System

Abstract

An advantage of some aspects of the invention is to provide a system that clarifies a factor of a play in a sport and creates a new choice.SOLUTION: A system includes a data collection part, an analysis part, and an option creation part. The data collection unit collects play data. The analysis unit analyzes the play data collected by the data collection unit. The option creating section creates a new option based on a result of the analysis by the analyzing section.SELECTED DRAWING: Figure 1

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] With conventional technology, the outcome of a sporting event is often attributed to luck, making it difficult to identify the specific factors that contribute to the outcome.

[0005] The system according to the embodiment aims to clarify the factors behind sports plays and create new options. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and an option creation unit. The data collection unit collects play data. The analysis unit analyzes the play data collected by the data collection unit. The option creation unit creates new options based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can clarify the factors behind sports plays and create new options. [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 sports analysis system according to an embodiment of the present invention is a system that elucidates the factors behind plays in sports such as soccer and basketball, and uses a generative AI to create new options. This allows the sports analysis system to elucidate the factors behind plays and obtain new knowledge.

[0029] A sports analysis system according to an embodiment includes a data collection unit, an analysis unit, and an option creation unit. The data collection unit collects play data. For example, it collects data such as player position information, ball movement, and pass success rates between players during a game. The data collection unit can also collect environmental data (weather, spectator cheers, etc.) during a game. For example, it collects weather data such as temperature, humidity, and wind speed in real time. The analysis unit analyzes the play data collected by the data collection unit. For example, it identifies player positioning, pass timing, and the opposing team's defensive positioning. The analysis unit can also estimate a player's emotional state and analyze the impact of emotional fluctuations on play. For example, it captures a player's facial expressions and movements with a camera and uses a generation AI to estimate the player's emotional state. The option creation unit creates new options based on the results of the analysis by the analysis unit. For example, it can suggest different positioning and pass timing. The option creation unit can also use the generation AI to suggest new tactics and play options that take a player's emotional state into account. For example, it can adopt an offensive tactic when a player is relaxed. As a result, the sports analysis system according to the embodiment can contribute to the development of sports by analyzing play data and creating new options. For example, the analysis results and new options can be provided as a report, which coaches and players can use as a reference to develop strategies for the next game.

[0030] The data collection unit can collect player position information, ball movement, and the success rate of passes between players. For example, the data collection unit collects player position information during a match using GPS data or a tracking system. For example, it tracks player movement in real time and records position information. The data collection unit also collects ball movement data such as speed, direction, and rotation. For example, it captures the ball's movement with a camera and analyzes the movement using image analysis technology. In addition, the data collection unit sets the conditions for determining a pass as successful and a calculation period in order to calculate the success rate of passes between players. For example, a pass that is not intercepted by the opposing team and is passed to a teammate is considered successful, and the success rate is calculated. In this way, by collecting data such as player position information and ball movement, the factors behind a play can be analyzed in detail.

[0031] The analysis unit can identify player positioning, pass timing, and the opposing team's defensive positioning. For example, the analysis unit identifies player positioning based on positioning data and tactical positioning. For example, it analyzes player positioning and proposes optimal positioning. The analysis unit also identifies pass timing based on evaluation criteria for pass origins and timing. For example, it analyzes pass timing and proposes optimal timing. The analysis unit also identifies the opposing team's defensive positioning based on the position of the defensive line and the positioning of zone defense. For example, it analyzes the opposing team's defensive positioning and proposes optimal attacking tactics. This allows more effective tactics to be developed by identifying the factors behind plays.

[0032] The option creation unit can propose different positioning and pass timing. For example, the option creation unit proposes different positioning based on positioning changes and tactical placement suggestions. For example, changing the positioning of players can improve the success rate of attacks. The option creation unit also proposes different pass timing based on changes to the pass origin and timing. For example, changing the timing of a pass can improve the success rate of a pass. In this way, proposing different positioning and pass timing can improve the success rate of plays.

[0033] The data collection unit can collect biometric data of players in real time using drones and wearable devices. For example, the data collection unit uses a drone to photograph players' movements during a game from the air and collect location information and movement data in real time. For example, it analyzes the player's running distance and speed. The data collection unit also monitors the heart rate and muscle movement of players wearing wearable devices in real time and integrates this data with play data for analysis. For example, it analyzes the player's fatigue level and performance fluctuations. The data collection unit also combines drones and wearable devices to simultaneously collect player movement and biometric data, and integrates this data with play data for analysis. For example, it analyzes the relationship between player movement and heart rate. In this way, collecting players' biometric data in real time improves the accuracy of play analysis.

