system

Abstract

The system according to this embodiment aims to provide matches at an appropriate difficulty level, adapting to the player's skill level and strategy. [Solution] The system according to the embodiment comprises an analysis unit, a setting unit, a learning unit, and a provision unit. The analysis unit analyzes the player's skill level or strategy. The setting unit sets the difficulty level based on the results analyzed by the analysis unit. The learning unit learns the game rules and character abilities and develops strategies. The provision unit learns through playing against the user and provides new challenges.

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 prior art, the difficulty level setting adapted to the player's skill level and strategy is not sufficiently carried out, and there is a risk of impairing the enjoyment of the game.

[0005] The system according to the embodiment aims to adapt to the player's skill level and strategy and provide a battle at an appropriate difficulty level.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a setting unit, a learning unit, and a provision unit. The analysis unit analyzes the player's skill level or strategy. The setting unit sets the difficulty level based on the results analyzed by the analysis unit. The learning unit learns the game rules and character abilities and develops strategies. The provision unit learns through matches with the user and provides new challenges. [Effects of the Invention]

[0007] The system according to this embodiment can adapt to the player's skill level and strategy, and provide matches at an appropriate difficulty level. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied 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 receiving 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 receiving 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 AI ​​opponent AI assistant for computer games according to an embodiment of the present invention is a system that adapts to the player's skill level and strategy to realize matches at an appropriate difficulty level. The AI ​​opponent AI assistant for computer games analyzes the player's skill level and strategy and sets an appropriate difficulty level. Next, the AI ​​opponent AI assistant for computer games learns the game rules and character abilities and develops advanced strategies. Furthermore, the AI ​​opponent AI assistant for computer games learns through matches with the user and constantly provides new challenges. This mechanism allows the player to always enjoy matches at an appropriate difficulty level, improving the enjoyment of the game. For example, the AI ​​opponent AI assistant for computer games collects and analyzes the player's past match data and play style. For example, by analyzing the player's win rate and the characteristics of the characters used, the player's skill level can be grasped. Next, the AI ​​opponent AI assistant for computer games sets an appropriate difficulty level based on the analysis results. For example, if the player's skill level is high, the difficulty level is increased to provide a challenging match for the player. On the other hand, if the player's skill level is low, the difficulty level is lowered to provide a match that the player can enjoy. Furthermore, the AI ​​opponent assistant in computer games learns the game rules and character abilities, and develops sophisticated strategies. For example, it can understand character traits and skills and formulate optimal strategies. This results in challenging matches for the player. Finally, the AI ​​opponent assistant in computer games learns through matches with the user and constantly provides new challenges. For example, it can learn the player's new strategies and develop counter-strategies. This allows the player to always enjoy new challenges, ensuring the enjoyment of the game is sustained. In this way, the AI ​​opponent assistant in computer games can adapt to the player's skill level and strategy, enabling matches at an appropriate difficulty level.

[0029] The AI ​​opponent AI assistant for a computer game according to this embodiment comprises an analysis unit, a setting unit, a learning unit, and a provision unit. The analysis unit analyzes the player's skill level and strategy. For example, the analysis unit collects the player's past battle data and play style to analyze the player's skill level. For example, the analysis unit can grasp the player's skill level by analyzing the player's win rate and the characteristics of the characters used. The setting unit sets the difficulty level based on the results analyzed by the analysis unit. For example, if the player's skill level is high, the setting unit increases the difficulty level to provide a challenging match for the player. The setting unit can also lower the difficulty level if the player's skill level is low to provide a match that the player can enjoy. The learning unit learns the game rules and character abilities and develops strategies. For example, the learning unit can understand the characteristics and skills of characters and formulate optimal strategies. For example, the learning unit learns the attack power, defense power, special abilities, etc., of characters and formulates strategies based on that. The provision unit learns through battles with the user and provides new challenges. The providing unit can, for example, learn the player's new strategies and develop counter-strategies. For instance, the providing unit can learn the player's attack and defense patterns and develop counter-strategies. This allows the AI ​​opponent AI assistant in the computer game according to the embodiment to adapt to the player's skill level and strategies, enabling matches at an appropriate difficulty level.

[0030] The analysis unit analyzes the player's skill level and strategy. For example, it collects the player's past match data and play style to analyze the player's skill level. Specifically, it collects detailed data on the win / loss record of past matches, the characters used and their characteristics, and the patterns of strategies and tactics chosen. This data is analyzed in real time by AI, and the player's behavior patterns and skill level are quantified. For example, the player's win rate, average damage, and frequency of use of specific characters and skills are analyzed. Furthermore, the analysis unit also evaluates the player's reaction speed and judgment to grasp the player's overall skill level. This allows the analysis unit to understand the player's strengths and weaknesses in detail and provide data for the next match. The analysis unit can also use AI to predict the player's actions and predict what strategies the player is likely to adopt. This allows the analysis unit to analyze the player's skill level and strategy in detail and provide the necessary information for the next steps, the configuration unit and the learning unit.

[0031] The settings unit sets the difficulty level based on the results analyzed by the analysis unit. For example, if the player's skill level is high, the settings unit will increase the difficulty level to provide a challenging match for the player. Specifically, if the settings unit determines that the player's skill level is high, it will set the AI ​​opponent's reaction speed to be faster or to employ more advanced strategies. Conversely, if the player's skill level is low, it can also lower the difficulty level to provide a match that the player can enjoy. For example, it will adjust the attack power of the AI ​​opponent or set it to employ simpler strategies. The settings unit can adjust the difficulty level considering not only the player's skill level but also the player's preferences and play style. For example, it can set the AI ​​opponent to prioritize using characters and skills that the player prefers. This allows the settings unit to provide matches at the optimal difficulty level for the player, enabling them to enjoy the game. Furthermore, the settings unit can collect player feedback and continuously improve the accuracy of the difficulty settings. This allows the settings unit to achieve flexible difficulty settings that match the player's skill level and preferences, maximizing the enjoyment of the game.

[0032] The learning unit learns the game rules and character abilities, and develops strategies. For example, the learning unit can understand character characteristics and skills and formulate optimal strategies. Specifically, the learning unit learns detailed data such as each character's attack power, defense power, movement speed, and special abilities, and builds strategies based on this. For example, it can devise tactics to make the most of a particular character's powerful attack skills, or develop strategies to exploit enemy characters' weaknesses. The learning unit uses AI to analyze past battle data and learns from successful and unsuccessful strategies. This allows the learning unit to always provide optimal strategies based on the latest information. Furthermore, the learning unit can learn the player's behavior patterns and devise new tactics to counter the player's strategies. For example, if a player repeatedly uses a particular attack pattern, the learning unit can learn that pattern and develop defensive strategies to counter it. This allows the learning unit to constantly provide evolving strategies, resulting in challenging and enjoyable battles for the player.

