System

Abstract

An object of a system according to an embodiment is to solve questions about rules of a card game or a board game and support smooth play.SOLUTION: A system according to an embodiment includes a play-video analysis unit, a similar game adjudication reference unit, and a rule question resolution unit. The play-video analysis unit analyzes a play-video. The similar game adjudication reference unit refers to adjudication information about a similar game on the basis of information obtained from the play-video analyzed by the play-video analysis unit. The rule question solving unit solves the question about the rule of the player based on the decision information of the similar game referred to by the similar game decision reference unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology makes it difficult to resolve detailed questions about the rules of card and board games, which can hinder smooth gameplay.

[0005] The system according to the embodiment aims to resolve questions about the rules of card games and board games and support smooth play. [Means for solving the problem]

[0006] The system according to the embodiment includes a gameplay video analysis unit, a similar game arbitration reference unit, and a rule question resolution unit. The gameplay video analysis unit analyzes gameplay videos. The similar game arbitration reference unit references arbitration information for similar games based on information obtained from the gameplay videos analyzed by the gameplay video analysis unit. The rule question resolution unit resolves player questions about the rules based on the arbitration information for similar games referenced by the similar game arbitration reference unit. [Effects of the Invention]

[0007] The system according to the embodiment can resolve questions about the rules of card games and board games and support smooth play. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The rule resolution system according to an embodiment of the present invention is a system that resolves situations in card games and board games where it is difficult to make a decision based on limited text, by using a generation AI to refer to gameplay videos and rulings from similar games to resolve detailed questions about the rules and support smooth play. This allows the rule resolution system to quickly resolve questions about the rules that players may encounter and support smooth game progress.

[0029] A rule solving system according to an embodiment includes a gameplay video analysis unit, a similar game ruling reference unit, and a rule question resolution unit. The gameplay video analysis unit analyzes gameplay videos. For example, the generation AI receives a URL or file of a gameplay video as input and analyzes the video to learn how the game progresses and the rules are applied. The generation AI analyzes, for example, the actions taken when a player uses a specific card and how pieces are moved on the board. The similar game ruling reference unit references ruling information for similar games based on information obtained from the gameplay video analyzed by the gameplay video analysis unit. For example, the generation AI references ruling information for similar games based on ruling examples from games in the same genre to generate answers to questions about rules that players may encounter. The generation AI receives input from a database of ruling information for similar games and FAQs and provides solutions based on that information. The rule question resolution unit resolves questions about rules that players may have based on the ruling information for similar games referenced by the similar game ruling reference unit. For example, the generation AI receives input from a prompt containing a player's question and generates an answer based on the gameplay video and rulings for similar games. As a result, the rule resolution system according to the embodiment can resolve player questions about the rules and support smooth gameplay. For example, even if a player does not understand the effect of a particular card, the generation AI can provide an appropriate answer, allowing the player to continue enjoying the game without any disruption to progress.

[0030] The gameplay video analysis unit learns specific strategies or play styles from gameplay videos and can propose optimal strategies to the player. The gameplay video analysis unit, for example, has a generation AI analyze gameplay videos and learn specific strategies and play styles. For example, it analyzes the timing when a player uses specific cards and how they move their pieces and proposes optimal strategies. The generation AI learns specific strategies and play styles from gameplay videos and proposes optimal strategies to the player. This makes it possible to propose optimal strategies to the player based on the results of gameplay video analysis.

[0031] The gameplay video analysis unit can predict the progress of the game in real time based on the analysis results of the gameplay video and advise the player on the next move. In the gameplay video analysis unit, for example, the generation AI analyzes the gameplay video and predicts the progress of the game in real time. For example, it predicts events that may occur in the next turn and the player's actions and advises the player on the next move. The generation AI predicts the progress of the game in real time based on the analysis results of the gameplay video and advises the player on the next move. In this way, it is possible to predict the progress of the game in real time based on the analysis results of the gameplay video and advise the player on the next move.

[0032] The gameplay video analysis unit can provide a game replay function based on the analysis results of the gameplay video, allowing players to study while looking back on past plays. In the gameplay video analysis unit, for example, the generation AI analyzes gameplay videos and provides a game replay function. For example, important scenes are highlighted so that players can study while looking back on past plays. The generation AI provides a game replay function based on the analysis results of the gameplay video, allowing players to study while looking back on past plays. In this way, the game replay function is provided based on the analysis results of the gameplay video, allowing players to study while looking back on past plays.

