system

Abstract

The system according to the embodiment aims to automate bug detection and game balance management in VRMMO games. [Solution] A system according to an embodiment includes a detection unit, a collection unit, a proposal unit, an adjustment unit, a generation unit, and a management unit. The detection unit automatically detects and fixes bugs. The collection unit collects player behavior data. The proposal unit proposes the implementation of new maps, enemies, or items based on the data collected by the collection unit. The adjustment unit adjusts the enemy appearance frequency or level according to the player's skill level or party composition. The generation unit generates new quests according to the player's behavior or the in-game situation. The management unit adjusts the drop rate of rare items according to the player's behavior or the in-game situation.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to detect bugs and manage game balance in VRMMO games due to their vast maps and diverse elements, which can lead to early withdrawal.

[0005] The system according to the embodiment aims to automate bug detection and game balance management in VRMMO games. [Means for solving the problem]

[0006] The system according to the embodiment includes a detection unit, a collection unit, a proposal unit, an adjustment unit, a generation unit, and a management unit. The detection unit automatically detects and fixes bugs. The collection unit collects player behavior data. The proposal unit proposes the implementation of new maps, enemies, or items based on the data collected by the collection unit. The adjustment unit adjusts the enemy appearance frequency or level according to the player's skill level or party composition. The generation unit generates new quests according to the player's behavior or the in-game situation. The management unit adjusts the drop rate of rare items according to the player's behavior or the in-game situation. [Effects of the Invention]

[0007] The system according to the embodiment can automate bug detection and game balance management in VRMMO games. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention automates game balance management in VRMMO games, benefiting both developers and players. This system includes a bug detection and bug repair function that automatically detects and fixes bugs that occur in the game. This function ensures a stable playing environment, even in VRMMO games with large maps and complex systems. Furthermore, it includes a data collection function that proposes new maps, enemies, and items. By collecting player behavior data and in-game statistics, the AI ​​generator proposes new maps, enemies, and items. This function ensures that game content is always fresh and keeps players engaged. Furthermore, it includes an enemy respawn management and enemy level adjustment function based on player skill and party composition, automatically adjusting the enemy appearance frequency and level according to the player's skill level and party composition. This function ensures that players can always enjoy the game at an appropriate difficulty level. Furthermore, it includes an automatic quest generation system, where the AI ​​generator automatically generates new quests based on player behavior and in-game conditions. This function ensures that players can always enjoy new challenges. Finally, the system includes a rare item drop management function that automatically adjusts the drop rate of rare items based on player behavior and in-game conditions. This allows players to obtain rare items at an appropriate frequency. These features allow VRMMO game developers to efficiently manage game balance, ensuring that players always enjoy a fresh and appropriately difficult game. For example, the system automatically detects and fixes bugs that occur in the game. This ensures a stable playing environment, even for VRMMO games with large maps and complex systems. The system also collects player behavior data and in-game statistics, and the generation AI suggests implementing new maps, enemies, and items. This ensures that game content is always fresh and keeps players engaged. Furthermore, the system automatically adjusts enemy appearance frequency and level based on the player's skill level and party composition.This allows players to always enjoy the game at an appropriate level of difficulty. In addition, the system's generation AI automatically generates new quests based on the player's actions and in-game situations, allowing players to always enjoy new challenges. Finally, the system automatically adjusts the drop rate of rare items based on the player's actions and in-game situations, allowing players to obtain rare items at an appropriate frequency. This allows the system to automatically manage the game balance of VRMMO games, providing a system that is beneficial to both developers and players.

[0029] A game balance management system according to an embodiment includes a detection unit, a collection unit, a proposal unit, an adjustment unit, a generation unit, and a management unit. The detection unit automatically detects and fixes bugs that occur in a game. For example, the detection unit analyzes in-game log data and identifies bugs. The detection unit can also collect and fix bug reports from players. The detection unit can also use AI to learn and predict bug occurrence patterns. The collection unit collects player behavior data and in-game statistical data. For example, the collection unit records player movement patterns. The collection unit can also collect items used by players and battle histories. The collection unit can also analyze player behavior patterns and collect data using AI. The proposal unit uses a generation AI to propose the implementation of new maps, enemies, and items based on the collected data. For example, the proposal unit analyzes player behavior data and proposes the design of a new map. The proposal unit can also propose the implementation of new enemies based on the player's battle history. The proposal unit can also use AI to propose the implementation of items that will attract players' interest. The adjustment unit automatically adjusts the frequency of enemy appearances and levels according to the player's skill level and party composition. For example, the adjustment unit analyzes the player's skill level and adjusts the frequency of enemy appearances. The adjustment unit can also adjust the enemy level based on the party composition. Furthermore, the adjustment unit can use AI to learn the player's skill level and party composition and adjust the frequency of enemy appearances and levels. The generation unit uses a generation AI to automatically generate new quests according to the player's actions and the in-game situation. For example, the generation unit analyzes player behavior data and generates new quests. The generation unit can also generate new quests based on the in-game situation. Furthermore, the generation unit can use AI to generate quests that will interest the player. The management unit automatically adjusts the drop rate of rare items according to the player's actions and the in-game situation. For example, the management unit analyzes player behavior data and adjusts the drop rate of rare items.The management unit can also adjust the drop rate of rare items based on the in-game situation. Furthermore, the management unit can use AI to learn player behavior patterns and adjust the drop rate of rare items. In this way, the game balance management system according to the embodiment can automatically manage the game balance of a VRMMO game, providing a system that is beneficial to both developers and players.

[0030] The detection unit can automatically detect and fix bugs that occur in a game. The detection unit, for example, analyzes in-game log data and identifies bugs. For example, the detection unit detects abnormal patterns in the log data and identifies the occurrence of a bug. The detection unit can also collect bug reports from players and fix them. For example, the detection unit automatically analyzes and fixes bugs reported by players. The detection unit can also use AI to learn and predict bug occurrence patterns. For example, the AI ​​learns past bug data and predicts future bug occurrences. This makes it possible to automatically detect and fix bugs in a game, thereby providing a stable playing environment.

