System

Abstract

An object of the system according to the embodiment is to dynamically evolve a story in accordance with a player's selection or action.SOLUTION: A system includes a reception part, a generation part, a provision part, a collection part, and an analysis part. The receiving unit receives a player's selection. The generation unit generates a story on the basis of the selection received by the reception unit. The provision unit provides the story generated by the generation unit to the player. The collection unit collects action data of a player. The analysis unit analyzes the data collected by the collection 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 had the challenge of making it difficult to provide a gaming experience in which the story changes dynamically based on the player's choices and actions.

[0005] The system according to the embodiment aims to dynamically evolve the story in response to the player's choices and actions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, a collection unit, and an analysis unit. The reception unit receives a player's selection. The generation unit generates a story based on the selection received by the reception unit. The provision unit provides the player with the story generated by the generation unit. The collection unit collects player behavior data. The analysis unit analyzes the data collected by the collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can dynamically evolve the story in response to the player's choices and actions. [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 system according to an embodiment of the present invention dynamically evolves a story in response to a player's choices and actions. This game system accepts player choices, and a generation AI analyzes the choices and generates a story. The generated story is provided to the player, allowing the player to create a unique story world based on their own actions. This allows players to enjoy a unique experience, resulting in a unique and unpredictable gaming experience not found in conventional games. For example, the game system accepts a player's choices, such as whether to interact with a specific character or go to a specific location. The generation AI then generates a story based on the player's choices and provides the story to the player. Furthermore, the system collects, analyzes, and reflects the player's behavioral data in the generated story. This allows players to create a unique story world based on their own actions. This game system aims to build a new gaming culture in which players can express themselves through games. This allows the game system to dynamically evolve a story in response to a player's choices and actions. For example, the game system accepts a player's choices, such as whether to interact with a specific character or go to a specific location. The generation AI then generates a story based on the player's choices and provides the story to the player. Furthermore, the system collects, analyzes, and reflects the player's behavioral data in the generated story. This allows players to create their own story world through their own actions, and the game system aims to build a new gaming culture in which players can express themselves through games.

[0029] A game system according to an embodiment includes a reception unit, a generation unit, a provision unit, a collection unit, and an analysis unit. The reception unit receives a player's selection. The player's selection may include, but is not limited to, interacting with a specific character or going to a specific location. The reception unit receives the selection by, for example, clicking an option. The reception unit may also receive the selection using voice input or gesture input. The generation unit generates a story based on the selection received by the reception unit using a generation AI. The generation AI generates the story using, for example, a text generation AI (e.g., LLM). The generation unit may also generate the story using a multimodal generation AI. For example, the generation AI determines and generates the story development based on the player's selection. The provision unit provides the story generated by the generation unit to the player. The provision unit provides the story using, for example, a screen display or audio output. The provision unit may also select an optimal display method depending on the player's device. For example, if the player is using a smartphone, a display method tailored to the screen size is provided. The collection unit collects the player's behavioral data. Examples of behavioral data include, but are not limited to, click data and movement data. The collection unit, for example, records the player's behavior as a log. The collection unit can also collect the player's biometric data using a sensor. The analysis unit analyzes the data collected by the collection unit and provides it to the generation unit. The analysis unit, for example, analyzes the behavioral data using a data analysis algorithm. The analysis unit can also estimate the player's emotions and analyze the data based on those emotions. For example, the analysis unit estimates the player's emotions using facial expression recognition or voice analysis and analyzes the data based on those emotions. This allows the game system according to the embodiment to dynamically evolve the story in response to the player's choices and actions. For example, the game system accepts a player's choices, such as whether to interact with a specific character or go to a specific location. Next, the generation AI generates a story based on the player's choices and provides the story to the player.Furthermore, the system collects and analyzes player behavior data and reflects it in the generated story. This allows players to create their own unique story world through their own actions. In this way, the game system aims to build a new gaming culture in which players can express themselves through games.

[0030] The generation unit can generate a story based on a player's selection. The generation unit uses a generation AI to generate a story based on the player's selection. The generation AI generates a story using, for example, a text generation AI (e.g., LLM). The generation unit can also generate a story using a multimodal generation AI. For example, the generation AI determines and generates a story development based on a player's selection. For example, if a player selects to interact with a specific character, the generation unit generates a story based on that interaction. Also, if a player selects to go to a specific location, the generation unit can generate a story based on that location. Furthermore, the generation unit can adjust the level of detail of the story based on the player's selection. For example, if an important choice is made, the generation unit generates a detailed story related to that choice. This enables story generation according to the player's selection. 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 generate a story using an AI model that receives a player's selection as input and outputs a story.

[0031] The providing unit can display the generated story to the player. The providing unit provides the story generated by the generating unit to the player. The providing unit provides the story using, for example, a screen display or audio output. The providing unit can also select the optimal display method depending on the player's device. For example, if the player is using a smartphone, a display method tailored to the screen size is provided. The providing unit can, for example, display the generated story in text format. The providing unit can also provide the generated story in audio format. Furthermore, the providing unit can add effects to visually display the generated story. For example, the providing unit can add visual effects to highlight important parts of the story. This allows the generated story to be provided to the player. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide the story using an AI model that inputs the generated story and outputs the optimal display method.

[0032] The collection unit can collect player behavioral data. The collection unit collects player behavioral data. The behavioral data includes, but is not limited to, click data and movement data. For example, the collection unit records the player's behavior as a log. The collection unit can also collect biometric data of the player using a sensor. For example, the collection unit collects the player's heart rate and electrodermal activity using a sensor. Furthermore, the collection unit can collect player behavioral data in real time. For example, the collection unit monitors the player's behavior in real time and collects data. In this way, the player's behavioral data can be collected. 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 collect behavioral data using an AI model that inputs the player's behavioral data and outputs a collection method.

[0033] The analysis unit can analyze the collected data and provide it to the generation unit. The analysis unit can analyze the data collected by the collection unit and provide it to the generation unit. The analysis unit can, for example, analyze the behavioral data using a data analysis algorithm. The analysis unit can also estimate the player's emotions and analyze the data based on those emotions. For example, the analysis unit can estimate the player's emotions using facial expression recognition or voice analysis and analyze the data based on those emotions. The analysis unit can also optimize the analysis algorithm by referring to the player's past behavioral data. For example, the analysis unit can select an optimal analysis algorithm based on the player's past behavioral data. This allows the collected data to be analyzed and provided to the generation unit. 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 analyze the data using an AI model that receives the collected data as input and outputs analysis results.

[0034] The generation unit can generate a story based on the analysis results. The generation unit generates a story based on the analysis results using a generation AI. The generation AI generates a story using, for example, a text generation AI (e.g., LLM). The generation unit can also generate a story using a multimodal generation AI. For example, the generation AI determines and generates a story development based on the analysis results. The generation unit generates a story based on, for example, the analysis results of player behavior data. The generation unit can also generate a story based on the analysis results of player emotion data. The generation unit can also adjust the level of detail of the story based on the analysis results. For example, a detailed story is generated based on important analysis results. This makes it possible to generate a story based on the analysis 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 generate a story using an AI model that receives the analysis results as input and outputs a story.

[0035] The providing unit can specifically change the in-game environment or characters based on the player's selection. The providing unit provides the player with the story generated by the generating unit. The providing unit can provide the story using, for example, a screen display or audio output. The providing unit can also select the optimal display method depending on the player's device. For example, if the player is using a smartphone, the providing unit can provide a display method tailored to the screen size. The providing unit can also change the in-game environment or characters based on the player's selection. For example, if the player selects to interact with a specific character, the character's behavior and dialogue can change. Also, if the player selects to go to a specific location, the environment of that location can change. This allows the in-game environment and characters to change based on the player's selection. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without AI. For example, the providing unit can realize in-game changes using an AI model that inputs the player's selection and outputs changes to the environment and characters.

[0036] The reception unit can analyze the player's past selection history and present appropriate options. The reception unit analyzes the player's past selection history and presents appropriate options. The past selection history includes, for example, options previously selected by the player and reactions to those options. The reception unit, for example, preferentially presents similar options based on options previously selected by the player. The reception unit can also find specific patterns from the player's past selection history and present options based on those patterns. For example, the reception unit can eliminate options that the player has avoided in the past and present optimal options. This makes it possible to present optimal options based on the player's past selection history. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can present options using an AI model that inputs the player's past selection history and outputs optimal options.

[0037] The reception unit can filter options based on the player's current game progress. The reception unit filters options based on the player's current game progress. The current game progress includes, for example, the quest the player is currently in progress of, the character the player is interacting with, and the player's current location. For example, if the player is currently in progress of a specific quest, the reception unit can preferentially present options related to that quest. Also, if the player is interacting with a specific character, the reception unit can present options related to that character. Furthermore, if the player is in a specific location, the reception unit can present options related to that location. In this way, options can be filtered based on the player's current game progress. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can present options using an AI model that uses the player's current game progress as input and filters options.

[0038] The reception unit can present appropriate options depending on the player's input method. The reception unit presents appropriate options depending on the player's input method. Input methods include, for example, voice input, text input, and gesture input. For example, if the player is using voice input, the reception unit can present options by voice. Also, if the player is using text input, the reception unit can present options by text. Furthermore, if the player is using gesture input, the reception unit can present options corresponding to the gesture. This makes it possible to present optimal options depending on the player's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can present options using an AI model that receives the player's input method as input and outputs optimal options.