[0033] The gameplay video analysis unit can automatically generate highlight scenes of the game based on the analysis results of the gameplay video, allowing players to easily rewatch important moments. In the gameplay video analysis unit, for example, the generation AI analyzes the gameplay video and automatically generates highlight scenes of the game. For example, it highlights the moment when a player uses a specific card or the moment when a player wins. The generation AI automatically generates highlight scenes of the game based on the analysis results of the gameplay video, allowing players to easily rewatch important moments. In this way, game highlight scenes are automatically generated based on the analysis results of the gameplay video, allowing players to easily rewatch important moments.

[0034] The similar game arbitration reference unit can automatically generate rules for a new game based on arbitration information for similar games and propose them to players. In the similar game arbitration reference unit, for example, a generation AI analyzes arbitration information for similar games and automatically generates rules for a new game. For example, new rules are proposed by referring to arbitration cases for games in the same genre. The generation AI automatically generates rules for a new game based on arbitration information for similar games and proposes them to players. In this way, rules for a new game can be automatically generated based on arbitration information for similar games and proposed to players.

[0035] The similar game adjudication reference unit adjusts the balance of the game based on the adjudication information of similar games, thereby providing a fairer gaming experience. In the similar game adjudication reference unit, for example, the generation AI analyzes the adjudication information of similar games and adjusts the balance of the game. For example, the strength of specific cards or pieces is adjusted to provide a fairer gaming experience. The generation AI adjusts the balance of the game based on the adjudication information of similar games, thereby providing a fairer gaming experience. In this way, the balance of the game is adjusted based on the adjudication information of similar games, thereby providing a fairer gaming experience.

[0036] The similar game arbitration reference unit can automatically generate a game tutorial based on arbitration information of similar games, allowing new players to easily learn the rules. In the similar game arbitration reference unit, for example, a generation AI analyzes arbitration information of similar games and automatically generates a game tutorial. For example, it can make it easy for new players to learn the rules based on past arbitration cases. The generation AI can automatically generate a game tutorial based on arbitration information of similar games, allowing new players to easily learn the rules. In this way, it is possible to automatically generate a game tutorial based on arbitration information of similar games, allowing new players to easily learn the rules.

[0037] The similar game arbitration reference unit automatically generates FAQs for a game based on arbitration information for similar games, making it easier for players to resolve their questions. In the similar game arbitration reference unit, for example, a generation AI analyzes arbitration information for similar games and automatically generates FAQs for a game. For example, based on past arbitration cases, it provides answers to questions about the rules that players may have. The generation AI automatically generates FAQs for a game based on arbitration information for similar games, making it easier for players to resolve their questions. In this way, FAQs for a game can be automatically generated based on arbitration information for similar games, making it easier for players to resolve their questions.

[0038] When providing an answer to a player's question, the rule question resolution unit simultaneously presents related rules and examples, thereby deepening the player's understanding. For example, when the generation AI provides an answer to a player's question, it simultaneously presents related rules and examples. For example, in response to the question, "If I play this card, what will happen next turn?", it presents related rules and past examples. When providing an answer to a player's question, the generation AI simultaneously presents related rules and examples, thereby deepening the player's understanding. This allows the generation AI to simultaneously present related rules and examples when providing an answer to a player's question, thereby deepening the player's understanding.

[0039] The rule question resolution unit can provide answers to the player's questions using audio or visuals, allowing for more intuitive understanding. In the rule question resolution unit, for example, the generation AI provides answers to the player's questions using audio or visuals. For example, how to use a specific card is explained using audio and the action is shown visually. The generation AI provides answers to the player's questions using audio or visuals, allowing for more intuitive understanding. In this way, the answer to the player's questions can be provided using audio or visuals, allowing for more intuitive understanding.

[0040] The rule question resolution unit can provide a platform where answers to players' questions are shared with other players and knowledge is shared throughout the community. The rule question resolution unit, for example, provides a platform where the generation AI can share answers to players' questions with other players. For example, answers about how to use a particular card are shared with the entire community. The generation AI can share answers to players' questions with other players and provide a platform where knowledge is shared throughout the community. This can provide a platform where answers to players' questions are shared with other players and knowledge is shared throughout the community.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The rule solving system further includes a voice recognition unit. The voice recognition unit recognizes questions about the rules uttered by the player and can transmit the questions as text data to the rule question solving unit. For example, if a player asks, "What is the effect of this card?", the voice recognition unit converts the question into text, and the rule question solving unit provides an appropriate answer. This allows players to solve their questions verbally without using their hands, making the game progress more smoothly.

