System

Abstract

An object of the system according to the embodiment is to generate a game story in real time based on an action or preference of a player.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects behavior data or preference data of the player. The analysis unit analyzes the data collected by the collection unit and learns a behavior pattern or a preference of the player. The generation unit generates a game story in real time based on a result learned by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to generate a game story in real time based on the player's behavior and preferences.

[0005] The system according to the embodiment aims to generate a game story in real time based on the player's actions and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects player behavioral data or preference data. The analysis unit analyzes the data collected by the collection unit to learn the player's behavioral patterns or preferences. The generation unit generates a game story in real time based on the results learned by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate a game story in real time based on the player's actions and preferences. [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 development and sales system according to an embodiment of the present invention is a system that learns player behavior and preferences and generates a game story in real time based on the player's behavior and preferences. The game development and sales system collects player behavioral data and preference data, analyzes this data using AI to learn the player's behavioral patterns and preferences, and generates a game story in real time based on the learned results. This mechanism allows players to enjoy a unique gaming experience that is unique to them. The game development and sales system can also provide custom gaming experiences for corporate events and PR campaigns. For example, it can provide advertisements promoting corporate products and services in the game. Furthermore, it is possible to collaborate with other industries to form business partnerships, and these promotions can also be reflected in the game. For example, collaboration with the film industry can incorporate movie characters and stories into the game. This allows players to enjoy a more diverse gaming experience. This allows the game development and sales system to generate a game story in real time based on the player's behavior and preferences. For example, by collecting player behavioral data and preference data, and using AI to analyze this data to learn the player's behavioral patterns and preferences, it can generate a game story in real time based on the learned results, allowing players to enjoy a unique gaming experience that is unique to them. In addition, it can provide custom gaming experiences for corporate events and PR campaigns, and provide in-game advertising to promote corporate products and services. Furthermore, it is possible to collaborate with other industries and have business partnerships reflected in the game. This allows players to enjoy a more diverse gaming experience.

[0029] A game development and sales system according to an embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects player behavior data or preference data. The player behavior data includes, but is not limited to, the player's movement history and operation history. For example, the collection unit collects the player's movement history as GPS data. The collection unit can also collect the player's operation history as log data. The collection unit also collects player preference data. The player preference data includes, but is not limited to, the player's selection history and preferred game genre. For example, the collection unit stores the player's selection history in a database. The collection unit can also collect the player's preferred game genre through a questionnaire survey. The analysis unit analyzes the data collected by the collection unit to learn the player's behavior patterns or preferences. The analysis is performed using, for example, but is not limited to, a machine learning algorithm. The analysis unit analyzes the player's behavior patterns using, for example, a machine learning algorithm. The analysis unit can also analyze the data using a data preprocessing method. The analysis unit also analyzes the player's behavioral patterns. Examples of behavioral patterns include, but are not limited to, frequency analysis and sequence analysis. The analysis unit, for example, analyzes the player's behavioral patterns using frequency analysis. The analysis unit can also analyze the player's behavioral patterns using sequence analysis. The generation unit generates a game story in real time based on the results learned by the analysis unit. The real-time processing is performed, for example, within an allowable delay time range, but is not limited to, an example. The generation unit, for example, performs real-time processing within an allowable delay time range. The generation unit can also generate the game story using real-time processing technology. The generation unit also generates the game story. Examples of game story generation include, but are not limited to, the use of a story template or a dynamic generation algorithm. The generation unit generates the game story using, for example, a story template. The generation unit can also generate the game story using a dynamic generation algorithm.As a result, the game development and sales system according to the embodiment can generate a game story in real time based on the player's behavior and preferences. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI (such as a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit inputs the player's behavioral patterns and preference data into the generation AI, which then generates a game story in real time.

[0030] The generation unit can provide a custom game experience for a corporate event or PR campaign. The custom game experience can include, for example, customization elements for each player and event types, but is not limited to these examples. For example, the generation unit can provide customization elements for each player for a corporate event. The generation unit can also provide specific event types for a PR campaign. This makes it possible to provide a custom game experience for a corporate event or PR campaign. Some or all of the above-described processing by the generation unit can be performed using, for example, a generation AI (such as a text generation AI or a multimodal generation AI), or can be performed without using a generation AI. For example, the generation unit can input data about a corporate event or PR campaign into the generation AI, which can then generate a custom game experience.

[0031] The generation unit can provide advertisements promoting the company's products and services within the game. Examples of advertisements include, but are not limited to, banner advertisements and interactive advertisements. For example, the generation unit can provide banner advertisements within the game. The generation unit can also provide interactive advertisements. This makes it possible to provide advertisements promoting the company's products and services within the game. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI (such as a text generation AI or a multimodal generation AI), or can be performed without using a generation AI. For example, the generation unit can input data on the company's products and services into the generation AI, which then generates the advertisement.

[0032] The generation unit can reflect business partnerships formed through collaboration with other industries. Business partnerships include, for example, joint projects and specific examples of the partnership content, but are not limited to these examples. The generation unit, for example, reflects joint projects with other industries. The generation unit can also reflect specific examples of the partnership content. This makes it possible to reflect business partnerships formed through collaboration with other industries. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI (such as a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit can input collaboration data with other industries into the generation AI, which then reflects the business partnership.