[0033] The AI ​​learns through matches with users and provides new challenges. For example, it can learn new strategies from players and develop counter-strategies. Specifically, when a player tries a new tactic or strategy, the AI ​​collects data in real time, analyzes it, and devises countermeasures. For example, if a player uses a new combo move for a particular character, the AI ​​learns the pattern of that combo move and devises defensive strategies or counter-attacks to counter it. The AI ​​can also predict the player's actions and what strategies they will take in the next match. This allows the AI ​​to constantly provide players with new challenges and maintain the enjoyment of the game. Furthermore, the AI ​​can collect player feedback and continuously improve the quality of matches. For example, it can adjust the AI's strategies and difficulty based on feedback provided by players after matches. This allows the AI ​​to provide the optimal match environment for players and maximize the enjoyment of the game.

[0034] The analysis unit can collect a player's past battle data and play style, and analyze the player's skill level. For example, the analysis unit can collect a player's past battle data and analyze win / loss records, characters used, and battle time. The analysis unit can also collect a player's play style and analyze it as offensive, defensive, balanced, etc. By analyzing the skill level based on the player's past battle data and play style, it becomes possible to set a more accurate difficulty level. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the player's past battle data into an AI and have the AI ​​perform the skill level analysis.

[0035] The settings unit can set an appropriate difficulty level based on the player's skill level. For example, if the player's skill level is high, the settings unit can increase the difficulty level to provide a challenging match for the player. Conversely, if the player's skill level is low, the settings unit can also decrease the difficulty level to provide a match that the player can enjoy. This makes it possible to set the difficulty level according to the player's skill level. Some or all of the above processing in the settings unit may be performed using AI, for example, or not using AI. For example, the settings unit can input the player's skill level into the AI ​​and have the AI ​​set the difficulty level.

[0036] The learning unit can understand the characteristics and skills of characters and formulate strategies. For example, the learning unit can understand the characteristics and skills of characters and formulate the optimal strategy. For example, the learning unit can learn the attack power, defense power, special abilities, etc. of characters and formulate strategies based on that. In this way, by understanding the characteristics and skills of characters, more advanced strategies can be developed. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input the characteristics and skills of characters into a generative AI and have the generative AI formulate strategies.

[0037] The service provider can learn new strategies through matches with users and develop counter-strategies. For example, the service provider can learn new strategies through matches with users and develop counter-strategies. For example, the service provider can learn new attack and defense patterns from players and develop counter-strategies. This allows the service provider to constantly provide new challenges through matches with users. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input match data with users into a generative AI and have the generative AI learn new strategies.

[0038] The analysis unit can dynamically analyze a player's skill level by collecting real-time gameplay data in addition to the player's past match data. For example, the analysis unit can collect data from a match the player is currently playing in real time and instantly analyze the skill level. The analysis unit can also analyze the player's real-time reaction speed and operational accuracy and reflect this in the skill level. Furthermore, the analysis unit can analyze the player's current strategic changes during a match in real time and dynamically adjust the skill level. This makes it possible to dynamically analyze skill levels by collecting real-time gameplay data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input real-time gameplay data into a generative AI and have the generative AI perform a dynamic analysis of skill levels.

[0039] The analysis unit can analyze the devices and operating methods used by the player and reflect this in the skill level evaluation. For example, the analysis unit can analyze the performance of the devices used by the player and reflect this in the skill level. The analysis unit can also analyze the player's operating methods (keyboard, mouse, controller, etc.) and evaluate the skill level. Furthermore, the analysis unit can analyze the player's device settings (sensitivity, key layout, etc.) and reflect this in the skill level. This makes the skill level evaluation more accurate by taking into account the devices and operating methods used by the player. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the player's device information into a generative AI and have the generative AI perform the skill level evaluation.

[0040] The analysis unit can analyze players' social media activity and reflect tendencies in their play style in its analysis. For example, the analysis unit can analyze players' social media posts to analyze tendencies in their play style. It can also analyze gameplay videos shared by players to evaluate their play style. Furthermore, the analysis unit can analyze players' social media friendships to analyze the influence of their play style. This allows for a more accurate understanding of play style tendencies by analyzing social media activity. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input players' social media data into a generative AI and have the generative AI perform an analysis of play style tendencies.

[0041] The analysis unit can analyze differences in play styles across regions by considering the players' geographical location information. For example, the analysis unit analyzes differences in play styles across regions based on the players' geographical location information. The analysis unit can also analyze differences in play styles by considering the internet connection speed of each region. Furthermore, the analysis unit can analyze differences in play styles by considering the cultural background of each region. In this way, differences in play styles across regions can be analyzed by considering geographical location information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the players' geographical location information into a generative AI and have the generative AI perform the analysis of differences in play styles across regions.

[0042] The settings unit can optimize the difficulty setting algorithm based on the player's past match results. For example, the settings unit can optimize the difficulty setting algorithm based on the player's past win rate. The settings unit can also adjust the difficulty setting algorithm considering the player's past match time. Furthermore, the settings unit can optimize the difficulty setting algorithm based on the skill levels of the player's past opponents. This allows for more accurate difficulty setting by optimizing the difficulty setting algorithm based on past match results. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input the player's past match results into a generating AI and have the generating AI perform the optimization of the difficulty setting algorithm.

[0043] The settings unit can dynamically adjust the difficulty setting, taking into account the player's play time and frequency. For example, if the player plays for a long time, the settings unit can increase the difficulty to provide a challenging match. It can also adjust the difficulty to provide a balanced match if the player plays frequently. Furthermore, if the player plays for a short time, the settings unit can lower the difficulty to provide a more relaxed match. This allows for more appropriate difficulty settings by dynamically adjusting the difficulty setting according to play time and frequency. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input data on the player's play time and frequency into a generating AI, and have the generating AI perform the dynamic adjustment of the difficulty setting.

[0044] The settings unit can set the optimal difficulty level by taking into account the player's device information. For example, the settings unit can adjust the difficulty level by considering the performance of the device the player is using. The settings unit can also set the difficulty level by considering the player's device settings (sensitivity, key layout, etc.). Furthermore, the settings unit can also set the difficulty level by considering how the player operates their device (keyboard, mouse, controller, etc.). This makes it possible to set the optimal difficulty level by considering device information. Some or all of the above processing in the settings unit may be performed using AI, for example, or without using AI. For example, the settings unit can input the player's device information into a generating AI and have the generating AI perform the difficulty level setting.

