System

Abstract

The system according to the embodiment aims to perform a match or practice while learning professional play styles.SOLUTION: A system according to an embodiment includes a collection unit, a learning unit, a selection unit, a match-up unit, and a wall-hitting unit. The collection unit collects professional play data. The learning unit learns the data collected by the collection unit. The selection unit selects a game that the user wants to play. The AI functions as an opponent based on the game selected by the selection unit. The wall-hitting unit provides a function for the user to hit the wall.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to learn the playing style of professionals while playing against or practicing against them.

[0005] The system according to the embodiment aims to allow players to play matches and practice while learning the playing style of professionals. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a learning unit, a selection unit, a competition unit, and a hitting-against-the-wall unit. The collection unit collects professional play data. The learning unit studies the data collected by the collection unit. The selection unit selects a game in which the user wants to play. In the competition unit, an AI functions as the opponent based on the game selected by the selection unit. The hitting-against-the-wall unit provides a function for the user to hit-against the wall. [Effects of the Invention]

[0007] The system according to the embodiment allows players to play against or practice while learning the playing style of professionals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A system according to an embodiment of the present invention generates an AI that has learned the playing styles of professionals in games such as Go, Shogi, Chess, and card games, allowing anyone to easily play against or practice against professionals. This system collects professional play data, trains the AI ​​to learn it, and allows users to select the game they want to play and play against the AI. The AI ​​reproduces the professional's playing style and acts as a real-time opponent for the user. Users can also practice against the AI, which can be useful for practice and skill improvement. This allows users to play against or practice while learning the professional's playing style, which can be useful for skill improvement. For example, by playing against a Go professional while learning his or her playing style, users can improve their own skills. Furthermore, by practicing Shogi against a professional, users can improve their skills while practicing alone.

[0029] A game competition system according to an embodiment includes a collection unit, a learning unit, a selection unit, a competition unit, and a hitting-against-a-wall unit. The collection unit collects professional play data. The professional play data includes, for example, professional match data for various games such as Go, Shogi, chess, and card games. The collection unit, for example, collects match records from the Internet. The collection unit can also collect data by analyzing professional match videos. The collection unit can also acquire professional match results from a database. The learning unit allows an AI to learn professional playing styles based on the data collected by the collection unit. The learning unit, for example, analyzes the data using a machine learning algorithm to train the AI. The learning unit can also preprocess the data to enable the AI ​​to learn efficiently. The learning unit can also evaluate the AI's learning results and adjust the algorithm as necessary. The selection unit selects a game the user wants to play. For example, the user selects a game through an interface. The selection unit can also suggest optimal games based on the user's past selection history. Furthermore, the selection unit can adjust the difficulty of the game according to the user's skill level. The competition unit functions as an AI opponent based on the game selected by the selection unit. For example, the competition unit reproduces a professional's playing style and competes against the user. The competition unit can also adjust the AI's behavior in real time according to the user's movements. Furthermore, the competition unit can record the results of the match and provide feedback to the user. The hitting-against-the-wall unit functions as an AI opponent for the user to practice alone. For example, the AI ​​responds appropriately to the user's movements. The hitting-against-the-wall unit can also record the user's practice data and provide advice for skill improvement. Furthermore, the hitting-against-the-wall unit can set practice scenarios to enable the user to practice efficiently. As a result, the game competition system according to the embodiment allows the user to easily play against or hit-against professionals using an AI that has learned the professional's playing style. For example, the user can play against or practice while learning the professional's playing style, which can help improve their skills.

[0030] The collection unit can collect professional match data for each game of Go, Shogi, chess, and card games. The collection unit, for example, collects match records on the Internet. The collection unit can also collect data by analyzing videos of professional matches. For example, the collection unit uses video analysis technology to extract match moves as data. The collection unit can also obtain professional match results from a database. For example, the collection unit obtains match results and detailed tactical information from the database. In this way, by collecting professional match data for each game, the AI ​​can learn various playing styles.

[0031] The learning unit enables the AI ​​to learn professional playing styles based on the collected data. The learning unit, for example, uses a machine learning algorithm to analyze the data and have the AI ​​learn from it. For example, the learning unit uses deep learning to learn professional playing styles. The learning unit can also preprocess the data to enable the AI ​​to learn efficiently. For example, the learning unit can remove noise from the data and provide accurate data. The learning unit can also evaluate the AI's learning results and adjust the algorithm as needed. For example, the learning unit can evaluate the accuracy of the learning results and adjust the algorithm parameters. This allows the AI ​​to learn professional playing styles, providing a realistic competitive experience.

[0032] The selection unit allows the user to select a game in which they want to play. For example, the selection unit allows the user to select a game through an interface. For example, the selection unit allows the user to select a game from an on-screen menu. The selection unit can also suggest an optimal game based on the user's past selection history. For example, the selection unit can analyze the history of games the user has previously selected and suggest related games. The selection unit can also adjust the difficulty of the game according to the user's skill level. For example, the selection unit can evaluate the user's skill level and suggest a game with an appropriate difficulty level. This allows the user to select a game they want to play and play against an AI in their preferred game.

[0033] The match unit can have an AI function as an opponent based on the selected game. For example, the AI ​​reproduces a professional playing style and competes against the user. For example, the match unit has the AI ​​compete against the user using professional tactics. The match unit can also adjust the AI's behavior in real time according to the user's movements. For example, the match unit analyzes the user's movements, and the AI ​​responds appropriately. The match unit can also record the results of the match and provide feedback to the user. For example, the match unit analyzes the results of the match and suggests areas for improvement to the user. This allows the AI ​​to function as an opponent based on the selected game, providing a realistic match experience.

[0034] The hitting-against-the-wall section allows the AI ​​to function as an opponent so that the user can practice alone. In the hitting-against-the-wall section, for example, the AI ​​responds appropriately to the user's movements. For example, the hitting-against-the-wall section returns an appropriate move to the user's move. The hitting-against-the-wall section can also record the user's practice data and provide advice for improving skills. For example, the hitting-against-the-wall section can analyze the user's practice data and suggest areas for improvement. The hitting-against-the-wall section can also set practice scenarios to enable the user to practice efficiently. For example, the hitting-against-the-wall section provides a scenario for practicing a specific tactic. This allows the AI ​​to function as an opponent so that the user can practice alone, helping to improve skills.

[0035] The collection unit can analyze the professional's past match history and select an appropriate collection method. The collection unit, for example, acquires the professional's past match history from a database. For example, the collection unit acquires match results and detailed tactical information from the database. The collection unit can also analyze the professional's past match history and extract particularly excellent plays. For example, the collection unit prioritizes collecting matches in which specific tactics or strategies were frequently used from the professional's match history. The collection unit can also collect matches with different opponents in a balanced manner. For example, the collection unit collects matches with different opponents in a balanced manner based on the professional's match history. This enables more effective data collection by analyzing the professional's past match history.

[0036] When collecting match data, the collection unit can filter the data based on the professional's current training status and strategy. The collection unit, for example, acquires the professional's training status from a database. For example, the collection unit acquires the professional's training menu and progress from the database. The collection unit can also analyze the professional's current strategy and filter data based on specific tactics and strategies. For example, if the professional is trying out a new strategy, the collection unit can prioritize collecting data based on that strategy. The collection unit can also collect data focusing on specific skills and techniques, taking into account the professional's training status. For example, if the professional is performing a specific training menu, the collection unit collects match data related to that menu. This allows for more accurate data collection by filtering data based on the professional's training status and strategy.

[0037] When collecting match data, the collection unit can select a collection method based on changes in the professional's playing style. For example, the collection unit identifies a period when the professional's playing style changed and focuses on collecting data before and after that period. For example, the collection unit retrieves from a database the period when the professional's playing style changed and collects match data before and after that period. The collection unit can also analyze the changes in the professional's playing style and collect data on different styles in a balanced manner. For example, the collection unit analyzes the changes in the professional's playing style and collects data on different styles in a balanced manner. The collection unit can also track the evolution of the professional's playing style and collect data that reflects the changes. For example, the collection unit tracks the evolution of the professional's playing style and collects data that reflects the changes. This enables more diverse data collection by taking into account the changes in the professional's playing style.

[0038] When collecting match data, the collection unit can prioritize collection of highly relevant data based on the geographical location information of the pros. The collection unit, for example, acquires the geographical location information of the pros from GPS data. For example, the collection unit identifies the pros' current location using the GPS data. Furthermore, when the pros are competing in a specific region, the collection unit can prioritize collection of data for that region. For example, when the pros are competing in a specific region, the collection unit prioritizes collection of match data for that region. Furthermore, when the pros are competing in different regions, the collection unit can collect data that reflects differences in playing styles for each region. For example, when the pros are competing in different regions, the collection unit collects data that reflects differences in playing styles for each region. Furthermore, when the pros are competing overseas, the collection unit can collect data that reflects tactics and strategies unique to that region. For example, when the pros are competing overseas, the collection unit collects data that reflects tactics and strategies unique to that region. In this way, by taking the pros' geographical location information into consideration, data that reflects playing styles unique to that region can be collected.

[0039] When collecting match data, the collection unit can analyze the social media activities of the professionals and collect related data. The collection unit, for example, analyzes the social media activities of the professionals. For example, the collection unit collects data based on match videos and commentary posted by the professionals on social media. The collection unit can also analyze the content of the professionals' social media posts and collect data related to specific tactics and strategies. For example, the collection unit can analyze the content of the professionals' social media posts and collect data related to specific tactics and strategies. The collection unit can also collect the latest match data by referring to the social media activities of the professionals. For example, the collection unit collects the latest match data by referring to the social media activities of the professionals. In this way, the latest match data can be collected by analyzing the social media activities of the professionals.

