System

Abstract

A system according to an embodiment is directed to allowing real professional athletes and virtual athletes generated by a AI to compete in the same league.SOLUTION: A system according to an embodiment includes a player performance evaluation unit, a AI prediction unit, and a point awarding unit. The player performance evaluation unit evaluates the performance of the real player. The AI prediction unit predicts future performance. The point awarding unit awards points based on the performance evaluated by the player performance evaluation unit and the AI prediction unit.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] Conventional technology has focused on fantasy sports based on the performance evaluations of real athletes, and there has been a problem in that it is difficult to realize competition with virtual athletes.

[0005] The system according to the embodiment aims to have real professional athletes and virtual athletes generated by AI compete in the same league. [Means for solving the problem]

[0006] The system according to the embodiment includes a player performance evaluation unit, an AI prediction unit, and a point assignment unit. The player performance evaluation unit evaluates the performance of a real player. The AI ​​prediction unit predicts future performance. The point assignment unit assigns points based on the performance evaluated by the player performance evaluation unit and the AI ​​prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment allows real professional athletes and virtual athletes generated by AI to compete in the same league. [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 fantasy AI league system according to an embodiment of the present invention is a system in which real professional athletes and virtual athletes generated by AI compete in the same league. This allows the fantasy AI league system to provide a new style of fantasy sports in which real athletes and virtual athletes compete in the same league.

[0029] A fantasy AI league system according to an embodiment includes a player performance evaluation unit, an AI prediction unit, and a point assignment unit. The player performance evaluation unit evaluates the performance of real-world players. For example, the player performance evaluation unit collects data on player movements and plays during a game in real time and evaluates the performance based on the data. The player performance evaluation unit can also analyze past game data to evaluate player performance. The player performance evaluation unit can also evaluate performance taking into account the player's health status and training data. The AI ​​prediction unit predicts future performance. For example, the AI ​​prediction unit predicts a player's future performance using a generative AI (e.g., a text generation AI or a multimodal generative AI). The AI ​​prediction unit can also predict future performance by analyzing past game data, the player's health status, and training data. The AI ​​prediction unit can also predict future performance taking into account the player's psychological state and motivation. The point assignment unit assigns points based on the performance evaluated by the player performance evaluation unit and the AI ​​prediction unit. For example, the point assignment unit calculates points based on the player's performance and provides feedback to the user. The point-assigning unit can also assign points based on future performance predicted by the AI. The point-assigning unit can also adjust points based on data updated in real time. As a result, the fantasy AI league system according to the embodiment can provide a new style of fantasy sports in which real players and virtual players compete in the same league.

[0030] The AI ​​prediction unit can predict future performance by taking into account a player's psychological state and motivation. For example, the AI ​​prediction unit analyzes a player's psychological state and predicts future performance based on that data. For example, it analyzes a player's interviews and social media posts to evaluate their psychological state. The AI ​​prediction unit also builds a predictive model that takes a player's motivation into account and predicts future performance based on that model. For example, it analyzes a player's goal setting and pre-match routine. The AI ​​prediction unit also monitors a player's psychological state and motivation in real time and dynamically predicts future performance based on that data. For example, it analyzes a player's facial expressions and behavior during a match. This allows for more accurate performance predictions by taking a player's psychological state and motivation into account.

[0031] The AI ​​prediction unit can predict future performance by taking into account external factors such as weather and the importance of the game. For example, the AI ​​prediction unit incorporates weather data into a player's performance evaluation and predicts future performance based on that data. For example, it analyzes the impact of rain and wind speed on a player's performance. The AI ​​prediction unit also builds a performance evaluation model that takes into account the importance of the game and predicts future performance based on that model. For example, it evaluates the importance of finals and league matches. The AI ​​prediction unit also incorporates external factors (for example, the number of spectators and the characteristics of the game venue) into the performance evaluation and predicts future performance based on that data. For example, it analyzes the differences between home games and away games. By taking external factors into account, this enables more realistic performance predictions.

[0032] The system can be applied to different sports and competitive events. For example, the system applies a player performance evaluation model to different sports, and AI predicts future performance based on that model. For example, the system evaluates performance in different sports, such as soccer and basketball. The system also applies the performance evaluation model to different competitive events, and AI predicts future performance based on that data. For example, the system evaluates performance in large-scale events such as the Olympics and the World Cup. The system also generalizes the performance evaluation model so that it can be used for different sports and competitive events. For example, it builds a performance evaluation model that takes into account the characteristics of each sport. This increases versatility by applying it to different sports and competitive events.