[0033] The generation unit can incorporate movie characters and stories into the game through collaboration with the movie industry. Movie characters include, but are not limited to, the use of character rights and roles in the game. For example, the generation unit reflects the use of movie character rights. The generation unit can also reflect the role of the character in the game. Movie stories include, but are not limited to, methods of incorporating parts of the story into the game and altering the story. For example, the generation unit incorporates parts of the movie story into the game. The generation unit can also alter the story. This allows movie characters and stories to be incorporated into the game through collaboration with the movie industry. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI (such as a text generation AI or a multimodal generation AI) or without using a generation AI. For example, the generation unit inputs movie character and story data into the generation AI, which then incorporates the data into the game.

[0034] The collection unit can analyze the player's past game play history and select the optimal data collection method. For example, the collection unit can prioritize collection of related data based on the player's past favorite game genres. The collection unit can also analyze the player's past play time periods and concentrate data collection on those time periods. The collection unit can also analyze the player's past in-game behavior patterns and collect data when specific behaviors occur. This enables efficient data collection by selecting the optimal data collection method based on the player's past game play history. 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 past game play history data into a generation AI, which can select the optimal data collection method.

[0035] When collecting behavioral data and preference data, the collection unit can filter the data based on the player's current game progress. For example, when the player is in the early stages of the game, the collection unit collects basic behavioral data. Furthermore, when the player is in the middle stages of the game, the collection unit can collect more detailed preference data. Furthermore, when the player is in the late stages of the game, the collection unit can focus on collecting specific behavioral data. This enables appropriate data collection by filtering data based on the player's current game progress. Some or all of the above-described processing in 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 game progress data to a generation AI, which then performs filtering.

[0036] When collecting behavioral data and preference data, the collection unit can select the optimal collection means depending on the player's input method. For example, if the player uses voice input, the collection unit can prioritize collecting voice data. Furthermore, if the player uses text input, the collection unit can also prioritize collecting text data. Furthermore, if the player uses 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 in 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 a generation AI, which can select the optimal collection means.

[0037] When collecting behavioral data and preference 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 travel destination. Furthermore, if the player is at home, the collection unit can prioritize collecting data related to the player's home. This enables more appropriate data collection by prioritizing the collection of highly relevant data by taking into account the player's geographical location information. 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 the player's geographical location information data into the generation AI, which can then prioritize collecting highly relevant data.

[0038] When collecting behavioral data and preference data, the collection unit can analyze the player's social media activities and collect related data. For example, the collection unit can collect related data based on gameplay content shared by the player on social media. The collection unit can also analyze the player's friendships on social media and collect shared preference data. The collection unit can also analyze the player's social media posts and collect related behavioral data. This enables more extensive data collection by analyzing the player's social media activities and collecting related data. Some or all of the above-described 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 a generation AI, which can collect related data.

[0039] When collecting behavioral data and preference data, the collection unit can customize the collection method by reflecting the player's past feedback. For example, the collection unit adjusts the collection method based on feedback provided by the player in the past. The collection unit can also prioritize the use of specific data collection means based on the player's past feedback. The collection unit can also analyze the player's past feedback and customize the type of collected data. This enables more appropriate data collection by customizing the collection method 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, which can then customize the collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the player's behavioral data. For example, the analysis unit can perform a detailed analysis on behavioral data with high importance. The analysis unit can also perform a simplified analysis on behavioral data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on behavioral data with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the player's behavioral data. Some or all of the above-described 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 player's behavioral data to a generation AI, which can adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of player behavior data. For example, the analysis unit can apply an analysis algorithm dedicated to combat to combat behavior data. The analysis unit can also apply an analysis algorithm dedicated to exploration to exploration behavior data. The analysis unit can also apply an analysis algorithm dedicated to interaction to interaction behavior data. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of player behavior data. 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 player behavior data to a generation AI, which can then apply different analysis algorithms.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the player's past analysis results. For example, the analysis unit reflects feedback in the current analysis based on the player's past analysis results. The analysis unit can also extract specific behavioral patterns from the player's past analysis results and use them in the current analysis. The analysis unit can also analyze the player's past analysis results and optimize the analysis algorithm. This improves the accuracy of the analysis by referring to the player's past analysis results. 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 player's past analysis result data into the generation AI, which can improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the player's behavioral data. For example, the analysis unit prioritizes analysis of the most recent behavioral data. The analysis unit can also adjust the priority based on the time of submission while referring to past behavioral data. The analysis unit can also prioritize analysis of behavioral data submitted within a specific period. This enables efficient analysis by determining the priority of analysis based on the time of submission of the player's behavioral data. Some or all of the above-described 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 data on the time of submission of the player's behavioral data to the generation AI, and the generation AI can determine the priority of analysis.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the player's behavioral data. For example, the analysis unit prioritizes analysis of highly relevant behavioral data. The analysis unit can also postpone analysis of low-relevance behavioral data. The analysis unit can also analyze moderately relevant behavioral data in an appropriate order. In this way, adjusting the order of analysis based on the relevance of the player's behavioral data enables analysis in an appropriate order. Some or all of the above-described 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 relevance data of the player's behavioral data to the generation AI, and the generation AI can adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the player's level of expertise. For example, if the player is a beginner, the analysis unit can provide analysis results that avoid technical terminology. Furthermore, if the player is an intermediate player, the analysis unit can provide analysis results using appropriate technical terminology. Furthermore, if the player is an advanced player, the analysis unit can provide analysis results using detailed technical terminology. By adjusting the use of technical terminology in the analysis according to the player's level of expertise, appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the player's level of expertise data into the generation AI, which can then adjust the use of technical terminology.