[0039] The reception unit can specifically present highly relevant options based on the geographical location information of the player. The reception unit presents highly relevant options taking into account the geographical location information of the player. The geographical location information is obtained, for example, using GPS data or a location information service. For example, if the player is in a specific area, the reception unit can present options related to that area. Also, if the player is traveling, the reception unit can present options related to the player's travel destination. Furthermore, if the player is at home, the reception unit can present options related to the player's home. In this way, highly relevant options can be presented based on the player's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can present options using an AI model that inputs the player's geographical location information and outputs highly relevant options.

[0040] The reception unit can analyze the player's social media activity and present relevant options. The reception unit analyzes the player's social media activity and presents relevant options. Social media activity includes, for example, the content of posts and the number of likes. The reception unit, for example, presents options related to topics in which the player has shown interest on social media. The reception unit can also present relevant options based on options selected by the player's friends. Furthermore, the reception unit can analyze the content of the player's social media posts and present relevant options. This makes it possible to present relevant options based on the player's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can present options using an AI model that inputs the player's social media activity and outputs relevant options.

[0041] The reception unit can customize the method of presenting options by reflecting the player's past feedback. The reception unit customizes the method of presenting options by reflecting the player's past feedback. Past feedback includes, for example, options for which the player has previously given favorable feedback and options for which the player has previously given negative feedback. The reception unit, for example, preferentially presents options for which the player has previously given favorable feedback. It can also exclude options for which the player has previously given negative feedback. Furthermore, the reception unit can analyze the player's feedback and customize the optimal method of presenting options. For example, the reception unit adjusts the display position and display format of options based on the player's feedback. This makes it possible to customize the method of presenting options based on the player's past feedback. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can customize the method of presenting options using an AI model that inputs the player's past feedback and outputs a method of presenting options.

[0042] The generation unit can adjust the level of detail of the story based on the importance of the player's choice. The generation unit uses a generation AI to adjust the level of detail of the story based on the importance of the player's choice. The importance of a choice is evaluated, for example, based on the influence of the choice or the frequency of the choice. For example, if the player makes an important choice, the generation unit generates a detailed story related to that choice. In addition, if the player makes a less important choice, the generation unit can generate a concise story. Furthermore, the generation unit can dynamically adjust the level of detail of the story according to the importance of the player's choice. For example, the level of detail of the story is adjusted in real time based on the importance of the player's choice. This makes it possible to adjust the level of detail of the story based on the importance of the player's choice. 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 adjust the level of detail of the story using an AI model that inputs the importance of the player's choice and outputs the level of detail of the story.

[0043] The generation unit can apply different generation algorithms depending on the category selected by the player. The generation unit uses a generation AI to apply different generation algorithms depending on the category selected by the player. The selected categories are classified based on, for example, genre or theme. For example, when the player selects the action category, the generation unit applies a generation algorithm specialized for action scenes. Furthermore, when the player selects the story category, the generation unit can also apply a generation algorithm with a strong story element. Furthermore, when the player selects the puzzle category, the generation unit can also apply a generation algorithm including puzzle elements. In this way, different generation algorithms can be applied depending on the category selected by the player. 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 generate a story using an AI model that inputs the category selected by the player and outputs a generation algorithm.

[0044] The generation unit can improve the accuracy of generation by referring to the player's past story progression. The generation unit uses a generation AI to improve the accuracy of generation by referring to the player's past story progression. Past story progression includes, for example, stories the player has progressed through in the past and their progression patterns. For example, the generation unit generates a similar story based on a story the player has progressed through in the past. The generation unit can also analyze the player's preferences from the player's past story progression and generate a story based on that. Furthermore, the generation unit can maintain the consistency of the story by referring to the player's past story progression. This can improve the accuracy of generation by referring to the player's past story progression. 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 generate a story using an AI model that uses the player's past story progression as input and improves the accuracy of generation.

[0045] The generation unit can determine the priority of the story based on the time of the player's selection. The generation unit uses a generation AI to determine the priority of the story based on the time of the player's selection. The time of selection is evaluated, for example, based on the timing and frequency of the selection. For example, the generation unit prioritizes the content selected by the player at an early stage in the story. The generation unit can also postpone the content selected by the player at a later stage. Furthermore, the generation unit can dynamically adjust the order of story development according to the time of the player's selection. For example, the order of story development is adjusted in real time based on the time of the player's selection. This makes it possible to determine the priority of the story based on the time of the player's selection. 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 determine the priority of the story using an AI model that inputs the time of the player's selection and outputs the priority of the story.

[0046] The generation unit can adjust the order of the story based on the relevance of the player's selection. The generation unit adjusts the order of the story based on the relevance of the player's selection using a generation AI. The relevance of the selection is evaluated based on, for example, the influence of the selection or the type of selection. For example, the generation unit prioritizes reflecting highly relevant selections in the story. The generation unit can also postpone less relevant selections. Furthermore, the generation unit can dynamically adjust the order of story development according to the relevance of the player's selection. For example, the generation unit adjusts the order of story development in real time based on the relevance of the player's selection. This makes it possible to adjust the order of the story based on the relevance of the player's selection. 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 adjust the order of the story using an AI model that inputs the relevance of the player's selection and outputs the order of the story.

[0047] The generation unit can adjust the use of story terminology according to the player's level of expertise. The generation unit uses a generation AI to adjust the use of story terminology according to the player's level of expertise. The expertise level is evaluated, for example, based on evaluation criteria and an evaluation method for the player's level of knowledge. For example, if the player has expertise, the generation unit generates a story that uses a lot of technical terminology. Furthermore, if the player does not have expertise, the generation unit can generate a story that explains things in simple terms. Furthermore, the generation unit can dynamically adjust the use of story terminology according to the player's level of expertise. For example, the generation unit adjusts the use of story terminology in real time based on the player's level of expertise. This makes it possible to adjust the use of story terminology according to the player's level of expertise. 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 generate a story using an AI model that inputs the player's level of expertise and outputs the use of story terminology.

[0048] The providing unit can select an appropriate display method by referring to the player's past operation history. The providing unit selects an appropriate display method by referring to the player's past operation history. The past operation history includes, for example, operations performed by the player in the past and reactions to those operations. The providing unit, for example, provides an optimal display method based on display methods preferred by the player in the past. The providing unit can also select a display method with high visibility based on the player's past operation history. Furthermore, the providing unit can analyze the player's past operation history and customize the optimal display method. For example, the providing unit can adjust the display position and display format based on the player's operation history. This makes it possible to select an optimal display method based on the player's past operation history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can select a display method using an AI model that inputs the player's past operation history and outputs a display method.

[0049] The providing unit can customize the display content according to the player's current task. The providing unit customizes the display content according to the player's current task. The current task includes, for example, the quest the player is currently performing, the character the player is interacting with, and the player's current location. For example, when the player is performing a specific quest, the providing unit can prioritize displaying information related to that quest. Also, when the player is interacting with a specific character, the providing unit can display information related to that character. Furthermore, when the player is in a specific location, the providing unit can display information related to that location. This allows the display content to be customized according to the player's current task. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the display content using an AI model that inputs the player's current task and outputs display content.

[0050] The providing unit can improve the display method by reflecting the player's feedback. The providing unit improves the display method by reflecting the player's feedback. The feedback includes, for example, display methods for which the player has given favorable feedback and display methods for which the player has given negative feedback. For example, the providing unit can preferentially use display methods for which the player has given favorable feedback. The providing unit can also exclude display methods for which the player has given negative feedback. Furthermore, the providing unit can analyze the player's feedback and customize the optimal display method. For example, the providing unit can adjust the display position and display format based on the player's feedback. This allows the display method to be improved based on the player's feedback. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can improve the display method using an AI model that receives the player's feedback as input and outputs a display method.

[0051] The providing unit can specifically select an appropriate display method based on the player's device information. The providing unit selects an appropriate display method based on the player's device information. The device information includes, for example, the device type and device settings. For example, if the player is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the player is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the player is using a smartwatch, the providing unit can provide a display method that is simple and highly visible. This allows the optimal display method to be selected based on the player's device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can select a display method using an AI model that inputs the player's device information and outputs a display method.

[0052] The providing unit can make the display content multilingual in accordance with the player's language setting. The providing unit can make the display content multilingual in accordance with the player's language setting. Language settings include, for example, the device's language setting and a language selected by the player. The providing unit can, for example, automatically set the story language based on the player's device's language setting. If the player uses multiple languages, the providing unit can also provide a language switching function. Furthermore, if the player selects a specific language, the story can be provided in that language. This makes it possible to make the display content multilingual in accordance with the player's language setting. Some or all of the above-described processing by the providing unit can be performed, for example, using AI, or can be performed without AI. For example, the providing unit can provide the display content using an AI model that uses the player's language setting as input and makes the display content multilingual.

[0053] The providing unit can customize the display method according to the player's visual and auditory characteristics. The providing unit customizes the display method according to the player's visual and auditory characteristics. Visual and auditory characteristics include, for example, the characteristics of a player with a visual impairment or a player with a hearing impairment. For example, the providing unit can provide a display method with enhanced audio guidance to a player with a visual impairment. The providing unit can also provide a display method with enhanced subtitles and visual effects to a player with a hearing impairment. Furthermore, the providing unit can customize the optimal display method according to the visual and auditory characteristics. For example, the display contrast and font size can be adjusted according to the visual characteristics. This allows the display method to be customized according to the player's visual and auditory characteristics. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide a display method using an AI model that uses the player's visual and auditory characteristics as input and customizes the display method.