[0043] The rule solving system further includes a history management unit. The history management unit records questions about rules that players have previously asked and the answers to those questions, and can quickly provide answers when the player has the same question again. For example, if a player previously asked "What is the effect of this card?", the answer is saved as history, and the answer is immediately displayed when the player asks the same question again. This allows players to easily look back on past questions, improving learning effectiveness.

[0044] The rule resolution system also includes a translation unit. The translation unit can automatically translate rules and ruling information provided in different languages ​​and provide it to players. For example, rules written in English can be translated into Japanese and displayed. This allows players who speak different languages ​​to smoothly progress through the game, supporting the formation of an international gaming community.

[0045] The rule resolution system further includes a customization section. The customization section allows the player to customize the settings of the rule resolution system according to their own play style and preferences. For example, it is possible to set the system to preferentially display answers specialized for a particular game genre, or to change the way answers are displayed from text to audio. This allows the player to use the rule resolution system in a way that is best suited to them.

[0046] The rule resolution system further includes a notification unit. The notification unit can notify players in real time when important rule changes or new ruling information is added during a game. For example, the notification unit sends a notification to players when a new card effect is added or an existing rule is changed. This allows players to always be aware of the latest rule information and progress smoothly through the game.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The gameplay video analysis unit analyzes the gameplay video. For example, the generation AI receives the URL or file of the gameplay video as input, analyzes the video, and learns how the game progresses and the rules apply. The generation AI analyzes the actions taken when a player uses a specific card and how the pieces on the board are moved. Step 2: The Similar Game Ruling Reference Unit references ruling information for similar games based on information obtained from the gameplay video analyzed by the gameplay video analysis unit. For example, the generation AI references ruling examples from games in the same genre to generate answers to questions about the rules that players may encounter. The generation AI receives as input a database of ruling information and FAQs for similar games and provides solutions based on that information. Step 3: The rule question resolution unit resolves the player's question about the rules based on the ruling information of similar games referenced by the similar game ruling reference unit. For example, the generation AI receives a prompt containing the player's question as input and generates an answer by referring to gameplay videos and rulings of similar games. In this way, the rule resolution system according to the embodiment can resolve the player's question about the rules and support smooth gameplay.

[0049] (Example 2) The rule resolution system according to an embodiment of the present invention is a system that resolves situations in card games and board games where it is difficult to make a decision based on limited text, by using a generation AI to refer to gameplay videos and rulings from similar games to resolve detailed questions about the rules and support smooth play. This allows the rule resolution system to quickly resolve questions about the rules that players may encounter and support smooth game progress.

[0050] A rule solving system according to an embodiment includes a gameplay video analysis unit, a similar game ruling reference unit, and a rule question resolution unit. The gameplay video analysis unit analyzes gameplay videos. For example, the generation AI receives a URL or file of a gameplay video as input and analyzes the video to learn how the game progresses and the rules are applied. The generation AI analyzes, for example, the actions taken when a player uses a specific card and how pieces are moved on the board. The similar game ruling reference unit references ruling information for similar games based on information obtained from the gameplay video analyzed by the gameplay video analysis unit. For example, the generation AI references ruling information for similar games based on ruling examples from games in the same genre to generate answers to questions about rules that players may encounter. The generation AI receives input from a database of ruling information for similar games and FAQs and provides solutions based on that information. The rule question resolution unit resolves questions about rules that players may have based on the ruling information for similar games referenced by the similar game ruling reference unit. For example, the generation AI receives input from a prompt containing a player's question and generates an answer based on the gameplay video and rulings for similar games. As a result, the rule resolution system according to the embodiment can resolve player questions about the rules and support smooth gameplay. For example, even if a player does not understand the effect of a particular card, the generation AI can provide an appropriate answer, allowing the player to continue enjoying the game without any disruption to progress.

[0051] The gameplay video analysis unit learns specific strategies or play styles from gameplay videos and can propose optimal strategies to the player. The gameplay video analysis unit, for example, has a generation AI analyze gameplay videos and learn specific strategies and play styles. For example, it analyzes the timing when a player uses specific cards and how they move their pieces and proposes optimal strategies. The generation AI learns specific strategies and play styles from gameplay videos and proposes optimal strategies to the player. This makes it possible to propose optimal strategies to the player based on the results of gameplay video analysis.

