System

Abstract

An object of the system according to the embodiment is to reproduce a specific game situation and provide an effective practice program.SOLUTION: A system according to an embodiment includes a game situation reproducer, a practice program generator, and a feedback provider. The game state reproducing section reproduces the specific game state based on the past game AI or simulation. The practice program generation unit generates an appropriate practice program based on the game situation reproduced by the game situation reproduction unit. The feedback providing unit analyzes an execution result of the practice program generated by the practice program generating unit and provides feedback to the user.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, it was difficult to replicate specific game situations for self-practice, making it difficult to practice effectively to avoid repeating mistakes made in a game.

[0005] The system according to the embodiment aims to reproduce a specific match situation and provide an effective training program. [Means for solving the problem]

[0006] The system according to the embodiment includes a game situation reproducing unit, a practice program generating unit, and a feedback providing unit. The game situation reproducing unit reproduces a specific game situation based on past game data or a simulation using a generation AI. The practice program generating unit generates an appropriate practice program based on the game situation reproduced by the game situation reproducing unit. The feedback providing unit analyzes the execution results of the practice program generated by the practice program generating unit and provides feedback to the user. [Effects of the Invention]

[0007] The system according to the embodiment can reproduce specific game situations and provide effective training programs. [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) The self-practice system according to an embodiment of the present invention is a system that facilitates self-practice based on simulated match situations. This system can recreate specific scenes and situations during a match and conduct practice tailored to those situations. This allows the self-practice system to reduce mistakes during matches and improve performance.

[0029] The self-practice system according to the embodiment includes a game situation reproducing unit, a practice program generating unit, and a feedback providing unit. The game situation reproducing unit uses a generation AI to reproduce a specific game situation based on past game data or a simulation. For example, the generation AI analyzes past game data to reproduce a specific game situation. The generation AI can also generate a specific game situation using a simulation. The generation AI generates a game situation based on a prompt entered by a user. The practice program generating unit generates an appropriate practice program based on the game situation reproduced by the game situation reproducing unit. For example, the generation AI generates a batting practice program based on the reproduced game situation. The generation AI can also generate a program in which the pitcher randomly sets the type and trajectory of a pitch and the batter practices hitting that ball. The generation AI generates the practice program based on a prompt entered by a user. The feedback providing unit analyzes the execution results of the practice program generated by the practice program generating unit and provides feedback to the user. For example, the generation AI analyzes practice result data to identify batting success rates and issues. The generation AI can also provide feedback that the user needs to address specific types and trajectories of pitches. The generating AI generates feedback based on the practice result data entered by the user. This allows the self-practice system according to the embodiment to recreate a match situation, generate an appropriate practice program, and provide feedback to help the user improve their performance. For example, a user can improve their performance in a match by recreating a specific situation in the match and practicing according to that situation. Furthermore, the feedback provided by the generating AI enables efficient practice.

[0030] The game situation reproduction unit can also reproduce the psychological state or physical condition of the players, allowing for more realistic simulations. For example, the generation AI in the game situation reproduction unit analyzes the players' past psychological state and physical condition data and reflects this in the game situation. For example, it reproduces situations in which a player is tense or fatigue is accumulating. Furthermore, in reproducing the game situation, the player's psychological state and physical condition are monitored in real time and a simulation is performed based on that data. For example, heart rate and stress level are analyzed. Furthermore, by incorporating the players' psychological state and physical condition into the simulation, the generation AI reproduces fluctuations in performance during a game. For example, it reproduces a drop in performance under pressure. In this way, by reproducing the players' psychological state and physical condition, more realistic game situations can be simulated.

[0031] The game situation reproducing unit can reproduce the game situation while taking into account at least one external factor of weather or spectator reaction. In the game situation reproducing unit, for example, the generation AI analyzes weather data in addition to past game data and reflects it in the game situation. For example, it reproduces rainy weather or strong wind conditions. It also analyzes spectator reaction data and incorporates it into the game situation. For example, it reproduces audio data of cheers and booing. It also analyzes environmental data of the game venue as an external factor and reflects it in the simulation. For example, it reproduces the brightness of lighting and the acoustic environment. In this way, by taking into account external factors such as weather and spectator reaction, it is possible to reproduce a more realistic game situation.

[0032] The game situation reproducing unit can reproduce game situations of different sports or competitions, enabling cross-sport training. For example, the generation AI analyzes game data from different sports and reproduces common game situations. For example, it combines game situations from soccer and basketball. It also reproduces game situations from different competitions to provide cross-sport training. For example, it combines baseball batting practice with tennis serving practice. The generation AI also analyzes game situations from different sports and generates a training program that helps improve a player's performance. For example, it combines soccer dribbling practice with basketball shooting practice. This enables cross-sport training by reproducing game situations from different sports or competitions.

[0033] The game situation reproduction unit can use VR technology to provide an experience of players actually being at the game site. The game situation reproduction unit, for example, uses VR technology to reproduce the game situation and provide an experience of players actually being at the game site. For example, a VR headset is used to reproduce the realism of the game. Also, the game situation is reproduced in a VR environment, and players practice on the spot. For example, batting practice is conducted in a VR space. Also, the game situation is reproduced using VR technology, and players simulate tactics on the spot. For example, team tactics are practiced in a VR space. In this way, by using VR technology, it is possible to provide an experience of players actually being at the game site.

