System

Abstract

An object of a system according to an embodiment is to reduce a tutorial time of a game and improve user experience by providing an operation explanation to a user in a timely manner.SOLUTION: A system according to an embodiment includes an action learning unit, an analysis unit, and an explanation providing unit. The action learning unit collects data in the game and action data of the user. The analysis unit analyzes the data in the game and the action data of the user collected by the action learning unit. The explanation providing unit provides an operating explanation to the user in a timely manner based on a result of the analysis by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques tend to make game tutorials long, which can detract from the user experience.

[0005] The system according to the embodiment aims to reduce the tutorial time for the game and improve the user experience by providing the user with timely operation instructions. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavior learning unit, an analysis unit, and an explanation providing unit. The behavior learning unit collects in-game data and user behavior data. The analysis unit analyzes the in-game data and user behavior data collected by the behavior learning unit. The explanation providing unit provides timely operation explanations to the user based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the tutorial time for the game and improve the user experience by providing the user with timely operation instructions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A game tutorial service according to an embodiment of the present invention is a system in which AI learns about the game and the user's behavior and provides necessary operation instructions in a timely manner, thereby improving the user experience and reducing the tutorial time.

[0029] A game tutorial service according to an embodiment includes a behavior learning unit, an analysis unit, and an explanation providing unit. The behavior learning unit collects in-game data and user behavior data. For example, the behavior learning unit collects game progress, scores, and item usage. The behavior learning unit can also collect the user's operation history, play time, and selected options. The analysis unit analyzes the in-game data and user behavior data collected by the behavior learning unit. For example, the analysis unit analyzes which operations the user spends time on and which parts the user is struggling with. The analysis unit can also analyze the user's play style and past game history. The explanation providing unit provides timely operation instructions to the user based on the results of the analysis by the analysis unit. For example, when the user attempts a new operation, the explanation providing unit displays an explanation of the operation on the screen. The explanation providing unit can also provide pop-up displays and audio guides in real time. As a result, the game tutorial service according to an embodiment can improve the user experience by providing timely operation instructions based on the user's behavior.

[0030] The behavioral learning unit can learn the user's play style and provide operation instructions based on that play style. For example, the behavioral learning unit uses a generation AI to learn the user's play style and provide operation instructions based on that individual play style. For example, for a user who frequently uses attacks in action games, the unit can focus on explaining attack combo techniques. The behavioral learning unit can also customize the content of the operation instructions according to the user's play style. For example, for a user with a defensive play style, the unit can provide detailed explanations on how to use defensive techniques. This makes it possible to meet individual needs by providing operation instructions based on the user's play style.

[0031] The behavioral learning unit can analyze the user's past game history and provide a customized tutorial based on the past experience. For example, the behavioral learning unit uses a generation AI to analyze the user's past game history and provide a customized tutorial based on the past experience. For example, basic operations are omitted for a user who has played games of the same genre in the past. The behavioral learning unit can also provide a tutorial that focuses on specific operations based on the user's past game history. For example, operations that the user has had difficulty with in the past are given intensive explanation. This allows the user's learning efficiency to be improved by providing a customized tutorial based on the user's past experience.

[0032] The behavioral learning unit can compare the behavioral data of other users, identify common stumbling blocks, and issue advance warnings. For example, the behavioral learning unit uses a generation AI to compare the behavioral data of other users, identify common stumbling blocks, and issue advance warnings. For example, a warning message can be displayed before an operation that many users fail at. The behavioral learning unit can also provide hints and advice on operations based on common stumbling blocks. For example, if a user fails frequently at a particular stage, the unit can explain how to clear that stage. In this way, by comparing the behavioral data of other users to identify common stumbling blocks and issuing advance warnings, it is possible to reduce user stumbling blocks.

[0033] The explanation providing unit can analyze the user's operation speed in real time and automatically provide auxiliary explanations if the operation is slow. For example, the explanation providing unit uses a generation AI to analyze the user's operation speed in real time and automatically provide auxiliary explanations if the operation is slow. For example, the operation steps are explained again to a user who is slow in operation. The explanation providing unit can also adjust the content of the auxiliary explanation according to the user's operation speed. For example, tricks for operation are explained in detail to a user who is slow in operation. In this way, auxiliary explanations can be provided according to the user's operation speed, thereby helping the user's understanding.

[0034] The explanation providing unit can analyze the user's gaze tracking data and provide an explanation of a specific UI element when the gaze stops on that element. For example, the explanation providing unit uses a generation AI to analyze the user's gaze tracking data and provide an explanation of that element when the gaze stops on that element. For example, if the user is looking at a specific button, the explanation providing unit can explain the function of that button. The explanation providing unit can also customize the explanation of UI elements based on the user's gaze tracking data. For example, it can provide a detailed explanation for a UI element on which the user fixates their gaze for a long time. This can help the user understand by providing an appropriate explanation based on the user's gaze tracking data.