[0031] The collection unit can collect player behavior data or in-game statistical data. The collection unit, for example, records the player's movement patterns. For example, the collection unit records which areas the player has moved through. The collection unit can also collect items used by the player and battle history. For example, the collection unit records the types of items used by the player and how often they were used. The collection unit can also use AI to analyze the player's behavior patterns and collect data. For example, the AI ​​learns the player's behavior patterns and collects important data. In this way, collecting player behavior data and in-game statistical data can be used to improve the game.

[0032] The suggestion unit allows the generation AI to propose the implementation of new maps, enemies, or items based on the collected data. The suggestion unit, for example, analyzes player behavior data and proposes new map designs. For example, the suggestion unit designs a new map based on areas frequently visited by players. The suggestion unit can also propose the implementation of new enemies based on the player's battle history. For example, the suggestion unit designs new enemies based on data on enemies that the player struggled with. The suggestion unit can also use AI to suggest the implementation of items that will attract the player's interest. For example, the AI ​​learns the player's item usage history and suggests new items. This makes it possible to use generation AI to suggest new content.

[0033] The adjustment unit can automatically adjust the frequency or level of enemy appearances according to the player's skill level or party composition. The adjustment unit, for example, analyzes the player's skill level and adjusts the frequency of enemy appearances. For example, if the player's skill level is high, the adjustment unit increases the frequency of enemy appearances. The adjustment unit can also adjust the enemy level based on the party composition. For example, if the party composition is powerful, the adjustment unit increases the enemy level. The adjustment unit can also use AI to learn the player's skill level and party composition and adjust the frequency or level of enemy appearances. For example, the AI ​​learns the player's skill level and party composition and sets the optimal frequency or level of enemy appearances. This allows the player to enjoy the game at an appropriate difficulty level according to the player's skill level and party composition.

[0034] The generation unit can automatically generate new quests based on the player's behavior and the situation in the game. The generation unit, for example, analyzes player behavior data and generates new quests. For example, the generation unit designs new quests based on areas frequently visited by players. The generation unit can also generate new quests based on the situation in the game. For example, the generation unit generates new quests based on in-game events or seasons. The generation unit can also use AI to generate quests that will attract the player's interest. For example, the AI ​​learns the player's behavior patterns and generates optimal quests. This allows the player to always enjoy new challenges.

[0035] The management unit can automatically adjust the drop rate of rare items according to player behavior and in-game conditions. The management unit, for example, analyzes player behavior data and adjusts the drop rate of rare items. For example, if a player spends a lot of time in a particular area, the management unit increases the drop rate of rare items in that area. The management unit can also adjust the drop rate of rare items based on in-game conditions. For example, the management unit adjusts the drop rate of rare items according to in-game events or seasons. The management unit can also use AI to learn player behavior patterns and adjust the drop rate of rare items. For example, AI learns player behavior patterns and sets the optimal drop rate. This allows players to obtain rare items at an appropriate frequency.

[0036] The detection unit can analyze the frequency of bug occurrence and prioritize fixing bugs that occur frequently. The detection unit, for example, analyzes in-game log data, identifies bugs that occur frequently, and prioritizes fixing them. For example, the detection unit counts the number of times bugs occur in the log data and identifies bugs that occur frequently. The detection unit can also collect report data from players and prioritize fixing bugs that are reported most frequently. For example, the detection unit can tally the number of bugs reported by players and identify bugs that occur frequently. The detection unit can also monitor the frequency of bug occurrence in real time and quickly fix bugs that occur more frequently. For example, the detection unit can analyze log data in real time and monitor the frequency of bug occurrence. This can improve the stability of the game by prioritizing fixing bugs that occur frequently.

[0037] The detection unit can evaluate the scope of impact of a bug and prioritize fixing bugs with a large impact. For example, the detection unit evaluates the impact of a bug on the entire game and prioritizes fixing bugs with a large impact. For example, the detection unit evaluates whether a bug affects a major function of the game. The detection unit can also identify bugs that have a large impact on the player's game progress and prioritize fixing them. For example, the detection unit evaluates whether a bug hinders the player's quest progress. If bugs are concentrated in a specific function or area, the detection unit can evaluate the scope of their impact and prioritize fixing them. For example, the detection unit evaluates whether bugs frequently occur in a specific area. This makes it possible to improve the game experience by prioritizing fixing bugs with a large impact.

[0038] The detection unit can geographically map the locations of bug occurrences and prioritize fixing bugs that occur in specific areas. The detection unit, for example, analyzes geographic data within the game to identify bugs that frequently occur in specific areas and prioritize fixing them. For example, the detection unit can count the number of bug occurrences in the geographic data and identify bug occurrences in specific areas. The detection unit can also collect player movement data and map and fix bug occurrences in specific areas. For example, it can identify bug occurrences in areas frequently visited by players. The detection unit can also monitor the locations of bug occurrences in real time and quickly fix bug occurrences in specific areas. For example, it can analyze geographic data in real time and monitor the locations of bug occurrences. This can improve the stability of the game by prioritizing fixing bug occurrences in specific areas.

[0039] The detection unit can analyze the causes of bug occurrence and fix bugs caused by similar causes collectively. For example, the detection unit analyzes the causes of bug occurrence, identifies bugs caused by similar causes, and fixes them collectively. For example, the detection unit identifies the causes of bug occurrence and groups bugs caused by similar causes. The detection unit can also collect report data from players, identify bugs caused by a common cause, and fix them collectively. For example, the detection unit can analyze the causes of bugs reported by players and identify the common cause. The detection unit can also monitor the causes of bug occurrence in real time and quickly fix bugs caused by similar causes. For example, the detection unit can analyze the causes of bug occurrence in real time and identify bugs caused by similar causes. This allows bugs caused by similar causes to be fixed collectively, thereby efficiently fixing bugs.

