system

Abstract

The system according to this embodiment aims to personalize the game environment according to the user's play style and preferences. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and an update unit. The collection unit collects the user's play style, preferences, and feedback. The analysis unit analyzes the data collected by the collection unit. The generation unit generates the game environment, characters, and quests based on the data analyzed by the analysis unit. The update unit updates the game environment, characters, and quests generated by the generation unit in real time.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the personalization of the game environment according to the play style and preferences of the user has not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to personalize the game environment according to the play style and preferences of the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and an update unit. The collection unit collects the user's play style, preferences, and feedback. The analysis unit analyzes the data collected by the collection unit. The generation unit generates the game environment, characters, and quests based on the data analyzed by the analysis unit. The update unit updates the game environment, characters, and quests generated by the generation unit in real time. [Effects of the Invention]

[0007] The system according to this embodiment can personalize the game environment according to the user's play style and preferences. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The game customization system according to an embodiment of the present invention is a system that analyzes a user's play style, preferences, and feedback in real time and generates a game environment, characters, and quests tailored to each individual user. This game customization system collects the user's play style, preferences, and feedback in real time, and the collected data is analyzed by AI to generate the optimal game environment, characters, and quests for the user. As a result, users can always enjoy a fresh and personalized gaming experience, improving immersion and satisfaction in the game. For example, if a user frequently uses a particular character, quests and items related to that character can be generated. Next, the collected data is analyzed by AI. Based on the collected data, the AI ​​determines the user's play style and preferences and generates the optimal game environment, characters, and quests for the user. For example, if a user prefers action games, quests and characters with strong action elements can be generated. Furthermore, the generated game environment, characters, and quests are updated in real time based on user feedback. For example, if a user provides feedback that a particular quest is "too difficult," the difficulty level of that quest can be adjusted. As a result, users can always enjoy a gaming experience that suits them. This mechanism improves user engagement and increases average play time. Furthermore, improved user satisfaction leads to higher repeat customer rates and increased acquisition of new users. For example, a personalized experience can be expected to increase average playtime by 30% and repeat customer rates by 50%. In addition, the AI ​​keeps user profiles constantly up-to-date. This allows for quick responses to changes in user behavior and preferences, providing the optimal gaming experience. For example, when a user starts using a new character, quests and items related to that character can be instantly generated. In this way, real-time game customization using AI allows users to always enjoy a fresh and personalized gaming experience. This improves game immersion and satisfaction, and increases user engagement.This allows the game customization system to generate and update game environments, characters, and quests in real time based on the user's play style, preferences, and feedback, thereby providing a personalized gaming experience.

[0029] The game customization system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and an update unit. The collection unit collects the user's play style, preferences, and feedback. The collection unit collects data such as the user's actions, selected characters, and completed quests. The collection unit collects detailed data such as what actions the user took, which characters they chose, and which quests they completed. For example, if the user frequently uses a particular character, the collection unit collects data to generate quests and items related to that character. The analysis unit analyzes the data collected by the collection unit. The analysis unit determines the user's play style and preferences based on the collected data. For example, if the user prefers action games, the analysis unit analyzes data to generate quests and characters with strong action elements. The generation unit generates game environments, characters, and quests based on the data analyzed by the analysis unit. The generation unit generates game environments, characters, and quests based on the user's play style and preferences. For example, if the user prefers action games, the generation unit generates quests and characters with strong action elements. The update unit updates the game environment, characters, and quests generated by the generation unit in real time. The update unit also updates the game environment, characters, and quests generated based on user feedback in real time. For example, if a user provides feedback that a particular quest is "too difficult," the update unit adjusts the difficulty level of that quest. In this way, the game customization system according to the embodiment can provide a personalized gaming experience by generating and updating the game environment, characters, and quests in real time based on the user's play style, preferences, and feedback.

[0030] The data collection unit collects user play styles, preferences, and feedback. Specifically, it collects data such as user actions, selected characters, and completed quests. For example, it collects detailed data such as what actions the user took in the game, which characters they chose, and which quests they completed. If a user frequently uses a particular character, the data collection unit collects data to generate quests and items related to that character. This allows the data collection unit to understand the user's play style and preferences in detail. Furthermore, the data collection unit monitors the user's choices and actions in the game in real time and accumulates data. For example, if a user prefers to play a particular game mode, the data collection unit focuses on collecting data related to that mode. The data collection unit also provides an interface for collecting user feedback, allowing users to input what they felt and what improvements they could make during gameplay. This allows the data collection unit to understand not only the user's play style and preferences, but also their opinions and requests. The collected data is stored in a central database and made accessible to the analysis and generation units. This allows the data collection unit to gain a detailed understanding of the user's play style and preferences, providing foundational data for personalizing the gaming experience.

[0031] The analysis unit analyzes the data collected by the data collection unit. Specifically, it determines the user's play style and preferences based on the collected data. For example, if a user prefers action games, it analyzes data to generate quests and characters with strong action elements. The analysis unit uses AI to analyze data and understand the user's play style and preferences in detail. For example, if a user frequently uses a particular character, it analyzes data to generate quests and items related to that character. The analysis unit analyzes the user's behavior patterns and selection tendencies to determine what kind of play style the user has. For example, if a user has an aggressive play style, it determines that they tend to prefer characters and weapons with high attack power. The analysis unit also analyzes user feedback to understand what kind of requests and opinions users have. For example, if a user provides feedback that a particular quest is "too difficult," it analyzes data to adjust the difficulty of that quest. In this way, the analysis unit can understand the user's play style and preferences in detail and provide foundational data for personalizing the game experience. Furthermore, the analysis unit can also utilize historical data and statistical information to analyze long-term trends and patterns. This allows the analysis unit to gain a detailed understanding of the user's play style and preferences, providing foundational data for personalizing the game experience.

[0032] The generation unit generates game environments, characters, and quests based on data analyzed by the analysis unit. Specifically, it generates game environments, characters, and quests based on the user's play style and preferences. For example, if a user prefers action games, it generates quests and characters with strong action elements. The generation unit uses AI to generate game environments, characters, and quests, providing a personalized game experience tailored to the user's play style and preferences. For example, if a user frequently uses a particular character, it generates quests and items related to that character. The generation unit dynamically generates in-game elements based on the user's play style and preferences, providing the user with the optimal game experience. For example, if a user prefers exploration, it generates quests and areas with strong exploration elements. The generation unit can also adjust in-game elements based on user feedback. For example, if a user provides feedback that a particular quest is "too difficult," it adjusts the difficulty of that quest. This allows the generation unit to provide a personalized game experience tailored to the user's play style and preferences. Furthermore, the generation unit can generate in-game elements in real time and optimize the game experience to match the user's play style and preferences. This allows the generation unit to provide a personalized game experience tailored to the user's play style and preferences.

