system

Abstract

The system according to this embodiment aims to allow mahjong players to receive strategic advice in real time. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a provision unit, and a display unit. The acquisition unit acquires the player's hand and the state of the game. The analysis unit analyzes the information acquired by the acquisition unit and generates strategic advice and candidates for the next move. The provision unit provides the advice and information generated by the analysis unit to the player. The display unit visually displays the information provided by the provision unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

[0005] , , ,

[0003] , , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

[0007] The system according to this embodiment allows mahjong players to receive strategic advice in real time. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The Mahjong Support Agent System according to an embodiment of the present invention is a glasses-type device for Mahjong players to strategically enjoy the game. This Mahjong Support Agent System acquires the player's hand and the state of the game in real time, and a generating AI analyzes this information to generate strategic advice and candidate next moves. The generated advice and information are provided to the player through a bone conduction speaker. In addition, the completed forms and probabilities of winning hands are projected onto the glasses portion, providing visual support. This supports the improvement of players' skills, from beginners to advanced players. For example, an acquisition unit is used to acquire the player's hand and the state of the game in real time. The acquisition unit plays the role of acquiring the player's hand and the state of the game. Next, an analysis unit is used to analyze the acquired information. The generating AI analyzes the hand and the state of the game through the analysis unit and generates strategic advice and candidate next moves. The generated advice and information are provided to the player through a provision unit. The provision unit plays the role of providing advice to the player through a bone conduction speaker. Furthermore, a display unit is used so that the completed forms and probabilities of winning hands are projected onto the glasses portion. The display unit provides visual support, allowing players to strategically progress through the game while visually confirming information. This device supports skill improvement for a wide range of players, from beginners to advanced players. Beginners can easily understand complex rules and strategies, while advanced players can receive support to execute more advanced strategies. This enhances the enjoyment of mahjong and increases player satisfaction. In this way, the mahjong support agent system can support player skill improvement and amplify the enjoyment of mahjong.

[0029] The mahjong support agent system according to this embodiment comprises an acquisition unit, an analysis unit, a provision unit, and a display unit. The acquisition unit acquires the player's hand and the state of the game. The acquisition unit can acquire the hand and the state of the game in real time, for example, using a camera or sensors. The acquisition unit can, for example, acquire images of the hand using a camera and identify the types of the hand using image recognition technology. The acquisition unit can also acquire the state of the game using sensors and identify the arrangement of the cards on the game. For example, the acquisition unit can acquire images of the hand using a camera and identify the types of the hand using image recognition technology. Furthermore, the acquisition unit can acquire the state of the game using sensors and identify the arrangement of the cards on the game. The analysis unit analyzes the information acquired by the acquisition unit and generates strategic advice and candidates for the next move. The analysis unit can, for example, analyze the hand and the state of the game using a generation AI and generate strategic advice and candidates for the next move. The generation AI takes the hand and the state of the game as input and outputs strategic advice and candidates for the next move. The generating AI can, for example, analyze the player's hand and the state of the game and propose the optimal strategy. The providing unit provides the player with advice and information generated by the analysis unit. The providing unit can, for example, provide advice to the player using a bone conduction speaker. Furthermore, the providing unit can recognize the player's voice using speech recognition technology and provide advice in response to the player's questions. The display unit visually displays the information provided by the providing unit. The display unit can, for example, project completed hands and probabilities onto the glasses portion of the glasses portion of the glasses. Furthermore, the display unit can also display completed hands and probabilities using AR technology. As a result, the mahjong support agent system according to this embodiment can acquire, analyze, provide, and display the player's hand and the state of the game in real time, thereby providing strategic advice.

[0030] The acquisition unit acquires the player's hand and the state of the table. The acquisition unit can acquire the hand and the state of the table in real time, for example, using cameras and sensors. Specifically, a camera is installed on the table and photographs the player's hand and the arrangement of cards on the table. This acquires an image of the hand, and the type and number of tiles in the hand can be identified using image recognition technology. The image recognition technology uses a model based on deep learning to analyze the image of the hand and identify the type of each tile with high accuracy. For example, it can accurately identify each tile such as characters, bamboo, dots, and honor tiles. Furthermore, the arrangement of cards on the table is acquired using sensors. The sensors detect the position and orientation of cards placed on the table and accurately grasp the state of the table. This allows the status of tiles played and discarded tiles to be acquired in real time. In addition, the acquisition unit centrally manages this data and transmits it to the analysis unit. The data acquisition unit can adjust the frequency and accuracy of data acquisition, enabling real-time situation monitoring and providing players with quick and accurate information.

[0031] The analysis unit analyzes the information acquired by the acquisition unit and generates strategic advice and candidate next moves. For example, the analysis unit can use a generative AI to analyze the hand and the situation on the table and generate strategic advice and candidate next moves. The generative AI uses models based on deep learning and reinforcement learning, takes the hand and the situation on the table as input, and outputs the optimal strategy and candidate next moves. Specifically, the generative AI analyzes the combination of tiles in the hand and the situation of discarded tiles on the table and proposes the most advantageous strategy for the player. For example, the generative AI determines which tiles to discard and which tiles to draw from the hand and provides advice to the player. The generative AI can also analyze the situation on the table and predict the possibilities of other players' hands and strategies. This allows players to predict the movements of other players and play more strategically. Furthermore, the analysis unit can also utilize past play data and statistical information to perform long-term strategy and trend analysis. This allows players to receive strategic advice based on their past gameplay data, enabling them to achieve a higher level of gameplay.

[0032] The service provider delivers advice and information generated by the analysis unit to the player. For example, the service provider can provide advice to the player using bone conduction speakers. Bone conduction speakers transmit sound through the bones without directly transmitting sound to the player's ears, allowing the player to receive advice without being blocked by ambient noise. This allows the player to receive necessary advice while concentrating on the game. The service provider can also recognize the player's voice using speech recognition technology and provide advice in response to the player's questions. Speech recognition technology analyzes the player's voice and understands the content of the question to generate appropriate advice. For example, if the player asks, "What should I discard next?", the service provider can suggest the optimal tile based on the information from the analysis unit. Furthermore, the service provider can collect player feedback and continuously improve the accuracy and effectiveness of the advice. This allows the service provider to provide players with quick and accurate advice and support their game strategy.

[0033] The display unit visually displays information provided by the provider unit. For example, the display unit can project completed hand shapes and probabilities onto the glasses portion. The information projected onto the glasses portion provides necessary information without obstructing the player's view, allowing the player to check completed hand shapes and probabilities while concentrating on the game. Furthermore, the display unit can also display completed hand shapes and probabilities using AR technology. By using AR technology, players can check the information by overlaying it with their actual hand and the situation on the table, allowing them to grasp the information more intuitively. For example, when a player checks their hand, the completed hand shapes and probabilities are visually displayed, which can be used as a reference when deciding on the next move. The display unit can also detect the player's gaze and gestures and display necessary information. This allows players to check information without using their hands, enabling smoother game progression. By providing visual information to the player, the display unit can support game strategy and assist the player's decision-making.

[0034] The acquisition unit can acquire the player's hand and the state of the game board in real time. The acquisition unit can acquire the hand and the state of the game board in real time, for example, using a camera or sensors. The acquisition unit can acquire images of the hand using a camera and identify the types of cards in the hand using image recognition technology. The acquisition unit can also acquire the state of the game board using sensors and identify the arrangement of cards on the board. For example, the acquisition unit can acquire images of the hand using a camera and identify the types of cards in the hand using image recognition technology. Furthermore, the acquisition unit can acquire the state of the game board using sensors and identify the arrangement of cards on the board. This makes it possible to provide advice based on the latest information by acquiring the player's hand and the state of the game board in real time. The specific definition and criteria of real time need to be clarified, for example, by the frequency of acquisition and the delay time. Some or all of the above processing in the acquisition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the acquisition unit can input images of the hand acquired by a camera into a generative AI and have the generative AI perform the identification of the types of cards in the hand.