[0033] The system has a function that allows users to simulate future performance predicted by the AI ​​prediction unit. The system provides, for example, a function that allows users to simulate future performance predicted by the AI. For example, a simulation tool is developed that predicts player performance and creates a strategy based on the results. The system also uses the simulation function to build a system that allows users to try out different strategies. For example, the system changes player placement or tactics and simulates the effects. The system also provides an interface that allows users to refine their strategy based on the simulation results. For example, the system visually displays the simulation results so that the user can intuitively understand them. This helps users refine their strategy by simulating future performance.

[0034] The system can visualize an AI prediction algorithm to allow users to intuitively understand how it works. For example, the system visualizes an AI prediction algorithm to allow users to intuitively understand how it works. For example, the system may show the algorithm flow using a flowchart or graph. The system may also provide an interface that allows users to operate the AI ​​prediction algorithm. For example, the system may visually display each step of the algorithm to allow users to check its contents. The system may also develop tools that visualize the AI ​​prediction algorithm to allow users to learn how it works. For example, the system may provide an interactive tutorial. By visualizing the AI ​​prediction algorithm, the system may allow users to intuitively understand how it works.

[0035] The system can publish the update history of the data sources and predictive models used by the AI, allowing users to confirm their reliability. For example, the system may publish the update history of the data sources and predictive models used by the AI, allowing users to confirm their reliability. For example, the system may display the origin of the data source and the update date and time. The system may also provide an interface that allows users to check the update history of the AI's predictive models. For example, the system may list the model versions and changes. The system may also build a system to evaluate the reliability of the AI's data sources and predictive models, and provide the results to users. For example, the system may evaluate the quality of the data and the accuracy of the model. By publishing the update history of the data sources and predictive models, users may be able to confirm their reliability.

[0036] The system can use real-time data to evaluate not only player performance but also match tactics and strategies. For example, a system is constructed that uses real-time data to evaluate not only player performance but also match tactics and strategies. For example, the system analyzes player movements and positioning during a match and evaluates the effectiveness of tactics. The system also develops a system that proposes changes to tactics and strategies based on real-time data during a match. For example, the system analyzes player performance data and proposes optimal tactics in real time. The system also uses real-time data to evaluate match tactics and strategies, and constructs a system that formulates a strategy for the next match based on the results. For example, the system analyzes data from past matches and simulates the tactics for the next match. In this way, it becomes possible to evaluate match tactics and strategies using real-time data.

[0037] The system displays real-time data on a dashboard that can be customized by the user, and can support immediate adjustments to strategies. For example, the system provides a dashboard that allows the user to customize real-time data, and builds a system that supports immediate adjustments to strategies. For example, player performance data is displayed in real time, allowing the user to adjust strategies based on that data. The system also displays the real-time data on the dashboard, allowing the user to intuitively understand it. For example, the data is visualized using graphs and charts. The system also provides an interface that allows the user to immediately adjust strategies based on the real-time data. For example, a function is added that allows the user to change player placements and tactics in real time. This allows the user to customize and display real-time data, and supports immediate adjustments to strategies.

[0038] The system can diversify tournament formats and increase the types of tournaments that users can choose from. For example, the system is constructed to diversify tournament formats and increase the types of tournaments that users can choose from. For example, different types of tournaments are provided, such as single elimination and double elimination. The system also provides a function that allows users to customize the tournament format. For example, the system allows users to set tournament rules and participation conditions. The system also diversifies tournament formats to accommodate different sports and competitive events. For example, tournaments for different sports such as soccer and basketball are provided. In this way, diversifying tournament formats increases users' options and enhances their enjoyment.

[0039] The system can diversify the types of rewards and provide rewards and incentives to increase user motivation. For example, the system builds a system that diversifies the types of rewards and provides rewards and incentives to increase user motivation. For example, different types of rewards are provided, such as cash, gift cards, and bonus items. The system also develops a system that provides rewards according to user performance. For example, rewards are set in stages according to tournament results. The system also diversifies the types of rewards and increases the reward options that users can select. For example, the system allows users to select rewards according to their preferences. In this way, diversifying the types of rewards increases user motivation.