[0035] The explanation providing unit can analyze the user's voice commands and provide operation instructions in voice. For example, the explanation providing unit uses a generation AI to analyze the user's voice commands and provide operation instructions in voice. For example, if the user asks, "What do I do next?", the next operation procedure is explained in voice. The explanation providing unit can also customize the content of the operation instructions based on the user's voice commands. For example, if the user asks about a specific operation, the details of that operation are explained in voice. This makes it possible to improve user convenience by providing operation instructions based on the user's voice commands.

[0036] The explanation providing unit can analyze the user's gestures and provide operation instructions based on the gestures. For example, the explanation providing unit uses a generation AI to analyze the user's gestures and provide operation instructions based on the gestures. For example, if the user performs a specific gesture, the explanation providing unit explains the meaning of the gesture. The explanation providing unit can also customize the content of the operation instructions based on the user's gestures. For example, if the user repeatedly performs a specific gesture, the explanation providing unit provides detailed explanations on how to use the gesture. This can improve user convenience by providing operation instructions based on the user's gestures.

[0037] The analysis unit can evaluate the user's operation proficiency in real time and dynamically adjust the tutorial content according to the proficiency. For example, the analysis unit uses a generation AI to evaluate the user's operation proficiency in real time and dynamically adjust the tutorial content according to the proficiency. For example, the analysis unit can speed up the tutorial progress for users who are smooth at operation. The analysis unit can also customize the tutorial content based on the user's operation proficiency. For example, the analysis unit can provide detailed explanations of operation procedures for users who are not good at operation. In this way, the user's learning efficiency can be improved by dynamically adjusting the tutorial content according to the user's operation proficiency.

[0038] The analysis unit can analyze the user's operation history and omit operations that have already been mastered. For example, the analysis unit uses a generation AI to analyze the user's operation history and omit operations that have already been mastered. For example, for a user who has successfully performed the same operation in the past, the explanation of that operation is omitted. The analysis unit can also omit specific operations based on the user's operation history. For example, for a user who has successfully performed a specific operation multiple times, the explanation of that operation is omitted. In this way, the tutorial time can be shortened by omitting operations that have already been mastered based on the user's operation history.

[0039] The analysis unit can analyze the user's play time and provide an effective tutorial in a short amount of time. For example, the analysis unit uses a generation AI to analyze the user's play time and provide an effective tutorial in a short amount of time. For example, for a user with a short play time, only important operations are explained. The analysis unit can also customize the content of the tutorial based on the user's play time. For example, detailed explanations are provided for a user with a long play time. This allows the user's time to be used effectively by providing an effective tutorial in a short amount of time based on the user's play time.

[0040] The analysis unit can analyze the user's operational errors and omit the tutorial if there are few errors. For example, the analysis unit uses a generation AI to analyze the user's operational errors and omit the tutorial if there are few errors. For example, a user who makes few operational errors is prompted to proceed to the next step. The analysis unit can also adjust the content of the tutorial based on the user's operational errors. For example, the analysis unit can provide detailed explanations of the operation procedures for a user who makes many operational errors. This allows the user's time to be used more effectively by omitting the tutorial if there are few operational errors.

[0041] The explanation providing unit can receive feedback from the game developer in real time and dynamically update the tutorial content. For example, the explanation providing unit uses a generation AI to receive feedback from the game developer in real time and dynamically update the tutorial content. For example, when a new operation is added, the explanation for that operation is immediately reflected. The explanation providing unit can also customize the tutorial content based on feedback from the game developer. For example, the content is changed according to the user's progress. In this way, by receiving feedback from the game developer in real time and dynamically updating the tutorial content, it is possible to provide a tutorial based on the latest information.

[0042] The explanation providing unit can analyze game update information and automatically update the tutorial when a new operation is added. For example, the generation AI analyzes game update information and automatically updates the tutorial when a new operation is added. For example, when a new skill is added, the explanation providing unit explains how to use the skill. The explanation providing unit can also customize the content of the tutorial based on the game update information. For example, when a new function is added, an explanation of the function is added. In this way, the latest operation methods can be provided to the user by automatically updating the tutorial based on game update information.

[0043] The explanation providing unit can conduct user tests in collaboration with game developers and optimize the tutorial based on the test results. For example, the explanation providing unit uses a generation AI to conduct user tests in collaboration with game developers and optimize the tutorial based on the test results. For example, the explanation providing unit adjusts the content of the tutorial based on feedback obtained from the user tests. The explanation providing unit can also customize the content of the tutorial based on the results of the user tests. For example, the explanation providing unit changes the content according to the user's progress. In this way, by conducting user tests in collaboration with game developers and optimizing the tutorial based on the test results, it is possible to provide a tutorial that meets the needs of the user.