[0035] The analysis unit can analyze the acquired information and generate strategic advice and candidate next moves. For example, the analysis unit can use a generative AI to analyze the hand and the game situation to generate strategic advice and candidate next moves. The generative AI, for example, takes the hand and the game situation as input and outputs strategic advice and candidate next moves. The generative AI can, for example, analyze the hand and the game situation and propose the optimal strategy. For example, the generative AI analyzes the hand and the game situation and generates candidate next moves. The generative AI can, for example, analyze the hand and the game situation, perform a risk assessment, and propose the optimal strategy. Thus, by analyzing the acquired information, strategic advice and candidate next moves can be generated. The specific methods and criteria for analysis need to be clearly defined, for example, by the algorithm used and the depth of analysis. The specific content and criteria for strategic advice need to be clearly defined, for example, by candidate next moves and risk assessment. The specific selection criteria and methods for candidate next moves need to be clearly defined, for example, by a probabilistic approach or prediction based on past data. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the hand and the situation on the board into the generation AI and have the generation AI perform strategic advice and generate candidates for the next move.

[0036] The service provider can provide advice to the player through a bone conduction speaker. The service provider can, for example, provide advice to the player using a bone conduction speaker. The service provider can, for example, provide advice to the player using a bone conduction speaker. Furthermore, the service provider can recognize the player's voice using speech recognition technology and provide advice in response to the player's questions. For example, the service provider recognizes the player's voice and provides advice in response to the player's questions. This allows for direct feedback to the player by providing advice through a bone conduction speaker. The specific type and method of implementation of the bone conduction speaker need to be clarified, for example, by the method of attachment and the characteristics of the sound quality. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can provide advice generated by a generative AI to the player through a bone conduction speaker.

[0037] The display unit can project the completed forms and probabilities of the winning hands onto the glasses portion. The display unit can, for example, project the completed forms and probabilities of the winning hands onto the glasses portion. The display unit can, for example, project the completed forms and probabilities of the winning hands onto the glasses portion. Furthermore, the display unit can also display the completed forms and probabilities of the winning hands using AR technology. For example, the display unit can display the completed forms and probabilities of the winning hands using AR technology. This allows for visual support to the player by projecting the completed forms and probabilities of the winning hands onto the glasses portion. The specific structure and display method of the glasses portion need to be clarified, for example, by the type of display and the display resolution. The specific calculation method and criteria for the completed forms and probabilities of the winning hands need to be clarified, for example, by the probability calculation algorithm and the definition of the hands. Some or all of the above processing in the display unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the display unit can project the completed forms and probabilities of the winning hands generated by a generative AI onto the glasses portion.

[0038] The acquisition unit can analyze the player's past game history and select the optimal acquisition method. The acquisition unit can customize the acquisition method based on strategies the player has frequently used in the past, for example. The acquisition unit can optimize the acquisition method for specific situations based on the player's past game history, for example. The acquisition unit can analyze the player's past game history and adapt the acquisition method to the player's play style, for example. This allows the acquisition unit to provide the player with the optimal acquisition method by analyzing the player's past game history. The specific selection criteria and methods for the optimal acquisition method need to be clearly defined, for example, by a method based on past data analysis. Some or all of the above processing in the acquisition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the acquisition unit can input the player's past game history data into a generative AI and have the generative AI select the optimal acquisition method.

[0039] The data acquisition unit can filter the acquired hand and game situation based on the player's current strategy and playing style. For example, if the player is employing an aggressive strategy, the data acquisition unit will prioritize acquiring offensive information. For example, if the player is employing a defensive strategy, the data acquisition unit can prioritize acquiring defensive information. The data acquisition unit can adjust the types of information acquired based on the player's playing style. This allows for the provision of useful information to the player by filtering the information based on the player's current strategy and playing style. The specific methods and criteria for filtering need to be clarified, for example, by defining the method for selecting information based on strategy and playing style. Some or all of the above processing in the data acquisition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data acquisition unit can input the player's current strategy and playing style data into a generative AI and have the generative AI perform the information filtering.

[0040] The acquisition unit can prioritize acquiring highly relevant information based on the player's geographical location when acquiring hand tiles or the state of the game. For example, if a player is playing in a specific region, the acquisition unit will prioritize acquiring information based on the characteristics of that region. For example, the acquisition unit can acquire information that takes into account region-specific strategies based on the player's geographical location. For example, the acquisition unit can acquire information that reflects the tendencies of players in a region based on the player's location. This makes it possible to provide information that takes into account region-specific strategies by acquiring highly relevant information based on the player's geographical location. The specific methods for acquiring and using geographical location information need to be clarified, for example, by describing how GPS data is used. The specific criteria and methods for selecting highly relevant information need to be clarified, for example, by describing information based on region-specific strategies. Some or all of the above processing in the acquisition unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the acquisition unit can input the player's geographical location data into a generation AI and have the generation AI acquire highly relevant information.

[0041] The acquisition unit can analyze the player's social media activity and acquire relevant information when acquiring the player's hand or the state of the game. For example, the acquisition unit can acquire relevant information based on strategies shared by the player on social media. For example, the acquisition unit can analyze the player's social media activity and acquire information based on the player's interests. For example, the acquisition unit can acquire information related to the player's playing style based on the player's social media activity. This makes it possible to provide information based on the player's interests by analyzing the player's social media activity. The specific methods for analyzing and using social media activity need to be clarified, for example, by analyzing the content of posts or the number of followers. Some or all of the above processing in the acquisition unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the acquisition unit can input the player's social media activity data into a generative AI and have the generative AI acquire the relevant information.

[0042] The analysis unit can adjust the level of detail of its analysis based on the importance of the hand and the situation on the board during the analysis. For example, in important situations, the analysis unit can perform a detailed analysis and provide it to the player. For example, in less important situations, the analysis unit can perform a concise analysis and provide it to the player. The analysis unit can dynamically adjust the level of detail of its analysis according to the importance of the hand and the situation on the board. This allows the analysis unit to appropriately provide the player with the information they need by adjusting the level of detail of the analysis based on the importance of the hand and the situation on the board. The specific criteria and methods for evaluating importance need to be clarified, for example, by evaluating based on the influence of the hand and the situation on the board. The specific methods and criteria for adjusting the level of detail of the analysis need to be clarified, for example, by determining the depth of analysis according to importance. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input importance data of the hand and the situation on the board into a generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the hand and the situation on the board during analysis. For example, in an offensive situation, the analysis unit can apply an analysis algorithm specialized for offense. For example, in a defensive situation, the analysis unit can apply an analysis algorithm specialized for defense. For example, in a situation where a specific hand is being aimed for, the analysis unit can apply an analysis algorithm specialized for that hand. By applying different analysis algorithms depending on the category of the hand and the situation on the board, the analysis unit can provide the player with the best possible advice. The specific definitions and classification methods of the categories need to be clarified, for example, by classifying the types of hands and the situation on the board. The specific types and application methods of the analysis algorithms need to be clarified, for example, by selecting algorithms according to different categories. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input category data of the hand and the situation on the board into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on the timing of submission of hand and game situation data during analysis. For example, in important situations, the analysis unit can set a higher priority based on the submission timing. For example, in less important situations, the analysis unit can set a lower priority based on the submission timing. The analysis unit can dynamically adjust the priority of analysis according to the timing of submission of hand and game situation data. This allows the system to prioritize providing important information to the player by determining the priority of analysis based on the timing of submission of hand and game situation data. The specific evaluation criteria and methods for submission timing need to be clarified, for example, by evaluating the timing of submission of hand and game situation data. The specific methods and criteria for determining the priority of analysis need to be clarified, for example, by prioritizing according to submission timing. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input hand and game situation submission timing data into a generation AI and have the generation AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relationships between the hand and the situation on the table during analysis. For example, in important situations, the analysis unit can prioritize the analysis of highly relevant information. For example, in less important situations, the analysis unit can prioritize the analysis of less relevant information. The analysis unit can dynamically adjust the order of analysis according to the relationships between the hand and the situation on the table. This allows the system to appropriately provide the player with the necessary information by adjusting the order of analysis based on the relationships between the hand and the situation on the table. The specific criteria and methods for evaluating relationships need to be clarified, for example, by evaluating based on the degree of relevance between the hand and the situation on the table. The specific methods and criteria for adjusting the order of analysis need to be clarified, for example, by adjusting the order according to relationships. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input relationship data between the hand and the situation on the table into a generation AI and have the generation AI perform the adjustment of the order of analysis.