[0040] The system can adapt the tournament format to different sports and entertainment fields, catering to a wide range of users. For example, the system can adapt the tournament format to different sports, building a system that caters to a wide range of users. For example, it can provide tournaments for different sports, such as soccer and basketball. The system can also adapt the tournament format to the entertainment field, providing users with new experiences. For example, it can hold tournaments in e-sports and virtual reality. The system can also generalize the tournament format across different sports and entertainment fields, building a system that provides users with a consistent experience. For example, it can use the same tournament format for multiple sports or events. This allows the tournament format to be adapted to different fields, catering to a wide range of users.

[0041] The system can build a platform that allows users to share tournament results and promote community activation. For example, the system builds a platform that allows users to share tournament results and develops a system that promotes community activation. For example, the system provides a function that allows tournament results to be shared on social media. The system also provides an interface that allows users to share tournament results and interact with other users. For example, the system provides a discussion forum based on tournament results. The system also builds a system that shares tournament results in real time and allows users to engage in community activities based on the results. For example, an event is held based on the tournament results. In this way, sharing tournament results promotes community activation.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The system can be applied to different sports and competitive events. For example, a player performance evaluation model can be applied to different sports, and AI can predict future performance based on that model. For example, performance evaluation can be performed in different sports, such as soccer or basketball. The system can also apply the performance evaluation model to different competitive events, and AI can predict future performance based on that data. For example, performance evaluation can be performed in large-scale events such as the Olympics or the World Cup. The system can also generalize the performance evaluation model to make it applicable to different sports and competitive events. For example, a performance evaluation model can be constructed that takes into account the characteristics of each sport. This increases versatility by applying it to different sports and competitive events.

[0044] The system has a function that allows users to simulate future performance predicted by the AI ​​prediction unit. For example, a function is provided that allows users to simulate future performance predicted by the AI. For example, a simulation tool is developed that predicts player performance and creates strategies based on the results. The system also uses the simulation function to build a system that allows users to try out different strategies. For example, the system changes player placement or tactics and simulates the effects. The system also provides an interface that allows users to refine their strategies based on the simulation results. For example, the system visually displays the simulation results so that users can intuitively understand them. This allows users to simulate future performance and supports refinement of strategies.

[0045] The system can use real-time data to evaluate not only player performance but also match tactics and strategies. For example, a system is built that uses real-time data to evaluate not only player performance but also match tactics and strategies. For example, the system analyzes player movements and positioning during a match to evaluate the effectiveness of tactics. The system also develops a system that proposes changes to tactics and strategies based on real-time data during a match. For example, it analyzes player performance data and proposes optimal tactics in real time. The system also uses real-time data to evaluate match tactics and strategies, and builds a system that formulates a strategy for the next match based on the results. For example, it analyzes data from past matches and simulates the tactics for the next match. In this way, it becomes possible to use real-time data to evaluate match tactics and strategies.

[0046] The system can display real-time data on a dashboard that can be customized by the user, and support immediate adjustments to strategies. For example, a system is constructed that provides a dashboard that allows the user to customize real-time data and supports immediate adjustments to strategies. For example, player performance data is displayed in real time, allowing the user to adjust strategies based on that data. The system also displays the real-time data on the dashboard, allowing the user to intuitively understand it. For example, the data is visualized using graphs and charts. The system also provides an interface that allows the user to immediately adjust strategies based on the real-time data. For example, a function is added that allows the user to change player placements and tactics in real time. This allows the user to customize and display real-time data, and supports immediate adjustments to strategies.

[0047] The system can diversify tournament formats and increase the types of tournaments that users can choose from. For example, a system is constructed that diversifies tournament formats and increases the types of tournaments that users can choose from. For example, different types of tournaments are provided, such as single elimination and double elimination. The system also provides a function that allows users to customize the tournament format. For example, the system allows users to set tournament rules and participation conditions. The system also diversifies tournament formats to accommodate different sports and competitive events. For example, tournaments for different sports such as soccer and basketball are provided. In this way, diversifying tournament formats increases users' options and enhances their enjoyment.