[0045] The settings unit can analyze the player's in-game behavior history and reflect it in the difficulty settings. For example, the settings unit can adjust the difficulty settings based on the player's past behavior history. The settings unit can also analyze the player's in-game behavior patterns and reflect them in the difficulty settings. Furthermore, the settings unit can set the difficulty settings considering the player's specific actions (e.g., frequency of use of a specific character). This allows for more accurate difficulty settings by analyzing the in-game behavior history. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input the player's behavior history data into a generating AI and have the generating AI perform the difficulty setting adjustments.

[0046] The learning unit can learn not only character traits and skills but also environmental elements within the game to enhance strategies. For example, the learning unit can learn not only character traits and skills but also terrain and obstacle placement to enhance strategies. Furthermore, the learning unit can learn changes in weather and time of day within the game and reflect them in strategies. In addition, the learning unit can learn the placement and appearance patterns of items within the game to enhance strategies. This allows for the development of more advanced strategies by learning environmental elements within the game in addition to character traits and skills. Some or all of the above-described processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input environmental element data from the game into a generative AI and have the generative AI perform the strategy enhancement.

[0047] The learning unit can learn the behavior patterns of the player's opponents and incorporate them into its strategy. For example, the learning unit can learn the attack patterns of the player's opponents and incorporate them into its strategy. It can also learn the defensive patterns of the player's opponents and incorporate them into its strategy. Furthermore, the learning unit can learn the movement patterns of the player's opponents and incorporate them into its strategy. This allows for the development of more effective strategies by learning the opponent's behavior patterns. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input opponent behavior pattern data into a generative AI and have the generative AI implement the strategy.

[0048] The learning unit can analyze the player's past gameplay videos and use them as training data. For example, the learning unit can analyze the player's past gameplay videos and use them as training data. The learning unit can also learn specific strategies from the player's past gameplay videos. Furthermore, the learning unit can learn the optimal strategy based on the player's past gameplay videos. This allows for the learning of more effective strategies by analyzing past gameplay videos. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input gameplay videos into a generative AI and have the generative AI perform the use of the gameplay videos as training data.

[0049] The learning unit can incorporate the player's activities outside of the game (e.g., using a training app) into its learning process. For example, the learning unit can incorporate data from the training app the player uses into its learning process. It can also incorporate the player's activities outside of the game (e.g., using a fitness app) into its learning process. Furthermore, the learning unit can incorporate the player's activities outside of the game (e.g., using a reading app) into its learning process. This allows for the learning of more comprehensive strategies by incorporating activities outside of the game. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input data on activities outside of the game into a generative AI and incorporate it into its learning process.

[0050] The service provider can analyze the player's past match results and optimize new challenges for the next match. For example, the service provider can optimize new challenges for the next match based on the player's past match results. The service provider can also provide new challenges considering the skill levels of the player's past opponents. Furthermore, the service provider can analyze the player's strategies in past matches and provide new challenges based on that. In this way, new challenges for the next match can be optimized by analyzing past match results. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the player's past match result data into a generative AI and have the generative AI perform the optimization of new challenges.

[0051] The service provider can offer different strategies depending on the player's play style. For example, it can offer an aggressive strategy depending on the player's aggressive play style. It can also offer a defensive strategy depending on the player's defensive play style. Furthermore, it can offer a balanced strategy depending on the player's balanced play style. By offering strategies tailored to play style, more appropriate matches become possible. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the player's play style data into a generative AI and have the generative AI execute the provision of different strategies.

[0052] The service provider can offer new challenges tailored to each region, taking into account the player's geographical location. For example, the service provider can offer new challenges tailored to each region based on the player's geographical location. Furthermore, the service provider can offer new challenges while considering the cultural background of each region. In addition, the service provider can offer new challenges while considering the internet connection speed of each region. This allows for the provision of new challenges tailored to each region by considering geographical location. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the player's geographical location into a generative AI and have the generative AI execute the provision of new challenges tailored to each region.

[0053] The service provider can analyze the player's social media activity and provide relevant new challenges. For example, the service provider can analyze the player's social media posts and provide relevant new challenges. It can also analyze gameplay videos shared by the player and provide relevant new challenges. Furthermore, the service provider can analyze the player's social media friendships and provide relevant new challenges. Thus, by analyzing social media activity, relevant new challenges can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the player's social media data into a generative AI and have the generative AI perform the task of providing new challenges.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The analysis unit can collect the player's biometric information and use it to analyze their skill level. For example, the analysis unit can collect the player's heart rate and skin electrical responses to analyze their tension and concentration levels during gameplay. It can also collect the player's breathing patterns to analyze their relaxation level. Furthermore, the analysis unit can analyze the player's pupil movements to evaluate their visual concentration. By analyzing skill levels based on biometric information, it becomes possible to set a more accurate difficulty level. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input biometric information into a generative AI and have the generative AI perform the skill level analysis.

[0056] The learning unit can learn the player's tendencies in choices within the game and incorporate them into its strategy. For example, the learning unit can learn the player's tendencies in selecting items and skills and incorporate them into its strategy. It can also learn the player's tendencies in selecting routes and missions and incorporate them into its strategy. Furthermore, the learning unit can learn the player's tendencies in selecting dialogue options and incorporate them into its strategy. This allows for the development of more effective strategies by learning the player's tendencies in choices. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input choice tendency data into a generative AI and have the generative AI implement the strategy.

[0057] The analysis unit can analyze the player's in-game behavior patterns and reflect this in the evaluation of their skill level. For example, the analysis unit can analyze the player's attack and defense patterns and evaluate their skill level. It can also analyze the player's movement patterns and item usage patterns and evaluate their skill level. Furthermore, the analysis unit can analyze the player's dialogue patterns and choices and evaluate their skill level. This allows for a more accurate evaluation of skill levels by analyzing in-game behavior patterns. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input behavior pattern data into a generating AI and have the generating AI perform the skill level evaluation.

[0058] The learning unit can learn the player's in-game communication patterns and incorporate them into its strategy. For example, the learning unit can learn how the player interacts with other characters and incorporate that into its strategy. It can also learn how the player cooperates with other players and incorporate that into its strategy. Furthermore, the learning unit can learn how the player competes with other players and incorporate that into its strategy. By learning communication patterns, more effective strategies can be developed. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input communication pattern data into a generative AI and have the generative AI implement the strategy.