[0046] When generating a game story, the generation unit can adjust the level of detail of the generation based on the importance of the player's behavior patterns. For example, the generation unit generates a detailed story based on behavior patterns with high importance. The generation unit can also generate a simplified story based on behavior patterns with low importance. The generation unit can also generate a story with an appropriate level of detail based on behavior patterns with medium importance. This enables efficient story generation by adjusting the level of detail of the generation based on the importance of the player's behavior patterns. 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 player behavior pattern data into a generation AI, which can adjust the level of detail of the generation.

[0047] When generating a game story, the generation unit can apply different generation algorithms depending on the category of the player's preference data. For example, the generation unit can apply a generation algorithm that emphasizes action elements to a player who prefers action. The generation unit can also apply a generation algorithm that emphasizes exploration elements to a player who prefers exploration. The generation unit can also apply a generation algorithm that emphasizes interaction elements to a player who prefers interaction. This enables more appropriate story generation by applying different generation algorithms depending on the category of the player's preference data. Some or all of the above-mentioned 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 preference data into a generation AI, and the generation AI can apply different generation algorithms.

[0048] When generating a game story, the generation unit can improve the accuracy of generation by referring to the player's past game story generation results. The generation unit, for example, reflects feedback in the current story generation based on the player's past game story generation results. The generation unit can also extract specific preference patterns from the player's past game story generation results and use them in the current story generation. The generation unit can also analyze the player's past game story generation results and optimize the generation algorithm. In this way, the accuracy of generation is improved by referring to the player's past game story generation 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 game story generation result data into the generation AI, which can improve the accuracy of generation.

[0049] When generating a game story, the generation unit can determine the generation priority based on the time of submission of the player's behavioral data. The generation unit can determine the priority of story generation based on, for example, the latest behavioral data. The generation unit can also adjust the priority based on the time of submission while referring to past behavioral data. The generation unit can also determine the priority of story generation based on behavioral data submitted within a specific period. This enables efficient story generation by determining the generation priority based on the time of submission of the player's behavioral data. 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 data on the time of submission of the player's behavioral data into the generation AI, and the generation AI can determine the generation priority.

[0050] When generating a game story, the generation unit can adjust the order of generation based on the relevance of the player's behavioral data. The generation unit, for example, determines the order of story generation based on highly relevant behavioral data. The generation unit can also postpone behavioral data with low relevance. The generation unit can also reflect behavioral data with medium relevance in story generation in an appropriate order. In this way, by adjusting the order of generation based on the relevance of the player's behavioral data, it is possible to generate a story in an appropriate order. 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 relevance data of the player's behavioral data into the generation AI, and the generation AI can adjust the order of generation.

[0051] When generating a game story, the generation unit can adjust the use of technical terminology in the generation according to the player's level of expertise. For example, if the player is a beginner, the generation unit can generate a story that avoids technical terminology. Furthermore, if the player is an intermediate player, the generation unit can generate a story using appropriate technical terminology. Furthermore, if the player is an advanced player, the generation unit can generate a story using detailed technical terminology. By adjusting the use of technical terminology in the generation according to the player's level of expertise, an appropriate story 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 AI. For example, the generation unit can input the player's level of expertise data into the generation AI, which can then adjust the use of technical terminology.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] When collecting player behavioral data, the collection unit can adjust the collection method taking into account the player's device usage status. For example, if the player is using a smartphone, the collection unit can use a data collection method optimized for smartphones. Also, if the player is using a PC, the collection unit can use a data collection method optimized for PCs. Furthermore, if the player uses multiple devices, the collection unit can also integrate and collect data from each device. This enables efficient data collection by adjusting the collection method based on the player's device usage status.

[0054] When analyzing the player's behavioral data, the analysis unit can adjust the analysis method by taking into account the player's social background. For example, if the player is a student, the analysis unit can analyze the data by taking into account the influence of his / her studies. Also, if the player is working, the analysis unit can analyze the data by taking into account the influence of his / her work. Furthermore, if the player has a family, the analysis unit can analyze the data by taking into account the influence of his / her family. In this way, by adjusting the analysis method based on the player's social background, more appropriate data analysis is possible.

[0055] When collecting player behavioral data, the collection unit can adjust the collection method taking into account the player's internet connection status. For example, if the player has a high-speed internet connection, the collection unit can collect detailed data frequently. On the other hand, if the player has a slow internet connection, the collection unit can reduce the frequency of data collection and collect the minimum amount of data necessary. Furthermore, if the player is offline, the collection unit can temporarily store the offline data and collect the data when the player is online. This allows for efficient data collection by adjusting the collection method based on the player's internet connection status.

[0056] When analyzing player behavior data, the analysis unit can adjust the analysis method by taking into account the player's cultural background. For example, if the player belongs to a specific cultural sphere, the data can be analyzed by taking into account behavioral patterns unique to that culture. Also, if the player has a multicultural background, the data can be analyzed by taking into account multiple cultural factors. Furthermore, if the player is engaged in cross-cultural exchange, the data can be analyzed by taking into account the influence of that exchange. This allows for more appropriate data analysis by adjusting the analysis method based on the player's cultural background.