[0048] The system can diversify the types of rewards and provide rewards and incentives to increase user motivation. For example, a system is constructed that diversifies the types of rewards and provides rewards and incentives to increase user motivation. For example, different types of rewards are provided, such as cash, gift cards, and bonus items. The system also develops a system that provides rewards according to user performance. For example, rewards are set in stages according to tournament results. The system also diversifies the types of rewards and increases the reward options that users can select. For example, the system allows users to select rewards according to their preferences. In this way, diversifying the types of rewards increases user motivation.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The player performance evaluation unit evaluates the performance of real-world players. For example, it collects data on players' movements and plays during a match in real time and evaluates their performance based on that data. It can also analyze data from past matches to evaluate player performance. It can also evaluate performance by taking into account the player's health and training data. Step 2: The AI ​​prediction unit predicts future performance. For example, it uses generative AI (text generation AI or multimodal generation AI) to predict a player's future performance. It can also predict future performance by analyzing past match data, a player's health status, and training data. It can also predict future performance by taking into account a player's psychological state and motivation. Step 3: The point assigning unit assigns points based on the performance evaluated by the player performance evaluation unit and the AI ​​prediction unit. For example, points can be calculated based on the player's performance and fed back to the user. Points can also be assigned based on future performance predicted by the AI. Furthermore, points can be adjusted based on data updated in real time.

[0051] (Example 2) The fantasy AI league system according to an embodiment of the present invention is a system in which real professional athletes and virtual athletes generated by AI compete in the same league. This allows the fantasy AI league system to provide a new style of fantasy sports in which real athletes and virtual athletes compete in the same league.

[0052] A fantasy AI league system according to an embodiment includes a player performance evaluation unit, an AI prediction unit, and a point assignment unit. The player performance evaluation unit evaluates the performance of real-world players. For example, the player performance evaluation unit collects data on player movements and plays during a game in real time and evaluates the performance based on the data. The player performance evaluation unit can also analyze past game data to evaluate player performance. The player performance evaluation unit can also evaluate performance taking into account the player's health status and training data. The AI ​​prediction unit predicts future performance. For example, the AI ​​prediction unit predicts a player's future performance using a generative AI (e.g., a text generation AI or a multimodal generative AI). The AI ​​prediction unit can also predict future performance by analyzing past game data, the player's health status, and training data. The AI ​​prediction unit can also predict future performance taking into account the player's psychological state and motivation. The point assignment unit assigns points based on the performance evaluated by the player performance evaluation unit and the AI ​​prediction unit. For example, the point assignment unit calculates points based on the player's performance and provides feedback to the user. The point-assigning unit can also assign points based on future performance predicted by the AI. The point-assigning unit can also adjust points based on data updated in real time. As a result, the fantasy AI league system according to the embodiment can provide a new style of fantasy sports in which real players and virtual players compete in the same league.

[0053] The AI ​​prediction unit can predict future performance by taking into account a player's psychological state and motivation. For example, the AI ​​prediction unit analyzes a player's psychological state and predicts future performance based on that data. For example, it analyzes a player's interviews and social media posts to evaluate their psychological state. The AI ​​prediction unit also builds a predictive model that takes a player's motivation into account and predicts future performance based on that model. For example, it analyzes a player's goal setting and pre-match routine. The AI ​​prediction unit also monitors a player's psychological state and motivation in real time and dynamically predicts future performance based on that data. For example, it analyzes a player's facial expressions and behavior during a match. This allows for more accurate performance predictions by taking a player's psychological state and motivation into account.

[0054] The AI ​​prediction unit can predict future performance by taking into account external factors such as weather and the importance of the game. For example, the AI ​​prediction unit incorporates weather data into a player's performance evaluation and predicts future performance based on that data. For example, it analyzes the impact of rain and wind speed on a player's performance. The AI ​​prediction unit also builds a performance evaluation model that takes into account the importance of the game and predicts future performance based on that model. For example, it evaluates the importance of finals and league matches. The AI ​​prediction unit also incorporates external factors (for example, the number of spectators and the characteristics of the game venue) into the performance evaluation and predicts future performance based on that data. For example, it analyzes the differences between home games and away games. By taking external factors into account, this enables more realistic performance predictions.

[0055] The AI ​​prediction unit can use the emotion estimation function to analyze a player's emotional state in real time and predict future performance based on that emotion. For example, the AI ​​prediction unit uses the emotion estimation function to analyze a player's emotional state in real time and predict future performance based on that data. For example, it analyzes the player's facial expressions and voice and calculates an emotion score. The AI ​​prediction unit also monitors a player's emotional state in real time and dynamically adjusts performance predictions based on that data. For example, if a player is nervous, it will revise the performance prediction downward. The AI ​​prediction unit also analyzes a player's emotional state based on the emotion estimation data and builds a system that predicts future performance based on the results. For example, if a player is relaxed, it will revise the performance prediction upward. This makes it possible to make more dynamic and accurate performance predictions by taking a player's emotional state into consideration.