[0040] When collecting match data, the collection unit can customize the collection method by reflecting past feedback from professionals. The collection unit, for example, obtains past feedback from professionals from a database. For example, the collection unit obtains past feedback from professionals from the database and customizes the collection method. The collection unit can also adjust the type and amount of data to be collected based on the professional feedback. For example, the collection unit collects data that focuses on specific tactics and strategies based on the professional feedback. The collection unit can also improve the collection method by referring to the professional feedback and collect more accurate data. For example, the collection unit improves the collection method by referring to the professional feedback and collects more accurate data. In this way, by reflecting past feedback from professionals, more accurate data collection is possible.

[0041] During learning, the learning unit can optimize the learning algorithm based on changes in the professional's playing style. For example, the learning unit identifies a period when the professional's playing style changed and focuses on learning data before and after that period. For example, the learning unit retrieves from a database the period when the professional's playing style changed and learns match data before and after that period. The learning unit can also analyze the changes in the professional's playing style and learn data on different styles in a balanced manner. For example, the learning unit can analyze the changes in the professional's playing style and learn data on different styles in a balanced manner. The learning unit can also track the evolution of the professional's playing style and optimize an algorithm that reflects those changes. For example, the learning unit tracks the evolution of the professional's playing style and optimizes an algorithm that reflects those changes. In this way, a wider variety of playing styles can be learned by referring to the changes in the professional's playing style.

[0042] During learning, the learning unit can adjust the accuracy of learning based on the level of detail of professional match data. The learning unit, for example, prioritizes learning detailed match data to reproduce a playing style with high accuracy. For example, the learning unit obtains detailed match data from a database and uses it for learning. The learning unit can also learn simplified match data to reproduce a basic playing style. For example, the learning unit obtains simplified match data from a database and uses it for learning. The learning unit can also adjust parameters of the learning algorithm according to the level of detail of the match data. For example, the learning unit adjusts parameters of the learning algorithm according to the level of detail of the match data. In this way, by adjusting the accuracy of learning based on the level of detail of the match data, a playing style with higher accuracy can be reproduced.

[0043] During learning, the learning unit can improve the accuracy of learning by referring to the professional's past match results. The learning unit, for example, acquires the professional's past match results from a database. For example, the learning unit acquires the professional's past match results from the database and uses them for learning. The learning unit can also analyze the professional's past match results and prioritize learning data from victories. For example, the learning unit analyzes the professional's past match results and prioritize learning data from victories. The learning unit can also learn specific tactics and strategies based on the professional's past match results. For example, the learning unit learns specific tactics and strategies based on the professional's past match results. The learning unit can also adjust the parameters of the learning algorithm by referring to the professional's past match results. For example, the learning unit adjusts the parameters of the learning algorithm by referring to the professional's past match results. In this way, by referring to the professional's past match results, a playing style can be reproduced with greater accuracy.

[0044] During learning, the learning unit can weight the learning data based on the time of submission of professional match data. For example, the learning unit prioritizes learning the most recent match data to reflect the current playing style. For example, the learning unit obtains the most recent match data from a database and uses it for learning. The learning unit can also weight and learn past match data to reflect changes in playing style. For example, the learning unit obtains past match data from a database and weights it to use for learning. The learning unit can also adjust the weighting of the learning data based on the time of submission to reproduce an optimal playing style. For example, the learning unit adjusts the weighting of the learning data based on the time of submission to reproduce an optimal playing style. In this way, weighting based on the time of submission of the match data can reflect the latest playing style.

[0045] During learning, the learning unit can improve the accuracy of learning based on related literature written by professionals. The learning unit, for example, refers to related literature written by professionals to learn specific tactics and strategies. For example, the learning unit refers to academic papers and technical reports written by professionals to learn specific tactics and strategies. The learning unit can also adjust parameters of the learning algorithm based on related literature written by professionals. For example, the learning unit adjusts parameters of the learning algorithm based on related literature written by professionals. The learning unit can also select learning data with reference to related literature written by professionals to improve accuracy. For example, the learning unit selects learning data with reference to related literature written by professionals to improve accuracy. In this way, by referring to related literature written by professionals, a playing style can be reproduced with greater accuracy.

[0046] During learning, the learning unit can apply different learning algorithms depending on the category of the professional's playing style. For example, the learning unit applies a learning algorithm specialized for offensive tactics to an offensive playing style. For example, the learning unit performs learning using an algorithm specialized for offensive playing styles. The learning unit can also apply a learning algorithm specialized for defensive tactics to a defensive playing style. For example, the learning unit performs learning using an algorithm specialized for defensive playing styles. The learning unit can also apply a learning algorithm that takes into account both offense and defense to a balanced playing style. For example, the learning unit performs learning using an algorithm specialized for a balanced playing style. In this way, by applying a learning algorithm depending on the category of playing style, a wider variety of playing styles can be reproduced.

[0047] When selecting a game, the selection unit can present optimal options by referring to the user's past selection history. The selection unit, for example, acquires the user's past selection history from a database. For example, the selection unit acquires the user's past selection history from the database and presents optimal options. The selection unit can also preferentially display games that have been frequently selected based on the user's past selection history. For example, the selection unit preferentially displays games that the user has frequently selected in the past. The selection unit can also suggest games that were selected during a specific time period based on the user's past selection history. For example, the selection unit suggests games that were selected during a specific time period based on the user's past selection history. The selection unit can also suggest related games based on the user's past selection history. For example, the selection unit suggests related games based on the user's past selection history. This enables a more appropriate game selection by referring to the user's past selection history.

[0048] When selecting a game, the selection unit can filter options based on the user's current skill level. The selection unit, for example, acquires the user's skill level from a database. For example, the selection unit acquires the user's skill level from the database and filters the options. The selection unit can also suggest games with an appropriate difficulty level based on the user's skill level. For example, the selection unit evaluates the user's skill level and suggests games with an appropriate difficulty level. The selection unit can also analyze the user's skill level and suggest challenging games or games suitable for practice. For example, the selection unit analyzes the user's skill level and suggests challenging games or games suitable for practice. The selection unit can also filter games for beginners or advanced players based on the user's skill level. For example, the selection unit filters games for beginners or advanced players based on the user's skill level. This allows for more appropriate game selection by filtering options based on the user's skill level.

[0049] The selection unit can customize options according to the user's play style when selecting a game. The selection unit, for example, acquires the user's play style from a database. For example, the selection unit acquires the user's play style from the database and customizes the options. The selection unit can also suggest appropriate games according to the user's play style. For example, if the user has an offensive play style, the selection unit can suggest games that require offensive tactics. For example, if the user has a defensive play style, the selection unit can suggest games that require defensive tactics. For example, if the user has a defensive play style, the selection unit can suggest games that require defensive tactics. The selection unit can also suggest balanced games based on the user's play style. For example, the selection unit can suggest balanced games based on the user's play style. In this way, customizing the options according to the user's play style enables more appropriate game selection.

[0050] When selecting a game, the selection unit can prioritize and present highly relevant games based on the user's geographical location information. The selection unit, for example, acquires the user's geographical location information from GPS data. For example, the selection unit identifies the user's current location using the GPS data. Furthermore, if the user is in a specific region, the selection unit can also suggest games that are popular in that region. For example, if the user is in a specific region, the selection unit can suggest games that are popular in that region. Furthermore, the selection unit can also suggest games that require region-specific tactics and strategies based on the user's geographical location information. For example, the selection unit can suggest games that require region-specific tactics and strategies based on the user's geographical location information. Furthermore, if the user is traveling, the selection unit can also suggest games related to the culture or history of the region. For example, if the user is traveling, the selection unit can suggest games related to the culture or history of the region. This makes it possible to select region-specific games by taking the user's geographical location information into consideration.

[0051] When selecting a game, the selection unit can analyze the user's social media activity and present related games. The selection unit, for example, analyzes the user's social media activity. For example, the selection unit preferentially suggests games that the user is talking about on social media. The selection unit can also analyze the content of the user's posts on social media and present related games. For example, the selection unit can analyze the content of the user's posts on social media and present related games. The selection unit can also suggest related games by taking into account the activities of the user's friends on social media. For example, the selection unit suggests related games by taking into account the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more appropriate game selection is possible.

[0052] The selection unit can customize options by reflecting the user's past feedback when selecting a game. The selection unit, for example, acquires the user's past feedback from a database. For example, the selection unit acquires the user's past feedback from the database and customizes the options. The selection unit can also adjust the display order of options based on the user's feedback. For example, the selection unit adjusts the display order of options based on feedback provided by the user in the past. The selection unit can also reflect the user's feedback and preferentially suggest games that require specific tactics or strategies. For example, the selection unit reflects the user's feedback and preferentially suggest games that require specific tactics or strategies. The selection unit can also customize options by referring to the user's feedback and suggest more appropriate games. For example, the selection unit customizes options by referring to the user's feedback and suggests more appropriate games. In this way, a more appropriate game can be selected by reflecting the user's past feedback.

[0053] During a match, the match unit can select an optimal match strategy by referring to the user's past match history. The match unit, for example, acquires the user's past match history from a database. For example, the match unit acquires the user's past match history from the database and selects an optimal match strategy. The match unit can also select the most effective strategy based on the user's past match history. For example, the match unit analyzes the user's past match history and selects the most effective strategy. The match unit can also learn specific tactics or strategies from the user's past match history and select a match strategy based on the tactics or strategies. For example, the match unit learns specific tactics or strategies from the user's past match history and selects a match strategy based on the tactics or strategies. The match unit can also provide matches in which different tactics or strategies are tried based on the user's past match history. For example, the match unit provides matches in which different tactics or strategies are tried based on the user's past match history. In this way, a more effective match strategy can be selected by referring to the user's past match history.