[0057] When collecting player behavioral data, the collection unit can adjust the collection method taking into account the player's gameplay environment. For example, if the player is playing in a quiet environment, the collection unit can collect detailed audio data. Alternatively, if the player is playing in a noisy environment, the collection unit can refrain from collecting audio data and prioritize collecting other data. Furthermore, if the player is playing while moving, the collection unit can collect GPS data to understand the player's movement patterns. This allows for efficient data collection by adjusting the collection method based on the player's gameplay environment.

[0058] When analyzing a player's behavioral data, the analysis unit can adjust the analysis method taking into account the player's learning style. For example, if the player is a visual learner, the analysis unit can prioritize analyzing visual behavioral data. Also, if the player is an auditory learner, the analysis unit can prioritize analyzing auditory behavioral data. Furthermore, if the player is an experiential learner, the analysis unit can prioritize analyzing experiential behavioral data. This allows for more appropriate data analysis by adjusting the analysis method based on the player's learning style.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects player behavior data or preference data. The player behavior data includes, for example, the player's movement history and operation history. The collection unit collects the player's movement history as GPS data and the operation history as log data. The player preference data includes, for example, the player's selection history and preferred game genre. The collection unit saves the player's selection history in a database and collects the player's preferred game genre through a questionnaire survey. Step 2: The analysis unit analyzes the data collected by the collection unit and learns the player's behavioral patterns or preferences. The analysis is performed using, for example, a machine learning algorithm. The analysis unit analyzes the player's behavioral patterns using a machine learning algorithm and analyzes the data using a data preprocessing method. The behavioral patterns include, for example, frequency analysis and sequence analysis. The analysis unit analyzes the player's behavioral patterns using frequency analysis and sequence analysis. Step 3: The generation unit generates a game story in real time based on the results learned by the analysis unit. Real-time processing is performed, for example, within the allowable delay time range. The generation unit performs real-time processing within the allowable delay time range and generates the game story using real-time processing technology. The game story may include, for example, the use of a story template or a dynamic generation algorithm. The generation unit generates the game story using the story template or dynamic generation algorithm. Some or all of the processing in the generation unit may be performed using generation AI (such as text generation AI or multimodal generation AI).

[0061] (Example 2) A game development and sales system according to an embodiment of the present invention is a system that learns player behavior and preferences and generates a game story in real time based on the player's behavior and preferences. The game development and sales system collects player behavioral data and preference data, analyzes this data using AI to learn the player's behavioral patterns and preferences, and generates a game story in real time based on the learned results. This mechanism allows players to enjoy a unique gaming experience that is unique to them. The game development and sales system can also provide custom gaming experiences for corporate events and PR campaigns. For example, it can provide advertisements promoting corporate products and services in the game. Furthermore, it is possible to collaborate with other industries to form business partnerships, and these promotions can also be reflected in the game. For example, collaboration with the film industry can incorporate movie characters and stories into the game. This allows players to enjoy a more diverse gaming experience. This allows the game development and sales system to generate a game story in real time based on the player's behavior and preferences. For example, by collecting player behavioral data and preference data, and using AI to analyze this data to learn the player's behavioral patterns and preferences, it can generate a game story in real time based on the learned results, allowing players to enjoy a unique gaming experience that is unique to them. In addition, it can provide custom gaming experiences for corporate events and PR campaigns, and provide in-game advertising to promote corporate products and services. Furthermore, it is possible to collaborate with other industries and have business partnerships reflected in the game. This allows players to enjoy a more diverse gaming experience.

[0062] A game development and sales system according to an embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects player behavior data or preference data. The player behavior data includes, but is not limited to, the player's movement history and operation history. For example, the collection unit collects the player's movement history as GPS data. The collection unit can also collect the player's operation history as log data. The collection unit also collects player preference data. The player preference data includes, but is not limited to, the player's selection history and preferred game genre. For example, the collection unit stores the player's selection history in a database. The collection unit can also collect the player's preferred game genre through a questionnaire survey. The analysis unit analyzes the data collected by the collection unit to learn the player's behavior patterns or preferences. The analysis is performed using, for example, but is not limited to, a machine learning algorithm. The analysis unit analyzes the player's behavior patterns using, for example, a machine learning algorithm. The analysis unit can also analyze the data using a data preprocessing method. The analysis unit also analyzes the player's behavioral patterns. Examples of behavioral patterns include, but are not limited to, frequency analysis and sequence analysis. The analysis unit, for example, analyzes the player's behavioral patterns using frequency analysis. The analysis unit can also analyze the player's behavioral patterns using sequence analysis. The generation unit generates a game story in real time based on the results learned by the analysis unit. The real-time processing is performed, for example, within an allowable delay time range, but is not limited to, an example. The generation unit, for example, performs real-time processing within an allowable delay time range. The generation unit can also generate the game story using real-time processing technology. The generation unit also generates the game story. Examples of game story generation include, but are not limited to, the use of a story template or a dynamic generation algorithm. The generation unit generates the game story using, for example, a story template. The generation unit can also generate the game story using a dynamic generation algorithm.As a result, the game development and sales system according to the embodiment can generate a game story in real time based on the player's behavior and preferences. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI (such as a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit inputs the player's behavioral patterns and preference data into the generation AI, which then generates a game story in real time.