[0046] The service provider can select the optimal service provider method by referring to the player's past game history when providing advice. For example, the service provider may prioritize advice provider methods that the player has preferred to use in the past. For example, the service provider may select the optimal service provider method for a specific situation based on the player's past game history. For example, the service provider may analyze the player's past game history and tailor the service provider method to the player's play style. This allows the service provider to select the most suitable advice provider method for the player by referring to the player's past game history. The specific methods for referring to and using past game history need to be clarified, for example, in the method of analyzing the history data. The specific criteria and methods for selecting the optimal service provider method need to be clarified, for example, in the selection of a service provider method based on past history. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the player's past game history data into a generative AI and have the generative AI select the optimal service provider method.

[0047] The service provider can customize the content of its advice based on the player's current strategy and play style. For example, if the player is employing an offensive strategy, the service provider can provide offensive advice. For example, if the player is employing a defensive strategy, the service provider can provide defensive advice. The service provider can customize the content of its advice based on the player's play style. This allows the service provider to provide beneficial advice to the player by customizing the content based on the player's current strategy and play style. The specific methods for evaluating and using the current strategy and play style need to be clarified, for example, in the method of analyzing the play style. The specific methods and criteria for customizing the content of the advice need to be clarified, for example, in the adjustment of content according to strategy and play style. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the player's current strategy and play style data into a generative AI and have the generative AI perform the customization of the content of the advice.

[0048] The service provider can select the optimal service delivery method when providing advice, taking into account the player's geographical location information. For example, if a player is playing in a specific region, the service provider can provide advice based on the characteristics of that region. For example, the service provider can provide advice that takes into account region-specific strategies based on the player's geographical location information. For example, the service provider can provide advice that reflects the tendencies of players in a region based on the player's location information. In this way, by taking into account the player's geographical location information, it is possible to provide advice that reflects region-specific strategies. The specific methods for acquiring and using geographical location information need to be clarified, for example, by describing how GPS data is used. The specific criteria and methods for selecting the optimal service delivery method need to be clarified, for example, by describing how to select a service delivery method based on geographical location information. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the player's geographical location information data into a generative AI and have the generative AI select the optimal service delivery method.

[0049] The service provider can analyze the player's social media activity when providing advice and propose content accordingly. For example, the service provider can provide relevant advice based on strategies shared by the player on social media. For example, the service provider can analyze the player's social media activity and provide advice based on the player's interests. For example, the service provider can provide advice related to the player's play style based on the player's social media activity. This allows the service provider to provide advice based on the player's interests by analyzing the player's social media activity. The specific methods and uses of analyzing social media activity need to be clarified, for example, by analyzing the content of posts or the number of followers. The specific methods and criteria for proposing content need to be clarified, for example, by proposing content based on social media activity. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the player's social media activity data into a generative AI and have the generative AI execute the proposal of content.

[0050] The display unit can select the optimal display method by referring to the player's past game history when displaying information. For example, the display unit may prioritize display methods that the player has preferred to use in the past. For example, the display unit may select the optimal display method for a specific situation based on the player's past game history. For example, the display unit may analyze the player's past game history and adjust the display method to match the player's play style. This allows the display unit to select the optimal display method for the player by referring to their past game history. The specific methods for referencing and using past game history need to be clarified, for example, in the method for analyzing history data. The specific criteria and methods for selecting the optimal display method need to be clarified, for example, in the selection of a display method based on past history. Some or all of the above-described processes in the display unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the display unit can input the player's past game history data into a generation AI and have the generation AI select the optimal display method.

[0051] The display unit can customize the displayed content based on the player's current strategy and play style. For example, if the player is employing an offensive strategy, the display unit can prioritize displaying offensive information. For example, if the player is employing a defensive strategy, the display unit can prioritize displaying defensive information. The display unit can customize the displayed content based on the player's play style. This allows the display unit to provide the player with useful information by customizing the displayed content based on the player's current strategy and play style. The specific methods for evaluating and using the current strategy and play style need to be clarified, for example, in the play style analysis method. The specific methods and criteria for customizing the displayed content need to be clarified, for example, in the content adjustment according to strategy and play style. Some or all of the above processing in the display unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the display unit can input the player's current strategy and play style data into a generating AI and have the generating AI perform the customization of the displayed content.

[0052] The display unit can select the optimal display method when displaying information, taking into account the player's geographical location. For example, if a player is playing in a specific region, the display unit can provide a display method based on the characteristics of that region. For example, the display unit can provide a display method that takes into account region-specific strategies based on the player's geographical location. For example, the display unit can provide a display method that reflects the tendencies of players in a region based on the player's location information. In this way, by taking into account the player's geographical location, it is possible to provide information that reflects region-specific strategies. The specific methods for acquiring and using geographical location information need to be clarified, for example, by describing how GPS data is used. The specific criteria and methods for selecting the optimal display method need to be clarified, for example, by describing how to select a display method based on geographical location information. Some or all of the above processing in the display unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the display unit can input the player's geographical location data into a generation AI and have the generation AI select the optimal display method.

[0053] The display unit can analyze the player's social media activity and suggest content to display at the time of display. For example, the display unit can display relevant information based on strategies shared by the player on social media. For example, the display unit can analyze the player's social media activity and display information based on the player's interests. For example, the display unit can display information related to the player's play style based on the player's social media activity. In this way, by analyzing the player's social media activity, it is possible to provide information based on the player's interests. The specific methods and uses of analyzing social media activity need to be clarified, for example, by analyzing posted content or followers. The specific methods and criteria for suggesting content to display need to be clarified, for example, by suggesting content based on social media activity. Some or all of the above processing in the display unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the display unit can input the player's social media activity data into a generative AI and have the generative AI perform the suggestion of content to display.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The acquisition unit can analyze the player's past game history and select the optimal acquisition timing. For example, it can adjust the acquisition timing based on strategies the player has successfully used in the past. It can also change the acquisition timing to avoid strategies the player has failed with in the past. It can select the optimal acquisition timing for a specific situation from the player's past game history. In this way, by analyzing the player's past game history, it can provide the optimal acquisition timing.

[0056] The system can customize the advice provided by referencing the player's past game history. For example, it can provide advice based on strategies the player has preferred to use in the past. It can select the most appropriate advice for a specific situation from the player's past game history. By analyzing the player's past game history, the system can tailor the advice to the player's play style. This allows the system to provide optimal advice by referencing the player's past game history.

[0057] The display can be customized based on the player's current strategy and play style. For example, if the player is employing an offensive strategy, information related to offense can be prioritized. If the player is employing a defensive strategy, information related to defense can be prioritized. The display content can be customized based on the player's play style. This allows the system to provide the player with useful information by customizing the displayed content based on their current strategy and play style.

