System

Abstract

An object of the system according to the embodiment is to analyze game data and provide accurate advice to a player.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects game data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates advice based on the analysis result obtained by the analysis unit. The providing unit provides the advice generated by the generating unit to the player.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately provide players with accurate advice based on game data, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze game data and provide accurate advice to the player. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects game data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates advice based on the analysis results obtained by the analysis unit. The provision unit provides the advice generated by the generation unit to the player. [Effects of the Invention]

[0007] The system according to the embodiment can analyze game data and provide accurate advice to the player. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A game advice system according to an embodiment of the present invention collects game data, analyzes it using an AI, and provides appropriate advice to a player. The game advice system collects game data, analyzes the game situation in real time based on the data learned by the AI, and provides optimal advice to the player. For example, the game advice system collects player operation data and in-game event data. For example, data such as what operations the player performed and what events occurred in the game is collected. Next, the game advice system trains the AI ​​to learn the collected data. The AI ​​analyzes the collected data to learn the game progress and the player's behavioral patterns. For example, it learns what results will occur in the game when the player performs a specific operation. Furthermore, the game advice system analyzes the game situation in real time based on the data learned by the AI. The AI ​​monitors the player's operations and in-game events in real time to grasp the current game situation. For example, if the player is attacked by an enemy character, the AI ​​immediately analyzes the situation and provides appropriate advice. Finally, the game advice system provides optimal advice to the player based on the analysis results. The AI ​​generates advice based on the current game situation and presents it to the player. For example, if a player is attacked by an enemy character, the AI ​​will provide advice such as "strengthen your defense" or "take evasive action." This allows the game advice system to provide players with appropriate advice tailored to their game progress, thereby improving their game performance. This allows players to provide appropriate advice tailored to their game progress, thereby improving their game performance. For example, a beginner player can learn basic game controls and strategies by receiving advice from the AI. Furthermore, an advanced player can execute more advanced strategies by receiving advice from the AI. This allows all players to enjoy the game more.

[0029] A game advice system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects game data. The game data includes, but is not limited to, player operation data and in-game event data. The collection unit collects data such as what operations the player performed and what events occurred in the game. The collection unit can also collect player operation data in real time. For example, the collection unit records the timing at which the player presses a button and the type of operation. The collection unit can also collect in-game event data. For example, the collection unit records information about specific events that occur in the game. The analysis unit analyzes the data collected by the collection unit. The analysis aims to understand the progress of the game and the player's behavioral patterns based on the collected data. For example, the analysis unit analyzes what results occur in the game when the player performs a specific operation. The analysis unit can also analyze the player's behavioral patterns. For example, the analysis unit analyzes what operations the player frequently performs and what options the player chooses. The analysis unit can also analyze the progress of the game. For example, the analysis unit analyzes the player's score and determines which stages the player has cleared. The generation unit generates advice based on the analysis results obtained by the analysis unit. The advice may be provided in the form of, for example, text or audio, but is not limited to these examples. For example, the generation unit generates advice such as "strengthen your defenses" or "take evasive action" based on the analysis results. The generation unit can also generate advice based on the player's behavioral patterns. For example, the generation unit generates advice such as "that operation is effective" for an operation frequently performed by the player. Furthermore, the generation unit can also generate advice based on the progress of the game. For example, when the player clears a specific stage, the generation unit generates advice such as "this strategy will be effective in the next stage." The provision unit provides the advice generated by the generation unit to the player. The provision is performed in the form of, for example, text or audio, but is not limited to these examples.For example, the providing unit displays the generated advice on a screen. The providing unit can also play back the generated advice by voice. For example, the providing unit provides the advice by voice using voice synthesis technology. This allows the game advice system according to the embodiment to provide accurate advice to the player. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide advice using an AI model that receives the advice generated by the generating unit as input and outputs advice.

[0030] The collection unit can collect player operation data or in-game event data. Operation data includes, but is not limited to, button press information and operation timing. For example, the collection unit records the timing at which the player presses a button and the type of operation. The collection unit can also collect in-game event data. For example, the collection unit records information about specific events that occur in the game. By collecting player operation data and in-game event data, a dataset for AI learning can be constructed. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input player operation data into a generation AI and have the generation AI analyze the operation data.

[0031] The analysis unit analyzes the collected data to understand the progress of the game and the player's behavioral patterns. The analysis unit, for example, understands the progress of the game based on the collected data. For example, the analysis unit analyzes which stages the player has cleared and the player's score. The analysis unit can also analyze the player's behavioral patterns. For example, the analysis unit analyzes what operations the player frequently performs and what options the player chooses. The analysis unit can also analyze what results occur in the game when the player performs a specific operation. For example, the analysis unit analyzes what events occur in the game when the player presses a specific button. In this way, by analyzing the collected data, the progress of the game and the player's behavioral patterns can be understood. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and cause the generation AI to analyze the progress of the game and the player's behavioral patterns.

[0032] The generation unit can generate advice in text or voice based on the analysis results. The generation unit generates advice in text based on the analysis results. For example, the generation unit generates advice such as "strengthen your defenses" or "take evasive action." The generation unit can also generate advice in voice based on the analysis results. For example, the generation unit provides advice in voice using voice synthesis technology. The generation unit can also generate advice based on the player's behavioral patterns. For example, the generation unit generates advice such as "that operation is effective" for an operation frequently performed by the player. In this way, appropriate advice can be provided to the player by generating advice in text or voice based on the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit inputs the analysis results into a generation AI and causes the generation AI to generate advice.

[0033] The providing unit can provide the generated advice to the player. The providing unit, for example, displays the generated advice on a screen. For example, the providing unit displays the generated advice in text. The providing unit can also play the generated advice as audio. For example, the providing unit provides the advice as audio using voice synthesis technology. By providing the generated advice to the player, the player can receive appropriate advice according to the progress of the game. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide advice using an AI model that receives the advice generated by the generating unit as input and outputs advice.