[0033] The update unit updates the game environment, characters, and quests generated by the generation unit in real time. Specifically, it updates the game environment, characters, and quests generated based on user feedback in real time. For example, if a user provides feedback that a particular quest is "too difficult," the update unit adjusts the difficulty of that quest. The update unit reflects user feedback in real time and dynamically updates elements within the game. For example, if a user provides feedback that a particular character's abilities are "too strong," the update unit adjusts those abilities. By collecting user feedback and adjusting elements within the game in real time, the update unit provides users with the optimal gaming experience. Furthermore, the update unit can also dynamically update elements within the game based on the user's play style and preferences. For example, if a user prefers to play a particular game mode, the update unit will focus on updating elements related to that mode. This allows the update unit to provide a personalized gaming experience tailored to the user's play style and preferences. Furthermore, by collecting user feedback and adjusting elements within the game in real time, the update unit provides users with the optimal gaming experience. This allows the update unit to provide a personalized gaming experience tailored to the user's play style and preferences.

[0034] The data collection unit can collect data such as user behavior, selected characters, and completed quests. For example, the data collection unit can collect user behavior. The data collection unit can collect data such as user movement patterns, choice selections, and play time. The data collection unit can also collect data on the character selected by the user. The data collection unit can collect data such as character type, skills, and equipment. Furthermore, the data collection unit can collect data on quests completed by the user. The data collection unit can collect data such as quest type, difficulty, and completion time. This allows the data collection unit to perform more detailed analysis by collecting data based on user behavior and choices. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user behavior data into a generating AI and have the generating AI perform the analysis of the behavior data.

[0035] The analysis unit can determine the user's play style and preferences based on the collected data. For example, the analysis unit can determine the user's play style based on the collected data. The analysis unit can analyze the user's behavioral data and determine the user's play style. For example, if the analysis unit determines that the user has an aggressive play style, it will generate a game environment based on that play style. The analysis unit can also determine the user's preferences based on the collected data. The analysis unit can analyze data on the character the user has selected and the quests they have completed and determine the user's preferences. For example, if the analysis unit determines that the user likes a particular character, it will generate quests and items related to that character. In this way, the analysis unit can generate a more appropriate game environment, character, and quest by determining the user's play style and preferences. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI perform the determination of play style and preferences.

[0036] The generation unit can generate game environments, characters, and quests based on the user's play style and preferences. For example, the generation unit can generate a game environment based on the user's play style. The generation unit can generate action-oriented quests and characters depending on the user's play style. For example, if the user prefers action games, the generation unit will generate action-oriented quests. The generation unit can also generate characters based on the user's preferences. The generation unit can generate specific characters and items depending on the user's preferences. For example, if the user prefers a particular character, the generation unit will generate items related to that character. In this way, the generation unit can provide a personalized gaming experience by generating game environments, characters, and quests based on the user's play style and preferences. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's play style and preferences into a generation AI and have the generation AI perform the generation of game environments, characters, and quests.

[0037] The update unit can update the game environment, characters, and quests generated based on user feedback in real time. For example, the update unit can update the game environment based on user feedback. The update unit can analyze user feedback and adjust the difficulty and content of the game environment. For example, if a user provides feedback that a particular quest is "too difficult," the update unit will adjust the difficulty of that quest. The update unit can also update characters based on user feedback. The update unit can adjust the character's design and skills based on user feedback. For example, if a user provides feedback that a particular character is "not appealing," the update unit will change the character's design. In this way, the update unit can always provide the optimal game experience by updating the game environment, characters, and quests in real time based on user feedback. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input user feedback data into a generation AI and have the generation AI perform updates to the game environment, characters, and quests.

[0038] The generation unit can keep the user's profile up to date. For example, the generation unit collects data on the user's behavior and preferences and updates the profile. The generation unit can analyze the user's behavior and preference data and keep the profile up to date. For example, when a user starts using a new character, the generation unit instantly generates quests and items related to that character. This allows the generation unit to quickly respond to changes in the user's behavior and preferences by keeping the user's profile up to date. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user behavior and preference data into a generation AI and have the generation AI perform the profile update.

[0039] The data collection unit can analyze the user's past play history and select the optimal data collection method. For example, the data collection unit can analyze data such as the user's play time, completed quests, and characters used. For example, the data collection unit can prioritize collecting actions that the user has frequently performed in the past. The data collection unit can also focus on collecting data related to characters that the user has selected in the past. Furthermore, based on data from quests the user has completed in the past, the data collection unit can collect data on quests that the user is most likely to complete next. This allows the data collection unit to collect data more effectively by analyzing the user's past play history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past play history data into a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can filter data based on the user's current game progress and areas of interest during data collection. For example, the data collection unit can filter data based on the user's current game progress. The data collection unit can prioritize collecting data related to the quest the user is currently in progress on. The data collection unit can also filter data based on the user's areas of interest. The data collection unit can collect data related to game elements that the user is interested in. Furthermore, the data collection unit can collect data related to the character the user is currently using. This allows the data collection unit to collect more relevant data by filtering data based on the user's current game progress and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current game progress and areas of interest into a generating AI and have the generating AI perform data filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can collect data considering the user's geographical location information. If the user is in a specific region, the data collection unit can collect data on game events related to that region. If the user is traveling, the data collection unit can also collect data on game elements related to the travel destination. Furthermore, if the user is at home, the data collection unit can collect data related to playing at home. In this way, the data collection unit can collect more relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the user's social media activity. The data collection unit can collect data on gameplay shared by the user on social media. The data collection unit can also collect information on game-related accounts that the user follows on social media. Furthermore, the data collection unit can collect data on game communities that the user participates in on social media. This allows the data collection unit to collect more relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit can perform detailed analysis on important data. For example, the analysis unit can perform detailed analysis on data of high user interest. The analysis unit can also perform simplified analysis on less important data. For example, the analysis unit can perform simplified analysis on data of low user interest. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit prioritizes the analysis of high-importance data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the data category. For game play data, the analysis unit can apply a play style analysis algorithm. For example, the analysis unit can apply a clustering algorithm to analyze the user's play style. The analysis unit can also apply an emotion analysis algorithm to user feedback data. For example, the analysis unit can apply a natural language processing algorithm to analyze the emotion of the user's feedback. Furthermore, the analysis unit can apply a preference analysis algorithm to user preference data. For example, the analysis unit can apply a collaborative filtering algorithm to analyze the user's preferences. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI select the analysis algorithm to apply.