[0044] The explanation providing unit can collect opinions from game developers and integrate the opinions of multiple developers to provide an optimal tutorial. For example, the explanation providing unit uses a generation AI to collect opinions from game developers and integrate the opinions of multiple developers to provide an optimal tutorial. For example, a tutorial that reflects the opinions of different developers is created. The explanation providing unit can also customize the content of the tutorial based on the opinions of game developers. For example, explanations of specific operations can be added based on the developer's opinions. In this way, by integrating the opinions of multiple game developers to provide an optimal tutorial, a tutorial that is suitable for the user from a more multifaceted perspective can be provided.

[0045] The analysis unit can analyze the user's play history and provide advice based on the user's individual play style. For example, the analysis unit uses a generation AI to analyze the user's play history and provide advice based on the user's individual play style. For example, a user with an offensive play style can be recommended equipment and skills to maximize attack power. The analysis unit can also customize the content of advice according to the user's play style. For example, a user with a defensive play style can be provided with advice to strengthen defense power. This makes it possible to improve the user's gaming experience by providing advice that suits the user's individual play style based on the user's play history.

[0046] The explanation providing unit can collect user feedback and improve the tutorial based on the feedback. For example, the generation AI collects user feedback and improves the tutorial based on the feedback. For example, it simplifies parts that the user finds difficult. The explanation providing unit can also customize the content of the tutorial based on user feedback. For example, it creates a tutorial that reflects the user's opinions. In this way, by improving the tutorial based on user feedback, it is possible to provide a tutorial that meets the user's needs.

[0047] The analysis unit can analyze the user's play data and provide a customized tutorial according to the play style. For example, the analysis unit uses a generation AI to analyze the user's play data and provide a customized tutorial according to the play style. For example, a user with an offensive play style can be provided with a tutorial on offensive techniques. The analysis unit can also customize the content of the tutorial based on the user's play style. For example, a user with a defensive play style can be provided with a detailed tutorial on defensive techniques. This allows the user's learning efficiency to be improved by providing a customized tutorial according to the play style based on the user's play data.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The analysis unit can analyze the user's operation history and omit operations that have already been mastered. For example, for a user who has successfully performed the same operation in the past, the explanation of that operation is omitted. The analysis unit can also omit specific operations based on the user's operation history. For example, for a user who has successfully performed a specific operation multiple times, the explanation of that operation is omitted. In this way, by omitting operations that have already been mastered based on the user's operation history, the tutorial time can be shortened.

[0050] The explanation providing unit can analyze the user's gaze tracking data and provide an explanation of a specific UI element when the user's gaze lingers on that element. For example, if the user gazes at a specific button, the explanation providing unit can explain the function of that button. The explanation providing unit can also customize the explanation of UI elements based on the user's gaze tracking data. For example, the explanation providing unit can provide a detailed explanation for a UI element on which the user gazes for a long time. This can help the user understand the content by providing an appropriate explanation based on the user's gaze tracking data.

[0051] The explanation providing unit can analyze the user's voice commands and provide audio instructions for operations. For example, if the user asks, "What do I do next?", the next operation procedure is explained audio. The explanation providing unit can also customize the content of the operation instructions based on the user's voice commands. For example, if the user asks about a specific operation, the details of that operation are explained audio. This makes it possible to improve user convenience by providing operation instructions based on the user's voice commands.

[0052] The explanation providing unit can analyze the user's gestures and provide operation instructions based on the gestures. For example, if the user performs a specific gesture, the explanation providing unit can explain the meaning of the gesture. The explanation providing unit can also customize the content of the operation instructions based on the user's gestures. For example, if the user repeatedly performs a specific gesture, the explanation providing unit can explain in detail how to use the gesture. In this way, by providing operation instructions based on the user's gestures, it is possible to improve user convenience.

[0053] The analysis unit can analyze the user's play time and provide an effective tutorial in a short time. For example, for a user who plays for a short time, only important operations can be explained. The analysis unit can also customize the content of the tutorial based on the user's play time. For example, for a user who plays for a long time, detailed explanations can be provided. In this way, by providing an effective tutorial in a short time based on the user's play time, the user's time can be used effectively.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The behavioral learning unit collects in-game data and user behavioral data. For example, the behavioral learning unit collects game progress, scores, and item usage. The behavioral learning unit can also collect the user's operation history, play time, and selected options. Step 2: The analysis unit analyzes the in-game data collected by the behavioral learning unit and the user's behavioral data. For example, the analysis unit analyzes which operations the user spends time on and where the user has trouble. The analysis unit can also analyze the user's play style and past game history. Step 3: The explanation providing unit provides the user with a timely explanation for an operation based on the results of the analysis by the analysis unit. For example, when the user attempts a new operation, the explanation providing unit displays an explanation for that operation on the screen. The explanation providing unit can also provide a pop-up display or audio guidance in real time.