[0054] The collection unit can analyze the player's past behavioral data and select the optimal collection method. The collection unit analyzes the player's past behavioral data and selects the optimal collection method. The past behavioral data includes, for example, the player's past actions and reactions to those actions. The collection unit selects the optimal collection method, for example, based on the player's frequent past actions. The collection unit can also find an efficient collection method from the player's past behavioral data. Furthermore, the collection unit can analyze the player's behavioral patterns and customize the optimal collection method. For example, the collection unit adjusts the collection means and collection timing based on the player's behavioral patterns. This makes it possible to select the optimal collection method based on the player's past behavioral data. 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 select the collection method using an AI model that inputs the player's past behavioral data and outputs a collection method.

[0055] The collection unit can filter behavioral data based on the player's current game progress. The collection unit filters behavioral data based on the player's current game progress. The current game progress includes, for example, the quest the player is currently in progress of, the character the player is currently interacting with, and the player's current location. For example, when the player is currently in progress of a specific quest, the collection unit prioritizes collecting behavioral data related to that quest. Also, when the player is interacting with a specific character, the collection unit can collect behavioral data related to the interaction. Furthermore, when the player is in a specific location, the collection unit can collect behavioral data related to the location. This allows filtering behavioral data based on the player's current game progress. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect behavioral data using an AI model that uses the player's current game progress as input and filters the behavioral data.

[0056] The collection unit can select the optimal collection means depending on the input method of the player. The collection unit selects the optimal collection means depending on the input method of the player. Input methods include, for example, voice input, text input, and gesture input. For example, if the player is using voice input, the collection unit can prioritize collecting voice data. Also, if the player is using text input, the collection unit can prioritize collecting text data. Furthermore, if the player is using gesture input, the collection unit can prioritize collecting gesture data. This makes it possible to select the optimal collection means depending on the input method of the player. Some or all of the above-mentioned 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 select the collection means using an AI model that receives the player's input method as input and outputs a collection means.

[0057] The collection unit can prioritize collecting highly relevant data in consideration of the geographical location information of the player. The collection unit prioritizes collecting highly relevant data in consideration of the geographical location information of the player. Geographical location information is obtained, for example, using GPS data or location information services. For example, if the player is in a specific area, the collection unit can prioritize collecting behavioral data related to that area. Also, if the player is traveling, the collection unit can prioritize collecting behavioral data related to the travel destination. Furthermore, if the player is at home, the collection unit can prioritize collecting behavioral data related to the home. This makes it possible to prioritize collecting highly relevant data based on the geographical location information of the player. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect behavioral data using an AI model that inputs the geographical location information of the player and outputs highly relevant data.

[0058] The collection unit can analyze the player's social media activity and collect related data. The collection unit analyzes the player's social media activity and collects related data. Social media activity includes, for example, the content of posts and the number of likes. The collection unit, for example, collects behavioral data related to topics in which the player has shown interest on social media. The collection unit can also collect related behavioral data by referring to the activities of the player's friends. Furthermore, the content of the player's social media posts can be analyzed to collect related behavioral data. This makes it possible to collect related data based on the player's social media activity. 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 collect behavioral data using an AI model that inputs the player's social media activity and outputs related data.

[0059] The collection unit can customize the collection method by reflecting the player's past feedback. The collection unit customizes the collection method by reflecting the player's past feedback. Past feedback includes, for example, collection methods for which the player has given positive feedback and collection methods for which the player has given negative feedback. The collection unit, for example, preferentially uses collection methods for which the player has given positive feedback. It can also exclude collection methods for which the player has given negative feedback. Furthermore, the collection unit can analyze the player's feedback and customize the optimal collection method. For example, it adjusts the collection means and collection timing based on the player's feedback. This makes it possible to customize the collection method based on the player's past feedback. 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 customize the collection method using an AI model that inputs the player's past feedback and outputs a collection method.

[0060] The analysis unit can optimize the analysis algorithm by referring to the player's past behavioral data. The analysis unit optimizes the analysis algorithm by referring to the player's past behavioral data. The past behavioral data includes, for example, the player's past actions and reactions to those actions. The analysis unit, for example, selects an optimal analysis algorithm based on the player's past behavioral data. The analysis unit can also find an efficient analysis algorithm from the player's past behavioral data. Furthermore, the analysis unit can analyze the player's behavioral patterns and customize an optimal analysis algorithm. For example, the analysis unit adjusts the analysis algorithm based on the player's behavioral patterns. This allows the analysis algorithm to be optimized based on the player's past 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 optimize the analysis algorithm using an AI model that inputs the player's past behavioral data and outputs an analysis algorithm.

[0061] The analysis unit can improve the accuracy of the analysis based on the player's current game progress. The analysis unit improves the accuracy of the analysis based on the player's current game progress. The current game progress includes, for example, the quest the player is currently in progress of, the character the player is interacting with, and the player's current location. For example, if the player is currently in progress of a specific quest, the analysis unit prioritizes analyzing data related to that quest. Also, if the player is interacting with a specific character, the analysis unit can analyze data related to that interaction. Furthermore, if the player is in a specific location, the analysis unit can analyze data related to that location. This improves the accuracy of the analysis based on the player's current game progress. 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 current game progress and analyze the data using an AI model that improves the accuracy of the analysis.

[0062] The analysis unit can improve the analysis method by reflecting player feedback. The analysis unit improves the analysis method by reflecting player feedback. Feedback includes, for example, analysis methods for which players have given positive feedback and analysis methods for which players have given negative feedback. The analysis unit, for example, preferentially uses analysis methods for which players have given positive feedback. It can also exclude analysis methods for which players have given negative feedback. Furthermore, the analysis unit can analyze player feedback and customize an optimal analysis method. For example, it can adjust the analysis algorithm or the timing of analysis based on player feedback. This allows the analysis method to be improved based on player feedback. 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 improve the analysis method by using an AI model that receives player feedback as input and outputs an analysis method.

[0063] The analysis unit can improve the accuracy of the analysis by taking into account the geographical location information of the player. The analysis unit improves the accuracy of the analysis by taking into account the geographical location information of the player. Geographical location information is obtained, for example, using GPS data or a location information service. For example, if the player is in a specific area, the analysis unit can prioritize analyzing data related to that area. Also, if the player is traveling, the analysis unit can prioritize analyzing data related to the travel destination. Furthermore, if the player is at home, the analysis unit can prioritize analyzing data related to the home. This can improve the accuracy of the analysis based on the geographical location information of the player. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze data using an AI model that uses the geographical location information of the player as input and improves the accuracy of the analysis.

[0064] The analysis unit can analyze the player's social media activity and analyze the related data. The analysis unit analyzes the player's social media activity and analyzes the related data. Social media activity includes, for example, the content of posts and the number of likes. The analysis unit, for example, analyzes data related to topics in which the player has shown interest on social media. The analysis unit can also analyze the related data by referring to the actions of the player's friends. Furthermore, the analysis unit can analyze the content of the player's social media posts and analyze the related data. This makes it possible to analyze the related data based on the player's social media activity. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the data using an AI model that inputs the player's social media activity and outputs related data.

[0065] The analysis unit can adjust the analysis algorithm by reflecting the player's past feedback. The analysis unit adjusts the analysis algorithm by reflecting the player's past feedback. Past feedback includes, for example, analysis algorithms for which the player has given positive feedback and analysis algorithms for which the player has given negative feedback. The analysis unit, for example, preferentially uses analysis algorithms for which the player has given positive feedback. It can also exclude analysis algorithms for which the player has given negative feedback. Furthermore, the analysis unit can analyze the player's feedback and customize an optimal analysis algorithm. For example, the analysis unit adjusts the analysis algorithm based on the player's feedback. This allows the analysis algorithm to be adjusted based on the player's past feedback. 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 adjust the analysis algorithm using an AI model that inputs the player's past feedback and outputs an analysis algorithm.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The generator not only generates a story based on the player's choices, but can also dynamically change the in-game weather and time of day according to the player's choices. For example, if the player makes a specific choice, the in-game weather can change from sunny to rainy in response to that choice. Also, if the player is playing the game at night, the in-game time can be set to night. This allows for a more realistic gaming experience tailored to the player's choices and playing environment. Furthermore, the generator can change the in-game music and sound effects based on the player's choices. For example, tense music can be played during tense scenes, and calm music can be played during relaxing scenes.

[0068] The providing unit not only displays the generated story to the player, but also adjusts the display method according to the remaining battery level of the player's device. For example, if the battery level is low, the unit lowers the quality of the graphics to reduce energy consumption. On the other hand, if the battery level is sufficient, the unit can provide high-quality graphics. Furthermore, the providing unit can automatically adjust the screen brightness of the player's device. For example, if the player is playing in a dark environment, the unit lowers the screen brightness to reduce eye strain. This makes it possible to provide the optimal display method according to the state of the player's device.

[0069] In addition to collecting player behavior data, the collection unit can also collect player posture data during gameplay and provide feedback to encourage posture improvement. For example, if a player plays in the same posture for a long period of time, a notification urging the player to change posture can be displayed. Also, if a player plays in an improper posture, a guide showing the player how to correct posture can be provided. Furthermore, the collection unit can analyze the player's posture data and evaluate long-term health risks. This allows players to maintain a healthy posture while enjoying the game.