[0059] The analysis unit can analyze a player's item usage patterns in the game and reflect this in the evaluation of their skill level. For example, the analysis unit can analyze which items a player uses and when, and evaluate their skill level. It can also analyze how a player combines items and evaluate their skill level. Furthermore, the analysis unit can analyze how a player manages items and evaluate their skill level. This allows for a more accurate evaluation of skill levels by analyzing item usage patterns. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input item usage pattern data into a generating AI and have the generating AI perform the skill level evaluation.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The analysis unit analyzes the player's skill level and strategy. For example, the analysis unit collects the player's past match data and play style to analyze the player's skill level. For instance, the analysis unit can determine the player's skill level by analyzing the player's win rate and the characteristics of the characters they use. Step 2: The settings unit sets the difficulty level based on the results analyzed by the analysis unit. For example, if the player's skill level is high, the settings unit will increase the difficulty level to provide a challenging match for the player. Conversely, if the player's skill level is low, the settings unit can also decrease the difficulty level to provide a match that the player can enjoy. Step 3: The learning unit learns the game rules and character abilities, and develops strategies. For example, the learning unit can understand the characteristics and skills of characters and formulate optimal strategies. For example, the learning unit learns about the characters' attack power, defense power, special abilities, etc., and formulates strategies based on that. Step 4: The provider learns through playing against the user and provides new challenges. For example, the provider can learn new strategies from the player and develop counter-strategies. For example, the provider can learn the player's attack and defense patterns and develop counter-strategies.

[0062] (Example of form 2) The AI ​​opponent AI assistant for computer games according to an embodiment of the present invention is a system that adapts to the player's skill level and strategy to realize matches at an appropriate difficulty level. The AI ​​opponent AI assistant for computer games analyzes the player's skill level and strategy and sets an appropriate difficulty level. Next, the AI ​​opponent AI assistant for computer games learns the game rules and character abilities and develops advanced strategies. Furthermore, the AI ​​opponent AI assistant for computer games learns through matches with the user and constantly provides new challenges. This mechanism allows the player to always enjoy matches at an appropriate difficulty level, improving the enjoyment of the game. For example, the AI ​​opponent AI assistant for computer games collects and analyzes the player's past match data and play style. For example, by analyzing the player's win rate and the characteristics of the characters used, the player's skill level can be grasped. Next, the AI ​​opponent AI assistant for computer games sets an appropriate difficulty level based on the analysis results. For example, if the player's skill level is high, the difficulty level is increased to provide a challenging match for the player. On the other hand, if the player's skill level is low, the difficulty level is lowered to provide a match that the player can enjoy. Furthermore, the AI ​​opponent assistant in computer games learns the game rules and character abilities, and develops sophisticated strategies. For example, it can understand character traits and skills and formulate optimal strategies. This results in challenging matches for the player. Finally, the AI ​​opponent assistant in computer games learns through matches with the user and constantly provides new challenges. For example, it can learn the player's new strategies and develop counter-strategies. This allows the player to always enjoy new challenges, ensuring the enjoyment of the game is sustained. In this way, the AI ​​opponent assistant in computer games can adapt to the player's skill level and strategy, enabling matches at an appropriate difficulty level.

[0063] The AI ​​opponent AI assistant for a computer game according to this embodiment comprises an analysis unit, a setting unit, a learning unit, and a provision unit. The analysis unit analyzes the player's skill level and strategy. For example, the analysis unit collects the player's past battle data and play style to analyze the player's skill level. For example, the analysis unit can grasp the player's skill level by analyzing the player's win rate and the characteristics of the characters used. The setting unit sets the difficulty level based on the results analyzed by the analysis unit. For example, if the player's skill level is high, the setting unit increases the difficulty level to provide a challenging match for the player. The setting unit can also lower the difficulty level if the player's skill level is low to provide a match that the player can enjoy. The learning unit learns the game rules and character abilities and develops strategies. For example, the learning unit can understand the characteristics and skills of characters and formulate optimal strategies. For example, the learning unit learns the attack power, defense power, special abilities, etc., of characters and formulates strategies based on that. The provision unit learns through battles with the user and provides new challenges. The providing unit can, for example, learn the player's new strategies and develop counter-strategies. For instance, the providing unit can learn the player's attack and defense patterns and develop counter-strategies. This allows the AI ​​opponent AI assistant in the computer game according to the embodiment to adapt to the player's skill level and strategies, enabling matches at an appropriate difficulty level.

[0064] The analysis unit analyzes the player's skill level and strategy. For example, it collects the player's past match data and play style to analyze the player's skill level. Specifically, it collects detailed data on the win / loss record of past matches, the characters used and their characteristics, and the patterns of strategies and tactics chosen. This data is analyzed in real time by AI, and the player's behavior patterns and skill level are quantified. For example, the player's win rate, average damage, and frequency of use of specific characters and skills are analyzed. Furthermore, the analysis unit also evaluates the player's reaction speed and judgment to grasp the player's overall skill level. This allows the analysis unit to understand the player's strengths and weaknesses in detail and provide data for the next match. The analysis unit can also use AI to predict the player's actions and predict what strategies the player is likely to adopt. This allows the analysis unit to analyze the player's skill level and strategy in detail and provide the necessary information for the next steps, the configuration unit and the learning unit.

[0065] The settings unit sets the difficulty level based on the results analyzed by the analysis unit. For example, if the player's skill level is high, the settings unit will increase the difficulty level to provide a challenging match for the player. Specifically, if the settings unit determines that the player's skill level is high, it will set the AI ​​opponent's reaction speed to be faster or to employ more advanced strategies. Conversely, if the player's skill level is low, it can also lower the difficulty level to provide a match that the player can enjoy. For example, it will adjust the attack power of the AI ​​opponent or set it to employ simpler strategies. The settings unit can adjust the difficulty level considering not only the player's skill level but also the player's preferences and play style. For example, it can set the AI ​​opponent to prioritize using characters and skills that the player prefers. This allows the settings unit to provide matches at the optimal difficulty level for the player, enabling them to enjoy the game. Furthermore, the settings unit can collect player feedback and continuously improve the accuracy of the difficulty settings. This allows the settings unit to achieve flexible difficulty settings that match the player's skill level and preferences, maximizing the enjoyment of the game.

[0066] The learning unit learns the game rules and character abilities, and develops strategies. For example, the learning unit can understand character characteristics and skills and formulate optimal strategies. Specifically, the learning unit learns detailed data such as each character's attack power, defense power, movement speed, and special abilities, and builds strategies based on this. For example, it can devise tactics to make the most of a particular character's powerful attack skills, or develop strategies to exploit enemy characters' weaknesses. The learning unit uses AI to analyze past battle data and learns from successful and unsuccessful strategies. This allows the learning unit to always provide optimal strategies based on the latest information. Furthermore, the learning unit can learn the player's behavior patterns and devise new tactics to counter the player's strategies. For example, if a player repeatedly uses a particular attack pattern, the learning unit can learn that pattern and develop defensive strategies to counter it. This allows the learning unit to constantly provide evolving strategies, resulting in challenging and enjoyable battles for the player.