[0054] The match unit can customize a match technique based on the user's current skill level during a match. The match unit, for example, acquires the user's skill level from a database. For example, the match unit acquires the user's skill level from the database and customizes the match technique. The match unit can also provide a match of an appropriate difficulty level based on the user's skill level. For example, the match unit evaluates the user's skill level and provides a match of an appropriate difficulty level. The match unit can also analyze the user's skill level and provide a challenging match or a match suitable for practice. For example, the match unit analyzes the user's skill level and provides a challenging match or a match suitable for practice. The match unit can also customize a match for beginners or advanced players based on the user's skill level. For example, the match unit customizes a match for beginners or advanced players based on the user's skill level. In this way, customizing the match technique based on the user's skill level enables more appropriate matches.

[0055] The match unit can improve the match method by reflecting user feedback during a match. The match unit, for example, acquires user feedback from a database. For example, the match unit acquires user feedback from the database and improves the match method. The match unit can also adjust the difficulty level and tactics of the match based on the feedback provided by the user. For example, the match unit adjusts the difficulty level and tactics of the match based on the feedback provided by the user. The match unit can also improve specific tactics and strategies by reflecting user feedback. For example, the match unit improves specific tactics and strategies by reflecting user feedback. The match unit can also customize the match method by referring to user feedback and provide a more appropriate match. For example, the match unit customizes the match method by referring to user feedback and provides a more appropriate match. In this way, a more appropriate match can be achieved by reflecting user feedback.

[0056] During a match, the match unit can select an optimal match method based on the user's geographical location information. The match unit, for example, acquires the user's geographical location information from GPS data. For example, the match unit uses the GPS data to identify the user's current location. Furthermore, if the user is in a specific region, the match unit can provide a match that reflects tactics and strategies popular in that region. For example, if the user is in a specific region, the match unit can provide a match that requires tactics and strategies specific to that region based on the user's geographical location information. For example, the match unit can provide a match that requires tactics and strategies specific to that region based on the user's geographical location information. Furthermore, if the user is traveling, the match unit can provide a match that reflects tactics and strategies related to the culture and history of that region. For example, if the user is traveling, the match unit can provide a match that reflects tactics and strategies related to the culture and history of that region. In this way, a region-specific match method can be selected by taking the user's geographical location information into consideration.

[0057] During a match, the match unit can analyze the user's social media activity and suggest a match technique. The match unit, for example, analyzes the user's social media activity. For example, the match unit provides a match that reflects the tactics and strategies that the user is talking about on social media. The match unit can also analyze the content of the user's social media posts and suggest related tactics and strategies. For example, the match unit can analyze the content of the user's social media posts and suggest related tactics and strategies. The match unit can also refer to the activity of the user's friends on social media to suggest related tactics and strategies. For example, the match unit refers to the activity of the user's friends on social media to suggest related tactics and strategies. In this way, by analyzing the user's social media activity, a more appropriate match technique can be suggested.

[0058] During a match, the match unit can customize the match method by reflecting the user's past feedback. The match unit, for example, acquires the user's past feedback from a database. For example, the match unit acquires the user's past feedback from the database and customizes the match method. The match unit can also adjust the difficulty and tactics of the match based on the feedback provided by the user. For example, the match unit adjusts the difficulty and tactics of the match based on the feedback provided by the user. The match unit can also reflect the user's feedback and improve specific tactics and strategies. For example, the match unit reflects the user's feedback and improves specific tactics and strategies. The match unit can also customize the match method by referring to the user's feedback and provide a more appropriate match. For example, the match unit customizes the match method by referring to the user's feedback and provides a more appropriate match. In this way, a more appropriate match can be achieved by reflecting the user's past feedback.

[0059] When hitting the ball against a wall, the hitting unit can select the optimal practice method by referring to the user's past practice history. The hitting unit, for example, acquires the user's past practice history from a database. For example, the hitting unit acquires the user's past practice history from the database and selects the optimal practice method. The hitting unit can also select the most effective practice method based on the user's past practice history. For example, the hitting unit analyzes the user's past practice history and selects the most effective practice method. The hitting unit can also select a method to focus on practicing a specific skill or technique based on the user's past practice history. For example, the hitting unit selects a method to focus on practicing a specific skill or technique based on the user's past practice history. The hitting unit can also provide hitting units that allow users to try different practice methods based on the user's past practice history. For example, the hitting unit provides hitting units that allow users to try different practice methods based on the user's past practice history. In this way, a more effective practice method can be selected by referring to the user's past practice history.

[0060] The hitting-against-the-wall unit can customize the practice technique based on the user's current skill level when hitting the ball against the wall. The hitting-against-the-wall unit, for example, acquires the user's skill level from a database. For example, the hitting-against-the-wall unit acquires the user's skill level from the database and customizes the practice technique. The hitting-against-the-wall unit can also provide practice sessions of an appropriate level of difficulty according to the user's skill level. For example, the hitting-against-the-wall unit evaluates the user's skill level and provides practice sessions of an appropriate level of difficulty. The hitting-against-the-wall unit can also analyze the user's skill level and provide challenging practice sessions or basic practice sessions. For example, the hitting-against-the-wall unit analyzes the user's skill level and provides challenging practice sessions or basic practice sessions. The hitting-against-the-wall unit can also customize practice sessions for beginners or advanced players based on the user's skill level. For example, the hitting-against-the-wall unit customizes practice sessions for beginners or advanced players based on the user's skill level. This allows for more appropriate practice by customizing the practice technique based on the user's skill level.

[0061] The hitting-against-the-wall unit can improve the practice method by reflecting the user's feedback when hitting the ball against the wall. The hitting-against-the-wall unit, for example, acquires the user's feedback from a database. For example, the hitting-against-the-wall unit acquires the user's feedback from the database and improves the practice method. The hitting-against-the-wall unit can also adjust the difficulty level and technique of practice based on the feedback provided by the user. For example, the hitting-against-the-wall unit adjusts the difficulty level and technique of practice based on the feedback provided by the user. The hitting-against-the-wall unit can also improve the method of focusing on practicing a specific skill or technique by reflecting the user's feedback. For example, the hitting-against-the-wall unit can improve the method of focusing on practicing a specific skill or technique by reflecting the user's feedback. The hitting-against-the-wall unit can also customize the practice method by taking the user's feedback into consideration and provide more effective practice. For example, the hitting-against-the-wall unit customizes the practice method by taking the user's feedback into consideration and provide more effective practice. In this way, more effective practice is possible by reflecting the user's feedback.

[0062] When hitting the ball against a wall, the hitting-against-wall section can select the optimal practice method by taking into account the user's geographical location information. The hitting-against-wall section, for example, acquires the user's geographical location information from GPS data. For example, the hitting-against-wall section identifies the user's current location using GPS data. Furthermore, when the user is in a specific region, the hitting-against-wall section can provide practice sessions that reflect tactics and strategies that are popular in that region. For example, when the user is in a specific region, the hitting-against-wall section can provide practice sessions that require tactics and strategies that are unique to that region, based on the user's geographical location information. For example, the hitting-against-wall section can provide practice sessions that require tactics and strategies that are unique to that region, based on the user's geographical location information. Furthermore, when the user is traveling, the hitting-against-wall section can provide practice sessions that reflect tactics and strategies that are related to the culture and history of that region. For example, when the user is traveling, the hitting-against-wall section can provide practice sessions that reflect tactics and strategies that are related to the culture and history of that region. In this way, a region-specific practice method can be selected by taking into account the user's geographical location information.

[0063] The hitting-against-the-wall section can analyze the user's social media activity and suggest practice techniques when hitting the wall. The hitting-against-the-wall section, for example, analyzes the user's social media activity. For example, the hitting-against-the-wall section provides practice sessions that reflect the tactics and strategies that the user is discussing on social media. The hitting-against-the-wall section can also analyze the content of the user's social media posts and suggest related tactics and strategies. For example, the hitting-against-the-wall section can analyze the content of the user's social media posts and suggest related tactics and strategies. The hitting-against-the-wall section can also refer to the activity of the user's friends on social media to suggest related tactics and strategies. For example, the hitting-against-the-wall section refers to the activity of the user's friends on social media to suggest related tactics and strategies. In this way, by analyzing the user's social media activity, more appropriate practice techniques can be suggested.

[0064] The hitting-against-the-wall unit can customize the practice method by reflecting the user's past feedback when hitting the ball against the wall. The hitting-against-the-wall unit, for example, acquires the user's past feedback from a database. For example, the hitting-against-the-wall unit acquires the user's past feedback from the database and customizes the practice method. The hitting-against-the-wall unit can also adjust the difficulty level and technique of practice based on the feedback provided by the user. For example, the hitting-against-the-wall unit adjusts the difficulty level and technique of practice based on the feedback provided by the user. The hitting-against-the-wall unit can also improve the method of focusing on practicing a specific skill or technique by reflecting the user's feedback. For example, the hitting-against-the-wall unit can improve the method of focusing on practicing a specific skill or technique by reflecting the user's feedback. The hitting-against-the-wall unit can also customize the practice method by taking the user's feedback into consideration and provide more effective practice. For example, the hitting-against-the-wall unit customizes the practice method by taking the user's feedback into consideration and provide more effective practice. In this way, more effective practice is possible by reflecting the user's past feedback.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] When collecting professional match data, the collection unit can also filter the data based on the professional's current training status and strategy. For example, if the professional is trying out a new tactic, data based on that tactic can be collected preferentially. It can also collect data focusing on specific skills and techniques, taking into account the professional's training menu and progress. Furthermore, if the professional is following a specific training menu, collecting match data related to that menu allows for more accurate data collection.