[0045] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can determine the priority of analysis based on the data collection period. The analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also postpone the analysis of older data. For example, the analysis unit postpones the analysis of older data. Furthermore, the analysis unit can adjust the priority of analysis according to the data collection period. For example, the analysis unit adjusts the priority of analysis according to the data collection period. In this way, the analysis unit can prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI perform the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit prioritizes the analysis of data of high user interest. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit postpones the analysis of data of low user interest. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. For example, the analysis unit adjusts the order of analysis according to the relevance of the data. This enables efficient analysis by allowing the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0047] The generation unit can adjust the level of detail of the generated content based on the user's play style and preferences during the generation process. For example, the generation unit can adjust the level of detail of the generated content based on the user's play style. Depending on the user's play style, the generation unit can generate quests with strong action elements. For example, if the user prefers action games, the generation unit will generate quests with strong action elements. The generation unit can also adjust the level of detail of the generated content based on the user's preferences. Depending on the user's preferences, the generation unit can generate characters with detailed storylines. For example, if the user prefers story-driven games, the generation unit will generate characters with detailed storylines. Furthermore, the generation unit can adjust the level of detail of the generated content based on the user's play style and preferences. If the user prefers exploration, the generation unit can generate vast game environments. In this way, the generation unit can provide the user with the optimal game experience by adjusting the level of detail of the generated content based on the user's play style and preferences. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input data on the user's play style and preferences into the generation AI, and have the generation AI adjust the level of detail in the generation process.

[0048] The generation unit can generate the optimal game environment, characters, and quests by referring to the user's past play history during the generation process. For example, the generation unit can refer to the user's past play history. The generation unit can refer to data such as the user's play time, completed quests, and characters used. For example, the generation unit can generate quests related to characters the user has preferred to use in the past. The generation unit can also generate items related to quests the user has completed in the past. Furthermore, the generation unit can generate new areas related to game environments the user has visited in the past. In this way, the generation unit can provide a more personalized gaming experience by referring to the user's past play history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past play history data into a generation AI and have the generation AI perform the generation of the optimal game environment, characters, and quests.

[0049] The generation unit can generate highly relevant game environments, characters, and quests while considering the user's geographical location information during generation. For example, the generation unit can generate a game environment considering the user's geographical location information. If the user is in a specific region, the generation unit can generate a game environment related to that region. Also, if the user is traveling, the generation unit can generate characters related to the travel destination. Furthermore, if the user is at home, the generation unit can generate quests related to playing at home. In this way, the generation unit can provide a more relevant gaming experience by generating game environments, characters, and quests while considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI execute the generation of highly relevant game environments, characters, and quests.

[0050] The generation unit can analyze the user's social media activity during generation and generate relevant game environments, characters, and quests. For example, the generation unit can analyze the user's social media activity. The generation unit can generate quests related to gameplay shared by the user on social media. The generation unit can also generate characters related to game-related accounts that the user follows on social media. Furthermore, the generation unit can generate game environments related to game communities that the user participates in on social media. In this way, the generation unit can provide a more relevant game experience by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation of relevant game environments, characters, and quests.

[0051] The update unit can analyze user feedback during updates to select the optimal update method. For example, the update unit analyzes user feedback. If a user provides feedback that a particular quest is "too difficult," the update unit can adjust the difficulty of that quest. Also, if a user provides feedback that a particular character is "unappealing," the update unit can change the design of that character. Furthermore, if a user provides feedback that a particular game environment is "boring," the update unit can add new elements to that environment. In this way, the update unit can make more appropriate updates by analyzing user feedback. Some or all of the above processes in the update unit may be performed using AI, for example, or not. For example, the update unit can input user feedback data into a generating AI and have the generating AI select the optimal update method.

[0052] The update unit can customize the content of updates based on the user's current game progress. For example, the update unit can customize the content of updates based on the user's current game progress. If the user is progressing through a specific quest, the update unit can prioritize updates related to that quest. Also, if the user starts using a new character, the update unit can perform updates related to that character. Furthermore, if the user is in a specific game environment, the update unit can perform updates related to that environment. In this way, the update unit can perform more appropriate updates by customizing the content of updates based on the user's current game progress. Some or all of the above processing in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input the user's current game progress data into a generating AI and have the generating AI perform the customization of the update content.

[0053] The update unit can select the optimal update method by considering the user's geographical location information during the update process. For example, the update unit can perform updates while considering the user's geographical location information. If the user is in a specific region, the update unit can update game events related to that region. If the user is traveling, the update unit can update game elements related to the travel destination. Furthermore, if the user is at home, the update unit can perform updates related to playing at home. In this way, the update unit can perform more relevant updates by considering the user's geographical location information. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal update method.

[0054] The update unit can analyze the user's social media activity and suggest update content during the update process. For example, the update unit can analyze the user's social media activity. The update unit can perform updates related to gameplay shared by the user on social media. It can also perform updates related to game-related accounts that the user follows on social media. Furthermore, the update unit can perform updates related to game communities that the user participates in on social media. This allows the update unit to perform more relevant updates by analyzing the user's social media activity. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's social media activity data into a generating AI and have the generating AI suggest update content.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The analytics unit can customize in-game ad displays based on the user's play style and preferences. For example, if a user prefers action games, ads related to action games will be prioritized. Also, if a user frequently uses a particular character, ads for products and services related to that character can be displayed. Furthermore, the content and frequency of ads can be adjusted based on user feedback. This maximizes the effectiveness of ads by displaying those that are most relevant to the user.

[0057] The generation unit can customize in-game music and sound effects based on the user's play style and preferences. For example, if the user prefers a relaxed play style, calming music can be played. Conversely, if the user prefers action games, fast-paced music and sound effects can be used. Furthermore, the type and volume of music and sound effects can be adjusted based on user feedback. This allows for the provision of the optimal sound environment for the user.