[0034] The data collection unit collects environmental data during a match and can analyze the impact of these factors on play. For example, the data collection unit collects weather data (temperature, humidity, wind speed, etc.) during a match in real time and integrates it with play data for analysis. For example, it analyzes the impact of temperature fluctuations on player performance. The data collection unit also uses audio analysis technology to collect cheers and boos from spectators and integrates it with play data for analysis. For example, it analyzes the impact of spectator cheers on players' motivation. The data collection unit also monitors the lighting and acoustic environment of the match venue with sensors and integrates it with play data for analysis. For example, it analyzes the impact of lighting brightness and the acoustic environment on players' concentration. In this way, collecting environmental data and analyzing its impact on play improves the accuracy of tactics.

[0035] The data collection unit collects and analyzes similar data for different sports, enabling knowledge applicable to sports in general to be obtained. For example, the data collection unit collects information on the positions of players and the movement of the ball during a tennis match and analyzes it in the same way as for soccer and basketball. For example, the data collection unit compares the movement patterns and tactics of players. The data collection unit also collects biometric data (heart rate, muscle movement, etc.) of players during a rugby match and analyzes it in the same way as for soccer and basketball. For example, the data collection unit compares the players' fatigue levels and performance fluctuations. The data collection unit also collects environmental data (weather, spectator cheers, etc.) during matches of different sports and analyzes it in the same way as for soccer and basketball. For example, the data collection unit compares the effects of environmental factors on players' performance. In this way, data collection and analysis for different sports allows knowledge applicable to sports in general to be obtained.

[0036] The data collection unit can include audio data and the content of players' conversations in the collection of play data, and analyze the impact of communication quality on play. For example, the data collection unit collects the content of conversations between players during a game using audio analysis technology, and integrates and analyzes it with play data. For example, it analyzes the impact of communication quality between players on pass success rates. The data collection unit also collects coaches' instructions and advice during a game using audio analysis technology, and integrates and analyzes it with play data. For example, it analyzes the impact of coaches' instructions on players' movements. The data collection unit also collects players' tone of voice and emotions during a game using audio analysis technology, and integrates and analyzes it with play data. For example, it analyzes the impact of players' emotional states on play results. In this way, by collecting audio data and the content of players' conversations, it is possible to analyze the impact of communication quality on play.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The sports analysis system can further include a nutrition monitoring unit that monitors a player's nutritional status and analyzes the impact on playing performance. For example, it records the player's diet and calorie intake and analyzes nutritional balance. The nutrition monitoring unit can also measure a player's blood sugar level and vitamin levels and analyze the impact on playing performance. For example, it can analyze the impact of fluctuations in blood sugar levels on a player's stamina and concentration. Furthermore, the nutrition monitoring unit can monitor a player's hydration status and analyze the impact of dehydration on playing. In this way, by monitoring a player's nutritional status, playing performance can be optimized.

[0039] The sports analysis system can further include a sleep monitoring unit that monitors a player's sleep state and analyzes the impact on playing performance. For example, the system can record the player's sleep time and sleep quality and analyze the impact on playing performance. The sleep monitoring unit can also measure the player's heart rate and breathing pattern while sleeping to evaluate the quality of sleep. For example, the system can analyze the impact of deep sleep on a player's recovery ability and concentration. The sleep monitoring unit can also monitor the player's sleeping environment (temperature, humidity, noise, etc.) and analyze the impact on sleep quality. In this way, monitoring a player's sleep state can improve playing performance.

[0040] The sports analysis system can further include a muscle monitoring unit that monitors the muscle fatigue state of a player and analyzes the impact on playing performance. For example, the muscle monitoring unit measures the stiffness and flexibility of the player's muscles and analyzes the impact on playing performance. The muscle monitoring unit can also monitor the muscle recovery state of a player and analyze the impact on playing performance. For example, the muscle monitoring unit can analyze the impact of muscle recovery on a player's stamina and speed. The muscle monitoring unit can also measure the temperature and blood flow of the player's muscles and analyze the impact on playing performance. In this way, by monitoring the muscle fatigue state of a player, playing performance can be optimized.