[0034] The providing unit may include a method for displaying advice in text and a method for providing advice in audio. The providing unit, for example, displays the generated advice in text on a screen. For example, the providing unit displays the generated advice at a specific position on the screen. The providing unit may also play the generated advice in audio. For example, the providing unit may provide the advice in audio using speech synthesis technology. By providing both a method for displaying advice in text and a method for providing advice in audio, it is possible to provide advice according to the player's preferences. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may provide advice using an AI model that receives the advice generated by the generating unit as input and outputs advice.

[0035] The collection unit can analyze the player's past operation history and select an appropriate data collection method. For example, the collection unit can prioritize collection of operations that the player frequently performed in the past. The collection unit can also prioritize collection of important operation data based on the success rate of the player's past operations. Furthermore, the collection unit can predict operations that will be performed during a specific time period from the player's past operation history and concentrate data collection during that time period. In this way, the optimal data collection method can be selected by analyzing the player's past operation history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the player's past operation history data into a generation AI and have the generation AI select a data collection method.

[0036] When collecting data, the collection unit can filter the data based on the player's current game progress. For example, when the player enters a boss battle, the collection unit prioritizes collecting data related to that battle. Also, when the player is exploring, the collection unit can collect data related to the exploration and postpone battle data. Furthermore, immediately after the player completes a mission, the collection unit can collect data related to that mission and prepare for the next mission. By filtering data based on the player's current game progress, more relevant data can be collected. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the player's game progress data to a generation AI and have the generation AI perform data filtering.

[0037] When collecting data, the collection unit can select the optimal collection means depending on the player's input method. For example, if the player is using voice input, the collection unit can prioritize collecting voice data. Furthermore, if the player is using text input, the collection unit can also prioritize collecting text data. Furthermore, if the player is using gesture input, the collection unit can also prioritize collecting gesture data. This enables efficient data collection by selecting the optimal collection means depending on the player's input method. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the player's input method data to the generation AI and cause the generation AI to select the optimal collection means.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the player's geographical location information. For example, if the player is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, if the player is traveling, the collection unit can prioritize collecting data related to the player's movement. Furthermore, if the player is participating in a specific event, the collection unit can prioritize collecting data related to the event. In this way, by collecting data by taking into account the player's geographical location information, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the player's geographical location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0039] When collecting data, the collection unit can analyze the player's social media activities and collect related data. For example, the collection unit can collect gameplay videos shared by the player on social media. The collection unit can also analyze the player's social media posts and collect related game data. Furthermore, the collection unit can collect related data by referring to the activities of the player's friends on social media. In this way, related data can be collected by analyzing the player's social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the player's social media activity data into the generation AI and cause the generation AI to collect related data.

[0040] The collection unit can customize the collection method by reflecting the player's past feedback when collecting data. For example, the collection unit adjusts the type of data to collect based on feedback provided by the player in the past. The collection unit can also prioritize the use of a specific data collection method based on the player's past feedback. Furthermore, the collection unit can adjust the frequency and timing of data collection by referring to the player's feedback. In this way, the collection method can be customized by reflecting the player's past feedback. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the player's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0041] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account interrelationships within the game. For example, the analysis unit can analyze the interrelationships between player operations and in-game events to improve accuracy. The analysis unit can also analyze the interrelationships between player behavior patterns and in-game results to improve accuracy. Furthermore, the analysis unit can analyze the interrelationships between player choices and in-game progress to improve accuracy. In this way, the accuracy of the analysis is improved by taking into account the interrelationships within the game. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input in-game interrelationship data into a generation AI and cause the generation AI to improve the accuracy of the analysis.

[0042] The analysis unit can perform the analysis while taking into account the player's attribute information. The analysis unit can perform the analysis while taking into account, for example, the player's age and gender. The analysis unit can also perform the analysis while taking into account the player's gaming experience and skill level. Furthermore, the analysis unit can perform the analysis while taking into account the player's interests and concerns. This allows for more personalized analysis by taking into account the player's attribute information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the player's attribute information data into the generation AI and have the generation AI perform the analysis.

[0043] During analysis, the analysis unit can weight the analysis based on the progress of the game. For example, in the early stages of the game, the analysis unit can emphasize analysis of basic operation data. In addition, in the middle stages of the game, the analysis unit can emphasize analysis of strategic data. Furthermore, in the late stages of the game, the analysis unit can emphasize analysis of the player's final behavioral patterns. In this way, weighting the analysis based on the progress of the game enables analysis that focuses on more important data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input game progress data to the generation AI and have the generation AI perform the weighting of the analysis.

[0044] The analysis unit can perform the analysis while taking into account the geographical distribution within the game. For example, the analysis unit analyzes the behavioral patterns of players in a specific area within the game. The analysis unit can also analyze player movement data while taking into account geographical factors within the game. Furthermore, the analysis unit can analyze player strategies based on the geographical distribution within the game. This allows for more detailed analysis by taking into account the geographical distribution within the game. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the geographical distribution data within the game to a generation AI and have the generation AI perform the analysis.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related game data. The analysis unit can improve the accuracy of the analysis by, for example, referring to data of other players. The analysis unit can also improve the accuracy of the analysis by referring to past game data. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to data provided by the game developer. In this way, the accuracy of the analysis is improved by referring to related game data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input related game data to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0046] The analysis unit can perform the analysis taking into account the market value of the game. The analysis unit can perform the analysis taking into account, for example, the popularity of the game. The analysis unit can also perform the analysis taking into account sales data of the game. Furthermore, the analysis unit can also perform the analysis taking into account evaluation data of the game. By taking the market value of the game into account, analysis can be performed from a more business perspective. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input market value data of the game into the generation AI and have the generation AI perform the analysis.

[0047] When generating advice, the generation unit can adjust the level of detail of the advice based on the importance of the game. For example, the generation unit provides detailed advice in important scenes. The generation unit can also provide concise advice in less important scenes. Furthermore, the generation unit can adjust the level of detail of the advice according to the player's progress. In this way, by adjusting the level of detail of the advice based on the importance of the game, more appropriate advice can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input game importance data to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0048] When generating advice, the generation unit can apply different advice algorithms depending on the game category. For example, the generation unit applies an algorithm that provides strategic advice to an RPG game. The generation unit can also apply an algorithm that provides advice regarding real-time operations to an action game. The generation unit can also apply an algorithm that provides advice regarding solutions to a puzzle game. In this way, by applying different advice algorithms depending on the game category, more appropriate advice can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input game category data to the generation AI and cause the generation AI to apply the advice algorithm.