[0067] The match unit can also select an optimal match strategy by referring to the user's past match history. For example, it can analyze the user's past match history and select the most effective strategy. It can also learn specific tactics and strategies from the user's past match history and select a match strategy based on those. It can also provide matches that allow different tactics and strategies to be tried based on the user's past match history. In this way, by referring to the user's past match history, a more effective match strategy can be selected.

[0068] The collection unit can also analyze the past match history of a professional player and select an appropriate collection method. For example, it can retrieve the past match history of a professional player from a database and extract particularly excellent plays. It can also prioritize the collection of matches in which a particular tactic or strategy was frequently used from the professional player's match history. Furthermore, by collecting matches against different opponents in a balanced manner, it becomes possible to collect more diverse data. This allows for more effective data collection by analyzing the past match history of a professional player.

[0069] During learning, the learning unit can also optimize the learning algorithm based on the evolution of a professional's playing style. For example, it can identify the period when a professional's playing style changed and focus on learning data from before and after that period. It can also analyze the evolution of a professional's playing style and learn data from different styles in a balanced manner. Furthermore, by tracking the evolution of a professional's playing style and optimizing the algorithm to reflect those changes, it can learn a wider variety of playing styles. This allows it to learn a wider variety of playing styles by referring to the evolution of professional playing styles.

[0070] When selecting a game, the selection unit can also prioritize and present games that are highly relevant based on the user's geographical location information. For example, if the user's geographical location information is obtained from GPS data and the user is in a specific region, games that are popular in that region can be suggested. Games that require tactics or strategies specific to that region can also be suggested. Furthermore, if the user is traveling, games related to the culture and history of that region can be suggested, enabling region-specific game selection. This allows region-specific game selection by taking the user's geographical location information into consideration.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The collection unit collects professional play data. The professional play data includes professional match data for various games such as Go, Shogi, chess, and card games. The collection unit not only collects match records from the Internet, but can also collect data by analyzing professional match videos. It can also obtain professional match results from a database. Step 2: The learning unit uses the data collected by the collection unit to train the AI ​​to learn professional playing styles. The learning unit analyzes the data using machine learning algorithms and trains the AI. It can also preprocess the data to enable the AI ​​to learn efficiently. It can also evaluate the AI's learning results and adjust the algorithm as needed. Step 3: The selection unit selects the game the user wants to play. The selection unit not only allows the user to select a game through the interface, but can also suggest the most suitable game based on the user's past selection history. It can also adjust the difficulty of the game according to the user's skill level. Step 4: In the match section, an AI acts as an opponent based on the game selected by the selection section. The match section reproduces the professional playing style and plays against the user. The AI ​​can also adjust its behavior in real time according to the user's movements. Furthermore, it can record the results of the match and provide feedback to the user. Step 5: In the wall-hitting section, the AI ​​acts as an opponent for the user to practice alone. The AI ​​responds appropriately to the user's movements. It can also record the user's practice data and provide advice to improve skills. It can also set up practice scenarios to help users practice efficiently.

[0073] (Example 2) A system according to an embodiment of the present invention generates an AI that has learned the playing styles of professionals in games such as Go, Shogi, Chess, and card games, allowing anyone to easily play against or practice against professionals. This system collects professional play data, trains the AI ​​to learn it, and allows users to select the game they want to play and play against the AI. The AI ​​reproduces the professional's playing style and acts as a real-time opponent for the user. Users can also practice against the AI, which can be useful for practice and skill improvement. This allows users to play against or practice while learning the professional's playing style, which can be useful for skill improvement. For example, by playing against a Go professional while learning his or her playing style, users can improve their own skills. Furthermore, by practicing Shogi against a professional, users can improve their skills while practicing alone.

[0074] A game competition system according to an embodiment includes a collection unit, a learning unit, a selection unit, a competition unit, and a hitting-against-a-wall unit. The collection unit collects professional play data. The professional play data includes, for example, professional match data for various games such as Go, Shogi, chess, and card games. The collection unit, for example, collects match records from the Internet. The collection unit can also collect data by analyzing professional match videos. The collection unit can also acquire professional match results from a database. The learning unit allows an AI to learn professional playing styles based on the data collected by the collection unit. The learning unit, for example, analyzes the data using a machine learning algorithm to train the AI. The learning unit can also preprocess the data to enable the AI ​​to learn efficiently. The learning unit can also evaluate the AI's learning results and adjust the algorithm as necessary. The selection unit selects a game the user wants to play. For example, the user selects a game through an interface. The selection unit can also suggest optimal games based on the user's past selection history. Furthermore, the selection unit can adjust the difficulty of the game according to the user's skill level. The competition unit functions as an AI opponent based on the game selected by the selection unit. For example, the competition unit reproduces a professional's playing style and competes against the user. The competition unit can also adjust the AI's behavior in real time according to the user's movements. Furthermore, the competition unit can record the results of the match and provide feedback to the user. The hitting-against-the-wall unit functions as an AI opponent for the user to practice alone. For example, the AI ​​responds appropriately to the user's movements. The hitting-against-the-wall unit can also record the user's practice data and provide advice for skill improvement. Furthermore, the hitting-against-the-wall unit can set practice scenarios to enable the user to practice efficiently. As a result, the game competition system according to the embodiment allows the user to easily play against or hit-against professionals using an AI that has learned the professional's playing style. For example, the user can play against or practice while learning the professional's playing style, which can help improve their skills.

[0075] The collection unit can collect professional match data for each game of Go, Shogi, chess, and card games. The collection unit, for example, collects match records on the Internet. The collection unit can also collect data by analyzing videos of professional matches. For example, the collection unit uses video analysis technology to extract match moves as data. The collection unit can also obtain professional match results from a database. For example, the collection unit obtains match results and detailed tactical information from the database. In this way, by collecting professional match data for each game, the AI ​​can learn various playing styles.

[0076] The learning unit enables the AI ​​to learn professional playing styles based on the collected data. The learning unit, for example, uses a machine learning algorithm to analyze the data and have the AI ​​learn from it. For example, the learning unit uses deep learning to learn professional playing styles. The learning unit can also preprocess the data to enable the AI ​​to learn efficiently. For example, the learning unit can remove noise from the data and provide accurate data. The learning unit can also evaluate the AI's learning results and adjust the algorithm as needed. For example, the learning unit can evaluate the accuracy of the learning results and adjust the algorithm parameters. This allows the AI ​​to learn professional playing styles, providing a realistic competitive experience.

[0077] The selection unit allows the user to select a game in which they want to play. For example, the selection unit allows the user to select a game through an interface. For example, the selection unit allows the user to select a game from an on-screen menu. The selection unit can also suggest an optimal game based on the user's past selection history. For example, the selection unit can analyze the history of games the user has previously selected and suggest related games. The selection unit can also adjust the difficulty of the game according to the user's skill level. For example, the selection unit can evaluate the user's skill level and suggest a game with an appropriate difficulty level. This allows the user to select a game they want to play and play against an AI in their preferred game.

[0078] The match unit can have an AI function as an opponent based on the selected game. For example, the AI ​​reproduces a professional playing style and competes against the user. For example, the match unit has the AI ​​compete against the user using professional tactics. The match unit can also adjust the AI's behavior in real time according to the user's movements. For example, the match unit analyzes the user's movements, and the AI ​​responds appropriately. The match unit can also record the results of the match and provide feedback to the user. For example, the match unit analyzes the results of the match and suggests areas for improvement to the user. This allows the AI ​​to function as an opponent based on the selected game, providing a realistic match experience.

[0079] The hitting-against-the-wall section allows the AI ​​to function as an opponent so that the user can practice alone. In the hitting-against-the-wall section, for example, the AI ​​responds appropriately to the user's movements. For example, the hitting-against-the-wall section returns an appropriate move to the user's move. The hitting-against-the-wall section can also record the user's practice data and provide advice for improving skills. For example, the hitting-against-the-wall section can analyze the user's practice data and suggest areas for improvement. The hitting-against-the-wall section can also set practice scenarios to enable the user to practice efficiently. For example, the hitting-against-the-wall section provides a scenario for practicing a specific tactic. This allows the AI ​​to function as an opponent so that the user can practice alone, helping to improve skills.

[0080] The collection unit can analyze the professional's emotions and adjust the timing of collecting match data based on the analyzed professional's emotions. The collection unit, for example, analyzes the professional's facial expressions and voice to estimate the professional's emotions. For example, the collection unit can estimate the professional's emotions using facial expression recognition technology. The collection unit can also estimate the professional's emotions using voice analysis technology. For example, the collection unit can analyze the professional's tone and speed of voice to estimate the professional's emotions. The collection unit can also adjust the timing of collecting match data based on the estimated emotions. For example, the collection unit can collect match data when the professional is relaxed to reflect a natural playing style. The collection unit can also collect match data when the professional is concentrating to learn the most accurate playing style. In this way, by adjusting the collection timing based on the professional's emotions, a more natural playing style can be reflected.

[0081] The collection unit can analyze the professional's past match history and select an appropriate collection method. The collection unit, for example, acquires the professional's past match history from a database. For example, the collection unit acquires match results and detailed tactical information from the database. The collection unit can also analyze the professional's past match history and extract particularly excellent plays. For example, the collection unit prioritizes collecting matches in which specific tactics or strategies were frequently used from the professional's match history. The collection unit can also collect matches with different opponents in a balanced manner. For example, the collection unit collects matches with different opponents in a balanced manner based on the professional's match history. This enables more effective data collection by analyzing the professional's past match history.