[0070] The analysis unit not only analyzes the collected data, but also analyzes the player's gameplay style and provides optimal gameplay advice. For example, if a player has a specific gameplay style, it can suggest effective strategies and techniques based on that style. It can also provide appropriate guides and tutorials when a player tries a new gameplay style. Furthermore, the analysis unit can provide specific advice to improve the player's skills based on the player's past gameplay data. This allows players to improve their gameplay and enjoy it more.

[0071] The generator not only generates a story based on the player's choices, but can also dynamically adjust the in-game economic system according to the player's choices. For example, if a player chooses to purchase a specific item, the price of that item will fluctuate according to demand. Also, if a player completes a specific quest, it can affect the in-game market. This allows the player's choices to be reflected in the in-game economy in real time, providing a more dynamic gaming experience. Furthermore, the generator can adjust the supply of in-game resources based on the player's choices. For example, if a player uses a lot of a particular resource, the supply of that resource may decrease.

[0072] The providing unit can not only change the in-game environment and characters based on the player's selection, but can also customize in-game voice navigation according to the player's selection. For example, if the player selects to interact with a specific character, the character's tone of voice and speaking style can change. Also, if the player selects to go to a specific location, voice guidance related to that location can be provided. Furthermore, the providing unit can dynamically change the content of the voice navigation based on the player's selection. For example, if the player is in the middle of a specific quest, voice navigation related to that quest can be provided. This makes it possible to provide more personalized voice navigation according to the player's selection.

[0073] The reception unit can not only analyze the player's past selection history, but also adjust the probability of an event occurring in the game based on the player's selection history. For example, if the player frequently made a particular choice in the past, the reception unit can increase the probability of an event occurring related to that choice. Also, if the player avoids a particular choice, the reception unit can decrease the probability of an event occurring related to that choice. Furthermore, the reception unit can generate new events based on the player's selection history. For example, if the player repeatedly makes a particular choice, a new event related to that choice may occur. This makes it possible to provide a more dynamic game experience based on the player's selection history.

[0074] The reception unit not only filters options based on the player's current game progress, but also dynamically adjusts in-game rewards according to the player's progress. For example, when a player is in the middle of a specific quest, the reward for that quest may vary according to the player's progress. Also, when a player is interacting with a specific character, the reward provided by that character may be adjusted based on the player's progress. Furthermore, the reception unit may dynamically change the type and amount of rewards based on the player's progress. For example, when a player completes a specific task, the reward associated with that task may increase. This makes it possible to provide a more personalized reward system according to the player's progress.

[0075] The processing flow of the first embodiment will be briefly explained below.

[0076] Step 1: The reception unit accepts the player's selection. The player's selection may include interacting with a specific character or going to a specific location. The reception unit accepts the selection by the player clicking on an option, or by voice input or gesture input. Step 2: The generation unit uses a generation AI to generate a story based on the selections received by the reception unit. The generation AI generates the story using a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI determines and generates the development of the story based on the player's selections. Step 3: The providing unit provides the story generated by the generating unit to the player. The providing unit provides the story using screen display and audio output. The providing unit can also select the optimal display method depending on the device of the player. For example, if the player is using a smartphone, the providing unit provides a display method that matches the screen size. Step 4: The collection unit collects the player's behavioral data. The behavioral data includes click data, movement data, etc. The collection unit not only records the player's behavior as a log, but can also collect the player's biometric data using sensors. Step 5: The analysis unit analyzes the data collected by the collection unit and provides it to the generation unit. In addition to analyzing the behavioral data using a data analysis algorithm, the analysis unit can also estimate the player's emotions and analyze the data based on those emotions. For example, the analysis unit can estimate the player's emotions using facial expression recognition or voice analysis and analyze the data based on those emotions.

[0077] (Example 2) A game system according to an embodiment of the present invention dynamically evolves a story in response to a player's choices and actions. This game system accepts player choices, and a generation AI analyzes the choices and generates a story. The generated story is provided to the player, allowing the player to create a unique story world based on their own actions. This allows players to enjoy a unique experience, resulting in a unique and unpredictable gaming experience not found in conventional games. For example, the game system accepts a player's choices, such as whether to interact with a specific character or go to a specific location. The generation AI then generates a story based on the player's choices and provides the story to the player. Furthermore, the system collects, analyzes, and reflects the player's behavioral data in the generated story. This allows players to create a unique story world based on their own actions. This game system aims to build a new gaming culture in which players can express themselves through games. This allows the game system to dynamically evolve a story in response to a player's choices and actions. For example, the game system accepts a player's choices, such as whether to interact with a specific character or go to a specific location. The generation AI then generates a story based on the player's choices and provides the story to the player. Furthermore, the system collects, analyzes, and reflects the player's behavioral data in the generated story. This allows players to create their own story world through their own actions, and the game system aims to build a new gaming culture in which players can express themselves through games.

[0078] A game system according to an embodiment includes a reception unit, a generation unit, a provision unit, a collection unit, and an analysis unit. The reception unit receives a player's selection. The player's selection may include, but is not limited to, interacting with a specific character or going to a specific location. The reception unit receives the selection by, for example, clicking an option. The reception unit may also receive the selection using voice input or gesture input. The generation unit generates a story based on the selection received by the reception unit using a generation AI. The generation AI generates the story using, for example, a text generation AI (e.g., LLM). The generation unit may also generate the story using a multimodal generation AI. For example, the generation AI determines and generates the story development based on the player's selection. The provision unit provides the story generated by the generation unit to the player. The provision unit provides the story using, for example, a screen display or audio output. The provision unit may also select an optimal display method depending on the player's device. For example, if the player is using a smartphone, a display method tailored to the screen size is provided. The collection unit collects the player's behavioral data. Examples of behavioral data include, but are not limited to, click data and movement data. The collection unit, for example, records the player's behavior as a log. The collection unit can also collect the player's biometric data using a sensor. The analysis unit analyzes the data collected by the collection unit and provides it to the generation unit. The analysis unit, for example, analyzes the behavioral data using a data analysis algorithm. The analysis unit can also estimate the player's emotions and analyze the data based on those emotions. For example, the analysis unit estimates the player's emotions using facial expression recognition or voice analysis and analyzes the data based on those emotions. This allows the game system according to the embodiment to dynamically evolve the story in response to the player's choices and actions. For example, the game system accepts a player's choices, such as whether to interact with a specific character or go to a specific location. Next, the generation AI generates a story based on the player's choices and provides the story to the player.Furthermore, the system collects and analyzes player behavior data and reflects it in the generated story. This allows players to create their own unique story world through their own actions. In this way, the game system aims to build a new gaming culture in which players can express themselves through games.

[0079] The generation unit can generate a story based on a player's selection. The generation unit uses a generation AI to generate a story based on the player's selection. The generation AI generates a story using, for example, a text generation AI (e.g., LLM). The generation unit can also generate a story using a multimodal generation AI. For example, the generation AI determines and generates a story development based on a player's selection. For example, if a player selects to interact with a specific character, the generation unit generates a story based on that interaction. Also, if a player selects to go to a specific location, the generation unit can generate a story based on that location. Furthermore, the generation unit can adjust the level of detail of the story based on the player's selection. For example, if an important choice is made, the generation unit generates a detailed story related to that choice. This enables story generation according to the player's selection. 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 generate a story using an AI model that receives a player's selection as input and outputs a story.

[0080] The providing unit can display the generated story to the player. The providing unit provides the story generated by the generating unit to the player. The providing unit provides the story using, for example, a screen display or audio output. The providing unit can also select the optimal display method depending on the player's device. For example, if the player is using a smartphone, a display method tailored to the screen size is provided. The providing unit can, for example, display the generated story in text format. The providing unit can also provide the generated story in audio format. Furthermore, the providing unit can add effects to visually display the generated story. For example, the providing unit can add visual effects to highlight important parts of the story. This allows the generated story to be provided to the player. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide the story using an AI model that inputs the generated story and outputs the optimal display method.

[0081] The collection unit can collect player behavioral data. The collection unit collects player behavioral data. The behavioral data includes, but is not limited to, click data and movement data. For example, the collection unit records the player's behavior as a log. The collection unit can also collect biometric data of the player using a sensor. For example, the collection unit collects the player's heart rate and electrodermal activity using a sensor. Furthermore, the collection unit can collect player behavioral data in real time. For example, the collection unit monitors the player's behavior in real time and collects data. In this way, the player's behavioral data can be collected. 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 collect behavioral data using an AI model that inputs the player's behavioral data and outputs a collection method.

[0082] The analysis unit can analyze the collected data and provide it to the generation unit. The analysis unit can analyze the data collected by the collection unit and provide it to the generation unit. The analysis unit can, for example, analyze the behavioral data using a data analysis algorithm. The analysis unit can also estimate the player's emotions and analyze the data based on those emotions. For example, the analysis unit can estimate the player's emotions using facial expression recognition or voice analysis and analyze the data based on those emotions. The analysis unit can also optimize the analysis algorithm by referring to the player's past behavioral data. For example, the analysis unit can select an optimal analysis algorithm based on the player's past behavioral data. This allows the collected data to be analyzed and provided to the generation unit. 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 analyze the data using an AI model that receives the collected data as input and outputs analysis results.