[0058] The collection department can customize the in-game reward system based on the user's play style and preferences. For example, if a user frequently completes a particular quest, the rewards associated with that quest can be increased. Also, if a user likes a particular character, items related to that character can be offered as rewards. Furthermore, the content and frequency of rewards can be adjusted based on user feedback. This allows for the provision of an attractive reward system for users.

[0059] The analysis unit can customize the in-game story progression based on the user's play style and preferences. For example, if a user prefers story-driven games, it can provide quests with detailed storylines. If a user prefers action-driven games, it can provide a story progression with strong action elements. Furthermore, it can adjust the story content and pace based on user feedback. This allows for a more engaging story experience for the user.

[0060] The collection unit can customize in-game tutorials based on the user's play style and preferences. For example, it can provide a detailed tutorial for beginners, and a simplified tutorial for experienced players. Furthermore, it can adjust the content and pace of the tutorial based on user feedback. This allows for the provision of the optimal learning experience for each user.

[0061] The update section allows for customization of the in-game item shop based on the user's play style and preferences. For example, if a user likes a particular character, items related to that character will be displayed preferentially. Similarly, if a user likes a particular game element, items related to that element can be displayed. Furthermore, the content and price of items can be adjusted based on user feedback. This allows for the provision of an item shop that is appealing to users.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit gathers user play styles, preferences, and feedback. For example, it collects data such as user actions, selected characters, and completed quests, gathering detailed information about what actions users took, which characters they chose, and which quests they completed. Step 2: The analysis unit analyzes the data collected by the collection unit. Based on the collected data, the analysis unit determines the user's play style and preferences. For example, if the user prefers action games, the analysis unit analyzes data to generate quests and characters with strong action elements. Step 3: The generation unit generates the game environment, characters, and quests based on the data analyzed by the analysis unit. The generation unit generates the game environment, characters, and quests based on the user's play style and preferences. For example, if the user prefers action games, it will generate quests and characters with strong action elements. Step 4: The update unit updates the game environment, characters, and quests generated by the generation unit in real time. The update unit updates the game environment, characters, and quests generated based on user feedback in real time. For example, if a user provides feedback that a particular quest is "too difficult," the difficulty level of that quest will be adjusted.

[0064] (Example of form 2) The game customization system according to an embodiment of the present invention is a system that analyzes a user's play style, preferences, and feedback in real time and generates a game environment, characters, and quests tailored to each individual user. This game customization system collects the user's play style, preferences, and feedback in real time, and the collected data is analyzed by AI to generate the optimal game environment, characters, and quests for the user. As a result, users can always enjoy a fresh and personalized gaming experience, improving immersion and satisfaction in the game. For example, if a user frequently uses a particular character, quests and items related to that character can be generated. Next, the collected data is analyzed by AI. Based on the collected data, the AI ​​determines the user's play style and preferences and generates the optimal game environment, characters, and quests for the user. For example, if a user prefers action games, quests and characters with strong action elements can be generated. Furthermore, the generated game environment, characters, and quests are updated in real time based on user feedback. For example, if a user provides feedback that a particular quest is "too difficult," the difficulty level of that quest can be adjusted. As a result, users can always enjoy a gaming experience that suits them. This mechanism improves user engagement and increases average play time. Furthermore, improved user satisfaction leads to higher repeat customer rates and increased acquisition of new users. For example, a personalized experience can be expected to increase average playtime by 30% and repeat customer rates by 50%. In addition, the AI ​​keeps user profiles constantly up-to-date. This allows for quick responses to changes in user behavior and preferences, providing the optimal gaming experience. For example, when a user starts using a new character, quests and items related to that character can be instantly generated. In this way, real-time game customization using AI allows users to always enjoy a fresh and personalized gaming experience. This improves game immersion and satisfaction, and increases user engagement.This allows the game customization system to generate and update game environments, characters, and quests in real time based on the user's play style, preferences, and feedback, thereby providing a personalized gaming experience.

[0065] The game customization system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and an update unit. The collection unit collects the user's play style, preferences, and feedback. The collection unit collects data such as the user's actions, selected characters, and completed quests. The collection unit collects detailed data such as what actions the user took, which characters they chose, and which quests they completed. For example, if the user frequently uses a particular character, the collection unit collects data to generate quests and items related to that character. The analysis unit analyzes the data collected by the collection unit. The analysis unit determines the user's play style and preferences based on the collected data. For example, if the user prefers action games, the analysis unit analyzes data to generate quests and characters with strong action elements. The generation unit generates game environments, characters, and quests based on the data analyzed by the analysis unit. The generation unit generates game environments, characters, and quests based on the user's play style and preferences. For example, if the user prefers action games, the generation unit generates quests and characters with strong action elements. The update unit updates the game environment, characters, and quests generated by the generation unit in real time. The update unit also updates the game environment, characters, and quests generated based on user feedback in real time. For example, if a user provides feedback that a particular quest is "too difficult," the update unit adjusts the difficulty level of that quest. In this way, the game customization system according to the embodiment can provide a personalized gaming experience by generating and updating the game environment, characters, and quests in real time based on the user's play style, preferences, and feedback.

[0066] The data collection unit collects user play styles, preferences, and feedback. Specifically, it collects data such as user actions, selected characters, and completed quests. For example, it collects detailed data such as what actions the user took in the game, which characters they chose, and which quests they completed. If a user frequently uses a particular character, the data collection unit collects data to generate quests and items related to that character. This allows the data collection unit to understand the user's play style and preferences in detail. Furthermore, the data collection unit monitors the user's choices and actions in the game in real time and accumulates data. For example, if a user prefers to play a particular game mode, the data collection unit focuses on collecting data related to that mode. The data collection unit also provides an interface for collecting user feedback, allowing users to input what they felt and what improvements they could make during gameplay. This allows the data collection unit to understand not only the user's play style and preferences, but also their opinions and requests. The collected data is stored in a central database and made accessible to the analysis and generation units. This allows the data collection unit to gain a detailed understanding of the user's play style and preferences, providing foundational data for personalizing the gaming experience.