[0058] The acquisition unit can prioritize acquiring highly relevant information based on the player's geographical location when acquiring hand tiles or the state of the game. For example, if a player is playing in a specific region, it can prioritize acquiring information based on the characteristics of that region. Based on the player's geographical location, it can acquire information that takes into account region-specific strategies. Based on the player's location, it can acquire information that reflects the tendencies of players in that region. As a result, by acquiring highly relevant information based on the player's geographical location, it becomes possible to provide information that takes into account region-specific strategies.

[0059] The service provider can analyze the player's social media activity when providing advice and propose content accordingly. For example, it can provide relevant advice based on strategies the player has shared on social media. It can analyze the player's social media activity and provide advice based on the player's interests and preferences. It can provide advice related to the player's play style based on the player's social media activity. In this way, by analyzing the player's social media activity, it is possible to provide advice based on the player's interests and preferences.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The acquisition unit acquires the player's hand and the state of the game board. The acquisition unit can acquire the hand and the state of the game board in real time, for example, using a camera or sensors. The acquisition unit uses a camera to acquire images of the hand and uses image recognition technology to identify the types of cards in the hand. The acquisition unit can also use sensors to acquire the state of the game board and identify the arrangement of cards on the board. Step 2: The analysis unit analyzes the information acquired by the acquisition unit and generates strategic advice and candidate next moves. The analysis unit can use the generation AI to analyze the hand and the situation on the board and generate strategic advice and candidate next moves. The generation AI takes the hand and the situation on the board as input and outputs strategic advice and candidate next moves. Step 3: The service provider delivers advice and information generated by the analysis unit to the player. The service provider can deliver advice to the player using bone conduction speakers. Furthermore, the service provider can recognize the player's voice using speech recognition technology and provide advice in response to the player's questions. Step 4: The display unit visually displays the information provided by the provider unit. The display unit can project the completed hand and its probability onto the glasses portion. Furthermore, the display unit can also display the completed hand and its probability using AR technology.

[0062] (Example of form 2) The Mahjong Support Agent System according to an embodiment of the present invention is a glasses-type device for Mahjong players to strategically enjoy the game. This Mahjong Support Agent System acquires the player's hand and the state of the game in real time, and a generating AI analyzes this information to generate strategic advice and candidate next moves. The generated advice and information are provided to the player through a bone conduction speaker. In addition, the completed forms and probabilities of winning hands are projected onto the glasses portion, providing visual support. This supports the improvement of players' skills, from beginners to advanced players. For example, an acquisition unit is used to acquire the player's hand and the state of the game in real time. The acquisition unit plays the role of acquiring the player's hand and the state of the game. Next, an analysis unit is used to analyze the acquired information. The generating AI analyzes the hand and the state of the game through the analysis unit and generates strategic advice and candidate next moves. The generated advice and information are provided to the player through a provision unit. The provision unit plays the role of providing advice to the player through a bone conduction speaker. Furthermore, a display unit is used so that the completed forms and probabilities of winning hands are projected onto the glasses portion. The display unit provides visual support, allowing players to strategically progress through the game while visually confirming information. This device supports skill improvement for a wide range of players, from beginners to advanced players. Beginners can easily understand complex rules and strategies, while advanced players can receive support to execute more advanced strategies. This enhances the enjoyment of mahjong and increases player satisfaction. In this way, the mahjong support agent system can support player skill improvement and amplify the enjoyment of mahjong.

[0063] The mahjong support agent system according to this embodiment comprises an acquisition unit, an analysis unit, a provision unit, and a display unit. The acquisition unit acquires the player's hand and the state of the game. The acquisition unit can acquire the hand and the state of the game in real time, for example, using a camera or sensors. The acquisition unit can, for example, acquire images of the hand using a camera and identify the types of the hand using image recognition technology. The acquisition unit can also acquire the state of the game using sensors and identify the arrangement of the cards on the game. For example, the acquisition unit can acquire images of the hand using a camera and identify the types of the hand using image recognition technology. Furthermore, the acquisition unit can acquire the state of the game using sensors and identify the arrangement of the cards on the game. The analysis unit analyzes the information acquired by the acquisition unit and generates strategic advice and candidates for the next move. The analysis unit can, for example, analyze the hand and the state of the game using a generation AI and generate strategic advice and candidates for the next move. The generation AI takes the hand and the state of the game as input and outputs strategic advice and candidates for the next move. The generating AI can, for example, analyze the player's hand and the state of the game and propose the optimal strategy. The providing unit provides the player with advice and information generated by the analysis unit. The providing unit can, for example, provide advice to the player using a bone conduction speaker. Furthermore, the providing unit can recognize the player's voice using speech recognition technology and provide advice in response to the player's questions. The display unit visually displays the information provided by the providing unit. The display unit can, for example, project completed hands and probabilities onto the glasses portion of the glasses portion of the glasses. Furthermore, the display unit can also display completed hands and probabilities using AR technology. As a result, the mahjong support agent system according to this embodiment can acquire, analyze, provide, and display the player's hand and the state of the game in real time, thereby providing strategic advice.

[0064] The acquisition unit acquires the player's hand and the state of the table. The acquisition unit can acquire the hand and the state of the table in real time, for example, using cameras and sensors. Specifically, a camera is installed on the table and photographs the player's hand and the arrangement of cards on the table. This acquires an image of the hand, and the type and number of tiles in the hand can be identified using image recognition technology. The image recognition technology uses a model based on deep learning to analyze the image of the hand and identify the type of each tile with high accuracy. For example, it can accurately identify each tile such as characters, bamboo, dots, and honor tiles. Furthermore, the arrangement of cards on the table is acquired using sensors. The sensors detect the position and orientation of cards placed on the table and accurately grasp the state of the table. This allows the status of tiles played and discarded tiles to be acquired in real time. In addition, the acquisition unit centrally manages this data and transmits it to the analysis unit. The data acquisition unit can adjust the frequency and accuracy of data acquisition, enabling real-time situation monitoring and providing players with quick and accurate information.

[0065] The analysis unit analyzes the information acquired by the acquisition unit and generates strategic advice and candidate next moves. For example, the analysis unit can use a generative AI to analyze the hand and the situation on the table and generate strategic advice and candidate next moves. The generative AI uses models based on deep learning and reinforcement learning, takes the hand and the situation on the table as input, and outputs the optimal strategy and candidate next moves. Specifically, the generative AI analyzes the combination of tiles in the hand and the situation of discarded tiles on the table and proposes the most advantageous strategy for the player. For example, the generative AI determines which tiles to discard and which tiles to draw from the hand and provides advice to the player. The generative AI can also analyze the situation on the table and predict the possibilities of other players' hands and strategies. This allows players to predict the movements of other players and play more strategically. Furthermore, the analysis unit can also utilize past play data and statistical information to perform long-term strategy and trend analysis. This allows players to receive strategic advice based on their past gameplay data, enabling them to achieve a higher level of gameplay.

[0066] The service provider delivers advice and information generated by the analysis unit to the player. For example, the service provider can provide advice to the player using bone conduction speakers. Bone conduction speakers transmit sound through the bones without directly transmitting sound to the player's ears, allowing the player to receive advice without being blocked by ambient noise. This allows the player to receive necessary advice while concentrating on the game. The service provider can also recognize the player's voice using speech recognition technology and provide advice in response to the player's questions. Speech recognition technology analyzes the player's voice and understands the content of the question to generate appropriate advice. For example, if the player asks, "What should I discard next?", the service provider can suggest the optimal tile based on the information from the analysis unit. Furthermore, the service provider can collect player feedback and continuously improve the accuracy and effectiveness of the advice. This allows the service provider to provide players with quick and accurate advice and support their game strategy.