[0049] When generating advice, the generation unit can improve the accuracy of the advice by referring to the player's past advice results. The generation unit, for example, analyzes the effects of advice the player has received in the past and improves the accuracy. The generation unit can also provide optimal advice based on the player's past advice history. Furthermore, the generation unit can also adjust the content of the advice by referring to the player's past advice results. In this way, the accuracy of the advice is improved by referring to the player's past advice results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the player's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0050] When generating advice, the generation unit can determine the priority of the advice based on the progress of the game. For example, during an important battle, the generation unit can prioritize providing advice related to the battle. Furthermore, during exploration, the generation unit can also prioritize providing advice related to the exploration. Furthermore, immediately after a mission is completed, the generation unit can prioritize providing advice related to the next mission. In this way, by determining the priority of advice based on the progress of the game, more appropriate advice can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input game progress data to the generation AI and cause the generation AI to determine the priority of the advice.

[0051] When generating advice, the generation unit can adjust the order of advice based on the relevance of the game. For example, the generation unit first provides advice related to the task the player is currently performing. The generation unit can also provide advice related to the next task the player should perform next. Furthermore, the generation unit can also provide advice related to a task the player has previously performed last. In this way, by adjusting the order of advice based on the relevance of the game, more appropriate advice can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input game relevance data to the generation AI and cause the generation AI to adjust the order of advice.

[0052] When generating advice, the generation unit can adjust the use of technical terminology in the advice according to the player's level of expertise. For example, the generation unit can provide advice to a beginner player in simple language, avoiding technical terminology. The generation unit can also provide advice to an intermediate player using technical terminology appropriately. Furthermore, the generation unit can provide detailed advice to an advanced player using a lot of technical terminology. In this way, by adjusting the use of technical terminology in the advice according to the player's level of expertise, more understandable advice can be provided. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the player's level of expertise data into the generation AI and cause the generation AI to use technical terminology in the advice.

[0053] When providing advice, the providing unit can select the optimal advice providing method by referring to the player's past operation history. For example, the providing unit can prioritize the use of a providing method (text, voice, etc.) that the player has previously preferred. The providing unit can also select a providing method that was effective in a specific situation from the player's past operation history. Furthermore, the providing unit can provide advice at the optimal timing based on the player's past operation history. In this way, the optimal advice providing method can be selected by referring to the player's past operation history. Some or all of the above-described processing by the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the player's past operation history data into the generating AI and cause the generating AI to select the optimal advice providing method.

[0054] When providing advice, the providing unit can customize the content to be provided according to the player's current task. For example, if the player is in combat, the providing unit can provide advice about combat. Furthermore, if the player is exploring, the providing unit can also provide advice about exploration. Furthermore, if the player has just completed a mission, the providing unit can provide advice about the next mission. In this way, by customizing the content to be provided according to the player's current task, more appropriate advice can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the player's current task data into the generating AI and cause the generating AI to customize the content to be provided.

[0055] The providing unit can improve the advice providing method by reflecting the player's feedback when providing advice. For example, if the player provides positive feedback in response to the advice provided, the providing unit continues the advice providing method. In addition, if the player provides negative feedback in response to the advice provided, the providing unit can change the advice providing method. Furthermore, the providing unit can adjust the content and timing of the advice based on the player's feedback. In this way, the advice providing method can be improved by reflecting the player's feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the player's feedback data into the generating AI and cause the generating AI to improve the advice providing method.

[0056] When providing advice, the providing unit can select the optimal advice providing method by taking into consideration the player's device information. For example, if the player is using a smartphone, the providing unit can select a advice providing method that matches the screen size. Furthermore, if the player is using a tablet, the providing unit can select a advice providing method optimized for a large screen. Furthermore, if the player is using a PC, the providing unit can select a high-resolution advice providing method. In this way, the optimal advice providing method can be selected by taking into consideration the player's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the player's device information data into the generating AI and cause the generating AI to select the optimal advice providing method.

[0057] When providing advice, the providing unit can make the advice content multilingual based on the player's language setting. The providing unit, for example, automatically sets the language of the advice based on the language setting of the player's device. The providing unit can also provide a language switching function when the player uses multiple languages. Furthermore, if the player selects a specific language, the providing unit can provide advice in that language. This allows the advice content to be multilingual based on the player's language setting, making it possible to accommodate a wider range of players. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the player's language setting data into a generating AI and cause the generating AI to execute the multilingual advice content.

[0058] When providing advice, the providing unit can customize the advice to be provided based on the player's gameplay style. For example, if the player has an aggressive play style, the providing unit can provide aggressive advice. Furthermore, if the player has a defensive play style, the providing unit can provide defensive advice. Furthermore, if the player has a balanced play style, the providing unit can provide balanced advice. In this way, by customizing the advice to be provided based on the player's gameplay style, more appropriate advice can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the player's gameplay style data to a generation AI and cause the generation AI to customize the advice to be provided.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The analysis unit can analyze the reaction speed of the player based on the player's operation data. For example, the analysis unit measures the time it takes for the player to press a button and analyzes the reaction speed. The analysis unit can also measure the time it takes for the player to perform a specific operation and analyze the reaction speed. Furthermore, the analysis unit can analyze the player's operation pattern based on the player's reaction speed. In this way, by analyzing the player's reaction speed, the player's operation pattern can be understood in more detail.

[0061] The collection unit can take into consideration the type of device used by the player when collecting the player's operation data. For example, if the player is using a smartphone, the collection unit can prioritize collecting touch operation data. Also, if the player is using a PC, the collection unit can prioritize collecting keyboard and mouse operation data. Furthermore, if the player is using a game controller, the collection unit can prioritize collecting controller operation data. This makes it possible to collect optimal operation data depending on the type of device used by the player.