[0056] (Example 2) A game tutorial service according to an embodiment of the present invention is a system in which AI learns about the game and the user's behavior and provides necessary operation instructions in a timely manner, thereby improving the user experience and reducing the tutorial time.

[0057] A game tutorial service according to an embodiment includes a behavior learning unit, an analysis unit, and an explanation providing unit. The behavior learning unit collects in-game data and user behavior data. For example, the behavior learning unit collects game progress, scores, and item usage. The behavior learning unit can also collect the user's operation history, play time, and selected options. The analysis unit analyzes the in-game data and user behavior data collected by the behavior learning unit. For example, the analysis unit analyzes which operations the user spends time on and which parts the user is struggling with. The analysis unit can also analyze the user's play style and past game history. The explanation providing unit provides timely operation instructions to the user based on the results of the analysis by the analysis unit. For example, when the user attempts a new operation, the explanation providing unit displays an explanation of the operation on the screen. The explanation providing unit can also provide pop-up displays and audio guides in real time. As a result, the game tutorial service according to an embodiment can improve the user experience by providing timely operation instructions based on the user's behavior.

[0058] The behavioral learning unit can learn the user's play style and provide operation instructions based on that play style. For example, the behavioral learning unit uses a generation AI to learn the user's play style and provide operation instructions based on that individual play style. For example, for a user who frequently uses attacks in action games, the unit can focus on explaining attack combo techniques. The behavioral learning unit can also customize the content of the operation instructions according to the user's play style. For example, for a user with a defensive play style, the unit can provide detailed explanations on how to use defensive techniques. This makes it possible to meet individual needs by providing operation instructions based on the user's play style.

[0059] The behavioral learning unit can analyze the user's past game history and provide a customized tutorial based on the past experience. For example, the behavioral learning unit uses a generation AI to analyze the user's past game history and provide a customized tutorial based on the past experience. For example, basic operations are omitted for a user who has played games of the same genre in the past. The behavioral learning unit can also provide a tutorial that focuses on specific operations based on the user's past game history. For example, operations that the user has had difficulty with in the past are given intensive explanation. This allows the user's learning efficiency to be improved by providing a customized tutorial based on the user's past experience.

[0060] The behavioral learning unit can use the emotion estimation function to analyze how the user feels about the operation and provide support according to the emotion. The behavioral learning unit, for example, uses the emotion estimation function to analyze how the user feels about the operation and provides support according to the emotion. For example, if the user feels anxious about the operation, an encouraging message is displayed. The behavioral learning unit can also provide hints for the operation or adjust the difficulty level according to the user's emotion. For example, if the user feels frustrated about the operation, a suggestion is made to lower the difficulty level of the operation. In this way, by providing support according to the user's emotion, it is possible to reduce the user's stress and improve the game experience.

[0061] The behavioral learning unit can analyze the user's real-time biometric data and provide operation instructions according to the user's stress level. For example, the generation AI analyzes the user's real-time biometric data (heart rate, facial expression, etc.) and provides operation instructions according to the user's stress level. For example, if the user's heart rate is elevated, advice on how to relax is displayed. The behavioral learning unit can also adjust the content of the operation instructions based on the user's biometric data. For example, if the user is nervous, the operation procedures are explained more slowly. This makes it possible to provide operation instructions according to the user's stress level, thereby reducing the user's stress and improving the game experience.

[0062] The behavioral learning unit can compare the behavioral data of other users, identify common stumbling blocks, and issue advance warnings. For example, the behavioral learning unit uses a generation AI to compare the behavioral data of other users, identify common stumbling blocks, and issue advance warnings. For example, a warning message can be displayed before an operation that many users fail at. The behavioral learning unit can also provide hints and advice on operations based on common stumbling blocks. For example, if a user fails frequently at a particular stage, the unit can explain how to clear that stage. In this way, by comparing the behavioral data of other users to identify common stumbling blocks and issuing advance warnings, it is possible to reduce user stumbling blocks.