[0067] The display unit visually displays information provided by the provider unit. For example, the display unit can project completed hand shapes and probabilities onto the glasses portion. The information projected onto the glasses portion provides necessary information without obstructing the player's view, allowing the player to check completed hand shapes and probabilities while concentrating on the game. Furthermore, the display unit can also display completed hand shapes and probabilities using AR technology. By using AR technology, players can check the information by overlaying it with their actual hand and the situation on the table, allowing them to grasp the information more intuitively. For example, when a player checks their hand, the completed hand shapes and probabilities are visually displayed, which can be used as a reference when deciding on the next move. The display unit can also detect the player's gaze and gestures and display necessary information. This allows players to check information without using their hands, enabling smoother game progression. By providing visual information to the player, the display unit can support game strategy and assist the player's decision-making.

[0068] The acquisition unit can acquire the player's hand and the state of the game board in real time. The acquisition unit can acquire the hand and the state of the game board in real time, for example, using a camera or sensors. The acquisition unit can acquire images of the hand using a camera and identify the types of cards in the hand using image recognition technology. The acquisition unit can also acquire the state of the game board using sensors and identify the arrangement of cards on the board. For example, the acquisition unit can acquire images of the hand using a camera and identify the types of cards in the hand using image recognition technology. Furthermore, the acquisition unit can acquire the state of the game board using sensors and identify the arrangement of cards on the board. This makes it possible to provide advice based on the latest information by acquiring the player's hand and the state of the game board in real time. The specific definition and criteria of real time need to be clarified, for example, by the frequency of acquisition and the delay time. Some or all of the above processing in the acquisition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the acquisition unit can input images of the hand acquired by a camera into a generative AI and have the generative AI perform the identification of the types of cards in the hand.

[0069] The analysis unit can analyze the acquired information and generate strategic advice and candidate next moves. For example, the analysis unit can use a generative AI to analyze the hand and the game situation to generate strategic advice and candidate next moves. The generative AI, for example, takes the hand and the game situation as input and outputs strategic advice and candidate next moves. The generative AI can, for example, analyze the hand and the game situation and propose the optimal strategy. For example, the generative AI analyzes the hand and the game situation and generates candidate next moves. The generative AI can, for example, analyze the hand and the game situation, perform a risk assessment, and propose the optimal strategy. Thus, by analyzing the acquired information, strategic advice and candidate next moves can be generated. The specific methods and criteria for analysis need to be clearly defined, for example, by the algorithm used and the depth of analysis. The specific content and criteria for strategic advice need to be clearly defined, for example, by candidate next moves and risk assessment. The specific selection criteria and methods for candidate next moves need to be clearly defined, for example, by a probabilistic approach or prediction based on past data. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the hand and the situation on the board into the generation AI and have the generation AI perform strategic advice and generate candidates for the next move.

[0070] The service provider can provide advice to the player through a bone conduction speaker. The service provider can, for example, provide advice to the player using a bone conduction speaker. The service provider can, for example, provide advice to the player using a bone conduction speaker. Furthermore, the service provider can recognize the player's voice using speech recognition technology and provide advice in response to the player's questions. For example, the service provider recognizes the player's voice and provides advice in response to the player's questions. This allows for direct feedback to the player by providing advice through a bone conduction speaker. The specific type and method of implementation of the bone conduction speaker need to be clarified, for example, by the method of attachment and the characteristics of the sound quality. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can provide advice generated by a generative AI to the player through a bone conduction speaker.

[0071] The display unit can project the completed forms and probabilities of the winning hands onto the glasses portion. The display unit can, for example, project the completed forms and probabilities of the winning hands onto the glasses portion. The display unit can, for example, project the completed forms and probabilities of the winning hands onto the glasses portion. Furthermore, the display unit can also display the completed forms and probabilities of the winning hands using AR technology. For example, the display unit can display the completed forms and probabilities of the winning hands using AR technology. This allows for visual support to the player by projecting the completed forms and probabilities of the winning hands onto the glasses portion. The specific structure and display method of the glasses portion need to be clarified, for example, by the type of display and the display resolution. The specific calculation method and criteria for the completed forms and probabilities of the winning hands need to be clarified, for example, by the probability calculation algorithm and the definition of the hands. Some or all of the above processing in the display unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the display unit can project the completed forms and probabilities of the winning hands generated by a generative AI onto the glasses portion.

[0072] The acquisition unit can estimate the player's emotions and adjust the timing of acquiring the player's hand and the state of the game based on the estimated emotions. For example, if the player is nervous, the acquisition unit can delay the acquisition timing to give the player time to think. For example, if the player is relaxed, the acquisition unit can speed up the acquisition timing to provide information quickly. For example, if the player is anxious, the acquisition unit can adjust the acquisition timing to match the player's pace. By adjusting the acquisition timing based on the player's emotions, it becomes possible to provide appropriate information according to the player's situation. The estimation of the player's emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The specific method and criteria for adjusting the acquisition timing need to be clarified, for example, by adjusting the timing according to changes in emotions. Some or all of the above processing in the acquisition unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the acquisition unit can input the player's emotional data into the generating AI and have the generating AI adjust the timing of the acquisition.

[0073] The acquisition unit can analyze the player's past game history and select the optimal acquisition method. The acquisition unit can customize the acquisition method based on strategies the player has frequently used in the past, for example. The acquisition unit can optimize the acquisition method for specific situations based on the player's past game history, for example. The acquisition unit can analyze the player's past game history and adapt the acquisition method to the player's play style, for example. This allows the acquisition unit to provide the player with the optimal acquisition method by analyzing the player's past game history. The specific selection criteria and methods for the optimal acquisition method need to be clearly defined, for example, by a method based on past data analysis. Some or all of the above processing in the acquisition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the acquisition unit can input the player's past game history data into a generative AI and have the generative AI select the optimal acquisition method.

[0074] The data acquisition unit can filter the acquired hand and game situation based on the player's current strategy and playing style. For example, if the player is employing an aggressive strategy, the data acquisition unit will prioritize acquiring offensive information. For example, if the player is employing a defensive strategy, the data acquisition unit can prioritize acquiring defensive information. The data acquisition unit can adjust the types of information acquired based on the player's playing style. This allows for the provision of useful information to the player by filtering the information based on the player's current strategy and playing style. The specific methods and criteria for filtering need to be clarified, for example, by defining the method for selecting information based on strategy and playing style. Some or all of the above processing in the data acquisition unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data acquisition unit can input the player's current strategy and playing style data into a generative AI and have the generative AI perform the information filtering.

[0075] The acquisition unit can estimate the player's emotions and determine the priority of information to acquire based on the estimated emotions. For example, if the player is tense, the acquisition unit will prioritize acquiring important information. For example, if the player is relaxed, the acquisition unit can prioritize acquiring detailed information. For example, if the player is anxious, the acquisition unit can prioritize acquiring information that can be acquired quickly. In this way, by prioritizing information based on the player's emotions, it is possible to provide the player with information that is important to them. The estimation of the player's emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The specific method and criteria for determining the priority of information need to be clarified, for example, by prioritizing based on the intensity of emotion. Some or all of the above processing in the acquisition unit may be performed using a generative AI or not. For example, the acquisition unit can input the player's emotion data into a generative AI and have the generative AI perform the determination of information priority.

[0076] The acquisition unit can prioritize acquiring highly relevant information based on the player's geographical location when acquiring hand tiles or the state of the game. For example, if a player is playing in a specific region, the acquisition unit will prioritize acquiring information based on the characteristics of that region. For example, the acquisition unit can acquire information that takes into account region-specific strategies based on the player's geographical location. For example, the acquisition unit can acquire information that reflects the tendencies of players in a region based on the player's location. This makes it possible to provide information that takes into account region-specific strategies by acquiring highly relevant information based on the player's geographical location. The specific methods for acquiring and using geographical location information need to be clarified, for example, by describing how GPS data is used. The specific criteria and methods for selecting highly relevant information need to be clarified, for example, by describing information based on region-specific strategies. Some or all of the above processing in the acquisition unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the acquisition unit can input the player's geographical location data into a generation AI and have the generation AI acquire highly relevant information.