[0052] The gameplay video analysis unit can predict the progress of the game in real time based on the analysis results of the gameplay video and advise the player on the next move. In the gameplay video analysis unit, for example, the generation AI analyzes the gameplay video and predicts the progress of the game in real time. For example, it predicts events that may occur in the next turn and the player's actions and advises the player on the next move. The generation AI predicts the progress of the game in real time based on the analysis results of the gameplay video and advises the player on the next move. In this way, it is possible to predict the progress of the game in real time based on the analysis results of the gameplay video and advise the player on the next move.

[0053] The gameplay video analysis unit uses the emotion estimation function to estimate the player's emotions from their facial expressions or tone of voice, and can provide advice according to those emotions. For example, the generation AI analyzes gameplay videos and estimates the player's emotions from their facial expressions and tone of voice. For example, if the player is nervous, it provides advice to relax. The generation AI uses the emotion estimation function to estimate the player's emotions from their facial expressions and tone of voice, and provides advice according to those emotions. This makes it possible to provide advice according to the player's emotions.

[0054] The gameplay video analysis unit can provide a game replay function based on the analysis results of the gameplay video, allowing players to study while looking back on past plays. In the gameplay video analysis unit, for example, the generation AI analyzes gameplay videos and provides a game replay function. For example, important scenes are highlighted so that players can study while looking back on past plays. The generation AI provides a game replay function based on the analysis results of the gameplay video, allowing players to study while looking back on past plays. In this way, the game replay function is provided based on the analysis results of the gameplay video, allowing players to study while looking back on past plays.

[0055] The gameplay video analysis unit can automatically generate highlight scenes of the game based on the analysis results of the gameplay video, allowing players to easily rewatch important moments. In the gameplay video analysis unit, for example, the generation AI analyzes the gameplay video and automatically generates highlight scenes of the game. For example, it highlights the moment when a player uses a specific card or the moment when a player wins. The generation AI automatically generates highlight scenes of the game based on the analysis results of the gameplay video, allowing players to easily rewatch important moments. In this way, game highlight scenes are automatically generated based on the analysis results of the gameplay video, allowing players to easily rewatch important moments.

[0056] The similar game arbitration reference unit can automatically generate rules for a new game based on arbitration information for similar games and propose them to players. In the similar game arbitration reference unit, for example, a generation AI analyzes arbitration information for similar games and automatically generates rules for a new game. For example, new rules are proposed by referring to arbitration cases for games in the same genre. The generation AI automatically generates rules for a new game based on arbitration information for similar games and proposes them to players. In this way, rules for a new game can be automatically generated based on arbitration information for similar games and proposed to players.

[0057] The similar game adjudication reference unit adjusts the balance of the game based on the adjudication information of similar games, thereby providing a fairer gaming experience. In the similar game adjudication reference unit, for example, the generation AI analyzes the adjudication information of similar games and adjusts the balance of the game. For example, the strength of specific cards or pieces is adjusted to provide a fairer gaming experience. The generation AI adjusts the balance of the game based on the adjudication information of similar games, thereby providing a fairer gaming experience. In this way, the balance of the game is adjusted based on the adjudication information of similar games, thereby providing a fairer gaming experience.

[0058] The similar game ruling reference unit uses the emotion estimation function to identify the ruling that the player is most satisfied with and is able to prioritize reference to that ruling. In the similar game ruling reference unit, for example, the generation AI analyzes ruling information of similar games and identifies the ruling that the player is most satisfied with. For example, based on the player's emotional response, the generation AI prioritizes reference to rulings that are highly satisfying. The generation AI uses the emotion estimation function to identify the ruling that the player is most satisfied with and is able to prioritize reference to that ruling. This allows the generation AI to identify the ruling that the player is most satisfied with and is able to prioritize reference to that ruling.

[0059] The similar game arbitration reference unit can automatically generate a game tutorial based on arbitration information of similar games, allowing new players to easily learn the rules. In the similar game arbitration reference unit, for example, a generation AI analyzes arbitration information of similar games and automatically generates a game tutorial. For example, it can make it easy for new players to learn the rules based on past arbitration cases. The generation AI can automatically generate a game tutorial based on arbitration information of similar games, allowing new players to easily learn the rules. In this way, it is possible to automatically generate a game tutorial based on arbitration information of similar games, allowing new players to easily learn the rules.