[0056] The system can be applied to different sports and competitive events. For example, the system applies a player performance evaluation model to different sports, and AI predicts future performance based on that model. For example, the system evaluates performance in different sports, such as soccer and basketball. The system also applies the performance evaluation model to different competitive events, and AI predicts future performance based on that data. For example, the system evaluates performance in large-scale events such as the Olympics and the World Cup. The system also generalizes the performance evaluation model so that it can be used for different sports and competitive events. For example, it builds a performance evaluation model that takes into account the characteristics of each sport. This increases versatility by applying it to different sports and competitive events.

[0057] The system has a function that allows users to simulate future performance predicted by the AI ​​prediction unit. The system provides, for example, a function that allows users to simulate future performance predicted by the AI. For example, a simulation tool is developed that predicts player performance and creates a strategy based on the results. The system also uses the simulation function to build a system that allows users to try out different strategies. For example, the system changes player placement or tactics and simulates the effects. The system also provides an interface that allows users to refine their strategy based on the simulation results. For example, the system visually displays the simulation results so that the user can intuitively understand them. This helps users refine their strategy by simulating future performance.

[0058] The system can use the emotion estimation function to analyze the emotions a user feels toward a player's performance and adjust rewards and incentives based on those emotions. For example, the system uses the emotion estimation function to analyze the emotions a user feels toward a player's performance in real time and adjust rewards and incentives based on that data. For example, if a user feels positive emotions, the system increases rewards. The system also builds a system that dynamically adjusts rewards and incentives based on the user's emotion data. For example, if a user is excited, the system provides a special reward. The system also analyzes the emotions a user feels toward a player's performance based on the emotion estimation data and customizes rewards and incentives based on the results. For example, the system provides benefits according to the user's emotions. In this way, by adjusting rewards and incentives based on the user's emotions, user satisfaction is improved.

[0059] The system can visualize an AI prediction algorithm to allow users to intuitively understand how it works. For example, the system visualizes an AI prediction algorithm to allow users to intuitively understand how it works. For example, the system may show the algorithm flow using a flowchart or graph. The system may also provide an interface that allows users to operate the AI ​​prediction algorithm. For example, the system may visually display each step of the algorithm to allow users to check its contents. The system may also develop tools that visualize the AI ​​prediction algorithm to allow users to learn how it works. For example, the system may provide an interactive tutorial. By visualizing the AI ​​prediction algorithm, the system may allow users to intuitively understand how it works.

[0060] The system can publish the update history of the data sources and predictive models used by the AI, allowing users to confirm their reliability. For example, the system may publish the update history of the data sources and predictive models used by the AI, allowing users to confirm their reliability. For example, the system may display the origin of the data source and the update date and time. The system may also provide an interface that allows users to check the update history of the AI's predictive models. For example, the system may list the model versions and changes. The system may also build a system to evaluate the reliability of the AI's data sources and predictive models, and provide the results to users. For example, the system may evaluate the quality of the data and the accuracy of the model. By publishing the update history of the data sources and predictive models, users may be able to confirm their reliability.

[0061] The system can use the emotion estimation function to analyze the emotions users have toward AI predictions and propose measures to improve transparency based on those emotions. For example, the system can use the emotion estimation function to analyze the emotions users have toward AI predictions in real time and propose measures to improve transparency based on that data. For example, if the user is feeling anxious, the system can provide a detailed explanation. The system can also build a system that makes specific proposals to improve AI transparency based on user emotion data. For example, it can focus on explaining areas where the user has questions. The system can also analyze the emotions users have toward AI predictions based on the emotion estimation data and provide feedback to improve transparency based on the results. For example, it can provide an explanation that reflects the user's emotions. In this way, the system can propose measures to improve transparency based on the user's emotions, thereby improving the reliability of the AI.

[0062] The system can use real-time data to evaluate not only player performance but also match tactics and strategies. For example, a system is constructed that uses real-time data to evaluate not only player performance but also match tactics and strategies. For example, the system analyzes player movements and positioning during a match and evaluates the effectiveness of tactics. The system also develops a system that proposes changes to tactics and strategies based on real-time data during a match. For example, the system analyzes player performance data and proposes optimal tactics in real time. The system also uses real-time data to evaluate match tactics and strategies, and constructs a system that formulates a strategy for the next match based on the results. For example, the system analyzes data from past matches and simulates the tactics for the next match. In this way, it becomes possible to evaluate match tactics and strategies using real-time data.