[0082] When collecting match data, the collection unit can filter the data based on the professional's current training status and strategy. The collection unit, for example, acquires the professional's training status from a database. For example, the collection unit acquires the professional's training menu and progress from the database. The collection unit can also analyze the professional's current strategy and filter data based on specific tactics and strategies. For example, if the professional is trying out a new strategy, the collection unit can prioritize collecting data based on that strategy. The collection unit can also collect data focusing on specific skills and techniques, taking into account the professional's training status. For example, if the professional is performing a specific training menu, the collection unit collects match data related to that menu. This allows for more accurate data collection by filtering data based on the professional's training status and strategy.

[0083] When collecting match data, the collection unit can select a collection method based on changes in the professional's playing style. For example, the collection unit identifies a period when the professional's playing style changed and focuses on collecting data before and after that period. For example, the collection unit retrieves from a database the period when the professional's playing style changed and collects match data before and after that period. The collection unit can also analyze the changes in the professional's playing style and collect data on different styles in a balanced manner. For example, the collection unit analyzes the changes in the professional's playing style and collects data on different styles in a balanced manner. The collection unit can also track the evolution of the professional's playing style and collect data that reflects the changes. For example, the collection unit tracks the evolution of the professional's playing style and collects data that reflects the changes. This enables more diverse data collection by taking into account the changes in the professional's playing style.

[0084] The collection unit can estimate the professional's emotions and determine the priority of the match data to be collected based on the estimated professional's emotions. The collection unit, for example, analyzes the professional's facial expressions and voice to estimate the emotions. For example, the collection unit can estimate the professional's emotions using facial expression recognition technology. The collection unit can also estimate the professional's emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the professional's voice to estimate the emotions. The collection unit also determines the priority of the match data to be collected based on the estimated emotions. For example, the collection unit can prioritize collecting data when the professional is concentrating to learn a highly accurate playing style. The collection unit can also prioritize collecting data when the professional is relaxed to reflect a natural playing style. In this way, by determining the priority of data based on the professional's emotions, more effective data collection is possible.

[0085] When collecting match data, the collection unit can prioritize collection of highly relevant data based on the geographical location information of the pros. The collection unit, for example, acquires the geographical location information of the pros from GPS data. For example, the collection unit identifies the pros' current location using the GPS data. Furthermore, when the pros are competing in a specific region, the collection unit can prioritize collection of data for that region. For example, when the pros are competing in a specific region, the collection unit prioritizes collection of match data for that region. Furthermore, when the pros are competing in different regions, the collection unit can collect data that reflects differences in playing styles for each region. For example, when the pros are competing in different regions, the collection unit collects data that reflects differences in playing styles for each region. Furthermore, when the pros are competing overseas, the collection unit can collect data that reflects tactics and strategies unique to that region. For example, when the pros are competing overseas, the collection unit collects data that reflects tactics and strategies unique to that region. In this way, by taking the pros' geographical location information into consideration, data that reflects playing styles unique to that region can be collected.

[0086] When collecting match data, the collection unit can analyze the social media activities of the professionals and collect related data. The collection unit, for example, analyzes the social media activities of the professionals. For example, the collection unit collects data based on match videos and commentary posted by the professionals on social media. The collection unit can also analyze the content of the professionals' social media posts and collect data related to specific tactics and strategies. For example, the collection unit can analyze the content of the professionals' social media posts and collect data related to specific tactics and strategies. The collection unit can also collect the latest match data by referring to the social media activities of the professionals. For example, the collection unit collects the latest match data by referring to the social media activities of the professionals. In this way, the latest match data can be collected by analyzing the social media activities of the professionals.

[0087] When collecting match data, the collection unit can customize the collection method by reflecting past feedback from professionals. The collection unit, for example, obtains past feedback from professionals from a database. For example, the collection unit obtains past feedback from professionals from the database and customizes the collection method. The collection unit can also adjust the type and amount of data to be collected based on the professional feedback. For example, the collection unit collects data that focuses on specific tactics and strategies based on the professional feedback. The collection unit can also improve the collection method by referring to the professional feedback and collect more accurate data. For example, the collection unit improves the collection method by referring to the professional feedback and collects more accurate data. In this way, by reflecting past feedback from professionals, more accurate data collection is possible.

[0088] The learning unit can estimate the professional's emotions and select learning data based on the estimated professional's emotions. The learning unit, for example, analyzes the professional's facial expressions and voice to estimate emotions. For example, the learning unit can estimate the professional's emotions using facial expression recognition technology. The learning unit can also estimate the professional's emotions using voice analysis technology. For example, the learning unit can analyze the professional's tone and speed of voice to estimate emotions. The learning unit also selects learning data based on the estimated emotions. For example, the learning unit prioritizes learning data from when the professional is relaxed to reproduce a natural playing style. The learning unit can also prioritize learning data from when the professional is concentrating to reproduce a playing style with high accuracy. In this way, by selecting learning data based on the professional's emotions, a more natural playing style can be reproduced.

[0089] During learning, the learning unit can optimize the learning algorithm based on changes in the professional's playing style. For example, the learning unit identifies a period when the professional's playing style changed and focuses on learning data before and after that period. For example, the learning unit retrieves from a database the period when the professional's playing style changed and learns match data before and after that period. The learning unit can also analyze the changes in the professional's playing style and learn data on different styles in a balanced manner. For example, the learning unit can analyze the changes in the professional's playing style and learn data on different styles in a balanced manner. The learning unit can also track the evolution of the professional's playing style and optimize an algorithm that reflects those changes. For example, the learning unit tracks the evolution of the professional's playing style and optimizes an algorithm that reflects those changes. In this way, a wider variety of playing styles can be learned by referring to the changes in the professional's playing style.

[0090] During learning, the learning unit can adjust the accuracy of learning based on the level of detail of professional match data. The learning unit, for example, prioritizes learning detailed match data to reproduce a playing style with high accuracy. For example, the learning unit obtains detailed match data from a database and uses it for learning. The learning unit can also learn simplified match data to reproduce a basic playing style. For example, the learning unit obtains simplified match data from a database and uses it for learning. The learning unit can also adjust parameters of the learning algorithm according to the level of detail of the match data. For example, the learning unit adjusts parameters of the learning algorithm according to the level of detail of the match data. In this way, by adjusting the accuracy of learning based on the level of detail of the match data, a playing style with higher accuracy can be reproduced.

[0091] During learning, the learning unit can improve the accuracy of learning by referring to the professional's past match results. The learning unit, for example, acquires the professional's past match results from a database. For example, the learning unit acquires the professional's past match results from the database and uses them for learning. The learning unit can also analyze the professional's past match results and prioritize learning data from victories. For example, the learning unit analyzes the professional's past match results and prioritize learning data from victories. The learning unit can also learn specific tactics and strategies based on the professional's past match results. For example, the learning unit learns specific tactics and strategies based on the professional's past match results. The learning unit can also adjust the parameters of the learning algorithm by referring to the professional's past match results. For example, the learning unit adjusts the parameters of the learning algorithm by referring to the professional's past match results. In this way, by referring to the professional's past match results, a playing style can be reproduced with greater accuracy.

[0092] The learning unit can estimate the professional's emotions and adjust the frequency of learning based on the estimated professional's emotions. The learning unit, for example, analyzes the professional's facial expressions and voice to estimate emotions. For example, the learning unit estimates the professional's emotions using facial expression recognition technology. The learning unit can also estimate the professional's emotions using voice analysis technology. For example, the learning unit estimates emotions by analyzing the tone and speed of the professional's voice. The learning unit also adjusts the frequency of learning based on the estimated emotions. For example, the learning unit performs learning when the professional is relaxed to reproduce a natural playing style. The learning unit can also perform learning when the professional is concentrating to reproduce a playing style with high accuracy. In this way, by adjusting the frequency of learning based on the professional's emotions, a more natural playing style can be reproduced.

[0093] During learning, the learning unit can weight the learning data based on the time of submission of professional match data. For example, the learning unit prioritizes learning the most recent match data to reflect the current playing style. For example, the learning unit obtains the most recent match data from a database and uses it for learning. The learning unit can also weight and learn past match data to reflect changes in playing style. For example, the learning unit obtains past match data from a database and weights it to use for learning. The learning unit can also adjust the weighting of the learning data based on the time of submission to reproduce an optimal playing style. For example, the learning unit adjusts the weighting of the learning data based on the time of submission to reproduce an optimal playing style. In this way, weighting based on the time of submission of the match data can reflect the latest playing style.

[0094] During learning, the learning unit can improve the accuracy of learning based on related literature written by professionals. The learning unit, for example, refers to related literature written by professionals to learn specific tactics and strategies. For example, the learning unit refers to academic papers and technical reports written by professionals to learn specific tactics and strategies. The learning unit can also adjust parameters of the learning algorithm based on related literature written by professionals. For example, the learning unit adjusts parameters of the learning algorithm based on related literature written by professionals. The learning unit can also select learning data with reference to related literature written by professionals to improve accuracy. For example, the learning unit selects learning data with reference to related literature written by professionals to improve accuracy. In this way, by referring to related literature written by professionals, a playing style can be reproduced with greater accuracy.