[0034] The training program generation unit can analyze a player's past training data and generate a training program that is optimal for each individual player. In the training program generation unit, for example, a generation AI analyzes a player's past training data and generates a training program that is optimal for each individual player. For example, the training content is set based on the player's strong and weak plays. In addition, a system is built that generates a training program that helps improve a player's performance based on past training data. For example, a player's growth curve is analyzed and optimal training content is suggested. In addition, the generation AI analyzes a player's training data and generates a training program that is optimal for each individual player, thereby achieving efficient training. For example, a training menu is customized based on the player's training history. In this way, the optimal training program can be generated for each individual player by analyzing the player's past training data.

[0035] The training program generation unit can set an optimal training load by taking into account the player's physical condition or fatigue level. In the training program generation unit, for example, the generation AI analyzes the player's physical condition data and reflects it in the training program. For example, the training load is adjusted based on the player's heart rate and fatigue level. In addition, when generating a training program, the player's physical condition and fatigue level are monitored in real time and the training content is set based on that data. For example, the training load is increased when the player is in good physical condition. In addition, a system is constructed in which the generation AI takes into account the player's physical condition and fatigue level and sets the optimal training load. For example, a training menu is customized based on the player's physical condition data. This makes it possible to set an optimal training load by taking into account the player's physical condition and fatigue level.

[0036] The practice program generation unit can generate practice programs for different sports or competitions, enabling cross-training. For example, the generation AI in the practice program generation unit analyzes practice data from different sports and generates a common practice program. For example, it combines training menus for soccer and basketball. It also generates practice programs for different sports to enable cross-training. For example, it combines baseball batting practice with tennis serving practice. The generation AI also analyzes practice programs for different sports and suggests training menus that will help improve a player's performance. For example, it combines soccer dribbling practice with basketball shooting practice. In this way, cross-training becomes possible by generating practice programs for different sports or competitions.

[0037] The practice program generation unit can use AR technology to provide an experience of players actually practicing on the spot. The practice program generation unit, for example, uses AR technology to recreate a practice program and provide an experience of players actually practicing on the spot. For example, AR glasses are used to recreate the sense of realism of practice. Furthermore, a practice program is recreated in an AR environment, and players practice on the spot. For example, batting practice is conducted in an AR space. Furthermore, AR technology is used to recreate a practice program, and players simulate tactics on the spot. For example, team tactics are practiced in an AR space. In this way, by using AR technology, it is possible to provide an experience of players actually practicing on the spot.

[0038] The feedback providing unit can analyze the results of practice and provide specific feedback to the user. In the feedback providing unit, for example, the generation AI analyzes practice result data and provides specific feedback to the user. For example, it analyzes batting success rates and issues and suggests areas for improvement. In addition, a system is constructed in which the generation AI provides feedback to the user based on the practice results. For example, it displays practice results and progress in graphs. In addition, the generation AI analyzes the results of practice and provides feedback to the user, achieving efficient training. For example, it analyzes the success and failure rates of practice and suggests areas for improvement. In this way, it is possible to provide specific feedback to the user by analyzing the results of practice.

[0039] The feedback providing unit can suggest specific improvements or next steps to the user based on the practice results. In the feedback providing unit, for example, the generation AI analyzes the practice results and suggests specific improvements to the user. For example, it suggests corrections to batting form or changes to the practice menu. In addition, a system is constructed in which the generation AI suggests the next step to the user based on the practice results. For example, it presents the next practice content and goal to work on. In addition, the generation AI analyzes the practice results and suggests specific improvements or next steps to the user, thereby realizing efficient training. For example, it sets the next goal based on the results of practice. In this way, efficient training can be achieved by suggesting specific improvements or next steps based on the practice results.

[0040] The feedback providing unit can analyze practice results from different sports or competitions to discover common issues and areas for improvement. For example, the generation AI in the feedback providing unit analyzes practice results from different sports to discover common issues. For example, it compares practice results from soccer and basketball to identify common areas for improvement. It also builds a system that discovers common issues and areas for improvement based on practice results from different sports. For example, it compares practice results from baseball and tennis to identify common issues. It also analyzes practice results from different sports to discover common issues and areas for improvement and proposes a new training program. For example, it conducts cross-sport training. In this way, it is possible to discover common issues and areas for improvement by analyzing practice results from different sports or competitions.

[0041] The feedback providing unit can integrate the practice results with other datasets to gain new insights. For example, the generative AI in the feedback providing unit can integrate the practice results with the player's health data to gain new insights. For example, it can analyze the relationship between the player's physical condition and practice results. It can also integrate the practice results with training data to build a system that can gain insights that are useful for improving player performance. For example, it can analyze the relationship between training content and practice results. The generative AI can also integrate the practice results with other datasets to gain new insights and propose a training program that will help improve the player's performance. For example, it can generate an optimal practice plan based on the health data and training data. In this way, new insights can be gained by integrating the practice results with other datasets.

[0042] The practice recording and analysis unit can record the user's practice data and analyze long-term changes in performance. In the practice recording and analysis unit, for example, the generation AI records the user's practice data and analyzes long-term changes in performance. For example, it analyzes practice data from the past month and graphs the progress of success rates. Furthermore, based on the practice data, the generation AI builds a system that analyzes changes in the user's performance. For example, it analyzes progress in practice results and changes in tasks. Furthermore, the generation AI records the user's practice data and analyzes long-term changes in performance, thereby achieving efficient training. For example, it visualizes performance improvement based on practice data. In this way, the generation AI can record the user's practice data and analyze long-term changes in performance, thereby achieving efficient training.