[0062] When analyzing a player's behavioral patterns, the analysis unit can refer to the player's past gameplay data. For example, the analysis unit can analyze what operations the player has performed in the past to understand the player's current behavioral patterns. The analysis unit can also analyze what options the player has chosen in the past to predict what options the player will choose in the future. Furthermore, the analysis unit can analyze what results the player has achieved in the past to predict what the current results will be. In this way, by referring to the player's past gameplay data, more accurate analysis of the player's behavioral patterns is possible.

[0063] The generation unit can take into consideration the player's current game progress when generating advice. For example, when the player enters a boss battle, the generation unit generates advice about the boss battle. In addition, when the player is exploring, the generation unit can also generate advice about exploration. Furthermore, the generation unit can also generate advice about the next mission immediately after the player completes a mission. In this way, appropriate advice can be generated according to the player's current game progress.

[0064] The collection unit can take the player's gameplay style into consideration when collecting the player's operation data. For example, if the player has an aggressive play style, the collection unit can prioritize collecting aggressive operation data. Also, if the player has a defensive play style, the collection unit can prioritize collecting defensive operation data. Furthermore, if the player has a balanced play style, the collection unit can prioritize collecting balanced operation data. This makes it possible to collect optimal operation data according to the player's gameplay style.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects game data. Game data includes player operation data and in-game event data. For example, the collection unit collects data such as what operations the player performed and what events occurred in the game. The collection unit can also collect player operation data in real time. For example, it can record the timing when the player pressed a button and the type of operation. Furthermore, the collection unit can also collect in-game event data. For example, it can record information about specific events that occurred in the game. Step 2: The analysis unit analyzes the data collected by the collection unit. The purpose of the analysis is to understand the game progress and the player's behavioral patterns based on the collected data. For example, it analyzes what results occur in the game when the player performs a specific operation. It can also analyze the player's behavioral patterns. For example, it analyzes what operations the player frequently performs and what options the player chooses. It can also analyze the game progress. For example, it analyzes which stages the player has cleared and the player's score. Step 3: The generator generates advice based on the analysis results obtained by the analyzer. The advice is provided in text or audio format. For example, advice such as "strengthen your defenses" or "take evasive action" is generated based on the analysis results. Advice can also be generated based on the player's behavioral patterns. For example, advice such as "That action is effective" is generated for an action that the player frequently performs. Advice can also be generated based on the player's progress in the game. For example, when the player clears a specific stage, advice such as "This strategy is effective in the next stage" is generated. Step 4: The providing unit provides the player with the advice generated by the generating unit. The advice is provided in the form of text or voice. For example, the generated advice is displayed on a screen. The advice can also be provided by voice using speech synthesis technology. This makes it possible to provide accurate advice to the player. Some or all of the processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the advice can be provided using an AI model that takes the advice generated by the generating unit as input and outputs advice.

[0067] (Example 2) A game advice system according to an embodiment of the present invention collects game data, analyzes it using an AI, and provides appropriate advice to a player. The game advice system collects game data, analyzes the game situation in real time based on the data learned by the AI, and provides optimal advice to the player. For example, the game advice system collects player operation data and in-game event data. For example, data such as what operations the player performed and what events occurred in the game is collected. Next, the game advice system trains the AI ​​to learn the collected data. The AI ​​analyzes the collected data to learn the game progress and the player's behavioral patterns. For example, it learns what results will occur in the game when the player performs a specific operation. Furthermore, the game advice system analyzes the game situation in real time based on the data learned by the AI. The AI ​​monitors the player's operations and in-game events in real time to grasp the current game situation. For example, if the player is attacked by an enemy character, the AI ​​immediately analyzes the situation and provides appropriate advice. Finally, the game advice system provides optimal advice to the player based on the analysis results. The AI ​​generates advice based on the current game situation and presents it to the player. For example, if a player is attacked by an enemy character, the AI ​​will provide advice such as "strengthen your defense" or "take evasive action." This allows the game advice system to provide players with appropriate advice tailored to their game progress, thereby improving their game performance. This allows players to provide appropriate advice tailored to their game progress, thereby improving their game performance. For example, a beginner player can learn basic game controls and strategies by receiving advice from the AI. Furthermore, an advanced player can execute more advanced strategies by receiving advice from the AI. This allows all players to enjoy the game more.

[0068] A game advice system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects game data. The game data includes, but is not limited to, player operation data and in-game event data. The collection unit collects data such as what operations the player performed and what events occurred in the game. The collection unit can also collect player operation data in real time. For example, the collection unit records the timing at which the player presses a button and the type of operation. The collection unit can also collect in-game event data. For example, the collection unit records information about specific events that occur in the game. The analysis unit analyzes the data collected by the collection unit. The analysis aims to understand the progress of the game and the player's behavioral patterns based on the collected data. For example, the analysis unit analyzes what results occur in the game when the player performs a specific operation. The analysis unit can also analyze the player's behavioral patterns. For example, the analysis unit analyzes what operations the player frequently performs and what options the player chooses. The analysis unit can also analyze the progress of the game. For example, the analysis unit analyzes the player's score and determines which stages the player has cleared. The generation unit generates advice based on the analysis results obtained by the analysis unit. The advice may be provided in the form of, for example, text or audio, but is not limited to these examples. For example, the generation unit generates advice such as "strengthen your defenses" or "take evasive action" based on the analysis results. The generation unit can also generate advice based on the player's behavioral patterns. For example, the generation unit generates advice such as "that operation is effective" for an operation frequently performed by the player. Furthermore, the generation unit can also generate advice based on the progress of the game. For example, when the player clears a specific stage, the generation unit generates advice such as "this strategy will be effective in the next stage." The provision unit provides the advice generated by the generation unit to the player. The provision is performed in the form of, for example, text or audio, but is not limited to these examples.For example, the providing unit displays the generated advice on a screen. The providing unit can also play back the generated advice by voice. For example, the providing unit provides the advice by voice using voice synthesis technology. This allows the game advice system according to the embodiment to provide accurate advice to the player. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide advice using an AI model that receives the advice generated by the generating unit as input and outputs advice.