[0067] The AI ​​learns through matches with users and provides new challenges. For example, it can learn new strategies from players and develop counter-strategies. Specifically, when a player tries a new tactic or strategy, the AI ​​collects data in real time, analyzes it, and devises countermeasures. For example, if a player uses a new combo move for a particular character, the AI ​​learns the pattern of that combo move and devises defensive strategies or counter-attacks to counter it. The AI ​​can also predict the player's actions and what strategies they will take in the next match. This allows the AI ​​to constantly provide players with new challenges and maintain the enjoyment of the game. Furthermore, the AI ​​can collect player feedback and continuously improve the quality of matches. For example, it can adjust the AI's strategies and difficulty based on feedback provided by players after matches. This allows the AI ​​to provide the optimal match environment for players and maximize the enjoyment of the game.

[0068] The analysis unit can collect a player's past battle data and play style, and analyze the player's skill level. For example, the analysis unit can collect a player's past battle data and analyze win / loss records, characters used, and battle time. The analysis unit can also collect a player's play style and analyze it as offensive, defensive, balanced, etc. By analyzing the skill level based on the player's past battle data and play style, it becomes possible to set a more accurate difficulty level. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the player's past battle data into an AI and have the AI ​​perform the skill level analysis.

[0069] The settings unit can set an appropriate difficulty level based on the player's skill level. For example, if the player's skill level is high, the settings unit can increase the difficulty level to provide a challenging match for the player. Conversely, if the player's skill level is low, the settings unit can also decrease the difficulty level to provide a match that the player can enjoy. This makes it possible to set the difficulty level according to the player's skill level. Some or all of the above processing in the settings unit may be performed using AI, for example, or not using AI. For example, the settings unit can input the player's skill level into the AI ​​and have the AI ​​set the difficulty level.

[0070] The learning unit can understand the characteristics and skills of characters and formulate strategies. For example, the learning unit can understand the characteristics and skills of characters and formulate the optimal strategy. For example, the learning unit can learn the attack power, defense power, special abilities, etc. of characters and formulate strategies based on that. In this way, by understanding the characteristics and skills of characters, more advanced strategies can be developed. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input the characteristics and skills of characters into a generative AI and have the generative AI formulate strategies.

[0071] The service provider can learn new strategies through matches with users and develop counter-strategies. For example, the service provider can learn new strategies through matches with users and develop counter-strategies. For example, the service provider can learn new attack and defense patterns from players and develop counter-strategies. This allows the service provider to constantly provide new challenges through matches with users. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input match data with users into a generative AI and have the generative AI learn new strategies.

[0072] The analysis unit can estimate the player's emotions and adjust the skill level analysis method based on the estimated emotions. For example, if the player is stressed, the analysis unit will analyze the skill level by emphasizing past successful experiences. If the player is relaxed, the analysis unit can also analyze the overall play style in detail. Furthermore, if the player is excited, the analysis unit can analyze the skill level by emphasizing an aggressive play style. This allows for a more appropriate analysis by adjusting the skill level analysis method according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the player's emotion data into a generative AI and have the generative AI adjust the skill level analysis method.

[0073] The analysis unit can dynamically analyze a player's skill level by collecting real-time gameplay data in addition to the player's past match data. For example, the analysis unit can collect data from a match the player is currently playing in real time and instantly analyze the skill level. The analysis unit can also analyze the player's real-time reaction speed and operational accuracy and reflect this in the skill level. Furthermore, the analysis unit can analyze the player's current strategic changes during a match in real time and dynamically adjust the skill level. This makes it possible to dynamically analyze skill levels by collecting real-time gameplay data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input real-time gameplay data into a generative AI and have the generative AI perform a dynamic analysis of skill levels.

[0074] The analysis unit can analyze the devices and operating methods used by the player and reflect this in the skill level evaluation. For example, the analysis unit can analyze the performance of the devices used by the player and reflect this in the skill level. The analysis unit can also analyze the player's operating methods (keyboard, mouse, controller, etc.) and evaluate the skill level. Furthermore, the analysis unit can analyze the player's device settings (sensitivity, key layout, etc.) and reflect this in the skill level. This makes the skill level evaluation more accurate by taking into account the devices and operating methods used by the player. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the player's device information into a generative AI and have the generative AI perform the skill level evaluation.

[0075] The analysis unit can estimate the player's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the player is nervous, the analysis unit can provide a simple and highly visible display method. If the player is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the player is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results according to the player's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a 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 the player's emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0076] The analysis unit can analyze players' social media activity and reflect tendencies in their play style in its analysis. For example, the analysis unit can analyze players' social media posts to analyze tendencies in their play style. It can also analyze gameplay videos shared by players to evaluate their play style. Furthermore, the analysis unit can analyze players' social media friendships to analyze the influence of their play style. This allows for a more accurate understanding of play style tendencies by analyzing social media activity. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input players' social media data into a generative AI and have the generative AI perform an analysis of play style tendencies.

[0077] The analysis unit can analyze differences in play styles across regions by considering the players' geographical location information. For example, the analysis unit analyzes differences in play styles across regions based on the players' geographical location information. The analysis unit can also analyze differences in play styles by considering the internet connection speed of each region. Furthermore, the analysis unit can analyze differences in play styles by considering the cultural background of each region. In this way, differences in play styles across regions can be analyzed by considering geographical location information. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the players' geographical location information into a generative AI and have the generative AI perform the analysis of differences in play styles across regions.

[0078] The settings unit can estimate the player's emotions and adjust the difficulty setting criteria based on the estimated emotions. For example, if the player is stressed, the settings unit can lower the difficulty to provide a relaxing match. Conversely, if the player is relaxed, the settings unit can increase the difficulty to provide a challenging match. Furthermore, if the player is excited, the settings unit can adjust the difficulty to provide a balanced match. This allows for more appropriate difficulty settings by adjusting the difficulty setting criteria according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the settings unit may be performed using an AI, or not using an AI. For example, the settings unit can input the player's emotion data into a generative AI and have the generative AI adjust the difficulty setting criteria.

[0079] The settings unit can optimize the difficulty setting algorithm based on the player's past match results. For example, the settings unit can optimize the difficulty setting algorithm based on the player's past win rate. The settings unit can also adjust the difficulty setting algorithm considering the player's past match time. Furthermore, the settings unit can optimize the difficulty setting algorithm based on the skill levels of the player's past opponents. This allows for more accurate difficulty setting by optimizing the difficulty setting algorithm based on past match results. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input the player's past match results into a generating AI and have the generating AI perform the optimization of the difficulty setting algorithm.