[0040] The collection unit can analyze the player's behavioral patterns and prioritize collecting data related to specific behaviors. The collection unit, for example, analyzes the player's movement patterns and prioritizes collecting behavioral data in specific areas. For example, the collection unit collects behavioral data in areas frequently visited by the player. The collection unit can also analyze the player's battle patterns and prioritize collecting battle data with specific enemies. For example, the collection unit collects data on enemies the player frequently fights. The collection unit can also analyze the player's item usage patterns and prioritize collecting data related to specific items. For example, the collection unit collects data on items frequently used by the player. In this way, priority collection of data related to specific behaviors can be used to improve the game.

[0041] The collection unit can adjust the frequency of data collection based on a specific event in the game. For example, the collection unit increases the frequency of data collection during a major event in the game. For example, the collection unit collects player behavior data more frequently during the event period. The collection unit can also adjust the frequency of data collection when the player completes a specific quest. For example, the collection unit intensively collects player behavior data after the quest is completed. The collection unit can also adjust the frequency of data collection based on a seasonal event in the game. For example, the collection unit increases the frequency of data collection during a seasonal event. In this way, adjusting the frequency of data collection based on a specific event makes it possible to efficiently collect important data.

[0042] The collection unit can collect data for each region taking into account the geographical location information of the player. The collection unit, for example, collects play data for a specific region based on the geographical location information of the player. For example, the collection unit records which region the player is playing in and collects data for that region. The collection unit can also collect bug occurrence data for each region and identify problems for each region. For example, the collection unit can identify bugs that frequently occur in a specific region. The collection unit can also collect player behavior patterns for each region and analyze trends specific to the region. For example, the collection unit can analyze player behavior patterns for each region and identify trends specific to the region. In this way, by collecting data for each region, it is possible to identify problems and trends specific to the region.

[0043] The collection unit can analyze players' social media activities and collect related data. For example, the collection unit can analyze players' social media posts and collect feedback data about the game. For example, the collection unit can collect comments and ratings about the game posted by players on social media. The collection unit can also analyze players' behavioral patterns on social media and associate them with in-game behavioral data. For example, the collection unit can analyze players' activities on social media and compare them with in-game behavioral data. The collection unit can also analyze trends on social media and use the results to suggest new content in the game. For example, the collection unit can identify popular topics on social media and suggest new content based on them. In this way, analyzing social media activities can identify players' behavioral patterns and feedback.

[0044] The suggestion unit can analyze the player's past play history and make optimal suggestions. The suggestion unit, for example, analyzes the player's past quest history and suggests similar quests. For example, the suggestion unit analyzes data on quests the player has previously completed and suggests similar quests. The suggestion unit can also analyze the player's past item acquisition history and suggest related items. For example, the suggestion unit analyzes data on items the player has previously acquired and suggests related items. The suggestion unit can also analyze the player's past battle history and suggest appropriate enemies. For example, the suggestion unit analyzes data on enemies the player has previously fought and suggests appropriate enemies. In this way, optimal suggestions can be made to the player by analyzing the player's past play history.

[0045] The suggestion unit can analyze trends within a game and make suggestions based on the latest trends. The suggestion unit, for example, analyzes popular quests within a game and suggests them to the player. For example, the suggestion unit identifies the most played quest within a game and suggests it to the player. The suggestion unit can also analyze popular items within a game and suggest them to the player. For example, the suggestion unit identifies the most used item within a game and suggests it to the player. The suggestion unit can also analyze popular enemies within a game and suggest them to the player. For example, the suggestion unit identifies the enemy that is most frequently fought within a game and suggests it to the player. This makes it possible to continue to attract the interest of players by making suggestions based on the latest trends.

[0046] The suggestion unit can make different suggestions for each region, taking into account the geographical location information of the player. The suggestion unit, for example, can suggest quests specific to each region based on the geographical location information of the player. For example, the suggestion unit can record which region the player is playing in and suggest quests specific to that region. The suggestion unit can also analyze player behavior patterns for each region and suggest items specific to the region. For example, the suggestion unit can analyze player behavior patterns for each region and suggest items specific to the region. The suggestion unit can also analyze trends for each region and suggest enemies specific to the region. For example, the suggestion unit can analyze player behavior patterns for each region and suggest enemies specific to the region. In this way, by making different suggestions for each region, it is possible to make suggestions that meet the specific needs of the region.

[0047] The suggestion unit can analyze the player's social media activities and make relevant suggestions. For example, the suggestion unit can analyze the player's social media posts and make suggestions based on feedback about the game. For example, the suggestion unit can collect comments and ratings about the game posted by the player on social media and make suggestions based on them. The suggestion unit can also analyze the player's behavioral patterns on social media and make suggestions by associating them with in-game behavioral data. For example, the suggestion unit can analyze the player's social media activities and compare them with in-game behavioral data. The suggestion unit can also analyze trends on social media and use this information to suggest new content in the game. For example, the suggestion unit can identify popular topics on social media and suggest new content based on them. In this way, by analyzing social media activities, suggestions can be made based on the player's interests.

[0048] The adjustment unit can analyze the player's past battle records and set optimal enemy appearance frequencies and levels. The adjustment unit, for example, analyzes the player's past battle records and causes enemies of an appropriate difficulty level to appear. For example, the adjustment unit analyzes the player's past battle record data and sets appropriate enemy appearance frequencies and levels. The adjustment unit can also adjust the enemy appearance frequency based on the player's win rate. For example, the adjustment unit increases the enemy appearance frequency if the player's win rate is high. The adjustment unit can also set enemy levels based on the player's past battle data. For example, the adjustment unit analyzes data on enemies the player has fought in the past and sets appropriate enemy levels. In this way, the analysis of past battle records can set optimal enemy appearance frequencies and levels for the player.