[0069] The collection unit can collect player operation data or in-game event data. Operation data includes, but is not limited to, button press information and operation timing. For example, the collection unit records the timing at which the player presses a button and the type of operation. The collection unit can also collect in-game event data. For example, the collection unit records information about specific events that occur in the game. By collecting player operation data and in-game event data, a dataset for AI learning can be constructed. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input player operation data into a generation AI and have the generation AI analyze the operation data.

[0070] The analysis unit analyzes the collected data to understand the progress of the game and the player's behavioral patterns. The analysis unit, for example, understands the progress of the game based on the collected data. For example, the analysis unit analyzes which stages the player has cleared and the player's score. The analysis unit can also analyze the player's behavioral patterns. For example, the analysis unit analyzes what operations the player frequently performs and what options the player chooses. The analysis unit can also analyze what results occur in the game when the player performs a specific operation. For example, the analysis unit analyzes what events occur in the game when the player presses a specific button. In this way, by analyzing the collected data, the progress of the game and the player's behavioral patterns can be understood. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and cause the generation AI to analyze the progress of the game and the player's behavioral patterns.

[0071] The generation unit can generate advice in text or voice based on the analysis results. The generation unit generates advice in text based on the analysis results. For example, the generation unit generates advice such as "strengthen your defenses" or "take evasive action." The generation unit can also generate advice in voice based on the analysis results. For example, the generation unit provides advice in voice using voice synthesis technology. The generation unit can also generate advice based on the player's behavioral patterns. For example, the generation unit generates advice such as "that operation is effective" for an operation frequently performed by the player. In this way, appropriate advice can be provided to the player by generating advice in text or voice based on the analysis results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit inputs the analysis results into a generation AI and causes the generation AI to generate advice.

[0072] The providing unit can provide the generated advice to the player. The providing unit, for example, displays the generated advice on a screen. For example, the providing unit displays the generated advice in text. The providing unit can also play the generated advice as audio. For example, the providing unit provides the advice as audio using voice synthesis technology. By providing the generated advice to the player, the player can receive appropriate advice according to the progress of the game. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide advice using an AI model that receives the advice generated by the generating unit as input and outputs advice.

[0073] The providing unit may include a method for displaying advice in text and a method for providing advice in audio. The providing unit, for example, displays the generated advice in text on a screen. For example, the providing unit displays the generated advice at a specific position on the screen. The providing unit may also play the generated advice in audio. For example, the providing unit may provide the advice in audio using speech synthesis technology. By providing both a method for displaying advice in text and a method for providing advice in audio, it is possible to provide advice according to the player's preferences. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may provide advice using an AI model that receives the advice generated by the generating unit as input and outputs advice.

[0074] The collection unit can estimate the player's emotions and adjust the timing of data collection based on the estimated player's emotions. For example, if the player is excited, the collection unit can collect data in real time and immediately analyze it. Alternatively, if the player is relaxed, the collection unit can collect data at regular intervals to reduce the load. Furthermore, if the player is stressed, the collection unit can reduce the frequency of data collection to avoid disrupting the player's concentration. This allows for more appropriate data collection by adjusting the timing of data collection according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the player's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0075] The collection unit can analyze the player's past operation history and select an appropriate data collection method. For example, the collection unit can prioritize collection of operations that the player frequently performed in the past. The collection unit can also prioritize collection of important operation data based on the success rate of the player's past operations. Furthermore, the collection unit can predict operations that will be performed during a specific time period from the player's past operation history and concentrate data collection during that time period. In this way, the optimal data collection method can be selected by analyzing the player's past operation history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the player's past operation history data into a generation AI and have the generation AI select a data collection method.

[0076] When collecting data, the collection unit can filter the data based on the player's current game progress. For example, when the player enters a boss battle, the collection unit prioritizes collecting data related to that battle. Also, when the player is exploring, the collection unit can collect data related to the exploration and postpone battle data. Furthermore, immediately after the player completes a mission, the collection unit can collect data related to that mission and prepare for the next mission. By filtering data based on the player's current game progress, more relevant data can be collected. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the player's game progress data to a generation AI and have the generation AI perform data filtering.

[0077] When collecting data, the collection unit can select the optimal collection means depending on the player's input method. For example, if the player is using voice input, the collection unit can prioritize collecting voice data. Furthermore, if the player is using text input, the collection unit can also prioritize collecting text data. Furthermore, if the player is using gesture input, the collection unit can also prioritize collecting gesture data. This enables efficient data collection by selecting the optimal collection means depending on the player's input method. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the player's input method data to the generation AI and cause the generation AI to select the optimal collection means.

[0078] The collection unit can estimate the player's emotions and determine the priority of data to be collected based on the estimated player's emotions. For example, if the player is excited, the collection unit can prioritize collecting battle data. Furthermore, if the player is relaxed, the collection unit can prioritize collecting exploration data. Furthermore, if the player is stressed, the collection unit can prioritize collecting data related to the player's operational errors. By determining the priority of data to be collected based on the player's emotions, more important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the player's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0079] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the player's geographical location information. For example, if the player is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, if the player is traveling, the collection unit can prioritize collecting data related to the player's movement. Furthermore, if the player is participating in a specific event, the collection unit can prioritize collecting data related to the event. In this way, by collecting data by taking into account the player's geographical location information, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the player's geographical location information data to the generation AI and cause the generation AI to collect highly relevant data.

[0080] When collecting data, the collection unit can analyze the player's social media activities and collect related data. For example, the collection unit can collect gameplay videos shared by the player on social media. The collection unit can also analyze the player's social media posts and collect related game data. Furthermore, the collection unit can collect related data by referring to the activities of the player's friends on social media. In this way, related data can be collected by analyzing the player's social media activities. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the player's social media activity data into the generation AI and cause the generation AI to collect related data.