[0077] The acquisition unit can analyze the player's social media activity and acquire relevant information when acquiring the player's hand or the state of the game. For example, the acquisition unit can acquire relevant information based on strategies shared by the player on social media. For example, the acquisition unit can analyze the player's social media activity and acquire information based on the player's interests. For example, the acquisition unit can acquire information related to the player's playing style based on the player's social media activity. This makes it possible to provide information based on the player's interests by analyzing the player's social media activity. The specific methods for analyzing and using social media activity need to be clarified, for example, by analyzing the content of posts or the number of followers. Some or all of the above processing in the acquisition unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the acquisition unit can input the player's social media activity data into a generative AI and have the generative AI acquire the relevant information.

[0078] The analysis unit can estimate the player's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the player is nervous, the analysis unit can provide simple and clear advice. If the player is relaxed, the analysis unit can provide advice that includes detailed explanations. If the player is anxious, the analysis unit can provide quick and concise advice. By adjusting the way advice is expressed based on the player's emotions, the system can provide advice that is easy for the player to understand. The estimation of the player's emotions is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The specific methods and criteria for adjusting the way advice is expressed need to be clearly defined, for example, in terms of wording and emphasis. Some or all of the above-described processes in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input the player's emotion data into a generative AI and have the generative AI adjust the way advice is expressed.

[0079] The analysis unit can adjust the level of detail of its analysis based on the importance of the hand and the situation on the board during the analysis. For example, in important situations, the analysis unit can perform a detailed analysis and provide it to the player. For example, in less important situations, the analysis unit can perform a concise analysis and provide it to the player. The analysis unit can dynamically adjust the level of detail of its analysis according to the importance of the hand and the situation on the board. This allows the analysis unit to appropriately provide the player with the information they need by adjusting the level of detail of the analysis based on the importance of the hand and the situation on the board. The specific criteria and methods for evaluating importance need to be clarified, for example, by evaluating based on the influence of the hand and the situation on the board. The specific methods and criteria for adjusting the level of detail of the analysis need to be clarified, for example, by determining the depth of analysis according to importance. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input importance data of the hand and the situation on the board into a generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of the hand and the situation on the board during analysis. For example, in an offensive situation, the analysis unit can apply an analysis algorithm specialized for offense. For example, in a defensive situation, the analysis unit can apply an analysis algorithm specialized for defense. For example, in a situation where a specific hand is being aimed for, the analysis unit can apply an analysis algorithm specialized for that hand. By applying different analysis algorithms depending on the category of the hand and the situation on the board, the analysis unit can provide the player with the best possible advice. The specific definitions and classification methods of the categories need to be clarified, for example, by classifying the types of hands and the situation on the board. The specific types and application methods of the analysis algorithms need to be clarified, for example, by selecting algorithms according to different categories. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the analysis unit can input category data of the hand and the situation on the board into a generative AI and have the generative AI execute the application of different analysis algorithms.

[0081] The analysis unit can estimate the player's emotions and adjust the length of advice based on the estimated emotions. For example, if the player is nervous, the analysis unit can provide short, concise advice. If the player is relaxed, the analysis unit can provide longer advice with detailed explanations. If the player is anxious, the analysis unit can provide quick and concise advice. By adjusting the length of advice based on the player's emotions, the system can provide the most appropriate advice for the player. The estimation of the player's emotions is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The specific method and criteria for adjusting the length of advice need to be clarified, for example, by adjusting the length according to the emotional state. Some or all of the above processing in the analysis unit may be performed using a generative AI or not. For example, the analysis unit can input the player's emotion data into a generative AI and have the generative AI adjust the length of the advice.

[0082] The analysis unit can determine the priority of analysis based on the timing of submission of hand and game situation data during analysis. For example, in important situations, the analysis unit can set a higher priority based on the submission timing. For example, in less important situations, the analysis unit can set a lower priority based on the submission timing. The analysis unit can dynamically adjust the priority of analysis according to the timing of submission of hand and game situation data. This allows the system to prioritize providing important information to the player by determining the priority of analysis based on the timing of submission of hand and game situation data. The specific evaluation criteria and methods for submission timing need to be clarified, for example, by evaluating the timing of submission of hand and game situation data. The specific methods and criteria for determining the priority of analysis need to be clarified, for example, by prioritizing according to submission timing. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input hand and game situation submission timing data into a generation AI and have the generation AI determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relationships between the hand and the situation on the table during analysis. For example, in important situations, the analysis unit can prioritize the analysis of highly relevant information. For example, in less important situations, the analysis unit can prioritize the analysis of less relevant information. The analysis unit can dynamically adjust the order of analysis according to the relationships between the hand and the situation on the table. This allows the system to appropriately provide the player with the necessary information by adjusting the order of analysis based on the relationships between the hand and the situation on the table. The specific criteria and methods for evaluating relationships need to be clarified, for example, by evaluating based on the degree of relevance between the hand and the situation on the table. The specific methods and criteria for adjusting the order of analysis need to be clarified, for example, by adjusting the order according to relationships. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input relationship data between the hand and the situation on the table into a generation AI and have the generation AI perform the adjustment of the order of analysis.

[0084] The advice delivery unit can estimate the player's emotions and adjust the method of providing advice based on the estimated emotions. For example, if the player is nervous, the advice delivery unit can provide advice in a calm voice. For example, if the player is relaxed, the advice delivery unit can provide advice in a cheerful voice. For example, if the player is anxious, the advice delivery unit can provide quick and concise advice. In this way, by adjusting the method of providing advice based on the player's emotions, the best possible advice can be provided to the player. The estimation of the player's emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. The specific methods and criteria for adjusting the method of providing advice need to be clarified, for example, by changing the method of delivery according to the emotional state. Some or all of the above processing in the advice delivery unit may be performed using a generative AI or not using a generative AI. For example, the advice delivery unit can input the player's emotion data into a generative AI and have the generative AI perform the adjustment of the method of providing advice.

[0085] The service provider can select the optimal service provider method by referring to the player's past game history when providing advice. For example, the service provider may prioritize advice provider methods that the player has preferred to use in the past. For example, the service provider may select the optimal service provider method for a specific situation based on the player's past game history. For example, the service provider may analyze the player's past game history and tailor the service provider method to the player's play style. This allows the service provider to select the most suitable advice provider method for the player by referring to the player's past game history. The specific methods for referring to and using past game history need to be clarified, for example, in the method of analyzing the history data. The specific criteria and methods for selecting the optimal service provider method need to be clarified, for example, in the selection of a service provider method based on past history. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the player's past game history data into a generative AI and have the generative AI select the optimal service provider method.

[0086] The service provider can customize the content of its advice based on the player's current strategy and play style. For example, if the player is employing an offensive strategy, the service provider can provide offensive advice. For example, if the player is employing a defensive strategy, the service provider can provide defensive advice. The service provider can customize the content of its advice based on the player's play style. This allows the service provider to provide beneficial advice to the player by customizing the content based on the player's current strategy and play style. The specific methods for evaluating and using the current strategy and play style need to be clarified, for example, in the method of analyzing the play style. The specific methods and criteria for customizing the content of the advice need to be clarified, for example, in the adjustment of content according to strategy and play style. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the player's current strategy and play style data into a generative AI and have the generative AI perform the customization of the content of the advice.

[0087] The service provider can estimate the player's emotions and determine the priority of advice based on the estimated emotions. For example, if the player is nervous, the service provider will prioritize important advice. For example, if the player is relaxed, the service provider can prioritize detailed advice. For example, if the player is anxious, the service provider can prioritize advice that can be delivered quickly. In this way, by determining the priority of advice based on the player's emotions, it is possible to prioritize advice that is important to the player. The estimation of the player's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. The specific method and criteria for determining the priority of advice need to be clarified, for example, by prioritizing based on the intensity of emotion. Some or all of the above processing in the service provider may be performed using a generative AI or not. For example, the service provider can input the player's emotion data into a generative AI and have the generative AI perform the determination of the priority of advice.