[0060] The similar game arbitration reference unit automatically generates FAQs for a game based on arbitration information for similar games, making it easier for players to resolve their questions. In the similar game arbitration reference unit, for example, a generation AI analyzes arbitration information for similar games and automatically generates FAQs for a game. For example, based on past arbitration cases, it provides answers to questions about the rules that players may have. The generation AI automatically generates FAQs for a game based on arbitration information for similar games, making it easier for players to resolve their questions. In this way, FAQs for a game can be automatically generated based on arbitration information for similar games, making it easier for players to resolve their questions.

[0061] The similar game adjudication reference unit can use the emotion estimation function to identify the rule that players find most confusing and strengthen the explanation for that rule. In the similar game adjudication reference unit, for example, the generation AI analyzes the adjudication information of similar games and identifies the rule that players find most confusing. For example, based on the player's emotional reaction, the generation AI can identify the rule that players find most confusing and strengthen the explanation for that rule. The generation AI can use the emotion estimation function to identify the rule that players find most confusing and strengthen the explanation for that rule. This makes it possible to identify the rule that players find most confusing and strengthen the explanation for that rule.

[0062] When providing an answer to a player's question, the rule question resolution unit simultaneously presents related rules and examples, thereby deepening the player's understanding. For example, when the generation AI provides an answer to a player's question, it simultaneously presents related rules and examples. For example, in response to the question, "If I play this card, what will happen next turn?", it presents related rules and past examples. When providing an answer to a player's question, the generation AI simultaneously presents related rules and examples, thereby deepening the player's understanding. This allows the generation AI to simultaneously present related rules and examples when providing an answer to a player's question, thereby deepening the player's understanding.

[0063] The rule question solving unit uses the emotion estimation function to measure the player's level of satisfaction when the question is solved, and can improve the quality of the answer based on that data. For example, the rule question solving unit uses the emotion estimation function to measure the player's level of satisfaction when the generation AI solves the player's question. For example, it estimates the level of satisfaction from the player's facial expression and tone of voice, and improves the quality of the answer based on that data. The generation AI uses the emotion estimation function to measure the player's level of satisfaction when the question is solved, and improves the quality of the answer based on that data. In this way, it is possible to measure the player's level of satisfaction when the question is solved, and improve the quality of the answer based on that data.

[0064] The rule question resolution unit can provide answers to the player's questions using audio or visuals, allowing for more intuitive understanding. In the rule question resolution unit, for example, the generation AI provides answers to the player's questions using audio or visuals. For example, how to use a specific card is explained using audio and the action is shown visually. The generation AI provides answers to the player's questions using audio or visuals, allowing for more intuitive understanding. In this way, the answer to the player's questions can be provided using audio or visuals, allowing for more intuitive understanding.

[0065] The rule question resolution unit can provide a platform where answers to players' questions are shared with other players and knowledge is shared throughout the community. The rule question resolution unit, for example, provides a platform where the generation AI can share answers to players' questions with other players. For example, answers about how to use a particular card are shared with the entire community. The generation AI can share answers to players' questions with other players and provide a platform where knowledge is shared throughout the community. This can provide a platform where answers to players' questions are shared with other players and knowledge is shared throughout the community.

[0066] The rule doubt resolution unit uses the emotion estimation function to identify the rule that players are most likely to have questions about and can strengthen the explanation for that rule. For example, the rule doubt resolution unit uses the generation AI to analyze the player's emotional reactions and identify the rule that players are most likely to have questions about. For example, it estimates emotions from the player's facial expressions and tone of voice, identifies the rule that players are most likely to have questions about, and strengthens the explanation for that rule. The generation AI uses the emotion estimation function to identify the rule that players are most likely to have questions about and strengthens the explanation for that rule. This makes it possible to identify the rule that players are most likely to have questions about and strengthen the explanation for that rule.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The rule solving system further includes a voice recognition unit. The voice recognition unit recognizes questions about the rules uttered by the player and can transmit the questions as text data to the rule question solving unit. For example, if a player asks, "What is the effect of this card?", the voice recognition unit converts the question into text, and the rule question solving unit provides an appropriate answer. This allows players to solve their questions verbally without using their hands, making the game progress more smoothly.

[0069] The rule solving system further includes a history management unit. The history management unit records questions about rules that players have previously asked and the answers to those questions, and can quickly provide answers when the player has the same question again. For example, if a player previously asked "What is the effect of this card?", the answer is saved as history, and the answer is immediately displayed when the player asks the same question again. This allows players to easily look back on past questions, improving learning effectiveness.

[0070] The rule resolution system also includes a translation unit. The translation unit can automatically translate rules and ruling information provided in different languages ​​and provide it to players. For example, rules written in English can be translated into Japanese and displayed. This allows players who speak different languages ​​to smoothly progress through the game, supporting the formation of an international gaming community.