[0063] The generation unit can provide a custom game experience for a corporate event or PR campaign. The custom game experience can include, for example, customization elements for each player and event types, but is not limited to these examples. For example, the generation unit can provide customization elements for each player for a corporate event. The generation unit can also provide specific event types for a PR campaign. This makes it possible to provide a custom game experience for a corporate event or PR campaign. Some or all of the above-described processing by the generation unit can be performed using, for example, a generation AI (such as a text generation AI or a multimodal generation AI), or can be performed without using a generation AI. For example, the generation unit can input data about a corporate event or PR campaign into the generation AI, which can then generate a custom game experience.

[0064] The generation unit can provide advertisements promoting the company's products and services within the game. Examples of advertisements include, but are not limited to, banner advertisements and interactive advertisements. For example, the generation unit can provide banner advertisements within the game. The generation unit can also provide interactive advertisements. This makes it possible to provide advertisements promoting the company's products and services within the game. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI (such as a text generation AI or a multimodal generation AI), or can be performed without using a generation AI. For example, the generation unit can input data on the company's products and services into the generation AI, which then generates the advertisement.

[0065] The generation unit can reflect business partnerships formed through collaboration with other industries. Business partnerships include, for example, joint projects and specific examples of the partnership content, but are not limited to these examples. The generation unit, for example, reflects joint projects with other industries. The generation unit can also reflect specific examples of the partnership content. This makes it possible to reflect business partnerships formed through collaboration with other industries. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI (such as a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit can input collaboration data with other industries into the generation AI, which then reflects the business partnership.

[0066] The generation unit can incorporate movie characters and stories into the game through collaboration with the movie industry. Movie characters include, but are not limited to, the use of character rights and roles in the game. For example, the generation unit reflects the use of movie character rights. The generation unit can also reflect the role of the character in the game. Movie stories include, but are not limited to, methods of incorporating parts of the story into the game and altering the story. For example, the generation unit incorporates parts of the movie story into the game. The generation unit can also alter the story. This allows movie characters and stories to be incorporated into the game through collaboration with the movie industry. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI (such as a text generation AI or a multimodal generation AI) or without using a generation AI. For example, the generation unit inputs movie character and story data into the generation AI, which then incorporates the data into the game.

[0067] The collection unit can estimate the player's emotions and adjust the timing of collecting behavioral data and preference data based on the emotion data. For example, if the player is excited, the collection unit can increase the collection timing and collect detailed data. Furthermore, if the player is relaxed, the collection unit can also slow down the collection timing and collect the minimum amount of data necessary. Furthermore, if the player is stressed, the collection unit can minimize the collection timing to reduce the burden on the player. This allows for more appropriate data collection by adjusting the timing of data collection based on 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 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, which can then adjust the collection timing.

[0068] The collection unit can analyze the player's past game play history and select the optimal data collection method. For example, the collection unit can prioritize collection of related data based on the player's past favorite game genres. The collection unit can also analyze the player's past play time periods and concentrate data collection on those time periods. The collection unit can also analyze the player's past in-game behavior patterns and collect data when specific behaviors occur. This enables efficient data collection by selecting the optimal data collection method based on the player's past game play history. 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 past game play history data into a generation AI, which can select the optimal data collection method.

[0069] When collecting behavioral data and preference data, the collection unit can filter the data based on the player's current game progress. For example, when the player is in the early stages of the game, the collection unit collects basic behavioral data. Furthermore, when the player is in the middle stages of the game, the collection unit can collect more detailed preference data. Furthermore, when the player is in the late stages of the game, the collection unit can focus on collecting specific behavioral data. This enables appropriate data collection by filtering data based on the player's current game progress. Some or all of the above-described processing in 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 game progress data to a generation AI, which then performs filtering.

[0070] When collecting behavioral data and preference data, the collection unit can select the optimal collection means depending on the player's input method. For example, if the player uses voice input, the collection unit can prioritize collecting voice data. Furthermore, if the player uses text input, the collection unit can also prioritize collecting text data. Furthermore, if the player uses 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 in 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 a generation AI, which can select the optimal collection means.

[0071] The collection unit can estimate the player's emotions and determine the priority of data to be collected based on the emotion data. For example, if the player is excited, the collection unit can prioritize collecting behavioral data. Furthermore, if the player is relaxed, the collection unit can also prioritize collecting preference data. Furthermore, if the player is stressed, the collection unit can reduce the amount of collected data to reduce the burden on the player. Thus, by prioritizing data based on the player's emotions, important data can be collected preferentially. Emotion estimation is realized 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 can be performed without using an AI. For example, the collection unit can input the player's emotion data into the generation AI, which can then prioritize the data.

[0072] When collecting behavioral data and preference 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 travel destination. Furthermore, if the player is at home, the collection unit can prioritize collecting data related to the player's home. This enables more appropriate data collection by prioritizing the collection of highly relevant data by taking into account the player's geographical location information. 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 the player's geographical location information data into the generation AI, which can then prioritize collecting highly relevant data.

[0073] When collecting behavioral data and preference data, the collection unit can analyze the player's social media activities and collect related data. For example, the collection unit can collect related data based on gameplay content shared by the player on social media. The collection unit can also analyze the player's friendships on social media and collect shared preference data. The collection unit can also analyze the player's social media posts and collect related behavioral data. This enables more extensive data collection by analyzing the player's social media activities and collecting related data. Some or all of the above-described 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 a generation AI, which can collect related data.