[0049] The adjustment unit can adjust the appearance frequency and level of enemies based on specific events in the game. For example, the adjustment unit increases the appearance frequency of enemies during major events in the game. For example, the adjustment unit increases the appearance frequency of enemies during the event period. The adjustment unit can also adjust the level of enemies during specific quests. For example, the adjustment unit increases the level of enemies during specific quests. The adjustment unit can also adjust the appearance frequency and level of enemies based on seasonal events. For example, the adjustment unit adjusts the appearance frequency and level of enemies during seasonal events. In this way, by adjusting the appearance frequency and level of enemies based on specific events, it is possible to provide an appropriate level of difficulty according to the event.

[0050] The adjustment unit can set different enemy appearance frequencies and levels for each region, taking into account the player's geographical location information. The adjustment unit, for example, adjusts the enemy appearance frequency in a specific region based on the player's geographical location information. For example, the adjustment unit records which region the player is playing in and adjusts the enemy appearance frequency for that region. The adjustment unit can also analyze player behavior patterns for each region and set region-specific enemy levels. For example, the adjustment unit can analyze player behavior patterns for each region and set region-specific enemy levels. The adjustment unit can also analyze trends for each region and set region-specific enemy appearance frequencies. For example, the adjustment unit can analyze player behavior patterns for each region and set region-specific enemy appearance frequencies. In this way, by setting different enemy appearance frequencies and levels for each region, it is possible to provide a region-specific game experience.

[0051] The adjustment unit can analyze a player's social media activity and adjust the frequency and level of related enemy appearances. For example, the adjustment unit can analyze a player's social media posts and adjust the frequency of enemy appearances based on feedback about the game. For example, the adjustment unit can collect comments and ratings about the game posted by players on social media and adjust the frequency of enemy appearances based on the comments and ratings. The adjustment unit can also analyze a player's behavioral patterns on social media and adjust the enemy levels by correlating them with in-game behavioral data. For example, the adjustment unit can analyze a player's social media activity and compare it with in-game behavioral data. The adjustment unit can also analyze trends on social media and adjust the frequency of new enemy appearances in the game. For example, the adjustment unit can identify popular topics on social media and adjust the frequency of new enemy appearances based on the identified topics. In this way, the analysis of social media activity can adjust the frequency and level of enemy appearances based on a player's interests.

[0052] The generation unit can analyze the player's past quest history and generate an optimal quest. The generation unit, for example, analyzes the player's past quest history and generates a similar quest. For example, the generation unit analyzes data on quests that the player has previously completed and generates a similar quest. The generation unit can also generate a quest of appropriate difficulty based on the player's past quest completion rate. For example, if the player has a high quest completion rate, the generation unit generates a quest with a high difficulty. The generation unit can also analyze the player's past quest reward history and generate a quest with a high reward. For example, the generation unit analyzes data on rewards the player has previously obtained and generates a quest with a high reward. In this way, the generation unit can provide the player with the optimal quest by analyzing the past quest history.

[0053] The generation unit can generate a quest based on a specific event in the game. For example, the generation unit generates a special quest during a major event in the game. For example, the generation unit generates a limited quest during the event period. The generation unit can also generate a time-limited quest based on a specific seasonal event. For example, the generation unit generates a special quest during the seasonal event period. The generation unit can also generate an area-limited quest based on an event in a specific area in the game. For example, the generation unit generates a quest based on an event in a specific area. In this way, by generating a quest based on a specific event, it is possible to provide a new challenge in accordance with the event.

[0054] The generation unit can generate different quests for each region, taking into account the geographical location information of the player. The generation unit, for example, generates region-specific quests based on the geographical location information of the player. For example, the generation unit records which region the player is playing in and generates a quest for that region. The generation unit can also analyze player behavior patterns for each region and generate region-specific quests. For example, the generation unit can analyze player behavior patterns for each region and generate region-specific quests. The generation unit can also analyze trends for each region and generate region-specific quests. For example, the generation unit can analyze player behavior patterns for each region and generate region-specific quests. In this way, by providing different quests for each region, it is possible to provide a region-specific gaming experience.

[0055] The generation unit can analyze a player's social media activity and generate a related quest. For example, the generation unit can analyze a player's social media posts and generate a quest based on feedback about the game. For example, the generation unit can collect comments and ratings about the game posted by the player on social media and generate a quest based on the comments and ratings. The generation unit can also analyze a player's behavioral patterns on social media and generate a quest by associating them with in-game behavioral data. For example, the generation unit can analyze a player's social media activity and compare it with in-game behavioral data. The generation unit can also analyze trends on social media and use the results to generate new in-game quests. For example, the generation unit can identify popular topics on social media and generate new quests based on those trends. In this way, analyzing social media activity can provide quests based on a player's interests.

[0056] The management unit can analyze the player's past item acquisition history and set an optimal drop rate. The management unit, for example, analyzes the player's past item acquisition history and sets an appropriate drop rate. For example, the management unit analyzes data on items previously acquired by the player and sets an optimal drop rate. The management unit can also adjust the drop rate based on the player's past item acquisition frequency. For example, the management unit reduces the drop rate if the player frequently acquires items. The management unit can also analyze the player's past item acquisition patterns and set an optimal drop rate. For example, the management unit analyzes the types and frequency of items previously acquired by the player and sets an optimal drop rate. In this way, the optimal drop rate can be set for the player by analyzing the player's past item acquisition history.

[0057] The management unit can adjust the drop rate of rare items based on specific events within the game. For example, the management unit increases the drop rate of rare items during major events within the game. For example, the management unit increases the drop rate of rare items during the event period. The management unit can also adjust the drop rate of rare items based on specific seasonal events. For example, the management unit adjusts the drop rate of rare items during the seasonal event period. The management unit can also adjust the drop rate of rare items based on events in specific areas within the game. For example, the management unit adjusts the drop rate of rare items based on events in specific areas. In this way, by adjusting the drop rate of rare items based on specific events, rare items can be obtained at an appropriate frequency depending on the event.