[0063] The system displays real-time data on a dashboard that can be customized by the user, and can support immediate adjustments to strategies. For example, the system provides a dashboard that allows the user to customize real-time data, and builds a system that supports immediate adjustments to strategies. For example, player performance data is displayed in real time, allowing the user to adjust strategies based on that data. The system also displays the real-time data on the dashboard, allowing the user to intuitively understand it. For example, the data is visualized using graphs and charts. The system also provides an interface that allows the user to immediately adjust strategies based on the real-time data. For example, a function is added that allows the user to change player placements and tactics in real time. This allows the user to customize and display real-time data, and supports immediate adjustments to strategies.

[0064] The system can use the emotion estimation function to analyze the emotions a user feels toward real-time data and adjust the data display method based on the emotions. For example, the system uses the emotion estimation function to analyze the emotions a user feels toward real-time data in real time and adjust the data display method based on the data. For example, if the user is feeling stressed, the data display is simplified. The system also builds a system that customizes the display method of real-time data based on the user's emotion data. For example, if the user is excited, the data display is made more detailed. The system also analyzes the emotions a user feels toward real-time data based on the emotion estimation data and adjusts the data display method based on the results. For example, the system displays data according to the user's emotions. In this way, adjusting the data display method based on the user's emotions improves the user's understanding.

[0065] The system can diversify tournament formats and increase the types of tournaments that users can choose from. For example, the system is constructed to diversify tournament formats and increase the types of tournaments that users can choose from. For example, different types of tournaments are provided, such as single elimination and double elimination. The system also provides a function that allows users to customize the tournament format. For example, the system allows users to set tournament rules and participation conditions. The system also diversifies tournament formats to accommodate different sports and competitive events. For example, tournaments for different sports such as soccer and basketball are provided. In this way, diversifying tournament formats increases users' options and enhances their enjoyment.

[0066] The system can diversify the types of rewards and provide rewards and incentives to increase user motivation. For example, the system builds a system that diversifies the types of rewards and provides rewards and incentives to increase user motivation. For example, different types of rewards are provided, such as cash, gift cards, and bonus items. The system also develops a system that provides rewards according to user performance. For example, rewards are set in stages according to tournament results. The system also diversifies the types of rewards and increases the reward options that users can select. For example, the system allows users to select rewards according to their preferences. In this way, diversifying the types of rewards increases user motivation.

[0067] The system can use the emotion estimation function to analyze the emotions users have toward tournaments and rewards, and adjust the tournament design and reward settings based on those emotions. For example, the system uses the emotion estimation function to analyze the emotions users have toward tournaments and rewards in real time, and adjust the tournament design and reward settings based on that data. For example, if the user is excited, the rewards are increased. The system also builds a system that dynamically adjusts the tournament design and reward settings based on the user's emotion data. For example, if the user is feeling stressed, the difficulty of the tournament is adjusted. The system also analyzes the emotions users have toward tournaments and rewards based on the emotion estimation data, and customizes the tournament design and reward settings based on the results. For example, it provides benefits according to the user's emotions. In this way, adjusting the tournament design and reward settings based on the user's emotions improves user satisfaction.

[0068] The system can adapt the tournament format to different sports and entertainment fields, catering to a wide range of users. For example, the system can adapt the tournament format to different sports, building a system that caters to a wide range of users. For example, it can provide tournaments for different sports, such as soccer and basketball. The system can also adapt the tournament format to the entertainment field, providing users with new experiences. For example, it can hold tournaments in e-sports and virtual reality. The system can also generalize the tournament format across different sports and entertainment fields, building a system that provides users with a consistent experience. For example, it can use the same tournament format for multiple sports or events. This allows the tournament format to be adapted to different fields, catering to a wide range of users.

[0069] The system can build a platform that allows users to share tournament results and promote community activation. For example, the system builds a platform that allows users to share tournament results and develops a system that promotes community activation. For example, the system provides a function that allows tournament results to be shared on social media. The system also provides an interface that allows users to share tournament results and interact with other users. For example, the system provides a discussion forum based on tournament results. The system also builds a system that shares tournament results in real time and allows users to engage in community activities based on the results. For example, an event is held based on the tournament results. In this way, sharing tournament results promotes community activation.