[0083] The generation unit can generate a story based on the analysis results. The generation unit generates a story based on the analysis results using a generation AI. The generation AI generates a story using, for example, a text generation AI (e.g., LLM). The generation unit can also generate a story using a multimodal generation AI. For example, the generation AI determines and generates a story development based on the analysis results. The generation unit generates a story based on, for example, the analysis results of player behavior data. The generation unit can also generate a story based on the analysis results of player emotion data. The generation unit can also adjust the level of detail of the story based on the analysis results. For example, a detailed story is generated based on important analysis results. This makes it possible to generate a story based on the analysis 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 generate a story using an AI model that receives the analysis results as input and outputs a story.

[0084] The providing unit can specifically change the in-game environment or characters based on the player's selection. The providing unit provides the player with the story generated by the generating unit. The providing unit can provide the story using, for example, a screen display or audio output. The providing unit can also select the optimal display method depending on the player's device. For example, if the player is using a smartphone, the providing unit can provide a display method tailored to the screen size. The providing unit can also change the in-game environment or characters based on the player's selection. For example, if the player selects to interact with a specific character, the character's behavior and dialogue can change. Also, if the player selects to go to a specific location, the environment of that location can change. This allows the in-game environment and characters to change based on the player's selection. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without AI. For example, the providing unit can realize in-game changes using an AI model that inputs the player's selection and outputs changes to the environment and characters.

[0085] The reception unit can estimate the player's emotions and adjust the way options are presented based on the estimated player's emotions. The reception unit can estimate the player's emotions and adjust the way options are presented based on the estimated player's emotions. The player's emotions can be estimated using, for example, facial expression recognition or voice analysis. For example, the reception unit can capture the player's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The reception unit can also record the player's voice and estimate the emotion using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice and calculate an emotion score. The reception unit can also adjust the way options are presented based on the player's emotions. For example, if the player is excited, visually stimulating options can be emphasized and presented. Alternatively, if the player is relaxed, options can be presented in calm colors. Alternatively, if the player is stressed, simple and intuitive options can be presented. This makes it possible to adjust the way options are presented depending on the player's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may adjust the way options are presented using an AI model that receives the player's emotional data as input and outputs the way options are presented.

[0086] The reception unit can analyze the player's past selection history and present appropriate options. The reception unit analyzes the player's past selection history and presents appropriate options. The past selection history includes, for example, options previously selected by the player and reactions to those options. The reception unit, for example, preferentially presents similar options based on options previously selected by the player. The reception unit can also find specific patterns from the player's past selection history and present options based on those patterns. For example, the reception unit can eliminate options that the player has avoided in the past and present optimal options. This makes it possible to present optimal options based on the player's past selection history. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can present options using an AI model that inputs the player's past selection history and outputs optimal options.

[0087] The reception unit can filter options based on the player's current game progress. The reception unit filters options based on the player's current game progress. The current game progress includes, for example, the quest the player is currently in progress of, the character the player is interacting with, and the player's current location. For example, if the player is currently in progress of a specific quest, the reception unit can preferentially present options related to that quest. Also, if the player is interacting with a specific character, the reception unit can present options related to that character. Furthermore, if the player is in a specific location, the reception unit can present options related to that location. In this way, options can be filtered based on the player's current game progress. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can present options using an AI model that uses the player's current game progress as input and filters options.

[0088] The reception unit can present appropriate options depending on the player's input method. The reception unit presents appropriate options depending on the player's input method. Input methods include, for example, voice input, text input, and gesture input. For example, if the player is using voice input, the reception unit can present options by voice. Also, if the player is using text input, the reception unit can present options by text. Furthermore, if the player is using gesture input, the reception unit can present options corresponding to the gesture. This makes it possible to present optimal options depending on the player's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can present options using an AI model that receives the player's input method as input and outputs optimal options.

[0089] The reception unit can estimate the player's emotions and prioritize options based on the estimated player's emotions. The reception unit can estimate the player's emotions and prioritize options based on the estimated player's emotions. The player's emotions can be estimated using, for example, facial expression recognition or voice analysis. For example, the reception unit can capture the player's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The reception unit can also record the player's voice and estimate the emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice and calculate an emotion score. The reception unit can also prioritize options based on the player's emotions. For example, if the player is excited, options with a high level of action can be presented preferentially. Alternatively, if the player is relaxed, options with a high level of storytelling can be presented preferentially. Alternatively, if the player is stressed, options with a high level of simplicity and intuition can be presented preferentially. This makes it possible to prioritize options based on the player's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may determine the priority of options using an AI model that receives the player's emotional data as input and outputs the priority of options.

[0090] The reception unit can specifically present highly relevant options based on the geographical location information of the player. The reception unit presents highly relevant options taking into account the geographical location information of the player. The geographical location information is obtained, for example, using GPS data or a location information service. For example, if the player is in a specific area, the reception unit can present options related to that area. Also, if the player is traveling, the reception unit can present options related to the player's travel destination. Furthermore, if the player is at home, the reception unit can present options related to the player's home. In this way, highly relevant options can be presented based on the player's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can present options using an AI model that inputs the player's geographical location information and outputs highly relevant options.

[0091] The reception unit can analyze the player's social media activity and present relevant options. The reception unit analyzes the player's social media activity and presents relevant options. Social media activity includes, for example, the content of posts and the number of likes. The reception unit, for example, presents options related to topics in which the player has shown interest on social media. The reception unit can also present relevant options based on options selected by the player's friends. Furthermore, the reception unit can analyze the content of the player's social media posts and present relevant options. This makes it possible to present relevant options based on the player's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can present options using an AI model that inputs the player's social media activity and outputs relevant options.

[0092] The reception unit can customize the method of presenting options by reflecting the player's past feedback. The reception unit customizes the method of presenting options by reflecting the player's past feedback. Past feedback includes, for example, options for which the player has previously given favorable feedback and options for which the player has previously given negative feedback. The reception unit, for example, preferentially presents options for which the player has previously given favorable feedback. It can also exclude options for which the player has previously given negative feedback. Furthermore, the reception unit can analyze the player's feedback and customize the optimal method of presenting options. For example, the reception unit adjusts the display position and display format of options based on the player's feedback. This makes it possible to customize the method of presenting options based on the player's past feedback. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can customize the method of presenting options using an AI model that inputs the player's past feedback and outputs a method of presenting options.

[0093] The generation unit can estimate the player's emotions and adjust the story development based on the estimated player's emotions. The generation unit can estimate the player's emotions using a generation AI and adjust the story development based on the estimated player's emotions. The player's emotions can be estimated using, for example, facial expression recognition or voice analysis. For example, the generation unit can capture the player's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The generation unit can also record the player's voice and estimate the emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the generation unit can adjust the story development based on the player's emotions. For example, if the player is excited, the generation unit can increase the number of action scenes. If the player is relaxed, the generation unit can increase the number of scenes with a strong story. If the player is stressed, the generation unit can increase the number of simple, intuitive scenes. This makes it possible to adjust the story development based on the player's emotions. Emotion estimation is achieved using, for example, an emotion estimation function using an emotion engine or generation AI. The generation AI may 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 generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may adjust the development of the story using an AI model that receives player emotion data as input and outputs the development of the story.

[0094] The generation unit can adjust the level of detail of the story based on the importance of the player's choice. The generation unit uses a generation AI to adjust the level of detail of the story based on the importance of the player's choice. The importance of a choice is evaluated, for example, based on the influence of the choice or the frequency of the choice. For example, if the player makes an important choice, the generation unit generates a detailed story related to that choice. In addition, if the player makes a less important choice, the generation unit can generate a concise story. Furthermore, the generation unit can dynamically adjust the level of detail of the story according to the importance of the player's choice. For example, the level of detail of the story is adjusted in real time based on the importance of the player's choice. This makes it possible to adjust the level of detail of the story based on the importance of the player's choice. 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 adjust the level of detail of the story using an AI model that inputs the importance of the player's choice and outputs the level of detail of the story.

[0095] The generation unit can apply different generation algorithms depending on the category selected by the player. The generation unit uses a generation AI to apply different generation algorithms depending on the category selected by the player. The selected categories are classified based on, for example, genre or theme. For example, when the player selects the action category, the generation unit applies a generation algorithm specialized for action scenes. Furthermore, when the player selects the story category, the generation unit can also apply a generation algorithm with a strong story element. Furthermore, when the player selects the puzzle category, the generation unit can also apply a generation algorithm including puzzle elements. In this way, different generation algorithms can be applied depending on the category selected by the player. 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 generate a story using an AI model that inputs the category selected by the player and outputs a generation algorithm.

[0096] The generation unit can improve the accuracy of generation by referring to the player's past story progression. The generation unit uses a generation AI to improve the accuracy of generation by referring to the player's past story progression. Past story progression includes, for example, stories the player has progressed through in the past and their progression patterns. For example, the generation unit generates a similar story based on a story the player has progressed through in the past. The generation unit can also analyze the player's preferences from the player's past story progression and generate a story based on that. Furthermore, the generation unit can maintain the consistency of the story by referring to the player's past story progression. This can improve the accuracy of generation by referring to the player's past story progression. 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 generate a story using an AI model that uses the player's past story progression as input and improves the accuracy of generation.