[0058] The management unit can set different rare item drop rates for each region, taking into account the player's geographical location information. The management unit, for example, adjusts the rare item drop rate in a specific region based on the player's geographical location information. For example, the management unit records which region the player is playing in and adjusts the rare item drop rate for that region. The management unit can also analyze player behavior patterns for each region and set region-specific rare item drop rates. For example, the management unit can analyze player behavior patterns for each region and set region-specific rare item drop rates. The management unit can also analyze trends for each region and set region-specific rare item drop rates. For example, the management unit can analyze player behavior patterns for each region and set region-specific rare item drop rates. In this way, by setting different rare item drop rates for each region, it is possible to provide a region-specific gaming experience.

[0059] The management unit can analyze players' social media activities and adjust the drop rate of related rare items. For example, the management unit can analyze players' social media posts and adjust the drop rate of rare items based on feedback about the game. For example, the management unit can collect comments and ratings about the game posted by players on social media and adjust the drop rate of rare items based on the comments and ratings. The management unit can also analyze players' behavioral patterns on social media and adjust the drop rate of rare items by correlating them with in-game behavioral data. For example, the management unit can analyze players' social media activities and compare them with in-game behavioral data. The management unit can also analyze trends on social media and adjust the drop rate of new rare items in the game. For example, the management unit can identify popular topics on social media and adjust the drop rate of new rare items based on the identified topics. In this way, the analysis of social media activities can adjust the drop rate of rare items based on players' interests.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The collection unit can analyze players' social media activities and collect data by associating them with in-game behavioral data. For example, it can collect comments and ratings about the game posted by players on social media and analyze players' behavioral patterns based on them. It can also analyze players' activities on social media and compare them with in-game behavioral data. It can also analyze trends on social media and use the results to suggest new content in the game. In this way, analyzing social media activities can help understand players' behavioral patterns and feedback.

[0062] The generation unit can analyze the player's past quest history and generate the most suitable quest. For example, it can analyze the player's past quest history and generate a similar quest. It can also generate a quest of appropriate difficulty based on the player's past quest completion rate. It can also analyze the player's past quest reward history and generate a quest with a high reward. In this way, it is possible to provide the player with the most suitable quest by analyzing their past quest history.

[0063] The suggestion unit can analyze the player's past play history and make optimal suggestions. For example, it can analyze the player's past quest history and suggest similar quests. It can also analyze the player's past item acquisition history and suggest related items. It can also analyze the player's past battle history and suggest appropriate enemies. In this way, it is possible to make optimal suggestions to the player by analyzing the player's past play history.

[0064] The collection unit can collect data for each region taking into account the geographical location information of the player. For example, play data for a specific region can be collected based on the geographical location information of the player. It can also collect bug occurrence data for each region and identify problems for each region. It can also collect player behavior patterns for each region and analyze trends specific to that region. In this way, by collecting data for each region, it is possible to identify problems and trends specific to that region.

[0065] The management unit can adjust the drop rate of rare items based on specific events within the game. For example, the drop rate of rare items can be increased during major events within the game. The management unit can also adjust the drop rate of rare items based on specific seasonal events. Furthermore, the management unit can also adjust the drop rate of rare items based on events in specific areas within the game. In this way, by adjusting the drop rate of rare items based on specific events, rare items can be obtained at an appropriate frequency depending on the event.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The detection unit automatically detects and fixes bugs that occur in the game. For example, the detection unit analyzes in-game log data and identifies bugs. The detection unit can also collect and fix bug reports from players. Furthermore, the detection unit can use AI to learn and predict bug occurrence patterns. Step 2: The collection unit collects player behavior data and in-game statistical data. For example, the collection unit records the player's movement patterns. The collection unit can also collect items used by the player and battle history. Furthermore, the collection unit can use AI to analyze the player's behavior patterns and collect data. Step 3: The proposal unit uses the generation AI to propose new maps, enemies, and items based on the collected data. For example, the proposal unit analyzes player behavior data and proposes new map designs. The proposal unit can also propose new enemy implementations based on the player's battle history. Furthermore, the proposal unit can use AI to propose the implementation of items that will attract the player's interest. Step 4: The adjustment unit automatically adjusts the enemy appearance frequency and level according to the player's skill level and party composition. For example, the adjustment unit analyzes the player's skill level and adjusts the enemy appearance frequency. The adjustment unit can also adjust the enemy level based on the party composition. Furthermore, the adjustment unit can use AI to learn the player's skill level and party composition and adjust the enemy appearance frequency and level. Step 5: The generator uses the AI ​​to automatically generate new quests based on the player's actions and the situation in the game. For example, the generator analyzes the player's behavior data and generates new quests. The generator can also generate new quests based on the situation in the game. Furthermore, the generator can use AI to generate quests that will attract the player's interest. Step 6: The management unit automatically adjusts the drop rate of rare items according to the player's behavior and the situation in the game. For example, the management unit analyzes the player's behavior data and adjusts the drop rate of rare items. The management unit can also adjust the drop rate of rare items based on the situation in the game. Furthermore, the management unit can use AI to learn the player's behavior patterns and adjust the drop rate of rare items.

[0068] (Example 2) A system according to an embodiment of the present invention automates game balance management in VRMMO games, benefiting both developers and players. This system includes a bug detection and bug repair function that automatically detects and fixes bugs that occur in the game. This function ensures a stable playing environment, even in VRMMO games with large maps and complex systems. Furthermore, it includes a data collection function that proposes new maps, enemies, and items. By collecting player behavior data and in-game statistics, the AI ​​generator proposes new maps, enemies, and items. This function ensures that game content is always fresh and keeps players engaged. Furthermore, it includes an enemy respawn management and enemy level adjustment function based on player skill and party composition, automatically adjusting the enemy appearance frequency and level according to the player's skill level and party composition. This function ensures that players can always enjoy the game at an appropriate difficulty level. Furthermore, it includes an automatic quest generation system, where the AI ​​generator automatically generates new quests based on player behavior and in-game conditions. This function ensures that players can always enjoy new challenges. Finally, the system includes a rare item drop management function that automatically adjusts the drop rate of rare items based on player behavior and in-game conditions. This allows players to obtain rare items at an appropriate frequency. These features allow VRMMO game developers to efficiently manage game balance, ensuring that players always enjoy a fresh and appropriately difficult game. For example, the system automatically detects and fixes bugs that occur in the game. This ensures a stable playing environment, even for VRMMO games with large maps and complex systems. The system also collects player behavior data and in-game statistics, and the generation AI suggests implementing new maps, enemies, and items. This ensures that game content is always fresh and keeps players engaged. Furthermore, the system automatically adjusts enemy appearance frequency and level based on the player's skill level and party composition.This allows players to always enjoy the game at an appropriate level of difficulty. In addition, the system's generation AI automatically generates new quests based on the player's actions and in-game situations, allowing players to always enjoy new challenges. Finally, the system automatically adjusts the drop rate of rare items based on the player's actions and in-game situations, allowing players to obtain rare items at an appropriate frequency. This allows the system to automatically manage the game balance of VRMMO games and provide a system that is beneficial to both developers and players.