[0081] The collection unit can customize the collection method by reflecting the player's past feedback when collecting data. For example, the collection unit adjusts the type of data to collect based on feedback provided by the player in the past. The collection unit can also prioritize the use of a specific data collection method based on the player's past feedback. Furthermore, the collection unit can adjust the frequency and timing of data collection by referring to the player's feedback. In this way, the collection method can be customized by reflecting the player's past feedback. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the player's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0082] The analysis unit can estimate the player's emotions and adjust the analysis criteria based on the estimated player's emotions. For example, if the player is excited, the analysis unit can prioritize analyzing battle data. Furthermore, if the player is relaxed, the analysis unit can prioritize analyzing exploration data. Furthermore, if the player is stressed, the analysis unit can prioritize analyzing data related to operational errors. This allows for more appropriate analysis by adjusting the analysis criteria according to the player's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the player's emotion data into the generation AI and have the generation AI adjust the analysis criteria.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account interrelationships within the game. For example, the analysis unit can analyze the interrelationships between player operations and in-game events to improve accuracy. The analysis unit can also analyze the interrelationships between player behavior patterns and in-game results to improve accuracy. Furthermore, the analysis unit can analyze the interrelationships between player choices and in-game progress to improve accuracy. In this way, the accuracy of the analysis is improved by taking into account the interrelationships within the game. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input in-game interrelationship data into a generation AI and cause the generation AI to improve the accuracy of the analysis.

[0084] The analysis unit can perform the analysis while taking into account the player's attribute information. The analysis unit can perform the analysis while taking into account, for example, the player's age and gender. The analysis unit can also perform the analysis while taking into account the player's gaming experience and skill level. Furthermore, the analysis unit can perform the analysis while taking into account the player's interests and concerns. This allows for more personalized analysis by taking into account the player's attribute information. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the player's attribute information data into the generation AI and have the generation AI perform the analysis.

[0085] During analysis, the analysis unit can weight the analysis based on the progress of the game. For example, in the early stages of the game, the analysis unit can emphasize analysis of basic operation data. In addition, in the middle stages of the game, the analysis unit can emphasize analysis of strategic data. Furthermore, in the late stages of the game, the analysis unit can emphasize analysis of the player's final behavioral patterns. In this way, weighting the analysis based on the progress of the game enables analysis that focuses on more important data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input game progress data to the generation AI and have the generation AI perform the weighting of the analysis.

[0086] The analysis unit can estimate the player's emotions and adjust the display method of the analysis results based on the estimated player's emotions. For example, if the player is excited, the analysis unit can provide a visually stimulating display method. Furthermore, if the player is relaxed, the analysis unit can provide a calming display method. Furthermore, if the player is stressed, the analysis unit can provide a simple, highly visible display method. This allows for more appropriate display by adjusting the display method of the analysis results according to the player's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the player's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0087] The analysis unit can perform the analysis while taking into account the geographical distribution within the game. For example, the analysis unit analyzes the behavioral patterns of players in a specific area within the game. The analysis unit can also analyze player movement data while taking into account geographical factors within the game. Furthermore, the analysis unit can analyze player strategies based on the geographical distribution within the game. This allows for more detailed analysis by taking into account the geographical distribution within the game. Some or all of the above-described processing by the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the geographical distribution data within the game to a generation AI and have the generation AI perform the analysis.

[0088] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related game data. The analysis unit can improve the accuracy of the analysis by, for example, referring to data of other players. The analysis unit can also improve the accuracy of the analysis by referring to past game data. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to data provided by the game developer. In this way, the accuracy of the analysis is improved by referring to related game data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input related game data to the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0089] The analysis unit can perform the analysis taking into account the market value of the game. The analysis unit can perform the analysis taking into account, for example, the popularity of the game. The analysis unit can also perform the analysis taking into account sales data of the game. Furthermore, the analysis unit can also perform the analysis taking into account evaluation data of the game. By taking the market value of the game into account, analysis can be performed from a more business perspective. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input market value data of the game into the generation AI and have the generation AI perform the analysis.

[0090] The generation unit can estimate the player's emotions and adjust the way advice is expressed based on the estimated player's emotions. For example, if the player is excited, the generation unit can provide advice using positive expressions. If the player is relaxed, the generation unit can also provide advice using calm expressions. If the player is stressed, the generation unit can also provide advice using simple and clear expressions. This allows for more effective advice to be provided by adjusting the way advice is expressed according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the way advice is expressed.

[0091] When generating advice, the generation unit can adjust the level of detail of the advice based on the importance of the game. For example, the generation unit provides detailed advice in important scenes. The generation unit can also provide concise advice in less important scenes. Furthermore, the generation unit can adjust the level of detail of the advice according to the player's progress. In this way, by adjusting the level of detail of the advice based on the importance of the game, more appropriate advice can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input game importance data to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0092] When generating advice, the generation unit can apply different advice algorithms depending on the game category. For example, the generation unit applies an algorithm that provides strategic advice to an RPG game. The generation unit can also apply an algorithm that provides advice regarding real-time operations to an action game. The generation unit can also apply an algorithm that provides advice regarding solutions to a puzzle game. In this way, by applying different advice algorithms depending on the game category, more appropriate advice can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input game category data to the generation AI and cause the generation AI to apply the advice algorithm.

[0093] When generating advice, the generation unit can improve the accuracy of the advice by referring to the player's past advice results. The generation unit, for example, analyzes the effects of advice the player has received in the past and improves the accuracy. The generation unit can also provide optimal advice based on the player's past advice history. Furthermore, the generation unit can also adjust the content of the advice by referring to the player's past advice results. In this way, the accuracy of the advice is improved by referring to the player's past advice results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the player's past advice result data into the generation AI and cause the generation AI to improve the accuracy of the advice.

[0094] The generation unit can estimate the player's emotions and adjust the length of advice based on the estimated player's emotions. For example, if the player is excited, the generation unit can provide short, concise advice. If the player is relaxed, the generation unit can also provide detailed advice. If the player is stressed, the generation unit can also provide simple, clear advice. By adjusting the length of advice according to the player's emotions, more effective advice can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the length of the advice.

[0095] When generating advice, the generation unit can determine the priority of the advice based on the progress of the game. For example, during an important battle, the generation unit can prioritize providing advice related to the battle. Furthermore, during exploration, the generation unit can also prioritize providing advice related to the exploration. Furthermore, immediately after a mission is completed, the generation unit can prioritize providing advice related to the next mission. In this way, by determining the priority of advice based on the progress of the game, more appropriate advice can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input game progress data to the generation AI and cause the generation AI to determine the priority of the advice.