[0070] The system can use the emotion estimation function to adjust the difficulty of the tournament and the value of the rewards based on the emotions the user feels toward the tournament and the rewards. For example, the system uses the emotion estimation function to analyze the emotions the user feels toward the tournament and the rewards in real time, and adjust the difficulty of the tournament and the value of the rewards based on that data. For example, if the user is excited, the rewards are increased. The system also builds a system that dynamically adjusts the difficulty of the tournament and the value of the rewards based on the user's emotion data. For example, if the user is feeling stressed, the system adjusts the difficulty of the tournament. The system also analyzes the emotions the user feels toward the tournament and the rewards based on the emotion estimation data, and customizes the difficulty of the tournament and the value of the rewards based on the results. For example, the system provides benefits according to the user's emotions. In this way, user satisfaction is improved by adjusting the difficulty of the tournament and the value of the rewards based on the user's emotions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The system can analyze the emotions a user feels toward a player's performance and adjust rewards and incentives based on those emotions. For example, if a user feels positive emotions, it increases the reward. The system also builds a system that dynamically adjusts rewards and incentives based on the user's emotion data. For example, if a user is excited, it provides a special reward. The system also analyzes the emotions a user feels toward a player's performance based on emotion estimation data and customizes rewards and incentives based on the results. For example, it provides benefits according to the user's emotions. In this way, user satisfaction is improved by adjusting rewards and incentives based on the user's emotions.

[0073] The system can be applied to different sports and competitive events. For example, a player performance evaluation model can be applied to different sports, and AI can predict future performance based on that model. For example, performance evaluation can be performed in different sports, such as soccer or basketball. The system can also apply the performance evaluation model to different competitive events, and AI can predict future performance based on that data. For example, performance evaluation can be performed in large-scale events such as the Olympics or the World Cup. The system can also generalize the performance evaluation model to make it applicable to different sports and competitive events. For example, a performance evaluation model can be constructed that takes into account the characteristics of each sport. This increases versatility by applying it to different sports and competitive events.

[0074] The system has a function that allows users to simulate future performance predicted by the AI ​​prediction unit. For example, a function is provided that allows users to simulate future performance predicted by the AI. For example, a simulation tool is developed that predicts player performance and creates strategies based on the results. The system also uses the simulation function to build a system that allows users to try out different strategies. For example, the system changes player placement or tactics and simulates the effects. The system also provides an interface that allows users to refine their strategies based on the simulation results. For example, the system visually displays the simulation results so that users can intuitively understand them. This allows users to simulate future performance and supports refinement of strategies.

[0075] The system can use the emotion estimation function to analyze the emotions users have toward AI predictions and propose measures to improve transparency based on those emotions. For example, the emotion estimation function can be used to analyze the emotions users have toward AI predictions in real time and propose measures to improve transparency based on that data. For example, if the user is feeling anxious, a detailed explanation can be provided. The system can also build a system that makes specific proposals to improve AI transparency based on user emotion data. For example, it can focus on explaining areas where the user has questions. The system can also analyze the emotions users have toward AI predictions based on emotion estimation data and provide feedback to improve transparency based on the results. For example, it can provide an explanation that reflects the user's emotions. In this way, by proposing measures to improve transparency based on user emotions, the reliability of the AI ​​can be improved.

[0076] The system can use real-time data to evaluate not only player performance but also match tactics and strategies. For example, a system is built that uses real-time data to evaluate not only player performance but also match tactics and strategies. For example, the system analyzes player movements and positioning during a match to evaluate the effectiveness of tactics. The system also develops a system that proposes changes to tactics and strategies based on real-time data during a match. For example, it analyzes player performance data and proposes optimal tactics in real time. The system also uses real-time data to evaluate match tactics and strategies, and builds a system that formulates a strategy for the next match based on the results. For example, it analyzes data from past matches and simulates the tactics for the next match. In this way, it becomes possible to use real-time data to evaluate match tactics and strategies.

[0077] The system can display real-time data on a dashboard that can be customized by the user, and support immediate adjustments to strategies. For example, a system is constructed that provides a dashboard that allows the user to customize real-time data and supports immediate adjustments to strategies. For example, player performance data is displayed in real time, allowing the user to adjust strategies based on that data. The system also displays the real-time data on the dashboard, allowing the user to intuitively understand it. For example, the data is visualized using graphs and charts. The system also provides an interface that allows the user to immediately adjust strategies based on the real-time data. For example, a function is added that allows the user to change player placements and tactics in real time. This allows the user to customize and display real-time data, and supports immediate adjustments to strategies.