[0069] A game balance management system according to an embodiment includes a detection unit, a collection unit, a proposal unit, an adjustment unit, a generation unit, and a management unit. The detection unit automatically detects and fixes bugs that occur in a game. For example, the detection unit analyzes in-game log data and identifies bugs. The detection unit can also collect and fix bug reports from players. The detection unit can also use AI to learn and predict bug occurrence patterns. The collection unit collects player behavior data and in-game statistical data. For example, the collection unit records player movement patterns. The collection unit can also collect items used by players and battle histories. The collection unit can also analyze player behavior patterns and collect data using AI. The proposal unit uses a generation AI to propose the implementation of new maps, enemies, and items based on the collected data. For example, the proposal unit analyzes player behavior data and proposes the design of a new map. The proposal unit can also propose the implementation of new enemies based on the player's battle history. The proposal unit can also use AI to propose the implementation of items that will attract players' interest. The adjustment unit automatically adjusts the frequency of enemy appearances and levels according to the player's skill level and party composition. For example, the adjustment unit analyzes the player's skill level and adjusts the frequency of enemy appearances. The adjustment unit can also adjust the enemy level based on the party composition. Furthermore, the adjustment unit can use AI to learn the player's skill level and party composition and adjust the frequency of enemy appearances and levels. The generation unit uses a generation AI to automatically generate new quests according to the player's actions and the in-game situation. For example, the generation unit analyzes player behavior data and generates new quests. The generation unit can also generate new quests based on the in-game situation. Furthermore, the generation unit can use AI to generate quests that will interest the player. The management unit automatically adjusts the drop rate of rare items according to the player's actions and the in-game situation. For example, the management unit analyzes player behavior data and adjusts the drop rate of rare items.The management unit can also adjust the drop rate of rare items based on the in-game situation. Furthermore, the management unit can use AI to learn player behavior patterns and adjust the drop rate of rare items. In this way, the game balance management system according to the embodiment can automatically manage the game balance of a VRMMO game, providing a system that is beneficial to both developers and players.

[0070] The detection unit can automatically detect and fix bugs that occur in a game. The detection unit, for example, analyzes in-game log data and identifies bugs. For example, the detection unit detects abnormal patterns in the log data and identifies the occurrence of a bug. The detection unit can also collect bug reports from players and fix them. For example, the detection unit automatically analyzes and fixes bugs reported by players. The detection unit can also use AI to learn and predict bug occurrence patterns. For example, the AI ​​learns past bug data and predicts future bug occurrences. This makes it possible to automatically detect and fix bugs in a game, thereby providing a stable playing environment.

[0071] The collection unit can collect player behavior data or in-game statistical data. The collection unit, for example, records the player's movement patterns. For example, the collection unit records which areas the player has moved through. The collection unit can also collect items used by the player and battle history. For example, the collection unit records the types of items used by the player and how often they were used. The collection unit can also use AI to analyze the player's behavior patterns and collect data. For example, the AI ​​learns the player's behavior patterns and collects important data. In this way, collecting player behavior data and in-game statistical data can be used to improve the game.

[0072] The suggestion unit allows the generation AI to propose the implementation of new maps, enemies, or items based on the collected data. The suggestion unit, for example, analyzes player behavior data and proposes new map designs. For example, the suggestion unit designs a new map based on areas frequently visited by players. The suggestion unit can also propose the implementation of new enemies based on the player's battle history. For example, the suggestion unit designs new enemies based on data on enemies that the player struggled with. The suggestion unit can also use AI to suggest the implementation of items that will attract the player's interest. For example, the AI ​​learns the player's item usage history and suggests new items. This makes it possible to use generation AI to suggest new content.

[0073] The adjustment unit can automatically adjust the frequency or level of enemy appearances according to the player's skill level or party composition. The adjustment unit, for example, analyzes the player's skill level and adjusts the frequency of enemy appearances. For example, if the player's skill level is high, the adjustment unit increases the frequency of enemy appearances. The adjustment unit can also adjust the enemy level based on the party composition. For example, if the party composition is powerful, the adjustment unit increases the enemy level. The adjustment unit can also use AI to learn the player's skill level and party composition and adjust the frequency or level of enemy appearances. For example, the AI ​​learns the player's skill level and party composition and sets the optimal frequency or level of enemy appearances. This allows the player to enjoy the game at an appropriate difficulty level according to the player's skill level and party composition.

[0074] The generation unit can automatically generate new quests based on the player's behavior and the situation in the game. The generation unit, for example, analyzes player behavior data and generates new quests. For example, the generation unit designs new quests based on areas frequently visited by players. The generation unit can also generate new quests based on the situation in the game. For example, the generation unit generates new quests based on in-game events or seasons. The generation unit can also use AI to generate quests that will attract the player's interest. For example, the AI ​​learns the player's behavior patterns and generates optimal quests. This allows the player to always enjoy new challenges.