[0095] During learning, the learning unit can apply different learning algorithms depending on the category of the professional's playing style. For example, the learning unit applies a learning algorithm specialized for offensive tactics to an offensive playing style. For example, the learning unit performs learning using an algorithm specialized for offensive playing styles. The learning unit can also apply a learning algorithm specialized for defensive tactics to a defensive playing style. For example, the learning unit performs learning using an algorithm specialized for defensive playing styles. The learning unit can also apply a learning algorithm that takes into account both offense and defense to a balanced playing style. For example, the learning unit performs learning using an algorithm specialized for a balanced playing style. In this way, by applying a learning algorithm depending on the category of playing style, a wider variety of playing styles can be reproduced.

[0096] The selection unit can estimate the user's emotion and adjust the game selection interface based on the estimated user's emotion. The selection unit, for example, analyzes the user's facial expressions and voice to estimate the emotion. For example, the selection unit can estimate the user's emotion using facial expression recognition technology. The selection unit can also estimate the user's emotion using voice analysis technology. For example, the selection unit can analyze the tone and speed of the user's voice to estimate the emotion. The selection unit can also adjust the game selection interface based on the estimated emotion. For example, the selection unit can provide a colorful and fun interface when the user is relaxed. The selection unit can also provide a simple and highly visible interface when the user is concentrating. This allows the interface to be adjusted based on the user's emotion, making game selection more comfortable.

[0097] When selecting a game, the selection unit can present optimal options by referring to the user's past selection history. The selection unit, for example, acquires the user's past selection history from a database. For example, the selection unit acquires the user's past selection history from the database and presents optimal options. The selection unit can also preferentially display games that have been frequently selected based on the user's past selection history. For example, the selection unit preferentially displays games that the user has frequently selected in the past. The selection unit can also suggest games that were selected during a specific time period based on the user's past selection history. For example, the selection unit suggests games that were selected during a specific time period based on the user's past selection history. The selection unit can also suggest related games based on the user's past selection history. For example, the selection unit suggests related games based on the user's past selection history. This enables a more appropriate game selection by referring to the user's past selection history.

[0098] When selecting a game, the selection unit can filter options based on the user's current skill level. The selection unit, for example, acquires the user's skill level from a database. For example, the selection unit acquires the user's skill level from the database and filters the options. The selection unit can also suggest games with an appropriate difficulty level based on the user's skill level. For example, the selection unit evaluates the user's skill level and suggests games with an appropriate difficulty level. The selection unit can also analyze the user's skill level and suggest challenging games or games suitable for practice. For example, the selection unit analyzes the user's skill level and suggests challenging games or games suitable for practice. The selection unit can also filter games for beginners or advanced players based on the user's skill level. For example, the selection unit filters games for beginners or advanced players based on the user's skill level. This allows for more appropriate game selection by filtering options based on the user's skill level.

[0099] The selection unit can customize options according to the user's play style when selecting a game. The selection unit, for example, acquires the user's play style from a database. For example, the selection unit acquires the user's play style from the database and customizes the options. The selection unit can also suggest appropriate games according to the user's play style. For example, if the user has an offensive play style, the selection unit can suggest games that require offensive tactics. For example, if the user has a defensive play style, the selection unit can suggest games that require defensive tactics. For example, if the user has a defensive play style, the selection unit can suggest games that require defensive tactics. The selection unit can also suggest balanced games based on the user's play style. For example, the selection unit can suggest balanced games based on the user's play style. In this way, customizing the options according to the user's play style enables more appropriate game selection.

[0100] The selection unit can estimate the user's emotion and adjust the display order of options based on the estimated user's emotion. The selection unit, for example, analyzes the user's facial expressions and voice to estimate the emotion. For example, the selection unit can estimate the user's emotion using facial expression recognition technology. The selection unit can also estimate the user's emotion using voice analysis technology. For example, the selection unit can estimate the emotion by analyzing the tone and speed of the user's voice. The selection unit can also adjust the display order of options based on the estimated emotion. For example, the selection unit can prioritize displaying colorful and fun games when the user is relaxed. The selection unit can also prioritize displaying simple and highly visible games when the user is concentrating. In this way, adjusting the display order based on the user's emotion enables a more comfortable game selection.

[0101] When selecting a game, the selection unit can prioritize and present highly relevant games based on the user's geographical location information. The selection unit, for example, acquires the user's geographical location information from GPS data. For example, the selection unit identifies the user's current location using the GPS data. Furthermore, if the user is in a specific region, the selection unit can also suggest games that are popular in that region. For example, if the user is in a specific region, the selection unit can suggest games that are popular in that region. Furthermore, the selection unit can also suggest games that require region-specific tactics and strategies based on the user's geographical location information. For example, the selection unit can suggest games that require region-specific tactics and strategies based on the user's geographical location information. Furthermore, if the user is traveling, the selection unit can also suggest games related to the culture or history of the region. For example, if the user is traveling, the selection unit can suggest games related to the culture or history of the region. This makes it possible to select region-specific games by taking the user's geographical location information into consideration.

[0102] When selecting a game, the selection unit can analyze the user's social media activity and present related games. The selection unit, for example, analyzes the user's social media activity. For example, the selection unit preferentially suggests games that the user is talking about on social media. The selection unit can also analyze the content of the user's posts on social media and present related games. For example, the selection unit can analyze the content of the user's posts on social media and present related games. The selection unit can also suggest related games by taking into account the activities of the user's friends on social media. For example, the selection unit suggests related games by taking into account the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more appropriate game selection is possible.

[0103] The selection unit can customize options by reflecting the user's past feedback when selecting a game. The selection unit, for example, acquires the user's past feedback from a database. For example, the selection unit acquires the user's past feedback from the database and customizes the options. The selection unit can also adjust the display order of options based on the user's feedback. For example, the selection unit adjusts the display order of options based on feedback provided by the user in the past. The selection unit can also reflect the user's feedback and preferentially suggest games that require specific tactics or strategies. For example, the selection unit reflects the user's feedback and preferentially suggest games that require specific tactics or strategies. The selection unit can also customize options by referring to the user's feedback and suggest more appropriate games. For example, the selection unit customizes options by referring to the user's feedback and suggests more appropriate games. In this way, a more appropriate game can be selected by reflecting the user's past feedback.

[0104] The match unit can estimate the user's emotions and adjust the difficulty of the match based on the estimated user's emotions. The match unit, for example, analyzes the user's facial expressions and voice to estimate the emotions. For example, the match unit can estimate the user's emotions using facial expression recognition technology. The match unit can also estimate the user's emotions using voice analysis technology. For example, the match unit can analyze the tone and speed of the user's voice to estimate the emotions. The match unit can also adjust the difficulty of the match based on the estimated emotions. For example, the match unit can provide a match of moderate difficulty when the user is relaxed. Also, the match unit can provide a match of challenging difficulty when the user is concentrating. In this way, adjusting the difficulty of the match based on the user's emotions enables a more appropriate match.

[0105] During a match, the match unit can select an optimal match strategy by referring to the user's past match history. The match unit, for example, acquires the user's past match history from a database. For example, the match unit acquires the user's past match history from the database and selects an optimal match strategy. The match unit can also select the most effective strategy based on the user's past match history. For example, the match unit analyzes the user's past match history and selects the most effective strategy. The match unit can also learn specific tactics or strategies from the user's past match history and select a match strategy based on the tactics or strategies. For example, the match unit learns specific tactics or strategies from the user's past match history and selects a match strategy based on the tactics or strategies. The match unit can also provide matches in which different tactics or strategies are tried based on the user's past match history. For example, the match unit provides matches in which different tactics or strategies are tried based on the user's past match history. In this way, a more effective match strategy can be selected by referring to the user's past match history.

[0106] The match unit can customize a match technique based on the user's current skill level during a match. The match unit, for example, acquires the user's skill level from a database. For example, the match unit acquires the user's skill level from the database and customizes the match technique. The match unit can also provide a match of an appropriate difficulty level based on the user's skill level. For example, the match unit evaluates the user's skill level and provides a match of an appropriate difficulty level. The match unit can also analyze the user's skill level and provide a challenging match or a match suitable for practice. For example, the match unit analyzes the user's skill level and provides a challenging match or a match suitable for practice. The match unit can also customize a match for beginners or advanced players based on the user's skill level. For example, the match unit customizes a match for beginners or advanced players based on the user's skill level. In this way, customizing the match technique based on the user's skill level enables more appropriate matches.

[0107] The match unit can improve the match method by reflecting user feedback during a match. The match unit, for example, acquires user feedback from a database. For example, the match unit acquires user feedback from the database and improves the match method. The match unit can also adjust the difficulty level and tactics of the match based on the feedback provided by the user. For example, the match unit adjusts the difficulty level and tactics of the match based on the feedback provided by the user. The match unit can also improve specific tactics and strategies by reflecting user feedback. For example, the match unit improves specific tactics and strategies by reflecting user feedback. The match unit can also customize the match method by referring to user feedback and provide a more appropriate match. For example, the match unit customizes the match method by referring to user feedback and provides a more appropriate match. In this way, a more appropriate match can be achieved by reflecting user feedback.

[0108] The match unit can estimate the user's emotions and determine the priority of matches based on the estimated user's emotions. The match unit, for example, analyzes the user's facial expressions and voice to estimate the emotions. For example, the match unit can estimate the user's emotions using facial expression recognition technology. The match unit can also estimate the user's emotions using voice analysis technology. For example, the match unit can estimate the user's emotions by analyzing the tone and speed of the user's voice. The match unit can also determine the priority of matches based on the estimated emotions. For example, the match unit can prioritize providing matches with a moderate level of difficulty when the user is relaxed. The match unit can also prioritize providing matches with a challenging level of difficulty when the user is concentrating. In this way, by determining the priority of matches based on the user's emotions, more appropriate matches can be achieved.