[0071] The rule resolution system further includes a customization section. The customization section allows the player to customize the settings of the rule resolution system according to their own play style and preferences. For example, it is possible to set the system to preferentially display answers specialized for a particular game genre, or to change the way answers are displayed from text to audio. This allows the player to use the rule resolution system in a way that is best suited to them.

[0072] The rule resolution system further includes a notification unit. The notification unit can notify players in real time when important rule changes or new ruling information is added during a game. For example, the notification unit sends a notification to players when a new card effect is added or an existing rule is changed. This allows players to always be aware of the latest rule information and progress smoothly through the game.

[0073] The rule solving system can estimate the player's emotions and provide advice to help the player relax based on the estimated emotions. For example, if the player is feeling tense, the system can advise the player to take deep breaths or suggest playing relaxing music. This allows the player to enjoy the game in a relaxed state and improve their concentration.

[0074] The rule resolution system can estimate the player's emotions and suggest in-game events that the player can enjoy based on the estimated emotions. For example, if the player is bored, it can suggest new challenges or mini-games. This allows the player to enjoy the game while always receiving new stimulation, and keeps them playing without getting bored.

[0075] The rule resolution system can estimate the player's emotions and provide rewards that will most satisfy the player based on the estimated emotions. For example, if the player is happy, it can provide special items or bonus points. This will allow the player to enjoy the game even more and increase their motivation.

[0076] The rule resolution system can estimate the player's emotions and provide more detailed explanations or additional support if the player is confused based on the estimated emotions. For example, if a player is confused about a particular rule, it can provide explanations using visuals or videos. This makes it easier for players to resolve their doubts and progress smoothly in the game.

[0077] The rule resolution system estimates the player's emotions and, based on the estimated emotions, can temporarily adjust the game difficulty if the player is feeling stressed. For example, if the player is feeling stressed, the system can reduce the strength of enemies or ease the conditions for clearing the game. This allows the player to reduce stress and enjoy the game.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The gameplay video analysis unit analyzes the gameplay video. For example, the generation AI receives the URL or file of the gameplay video as input, analyzes the video, and learns how the game progresses and the rules apply. The generation AI analyzes the actions taken when a player uses a specific card and how the pieces on the board are moved. Step 2: The Similar Game Ruling Reference Unit references ruling information for similar games based on information obtained from the gameplay video analyzed by the gameplay video analysis unit. For example, the generation AI references ruling examples from games in the same genre to generate answers to questions about the rules that players may encounter. The generation AI receives as input a database of ruling information and FAQs for similar games and provides solutions based on that information. Step 3: The rule question resolution unit resolves the player's question about the rules based on the ruling information of similar games referenced by the similar game ruling reference unit. For example, the generation AI receives a prompt containing the player's question as input and generates an answer by referring to gameplay videos and rulings of similar games. In this way, the rule resolution system according to the embodiment can resolve the player's question about the rules and support smooth gameplay.

[0080] 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.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0093] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0147] 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 gameplay video analysis unit that analyzes gameplay videos; a similar game arbitration reference unit that refers to arbitration information of similar games based on information obtained from the gameplay video analyzed by the gameplay video analysis unit; a rule question resolution unit that resolves player questions about the rules based on the ruling information of the similar games referenced by the similar game ruling reference unit. A system characterized by:

2. The gameplay video analysis unit Based on the analysis results of the gameplay video, the progress of the game is predicted in real time and the player is advised on the next move.

2. The system of claim 1.

3. The gameplay video analysis unit Based on the analysis results of the gameplay video, a game replay function is provided, allowing the player to learn by looking back on past play.

2. The system of claim 1.

4. The similar game arbitration reference unit: A game tutorial is automatically generated based on the ruling information of the similar game, allowing new players to easily learn the rules.

2. The system of claim 1.

5. The rule question resolution unit The answer to the player's question is provided using audio or visuals, making it easier to understand intuitively.

2. The system of claim 1.

6. The gameplay video analysis unit Inferring the player's emotions from their facial expressions or tone of voice and providing advice according to their emotions 2. The system of claim 1.

7. The similar game arbitration reference unit: Identifying the ruling that the player is most satisfied with and giving priority to that ruling 2. The system of claim 1.

8. The rule question resolution unit Measure the satisfaction of the player when solving the question, and improve the quality of the answer based on that data.

2. The system of claim 1.

Images