[0078] The system can use the emotion estimation function to analyze the emotions a user feels toward real-time data and adjust the data display method based on that emotion. For example, the emotion estimation function can be used to analyze the emotions a user feels toward real-time data in real time and adjust the data display method based on that data. For example, if the user is feeling stressed, the data display can be simplified. The system also builds a system that customizes the display method of real-time data based on the user's emotion data. For example, if the user is excited, the data display can be made more detailed. The system also analyzes the emotions a user feels toward real-time data based on the emotion estimation data and adjusts the data display method based on the results. For example, the system displays data according to the user's emotion. In this way, adjusting the data display method based on the user's emotion improves the user's understanding.

[0079] The system can diversify tournament formats and increase the types of tournaments that users can choose from. For example, a system is constructed that diversifies tournament formats and increases the types of tournaments that users can choose from. For example, different types of tournaments are provided, such as single elimination and double elimination. The system also provides a function that allows users to customize the tournament format. For example, the system allows users to set tournament rules and participation conditions. The system also diversifies tournament formats to accommodate different sports and competitive events. For example, tournaments for different sports such as soccer and basketball are provided. In this way, diversifying tournament formats increases users' options and enhances their enjoyment.

[0080] The system can diversify the types of rewards and provide rewards and incentives to increase user motivation. For example, a system is constructed that diversifies the types of rewards and provides rewards and incentives to increase user motivation. For example, different types of rewards are provided, such as cash, gift cards, and bonus items. The system also develops a system that provides rewards according to user performance. For example, rewards are set in stages according to tournament results. The system also diversifies the types of rewards and increases the reward options that users can select. For example, the system allows users to select rewards according to their preferences. In this way, diversifying the types of rewards increases user motivation.

[0081] The system can use the emotion estimation function to adjust the difficulty of the tournament and the value of the rewards based on the emotions the user feels toward the tournament and the rewards. For example, the emotion estimation function can be used to analyze the emotions the user feels toward the tournament and the rewards in real time, and adjust the difficulty of the tournament and the value of the rewards based on that data. For example, if the user is excited, the rewards can be increased. The system can also build a system that dynamically adjusts the difficulty of the tournament and the value of the rewards based on the user's emotion data. For example, if the user is feeling stressed, the difficulty of the tournament can be adjusted. The system can also analyze the emotions the user feels toward the tournament and the rewards based on the emotion estimation data, and customize the difficulty of the tournament and the value of the rewards based on the results. For example, the system can provide benefits according to the user's emotions. In this way, adjusting the difficulty of the tournament and the value of the rewards based on the user's emotions improves user satisfaction.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The player performance evaluation unit evaluates the performance of real-world players. For example, it collects data on players' movements and plays during a match in real time and evaluates their performance based on that data. It can also analyze data from past matches to evaluate player performance. It can also evaluate performance by taking into account the player's health and training data. Step 2: The AI ​​prediction unit predicts future performance. For example, it uses generative AI (text generation AI or multimodal generation AI) to predict a player's future performance. It can also predict future performance by analyzing past match data, a player's health status, and training data. It can also predict future performance by taking into account a player's psychological state and motivation. Step 3: The point assigning unit assigns points based on the performance evaluated by the player performance evaluation unit and the AI ​​prediction unit. For example, points can be calculated based on the player's performance and fed back to the user. Points can also be assigned based on future performance predicted by the AI. Furthermore, points can be adjusted based on data updated in real time.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] 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]

[0151] 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. It is a system in which real professional athletes and virtual athletes generated by AI compete in the same league. a player performance evaluation unit that evaluates the performance of real players; AI prediction section that predicts future performance, a point awarding unit that awards points based on the performance evaluated by the player performance evaluation unit and the AI ​​prediction unit. A system characterized by:

2. The AI ​​prediction unit Predicting future performance by taking into account the player's psychological state and motivation 2. The system of claim 1.

3. The AI ​​prediction unit Predict future performance by taking into account external factors such as weather and the importance of the match 2. The system of claim 1.

4. The AI ​​prediction unit Analyzing the emotional state of the player in real time and predicting the future performance based on the emotional state.

2. The system of claim 1.

5. The system comprises: Applicable to different sports and competitive events 2. The system of claim 1.

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