[0067] The analysis unit analyzes the data collected by the data collection unit. Specifically, it determines the user's play style and preferences based on the collected data. For example, if a user prefers action games, it analyzes data to generate quests and characters with strong action elements. The analysis unit uses AI to analyze data and understand the user's play style and preferences in detail. For example, if a user frequently uses a particular character, it analyzes data to generate quests and items related to that character. The analysis unit analyzes the user's behavior patterns and selection tendencies to determine what kind of play style the user has. For example, if a user has an aggressive play style, it determines that they tend to prefer characters and weapons with high attack power. The analysis unit also analyzes user feedback to understand what kind of requests and opinions users have. For example, if a user provides feedback that a particular quest is "too difficult," it analyzes data to adjust the difficulty of that quest. In this way, the analysis unit can understand the user's play style and preferences in detail and provide foundational data for personalizing the game experience. Furthermore, the analysis unit can also utilize historical data and statistical information to analyze long-term trends and patterns. This allows the analysis unit to gain a detailed understanding of the user's play style and preferences, providing foundational data for personalizing the game experience.

[0068] The generation unit generates game environments, characters, and quests based on data analyzed by the analysis unit. Specifically, it generates game environments, characters, and quests based on the user's play style and preferences. For example, if a user prefers action games, it generates quests and characters with strong action elements. The generation unit uses AI to generate game environments, characters, and quests, providing a personalized game experience tailored to the user's play style and preferences. For example, if a user frequently uses a particular character, it generates quests and items related to that character. The generation unit dynamically generates in-game elements based on the user's play style and preferences, providing the user with the optimal game experience. For example, if a user prefers exploration, it generates quests and areas with strong exploration elements. The generation unit can also adjust in-game elements based on user feedback. For example, if a user provides feedback that a particular quest is "too difficult," it adjusts the difficulty of that quest. This allows the generation unit to provide a personalized game experience tailored to the user's play style and preferences. Furthermore, the generation unit can generate in-game elements in real time and optimize the game experience to match the user's play style and preferences. This allows the generation unit to provide a personalized game experience tailored to the user's play style and preferences.

[0069] The update unit updates the game environment, characters, and quests generated by the generation unit in real time. Specifically, it updates the game environment, characters, and quests generated based on user feedback in real time. For example, if a user provides feedback that a particular quest is "too difficult," the update unit adjusts the difficulty of that quest. The update unit reflects user feedback in real time and dynamically updates elements within the game. For example, if a user provides feedback that a particular character's abilities are "too strong," the update unit adjusts those abilities. By collecting user feedback and adjusting elements within the game in real time, the update unit provides users with the optimal gaming experience. Furthermore, the update unit can also dynamically update elements within the game based on the user's play style and preferences. For example, if a user prefers to play a particular game mode, the update unit will focus on updating elements related to that mode. This allows the update unit to provide a personalized gaming experience tailored to the user's play style and preferences. Furthermore, by collecting user feedback and adjusting elements within the game in real time, the update unit provides users with the optimal gaming experience. This allows the update unit to provide a personalized gaming experience tailored to the user's play style and preferences.

[0070] The data collection unit can collect data such as user behavior, selected characters, and completed quests. For example, the data collection unit can collect user behavior. The data collection unit can collect data such as user movement patterns, choice selections, and play time. The data collection unit can also collect data on the character selected by the user. The data collection unit can collect data such as character type, skills, and equipment. Furthermore, the data collection unit can collect data on quests completed by the user. The data collection unit can collect data such as quest type, difficulty, and completion time. This allows the data collection unit to perform more detailed analysis by collecting data based on user behavior and choices. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user behavior data into a generating AI and have the generating AI perform the analysis of the behavior data.

[0071] The analysis unit can determine the user's play style and preferences based on the collected data. For example, the analysis unit can determine the user's play style based on the collected data. The analysis unit can analyze the user's behavioral data and determine the user's play style. For example, if the analysis unit determines that the user has an aggressive play style, it will generate a game environment based on that play style. The analysis unit can also determine the user's preferences based on the collected data. The analysis unit can analyze data on the character the user has selected and the quests they have completed and determine the user's preferences. For example, if the analysis unit determines that the user likes a particular character, it will generate quests and items related to that character. In this way, the analysis unit can generate a more appropriate game environment, character, and quest by determining the user's play style and preferences. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI perform the determination of play style and preferences.

[0072] The generation unit can generate game environments, characters, and quests based on the user's play style and preferences. For example, the generation unit can generate a game environment based on the user's play style. The generation unit can generate action-oriented quests and characters depending on the user's play style. For example, if the user prefers action games, the generation unit will generate action-oriented quests. The generation unit can also generate characters based on the user's preferences. The generation unit can generate specific characters and items depending on the user's preferences. For example, if the user prefers a particular character, the generation unit will generate items related to that character. In this way, the generation unit can provide a personalized gaming experience by generating game environments, characters, and quests based on the user's play style and preferences. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's play style and preferences into a generation AI and have the generation AI perform the generation of game environments, characters, and quests.

[0073] The update unit can update the game environment, characters, and quests generated based on user feedback in real time. For example, the update unit can update the game environment based on user feedback. The update unit can analyze user feedback and adjust the difficulty and content of the game environment. For example, if a user provides feedback that a particular quest is "too difficult," the update unit will adjust the difficulty of that quest. The update unit can also update characters based on user feedback. The update unit can adjust the character's design and skills based on user feedback. For example, if a user provides feedback that a particular character is "not appealing," the update unit will change the character's design. In this way, the update unit can always provide the optimal game experience by updating the game environment, characters, and quests in real time based on user feedback. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input user feedback data into a generation AI and have the generation AI perform updates to the game environment, characters, and quests.

[0074] The generation unit can keep the user's profile up to date. For example, the generation unit collects data on the user's behavior and preferences and updates the profile. The generation unit can analyze the user's behavior and preference data and keep the profile up to date. For example, when a user starts using a new character, the generation unit instantly generates quests and items related to that character. This allows the generation unit to quickly respond to changes in the user's behavior and preferences by keeping the user's profile up to date. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user behavior and preference data into a generation AI and have the generation AI perform the profile update.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can estimate the user's emotions. The data collection unit can estimate emotions using the user's facial recognition, voice analysis, behavioral patterns, etc. For example, if the user is excited, the data collection unit will refrain from collecting data so as not to disrupt the game's progress. Also, if the user is relaxed, the data collection unit can collect data frequently for detailed analysis. Furthermore, if the user is stressed, the data collection unit can minimize data collection and prioritize the game experience. In this way, the data collection unit can collect data without compromising the game experience by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the timing of data collection.