[0043] The practice recording and analysis unit can suggest specific improvements or next steps to the user based on the practice data. In the practice recording and analysis unit, for example, a generation AI analyzes the practice data and suggests specific improvements to the user. For example, it suggests corrections to batting form or changes to the practice menu. In addition, a system is constructed in which the generation AI suggests next steps to the user based on the practice data. For example, it presents the next practice content and goals to work on. In addition, the generation AI analyzes the practice data and suggests specific improvements or next steps to the user, thereby realizing efficient training. For example, it sets the next goal based on the results of practice. In this way, efficient training can be achieved by suggesting specific improvements or next steps based on the practice data.

[0044] The practice recording and analysis unit can analyze practice data from different sports or competitions to discover common issues and areas for improvement. In the practice recording and analysis unit, for example, the generation AI analyzes practice data from different sports to discover common issues. For example, it compares practice data from soccer and basketball to identify common areas for improvement. It also builds a system that discovers common issues and areas for improvement based on practice data from different sports. For example, it compares practice data from baseball and tennis to identify common issues. It also analyzes practice data from different sports to discover common issues and areas for improvement, and proposes new training programs. For example, it performs cross-sport training. This makes it possible to discover common issues and areas for improvement by analyzing practice data from different sports or competitions.

[0045] The practice recording and analysis unit can integrate the practice data with the other datasets to gain new insights. In the practice recording and analysis unit, for example, the generative AI integrates the practice data with the player's health data to gain new insights. For example, it analyzes the relationship between the player's physical condition and practice results. It also integrates the practice data with training data to build a system that gains insights useful for improving player performance. For example, it analyzes the relationship between training content and practice results. It also integrates the practice data with other datasets to gain new insights and proposes a training program useful for improving player performance. For example, it generates an optimal practice plan based on the health data and training data. In this way, new insights can be gained by integrating the practice data with other datasets.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The game situation reproduction unit can also reproduce the psychological state or physical condition of the players, allowing for more realistic simulations. For example, the generation AI can analyze the players' past psychological state and physical condition data and reflect this in the game situation. It can reproduce situations in which a player is tense or fatigued. Furthermore, when reproducing the game situation, it can monitor the players' psychological state and physical condition in real time and perform simulations based on that data. For example, it can analyze heart rate and stress levels. Furthermore, by incorporating the players' psychological state and physical condition into the simulation, the generation AI can reproduce fluctuations in performance during a game. For example, it can reproduce a drop in performance under pressure. By reproducing the players' psychological state and physical condition, it is possible to simulate more realistic game situations.

[0048] The game situation reproduction unit can reproduce the game situation while taking into account at least one external factor, either weather or spectator reaction. For example, the generation AI can analyze weather data in addition to past game data and reflect it in the game situation. Rainy or strong wind conditions can be reproduced. Spectator reaction data can also be analyzed and incorporated into the game situation. Audio data of cheers and boos can be reproduced. Furthermore, environmental data of the game venue can be analyzed as an external factor and reflected in the simulation. Lighting brightness and acoustic environment can be reproduced. This allows for more realistic game situations to be reproduced by taking into account external factors such as weather and spectator reaction.

[0049] The game situation reproduction unit can reproduce game situations from different sports or competitions, enabling cross-sport training. For example, the generation AI analyzes game data from different sports and reproduces common game situations. It can combine soccer and basketball game situations. It can also reproduce game situations from different sports to enable cross-sport training. It can combine baseball batting practice with tennis serving practice. Furthermore, the generation AI can analyze game situations from different sports and generate training programs that help improve player performance. It can combine soccer dribbling practice with basketball shooting practice. This enables cross-sport training by reproducing game situations from different sports or competitions.

[0050] The game situation reproduction unit can use VR technology to provide the experience of players actually being there. For example, VR technology can be used to reproduce the game situation, providing an experience as if the players were actually there. A VR headset can be used to reproduce the realism of the game. In addition, the game situation can be reproduced in a VR environment, allowing players to practice on the spot. Batting practice can be conducted in a VR space. Furthermore, VR technology can be used to reproduce the game situation, allowing players to simulate tactics on the spot. Team tactics can be practiced in a VR space. In this way, VR technology can be used to provide an experience as if the players were actually there.

[0051] The training program generation unit can analyze a player's past training data and generate a training program that is optimal for each individual player. For example, the generation AI can analyze a player's past training data and generate a training program that is optimal for each individual player. Training content can be set based on a player's strong and weak plays. It is also possible to build a system that generates training programs that help improve a player's performance based on past training data. A player's growth curve can be analyzed and optimal training content can be suggested. Furthermore, the generation AI can analyze a player's training data and generate a training program that is optimal for each individual player, thereby achieving efficient training. Training menus can be customized based on the player's training history. In this way, the optimal training program can be generated for each individual player by analyzing the player's past training data.