[0109] During a match, the match unit can select an optimal match method based on the user's geographical location information. The match unit, for example, acquires the user's geographical location information from GPS data. For example, the match unit uses the GPS data to identify the user's current location. Furthermore, if the user is in a specific region, the match unit can provide a match that reflects tactics and strategies popular in that region. For example, if the user is in a specific region, the match unit can provide a match that requires tactics and strategies specific to that region based on the user's geographical location information. For example, the match unit can provide a match that requires tactics and strategies specific to that region based on the user's geographical location information. Furthermore, if the user is traveling, the match unit can provide a match that reflects tactics and strategies related to the culture and history of that region. For example, if the user is traveling, the match unit can provide a match that reflects tactics and strategies related to the culture and history of that region. In this way, a region-specific match method can be selected by taking the user's geographical location information into consideration.

[0110] During a match, the match unit can analyze the user's social media activity and suggest a match technique. The match unit, for example, analyzes the user's social media activity. For example, the match unit provides a match that reflects the tactics and strategies that the user is talking about on social media. The match unit can also analyze the content of the user's social media posts and suggest related tactics and strategies. For example, the match unit can analyze the content of the user's social media posts and suggest related tactics and strategies. The match unit can also refer to the activity of the user's friends on social media to suggest related tactics and strategies. For example, the match unit refers to the activity of the user's friends on social media to suggest related tactics and strategies. In this way, by analyzing the user's social media activity, a more appropriate match technique can be suggested.

[0111] During a match, the match unit can customize the match method by reflecting the user's past feedback. The match unit, for example, acquires the user's past feedback from a database. For example, the match unit acquires the user's past feedback from the database and customizes the match method. The match unit can also adjust the difficulty and tactics of the match based on the feedback provided by the user. For example, the match unit adjusts the difficulty and tactics of the match based on the feedback provided by the user. The match unit can also reflect the user's feedback and improve specific tactics and strategies. For example, the match unit reflects the user's feedback and improves specific tactics and strategies. The match unit can also customize the match method by referring to the user's feedback and provide a more appropriate match. For example, the match unit customizes the match method by referring to the user's feedback and provides a more appropriate match. In this way, a more appropriate match can be achieved by reflecting the user's past feedback.

[0112] The hitting-against-the-wall unit can estimate the user's emotions and adjust the hitting-against-the-wall method based on the estimated user's emotions. The hitting-against-the-wall unit, for example, analyzes the user's facial expressions and voice to estimate the user's emotions. For example, the hitting-against-the-wall unit estimates the user's emotions using facial expression recognition technology. The hitting-against-the-wall unit can also estimate the user's emotions using voice analysis technology. For example, the hitting-against-the-wall unit estimates the user's emotions by analyzing the tone and speed of the user's voice. The hitting-against-the-wall unit can also adjust the hitting-against-the-wall method based on the estimated emotions. For example, the hitting-against-the-wall unit can provide hitting-against-the-wall of a moderate difficulty when the user is relaxed. The hitting-against-the-wall unit can also provide hitting-against-the-wall of a challenging difficulty when the user is concentrating. This allows for more appropriate practice by adjusting the hitting-against-the-wall method based on the user's emotions.

[0113] When hitting the ball against a wall, the hitting unit can select the optimal practice method by referring to the user's past practice history. The hitting unit, for example, acquires the user's past practice history from a database. For example, the hitting unit acquires the user's past practice history from the database and selects the optimal practice method. The hitting unit can also select the most effective practice method based on the user's past practice history. For example, the hitting unit analyzes the user's past practice history and selects the most effective practice method. The hitting unit can also select a method to focus on practicing a specific skill or technique based on the user's past practice history. For example, the hitting unit selects a method to focus on practicing a specific skill or technique based on the user's past practice history. The hitting unit can also provide hitting units that allow users to try different practice methods based on the user's past practice history. For example, the hitting unit provides hitting units that allow users to try different practice methods based on the user's past practice history. In this way, a more effective practice method can be selected by referring to the user's past practice history.

[0114] The hitting-against-the-wall unit can customize the practice technique based on the user's current skill level when hitting the ball against the wall. The hitting-against-the-wall unit, for example, acquires the user's skill level from a database. For example, the hitting-against-the-wall unit acquires the user's skill level from the database and customizes the practice technique. The hitting-against-the-wall unit can also provide practice sessions of an appropriate level of difficulty according to the user's skill level. For example, the hitting-against-the-wall unit evaluates the user's skill level and provides practice sessions of an appropriate level of difficulty. The hitting-against-the-wall unit can also analyze the user's skill level and provide challenging practice sessions or basic practice sessions. For example, the hitting-against-the-wall unit analyzes the user's skill level and provides challenging practice sessions or basic practice sessions. The hitting-against-the-wall unit can also customize practice sessions for beginners or advanced players based on the user's skill level. For example, the hitting-against-the-wall unit customizes practice sessions for beginners or advanced players based on the user's skill level. This allows for more appropriate practice by customizing the practice technique based on the user's skill level.

[0115] The hitting-against-the-wall unit can improve the practice method by reflecting the user's feedback when hitting the ball against the wall. The hitting-against-the-wall unit, for example, acquires the user's feedback from a database. For example, the hitting-against-the-wall unit acquires the user's feedback from the database and improves the practice method. The hitting-against-the-wall unit can also adjust the difficulty level and technique of practice based on the feedback provided by the user. For example, the hitting-against-the-wall unit adjusts the difficulty level and technique of practice based on the feedback provided by the user. The hitting-against-the-wall unit can also improve the method of focusing on practicing a specific skill or technique by reflecting the user's feedback. For example, the hitting-against-the-wall unit can improve the method of focusing on practicing a specific skill or technique by reflecting the user's feedback. The hitting-against-the-wall unit can also customize the practice method by taking the user's feedback into consideration and provide more effective practice. For example, the hitting-against-the-wall unit customizes the practice method by taking the user's feedback into consideration and provide more effective practice. In this way, more effective practice is possible by reflecting the user's feedback.

[0116] The hitting-against-the-wall unit can estimate the user's emotions and determine the priority of hitting-against-the-wall based on the estimated user's emotions. The hitting-against-the-wall unit, for example, analyzes the user's facial expressions and voice to estimate the user's emotions. For example, the hitting-against-the-wall unit estimates the user's emotions using facial expression recognition technology. The hitting-against-the-wall unit can also estimate the user's emotions using voice analysis technology. For example, the hitting-against-the-wall unit estimates the user's emotions by analyzing the tone and speed of the user's voice. The hitting-against-the-wall unit also determines the priority of hitting-against-the-wall based on the estimated emotions. For example, if the user is relaxed, the hitting-against-the-wall unit can prioritize providing hitting-against-the-wall with a moderate level of difficulty. Also, if the user is concentrating, the hitting-against-the-wall unit can prioritize providing hitting-against-the-wall with a challenging level of difficulty. In this way, by determining the priority of hitting-against-the-wall based on the user's emotions, more appropriate practice is possible.

[0117] When hitting the ball against a wall, the hitting-against-wall section can select the optimal practice method by taking into account the user's geographical location information. The hitting-against-wall section, for example, acquires the user's geographical location information from GPS data. For example, the hitting-against-wall section identifies the user's current location using GPS data. Furthermore, when the user is in a specific region, the hitting-against-wall section can provide practice sessions that reflect tactics and strategies that are popular in that region. For example, when the user is in a specific region, the hitting-against-wall section can provide practice sessions that require tactics and strategies that are unique to that region, based on the user's geographical location information. For example, the hitting-against-wall section can provide practice sessions that require tactics and strategies that are unique to that region, based on the user's geographical location information. Furthermore, when the user is traveling, the hitting-against-wall section can provide practice sessions that reflect tactics and strategies that are related to the culture and history of that region. For example, when the user is traveling, the hitting-against-wall section can provide practice sessions that reflect tactics and strategies that are related to the culture and history of that region. In this way, a region-specific practice method can be selected by taking into account the user's geographical location information.

[0118] The hitting-against-the-wall section can analyze the user's social media activity and suggest practice techniques when hitting the wall. The hitting-against-the-wall section, for example, analyzes the user's social media activity. For example, the hitting-against-the-wall section provides practice sessions that reflect the tactics and strategies that the user is discussing on social media. The hitting-against-the-wall section can also analyze the content of the user's social media posts and suggest related tactics and strategies. For example, the hitting-against-the-wall section can analyze the content of the user's social media posts and suggest related tactics and strategies. The hitting-against-the-wall section can also refer to the activity of the user's friends on social media to suggest related tactics and strategies. For example, the hitting-against-the-wall section refers to the activity of the user's friends on social media to suggest related tactics and strategies. In this way, by analyzing the user's social media activity, more appropriate practice techniques can be suggested.