[0097] The generation unit can estimate the player's emotions and adjust the length of the story based on the estimated player's emotions. The generation unit can use a generation AI to estimate the player's emotions and adjust the length of the story based on the estimated player's emotions. The player's emotions can be estimated using, for example, facial expression recognition or voice analysis. For example, the generation unit can capture the player's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The generation unit can also record the player's voice and estimate the emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice and calculate an emotion score. The generation unit can also adjust the length of the story based on the player's emotions. For example, if the player is in a hurry, the generation unit can generate a short, to-the-point story. If the player is relaxed, the generation unit can generate a longer story with detailed explanations. If the player is excited, the generation unit can generate a story with visually stimulating effects. This allows the length of the story to be adjusted 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 may 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 generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may adjust the length of the story using an AI model that receives player emotion data as input and outputs the length of the story.

[0098] The generation unit can determine the priority of the story based on the time of the player's selection. The generation unit uses a generation AI to determine the priority of the story based on the time of the player's selection. The time of selection is evaluated, for example, based on the timing and frequency of the selection. For example, the generation unit prioritizes the content selected by the player at an early stage in the story. The generation unit can also postpone the content selected by the player at a later stage. Furthermore, the generation unit can dynamically adjust the order of story development according to the time of the player's selection. For example, the order of story development is adjusted in real time based on the time of the player's selection. This makes it possible to determine the priority of the story based on the time of the player's selection. 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 determine the priority of the story using an AI model that inputs the time of the player's selection and outputs the priority of the story.

[0099] The generation unit can adjust the order of the story based on the relevance of the player's selection. The generation unit adjusts the order of the story based on the relevance of the player's selection using a generation AI. The relevance of the selection is evaluated based on, for example, the influence of the selection or the type of selection. For example, the generation unit prioritizes reflecting highly relevant selections in the story. The generation unit can also postpone less relevant selections. Furthermore, the generation unit can dynamically adjust the order of story development according to the relevance of the player's selection. For example, the generation unit adjusts the order of story development in real time based on the relevance of the player's selection. This makes it possible to adjust the order of the story based on the relevance of the player's selection. 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 adjust the order of the story using an AI model that inputs the relevance of the player's selection and outputs the order of the story.

[0100] The generation unit can adjust the use of story terminology according to the player's level of expertise. The generation unit uses a generation AI to adjust the use of story terminology according to the player's level of expertise. The expertise level is evaluated, for example, based on evaluation criteria and an evaluation method for the player's level of knowledge. For example, if the player has expertise, the generation unit generates a story that uses a lot of technical terminology. Furthermore, if the player does not have expertise, the generation unit can generate a story that explains things in simple terms. Furthermore, the generation unit can dynamically adjust the use of story terminology according to the player's level of expertise. For example, the generation unit adjusts the use of story terminology in real time based on the player's level of expertise. This makes it possible to adjust the use of story terminology according to the player's level of expertise. 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 generate a story using an AI model that inputs the player's level of expertise and outputs the use of story terminology.

[0101] The providing unit can estimate the player's emotions and adjust the story display method based on the estimated player's emotions. The providing unit can estimate the player's emotions and adjust the story display method based on the estimated player's emotions. The player's emotions can be estimated using, for example, facial expression recognition or voice analysis. For example, the providing unit can capture the player's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The providing unit can also record the player's voice and estimate the emotions using voice analysis technology. For example, the providing unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the providing unit can adjust the story display method based on the player's emotions. For example, if the player is nervous, a simple, highly visible display method can be provided. Alternatively, if the player is relaxed, a display method including detailed information can be provided. Alternatively, if the player is in a hurry, a display method that focuses on the main points can be provided. This makes it possible to adjust the story display method based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may 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 providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may adjust the display method using an AI model that receives the player's emotional data as input and outputs the story display method.

[0102] The providing unit can select an appropriate display method by referring to the player's past operation history. The providing unit selects an appropriate display method by referring to the player's past operation history. The past operation history includes, for example, operations performed by the player in the past and reactions to those operations. The providing unit, for example, provides an optimal display method based on display methods preferred by the player in the past. The providing unit can also select a display method with high visibility based on the player's past operation history. Furthermore, the providing unit can analyze the player's past operation history and customize the optimal display method. For example, the providing unit can adjust the display position and display format based on the player's operation history. This makes it possible to select an optimal display method based on the player's past operation history. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can select a display method using an AI model that inputs the player's past operation history and outputs a display method.

[0103] The providing unit can customize the display content according to the player's current task. The providing unit customizes the display content according to the player's current task. The current task includes, for example, the quest the player is currently performing, the character the player is interacting with, and the player's current location. For example, when the player is performing a specific quest, the providing unit can prioritize displaying information related to that quest. Also, when the player is interacting with a specific character, the providing unit can display information related to that character. Furthermore, when the player is in a specific location, the providing unit can display information related to that location. This allows the display content to be customized according to the player's current task. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can customize the display content using an AI model that inputs the player's current task and outputs display content.

[0104] The providing unit can improve the display method by reflecting the player's feedback. The providing unit improves the display method by reflecting the player's feedback. The feedback includes, for example, display methods for which the player has given favorable feedback and display methods for which the player has given negative feedback. For example, the providing unit can preferentially use display methods for which the player has given favorable feedback. The providing unit can also exclude display methods for which the player has given negative feedback. Furthermore, the providing unit can analyze the player's feedback and customize the optimal display method. For example, the providing unit can adjust the display position and display format based on the player's feedback. This allows the display method to be improved based on the player's feedback. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can improve the display method using an AI model that receives the player's feedback as input and outputs a display method.

[0105] The providing unit can estimate the player's emotions and adjust the display order of the stories based on the estimated player's emotions. The providing unit can estimate the player's emotions and adjust the display order of the stories based on the estimated player's emotions. The player's emotions can be estimated using, for example, facial expression recognition or voice analysis. For example, the providing unit can capture the player's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The providing unit can also record the player's voice and estimate the emotions using voice analysis technology. For example, the providing unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the providing unit can adjust the display order of the stories based on the player's emotions. For example, if the player is excited, action scenes can be displayed first. Alternatively, if the player is relaxed, scenes with a strong storyline can be displayed first. Alternatively, if the player is stressed, simple and intuitive scenes can be displayed first. This makes it possible to adjust the display order of the stories based on the player's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may adjust the display order using an AI model that receives the player's emotional data as input and outputs the display order of the story.

[0106] The providing unit can specifically select an appropriate display method based on the player's device information. The providing unit selects an appropriate display method based on the player's device information. The device information includes, for example, the device type and device settings. For example, if the player is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the player is using a tablet, the providing unit can provide a display method optimized for a large screen. Furthermore, if the player is using a smartwatch, the providing unit can provide a display method that is simple and highly visible. This allows the optimal display method to be selected based on the player's device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can select a display method using an AI model that inputs the player's device information and outputs a display method.

[0107] The providing unit can make the display content multilingual in accordance with the player's language setting. The providing unit can make the display content multilingual in accordance with the player's language setting. Language settings include, for example, the device's language setting and a language selected by the player. The providing unit can, for example, automatically set the story language based on the player's device's language setting. If the player uses multiple languages, the providing unit can also provide a language switching function. Furthermore, if the player selects a specific language, the story can be provided in that language. This makes it possible to make the display content multilingual in accordance with the player's language setting. Some or all of the above-described processing by the providing unit can be performed, for example, using AI, or can be performed without AI. For example, the providing unit can provide the display content using an AI model that uses the player's language setting as input and makes the display content multilingual.

[0108] The providing unit can customize the display method according to the player's visual and auditory characteristics. The providing unit customizes the display method according to the player's visual and auditory characteristics. Visual and auditory characteristics include, for example, the characteristics of a player with a visual impairment or a player with a hearing impairment. For example, the providing unit can provide a display method with enhanced audio guidance to a player with a visual impairment. The providing unit can also provide a display method with enhanced subtitles and visual effects to a player with a hearing impairment. Furthermore, the providing unit can customize the optimal display method according to the visual and auditory characteristics. For example, the display contrast and font size can be adjusted according to the visual characteristics. This allows the display method to be customized according to the player's visual and auditory characteristics. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide a display method using an AI model that uses the player's visual and auditory characteristics as input and customizes the display method.

[0109] The collection unit can estimate the player's emotions and adjust the timing of behavioral data collection based on the estimated player's emotions. The collection unit can estimate the player's emotions and adjust the timing of behavioral data collection based on the estimated player's emotions. The player's emotions can be estimated using, for example, facial expression recognition or voice analysis. For example, the collection unit can capture the player's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The collection unit can also record the player's voice and estimate the emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the voice and calculate an emotion score. The collection unit can also adjust the timing of behavioral data collection based on the player's emotions. For example, if the player is excited, the collection unit can collect behavioral data frequently. Alternatively, if the player is relaxed, the collection unit can collect behavioral data at appropriate times. Alternatively, if the player is stressed, the collection unit can reduce the collection timing to reduce the burden on the player. This makes it possible to adjust the timing of behavioral data collection based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may adjust the collection timing using an AI model that receives the player's emotional data as input and outputs the timing of collecting behavioral data.

[0110] The collection unit can analyze the player's past behavioral data and select the optimal collection method. The collection unit analyzes the player's past behavioral data and selects the optimal collection method. The past behavioral data includes, for example, the player's past actions and reactions to those actions. The collection unit selects the optimal collection method, for example, based on the player's frequent past actions. The collection unit can also find an efficient collection method from the player's past behavioral data. Furthermore, the collection unit can analyze the player's behavioral patterns and customize the optimal collection method. For example, the collection unit adjusts the collection means and collection timing based on the player's behavioral patterns. This makes it possible to select the optimal collection method based on the player's past behavioral data. 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 select the collection method using an AI model that inputs the player's past behavioral data and outputs a collection method.