[0052] The training program generation unit can set the optimal training load by taking into account the player's physical condition or fatigue level. For example, the generation AI can analyze the player's physical condition data and reflect it in the training program. The training load can be adjusted based on the player's heart rate and fatigue level. In addition, when generating a training program, the player's physical condition and fatigue level can be monitored in real time and the training content can be set based on that data. The training load can be increased when the player is in good physical condition. Furthermore, a system can be built in which the generation AI takes into account the player's physical condition and fatigue level and sets the optimal training load. Training menus can be customized based on the player's physical condition data. This makes it possible to set the optimal training load by taking into account the player's physical condition and fatigue level.

[0053] The practice program generation unit can generate practice programs for different sports or competitions, enabling cross-training. For example, the generation AI can analyze practice data from different sports and generate a common practice program. Training menus for soccer and basketball can be combined. It is also possible to generate practice programs for different sports and enable cross-training. Baseball batting practice can be combined with tennis serving practice. Furthermore, the generation AI can analyze practice programs for different sports and suggest training menus that will help improve a player's performance. Soccer dribbling practice can be combined with basketball shooting practice. This makes cross-training possible by generating practice programs for different sports or competitions.

[0054] The practice program generation unit can use AR technology to provide the experience of players actually practicing on the spot. For example, AR technology can be used to recreate a practice program, providing an experience as if the players were actually practicing on the spot. AR glasses can be used to recreate the realism of practice. Also, a practice program can be recreated in an AR environment, allowing players to practice on the spot. Batting practice can be conducted in an AR space. Furthermore, AR technology can be used to recreate a practice program, allowing players to simulate tactics on the spot. Team tactics can be practiced in an AR space. In this way, AR technology can be used to provide an experience as if the players were actually practicing on the spot.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The game situation reproducing unit uses a generating AI to reproduce a specific game situation based on past game data or a simulation. For example, the generating AI analyzes past game data to reproduce a specific game situation. The generating AI can also generate a specific game situation using a simulation. The generating AI generates a game situation based on a prompt entered by a user. Step 2: The practice program generation unit generates an appropriate practice program based on the game situation reproduced by the game situation reproduction unit. For example, the generation AI generates a batting practice program based on the reproduced game situation. The generation AI can also generate a program in which the pitcher randomly sets the type and trajectory of the ball to be thrown and the batter practices hitting that ball. The generation AI generates a practice program based on prompts entered by the user. Step 3: The feedback providing unit analyzes the execution results of the practice program generated by the practice program generating unit and provides feedback to the user. For example, the generating AI analyzes the practice result data and identifies the batting success rate and issues. The generating AI can also provide feedback that the user's response to a specific type of pitch or course is an issue. The generating AI generates feedback based on the practice result data entered by the user.

[0057] (Example 2) The self-practice system according to an embodiment of the present invention is a system that facilitates self-practice based on simulated match situations. This system can recreate specific scenes and situations during a match and conduct practice tailored to those situations. This allows the self-practice system to reduce mistakes during matches and improve performance.

[0058] The self-practice system according to the embodiment includes a game situation reproducing unit, a practice program generating unit, and a feedback providing unit. The game situation reproducing unit uses a generation AI to reproduce a specific game situation based on past game data or a simulation. For example, the generation AI analyzes past game data to reproduce a specific game situation. The generation AI can also generate a specific game situation using a simulation. The generation AI generates a game situation based on a prompt entered by a user. The practice program generating unit generates an appropriate practice program based on the game situation reproduced by the game situation reproducing unit. For example, the generation AI generates a batting practice program based on the reproduced game situation. The generation AI can also generate a program in which the pitcher randomly sets the type and trajectory of a pitch and the batter practices hitting that ball. The generation AI generates the practice program based on a prompt entered by a user. The feedback providing unit analyzes the execution results of the practice program generated by the practice program generating unit and provides feedback to the user. For example, the generation AI analyzes practice result data to identify batting success rates and issues. The generation AI can also provide feedback that the user needs to address specific types and trajectories of pitches. The generating AI generates feedback based on the practice result data entered by the user. This allows the self-practice system according to the embodiment to recreate a match situation, generate an appropriate practice program, and provide feedback to help the user improve their performance. For example, a user can improve their performance in a match by recreating a specific situation in the match and practicing according to that situation. Furthermore, the feedback provided by the generating AI enables efficient practice.

[0059] The game situation reproduction unit can also reproduce the psychological state or physical condition of the players, allowing for more realistic simulations. For example, the generation AI in the game situation reproduction unit analyzes the players' past psychological state and physical condition data and reflects this in the game situation. For example, it reproduces situations in which a player is tense or fatigue is accumulating. Furthermore, in reproducing the game situation, the player's psychological state and physical condition are monitored in real time and a simulation is performed based on that data. For example, heart rate and stress level are analyzed. Furthermore, by incorporating the players' psychological state and physical condition into the simulation, the generation AI reproduces fluctuations in performance during a game. For example, it reproduces a drop in performance under pressure. In this way, by reproducing the players' psychological state and physical condition, more realistic game situations can be simulated.

[0060] The game situation reproducing unit can reproduce the game situation while taking into account at least one external factor of weather or spectator reaction. In the game situation reproducing unit, for example, the generation AI analyzes weather data in addition to past game data and reflects it in the game situation. For example, it reproduces rainy weather or strong wind conditions. It also analyzes spectator reaction data and incorporates it into the game situation. For example, it reproduces audio data of cheers and booing. It also analyzes environmental data of the game venue as an external factor and reflects it in the simulation. For example, it reproduces the brightness of lighting and the acoustic environment. In this way, by taking into account external factors such as weather and spectator reaction, it is possible to reproduce a more realistic game situation.