[0074] When collecting behavioral data and preference data, the collection unit can customize the collection method by reflecting the player's past feedback. For example, the collection unit adjusts the collection method based on feedback provided by the player in the past. The collection unit can also prioritize the use of specific data collection means based on the player's past feedback. The collection unit can also analyze the player's past feedback and customize the type of collected data. This enables more appropriate data collection by customizing the collection method 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, which can then customize the collection method.

[0075] The analysis unit can estimate the player's emotions and adjust the analysis method for behavioral patterns and preferences based on the emotion data. For example, if the player is excited, the analysis unit analyzes detailed behavioral patterns. Furthermore, if the player is relaxed, the analysis unit can also focus on analyzing preferences. Furthermore, if the player is stressed, the analysis unit can be simplified to reduce the burden of analysis. This enables more appropriate analysis by adjusting the analysis method based on 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 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, which can then adjust the analysis method.

[0076] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the player's behavioral data. For example, the analysis unit can perform a detailed analysis on behavioral data with high importance. The analysis unit can also perform a simplified analysis on behavioral data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on behavioral data with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the player's behavioral data. Some or all of the above-described 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 player's behavioral data to a generation AI, which can adjust the level of detail of the analysis.

[0077] During analysis, the analysis unit can apply different analysis algorithms depending on the category of player behavior data. For example, the analysis unit can apply an analysis algorithm dedicated to combat to combat behavior data. The analysis unit can also apply an analysis algorithm dedicated to exploration to exploration behavior data. The analysis unit can also apply an analysis algorithm dedicated to interaction to interaction behavior data. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of player behavior data. 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 player behavior data to a generation AI, which can then apply different analysis algorithms.

[0078] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the player's past analysis results. For example, the analysis unit reflects feedback in the current analysis based on the player's past analysis results. The analysis unit can also extract specific behavioral patterns from the player's past analysis results and use them in the current analysis. The analysis unit can also analyze the player's past analysis results and optimize the analysis algorithm. This improves the accuracy of the analysis by referring to the player's past analysis results. 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 player's past analysis result data into the generation AI, which can improve the accuracy of the analysis.

[0079] The analysis unit can estimate the player's emotions and determine the analysis priority based on the emotion data. For example, if the player is excited, the analysis unit can prioritize the analysis of behavioral data. Furthermore, if the player is relaxed, the analysis unit can also prioritize the analysis of preference data. Furthermore, if the player is stressed, the analysis unit can adjust the priority to reduce the burden of analysis. Thus, by determining the analysis priority based on the player's emotions, important analyses can be prioritized. Emotion estimation is realized 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, which can then determine the analysis priority.

[0080] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the player's behavioral data. For example, the analysis unit prioritizes analysis of the most recent behavioral data. The analysis unit can also adjust the priority based on the time of submission while referring to past behavioral data. The analysis unit can also prioritize analysis of behavioral data submitted within a specific period. This enables efficient analysis by determining the priority of analysis based on the time of submission of the player's behavioral data. Some or all of the above-described 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 data on the time of submission of the player's behavioral data to the generation AI, and the generation AI can determine the priority of analysis.

[0081] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the player's behavioral data. For example, the analysis unit prioritizes analysis of highly relevant behavioral data. The analysis unit can also postpone analysis of low-relevance behavioral data. The analysis unit can also analyze moderately relevant behavioral data in an appropriate order. In this way, adjusting the order of analysis based on the relevance of the player's behavioral data enables analysis in an appropriate order. Some or all of the above-described 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 relevance data of the player's behavioral data to the generation AI, and the generation AI can adjust the order of analysis.

[0082] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the player's level of expertise. For example, if the player is a beginner, the analysis unit can provide analysis results that avoid technical terminology. Furthermore, if the player is an intermediate player, the analysis unit can provide analysis results using appropriate technical terminology. Furthermore, if the player is an advanced player, the analysis unit can provide analysis results using detailed technical terminology. By adjusting the use of technical terminology in the analysis according to the player's level of expertise, appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the player's level of expertise data into the generation AI, which can then adjust the use of technical terminology.

[0083] The generation unit can estimate the player's emotions and adjust the game story generation method based on the emotion data. For example, if the player is excited, the generation unit can generate a story that emphasizes action elements. Furthermore, if the player is relaxed, the generation unit can generate a story that emphasizes exploration elements. Furthermore, if the player is stressed, the generation unit can generate a story that emphasizes soothing elements. By adjusting the game story generation method based on the player's emotions, a more appropriate story can be generated. 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 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, which can then adjust the game story generation method.

[0084] When generating a game story, the generation unit can adjust the level of detail of the generation based on the importance of the player's behavior patterns. For example, the generation unit generates a detailed story based on behavior patterns with high importance. The generation unit can also generate a simplified story based on behavior patterns with low importance. The generation unit can also generate a story with an appropriate level of detail based on behavior patterns with medium importance. This enables efficient story generation by adjusting the level of detail of the generation based on the importance of the player's behavior patterns. 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 player behavior pattern data into a generation AI, which can adjust the level of detail of the generation.