[0111] The collection unit can filter behavioral data based on the player's current game progress. The collection unit filters behavioral data based on the player's current game progress. The current game progress includes, for example, the quest the player is currently in progress of, the character the player is currently interacting with, and the player's current location. For example, when the player is currently in progress of a specific quest, the collection unit prioritizes collecting behavioral data related to that quest. Also, when the player is interacting with a specific character, the collection unit can collect behavioral data related to the interaction. Furthermore, when the player is in a specific location, the collection unit can collect behavioral data related to the location. This allows filtering behavioral data based on the player's current game progress. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect behavioral data using an AI model that uses the player's current game progress as input and filters the behavioral data.

[0112] The collection unit can select the optimal collection means depending on the input method of the player. The collection unit selects the optimal collection means depending on the input method of the player. Input methods include, for example, voice input, text input, and gesture input. For example, if the player is using voice input, the collection unit can prioritize collecting voice data. Also, if the player is using text input, the collection unit can prioritize collecting text data. Furthermore, if the player is using gesture input, the collection unit can prioritize collecting gesture data. This makes it possible to select the optimal collection means depending on the input method of the player. Some or all of the above-mentioned 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 select the collection means using an AI model that receives the player's input method as input and outputs a collection means.

[0113] The collection unit can estimate the player's emotions and prioritize the behavioral data to be collected based on the estimated player's emotions. The collection unit can estimate the player's emotions and prioritize the behavioral data to be collected based on the estimated player's emotions. The player's emotions can be estimated using, for example, facial expression recognition or voice analysis. For example, the collection unit can capture the player's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The collection unit can also record the player's voice and estimate the emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the voice and calculate an emotion score. The collection unit can also prioritize the behavioral data to be collected based on the player's emotions. For example, if the player is excited, it can prioritize collecting action-related behavioral data. Alternatively, if the player is relaxed, it can prioritize collecting story-related behavioral data. Alternatively, if the player is stressed, it can prioritize collecting simple, intuitive behavioral data. This makes it possible to prioritize the behavioral data to be collected based on the player's emotions. Emotion estimation can be achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may determine the priority of behavioral data to be collected using an AI model that receives the player's emotional data as input and outputs the priority of behavioral data.

[0114] The collection unit can prioritize collecting highly relevant data in consideration of the geographical location information of the player. The collection unit prioritizes collecting highly relevant data in consideration of the geographical location information of the player. Geographical location information is obtained, for example, using GPS data or location information services. For example, if the player is in a specific area, the collection unit can prioritize collecting behavioral data related to that area. Also, if the player is traveling, the collection unit can prioritize collecting behavioral data related to the travel destination. Furthermore, if the player is at home, the collection unit can prioritize collecting behavioral data related to the home. This makes it possible to prioritize collecting highly relevant data based on the geographical location information of the player. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can collect behavioral data using an AI model that inputs the geographical location information of the player and outputs highly relevant data.

[0115] The collection unit can analyze the player's social media activity and collect related data. The collection unit analyzes the player's social media activity and collects related data. Social media activity includes, for example, the content of posts and the number of likes. The collection unit, for example, collects behavioral data related to topics in which the player has shown interest on social media. The collection unit can also collect related behavioral data by referring to the activities of the player's friends. Furthermore, the content of the player's social media posts can be analyzed to collect related behavioral data. This makes it possible to collect related data based on the player's social media activity. 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 collect behavioral data using an AI model that inputs the player's social media activity and outputs related data.

[0116] The collection unit can customize the collection method by reflecting the player's past feedback. The collection unit customizes the collection method by reflecting the player's past feedback. Past feedback includes, for example, collection methods for which the player has given positive feedback and collection methods for which the player has given negative feedback. The collection unit, for example, preferentially uses collection methods for which the player has given positive feedback. It can also exclude collection methods for which the player has given negative feedback. Furthermore, the collection unit can analyze the player's feedback and customize the optimal collection method. For example, it adjusts the collection means and collection timing based on the player's feedback. This makes it possible to customize the collection method based on the player's past feedback. 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 customize the collection method using an AI model that inputs the player's past feedback and outputs a collection method.

[0117] The analysis unit can estimate the player's emotions and adjust the analysis method based on the estimated player's emotions. The analysis unit can estimate the player's emotions and adjust the analysis method based on the estimated player's emotions. The player's emotions can be estimated using, for example, facial expression recognition or voice analysis. For example, the analysis unit can capture the player's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The analysis unit can also record the player's voice and estimate the emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also adjust the analysis method based on the player's emotions. For example, if the player is excited, it can prioritize analyzing action-related data. Alternatively, if the player is relaxed, it can prioritize analyzing story-related data. Alternatively, if the player is stressed, it can prioritize analyzing simple and intuitive data. This allows the analysis method to be adjusted based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may adjust the analysis method using an AI model that receives the player's emotional data as input and outputs an analysis method.

[0118] The analysis unit can optimize the analysis algorithm by referring to the player's past behavioral data. The analysis unit optimizes the analysis algorithm by referring to the player's past behavioral data. The past behavioral data includes, for example, the player's past actions and reactions to those actions. The analysis unit, for example, selects an optimal analysis algorithm based on the player's past behavioral data. The analysis unit can also find an efficient analysis algorithm from the player's past behavioral data. Furthermore, the analysis unit can analyze the player's behavioral patterns and customize an optimal analysis algorithm. For example, the analysis unit adjusts the analysis algorithm based on the player's behavioral patterns. This allows the analysis algorithm to be optimized based on the player's past 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 optimize the analysis algorithm using an AI model that inputs the player's past behavioral data and outputs an analysis algorithm.

[0119] The analysis unit can improve the accuracy of the analysis based on the player's current game progress. The analysis unit improves the accuracy of the analysis based on the player's current game progress. The current game progress includes, for example, the quest the player is currently in progress of, the character the player is interacting with, and the player's current location. For example, if the player is currently in progress of a specific quest, the analysis unit prioritizes analyzing data related to that quest. Also, if the player is interacting with a specific character, the analysis unit can analyze data related to that interaction. Furthermore, if the player is in a specific location, the analysis unit can analyze data related to that location. This improves the accuracy of the analysis based on the player's current game progress. 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 current game progress and analyze the data using an AI model that improves the accuracy of the analysis.

[0120] The analysis unit can improve the analysis method by reflecting player feedback. The analysis unit improves the analysis method by reflecting player feedback. Feedback includes, for example, analysis methods for which players have given positive feedback and analysis methods for which players have given negative feedback. The analysis unit, for example, preferentially uses analysis methods for which players have given positive feedback. It can also exclude analysis methods for which players have given negative feedback. Furthermore, the analysis unit can analyze player feedback and customize an optimal analysis method. For example, it can adjust the analysis algorithm or the timing of analysis based on player feedback. This allows the analysis method to be improved based on player feedback. 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 improve the analysis method by using an AI model that receives player feedback as input and outputs an analysis method.

[0121] The analysis unit can estimate the player's emotions and adjust the display method of the analysis results based on the estimated player's emotions. The analysis unit can estimate the player's emotions and adjust the display method of the analysis results based on the estimated player's emotions. The player's emotions can be estimated using, for example, facial expression recognition or voice analysis. For example, the analysis unit can capture the player's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the player's voice and estimate the emotion using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. Furthermore, the analysis unit can adjust the display method of the analysis results based on the player's emotions. For example, if the player is excited, a visually stimulating display method can be provided. If the player is relaxed, a calm color scheme can be used. If the player is stressed, a simple and intuitive display method can be provided. This makes it possible to adjust the display method of the analysis results based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may 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 may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may adjust the display method using an AI model that receives the player's emotional data as input and outputs a display method for the analysis results.

[0122] The analysis unit can improve the accuracy of the analysis by taking into account the geographical location information of the player. The analysis unit improves the accuracy of the analysis by taking into account the geographical location information of the player. Geographical location information is obtained, for example, using GPS data or a location information service. For example, if the player is in a specific area, the analysis unit can prioritize analyzing data related to that area. Also, if the player is traveling, the analysis unit can prioritize analyzing data related to the travel destination. Furthermore, if the player is at home, the analysis unit can prioritize analyzing data related to the home. This can improve the accuracy of the analysis based on the geographical location information of the player. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze data using an AI model that uses the geographical location information of the player as input and improves the accuracy of the analysis.

[0123] The analysis unit can analyze the player's social media activity and analyze the related data. The analysis unit analyzes the player's social media activity and analyzes the related data. Social media activity includes, for example, the content of posts and the number of likes. The analysis unit, for example, analyzes data related to topics in which the player has shown interest on social media. The analysis unit can also analyze the related data by referring to the actions of the player's friends. Furthermore, the analysis unit can analyze the content of the player's social media posts and analyze the related data. This makes it possible to analyze the related data based on the player's social media activity. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can analyze the data using an AI model that inputs the player's social media activity and outputs related data.