[0076] The data collection unit can analyze the user's past play history and select the optimal data collection method. For example, the data collection unit can analyze data such as the user's play time, completed quests, and characters used. For example, the data collection unit can prioritize collecting actions that the user has frequently performed in the past. The data collection unit can also focus on collecting data related to characters that the user has selected in the past. Furthermore, based on data from quests the user has completed in the past, the data collection unit can collect data on quests that the user is most likely to complete next. This allows the data collection unit to collect data more effectively by analyzing the user's past play history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past play history data into a generating AI and have the generating AI select the optimal data collection method.

[0077] The data collection unit can filter data based on the user's current game progress and areas of interest during data collection. For example, the data collection unit can filter data based on the user's current game progress. The data collection unit can prioritize collecting data related to the quest the user is currently in progress on. The data collection unit can also filter data based on the user's areas of interest. The data collection unit can collect data related to game elements that the user is interested in. Furthermore, the data collection unit can collect data related to the character the user is currently using. This allows the data collection unit to collect more relevant data by filtering data based on the user's current game progress and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's current game progress and areas of interest into a generating AI and have the generating AI perform data filtering.

[0078] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, the data collection unit can estimate the user's emotions using methods such as facial recognition, voice analysis, and behavioral patterns. For example, if the user is excited, the data collection unit will prioritize collecting data that is important for the game's progress. Also, if the user is relaxed, the data collection unit can collect detailed data to improve the accuracy of the analysis. Furthermore, if the user is stressed, the data collection unit can minimize data collection so as not to impair the game experience. In this way, the data collection unit can collect important data without impairing the game experience by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI determine the priority of the data.

[0079] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can collect data considering the user's geographical location information. If the user is in a specific region, the data collection unit can collect data on game events related to that region. If the user is traveling, the data collection unit can also collect data on game elements related to the travel destination. Furthermore, if the user is at home, the data collection unit can collect data related to playing at home. In this way, the data collection unit can collect more relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0080] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the user's social media activity. The data collection unit can collect data on gameplay shared by the user on social media. The data collection unit can also collect information on game-related accounts that the user follows on social media. Furthermore, the data collection unit can collect data on game communities that the user participates in on social media. This allows the data collection unit to collect more relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions. The analysis unit can estimate emotions using the user's facial recognition, voice analysis, behavioral patterns, etc. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the way the analysis is expressed.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit can perform detailed analysis on important data. For example, the analysis unit can perform detailed analysis on data of high user interest. The analysis unit can also perform simplified analysis on less important data. For example, the analysis unit can perform simplified analysis on data of low user interest. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit prioritizes the analysis of high-importance data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the data category. For game play data, the analysis unit can apply a play style analysis algorithm. For example, the analysis unit can apply a clustering algorithm to analyze the user's play style. The analysis unit can also apply an emotion analysis algorithm to user feedback data. For example, the analysis unit can apply a natural language processing algorithm to analyze the emotion of the user's feedback. Furthermore, the analysis unit can apply a preference analysis algorithm to user preference data. For example, the analysis unit can apply a collaborative filtering algorithm to analyze the user's preferences. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI select the analysis algorithm to apply.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using facial recognition, voice analysis, behavioral patterns, etc. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis result. In this way, the analysis unit can provide the optimal analysis result for the user by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0085] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can determine the priority of analysis based on the data collection period. The analysis unit can prioritize the analysis of the most recent data. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also postpone the analysis of older data. For example, the analysis unit postpones the analysis of older data. Furthermore, the analysis unit can adjust the priority of analysis according to the data collection period. For example, the analysis unit adjusts the priority of analysis according to the data collection period. In this way, the analysis unit can prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI perform the determination of the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit prioritizes the analysis of data of high user interest. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit postpones the analysis of data of low user interest. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. For example, the analysis unit adjusts the order of analysis according to the relevance of the data. This enables efficient analysis by allowing the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of analysis.

[0087] The generation unit can estimate the user's emotions and adjust the way the game environment, characters, and quests are presented based on the estimated user emotions. For example, the generation unit can estimate the user's emotions using facial recognition, voice analysis, behavioral patterns, etc. For example, if the user is relaxed, the generation unit can generate a calm game environment. If the user is excited, the generation unit can generate a stimulating game environment. Furthermore, if the user is stressed, the generation unit can generate relaxing characters and quests. In this way, the generation unit can provide the user with the optimal game experience by adjusting the way the game environment, characters, and quests are presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the way the game environment, characters, and quests are presented.

[0088] The generation unit can adjust the level of detail of the generated content based on the user's play style and preferences during the generation process. For example, the generation unit can adjust the level of detail of the generated content based on the user's play style. Depending on the user's play style, the generation unit can generate quests with strong action elements. For example, if the user prefers action games, the generation unit will generate quests with strong action elements. The generation unit can also adjust the level of detail of the generated content based on the user's preferences. Depending on the user's preferences, the generation unit can generate characters with detailed storylines. For example, if the user prefers story-driven games, the generation unit will generate characters with detailed storylines. Furthermore, the generation unit can adjust the level of detail of the generated content based on the user's play style and preferences. If the user prefers exploration, the generation unit can generate vast game environments. In this way, the generation unit can provide the user with the optimal game experience by adjusting the level of detail of the generated content based on the user's play style and preferences. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input data on the user's play style and preferences into the generation AI, and have the generation AI adjust the level of detail in the generation process.

[0089] The generation unit can generate the optimal game environment, characters, and quests by referring to the user's past play history during the generation process. For example, the generation unit can refer to the user's past play history. The generation unit can refer to data such as the user's play time, completed quests, and characters used. For example, the generation unit can generate quests related to characters the user has preferred to use in the past. The generation unit can also generate items related to quests the user has completed in the past. Furthermore, the generation unit can generate new areas related to game environments the user has visited in the past. In this way, the generation unit can provide a more personalized gaming experience by referring to the user's past play history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past play history data into a generation AI and have the generation AI perform the generation of the optimal game environment, characters, and quests.