[0088] The service provider can select the optimal service delivery method when providing advice, taking into account the player's geographical location information. For example, if a player is playing in a specific region, the service provider can provide advice based on the characteristics of that region. For example, the service provider can provide advice that takes into account region-specific strategies based on the player's geographical location information. For example, the service provider can provide advice that reflects the tendencies of players in a region based on the player's location information. In this way, by taking into account the player's geographical location information, it is possible to provide advice that reflects region-specific strategies. The specific methods for acquiring and using geographical location information need to be clarified, for example, by describing how GPS data is used. The specific criteria and methods for selecting the optimal service delivery method need to be clarified, for example, by describing how to select a service delivery method based on geographical location information. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the player's geographical location information data into a generative AI and have the generative AI select the optimal service delivery method.

[0089] The service provider can analyze the player's social media activity when providing advice and propose content accordingly. For example, the service provider can provide relevant advice based on strategies shared by the player on social media. For example, the service provider can analyze the player's social media activity and provide advice based on the player's interests. For example, the service provider can provide advice related to the player's play style based on the player's social media activity. This allows the service provider to provide advice based on the player's interests by analyzing the player's social media activity. The specific methods and uses of analyzing social media activity need to be clarified, for example, by analyzing the content of posts or the number of followers. The specific methods and criteria for proposing content need to be clarified, for example, by proposing content based on social media activity. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the player's social media activity data into a generative AI and have the generative AI execute the proposal of content.

[0090] The display unit can estimate the player's emotions and adjust the way the displayed content is presented based on the estimated emotions. For example, if the player is nervous, the display unit can provide a simple and clear display. For example, if the player is relaxed, the display unit can provide a display that includes detailed information. For example, if the player is anxious, the display unit can provide a quick and concise display. By adjusting the way the displayed content is presented based on the player's emotions, information that is easy for the player to understand can be provided. The estimation of the player's emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The specific methods and criteria for adjusting the way the displayed content is presented need to be clarified, for example, by changing the presentation method according to the emotional state. Some or all of the above processing in the display unit may be performed using a generative AI or not. For example, the display unit can input the player's emotion data into a generative AI and have the generative AI perform the adjustment of the way the displayed content is presented.

[0091] The display unit can select the optimal display method by referring to the player's past game history when displaying information. For example, the display unit may prioritize display methods that the player has preferred to use in the past. For example, the display unit may select the optimal display method for a specific situation based on the player's past game history. For example, the display unit may analyze the player's past game history and adjust the display method to match the player's play style. This allows the display unit to select the optimal display method for the player by referring to their past game history. The specific methods for referencing and using past game history need to be clarified, for example, in the method for analyzing history data. The specific criteria and methods for selecting the optimal display method need to be clarified, for example, in the selection of a display method based on past history. Some or all of the above-described processes in the display unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the display unit can input the player's past game history data into a generation AI and have the generation AI select the optimal display method.

[0092] The display unit can customize the displayed content based on the player's current strategy and play style. For example, if the player is employing an offensive strategy, the display unit can prioritize displaying offensive information. For example, if the player is employing a defensive strategy, the display unit can prioritize displaying defensive information. The display unit can customize the displayed content based on the player's play style. This allows the display unit to provide the player with useful information by customizing the displayed content based on the player's current strategy and play style. The specific methods for evaluating and using the current strategy and play style need to be clarified, for example, in the play style analysis method. The specific methods and criteria for customizing the displayed content need to be clarified, for example, in the content adjustment according to strategy and play style. Some or all of the above processing in the display unit may be performed using a generating AI, or it may be performed without using a generating AI. For example, the display unit can input the player's current strategy and play style data into a generating AI and have the generating AI perform the customization of the displayed content.

[0093] The display unit can estimate the player's emotions and determine the priority of the displayed content based on the estimated emotions. For example, if the player is tense, the display unit can prioritize displaying important information. For example, if the player is relaxed, the display unit can prioritize displaying detailed information. For example, if the player is anxious, the display unit can prioritize displaying information that can be displayed quickly. In this way, by determining the priority of the displayed content based on the player's emotions, important information can be provided to the player preferentially. The estimation of the player's emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. The specific method and criteria for determining the priority of the displayed content need to be clarified, for example, by prioritizing based on the intensity of emotions. Some or all of the above processing in the display unit may be performed using a generative AI or not. For example, the display unit can input the player's emotion data into a generative AI and have the generative AI perform the determination of the priority of the displayed content.

[0094] The display unit can select the optimal display method when displaying information, taking into account the player's geographical location. For example, if a player is playing in a specific region, the display unit can provide a display method based on the characteristics of that region. For example, the display unit can provide a display method that takes into account region-specific strategies based on the player's geographical location. For example, the display unit can provide a display method that reflects the tendencies of players in a region based on the player's location information. In this way, by taking into account the player's geographical location, it is possible to provide information that reflects region-specific strategies. The specific methods for acquiring and using geographical location information need to be clarified, for example, by describing how GPS data is used. The specific criteria and methods for selecting the optimal display method need to be clarified, for example, by describing how to select a display method based on geographical location information. Some or all of the above processing in the display unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the display unit can input the player's geographical location data into a generation AI and have the generation AI select the optimal display method.

[0095] The display unit can analyze the player's social media activity and suggest content to display at the time of display. For example, the display unit can display relevant information based on strategies shared by the player on social media. For example, the display unit can analyze the player's social media activity and display information based on the player's interests. For example, the display unit can display information related to the player's play style based on the player's social media activity. In this way, by analyzing the player's social media activity, it is possible to provide information based on the player's interests. The specific methods and uses of analyzing social media activity need to be clarified, for example, by analyzing posted content or followers. The specific methods and criteria for suggesting content to display need to be clarified, for example, by suggesting content based on social media activity. Some or all of the above processing in the display unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the display unit can input the player's social media activity data into a generative AI and have the generative AI perform the suggestion of content to display.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The analysis unit can estimate the player's emotions and adjust the advice based on those emotions. For example, if the player is nervous, the analysis unit can suggest a safe, low-risk strategy. If the player is relaxed, the analysis unit can suggest a more aggressive strategy. If the player is anxious, the analysis unit can suggest a concise strategy that can be executed quickly. This allows the system to provide the optimal strategy tailored to the player's emotions.

[0098] The system can estimate the player's emotions and adjust the timing of advice delivery based on those estimates. For example, if the player is nervous, the system can delay giving advice to give the player time to think. If the player is relaxed, the system can provide advice quickly. If the player is anxious, the system can provide advice in a timely manner. This allows for advice to be delivered at the appropriate time according to the player's emotions.

[0099] The display unit can estimate the player's emotions and adjust the color and font of the displayed content based on those emotions. For example, if the player is tense, the display unit can show information in calm colors and large fonts. If the player is relaxed, the display unit can show information in bright colors and standard fonts. If the player is anxious, the display unit can show important information in emphasized colors and bold fonts. This provides visual support that is tailored to the player's emotions.

[0100] The information retrieval unit can estimate the player's emotions and adjust the level of detail of the information it retrieves based on those emotions. For example, if the player is tense, the unit can retrieve only essential information. If the player is relaxed, the unit can retrieve detailed information. If the player is anxious, the unit can prioritize retrieving information that can be obtained quickly. This allows the system to provide appropriate information tailored to the player's emotions.

[0101] The analysis unit can estimate the player's emotions and adjust the tone of advice based on those emotions. For example, if the player is nervous, the analysis unit can provide advice in a gentle tone. If the player is relaxed, the analysis unit can provide advice in a friendly tone. If the player is anxious, the analysis unit can provide advice in a quick and clear tone. This allows for advice to be provided in an appropriate tone according to the player's emotions.