[0080] The settings unit can dynamically adjust the difficulty setting, taking into account the player's play time and frequency. For example, if the player plays for a long time, the settings unit can increase the difficulty to provide a challenging match. It can also adjust the difficulty to provide a balanced match if the player plays frequently. Furthermore, if the player plays for a short time, the settings unit can lower the difficulty to provide a more relaxed match. This allows for more appropriate difficulty settings by dynamically adjusting the difficulty setting according to play time and frequency. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input data on the player's play time and frequency into a generating AI, and have the generating AI perform the dynamic adjustment of the difficulty setting.

[0081] The settings unit can estimate the player's emotions and adjust the difficulty setting notification method based on the estimated emotions. For example, if the player is nervous, the settings unit can notify the player of the difficulty setting in a calm voice. If the player is relaxed, the settings unit can notify the player of the difficulty setting in a cheerful voice. Furthermore, if the player is in a hurry, the settings unit can notify the player of the difficulty setting in a quick and concise voice. This allows for more appropriate information to be provided by adjusting the difficulty setting notification method according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the settings unit may be performed using AI, or not using AI. For example, the settings unit can input the player's emotion data into a generative AI and have the generative AI adjust the notification method.

[0082] The settings unit can set the optimal difficulty level by taking into account the player's device information. For example, the settings unit can adjust the difficulty level by considering the performance of the device the player is using. The settings unit can also set the difficulty level by considering the player's device settings (sensitivity, key layout, etc.). Furthermore, the settings unit can also set the difficulty level by considering how the player operates their device (keyboard, mouse, controller, etc.). This makes it possible to set the optimal difficulty level by considering device information. Some or all of the above processing in the settings unit may be performed using AI, for example, or without using AI. For example, the settings unit can input the player's device information into a generating AI and have the generating AI perform the difficulty level setting.

[0083] The settings unit can analyze the player's in-game behavior history and reflect it in the difficulty settings. For example, the settings unit can adjust the difficulty settings based on the player's past behavior history. The settings unit can also analyze the player's in-game behavior patterns and reflect them in the difficulty settings. Furthermore, the settings unit can set the difficulty settings considering the player's specific actions (e.g., frequency of use of a specific character). This allows for more accurate difficulty settings by analyzing the in-game behavior history. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can input the player's behavior history data into a generating AI and have the generating AI perform the difficulty setting adjustments.

[0084] The learning unit can estimate the player's emotions and adjust the learning algorithm based on the estimated emotions. For example, if the player is stressed, the learning unit can adjust the learning algorithm to learn a relaxing strategy. It can also adjust the learning algorithm to learn a challenging strategy if the player is relaxed. Furthermore, if the player is excited, the learning unit can adjust the learning algorithm to learn a balanced strategy. This allows for the learning of more appropriate strategies by adjusting the learning algorithm according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not. For example, the learning unit can input the player's emotion data into a generative AI and have the generative AI adjust the learning algorithm.

[0085] The learning unit can learn not only character traits and skills but also environmental elements within the game to enhance strategies. For example, the learning unit can learn not only character traits and skills but also terrain and obstacle placement to enhance strategies. Furthermore, the learning unit can learn changes in weather and time of day within the game and reflect them in strategies. In addition, the learning unit can learn the placement and appearance patterns of items within the game to enhance strategies. This allows for the development of more advanced strategies by learning environmental elements within the game in addition to character traits and skills. Some or all of the above-described processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input environmental element data from the game into a generative AI and have the generative AI perform the strategy enhancement.

[0086] The learning unit can learn the behavior patterns of the player's opponents and incorporate them into its strategy. For example, the learning unit can learn the attack patterns of the player's opponents and incorporate them into its strategy. It can also learn the defensive patterns of the player's opponents and incorporate them into its strategy. Furthermore, the learning unit can learn the movement patterns of the player's opponents and incorporate them into its strategy. This allows for the development of more effective strategies by learning the opponent's behavior patterns. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input opponent behavior pattern data into a generative AI and have the generative AI implement the strategy.

[0087] The learning unit can estimate the player's emotions and adjust the display method of the learning results based on the estimated emotions. For example, if the player is nervous, the learning unit can provide a simple and highly visible display method. If the player is relaxed, the learning unit can also provide a display method that includes detailed information. Furthermore, if the player is in a hurry, the learning unit can provide a concise display method. By adjusting the display method of the learning results according to the player's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input the player's emotion data into the generative AI and have the generative AI adjust the display method of the learning results.

[0088] The learning unit can analyze the player's past gameplay videos and use them as training data. For example, the learning unit can analyze the player's past gameplay videos and use them as training data. The learning unit can also learn specific strategies from the player's past gameplay videos. Furthermore, the learning unit can learn the optimal strategy based on the player's past gameplay videos. This allows for the learning of more effective strategies by analyzing past gameplay videos. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input gameplay videos into a generative AI and have the generative AI perform the use of the gameplay videos as training data.

[0089] The learning unit can incorporate the player's activities outside of the game (e.g., using a training app) into its learning process. For example, the learning unit can incorporate data from the training app the player uses into its learning process. It can also incorporate the player's activities outside of the game (e.g., using a fitness app) into its learning process. Furthermore, the learning unit can incorporate the player's activities outside of the game (e.g., using a reading app) into its learning process. This allows for the learning of more comprehensive strategies by incorporating activities outside of the game. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input data on activities outside of the game into a generative AI and incorporate it into its learning process.

[0090] The service provider can estimate the player's emotions and adjust how new challenges are presented based on the estimated emotions. For example, if the player is stressed, the service provider can offer a relaxing new challenge. If the player is relaxed, the service provider can also offer a challenging new challenge. Furthermore, if the player is excited, the service provider can offer a balanced new challenge. This allows for the provision of more appropriate challenges by adjusting how new challenges are presented according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the player's emotion data into a generative AI and have the generative AI adjust how new challenges are presented.

[0091] The service provider can analyze the player's past match results and optimize new challenges for the next match. For example, the service provider can optimize new challenges for the next match based on the player's past match results. The service provider can also provide new challenges considering the skill levels of the player's past opponents. Furthermore, the service provider can analyze the player's strategies in past matches and provide new challenges based on that. In this way, new challenges for the next match can be optimized by analyzing past match results. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the player's past match result data into a generative AI and have the generative AI perform the optimization of new challenges.

[0092] The service provider can offer different strategies depending on the player's play style. For example, it can offer an aggressive strategy depending on the player's aggressive play style. It can also offer a defensive strategy depending on the player's defensive play style. Furthermore, it can offer a balanced strategy depending on the player's balanced play style. By offering strategies tailored to play style, more appropriate matches become possible. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the player's play style data into a generative AI and have the generative AI execute the provision of different strategies.