[0075] The management unit can automatically adjust the drop rate of rare items according to player behavior and in-game conditions. The management unit, for example, analyzes player behavior data and adjusts the drop rate of rare items. For example, if a player spends a lot of time in a particular area, the management unit increases the drop rate of rare items in that area. The management unit can also adjust the drop rate of rare items based on in-game conditions. For example, the management unit adjusts the drop rate of rare items according to in-game events or seasons. The management unit can also use AI to learn player behavior patterns and adjust the drop rate of rare items. For example, AI learns player behavior patterns and sets the optimal drop rate. This allows players to obtain rare items at an appropriate frequency.

[0076] The detection unit can estimate the player's emotions and prioritize and fix bugs based on the results. For example, if the player is feeling frustrated, the detection unit uses an emotion engine to estimate this and prioritize fixing bugs that have the greatest impact. For example, the emotion engine analyzes the player's facial expressions and voice data to detect frustration. Furthermore, if the player is enjoying themselves, the detection unit uses the emotion engine to estimate this and determine that minor bugs can be fixed later. For example, the emotion engine analyzes the player's behavior data and detects that the player is enjoying themselves. Furthermore, if the detection unit uses the emotion engine to estimate this and quickly fix bugs that have a significant impact on the game experience. For example, the emotion engine analyzes the player's feedback data and detects dissatisfaction. This allows bugs to be prioritized and quickly fixed based on 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 can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The detection unit can analyze the frequency of bug occurrence and prioritize fixing bugs that occur frequently. The detection unit, for example, analyzes in-game log data, identifies bugs that occur frequently, and prioritizes fixing them. For example, the detection unit counts the number of times bugs occur in the log data and identifies bugs that occur frequently. The detection unit can also collect report data from players and prioritize fixing bugs that are reported most frequently. For example, the detection unit can tally the number of bugs reported by players and identify bugs that occur frequently. The detection unit can also monitor the frequency of bug occurrence in real time and quickly fix bugs that occur more frequently. For example, the detection unit can analyze log data in real time and monitor the frequency of bug occurrence. This can improve the stability of the game by prioritizing fixing bugs that occur frequently.

[0078] The detection unit can evaluate the scope of impact of a bug and prioritize fixing bugs with a large impact. For example, the detection unit evaluates the impact of a bug on the entire game and prioritizes fixing bugs with a large impact. For example, the detection unit evaluates whether a bug affects a major function of the game. The detection unit can also identify bugs that have a large impact on the player's game progress and prioritize fixing them. For example, the detection unit evaluates whether a bug hinders the player's quest progress. If bugs are concentrated in a specific function or area, the detection unit can evaluate the scope of their impact and prioritize fixing them. For example, the detection unit evaluates whether bugs frequently occur in a specific area. This makes it possible to improve the game experience by prioritizing fixing bugs with a large impact.

[0079] The detection unit can estimate the player's emotions and adjust the bug fix notification method based on the results. For example, if the player is feeling frustrated, the detection unit uses an emotion engine to estimate the player's frustration and provide a prompt notification of the bug fix. For example, the emotion engine can analyze the player's facial expressions and voice data to detect frustration. The detection unit can also use the emotion engine to estimate the player's relaxed state and provide a notification including detailed details of the bug fix. For example, the emotion engine can analyze the player's behavior data to detect relaxation. The detection unit can also use the emotion engine to estimate the player's excited state and provide a visually appealing notification. For example, the emotion engine can analyze the player's feedback data to detect excitement. This makes it possible to provide an appropriate bug fix notification method based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The detection unit can geographically map the locations of bug occurrences and prioritize fixing bugs that occur in specific areas. The detection unit, for example, analyzes geographic data within the game to identify bugs that frequently occur in specific areas and prioritize fixing them. For example, the detection unit can count the number of bug occurrences in the geographic data and identify bug occurrences in specific areas. The detection unit can also collect player movement data and map and fix bug occurrences in specific areas. For example, it can identify bug occurrences in areas frequently visited by players. The detection unit can also monitor the locations of bug occurrences in real time and quickly fix bug occurrences in specific areas. For example, it can analyze geographic data in real time and monitor the locations of bug occurrences. This can improve the stability of the game by prioritizing fixing bug occurrences in specific areas.

[0081] The detection unit can analyze the causes of bug occurrence and fix bugs caused by similar causes collectively. For example, the detection unit analyzes the causes of bug occurrence, identifies bugs caused by similar causes, and fixes them collectively. For example, the detection unit identifies the causes of bug occurrence and groups bugs caused by similar causes. The detection unit can also collect report data from players, identify bugs caused by a common cause, and fix them collectively. For example, the detection unit can analyze the causes of bugs reported by players and identify the common cause. The detection unit can also monitor the causes of bug occurrence in real time and quickly fix bugs caused by similar causes. For example, the detection unit can analyze the causes of bug occurrence in real time and identify bugs caused by similar causes. This allows bugs caused by similar causes to be fixed collectively, thereby efficiently fixing bugs.

[0082] The collection unit can estimate the player's emotions and adjust the type of data to be collected based on the results. For example, if the player is feeling frustrated, the collection unit uses an emotion engine to estimate this and prioritize collecting bug report data. For example, the emotion engine analyzes the player's facial expressions and voice data to detect frustration. The collection unit can also use the emotion engine to estimate if the player is enjoying themselves and collect data on play time and behavior patterns. For example, the emotion engine analyzes the player's behavior data to detect enjoyment. The collection unit can also use the emotion engine to estimate if the player is feeling dissatisfied and prioritize collecting feedback data. For example, the emotion engine analyzes the player's feedback data to detect dissatisfaction. This allows appropriate data to be collected depending on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The collection unit can analyze the player's behavioral patterns and prioritize collecting data related to specific behaviors. The collection unit, for example, analyzes the player's movement patterns and prioritizes collecting behavioral data in specific areas. For example, the collection unit collects behavioral data in areas frequently visited by the player. The collection unit can also analyze the player's battle patterns and prioritize collecting battle data with specific enemies. For example, the collection unit collects data on enemies the player frequently fights. The collection unit can also analyze the player's item usage patterns and prioritize collecting data related to specific items. For example, the collection unit collects data on items frequently used by the player. In this way, priority collection of data related to specific behaviors can be used to improve the game.