[0119] The hitting-against-the-wall unit can customize the practice method by reflecting the user's past feedback when hitting the ball against the wall. The hitting-against-the-wall unit, for example, acquires the user's past feedback from a database. For example, the hitting-against-the-wall unit acquires the user's past feedback from the database and customizes the practice method. The hitting-against-the-wall unit can also adjust the difficulty level and technique of practice based on the feedback provided by the user. For example, the hitting-against-the-wall unit adjusts the difficulty level and technique of practice based on the feedback provided by the user. The hitting-against-the-wall unit can also improve the method of focusing on practicing a specific skill or technique by reflecting the user's feedback. For example, the hitting-against-the-wall unit can improve the method of focusing on practicing a specific skill or technique by reflecting the user's feedback. The hitting-against-the-wall unit can also customize the practice method by taking the user's feedback into consideration and provide more effective practice. For example, the hitting-against-the-wall unit customizes the practice method by taking the user's feedback into consideration and provide more effective practice. In this way, more effective practice is possible by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, learning unit, selection unit, competition unit, and hitting-against-the-wall unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect match records on the Internet via the communication I / F 44 of the smart device 14. For example, the learning unit analyzes data using a machine learning algorithm by the specific processing unit 290 of the data processing device 12, and causes the AI ​​to learn. For example, the selection unit allows the user to select a game through an interface via the control unit 46A of the smart device 14. For example, the competition unit allows the AI ​​to reproduce a professional playing style by the specific processing unit 290 of the data processing device 12 and play against the user. For example, the hitting-against-the-wall unit allows the AI ​​to function as an opponent for the user to practice alone by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, learning unit, selection unit, competition unit, and hitting-against-the-wall unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect match records on the Internet via the communication I / F 44 of the smart glasses 214. For example, the learning unit analyzes data using a machine learning algorithm by the specific processing unit 290 of the data processing device 12, and causes the AI ​​to learn. For example, the selection unit allows the user to select a game through an interface via the control unit 46A of the smart glasses 214. For example, the competition unit allows the AI ​​to reproduce a professional playing style by the specific processing unit 290 of the data processing device 12 and compete against the user. For example, the hitting-against-the-wall unit allows the AI ​​to function as an opponent for the user to practice alone by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, learning unit, selection unit, competition unit, and hitting-against-the-wall unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect match records on the Internet via the communication I / F 44 of the headset-type terminal 314. The learning unit, for example, analyzes data using a machine learning algorithm by the specific processing unit 290 of the data processing device 12 and causes the AI ​​to learn. The selection unit, for example, allows the user to select a game through an interface via the control unit 46A of the headset-type terminal 314. For example, the competition unit, for example, causes the AI ​​to reproduce a professional playing style by the specific processing unit 290 of the data processing device 12 and compete against the user. For example, the hitting-against-the-wall unit, for example, causes the AI ​​to function as an opponent for the user to practice alone by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, learning unit, selection unit, competition unit, and hitting-against-the-wall unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect match records on the Internet via the communication I / F 44 of the robot 414. For example, the learning unit analyzes data using a machine learning algorithm by the specific processing unit 290 of the data processing device 12, and causes the AI ​​to learn. For example, the selection unit allows the user to select a game through an interface via the control unit 46A of the robot 414. For example, the competition unit allows the AI ​​to reproduce a professional playing style by the specific processing unit 290 of the data processing device 12 and compete against the user. For example, the hitting-against-the-wall unit allows the AI ​​to function as an opponent for the user to practice alone, by the control unit 46A of the robot 414.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The match unit can also estimate the user's emotions and adjust the difficulty of the match based on the estimated user's emotions. For example, if the user is relaxed, the match unit can provide a match of moderate difficulty, allowing the user to enjoy playing. Alternatively, if the user is concentrating, the match unit can provide a match of challenging difficulty, promoting the user's skill improvement. Furthermore, if the user is feeling stressed, the match unit can lower the difficulty and provide a match that allows the user to relax. This makes it possible to provide an optimal match experience according to the user's emotions.

[0122] When collecting professional match data, the collection unit can also filter the data based on the professional's current training status and strategy. For example, if the professional is trying out a new tactic, data based on that tactic can be collected preferentially. It can also collect data focusing on specific skills and techniques, taking into account the professional's training menu and progress. Furthermore, if the professional is following a specific training menu, collecting match data related to that menu allows for more accurate data collection.

[0123] The learning unit can also estimate the emotions of the pros and select learning data based on the estimated emotions of the pros. For example, it can prioritize learning data from when the pros were relaxed to reproduce a natural playing style. It can also prioritize learning data from when the pros were concentrating to reproduce a highly accurate playing style. Furthermore, by avoiding data from when the pros were feeling stressed, it is possible to learn a more stable playing style. In this way, by selecting learning data based on the emotions of the pros, it is possible to reproduce a more natural and accurate playing style.

[0124] The selection unit can also estimate the user's emotions and adjust the game selection interface based on the estimated user emotions. For example, if the user is relaxed, a colorful and fun interface is provided, allowing the user to enjoyably select a game. Alternatively, if the user is concentrating, a simple and highly visible interface is provided, allowing the user to efficiently select a game. Furthermore, if the user is feeling stressed, a calming interface can be provided, creating a relaxing environment. In this way, adjusting the interface based on the user's emotions allows for a more comfortable game selection.

[0125] The match unit can also select an optimal match strategy by referring to the user's past match history. For example, it can analyze the user's past match history and select the most effective strategy. It can also learn specific tactics and strategies from the user's past match history and select a match strategy based on those. It can also provide matches that allow different tactics and strategies to be tried based on the user's past match history. In this way, by referring to the user's past match history, a more effective match strategy can be selected.

[0126] The hitting-against-wall unit can also estimate the user's emotions and adjust the hitting-against-wall method based on the estimated user's emotions. For example, if the user is relaxed, it can provide hitting-against-wall with a moderate level of difficulty, allowing the user to practice while having fun. Alternatively, if the user is concentrating, it can provide hitting-against-wall with a challenging level of difficulty, promoting the user's skill improvement. Furthermore, if the user is feeling stressed, it can provide hitting-against-wall with a lower level of difficulty, allowing the user to relax. In this way, adjusting the hitting-against-wall method based on the user's emotions enables more appropriate practice.

[0127] The collection unit can also analyze the past match history of a professional player and select an appropriate collection method. For example, it can retrieve the past match history of a professional player from a database and extract particularly excellent plays. It can also prioritize the collection of matches in which a particular tactic or strategy was frequently used from the professional player's match history. Furthermore, by collecting matches against different opponents in a balanced manner, it becomes possible to collect more diverse data. This allows for more effective data collection by analyzing the past match history of a professional player.

[0128] During learning, the learning unit can also optimize the learning algorithm based on the evolution of a professional's playing style. For example, it can identify the period when a professional's playing style changed and focus on learning data from before and after that period. It can also analyze the evolution of a professional's playing style and learn data from different styles in a balanced manner. Furthermore, by tracking the evolution of a professional's playing style and optimizing the algorithm to reflect those changes, it can learn a wider variety of playing styles. This allows it to learn a wider variety of playing styles by referring to the evolution of professional playing styles.

[0129] When selecting a game, the selection unit can also prioritize and present games that are highly relevant based on the user's geographical location information. For example, if the user's geographical location information is obtained from GPS data and the user is in a specific region, games that are popular in that region can be suggested. Games that require tactics or strategies specific to that region can also be suggested. Furthermore, if the user is traveling, games related to the culture and history of that region can be suggested, enabling region-specific game selection. This allows region-specific game selection by taking the user's geographical location information into consideration.

[0130] The match unit can also estimate the user's emotions and determine the priority of matches based on the estimated user's emotions. For example, if the user is relaxed, matches of a moderate level of difficulty can be provided preferentially. Also, if the user is concentrating, matches of a challenging level of difficulty can be provided preferentially. Furthermore, if the user is feeling stressed, matches of a lower level of difficulty that allow for relaxation can be provided preferentially. In this way, by determining the priority of matches based on the user's emotions, more appropriate matches can be provided.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The collection unit collects professional play data. The professional play data includes professional match data for various games such as Go, Shogi, chess, and card games. The collection unit not only collects match records from the Internet, but can also collect data by analyzing professional match videos. It can also obtain professional match results from a database. Step 2: The learning unit uses the data collected by the collection unit to train the AI ​​to learn professional playing styles. The learning unit analyzes the data using machine learning algorithms and trains the AI. It can also preprocess the data to enable the AI ​​to learn efficiently. It can also evaluate the AI's learning results and adjust the algorithm as needed. Step 3: The selection unit selects the game the user wants to play. The selection unit not only allows the user to select a game through the interface, but can also suggest the most suitable game based on the user's past selection history. It can also adjust the difficulty of the game according to the user's skill level. Step 4: In the match section, an AI acts as an opponent based on the game selected by the selection section. The match section reproduces the professional playing style and plays against the user. The AI ​​can also adjust its behavior in real time according to the user's movements. Furthermore, it can record the results of the match and provide feedback to the user. Step 5: In the wall-hitting section, the AI ​​acts as an opponent for the user to practice alone. The AI ​​responds appropriately to the user's movements. It can also record the user's practice data and provide advice to improve skills. It can also set up practice scenarios to help users practice efficiently.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] 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.

[0182] 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.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects professional play data, a learning unit that learns the data collected by the collection unit; a selection unit for selecting a game in which a user wants to play; a competition unit in which an AI functions as an opponent based on the game selected by the selection unit; and a wall hitting section for a user to hit the ball against the wall. A system characterized by:

2. The collecting unit Collecting professional match data for Go, Shogi, Chess, and card games 2. The system of claim 1.

3. The learning unit AI learns professional playing styles based on collected data 2. The system of claim 1.

4. The selection unit The user selects the game they want to play.

2. The system of claim 1.

5. The competition section includes: AI acts as opponent based on the game selected 2. The system of claim 1.

6. The wall striking section is AI acts as an opponent for users to practice alone 2. The system of claim 1.

7. The collecting unit Analyze the emotions of pros and adjust the timing of collecting match data based on the analyzed emotions of pros 2. The system of claim 1.

8. The collecting unit Analyze past match histories of pros and select the appropriate collection method 2. The system of claim 1.

Images