[0061] The game situation reproduction unit can use the emotion estimation function to reproduce the emotional state of a player and generate a game situation based on that emotion. In the game situation reproduction unit, for example, the generation AI analyzes the player's past emotional data and reflects it in the game situation. For example, it reproduces situations where the player is nervous or excited. It also uses the emotion estimation function to monitor the player's real-time emotional state and performs a simulation based on that data. For example, it reproduces emotional changes during a game. In addition, the generation AI incorporates the player's emotional state into the simulation to reproduce fluctuations in performance during a game. For example, it reproduces emotional changes in situations where pressure is applied. In this way, by reproducing the player's emotional state, it is possible to generate a game situation based on emotions.

[0062] The game situation reproducing unit can reproduce game situations of different sports or competitions, enabling cross-sport training. For example, the generation AI analyzes game data from different sports and reproduces common game situations. For example, it combines game situations from soccer and basketball. It also reproduces game situations from different competitions to provide cross-sport training. For example, it combines baseball batting practice with tennis serving practice. The generation AI also analyzes game situations from different sports and generates a training program that helps improve a player's performance. For example, it combines soccer dribbling practice with basketball shooting practice. This enables cross-sport training by reproducing game situations from different sports or competitions.

[0063] The game situation reproduction unit can use VR technology to provide an experience of players actually being at the game site. The game situation reproduction unit, for example, uses VR technology to reproduce the game situation and provide an experience of players actually being at the game site. For example, a VR headset is used to reproduce the realism of the game. Also, the game situation is reproduced in a VR environment, and players practice on the spot. For example, batting practice is conducted in a VR space. Also, the game situation is reproduced using VR technology, and players simulate tactics on the spot. For example, team tactics are practiced in a VR space. In this way, by using VR technology, it is possible to provide an experience of players actually being at the game site.

[0064] The game situation reproduction unit uses the emotion estimation function to monitor in real time the emotions of the user as they reproduce the game situation, and can provide an optimal simulation. The game situation reproduction unit, for example, uses the emotion estimation function to monitor in real time the emotions of the user as they reproduce the game situation. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also builds a system that provides an optimal simulation based on the user's emotion data. For example, it adjusts the simulation content according to the user's emotional state. It also collects emotion estimation data in real time and provides a simulation based on the user's emotions. For example, it provides a simulation that allows the user to relax if they are nervous. In this way, it is possible to provide an optimal simulation by monitoring the user's emotions in real time.

[0065] The training program generation unit can analyze a player's past training data and generate a training program that is optimal for each individual player. In the training program generation unit, for example, a generation AI analyzes a player's past training data and generates a training program that is optimal for each individual player. For example, the training content is set based on the player's strong and weak plays. In addition, a system is built that generates a training program that helps improve a player's performance based on past training data. For example, a player's growth curve is analyzed and optimal training content is suggested. In addition, the generation AI analyzes a player's training data and generates a training program that is optimal for each individual player, thereby achieving efficient training. For example, a training menu is customized based on the player's training history. In this way, the optimal training program can be generated for each individual player by analyzing the player's past training data.

[0066] The training program generation unit can set an optimal training load by taking into account the player's physical condition or fatigue level. In the training program generation unit, for example, the generation AI analyzes the player's physical condition data and reflects it in the training program. For example, the training load is adjusted based on the player's heart rate and fatigue level. In addition, when generating a training program, the player's physical condition and fatigue level are monitored in real time and the training content is set based on that data. For example, the training load is increased when the player is in good physical condition. In addition, a system is constructed in which the generation AI takes into account the player's physical condition and fatigue level and sets the optimal training load. For example, a training menu is customized based on the player's physical condition data. This makes it possible to set an optimal training load by taking into account the player's physical condition and fatigue level.

[0067] The training program generation unit uses the emotion estimation function to generate a training program that corresponds to the player's emotional state, thereby maintaining motivation. The training program generation unit, for example, uses the emotion estimation function to monitor the player's emotional state in real time and generate a training program based on that data. For example, it sets training content that will make the player feel positive. The generation AI also analyzes the player's emotional data and suggests a training program to maintain motivation. For example, it sets a training menu that the player can enjoy. Furthermore, a system is constructed that generates a training program that corresponds to the player's emotional state based on the emotion estimation data. For example, it suggests training content that will help the player relax if the player is feeling stressed. In this way, motivation can be maintained by generating a training program that corresponds to the player's emotional state.

[0068] The practice program generation unit can generate practice programs for different sports or competitions, enabling cross-training. For example, the generation AI in the practice program generation unit analyzes practice data from different sports and generates a common practice program. For example, it combines training menus for soccer and basketball. It also generates practice programs for different sports to enable cross-training. For example, it combines baseball batting practice with tennis serving practice. The generation AI also analyzes practice programs for different sports and suggests training menus that will help improve a player's performance. For example, it combines soccer dribbling practice with basketball shooting practice. In this way, cross-training becomes possible by generating practice programs for different sports or competitions.