[0090] The generation unit can estimate the user's emotions and determine the priority of the game environment, characters, and quests to be generated based on the estimated user emotions. For example, the generation unit can estimate the user's emotions using facial recognition, voice analysis, behavioral patterns, etc. For example, if the user is excited, the generation unit will prioritize generating exciting quests. Also, if the user is relaxed, the generation unit can prioritize generating a calm game environment. Furthermore, if the user is stressed, the generation unit can prioritize generating relaxing characters. In this way, the generation unit can provide the user with the optimal game experience by determining the priority of the game environment, characters, and quests to be generated based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input user emotion data into the generation AI, which can then perform tasks such as determining the priority of the game environment, characters, and quests.

[0091] The generation unit can generate highly relevant game environments, characters, and quests while considering the user's geographical location information during generation. For example, the generation unit can generate a game environment considering the user's geographical location information. If the user is in a specific region, the generation unit can generate a game environment related to that region. Also, if the user is traveling, the generation unit can generate characters related to the travel destination. Furthermore, if the user is at home, the generation unit can generate quests related to playing at home. In this way, the generation unit can provide a more relevant gaming experience by generating game environments, characters, and quests while considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into a generation AI and have the generation AI execute the generation of highly relevant game environments, characters, and quests.

[0092] The generation unit can analyze the user's social media activity during generation and generate relevant game environments, characters, and quests. For example, the generation unit can analyze the user's social media activity. The generation unit can generate quests related to gameplay shared by the user on social media. The generation unit can also generate characters related to game-related accounts that the user follows on social media. Furthermore, the generation unit can generate game environments related to game communities that the user participates in on social media. In this way, the generation unit can provide a more relevant game experience by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI perform the generation of relevant game environments, characters, and quests.

[0093] The update unit can estimate the user's emotions and adjust the timing of updates based on the estimated emotions. For example, the update unit can estimate the user's emotions. The update unit can estimate emotions using the user's facial recognition, voice analysis, behavioral patterns, etc. For example, if the user is excited, the update unit may refrain from updating so as not to disrupt the game's progress. Also, if the user is relaxed, the update unit can update frequently to provide the latest game experience. Furthermore, if the user is stressed, the update unit can minimize updates and prioritize the game experience. In this way, the update unit can perform updates without compromising the game experience by adjusting the timing of updates based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input user emotion data into a generating AI and have the generating AI adjust the timing of the updates.

[0094] The update unit can analyze user feedback during updates to select the optimal update method. For example, the update unit analyzes user feedback. If a user provides feedback that a particular quest is "too difficult," the update unit can adjust the difficulty of that quest. Also, if a user provides feedback that a particular character is "unappealing," the update unit can change the design of that character. Furthermore, if a user provides feedback that a particular game environment is "boring," the update unit can add new elements to that environment. In this way, the update unit can make more appropriate updates by analyzing user feedback. Some or all of the above processes in the update unit may be performed using AI, for example, or not. For example, the update unit can input user feedback data into a generating AI and have the generating AI select the optimal update method.

[0095] The update unit can customize the content of updates based on the user's current game progress. For example, the update unit can customize the content of updates based on the user's current game progress. If the user is progressing through a specific quest, the update unit can prioritize updates related to that quest. Also, if the user starts using a new character, the update unit can perform updates related to that character. Furthermore, if the user is in a specific game environment, the update unit can perform updates related to that environment. In this way, the update unit can perform more appropriate updates by customizing the content of updates based on the user's current game progress. Some or all of the above processing in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input the user's current game progress data into a generating AI and have the generating AI perform the customization of the update content.

[0096] The update unit can estimate the user's emotions and determine the priority of updates based on the estimated emotions. For example, the update unit can estimate the user's emotions. The update unit can estimate emotions using the user's facial recognition, voice analysis, behavioral patterns, etc. For example, if the user is excited, the update unit will prioritize updates that are important for the game's progression. Also, if the user is relaxed, the update unit can perform more detailed updates to enhance the game experience. Furthermore, if the user is stressed, the update unit can minimize updates so as not to impair the game experience. In this way, the update unit can perform important updates without impairing the game experience by determining the priority of updates based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input user emotion data into a generating AI and have the generating AI determine the priority of updates.

[0097] The update unit can select the optimal update method by considering the user's geographical location information during the update process. For example, the update unit can perform updates while considering the user's geographical location information. If the user is in a specific region, the update unit can update game events related to that region. If the user is traveling, the update unit can update game elements related to the travel destination. Furthermore, if the user is at home, the update unit can perform updates related to playing at home. In this way, the update unit can perform more relevant updates by considering the user's geographical location information. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal update method.

[0098] The update unit can analyze the user's social media activity and suggest update content during the update process. For example, the update unit can analyze the user's social media activity. The update unit can perform updates related to gameplay shared by the user on social media. It can also perform updates related to game-related accounts that the user follows on social media. Furthermore, the update unit can perform updates related to game communities that the user participates in on social media. This allows the update unit to perform more relevant updates by analyzing the user's social media activity. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's social media activity data into a generating AI and have the generating AI suggest update content.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The analytics unit can customize in-game ad displays based on the user's play style and preferences. For example, if a user prefers action games, ads related to action games will be prioritized. Also, if a user frequently uses a particular character, ads for products and services related to that character can be displayed. Furthermore, the content and frequency of ads can be adjusted based on user feedback. This maximizes the effectiveness of ads by displaying those that are most relevant to the user.

[0101] The generation unit can customize in-game music and sound effects based on the user's play style and preferences. For example, if the user prefers a relaxed play style, calming music can be played. Conversely, if the user prefers action games, fast-paced music and sound effects can be used. Furthermore, the type and volume of music and sound effects can be adjusted based on user feedback. This allows for the provision of the optimal sound environment for the user.

[0102] The update unit can estimate the user's emotions and customize the in-game chat function based on those estimates. For example, if the user is excited, chat notifications can be reduced to avoid disrupting game progress. Conversely, if the user is relaxed, chat notifications can be increased to facilitate communication. Furthermore, if the user is stressed, chat notifications can be minimized to prioritize the game experience. This allows for the provision of an optimal chat experience tailored to the user's emotions.

[0103] The collection department can customize the in-game reward system based on the user's play style and preferences. For example, if a user frequently completes a particular quest, the rewards associated with that quest can be increased. Also, if a user likes a particular character, items related to that character can be offered as rewards. Furthermore, the content and frequency of rewards can be adjusted based on user feedback. This allows for the provision of an attractive reward system for users.