[0102] The acquisition unit can analyze the player's past game history and select the optimal acquisition timing. For example, it can adjust the acquisition timing based on strategies the player has successfully used in the past. It can also change the acquisition timing to avoid strategies the player has failed with in the past. It can select the optimal acquisition timing for a specific situation from the player's past game history. In this way, by analyzing the player's past game history, it can provide the optimal acquisition timing.

[0103] The system can customize the advice provided by referencing the player's past game history. For example, it can provide advice based on strategies the player has preferred to use in the past. It can select the most appropriate advice for a specific situation from the player's past game history. By analyzing the player's past game history, the system can tailor the advice to the player's play style. This allows the system to provide optimal advice by referencing the player's past game history.

[0104] The display can be customized based on the player's current strategy and play style. For example, if the player is employing an offensive strategy, information related to offense can be prioritized. If the player is employing a defensive strategy, information related to defense can be prioritized. The display content can be customized based on the player's play style. This allows the system to provide the player with useful information by customizing the displayed content based on their current strategy and play style.

[0105] The acquisition unit can prioritize acquiring highly relevant information based on the player's geographical location when acquiring hand tiles or the state of the game. For example, if a player is playing in a specific region, it can prioritize acquiring information based on the characteristics of that region. Based on the player's geographical location, it can acquire information that takes into account region-specific strategies. Based on the player's location, it can acquire information that reflects the tendencies of players in that region. As a result, by acquiring highly relevant information based on the player's geographical location, it becomes possible to provide information that takes into account region-specific strategies.

[0106] The service provider can analyze the player's social media activity when providing advice and propose content accordingly. For example, it can provide relevant advice based on strategies the player has shared on social media. It can analyze the player's social media activity and provide advice based on the player's interests and preferences. It can provide advice related to the player's play style based on the player's social media activity. In this way, by analyzing the player's social media activity, it is possible to provide advice based on the player's interests and preferences.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The acquisition unit acquires the player's hand and the state of the game board. The acquisition unit can acquire the hand and the state of the game board in real time, for example, using a camera or sensors. The acquisition unit uses a camera to acquire images of the hand and uses image recognition technology to identify the types of cards in the hand. The acquisition unit can also use sensors to acquire the state of the game board and identify the arrangement of cards on the board. Step 2: The analysis unit analyzes the information acquired by the acquisition unit and generates strategic advice and candidate next moves. The analysis unit can use the generation AI to analyze the hand and the situation on the board and generate strategic advice and candidate next moves. The generation AI takes the hand and the situation on the board as input and outputs strategic advice and candidate next moves. Step 3: The service provider delivers advice and information generated by the analysis unit to the player. The service provider can deliver advice to the player using bone conduction speakers. Furthermore, the service provider can recognize the player's voice using speech recognition technology and provide advice in response to the player's questions. Step 4: The display unit visually displays the information provided by the provider unit. The display unit can project the completed hand and its probability onto the glasses portion. Furthermore, the display unit can also display the completed hand and its probability using AR technology.

[0109] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0112] Each of the multiple elements described above, including the acquisition unit, analysis unit, provision unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires the hand and the state of the game in real time using the camera 42 and sensors of the smart device 14. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the acquired information and generates strategic advice and candidates for the next move. The provision unit provides advice to the player using, for example, the bone conduction speaker 40B of the smart device 14. The display unit projects the completed hand and its probability using, for example, the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the acquisition unit, analysis unit, provision unit, and display unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires the hand and the state of the game in real time using the camera 42 and sensors of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the acquired information and generates strategic advice and candidates for the next move. The provision unit provides advice to the player using, for example, the bone conduction speaker 240 of the smart glasses 214. The display unit projects the completed hand and its probability using, for example, the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the acquisition unit, analysis unit, provision unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires the hand and the state of the game in real time using the camera 42 and sensors of the headset terminal 314. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the acquired information and generates strategic advice and candidates for the next move. The provision unit provides advice to the player using, for example, the bone conduction speaker 240 of the headset terminal 314. The display unit projects the completed hand and its probability using, for example, the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the acquisition unit, analysis unit, provision unit, and display unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires the hand and the state of the game in real time using the camera 42 and sensors of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the acquired information and generates strategic advice and candidates for the next move. The provision unit provides advice to the player using, for example, the bone conduction speaker 240 of the robot 414. The display unit projects the completed hand and its probability using, for example, the display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) The acquisition unit acquires the player's hand and the state of the game, An analysis unit analyzes the information acquired by the acquisition unit and generates strategic advice and candidates for the next move, A provisioning unit that provides advice and information generated by the aforementioned analysis unit to the player, The system includes a display unit that visually displays the information provided by the aforementioned providing unit. A system characterized by the following features. (Note 2) The acquisition unit is, The game retrieves the players' hands and the state of the game board in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The acquired information is analyzed to generate strategic advice and potential next moves. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Providing advice to players through bone conduction speakers. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is The completed form and probability of the role are projected onto the glasses. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, The system estimates the player's emotions and adjusts the timing of acquiring hand tiles and the state of the game based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, Analyze the player's past game history and select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When acquiring hand information and the state of the game, filtering is performed based on the player's current strategy and play style. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, The system estimates the player's emotions and prioritizes the information to be retrieved based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When acquiring hand information and game state data, the system prioritizes retrieving highly relevant information based on the player's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring hand information and the state of the game, the system analyzes the player's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the player's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the hand and the situation on the board. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the hand and the state of the game. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the player's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when the hand and the state of the game were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationships between the hand and the situation on the board. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, The system estimates the player's emotions and adjusts the way advice is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing advice, the system will refer to the player's past gaming history to select the most appropriate method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, the content is customized based on the player's current strategy and play style. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates the player's emotions and prioritizes advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing advice, the optimal method of delivery will be selected, taking into account the player's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, we analyze the player's social media activity and propose content accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is The system estimates the player's emotions and adjusts the way the displayed content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is When displaying information, the system will refer to the player's past game history to select the most suitable display method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is When displayed, the content shown is customized based on the player's current strategy and play style. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is The system estimates the player's emotions and determines the priority of displayed content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is When displaying information, the system selects the optimal display method by considering the player's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is When displaying content, the system analyzes the player's social media activity to suggest what to display. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The acquisition unit acquires the player's hand and the state of the game, An analysis unit analyzes the information acquired by the acquisition unit and generates strategic advice and candidates for the next move, A provisioning unit that provides advice and information generated by the aforementioned analysis unit to the player, The system includes a display unit that visually displays the information provided by the aforementioned providing unit. A system characterized by the following features.

2. The acquisition unit is, The game retrieves the players' hands and the state of the game board in real time. The system according to feature 1.

3. The aforementioned analysis unit, The acquired information is analyzed to generate strategic advice and potential next moves. The system according to feature 1.

4. The aforementioned supply unit is, Providing advice to players through bone conduction speakers. The system according to feature 1.

5. The aforementioned display unit is The completed form and probability of the role are projected onto the glasses. The system according to feature 1.

6. The acquisition unit is, The system estimates the player's emotions and adjusts the timing of acquiring hand tiles and the state of the game based on those estimated emotions. The system according to feature 1.

7. The acquisition unit is, Analyze the player's past game history and select the optimal acquisition method. The system according to feature 1.

8. The acquisition unit is, When acquiring hand information and the state of the game, filtering is performed based on the player's current strategy and play style. The system according to feature 1.

9. The acquisition unit is, The system estimates the player's emotions and prioritizes the information to be retrieved based on those estimated emotions. The system according to feature 1.

10. The acquisition unit is, When acquiring hand information and game state data, the system prioritizes retrieving highly relevant information based on the player's geographical location. The system according to feature 1.

Images