[0069] The practice program generation unit can use AR technology to provide an experience of players actually practicing on the spot. The practice program generation unit, for example, uses AR technology to recreate a practice program and provide an experience of players actually practicing on the spot. For example, AR glasses are used to recreate the sense of realism of practice. Furthermore, a practice program is recreated in an AR environment, and players practice on the spot. For example, batting practice is conducted in an AR space. Furthermore, AR technology is used to recreate a practice program, and players simulate tactics on the spot. For example, team tactics are practiced in an AR space. In this way, by using AR technology, it is possible to provide an experience of players actually practicing on the spot.

[0070] The practice program generation unit uses the emotion estimation function to monitor the emotions of the user when they are performing the practice program in real time, and can provide an optimal practice program. The practice program generation unit, for example, uses the emotion estimation function to monitor the emotions of the user when they are performing the practice program in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also builds a system that provides an optimal practice program based on the user's emotion data. For example, it adjusts the practice content according to the user's emotional state. It also collects emotion estimation data in real time and provides a practice program based on the user's emotions. For example, it provides a practice program that allows the user to relax when they are nervous. In this way, it is possible to provide an optimal practice program by monitoring the user's emotions in real time.

[0071] The feedback providing unit can analyze the results of practice and provide specific feedback to the user. In the feedback providing unit, for example, the generation AI analyzes practice result data and provides specific feedback to the user. For example, it analyzes batting success rates and issues and suggests areas for improvement. In addition, a system is constructed in which the generation AI provides feedback to the user based on the practice results. For example, it displays practice results and progress in graphs. In addition, the generation AI analyzes the results of practice and provides feedback to the user, achieving efficient training. For example, it analyzes the success and failure rates of practice and suggests areas for improvement. In this way, it is possible to provide specific feedback to the user by analyzing the results of practice.

[0072] The feedback providing unit can use the emotion estimation function to collect the user's emotional reactions to the practice results and improve the feedback based on the reactions. The feedback providing unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the practice results. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also builds a system to improve the feedback based on the user's emotional reaction data. For example, it adjusts the feedback content according to the user's emotional state. It also collects emotion estimation data in real time and improves the feedback based on the user's emotional reactions to the practice results. For example, it prioritizes providing feedback for which the user has positive emotions. In this way, it is possible to improve the feedback by collecting the user's emotional reactions.

[0073] The feedback providing unit can suggest specific improvements or next steps to the user based on the practice results. In the feedback providing unit, for example, the generation AI analyzes the practice results and suggests specific improvements to the user. For example, it suggests corrections to batting form or changes to the practice menu. In addition, a system is constructed in which the generation AI suggests the next step to the user based on the practice results. For example, it presents the next practice content and goal to work on. In addition, the generation AI analyzes the practice results and suggests specific improvements or next steps to the user, thereby realizing efficient training. For example, it sets the next goal based on the results of practice. In this way, efficient training can be achieved by suggesting specific improvements or next steps based on the practice results.

[0074] The feedback providing unit can analyze practice results from different sports or competitions to discover common issues and areas for improvement. For example, the generation AI in the feedback providing unit analyzes practice results from different sports to discover common issues. For example, it compares practice results from soccer and basketball to identify common areas for improvement. It also builds a system that discovers common issues and areas for improvement based on practice results from different sports. For example, it compares practice results from baseball and tennis to identify common issues. It also analyzes practice results from different sports to discover common issues and areas for improvement and proposes a new training program. For example, it conducts cross-sport training. In this way, it is possible to discover common issues and areas for improvement by analyzing practice results from different sports or competitions.

[0075] The feedback providing unit can integrate the practice results with other datasets to gain new insights. For example, the generative AI in the feedback providing unit can integrate the practice results with the player's health data to gain new insights. For example, it can analyze the relationship between the player's physical condition and practice results. It can also integrate the practice results with training data to build a system that can gain insights that are useful for improving player performance. For example, it can analyze the relationship between training content and practice results. The generative AI can also integrate the practice results with other datasets to gain new insights and propose a training program that will help improve the player's performance. For example, it can generate an optimal practice plan based on the health data and training data. In this way, new insights can be gained by integrating the practice results with other datasets.

[0076] The feedback providing unit can use the emotion estimation function to collect the user's emotional reactions to the practice results and improve the feedback based on the reactions. The feedback providing unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the practice results. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also builds a system to improve the feedback based on the user's emotional reaction data. For example, it adjusts the feedback content according to the user's emotional state. It also collects emotion estimation data in real time and improves the feedback based on the user's emotional reactions to the practice results. For example, it prioritizes providing feedback for which the user has positive emotions. In this way, it is possible to improve the feedback by collecting the user's emotional reactions.

[0077] The practice recording and analysis unit can record the user's practice data and analyze long-term changes in performance. In the practice recording and analysis unit, for example, the generation AI records the user's practice data and analyzes long-term changes in performance. For example, it analyzes practice data from the past month and graphs the progress of success rates. Furthermore, based on the practice data, the generation AI builds a system that analyzes changes in the user's performance. For example, it analyzes progress in practice results and changes in tasks. Furthermore, the generation AI records the user's practice data and analyzes long-term changes in performance, thereby achieving efficient training. For example, it visualizes performance improvement based on practice data. In this way, the generation AI can record the user's practice data and analyze long-term changes in performance, thereby achieving efficient training.