[0124] The analysis unit can adjust the analysis algorithm by reflecting the player's past feedback. The analysis unit adjusts the analysis algorithm by reflecting the player's past feedback. Past feedback includes, for example, analysis algorithms for which the player has given positive feedback and analysis algorithms for which the player has given negative feedback. The analysis unit, for example, preferentially uses analysis algorithms for which the player has given positive feedback. It can also exclude analysis algorithms for which the player has given negative feedback. Furthermore, the analysis unit can analyze the player's feedback and customize an optimal analysis algorithm. For example, the analysis unit adjusts the analysis algorithm based on the player's feedback. This allows the analysis algorithm to be adjusted based on the player's past feedback. 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 adjust the analysis algorithm using an AI model that inputs the player's past feedback and outputs an analysis algorithm. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described reception unit, generation unit, provision unit, collection unit, and analysis unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the touch panel 38A or microphone 38B of the smart device 14 can be used to receive the player's selection. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a story using a generation AI. The provision unit provides the story to the player using, for example, the display 40A or speaker 40B of the smart device 14. The collection unit collects player behavior data using, for example, the camera 42 or sensor 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 and provides it to the generation unit. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, generation unit, provision unit, collection unit, and analysis unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the microphone 238 of the smart glasses 214 can be used to receive the player's selection. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a story using a generation AI. The provision unit provides the story to the player, for example, using the speaker 240 of the smart glasses 214. The collection unit collects player behavior data, for example, using the camera 42 or sensor 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 and provides it to the generation unit. === Hard Collateral 1-3 === Each of the multiple elements including the above-described reception unit, generation unit, provision unit, collection unit, and analysis unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the microphone 238 of the headset-type terminal 314 can be used to receive the player's selection. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a story using a generation AI. The provision unit provides the story to the player, for example, using the display 343 or speaker 240 of the headset-type terminal 314. The collection unit collects player behavior data, for example, using the camera 42 or sensor 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 and provides it to the generation unit. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, collection unit, and analysis unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the microphone 238 of the robot 414 can be used to receive the player's selection. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a story using a generation AI. The provision unit provides the story to the player, for example, using the speaker 240 of the robot 414. The collection unit collects player behavior data, for example, using the camera 42 or sensor 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 and provides it to the generation unit.

[0125] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0126] The game system may further include a health management unit that collects health data of the player and adjusts the difficulty of gameplay. For example, the game system may monitor the player's heart rate and stress level and adjust the game difficulty in real time based on this data. If the player shows a high stress level, the game difficulty may be lowered to reduce the burden on the player. Alternatively, if the player is relaxed, the game difficulty may be increased to provide a more challenging experience. Furthermore, the health management unit may analyze the player's health data over the long term and provide feedback to help improve the player's health. This allows players to manage their health while enjoying the game.

[0127] The generator not only generates a story based on the player's choices, but can also dynamically change the in-game weather and time of day according to the player's choices. For example, if the player makes a specific choice, the in-game weather can change from sunny to rainy in response to that choice. Also, if the player is playing the game at night, the in-game time can be set to night. This allows for a more realistic gaming experience tailored to the player's choices and playing environment. Furthermore, the generator can change the in-game music and sound effects based on the player's choices. For example, tense music can be played during tense scenes, and calm music can be played during relaxing scenes.

[0128] The providing unit not only displays the generated story to the player, but also adjusts the display method according to the remaining battery level of the player's device. For example, if the battery level is low, the unit lowers the quality of the graphics to reduce energy consumption. On the other hand, if the battery level is sufficient, the unit can provide high-quality graphics. Furthermore, the providing unit can automatically adjust the screen brightness of the player's device. For example, if the player is playing in a dark environment, the unit lowers the screen brightness to reduce eye strain. This makes it possible to provide the optimal display method according to the state of the player's device.

[0129] In addition to collecting player behavior data, the collection unit can also collect player posture data during gameplay and provide feedback to encourage posture improvement. For example, if a player plays in the same posture for a long period of time, a notification urging the player to change posture can be displayed. Also, if a player plays in an improper posture, a guide showing the player how to correct posture can be provided. Furthermore, the collection unit can analyze the player's posture data and evaluate long-term health risks. This allows players to maintain a healthy posture while enjoying the game.

[0130] The analysis unit not only analyzes the collected data, but also analyzes the player's gameplay style and provides optimal gameplay advice. For example, if a player has a specific gameplay style, it can suggest effective strategies and techniques based on that style. It can also provide appropriate guides and tutorials when a player tries a new gameplay style. Furthermore, the analysis unit can provide specific advice to improve the player's skills based on the player's past gameplay data. This allows players to improve their gameplay and enjoy it more.

[0131] The generator not only generates a story based on the player's choices, but can also dynamically adjust the in-game economic system according to the player's choices. For example, if a player chooses to purchase a specific item, the price of that item will fluctuate according to demand. Also, if a player completes a specific quest, it can affect the in-game market. This allows the player's choices to be reflected in the in-game economy in real time, providing a more dynamic gaming experience. Furthermore, the generator can adjust the supply of in-game resources based on the player's choices. For example, if a player uses a lot of a particular resource, the supply of that resource may decrease.

[0132] The providing unit can not only change the in-game environment and characters based on the player's selection, but can also customize in-game voice navigation according to the player's selection. For example, if the player selects to interact with a specific character, the character's tone of voice and speaking style can change. Also, if the player selects to go to a specific location, voice guidance related to that location can be provided. Furthermore, the providing unit can dynamically change the content of the voice navigation based on the player's selection. For example, if the player is in the middle of a specific quest, voice navigation related to that quest can be provided. This makes it possible to provide more personalized voice navigation according to the player's selection.

[0133] The reception unit can estimate the player's emotions and adjust the in-game character's response based on the estimated player's emotions. For example, if the player is excited, the character can respond more lively and energetic. Alternatively, if the player is relaxed, the character can respond calmly and calmly. Furthermore, if the player is stressed, the character can provide a message of encouragement or support. This allows for a more interactive character experience tailored to the player's emotions. Emotion estimation is performed using, for example, facial expression recognition or voice analysis. This allows for the character's response to be adjusted based on the player's emotions.

[0134] The reception unit can not only analyze the player's past selection history, but also adjust the probability of an event occurring in the game based on the player's selection history. For example, if the player frequently made a particular choice in the past, the reception unit can increase the probability of an event occurring related to that choice. Also, if the player avoids a particular choice, the reception unit can decrease the probability of an event occurring related to that choice. Furthermore, the reception unit can generate new events based on the player's selection history. For example, if the player repeatedly makes a particular choice, a new event related to that choice may occur. This makes it possible to provide a more dynamic game experience based on the player's selection history.

[0135] The reception unit not only filters options based on the player's current game progress, but also dynamically adjusts in-game rewards according to the player's progress. For example, when a player is in the middle of a specific quest, the reward for that quest may vary according to the player's progress. Also, when a player is interacting with a specific character, the reward provided by that character may be adjusted based on the player's progress. Furthermore, the reception unit may dynamically change the type and amount of rewards based on the player's progress. For example, when a player completes a specific task, the reward associated with that task may increase. This makes it possible to provide a more personalized reward system according to the player's progress.

[0136] The processing flow of the second embodiment will be briefly explained below.

[0137] Step 1: The reception unit accepts the player's selection. The player's selection may include interacting with a specific character or going to a specific location. The reception unit accepts the selection by the player clicking on an option, or by voice input or gesture input. Step 2: The generation unit uses a generation AI to generate a story based on the selections received by the reception unit. The generation AI generates the story using a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI determines and generates the development of the story based on the player's selections. Step 3: The providing unit provides the story generated by the generating unit to the player. The providing unit provides the story using screen display and audio output. The providing unit can also select the optimal display method depending on the device of the player. For example, if the player is using a smartphone, the providing unit provides a display method that matches the screen size. Step 4: The collection unit collects the player's behavioral data. The behavioral data includes click data, movement data, etc. The collection unit not only records the player's behavior as a log, but can also collect the player's biometric data using sensors. Step 5: The analysis unit analyzes the data collected by the collection unit and provides it to the generation unit. In addition to analyzing the behavioral data using a data analysis algorithm, the analysis unit can also estimate the player's emotions and analyze the data based on those emotions. For example, the analysis unit can estimate the player's emotions using facial expression recognition or voice analysis and analyze the data based on those emotions.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0142] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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 AI 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0158] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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 AI 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.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0174] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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 AI 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.

[0189] 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.

[0190] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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).

[0195] 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.

[0196] 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."

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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, in order to avoid confusion and to 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.

[0208] 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.

[0209] [Explanation of symbols]

[0210] 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 reception unit that receives a player's selection; a generation unit that generates a story based on the selection received by the reception unit; a providing unit that provides the story generated by the generation unit to a player; a collection unit that collects player behavior data; an analysis unit that analyzes the data collected by the collection unit; Equipped with A system characterized by:

2. The generation unit Generate a story based on player choices 2. The system of claim 1.

3. The providing unit Display the generated story to the player 2. The system of claim 1.

4. The collecting unit Collecting player behavior data 2. The system of claim 1.

5. The analysis unit Analyze the collected data and provide it to the generator 2. The system of claim 1.

6. The generation unit Generate a story based on the analysis results 2. The system of claim 1.

7. The providing unit Specific changes to the in-game environment or characters based on player choices 2. The system of claim 1.

8. The reception unit Inferring the player's emotions and adjusting the way choices are presented based on the inferred player emotions 2. The system of claim 1.

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