[0085] When generating a game story, the generation unit can apply different generation algorithms depending on the category of the player's preference data. For example, the generation unit can apply a generation algorithm that emphasizes action elements to a player who prefers action. The generation unit can also apply a generation algorithm that emphasizes exploration elements to a player who prefers exploration. The generation unit can also apply a generation algorithm that emphasizes interaction elements to a player who prefers interaction. This enables more appropriate story generation by applying different generation algorithms depending on the category of the player's preference data. Some or all of the above-mentioned 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 preference data into a generation AI, and the generation AI can apply different generation algorithms.

[0086] When generating a game story, the generation unit can improve the accuracy of generation by referring to the player's past game story generation results. The generation unit, for example, reflects feedback in the current story generation based on the player's past game story generation results. The generation unit can also extract specific preference patterns from the player's past game story generation results and use them in the current story generation. The generation unit can also analyze the player's past game story generation results and optimize the generation algorithm. In this way, the accuracy of generation is improved by referring to the player's past game story generation 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 game story generation result data into the generation AI, which can improve the accuracy of generation.

[0087] The generation unit can estimate the player's emotions and determine the priority of the game story to be generated based on the emotion data. For example, if the player is excited, the generation unit can prioritize generating action elements. Furthermore, if the player is relaxed, the generation unit can prioritize generating exploration elements. Furthermore, if the player is stressed, the generation unit can prioritize generating soothing elements. Thus, by determining the priority of the game story based on the player's emotions, important stories can be generated preferentially. The emotion estimation 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 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, which can then determine the priority of the game story.

[0088] When generating a game story, the generation unit can determine the generation priority based on the time of submission of the player's behavioral data. The generation unit can determine the priority of story generation based on, for example, the latest behavioral data. The generation unit can also adjust the priority based on the time of submission while referring to past behavioral data. The generation unit can also determine the priority of story generation based on behavioral data submitted within a specific period. This enables efficient story generation by determining the generation priority based on the time of submission of the player's behavioral data. 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 data on the time of submission of the player's behavioral data into the generation AI, and the generation AI can determine the generation priority.

[0089] When generating a game story, the generation unit can adjust the order of generation based on the relevance of the player's behavioral data. The generation unit, for example, determines the order of story generation based on highly relevant behavioral data. The generation unit can also postpone behavioral data with low relevance. The generation unit can also reflect behavioral data with medium relevance in story generation in an appropriate order. In this way, by adjusting the order of generation based on the relevance of the player's behavioral data, it is possible to generate a story in an appropriate order. 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 relevance data of the player's behavioral data into the generation AI, and the generation AI can adjust the order of generation.

[0090] When generating a game story, the generation unit can adjust the use of technical terminology in the generation according to the player's level of expertise. For example, if the player is a beginner, the generation unit can generate a story that avoids technical terminology. Furthermore, if the player is an intermediate player, the generation unit can generate a story using appropriate technical terminology. Furthermore, if the player is an advanced player, the generation unit can generate a story using detailed technical terminology. By adjusting the use of technical terminology in the generation according to the player's level of expertise, an appropriate story 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 AI. For example, the generation unit can input the player's level of expertise data into the generation AI, which can then adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and generation 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 behavior data and preference data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to learn the player's behavior patterns and preferences. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a game story in real time based on the learning results. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and generation 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 behavior data and preference data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to learn the player's behavior patterns and preferences. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a game story in real time based on the learning results. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects player behavior data and preference data using the camera 42 and microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to learn the player's behavior patterns and preferences. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a game story in real time based on the learning results. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and generation 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 behavior data and preference data using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to learn the player's behavior patterns and preferences. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a game story in real time based on the learning results.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] When analyzing a player's behavioral data, the analysis unit can adjust the analysis method taking into account the player's health condition. For example, if the player is tired, the analysis unit can reduce the frequency of data analysis to reduce the burden on the player. Also, if the player is in good health, the analysis unit can perform a detailed analysis and collect more accurate data. Furthermore, if the player is feeling stressed, the analysis unit can prioritize analyzing data that will help reduce stress. This allows for more appropriate data analysis by adjusting the analysis method based on the player's health condition.

[0093] When collecting player behavioral data, the collection unit can adjust the collection method taking into account the player's device usage status. For example, if the player is using a smartphone, the collection unit can use a data collection method optimized for smartphones. Also, if the player is using a PC, the collection unit can use a data collection method optimized for PCs. Furthermore, if the player uses multiple devices, the collection unit can also integrate and collect data from each device. This enables efficient data collection by adjusting the collection method based on the player's device usage status.

[0094] The generator can estimate the player's emotions and adjust the in-game difficulty based on the emotion data. For example, if the player is excited, the generator can increase the difficulty of the game to add a challenging element. Alternatively, if the player is relaxed, the generator can lower the difficulty of the game to add a relaxing element. Furthermore, if the player is stressed, the generator can adjust the difficulty of the game to reduce stress. In this way, a more appropriate gaming experience can be provided by adjusting the in-game difficulty based on the player's emotions.

[0095] When analyzing the player's behavioral data, the analysis unit can adjust the analysis method by taking into account the player's social background. For example, if the player is a student, the analysis unit can analyze the data by taking into account the influence of his / her studies. Also, if the player is working, the analysis unit can analyze the data by taking into account the influence of his / her work. Furthermore, if the player has a family, the analysis unit can analyze the data by taking into account the influence of his / her family. In this way, by adjusting the analysis method based on the player's social background, more appropriate data analysis is possible.