[0096] When generating advice, the generation unit can adjust the order of advice based on the relevance of the game. For example, the generation unit first provides advice related to the task the player is currently performing. The generation unit can also provide advice related to the next task the player should perform next. Furthermore, the generation unit can also provide advice related to a task the player has previously performed last. In this way, by adjusting the order of advice based on the relevance of the game, more appropriate advice can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input game relevance data to the generation AI and cause the generation AI to adjust the order of advice.

[0097] When generating advice, the generation unit can adjust the use of technical terminology in the advice according to the player's level of expertise. For example, the generation unit can provide advice to a beginner player in simple language, avoiding technical terminology. The generation unit can also provide advice to an intermediate player using technical terminology appropriately. Furthermore, the generation unit can provide detailed advice to an advanced player using a lot of technical terminology. In this way, by adjusting the use of technical terminology in the advice according to the player's level of expertise, more understandable advice can be provided. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input the player's level of expertise data into the generation AI and cause the generation AI to use technical terminology in the advice.

[0098] The providing unit can estimate the player's emotions and adjust the way in which advice is provided based on the estimated player's emotions. For example, if the player is excited, the providing unit can provide advice in a visually stimulating manner. Furthermore, if the player is relaxed, the providing unit can provide advice in a calm manner. Furthermore, if the player is stressed, the providing unit can provide advice in a simple and clear manner. By adjusting the way in which advice is provided according to the player's emotions, more effective advice can be provided. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the way in which advice is provided.

[0099] When providing advice, the providing unit can select the optimal advice providing method by referring to the player's past operation history. For example, the providing unit can prioritize the use of a providing method (text, voice, etc.) that the player has previously preferred. The providing unit can also select a providing method that was effective in a specific situation from the player's past operation history. Furthermore, the providing unit can provide advice at the optimal timing based on the player's past operation history. In this way, the optimal advice providing method can be selected by referring to the player's past operation history. Some or all of the above-described processing by the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the player's past operation history data into the generating AI and cause the generating AI to select the optimal advice providing method.

[0100] When providing advice, the providing unit can customize the content to be provided according to the player's current task. For example, if the player is in combat, the providing unit can provide advice about combat. Furthermore, if the player is exploring, the providing unit can also provide advice about exploration. Furthermore, if the player has just completed a mission, the providing unit can provide advice about the next mission. In this way, by customizing the content to be provided according to the player's current task, more appropriate advice can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the player's current task data into the generating AI and cause the generating AI to customize the content to be provided.

[0101] The providing unit can improve the advice providing method by reflecting the player's feedback when providing advice. For example, if the player provides positive feedback in response to the advice provided, the providing unit continues the advice providing method. In addition, if the player provides negative feedback in response to the advice provided, the providing unit can change the advice providing method. Furthermore, the providing unit can adjust the content and timing of the advice based on the player's feedback. In this way, the advice providing method can be improved by reflecting the player's feedback. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the player's feedback data into the generating AI and cause the generating AI to improve the advice providing method.

[0102] The providing unit can estimate the player's emotions and adjust the advice providing procedure based on the estimated player's emotions. For example, if the player is excited, the providing unit can provide quick advice. Furthermore, if the player is relaxed, the providing unit can provide detailed advice. Furthermore, if the player is stressed, the providing unit can provide simple and clear advice. By adjusting the advice providing procedure according to the player's emotions, more effective advice can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using an AI, for example, or without an AI. For example, the providing unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the advice providing procedure.

[0103] When providing advice, the providing unit can select the optimal advice providing method by taking into consideration the player's device information. For example, if the player is using a smartphone, the providing unit can select a advice providing method that matches the screen size. Furthermore, if the player is using a tablet, the providing unit can select a advice providing method optimized for a large screen. Furthermore, if the player is using a PC, the providing unit can select a high-resolution advice providing method. In this way, the optimal advice providing method can be selected by taking into consideration the player's device information. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the player's device information data into the generating AI and cause the generating AI to select the optimal advice providing method.

[0104] When providing advice, the providing unit can make the advice content multilingual based on the player's language setting. The providing unit, for example, automatically sets the language of the advice based on the language setting of the player's device. The providing unit can also provide a language switching function when the player uses multiple languages. Furthermore, if the player selects a specific language, the providing unit can provide advice in that language. This allows the advice content to be multilingual based on the player's language setting, making it possible to accommodate a wider range of players. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the player's language setting data into a generating AI and cause the generating AI to execute the multilingual advice content.

[0105] When providing advice, the providing unit can customize the advice to be provided based on the player's gameplay style. For example, if the player has an aggressive play style, the providing unit can provide aggressive advice. Furthermore, if the player has a defensive play style, the providing unit can provide defensive advice. Furthermore, if the player has a balanced play style, the providing unit can provide balanced advice. In this way, by customizing the advice to be provided based on the player's gameplay style, more appropriate advice can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the player's gameplay style data to a generation AI and cause the generation AI to customize the advice to be provided. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects player operation data and in-game event data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to understand the progress of the game and the player's behavioral patterns. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates advice based on the analysis results. The provision unit is realized by the control unit 46A of the smart device 14 and provides the generated advice to the player. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects player operation data and in-game event data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to understand the progress of the game and the player's behavioral patterns. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates advice based on the analysis results. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the generated advice to the player. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects player operation data and in-game event data using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to understand the progress of the game and the player's behavioral patterns. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates advice based on the analysis results. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides the generated advice to the player. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects player operation data and in-game event data using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to understand the progress of the game and the player's behavioral patterns. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates advice based on the analysis results. The provision unit is realized by the control unit 46A of the robot 414 and provides the generated advice to the player.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The analysis unit can analyze the reaction speed of the player based on the player's operation data. For example, the analysis unit measures the time it takes for the player to press a button and analyzes the reaction speed. The analysis unit can also measure the time it takes for the player to perform a specific operation and analyze the reaction speed. Furthermore, the analysis unit can analyze the player's operation pattern based on the player's reaction speed. In this way, by analyzing the player's reaction speed, the player's operation pattern can be understood in more detail.