[0041] The sports analysis system can further include a recovery monitoring unit that monitors a player's recovery status and analyzes the impact on playing performance. For example, it can record a player's rest time and recovery methods (such as massage and stretching) and analyze the impact on playing performance. The recovery monitoring unit can also measure a player's muscle recovery status and fatigue level and analyze the impact on playing performance. For example, it can analyze the impact of muscle recovery on a player's stamina and speed. Furthermore, the recovery monitoring unit can suggest methods to optimize a player's recovery (such as icing and nutritional supplementation) and monitor their effectiveness. In this way, by monitoring a player's recovery status, playing performance can be optimized.

[0042] The sports analysis system may further include a tactical understanding monitoring unit that monitors a player's tactical understanding and analyzes the impact on playing performance. For example, the system may test a player's knowledge and understanding of tactics and analyze the impact on playing performance. The tactical understanding monitoring unit may also evaluate a player's ability to adapt to and execute tactics and analyze the impact on playing performance. For example, the system may analyze the impact of tactical understanding on a player's judgment and teamwork. Furthermore, the tactical understanding monitoring unit may suggest training methods (such as video analysis and simulation) to improve a player's tactical understanding and monitor the effectiveness of these methods. In this way, monitoring a player's tactical understanding can improve playing performance.

[0043] The sports analysis system may further include a feedback collection unit that collects player feedback and analyzes the impact on playing performance. For example, the system may record player self-evaluations and feedback from coaches and analyze the impact on playing performance. The feedback collection unit may also adjust a training plan based on player feedback and monitor its effectiveness. For example, the system may change training content based on the feedback to improve playing performance. Furthermore, the feedback collection unit may collect player feedback in real time and immediately reflect the feedback on playing performance. In this way, playing performance can be optimized by collecting player feedback.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The data collection unit collects play data. For example, it collects data such as the positional information of players during the game, the movement of the ball, and the success rate of passes between players. The data collection unit can also collect environmental data during the game (weather, spectator cheers, etc.). For example, it collects weather data such as temperature, humidity, and wind speed in real time. Step 2: The analysis unit analyzes the play data collected by the data collection unit. For example, it identifies the players' positioning, the timing of passes, and the positioning of the opposing team's defense. The analysis unit can also estimate the players' emotional states and analyze the impact of emotional fluctuations on their play. For example, it can capture the players' facial expressions and movements with a camera and use generative AI to estimate their emotional states. Step 3: The option generator generates new options based on the results of the analysis by the analysis unit. For example, it may suggest different positioning or timing of passes. The option generator can also use generative AI to suggest new tactical or play options that take into account the emotional state of the player. For example, it may adopt an attacking tactic when the player is relaxed.

[0046] (Example 2) A sports analysis system according to an embodiment of the present invention is a system that elucidates the factors behind plays in sports such as soccer and basketball, and uses a generative AI to create new options. This allows the sports analysis system to elucidate the factors behind plays and obtain new knowledge.

[0047] A sports analysis system according to an embodiment includes a data collection unit, an analysis unit, and an option creation unit. The data collection unit collects play data. For example, it collects data such as player position information, ball movement, and pass success rates between players during a game. The data collection unit can also collect environmental data (weather, spectator cheers, etc.) during a game. For example, it collects weather data such as temperature, humidity, and wind speed in real time. The analysis unit analyzes the play data collected by the data collection unit. For example, it identifies player positioning, pass timing, and the opposing team's defensive positioning. The analysis unit can also estimate a player's emotional state and analyze the impact of emotional fluctuations on play. For example, it captures a player's facial expressions and movements with a camera and uses a generation AI to estimate the player's emotional state. The option creation unit creates new options based on the results of the analysis by the analysis unit. For example, it can suggest different positioning and pass timing. The option creation unit can also use the generation AI to suggest new tactics and play options that take a player's emotional state into account. For example, it can adopt an offensive tactic when a player is relaxed. As a result, the sports analysis system according to the embodiment can contribute to the development of sports by analyzing play data and creating new options. For example, the analysis results and new options can be provided as a report, which coaches and players can use as a reference to develop strategies for the next game.

[0048] The data collection unit can collect player position information, ball movement, and the success rate of passes between players. For example, the data collection unit collects player position information during a match using GPS data or a tracking system. For example, it tracks player movement in real time and records position information. The data collection unit also collects ball movement data such as speed, direction, and rotation. For example, it captures the ball's movement with a camera and analyzes the movement using image analysis technology. In addition, the data collection unit sets the conditions for determining a pass as successful and a calculation period in order to calculate the success rate of passes between players. For example, a pass that is not intercepted by the opposing team and is passed to a teammate is considered successful, and the success rate is calculated. In this way, by collecting data such as player position information and ball movement, the factors behind a play can be analyzed in detail.