[0096] When collecting player behavioral data, the collection unit can adjust the collection method taking into account the player's internet connection status. For example, if the player has a high-speed internet connection, the collection unit can collect detailed data frequently. On the other hand, if the player has a slow internet connection, the collection unit can reduce the frequency of data collection and collect the minimum amount of data necessary. Furthermore, if the player is offline, the collection unit can temporarily store the offline data and collect the data when the player is online. This allows for efficient data collection by adjusting the collection method based on the player's internet connection status.

[0097] The generator can estimate the player's emotions and adjust the reaction of the in-game character based on the emotion data. For example, if the player is excited, the generator can adjust the in-game character to show a positive reaction. Alternatively, if the player is relaxed, the generator can adjust the in-game character to show a calm reaction. Furthermore, if the player is stressed, the generator can adjust the in-game character to show a comforting reaction. In this way, by adjusting the reaction of the in-game character based on the player's emotions, a more appropriate game experience can be provided.

[0098] When analyzing player behavior data, the analysis unit can adjust the analysis method by taking into account the player's cultural background. For example, if the player belongs to a specific cultural sphere, the data can be analyzed by taking into account behavioral patterns unique to that culture. Also, if the player has a multicultural background, the data can be analyzed by taking into account multiple cultural factors. Furthermore, if the player is engaged in cross-cultural exchange, the data can be analyzed by taking into account the influence of that exchange. This allows for more appropriate data analysis by adjusting the analysis method based on the player's cultural background.

[0099] When collecting player behavioral data, the collection unit can adjust the collection method taking into account the player's gameplay environment. For example, if the player is playing in a quiet environment, the collection unit can collect detailed audio data. Alternatively, if the player is playing in a noisy environment, the collection unit can refrain from collecting audio data and prioritize collecting other data. Furthermore, if the player is playing while moving, the collection unit can collect GPS data to understand the player's movement patterns. This allows for efficient data collection by adjusting the collection method based on the player's gameplay environment.

[0100] The generation unit can estimate the player's emotions and adjust the in-game music and sound effects based on the emotion data. For example, if the player is excited, the generation unit can make the in-game music more up-tempo and emphasize the sound effects. Alternatively, if the player is relaxed, the generation unit can make the in-game music more calming and tone down the sound effects. Furthermore, if the player is feeling stressed, the generation unit can adjust the in-game music to be soothing and the sound effects to be relaxing. This makes it possible to provide a more appropriate gaming experience by adjusting the in-game music and sound effects based on the player's emotions.

[0101] When analyzing a player's behavioral data, the analysis unit can adjust the analysis method taking into account the player's learning style. For example, if the player is a visual learner, the analysis unit can prioritize analyzing visual behavioral data. Also, if the player is an auditory learner, the analysis unit can prioritize analyzing auditory behavioral data. Furthermore, if the player is an experiential learner, the analysis unit can prioritize analyzing experiential behavioral data. This allows for more appropriate data analysis by adjusting the analysis method based on the player's learning style.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects player behavior data or preference data. The player behavior data includes, for example, the player's movement history and operation history. The collection unit collects the player's movement history as GPS data and the operation history as log data. The player preference data includes, for example, the player's selection history and preferred game genre. The collection unit saves the player's selection history in a database and collects the player's preferred game genre through a questionnaire survey. Step 2: The analysis unit analyzes the data collected by the collection unit and learns the player's behavioral patterns or preferences. The analysis is performed using, for example, a machine learning algorithm. The analysis unit analyzes the player's behavioral patterns using a machine learning algorithm and analyzes the data using a data preprocessing method. The behavioral patterns include, for example, frequency analysis and sequence analysis. The analysis unit analyzes the player's behavioral patterns using frequency analysis and sequence analysis. Step 3: The generation unit generates a game story in real time based on the results learned by the analysis unit. Real-time processing is performed, for example, within the allowable delay time range. The generation unit performs real-time processing within the allowable delay time range and generates the game story using real-time processing technology. The game story may include, for example, the use of a story template or a dynamic generation algorithm. The generation unit generates the game story using the story template or dynamic generation algorithm. Some or all of the processing in the generation unit may be performed using generation AI (such as text generation AI or multimodal generation AI).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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 player behavior data or preference data; an analysis unit that analyzes the data collected by the collection unit and learns the behavioral patterns or preferences of players; a generation unit that generates a game story in real time based on the results learned by the analysis unit. A system characterized by:

2. The generation unit Providing custom gaming experiences for corporate events and PR campaigns 2. The system of claim 1.

3. The generation unit Providing in-game advertisements promoting companies' products or services 2. The system of claim 1.

4. The generation unit Reflecting business partnerships through collaboration with other industries 2. The system of claim 1.

5. The generation unit Collaborating with the film industry to incorporate movie characters and stories into the game 2. The system of claim 1.

6. The collecting unit Estimating player emotions and adjusting the timing of collecting behavioral data or preference data based on the estimated player emotions 2. The system of claim 1.

7. The collecting unit Analyze players' past gameplay history and select the best data collection method 2. The system of claim 1.

8. The collecting unit When collecting behavioral and preference data, filter it based on the player's current game progress.

2. The system of claim 1.

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