[0093] The service provider can estimate the player's emotions and adjust the notification method for new challenges based on the estimated emotions. For example, if the player is nervous, the service provider can notify them of a new challenge in a calm voice. If the player is relaxed, the service provider can notify them of a new challenge in a cheerful voice. Furthermore, if the player is in a hurry, the service provider can notify them of a new challenge quickly and concisely. This allows for more appropriate information to be provided by adjusting the notification method for new challenges according to the player'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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the player's emotion data into a generative AI and have the generative AI adjust the notification method.

[0094] The service provider can offer new challenges tailored to each region, taking into account the player's geographical location. For example, the service provider can offer new challenges tailored to each region based on the player's geographical location. Furthermore, the service provider can offer new challenges while considering the cultural background of each region. In addition, the service provider can offer new challenges while considering the internet connection speed of each region. This allows for the provision of new challenges tailored to each region by considering geographical location. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the player's geographical location into a generative AI and have the generative AI execute the provision of new challenges tailored to each region.

[0095] The service provider can analyze the player's social media activity and provide relevant new challenges. For example, the service provider can analyze the player's social media posts and provide relevant new challenges. It can also analyze gameplay videos shared by the player and provide relevant new challenges. Furthermore, the service provider can analyze the player's social media friendships and provide relevant new challenges. Thus, by analyzing social media activity, relevant new challenges can be provided. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the player's social media data into a generative AI and have the generative AI perform the task of providing new challenges.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The analysis unit can collect the player's biometric information and use it to analyze their skill level. For example, the analysis unit can collect the player's heart rate and skin electrical responses to analyze their tension and concentration levels during gameplay. It can also collect the player's breathing patterns to analyze their relaxation level. Furthermore, the analysis unit can analyze the player's pupil movements to evaluate their visual concentration. By analyzing skill levels based on biometric information, it becomes possible to set a more accurate difficulty level. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input biometric information into a generative AI and have the generative AI perform the skill level analysis.

[0098] The settings unit can estimate the player's emotions and adjust the in-game reward system based on the estimated emotions. For example, if the player is stressed, the settings unit can increase rewards to boost motivation. It can also set rewards to normal if the player is relaxed. Furthermore, if the player is excited, the settings unit can reduce rewards to provide a challenge. This allows for a more appropriate gaming experience by adjusting the reward system according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the settings unit may be performed using AI, or not. For example, the settings unit can input player emotion data into a generative AI and have the generative AI adjust the reward system.

[0099] The learning unit can learn the player's tendencies in choices within the game and incorporate them into its strategy. For example, the learning unit can learn the player's tendencies in selecting items and skills and incorporate them into its strategy. It can also learn the player's tendencies in selecting routes and missions and incorporate them into its strategy. Furthermore, the learning unit can learn the player's tendencies in selecting dialogue options and incorporate them into its strategy. This allows for the development of more effective strategies by learning the player's tendencies in choices. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input choice tendency data into a generative AI and have the generative AI implement the strategy.

[0100] The service provider can estimate the player's emotions and adjust in-game events based on those emotions. For example, if the player is stressed, the service provider can provide relaxing events. If the player is relaxed, the service provider can also provide challenging events. Furthermore, if the player is excited, the service provider can provide balanced events. This allows for a more appropriate gaming experience by adjusting in-game events according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the player's emotion data into a generative AI and have the generative AI perform the event adjustments.

[0101] The analysis unit can analyze the player's in-game behavior patterns and reflect this in the evaluation of their skill level. For example, the analysis unit can analyze the player's attack and defense patterns and evaluate their skill level. It can also analyze the player's movement patterns and item usage patterns and evaluate their skill level. Furthermore, the analysis unit can analyze the player's dialogue patterns and choices and evaluate their skill level. This allows for a more accurate evaluation of skill levels by analyzing in-game behavior patterns. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input behavior pattern data into a generating AI and have the generating AI perform the skill level evaluation.

[0102] The settings unit can estimate the player's emotions and dynamically adjust the game difficulty based on the estimated emotions. For example, if the player is stressed, the settings unit can lower the difficulty to provide a relaxing match. Conversely, if the player is relaxed, the settings unit can increase the difficulty to provide a challenging match. Furthermore, if the player is excited, the settings unit can adjust the difficulty to provide a balanced match. This allows for a more appropriate gaming experience by dynamically adjusting the difficulty according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the settings unit may be performed using AI, or not using AI. For example, the settings unit can input the player's emotion data into a generative AI and have the generative AI perform the dynamic adjustment of the difficulty.

[0103] The learning unit can learn the player's in-game communication patterns and incorporate them into its strategy. For example, the learning unit can learn how the player interacts with other characters and incorporate that into its strategy. It can also learn how the player cooperates with other players and incorporate that into its strategy. Furthermore, the learning unit can learn how the player competes with other players and incorporate that into its strategy. By learning communication patterns, more effective strategies can be developed. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input communication pattern data into a generative AI and have the generative AI implement the strategy.

[0104] The service provider can estimate the player's emotions and adjust in-game hints and advice based on those estimated emotions. For example, if the player is stressed, the service provider can provide relaxing hints and advice. If the player is relaxed, the service provider can also provide challenging hints and advice. Furthermore, if the player is excited, the service provider can provide balanced hints and advice. This allows for more appropriate information to be provided by adjusting hints and advice according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input the player's emotion data into a generative AI and have the generative AI adjust the hints and advice.

[0105] The analysis unit can analyze a player's item usage patterns in the game and reflect this in the evaluation of their skill level. For example, the analysis unit can analyze which items a player uses and when, and evaluate their skill level. It can also analyze how a player combines items and evaluate their skill level. Furthermore, the analysis unit can analyze how a player manages items and evaluate their skill level. This allows for a more accurate evaluation of skill levels by analyzing item usage patterns. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input item usage pattern data into a generating AI and have the generating AI perform the skill level evaluation.