[0063] The behavior learning unit can use the emotion estimation function to adjust the timing and content of the operation instructions so that the user feels positive emotions toward a particular operation. The behavior learning unit, for example, uses the emotion estimation function to adjust the timing and content of the operation instructions so that the user feels positive emotions toward a particular operation. For example, the behavior learning unit can provide an explanation that emphasizes successful experiences. The behavior learning unit can also change the content of the operation instructions depending on the user's emotions. For example, if the user feels joy toward an operation, the behavior learning unit can explain in detail the advantages of that operation. In this way, adjusting the timing and content of the operation instructions so that the user feels positive emotions toward a particular operation can improve the user's motivation.

[0064] The explanation providing unit can analyze the user's operation speed in real time and automatically provide auxiliary explanations if the operation is slow. For example, the explanation providing unit uses a generation AI to analyze the user's operation speed in real time and automatically provide auxiliary explanations if the operation is slow. For example, the operation steps are explained again to a user who is slow in operation. The explanation providing unit can also adjust the content of the auxiliary explanation according to the user's operation speed. For example, tricks for operation are explained in detail to a user who is slow in operation. In this way, auxiliary explanations can be provided according to the user's operation speed, thereby helping the user's understanding.

[0065] The explanation providing unit can analyze the user's gaze tracking data and provide an explanation of a specific UI element when the gaze stops on that element. For example, the explanation providing unit uses a generation AI to analyze the user's gaze tracking data and provide an explanation of that element when the gaze stops on that element. For example, if the user is looking at a specific button, the explanation providing unit can explain the function of that button. The explanation providing unit can also customize the explanation of UI elements based on the user's gaze tracking data. For example, it can provide a detailed explanation for a UI element on which the user fixates their gaze for a long time. This can help the user understand by providing an appropriate explanation based on the user's gaze tracking data.

[0066] The explanation providing unit can use the emotion estimation function to provide an explanation that gives a sense of security when the user feels anxious about the operation. For example, the explanation providing unit uses the emotion estimation function to provide an explanation that gives a sense of security when the user feels anxious about the operation. For example, the explanation providing unit provides an explanation that emphasizes that the operation is easy. The explanation providing unit can also provide operation hints or encouraging messages depending on the user's emotions. For example, if the user feels anxious about the operation, the explanation providing unit can carefully explain the operation steps. In this way, by providing an explanation that gives a sense of security when the user feels anxious about the operation, the user's stress can be reduced.

[0067] The explanation providing unit can analyze the user's voice commands and provide operation instructions in voice. For example, the explanation providing unit uses a generation AI to analyze the user's voice commands and provide operation instructions in voice. For example, if the user asks, "What do I do next?", the next operation procedure is explained in voice. The explanation providing unit can also customize the content of the operation instructions based on the user's voice commands. For example, if the user asks about a specific operation, the details of that operation are explained in voice. This makes it possible to improve user convenience by providing operation instructions based on the user's voice commands.

[0068] The explanation providing unit can analyze the user's gestures and provide operation instructions based on the gestures. For example, the explanation providing unit uses a generation AI to analyze the user's gestures and provide operation instructions based on the gestures. For example, if the user performs a specific gesture, the explanation providing unit explains the meaning of the gesture. The explanation providing unit can also customize the content of the operation instructions based on the user's gestures. For example, if the user repeatedly performs a specific gesture, the explanation providing unit provides detailed explanations on how to use the gesture. This can improve user convenience by providing operation instructions based on the user's gestures.

[0069] The analysis unit can evaluate the user's operation proficiency in real time and dynamically adjust the tutorial content according to the proficiency. For example, the analysis unit uses a generation AI to evaluate the user's operation proficiency in real time and dynamically adjust the tutorial content according to the proficiency. For example, the analysis unit can speed up the tutorial progress for users who are smooth at operation. The analysis unit can also customize the tutorial content based on the user's operation proficiency. For example, the analysis unit can provide detailed explanations of operation procedures for users who are not good at operation. In this way, the user's learning efficiency can be improved by dynamically adjusting the tutorial content according to the user's operation proficiency.

[0070] The analysis unit can analyze the user's operation history and omit operations that have already been mastered. For example, the analysis unit uses a generation AI to analyze the user's operation history and omit operations that have already been mastered. For example, for a user who has successfully performed the same operation in the past, the explanation of that operation is omitted. The analysis unit can also omit specific operations based on the user's operation history. For example, for a user who has successfully performed a specific operation multiple times, the explanation of that operation is omitted. In this way, the tutorial time can be shortened by omitting operations that have already been mastered based on the user's operation history.

[0071] The analysis unit can shorten the tutorial when the user is bored with the operation by using the emotion estimation function. The analysis unit, for example, uses the emotion estimation function to shorten the tutorial when the user is bored with the operation. For example, if the user's facial expression becomes expressionless, the tutorial ends early. The analysis unit can also adjust the content of the tutorial according to the user's emotion. For example, if the user is bored with the operation, unnecessary explanations are omitted. In this way, by shortening the tutorial when the user is bored with the operation, the user's interest can be maintained.