[0049] The analysis unit can identify player positioning, pass timing, and the opposing team's defensive positioning. For example, the analysis unit identifies player positioning based on positioning data and tactical positioning. For example, it analyzes player positioning and proposes optimal positioning. The analysis unit also identifies pass timing based on evaluation criteria for pass origins and timing. For example, it analyzes pass timing and proposes optimal timing. The analysis unit also identifies the opposing team's defensive positioning based on the position of the defensive line and the positioning of zone defense. For example, it analyzes the opposing team's defensive positioning and proposes optimal attacking tactics. This allows more effective tactics to be developed by identifying the factors behind plays.

[0050] The option creation unit can propose different positioning and pass timing. For example, the option creation unit proposes different positioning based on positioning changes and tactical placement suggestions. For example, changing the positioning of players can improve the success rate of attacks. The option creation unit also proposes different pass timing based on changes to the pass origin and timing. For example, changing the timing of a pass can improve the success rate of a pass. In this way, proposing different positioning and pass timing can improve the success rate of plays.

[0051] The analysis unit can estimate a player's emotional state and analyze the impact of emotional fluctuations on play. For example, the analysis unit estimates a player's emotional state based on facial expression analysis and heart rate fluctuations. For example, the analysis unit captures a player's facial expression with a camera and estimates the player's emotional state using a generation AI. The analysis unit also measures a player's heart rate and electrodermal activity with a wearable device and estimates the player's emotional state using a generation AI. For example, it analyzes heart rate fluctuations to estimate the player's state of tension or relaxation. The analysis unit also collects a player's statements and tone of voice using voice analysis technology and estimates the player's emotional state using a generation AI. For example, it analyzes a player's statements and tone of voice to estimate the player's emotional state. In this way, analyzing a player's emotional state improves the quality of play.

[0052] The data collection unit can collect biometric data of players in real time using drones and wearable devices. For example, the data collection unit uses a drone to photograph players' movements during a game from the air and collect location information and movement data in real time. For example, it analyzes the player's running distance and speed. The data collection unit also monitors the heart rate and muscle movement of players wearing wearable devices in real time and integrates this data with play data for analysis. For example, it analyzes the player's fatigue level and performance fluctuations. The data collection unit also combines drones and wearable devices to simultaneously collect player movement and biometric data, and integrates this data with play data for analysis. For example, it analyzes the relationship between player movement and heart rate. In this way, collecting players' biometric data in real time improves the accuracy of play analysis.

[0053] The data collection unit collects environmental data during a match and can analyze the impact of these factors on play. For example, the data collection unit collects weather data (temperature, humidity, wind speed, etc.) during a match in real time and integrates it with play data for analysis. For example, it analyzes the impact of temperature fluctuations on player performance. The data collection unit also uses audio analysis technology to collect cheers and boos from spectators and integrates it with play data for analysis. For example, it analyzes the impact of spectator cheers on players' motivation. The data collection unit also monitors the lighting and acoustic environment of the match venue with sensors and integrates it with play data for analysis. For example, it analyzes the impact of lighting brightness and the acoustic environment on players' concentration. In this way, collecting environmental data and analyzing its impact on play improves the accuracy of tactics.

[0054] The data collection unit collects and analyzes similar data for different sports, enabling knowledge applicable to sports in general to be obtained. For example, the data collection unit collects information on the positions of players and the movement of the ball during a tennis match and analyzes it in the same way as for soccer and basketball. For example, the data collection unit compares the movement patterns and tactics of players. The data collection unit also collects biometric data (heart rate, muscle movement, etc.) of players during a rugby match and analyzes it in the same way as for soccer and basketball. For example, the data collection unit compares the players' fatigue levels and performance fluctuations. The data collection unit also collects environmental data (weather, spectator cheers, etc.) during matches of different sports and analyzes it in the same way as for soccer and basketball. For example, the data collection unit compares the effects of environmental factors on players' performance. In this way, data collection and analysis for different sports allows knowledge applicable to sports in general to be obtained.