[0078] The practice recording and analysis unit can use the emotion estimation function to collect the user's emotional reactions to the practice data and improve the analysis results based on those reactions. The practice recording and analysis unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the practice data. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also builds a system that improves the analysis results based on the user's emotional reaction data. For example, it adjusts the analysis results according to the user's emotional state. It also collects emotion estimation data in real time and improves the analysis results based on the user's emotional reactions to the practice data. For example, it prioritizes providing analysis results in which the user has positive emotions. In this way, it is possible to improve the analysis results by collecting the user's emotional reactions.

[0079] The practice recording and analysis unit can suggest specific improvements or next steps to the user based on the practice data. In the practice recording and analysis unit, for example, a generation AI analyzes the practice data and suggests specific improvements to the user. For example, it suggests corrections to batting form or changes to the practice menu. In addition, a system is constructed in which the generation AI suggests next steps to the user based on the practice data. For example, it presents the next practice content and goals to work on. In addition, the generation AI analyzes the practice data and suggests specific improvements or next steps to the user, thereby realizing efficient training. For example, it sets the next goal based on the results of practice. In this way, efficient training can be achieved by suggesting specific improvements or next steps based on the practice data.

[0080] The practice recording and analysis unit can analyze practice data from different sports or competitions to discover common issues and areas for improvement. In the practice recording and analysis unit, for example, the generation AI analyzes practice data from different sports to discover common issues. For example, it compares practice data from soccer and basketball to identify common areas for improvement. It also builds a system that discovers common issues and areas for improvement based on practice data from different sports. For example, it compares practice data from baseball and tennis to identify common issues. It also analyzes practice data from different sports to discover common issues and areas for improvement, and proposes new training programs. For example, it performs cross-sport training. This makes it possible to discover common issues and areas for improvement by analyzing practice data from different sports or competitions.

[0081] The practice recording and analysis unit can integrate the practice data with the other datasets to gain new insights. In the practice recording and analysis unit, for example, the generative AI integrates the practice data with the player's health data to gain new insights. For example, it analyzes the relationship between the player's physical condition and practice results. It also integrates the practice data with training data to build a system that gains insights useful for improving player performance. For example, it analyzes the relationship between training content and practice results. It also integrates the practice data with other datasets to gain new insights and proposes a training program useful for improving player performance. For example, it generates an optimal practice plan based on the health data and training data. In this way, new insights can be gained by integrating the practice data with other datasets.

[0082] The practice recording and analysis unit can use the emotion estimation function to collect the user's emotional reactions to the practice data and improve the analysis results based on those reactions. The practice recording and analysis unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the practice data. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. It also builds a system that improves the analysis results based on the user's emotional reaction data. For example, it adjusts the analysis results according to the user's emotional state. It also collects emotion estimation data in real time and improves the analysis results based on the user's emotional reactions to the practice data. For example, it prioritizes providing analysis results in which the user has positive emotions. In this way, it is possible to improve the analysis results by collecting the user's emotional reactions.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The game situation reproduction unit can also reproduce the psychological state or physical condition of the players, allowing for more realistic simulations. For example, the generation AI can analyze the players' past psychological state and physical condition data and reflect this in the game situation. It can reproduce situations in which a player is tense or fatigued. Furthermore, when reproducing the game situation, it can monitor the players' psychological state and physical condition in real time and perform simulations based on that data. For example, it can analyze heart rate and stress levels. Furthermore, by incorporating the players' psychological state and physical condition into the simulation, the generation AI can reproduce fluctuations in performance during a game. For example, it can reproduce a drop in performance under pressure. By reproducing the players' psychological state and physical condition, it is possible to simulate more realistic game situations.

[0085] The game situation reproduction unit can reproduce the game situation while taking into account at least one external factor, either weather or spectator reaction. For example, the generation AI can analyze weather data in addition to past game data and reflect it in the game situation. Rainy or strong wind conditions can be reproduced. Spectator reaction data can also be analyzed and incorporated into the game situation. Audio data of cheers and boos can be reproduced. Furthermore, environmental data of the game venue can be analyzed as an external factor and reflected in the simulation. Lighting brightness and acoustic environment can be reproduced. This allows for more realistic game situations to be reproduced by taking into account external factors such as weather and spectator reaction.

[0086] The game situation reproduction unit can use the emotion estimation function to reproduce the emotional state of players and generate game situations based on those emotions. For example, the generation AI can analyze players' past emotional data and reflect it in the game situation. It can reproduce situations in which players are nervous or excited. The emotion estimation function can also be used to monitor players' real-time emotional states and perform simulations based on that data. It can reproduce emotional changes during a game. Furthermore, by incorporating players' emotional states into the simulation, the generation AI can reproduce fluctuations in performance during a game. It can reproduce emotional changes in situations where pressure is applied. In this way, by reproducing players' emotional states, it is possible to generate game situations based on emotions.

[0087] The game situation reproduction unit can reproduce game situations from different sports or competitions, enabling cross-sport training. For example, the generation AI analyzes game data from different sports and reproduces common game situations. It can combine soccer and basketball game situations. It can also reproduce game situations from different sports to enable cross-sport training. It can combine baseball batting practice with tennis serving practice. Furthermore, the generation AI can analyze game situations from different sports and generate training programs that help improve player performance. It can combine soccer dribbling practice with basketball shooting practice. This enables cross-sport training by reproducing game situations from different sports or competitions.