[0072] The analysis unit can analyze the user's play time and provide an effective tutorial in a short amount of time. For example, the analysis unit uses a generation AI to analyze the user's play time and provide an effective tutorial in a short amount of time. For example, for a user with a short play time, only important operations are explained. The analysis unit can also customize the content of the tutorial based on the user's play time. For example, detailed explanations are provided for a user with a long play time. This allows the user's time to be used effectively by providing an effective tutorial in a short amount of time based on the user's play time.

[0073] The analysis unit can analyze the user's operational errors and omit the tutorial if there are few errors. For example, the analysis unit uses a generation AI to analyze the user's operational errors and omit the tutorial if there are few errors. For example, a user who makes few operational errors is prompted to proceed to the next step. The analysis unit can also adjust the content of the tutorial based on the user's operational errors. For example, the analysis unit can provide detailed explanations of the operation procedures for a user who makes many operational errors. This allows the user's time to be used more effectively by omitting the tutorial if there are few operational errors.

[0074] The analysis unit can use the emotion estimation function to provide a detailed tutorial when the user is interested in the operation. The analysis unit, for example, uses the emotion estimation function to provide a detailed tutorial when the user is interested in the operation. For example, if the user's facial expression looks interested, a detailed explanation is added. The analysis unit can also customize the content of the tutorial according to the user's emotion. For example, if the user is interested in the operation, a step-by-step guide is provided. In this way, by providing a detailed tutorial when the user is interested in the operation, the user's motivation to learn can be increased.

[0075] The explanation providing unit can receive feedback from the game developer in real time and dynamically update the tutorial content. For example, the explanation providing unit uses a generation AI to receive feedback from the game developer in real time and dynamically update the tutorial content. For example, when a new operation is added, the explanation for that operation is immediately reflected. The explanation providing unit can also customize the tutorial content based on feedback from the game developer. For example, the content is changed according to the user's progress. In this way, by receiving feedback from the game developer in real time and dynamically updating the tutorial content, it is possible to provide a tutorial based on the latest information.

[0076] The explanation providing unit can analyze game update information and automatically update the tutorial when a new operation is added. For example, the generation AI analyzes game update information and automatically updates the tutorial when a new operation is added. For example, when a new skill is added, the explanation providing unit explains how to use the skill. The explanation providing unit can also customize the content of the tutorial based on the game update information. For example, when a new function is added, an explanation of the function is added. In this way, the latest operation methods can be provided to the user by automatically updating the tutorial based on game update information.

[0077] The explanation providing unit uses the emotion estimation function to enable game developers to analyze the user's emotional reactions and improve the tutorial based on the emotions. For example, the explanation providing unit uses the emotion estimation function to enable game developers to analyze the user's emotional reactions and improve the tutorial based on the emotions. For example, the explanation providing unit identifies areas in which the user feels dissatisfied and improves those areas. The explanation providing unit can also customize the content of the tutorial based on the user's emotions. For example, if the user feels joy in an operation, the explanation providing unit can provide a detailed explanation of the advantages of that operation. This allows game developers to analyze the user's emotional reactions and improve the tutorial based on the emotions, thereby increasing user satisfaction.

[0078] The explanation providing unit can conduct user tests in collaboration with game developers and optimize the tutorial based on the test results. For example, the explanation providing unit uses a generation AI to conduct user tests in collaboration with game developers and optimize the tutorial based on the test results. For example, the explanation providing unit adjusts the content of the tutorial based on feedback obtained from the user tests. The explanation providing unit can also customize the content of the tutorial based on the results of the user tests. For example, the explanation providing unit changes the content according to the user's progress. In this way, by conducting user tests in collaboration with game developers and optimizing the tutorial based on the test results, it is possible to provide a tutorial that meets the needs of the user.

[0079] The explanation providing unit can collect opinions from game developers and integrate the opinions of multiple developers to provide an optimal tutorial. For example, the explanation providing unit uses a generation AI to collect opinions from game developers and integrate the opinions of multiple developers to provide an optimal tutorial. For example, a tutorial that reflects the opinions of different developers is created. The explanation providing unit can also customize the content of the tutorial based on the opinions of game developers. For example, explanations of specific operations can be added based on the developer's opinions. In this way, by integrating the opinions of multiple game developers to provide an optimal tutorial, a tutorial that is suitable for the user from a more multifaceted perspective can be provided.