[0055] The data collection unit can include audio data and the content of players' conversations in the collection of play data, and analyze the impact of communication quality on play. For example, the data collection unit collects the content of conversations between players during a game using audio analysis technology, and integrates and analyzes it with play data. For example, it analyzes the impact of communication quality between players on pass success rates. The data collection unit also collects coaches' instructions and advice during a game using audio analysis technology, and integrates and analyzes it with play data. For example, it analyzes the impact of coaches' instructions on players' movements. The data collection unit also collects players' tone of voice and emotions during a game using audio analysis technology, and integrates and analyzes it with play data. For example, it analyzes the impact of players' emotional states on play results. In this way, by collecting audio data and the content of players' conversations, it is possible to analyze the impact of communication quality on play.

[0056] The data collection unit can use the emotion estimation function to monitor the stress and pressure felt by players while playing in real time and incorporate that data into analysis. The data collection unit, for example, uses the emotion estimation function to monitor the stress and pressure felt by players while playing in real time. For example, it analyzes the player's facial expressions and movements to measure stress levels. The data collection unit also uses a wearable device to monitor the player's heart rate and electrodermal activity in real time and analyzes stress and pressure using the emotion estimation function. For example, it analyzes the impact of heart rate fluctuations on stress levels. The data collection unit also collects the player's statements and tone of voice during a game using voice analysis technology and analyzes stress and pressure using the emotion estimation function. For example, it analyzes the impact of changes in voice tone on stress levels. In this way, the stress and pressure of players can be monitored in real time and the data can be incorporated into analysis to improve the quality of play.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The sports analysis system can further include a nutrition monitoring unit that monitors a player's nutritional status and analyzes the impact on playing performance. For example, it records the player's diet and calorie intake and analyzes nutritional balance. The nutrition monitoring unit can also measure a player's blood sugar level and vitamin levels and analyze the impact on playing performance. For example, it can analyze the impact of fluctuations in blood sugar levels on a player's stamina and concentration. Furthermore, the nutrition monitoring unit can monitor a player's hydration status and analyze the impact of dehydration on playing. In this way, by monitoring a player's nutritional status, playing performance can be optimized.

[0059] The sports analysis system can further include a sleep monitoring unit that monitors a player's sleep state and analyzes the impact on playing performance. For example, the system can record the player's sleep time and sleep quality and analyze the impact on playing performance. The sleep monitoring unit can also measure the player's heart rate and breathing pattern while sleeping to evaluate the quality of sleep. For example, the system can analyze the impact of deep sleep on a player's recovery ability and concentration. The sleep monitoring unit can also monitor the player's sleeping environment (temperature, humidity, noise, etc.) and analyze the impact on sleep quality. In this way, monitoring a player's sleep state can improve playing performance.

[0060] The sports analysis system can further include a psychological monitoring unit that monitors a player's psychological readiness and analyzes the impact on playing performance. For example, the system records the player's self-evaluation and motivation level and analyzes the impact on playing performance. The psychological monitoring unit can also measure a player's stress level and anxiety and analyze the impact on playing performance. For example, the system can analyze the impact that fluctuations in stress level have on a player's concentration and judgment. Furthermore, the psychological monitoring unit can monitor the effectiveness of a player's relaxation techniques (breathing techniques, meditation, etc.) and analyze the impact on playing performance. In this way, by monitoring a player's psychological readiness, playing performance can be optimized.

[0061] The sports analysis system can further include a muscle monitoring unit that monitors the muscle fatigue state of a player and analyzes the impact on playing performance. For example, the muscle monitoring unit measures the stiffness and flexibility of the player's muscles and analyzes the impact on playing performance. The muscle monitoring unit can also monitor the muscle recovery state of a player and analyze the impact on playing performance. For example, the muscle monitoring unit can analyze the impact of muscle recovery on a player's stamina and speed. The muscle monitoring unit can also measure the temperature and blood flow of the player's muscles and analyze the impact on playing performance. In this way, by monitoring the muscle fatigue state of a player, playing performance can be optimized.

[0062] The sports analysis system can further include a concentration monitoring unit that monitors a player's concentration and analyzes the impact on playing performance. For example, it measures a player's eye movement and attention and analyzes the impact on playing performance. The concentration monitoring unit can also measure a player's brain waves and analyze fluctuations in concentration. For example, it can analyze the impact of brain wave fluctuations on a player's reaction speed and judgment. Furthermore, the concentration monitoring unit can suggest training methods (such as mental exercises) to improve a player's concentration and monitor their effectiveness. In this way, by monitoring a player's concentration, playing performance can be improved.