[0088] The game situation reproduction unit can use VR technology to provide the experience of players actually being there. For example, VR technology can be used to reproduce the game situation, providing an experience as if the players were actually there. A VR headset can be used to reproduce the realism of the game. In addition, the game situation can be reproduced in a VR environment, allowing players to practice on the spot. Batting practice can be conducted in a VR space. Furthermore, VR technology can be used to reproduce the game situation, allowing players to simulate tactics on the spot. Team tactics can be practiced in a VR space. In this way, VR technology can be used to provide an experience as if the players were actually there.

[0089] The training program generation unit can analyze a player's past training data and generate a training program that is optimal for each individual player. For example, the generation AI can analyze a player's past training data and generate a training program that is optimal for each individual player. Training content can be set based on a player's strong and weak plays. It is also possible to build a system that generates training programs that help improve a player's performance based on past training data. A player's growth curve can be analyzed and optimal training content can be suggested. Furthermore, the generation AI can analyze a player's training data and generate a training program that is optimal for each individual player, thereby achieving efficient training. Training menus can be customized based on the player's training history. In this way, the optimal training program can be generated for each individual player by analyzing the player's past training data.

[0090] The training program generation unit can set the optimal training load by taking into account the player's physical condition or fatigue level. For example, the generation AI can analyze the player's physical condition data and reflect it in the training program. The training load can be adjusted based on the player's heart rate and fatigue level. In addition, when generating a training program, the player's physical condition and fatigue level can be monitored in real time and the training content can be set based on that data. The training load can be increased when the player is in good physical condition. Furthermore, a system can be built in which the generation AI takes into account the player's physical condition and fatigue level and sets the optimal training load. Training menus can be customized based on the player's physical condition data. This makes it possible to set the optimal training load by taking into account the player's physical condition and fatigue level.

[0091] The training program generation unit uses the emotion estimation function to generate a training program that corresponds to the player's emotional state, thereby maintaining motivation. For example, the emotion estimation function can be used to monitor the player's emotional state in real time and generate a training program based on that data. Training content that will make the player feel positive can be set. The generation AI can also analyze the player's emotional data and suggest training programs to maintain motivation. A training menu that the player can enjoy can be set. Furthermore, a system can be built that generates a training program that corresponds to the player's emotional state based on the emotion estimation data. Training content that will help the player relax when they are feeling stressed can be suggested. This allows motivation to be maintained by generating a training program that corresponds to the player's emotional state.

[0092] The practice program generation unit can generate practice programs for different sports or competitions, enabling cross-training. For example, the generation AI can analyze practice data from different sports and generate a common practice program. Training menus for soccer and basketball can be combined. It is also possible to generate practice programs for different sports and enable cross-training. Baseball batting practice can be combined with tennis serving practice. Furthermore, the generation AI can analyze practice programs for different sports and suggest training menus that will help improve a player's performance. Soccer dribbling practice can be combined with basketball shooting practice. This makes cross-training possible by generating practice programs for different sports or competitions.

[0093] The practice program generation unit can use AR technology to provide the experience of players actually practicing on the spot. For example, AR technology can be used to recreate a practice program, providing an experience as if the players were actually practicing on the spot. AR glasses can be used to recreate the realism of practice. Also, a practice program can be recreated in an AR environment, allowing players to practice on the spot. Batting practice can be conducted in an AR space. Furthermore, AR technology can be used to recreate a practice program, allowing players to simulate tactics on the spot. Team tactics can be practiced in an AR space. In this way, AR technology can be used to provide an experience as if the players were actually practicing on the spot.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The game situation reproducing unit uses a generating AI to reproduce a specific game situation based on past game data or a simulation. For example, the generating AI analyzes past game data to reproduce a specific game situation. The generating AI can also generate a specific game situation using a simulation. The generating AI generates a game situation based on a prompt entered by a user. Step 2: The practice program generation unit generates an appropriate practice program based on the game situation reproduced by the game situation reproduction unit. For example, the generation AI generates a batting practice program based on the reproduced game situation. The generation AI can also generate a program in which the pitcher randomly sets the type and trajectory of the ball to be thrown and the batter practices hitting that ball. The generation AI generates a practice program based on prompts entered by the user. Step 3: The feedback providing unit analyzes the execution results of the practice program generated by the practice program generating unit and provides feedback to the user. For example, the generating AI analyzes the practice result data and identifies the batting success rate and issues. The generating AI can also provide feedback that the user's response to a specific type of pitch or course is an issue. The generating AI generates feedback based on the practice result data entered by the user.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 AI 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.

[0113] 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.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 AI 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.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 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 processing similar to that of the specific processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] 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 AI 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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, in order to avoid confusion and to 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.

[0162] 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]

[0163] 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 match situation reproducing unit that uses a generation AI to reproduce a specific match situation based on past match data or simulations; a practice program generation unit that generates an appropriate practice program based on the game situation reproduced by the game situation reproduction unit; a feedback providing unit that analyzes the execution results of the practice program generated by the practice program generating unit and provides feedback to the user. A system characterized by:

2. The game situation reproducing unit Reproduce the psychological and physical state of the players to create a more realistic simulation 2. The system of claim 1.

3. The game situation reproducing unit Recreate match conditions taking into account at least one external factor: weather or crowd reaction 2. The system of claim 1.

4. The game situation reproducing unit Reproducing the emotional state of players and generating game situations based on those emotions 2. The system of claim 1.

5. The game situation reproducing unit Recreates match situations from different sports or competitions, enabling cross-sport training 2. The system of claim 1.

Images