[0080] The explanation providing unit uses the emotion estimation function to enable game developers to monitor users' emotional reactions in real time and instantly adjust the tutorial. The explanation providing unit, for example, uses the emotion estimation function to enable game developers to monitor users' emotional reactions in real time and instantly adjust the tutorial. For example, if a user is dissatisfied, the explanation providing unit can instantly improve that part. The explanation providing unit can also customize the content of the tutorial according to the user's emotions. For example, if a user is pleased with an operation, the explanation providing unit can provide a detailed explanation of the benefits of that operation. This allows game developers to monitor users' emotional reactions in real time and instantly adjust the tutorial, thereby improving user satisfaction.

[0081] The analysis unit can analyze the user's play history and provide advice based on the user's individual play style. For example, the analysis unit uses a generation AI to analyze the user's play history and provide advice based on the user's individual play style. For example, a user with an offensive play style can be recommended equipment and skills to maximize attack power. The analysis unit can also customize the content of advice according to the user's play style. For example, a user with a defensive play style can be provided with advice to strengthen defense power. This makes it possible to improve the user's gaming experience by providing advice that suits the user's individual play style based on the user's play history.

[0082] The analysis unit can use the emotion estimation function to adjust the content of the operation instructions so that the user feels positive emotions toward the operation. The analysis unit, for example, uses the emotion estimation function to adjust the content of the operation instructions so that the user feels positive emotions toward the operation. For example, the analysis unit provides an explanation that emphasizes successful experiences. The analysis unit can also change the content of the operation instructions depending on the user's emotions. For example, if the user feels joy toward an operation, the analysis unit can explain in detail the advantages of that operation. In this way, adjusting the content of the operation instructions so that the user feels positive emotions toward the operation can improve the user's motivation.

[0083] The explanation providing unit can collect user feedback and improve the tutorial based on the feedback. For example, the generation AI collects user feedback and improves the tutorial based on the feedback. For example, it simplifies parts that the user finds difficult. The explanation providing unit can also customize the content of the tutorial based on user feedback. For example, it creates a tutorial that reflects the user's opinions. In this way, by improving the tutorial based on user feedback, it is possible to provide a tutorial that meets the user's needs.

[0084] The analysis unit can analyze the user's play data and provide a customized tutorial according to the play style. For example, the analysis unit uses a generation AI to analyze the user's play data and provide a customized tutorial according to the play style. For example, a user with an offensive play style can be provided with a tutorial on offensive techniques. The analysis unit can also customize the content of the tutorial based on the user's play style. For example, a user with a defensive play style can be provided with a detailed tutorial on defensive techniques. This allows the user's learning efficiency to be improved by providing a customized tutorial according to the play style based on the user's play data.

[0085] The analysis unit can use the emotion estimation function to provide immediate support when the user feels negative emotions about the operation. For example, the analysis unit uses the emotion estimation function to provide immediate support when the user feels negative emotions about the operation. For example, if the user feels irritated about the operation, the analysis unit makes a suggestion to reduce the difficulty of the operation. The analysis unit can also customize the content of the support according to the user's emotions. For example, if the user feels anxious about the operation, the analysis unit can carefully explain the operation procedures. In this way, by providing immediate support when the user feels negative emotions about the operation, the user's stress can be reduced and the game experience can be improved.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The analysis unit can analyze the user's operation history and omit operations that have already been mastered. For example, for a user who has successfully performed the same operation in the past, the explanation of that operation is omitted. The analysis unit can also omit specific operations based on the user's operation history. For example, for a user who has successfully performed a specific operation multiple times, the explanation of that operation is omitted. In this way, by omitting operations that have already been mastered based on the user's operation history, the tutorial time can be shortened.

[0088] The explanation providing unit can analyze the user's gaze tracking data and provide an explanation of a specific UI element when the user's gaze lingers on that element. For example, if the user gazes at a specific button, the explanation providing unit can explain the function of that button. The explanation providing unit can also customize the explanation of UI elements based on the user's gaze tracking data. For example, the explanation providing unit can provide a detailed explanation for a UI element on which the user gazes for a long time. This can help the user understand the content by providing an appropriate explanation based on the user's gaze tracking data.

[0089] The explanation providing unit can analyze the user's voice commands and provide audio instructions for operations. For example, if the user asks, "What do I do next?", the next operation procedure is explained audio. The explanation providing unit can also customize the content of the operation instructions based on the user's voice commands. For example, if the user asks about a specific operation, the details of that operation are explained audio. This makes it possible to improve user convenience by providing operation instructions based on the user's voice commands.

[0090] The explanation providing unit can analyze the user's gestures and provide operation instructions based on the gestures. For example, if the user performs a specific gesture, the explanation providing unit can explain the meaning of the gesture. The explanation providing unit can also customize the content of the operation instructions based on the user's gestures. For example, if the user repeatedly performs a specific gesture, the explanation providing unit can explain in detail how to use the gesture. In this way, by providing operation instructions based on the user's gestures, it is possible to improve user convenience.