[0063] The sports analysis system can further include a communication monitoring unit that monitors players' communication skills and analyzes the impact on their playing performance. For example, it can record the content of conversations between players and the frequency of communication and analyze the impact on their playing performance. The communication monitoring unit can also analyze players' non-verbal communication (gestures, facial expressions, etc.) and analyze the impact on their playing performance. For example, it can analyze the impact of the quality of non-verbal communication on teamwork and teamwork. Furthermore, the communication monitoring unit can suggest training methods (such as role-playing) to improve players' communication skills and monitor their effectiveness. In this way, monitoring players' communication skills can improve their playing performance.

[0064] The sports analysis system can further include a motivation monitoring unit that monitors players' motivation and analyzes the impact on their playing performance. For example, the system records players' self-evaluations and goal setting and analyzes the impact on their playing performance. The motivation monitoring unit can also measure players' motivation levels and analyze the impact on their playing performance. For example, it can analyze the impact of fluctuations in motivation levels on players' effort and concentration. The motivation monitoring unit can also suggest methods for increasing players' motivation (such as reward systems and feedback on goal achievement) and monitor their effectiveness. In this way, monitoring players' motivation can improve their playing performance.

[0065] The sports analysis system can further include a recovery monitoring unit that monitors a player's recovery status and analyzes the impact on playing performance. For example, it can record a player's rest time and recovery methods (such as massage and stretching) and analyze the impact on playing performance. The recovery monitoring unit can also measure a player's muscle recovery status and fatigue level and analyze the impact on playing performance. For example, it can analyze the impact of muscle recovery on a player's stamina and speed. Furthermore, the recovery monitoring unit can suggest methods to optimize a player's recovery (such as icing and nutritional supplementation) and monitor their effectiveness. In this way, by monitoring a player's recovery status, playing performance can be optimized.

[0066] The sports analysis system may further include a tactical understanding monitoring unit that monitors a player's tactical understanding and analyzes the impact on playing performance. For example, the system may test a player's knowledge and understanding of tactics and analyze the impact on playing performance. The tactical understanding monitoring unit may also evaluate a player's ability to adapt to and execute tactics and analyze the impact on playing performance. For example, the system may analyze the impact of tactical understanding on a player's judgment and teamwork. Furthermore, the tactical understanding monitoring unit may suggest training methods (such as video analysis and simulation) to improve a player's tactical understanding and monitor the effectiveness of these methods. In this way, monitoring a player's tactical understanding can improve playing performance.

[0067] The sports analysis system may further include a feedback collection unit that collects player feedback and analyzes the impact on playing performance. For example, the system may record player self-evaluations and feedback from coaches and analyze the impact on playing performance. The feedback collection unit may also adjust a training plan based on player feedback and monitor its effectiveness. For example, the system may change training content based on the feedback to improve playing performance. Furthermore, the feedback collection unit may collect player feedback in real time and immediately reflect the feedback on playing performance. In this way, playing performance can be optimized by collecting player feedback.

[0068] The processing flow of the second embodiment will be briefly explained below.

[0069] Step 1: The data collection unit collects play data. For example, it collects data such as the positional information of players during the game, the movement of the ball, and the success rate of passes between players. The data collection unit can also collect environmental data during the game (weather, spectator cheers, etc.). For example, it collects weather data such as temperature, humidity, and wind speed in real time. Step 2: The analysis unit analyzes the play data collected by the data collection unit. For example, it identifies the players' positioning, the timing of passes, and the positioning of the opposing team's defense. The analysis unit can also estimate the players' emotional states and analyze the impact of emotional fluctuations on their play. For example, it can capture the players' facial expressions and movements with a camera and use generative AI to estimate their emotional states. Step 3: The option generator generates new options based on the results of the analysis by the analysis unit. For example, it may suggest different positioning or timing of passes. The option generator can also use generative AI to suggest new tactical or play options that take into account the emotional state of the player. For example, it may adopt an attacking tactic when the player is relaxed.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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).

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0104] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] In the robot 414, 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 robot 414 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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."

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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. [Explanation of symbols]

[0137] 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 data collection unit that collects play data; an analysis unit that analyzes the play data collected by the data collection unit; an option creation unit that creates new options based on the results of the analysis by the analysis unit; A system characterized by:

2. The data collection unit Collect player position information, ball movement, and pass success rates between said players The system of claim 1 .

3. The analysis unit Identify player positioning, passing timing, and opposing team's defensive positioning The system of claim 1 .

4. The option creation unit Suggest different positions and passing timings The system of claim 1 .

5. The analysis unit Estimating players' emotional states and analyzing the impact of emotional fluctuations on play The system of claim 1 .

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