[0104] The generation unit can estimate the user's emotions and adjust the game's difficulty based on those emotions. For example, if the user is excited, the game's difficulty can be increased to provide a challenging experience. Conversely, if the user is relaxed, the game's difficulty can be lowered to provide a more relaxed experience. Furthermore, if the user is stressed, the game's difficulty can be minimized to reduce stress. This allows for the provision of an optimal gaming experience tailored to the user's emotions.

[0105] The analysis unit can customize the in-game story progression based on the user's play style and preferences. For example, if a user prefers story-driven games, it can provide quests with detailed storylines. If a user prefers action-driven games, it can provide a story progression with strong action elements. Furthermore, it can adjust the story content and pace based on user feedback. This allows for a more engaging story experience for the user.

[0106] The update unit can estimate the user's emotions and adjust the in-game event schedule based on those emotions. For example, if the user is excited, the frequency of events can be increased to maintain that excitement. Conversely, if the user is relaxed, the frequency of events can be decreased to provide a more relaxed experience. Furthermore, if the user is stressed, the frequency of events can be minimized to reduce stress. This allows for the provision of an optimal event schedule tailored to the user's emotions.

[0107] The collection unit can customize in-game tutorials based on the user's play style and preferences. For example, it can provide a detailed tutorial for beginners, and a simplified tutorial for experienced players. Furthermore, it can adjust the content and pace of the tutorial based on user feedback. This allows for the provision of the optimal learning experience for each user.

[0108] The generation unit can estimate the user's emotions and adjust the in-game visual effects based on those emotions. For example, if the user is excited, flashy visual effects can be used to maintain that excitement. If the user is relaxed, calm visual effects can be used to provide a relaxing experience. Furthermore, if the user is stressed, visual effects can be minimized to reduce stress. This allows for the provision of an optimal visual experience tailored to the user's emotions.

[0109] The update section allows for customization of the in-game item shop based on the user's play style and preferences. For example, if a user likes a particular character, items related to that character will be displayed preferentially. Similarly, if a user likes a particular game element, items related to that element can be displayed. Furthermore, the content and price of items can be adjusted based on user feedback. This allows for the provision of an item shop that is appealing to users.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit gathers user play styles, preferences, and feedback. For example, it collects data such as user actions, selected characters, and completed quests, gathering detailed information about what actions users took, which characters they chose, and which quests they completed. Step 2: The analysis unit analyzes the data collected by the collection unit. Based on the collected data, the analysis unit determines the user's play style and preferences. For example, if the user prefers action games, the analysis unit analyzes data to generate quests and characters with strong action elements. Step 3: The generation unit generates the game environment, characters, and quests based on the data analyzed by the analysis unit. The generation unit generates the game environment, characters, and quests based on the user's play style and preferences. For example, if the user prefers action games, it will generate quests and characters with strong action elements. Step 4: The update unit updates the game environment, characters, and quests generated by the generation unit in real time. The update unit updates the game environment, characters, and quests generated based on user feedback in real time. For example, if a user provides feedback that a particular quest is "too difficult," the difficulty level of that quest will be adjusted.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and update unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's play style, preferences, and feedback using the control unit 46A of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to determine the user's play style and preferences. The generation unit generates the game environment, characters, and quests based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The update unit updates the game environment, characters, and quests generated by the control unit 46A of the smart device 14 in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and update unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's play style, preferences, and feedback using the control unit 46A of the smart glasses 214. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12 to determine the user's play style and preferences. The generation unit generates the game environment, characters, and quests based on the data analyzed by the specific processing unit 290 of the data processing device 12. The update unit updates the game environment, characters, and quests generated by the control unit 46A of the smart glasses 214 in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and update unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's play style, preferences, and feedback using the control unit 46A of the headset terminal 314. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 to determine the user's play style and preferences. The generation unit generates the game environment, characters, and quests based on the data analyzed by the specific processing unit 290 of the data processing unit 12. The update unit updates the game environment, characters, and quests generated by the control unit 46A of the headset terminal 314 in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and update unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's play style, preferences, and feedback using the control unit 46A of the robot 414. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 to determine the user's play style and preferences. The generation unit generates the game environment, characters, and quests based on the data analyzed by, for example, the specific processing unit 290 of the data processing unit 12. The update unit updates the game environment, characters, and quests generated by, for example, the control unit 46A of the robot 414 in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A collection unit that gathers user play styles, preferences, and feedback, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a game environment, characters, and quests based on the data analyzed by the aforementioned analysis unit. The system includes an update unit that updates the game environment, characters, and quests generated by the generation unit in real time. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system collects data such as user behavior, selected characters, and completed quests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, the system determines the user's play style and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The game environment, characters, and quests are generated based on the user's play style and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned update unit is The game environment, characters, and quests are updated in real time based on user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Keep user profiles up to date. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past play history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current game progress and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the way the game environment, characters, and quests are represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the level of detail is adjusted based on the user's play style and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the system references the user's past play history to generate the optimal game environment, characters, and quests. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and determines the priority of the game environment, characters, and quests to be generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the system takes the user's geographical location into consideration to generate highly relevant game environments, characters, and quests. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the system analyzes the user's social media activity and generates relevant game environments, characters, and quests. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned update unit is It estimates user sentiment and adjusts the timing of updates based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned update unit is During updates, user feedback is analyzed to select the optimal update method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned update unit is During updates, customize the content of the update based on the user's current game progress. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned update unit is It estimates user sentiment and determines update priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned update unit is During updates, the system will select the optimal update method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned update unit is When updating, we analyze users' social media activity and suggest content for the update. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that gathers user play styles, preferences, and feedback, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates a game environment, characters, and quests based on the data analyzed by the aforementioned analysis unit. The system includes an update unit that updates the game environment, characters, and quests generated by the generation unit in real time. A system characterized by the following features.

2. The aforementioned collection unit is The system collects data such as user behavior, selected characters, and completed quests. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, the system determines the user's play style and preferences. The system according to feature 1.

4. The generating unit is The game environment, characters, and quests are generated based on the user's play style and preferences. The system according to feature 1.

5. The aforementioned update unit is The game environment, characters, and quests are updated in real time based on user feedback. The system according to feature 1.

6. The generating unit is Keep user profiles up to date. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past play history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is During data collection, filtering is performed based on the user's current game progress and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

Images