[0091] The analysis unit can analyze the user's play time and provide an effective tutorial in a short time. For example, for a user who plays for a short time, only important operations can be explained. The analysis unit can also customize the content of the tutorial based on the user's play time. For example, for a user who plays for a long time, detailed explanations can be provided. In this way, by providing an effective tutorial in a short time based on the user's play time, the user's time can be used effectively.

[0092] The analysis unit can use the emotion estimation function to shorten the tutorial when the user is bored with the operation. For example, if the user's facial expression becomes expressionless, the analysis unit can end the tutorial early. The analysis unit can also adjust the content of the tutorial according to the user's emotion. For example, if the user is bored with the operation, unnecessary explanations can be omitted. In this way, by shortening the tutorial when the user is bored with the operation, the user's interest can be maintained.

[0093] The analysis unit can use the emotion estimation function to provide immediate support when the user feels negative emotions about the operation. For example, if the user feels frustrated about the operation, the analysis unit can suggest ways to make the operation easier. The analysis unit can also customize the content of support according to the user's emotions. For example, if the user feels anxious about the operation, the analysis unit can carefully explain the operation procedures. This allows immediate support to be provided when the user feels negative emotions about the operation, thereby reducing the user's stress and improving the game experience.

[0094] The explanation providing unit can use the emotion estimation function to provide an explanation that gives a sense of security when the user feels anxious about the operation. For example, the explanation can emphasize that the operation is easy. The explanation providing unit can also provide operation hints or encouraging messages depending on the user's emotions. For example, if the user feels anxious about the operation, the explanation can carefully explain the operation steps. In this way, by providing an explanation that gives a sense of security when the user feels anxious about the operation, the user's stress can be reduced.

[0095] The analysis unit can use the emotion estimation function to adjust the content of the operation instructions so that the user feels positive emotions toward the operation. For example, the analysis unit can provide an explanation that emphasizes successful experiences. The analysis unit can also change the content of the operation instructions depending on the user's emotions. For example, if the user feels joy toward an operation, the analysis unit can explain in detail the advantages of that operation. In this way, adjusting the content of the operation instructions so that the user feels positive emotions toward the operation can improve the user's motivation.

[0096] The explanation providing unit uses the emotion estimation function to enable game developers to monitor the user's emotional reactions in real time and instantly adjust the tutorial. For example, if the user is dissatisfied, the explanation providing unit can immediately improve that part. The explanation providing unit can also customize the content of the tutorial according to the user's emotions. For example, if the user is pleased with an operation, the explanation providing unit can provide a detailed explanation of the benefits of that operation. This allows game developers to monitor the user's emotional reactions in real time and instantly adjust the tutorial, thereby improving user satisfaction.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The behavioral learning unit collects in-game data and user behavioral data. For example, the behavioral learning unit collects game progress, scores, and item usage. The behavioral learning unit can also collect the user's operation history, play time, and selected options. Step 2: The analysis unit analyzes the in-game data collected by the behavioral learning unit and the user's behavioral data. For example, the analysis unit analyzes which operations the user spends time on and where the user has trouble. The analysis unit can also analyze the user's play style and past game history. Step 3: The explanation providing unit provides the user with a timely explanation for an operation based on the results of the analysis by the analysis unit. For example, when the user attempts a new operation, the explanation providing unit displays an explanation for that operation on the screen. The explanation providing unit can also provide a pop-up display or audio guidance in real time.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0140] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] 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.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a behavioral learning unit that collects in-game data and user behavioral data; an analysis unit that analyzes the in-game data collected by the behavior learning unit and the user's behavior data; an explanation providing unit that provides a user with an operation explanation in a timely manner based on the results of the analysis by the analysis unit. A system characterized by:

2. The behavior learning unit Analyzing how the user feels about the operation and providing support according to the emotion 2. The system of claim 1.

3. The behavior learning unit Analyzing real-time biological data of the user and providing the operation instructions according to the stress level.

2. The system of claim 1.

4. The explanation providing unit: The operation speed of the user is analyzed in real time, and auxiliary explanations are automatically provided when the operation speed is slow.

2. The system of claim 1.

5. The analysis unit The user's operational proficiency is evaluated in real time, and the content of the tutorial is dynamically adjusted according to the proficiency.

2. The system of claim 1.

6. The explanation providing unit: Receive real-time feedback from game developers and dynamically update tutorial content 2. The system of claim 1.

7. The analysis unit Analyzing the user's play history and providing advice based on the user's individual play style 2. The system of claim 1.

8. The analysis unit Providing immediate support if the user has negative feelings about the operation 2. The system of claim 1.

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