past match data. The past comparison unit evaluates current performance based on the player's past match data. For example, it may analyze a player's past match data to evaluate current performance. It may also compare a player's past performance with their current performance to identify areas for improvement. It may also analyze a player's past match data to evaluate their current performance. This allows current performance to be evaluated by comparing with the player's past match data.

[0118] The match analysis system may further include an opponent analysis unit that analyzes the movements of players on the opposing team and identifies their weak points. The opponent analysis unit analyzes the movement patterns of players on the opposing team and identifies their weak points. For example, it analyzes the movement patterns of players on the opposing team and identifies their weak points. It may also analyze performance data of players on the opposing team and identify their weak points. It may also analyze the positioning of players on the opposing team and identify their weak points. In this way, it is possible to identify weak points by analyzing the movements of players on the opposing team.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The collection unit collects game footage. Game footage can include live footage, recorded footage, and specific camera angles. The collection unit can use multiple cameras or drones to collect overviews of the game or aerial footage. Step 2: The analysis unit analyzes the footage collected by the collection unit to understand the situation of the game. Analysis is carried out using methods such as image analysis, motion analysis, and pattern recognition to analyze player positions and ball movement, and evaluate team tactics and player performance. It can also analyze the opposing team's tactics and propose tactical changes to counter them. Step 3: The suggestion unit generates advice such as tactical changes and player substitutions based on the analysis results obtained by the analysis unit. Suggestions include changes in formation, changes in playing style, substitution suggestions based on player fatigue assessments, and suggestions for responding quickly to tactical changes by the opposing team. Step 4: The advice provider provides the advice generated by the suggestion provider. The advice provider provides the advice in real time using a tablet or smartphone, and can also provide the advice by voice. The advice history can also be saved so that it can be reviewed later.

[0121] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a 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.

[0159] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0165] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0166] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0167] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0168] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0169] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0170] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0171] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0172] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0176] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0177] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0178] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0182] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0183] 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.

[0184] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0185] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0186] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0187] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0189] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0190] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0191] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0192] [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects match footage; an analysis unit that analyzes the video collected by the collection unit and grasps the situation of the game; a suggestion unit that suggests tactical changes or player substitutions based on the analysis results obtained by the analysis unit; a providing unit that provides the advice generated by the suggesting unit. A system characterized by:

2. The collecting unit Use multiple cameras to capture footage of the entire match 2. The system of claim 1.

3. The analysis unit Analyze player positions and ball movement to evaluate team tactics or player performance 2. The system of claim 1.

4. The proposal unit Evaluate player fatigue levels and suggest substitutions based on player fatigue levels 2. The system of claim 1.

5. The proposal unit Analyze the opposing team's specific tactics and suggest tactical changes to counter them 2. The system of claim 1.

6. The providing unit Get instant advice using your tablet or smartphone 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the camera position and shooting angle based on those emotions.

2. The system of claim 1.

8. The collecting unit Automatically detects specific moments in a match and captures them in high resolution 2. The system of claim 1.

9. The collecting unit Automatically adjust camera settings according to weather or lighting conditions 2. The system of claim 1.

Images