[0106] The service provider can estimate the player's emotions and adjust the in-game character's reactions based on the estimated emotions. For example, if the player is stressed, the character may offer words of encouragement. If the player is relaxed, the character may offer challenging words. Furthermore, if the player is excited, the character may offer balanced words. By adjusting the character's reactions according to the player's emotions, a more appropriate gaming experience can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the player's emotion data into a generative AI and have the generative AI adjust the character's reactions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The analysis unit analyzes the player's skill level and strategy. For example, the analysis unit collects the player's past match data and play style to analyze the player's skill level. For instance, the analysis unit can determine the player's skill level by analyzing the player's win rate and the characteristics of the characters they use. Step 2: The settings unit sets the difficulty level based on the results analyzed by the analysis unit. For example, if the player's skill level is high, the settings unit will increase the difficulty level to provide a challenging match for the player. Conversely, if the player's skill level is low, the settings unit can also decrease the difficulty level to provide a match that the player can enjoy. Step 3: The learning unit learns the game rules and character abilities, and develops strategies. For example, the learning unit can understand the characteristics and skills of characters and formulate optimal strategies. For example, the learning unit learns about the characters' attack power, defense power, special abilities, etc., and formulates strategies based on that. Step 4: The provider learns through playing against the user and provides new challenges. For example, the provider can learn new strategies from the player and develop counter-strategies. For example, the provider can learn the player's attack and defense patterns and develop counter-strategies.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12. For example, it collects the player's past battle data and play style and analyzes the player's skill level. For example, it can also be implemented by the control unit 46A of the smart device 14. The setting unit is implemented by the specific processing unit 290 of the data processing device 12 and sets the difficulty level based on the analysis results. For example, it can also be implemented by the control unit 46A of the smart device 14. The learning unit is implemented by the specific processing unit 290 of the data processing device 12 and learns the game rules and character abilities and develops strategies. For example, it can also be implemented by the control unit 46A of the smart device 14. The provision unit is implemented by the specific processing unit 290 of the data processing device 12 and learns through battles with the user and provides new challenges. For example, it can also be implemented by the control unit 46A of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12. For example, it collects the player's past battle data and play style and analyzes the player's skill level. For example, it can also be implemented by the control unit 46A of the smart glasses 214. The setting unit is implemented by the specific processing unit 290 of the data processing device 12 and sets the difficulty level based on the analysis results. For example, it can also be implemented by the control unit 46A of the smart glasses 214. The learning unit is implemented by the specific processing unit 290 of the data processing device 12 and learns the game rules and character abilities and develops strategies. For example, it can also be implemented by the control unit 46A of the smart glasses 214. The providing unit is implemented by the specific processing unit 290 of the data processing device 12 and learns through battles with the user and provides new challenges. For example, it can also be implemented by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12. For example, it collects the player's past battle data and play style and analyzes the player's skill level. For example, it can also be implemented by the control unit 46A of the headset terminal 314. The setting unit is implemented by the specific processing unit 290 of the data processing device 12 and sets the difficulty level based on the analysis results. For example, it can also be implemented by the control unit 46A of the headset terminal 314. The learning unit is implemented by the specific processing unit 290 of the data processing device 12 and learns the game rules and character abilities and develops strategies. For example, it can also be implemented by the control unit 46A of the headset terminal 314. The provision unit is implemented by the specific processing unit 290 of the data processing device 12 and learns through battles with the user and provides new challenges. For example, it can also be implemented by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12. For example, it collects the player's past battle data and play style and analyzes the player's skill level. For example, it is also implemented by the control unit 46A of the robot 414. The setting unit is implemented by the specific processing unit 290 of the data processing device 12 and sets the difficulty level based on the analysis results. For example, it is also implemented by the control unit 46A of the robot 414. The learning unit is implemented by the specific processing unit 290 of the data processing device 12 and learns the game rules and character abilities and develops strategies. For example, it is also implemented by the control unit 46A of the robot 414. The provision unit is implemented by the specific processing unit 290 of the data processing device 12 and learns through battles with the user and provides new challenges. For example, it is also implemented by the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) An analysis unit that analyzes the player's skill level or strategy, A setting unit sets the difficulty level based on the results of the analysis performed by the aforementioned analysis unit, The learning section involves learning the game rules and character abilities, and developing strategies. It includes a provisioning unit that learns through matches with users and provides new challenges. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The system collects players' past match data and playstyles, and analyzes their skill levels. The system described in Appendix 1, characterized by the features described herein. (Note 3) The setting unit is, Set an appropriate difficulty level based on the player's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 4) Understand the characteristics and skills of the characters and formulate a strategy. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Learn new strategies through matches against other users and develop counter-strategies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The system estimates the player's emotions and adjusts the skill level analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, In addition to the player's past match data, real-time gameplay data is collected and skill levels are dynamically analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system analyzes the devices and control methods used by players and reflects this in the evaluation of their skill level. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates the player's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, Analyze players' social media activity and incorporate trends in their play style into the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, By considering the players' geographical location, we analyze differences in play styles across different regions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The setting unit is, The system estimates the player's emotions and adjusts the difficulty settings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The setting unit is, The difficulty setting algorithm is optimized based on the player's past match results. The system described in Appendix 1, characterized by the features described herein. (Note 14) The setting unit is, The difficulty settings are dynamically adjusted based on the player's playtime and frequency. The system described in Appendix 1, characterized by the features described herein. (Note 15) The setting unit is, The system estimates the player's emotions and adjusts the difficulty setting notification method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The difficulty level is set considering the player's device information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The setting unit is, Analyze the player's in-game behavior history and reflect it in the difficulty settings. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, It estimates the player's emotions and adjusts the learning algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, In addition to character traits and skills, the game also learns environmental elements to enhance strategies. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, Learn the behavioral patterns of the player's opponents and incorporate them into your strategy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning unit, It estimates the player's emotions and adjusts how the learning results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned learning unit, Analyze players' past gameplay videos and use them as training data. The system described in Appendix 1, characterized by the features described herein. (Note 23) Incorporate players' activities outside of the game into their learning process. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The system estimates the player's emotions and adjusts how new challenges are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, Analyze the player's past match results and optimize new challenges for the next match. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, Offers different strategies depending on the player's play style. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, We estimate the player's emotions and adjust how new challenges are notified based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, Taking into account the player's geographical location, the game offers new challenges tailored to each region. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, Analyze players' social media activity and provide them with relevant new challenges. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 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. An analysis unit that analyzes the player's skill level or strategy, A setting unit sets the difficulty level based on the results of the analysis performed by the aforementioned analysis unit, The learning section involves learning the game rules and character abilities, and developing strategies. It includes a provisioning unit that learns through matches with users and provides new challenges. A system characterized by the following features.

2. The aforementioned analysis unit, The system collects players' past match data and playstyles, and analyzes their skill levels. The system according to feature 1.

3. The aforementioned setting unit is, The difficulty level is set based on the player's skill level. The system according to feature 1.

4. Understand the characteristics and skills of the characters and formulate a strategy. The system according to feature 1.

5. The aforementioned supply unit is, Learn new strategies through matches against other users and develop counter-strategies. The system according to feature 1.

6. The aforementioned analysis unit, The system estimates the player's emotions and adjusts the skill level analysis method based on those estimated emotions. The system according to feature 1.

7. The aforementioned analysis unit, In addition to the player's past match data, real-time gameplay data is collected and skill levels are dynamically analyzed. The system according to feature 1.

8. The aforementioned analysis unit, The system analyzes the devices and control methods used by players and reflects this in the evaluation of their skill level. The system according to feature 1.

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