[0108] The collection unit can take into consideration the type of device used by the player when collecting the player's operation data. For example, if the player is using a smartphone, the collection unit can prioritize collecting touch operation data. Also, if the player is using a PC, the collection unit can prioritize collecting keyboard and mouse operation data. Furthermore, if the player is using a game controller, the collection unit can prioritize collecting controller operation data. This makes it possible to collect optimal operation data depending on the type of device used by the player.

[0109] When analyzing a player's behavioral patterns, the analysis unit can refer to the player's past gameplay data. For example, the analysis unit can analyze what operations the player has performed in the past to understand the player's current behavioral patterns. The analysis unit can also analyze what options the player has chosen in the past to predict what options the player will choose in the future. Furthermore, the analysis unit can analyze what results the player has achieved in the past to predict what the current results will be. In this way, by referring to the player's past gameplay data, more accurate analysis of the player's behavioral patterns is possible.

[0110] The generation unit can take into consideration the player's current game progress when generating advice. For example, when the player enters a boss battle, the generation unit generates advice about the boss battle. In addition, when the player is exploring, the generation unit can also generate advice about exploration. Furthermore, the generation unit can also generate advice about the next mission immediately after the player completes a mission. In this way, appropriate advice can be generated according to the player's current game progress.

[0111] The providing unit may take into consideration the player's current emotional state when providing the generated advice to the player. For example, if the player is excited, the providing unit may provide the advice in a visually stimulating manner. If the player is relaxed, the providing unit may provide the advice in a calm manner. Furthermore, if the player is stressed, the providing unit may provide the advice in a simple and clear manner. This makes it possible to provide appropriate advice according to the player's emotional state.

[0112] The collection unit can estimate the player's emotions and adjust the frequency of data collection based on the estimated player's emotions. For example, the collection unit can increase the frequency of data collection when the player is excited. The collection unit can also decrease the frequency of data collection when the player is relaxed. Furthermore, the collection unit can adjust the frequency of data collection when the player is feeling stressed, thereby reducing the burden on the player. This makes it possible to collect appropriate data according to the player's emotions.

[0113] The analysis unit can estimate the player's emotions and determine the priority of analysis based on the estimated player's emotions. For example, if the player is excited, the analysis unit can prioritize analysis of battle data. Alternatively, if the player is relaxed, the analysis unit can prioritize analysis of exploration data. Furthermore, if the player is stressed, the analysis unit can prioritize analysis of data related to operational errors. This allows for appropriate analysis according to the player's emotions.

[0114] The generation unit can estimate the player's emotions and adjust the content of the advice based on the estimated player's emotions. For example, the generation unit can provide aggressive advice when the player is excited. The generation unit can also provide calm advice when the player is relaxed. Furthermore, the generation unit can also provide simple and clear advice when the player is stressed. This makes it possible to provide appropriate advice according to the player's emotions.

[0115] The providing unit can estimate the player's emotions and adjust the timing of providing advice based on the estimated player's emotions. For example, if the player is excited, the providing unit can provide advice immediately. Also, if the player is relaxed, the providing unit can provide advice at an appropriate timing. Furthermore, if the player is feeling stressed, the providing unit can adjust the timing of providing advice to reduce the burden on the player. This makes it possible to provide appropriate advice according to the player's emotions.

[0116] The collection unit can take the player's gameplay style into consideration when collecting the player's operation data. For example, if the player has an aggressive play style, the collection unit can prioritize collecting aggressive operation data. Also, if the player has a defensive play style, the collection unit can prioritize collecting defensive operation data. Furthermore, if the player has a balanced play style, the collection unit can prioritize collecting balanced operation data. This makes it possible to collect optimal operation data according to the player's gameplay style.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects game data. Game data includes player operation data and in-game event data. For example, the collection unit collects data such as what operations the player performed and what events occurred in the game. The collection unit can also collect player operation data in real time. For example, it can record the timing when the player pressed a button and the type of operation. Furthermore, the collection unit can also collect in-game event data. For example, it can record information about specific events that occurred in the game. Step 2: The analysis unit analyzes the data collected by the collection unit. The purpose of the analysis is to understand the game progress and the player's behavioral patterns based on the collected data. For example, it analyzes what results occur in the game when the player performs a specific operation. It can also analyze the player's behavioral patterns. For example, it analyzes what operations the player frequently performs and what options the player chooses. It can also analyze the game progress. For example, it analyzes which stages the player has cleared and the player's score. Step 3: The generator generates advice based on the analysis results obtained by the analyzer. The advice is provided in text or audio format. For example, advice such as "strengthen your defenses" or "take evasive action" is generated based on the analysis results. Advice can also be generated based on the player's behavioral patterns. For example, advice such as "That action is effective" is generated for an action that the player frequently performs. Advice can also be generated based on the player's progress in the game. For example, when the player clears a specific stage, advice such as "This strategy is effective in the next stage" is generated. Step 4: The providing unit provides the player with the advice generated by the generating unit. The advice is provided in the form of text or voice. For example, the generated advice is displayed on a screen. The advice can also be provided by voice using speech synthesis technology. This makes it possible to provide accurate advice to the player. Some or all of the processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the advice can be provided using an AI model that takes the advice generated by the generating unit as input and outputs advice.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects game data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates advice based on the analysis result obtained by the analysis unit; a providing unit that provides the advice generated by the generating unit to the player. A system characterized by:

2. The collecting unit Collecting player action data or in-game event data 2. The system of claim 1.

3. The analysis unit Analyze the collected data to understand the game progress and player behavior patterns 2. The system of claim 1.

4. The generation unit Generate advice in text or voice based on the analysis results 2. The system of claim 1.

5. The providing unit Providing generated advice to the player 2. The system of claim 1.

6. The providing unit Provides a way to display advice in text and a way to provide advice in audio 2. The system of claim 1.

7. The collecting unit Estimate player emotions and adjust data collection timing based on the estimated player emotions 2. The system of claim 1.

8. The collecting unit Analyze players' past behavior and select the appropriate data collection method 2. The system of claim 1.

Images