[0084] The collection unit can adjust the frequency of data collection based on a specific event in the game. For example, the collection unit increases the frequency of data collection during a major event in the game. For example, the collection unit collects player behavior data more frequently during the event period. The collection unit can also adjust the frequency of data collection when the player completes a specific quest. For example, the collection unit intensively collects player behavior data after the quest is completed. The collection unit can also adjust the frequency of data collection based on a seasonal event in the game. For example, the collection unit increases the frequency of data collection during a seasonal event. In this way, adjusting the frequency of data collection based on a specific event makes it possible to efficiently collect important data.

[0085] The collection unit can estimate the player's emotions and adjust the timing of data collection based on the results. For example, if the player is feeling frustrated, the collection unit uses an emotion engine to estimate this and immediately collect data. For example, the emotion engine analyzes the player's facial expressions and voice data to detect frustration. The collection unit can also use the emotion engine to estimate if the player is relaxed and collect data after the game ends. For example, the emotion engine analyzes the player's behavioral data to detect relaxation. The collection unit can also use the emotion engine to estimate if the player is excited and collect data in real time. For example, the emotion engine analyzes the player's feedback data to detect excitement. This allows data to be collected at an appropriate time depending on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] The collection unit can collect data for each region taking into account the geographical location information of the player. The collection unit, for example, collects play data for a specific region based on the geographical location information of the player. For example, the collection unit records which region the player is playing in and collects data for that region. The collection unit can also collect bug occurrence data for each region and identify problems for each region. For example, the collection unit can identify bugs that frequently occur in a specific region. The collection unit can also collect player behavior patterns for each region and analyze trends specific to the region. For example, the collection unit can analyze player behavior patterns for each region and identify trends specific to the region. In this way, by collecting data for each region, it is possible to identify problems and trends specific to the region.

[0087] The collection unit can analyze players' social media activities and collect related data. For example, the collection unit can analyze players' social media posts and collect feedback data about the game. For example, the collection unit can collect comments and ratings about the game posted by players on social media. The collection unit can also analyze players' behavioral patterns on social media and associate them with in-game behavioral data. For example, the collection unit can analyze players' activities on social media and compare them with in-game behavioral data. The collection unit can also analyze trends on social media and use the results to suggest new content in the game. For example, the collection unit can identify popular topics on social media and suggest new content based on them. In this way, analyzing social media activities can identify players' behavioral patterns and feedback.

[0088] The suggestion unit can estimate the player's emotions and adjust the suggestion content based on the results. For example, if the player is feeling frustrated, the suggestion unit uses an emotion engine to estimate and suggest content with a lower level of difficulty. For example, the emotion engine analyzes the player's facial expressions and voice data to detect frustration. The suggestion unit can also estimate and suggest challenging content if the player is enjoying themselves. For example, the emotion engine analyzes the player's behavioral data to detect enjoyment. The suggestion unit can also estimate and suggest content with a higher reward if the player is feeling dissatisfied. For example, the emotion engine analyzes the player's feedback data to detect dissatisfaction. This allows appropriate suggestions to be made based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The suggestion unit can analyze the player's past play history and make optimal suggestions. The suggestion unit, for example, analyzes the player's past quest history and suggests similar quests. For example, the suggestion unit analyzes data on quests the player has previously completed and suggests similar quests. The suggestion unit can also analyze the player's past item acquisition history and suggest related items. For example, the suggestion unit analyzes data on items the player has previously acquired and suggests related items. The suggestion unit can also analyze the player's past battle history and suggest appropriate enemies. For example, the suggestion unit analyzes data on enemies the player has previously fought and suggests appropriate enemies. In this way, optimal suggestions can be made to the player by analyzing the player's past play history.

[0090] The suggestion unit can analyze trends within a game and make suggestions based on the latest trends. The suggestion unit, for example, analyzes popular quests within a game and suggests them to the player. For example, the suggestion unit identifies the most played quest within a game and suggests it to the player. The suggestion unit can also analyze popular items within a game and suggest them to the player. For example, the suggestion unit identifies the most used item within a game and suggests it to the player. The suggestion unit can also analyze popular enemies within a game and suggest them to the player. For example, the suggestion unit identifies the enemy that is most frequently fought within a game and suggests it to the player. This makes it possible to continue to attract the interest of players by making suggestions based on the latest trends.

[0091] The suggestion unit can estimate the player's emotions and prioritize suggestions based on the results. For example, if the player is feeling frustrated, the suggestion unit uses an emotion engine to estimate the player's emotions and prioritize the most relaxing suggestions. For example, the emotion engine analyzes the player's facial expressions and voice data to detect frustration. The suggestion unit can also use the emotion engine to estimate the player's enjoyment and prioritize the most challenging suggestions. For example, the emotion engine analyzes the player's behavioral data to detect enjoyment. The suggestion unit can also use the emotion engine to estimate the player's dissatisfaction and prioritize the suggestions with the highest rewards. For example, the emotion engine analyzes the player's feedback data to detect dissatisfaction. This allows more appropriate suggestions to be made by prioritizing suggestions according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0092] The suggestion unit can make different suggestions for each region, taking into account the geographical location information of the player. The suggestion unit, for example, can suggest quests specific to each region based on the geographical location information of the player. For example, the suggestion unit can record which region the player is playing in and suggest quests specific to that region. The suggestion unit can also analyze player behavior patterns for each region and suggest items specific to the region. For example, the suggestion unit can analyze player behavior patterns for each region and suggest items specific to the region. The suggestion unit can also analyze trends for each region and suggest enemies specific to the region. For example, the suggestion unit can analyze player behavior patterns for each region and suggest enemies specific to the region. In this way, by making different suggestions for each region, it is possible to make suggestions that meet the specific needs of the region.

[0093] The suggestion unit can analyze the player's social media activities and make relevant suggestions. For example, the suggestion unit can analyze the player's social media posts and make suggestions based on feedback about the game. For example, the suggestion unit can collect comments and ratings about the game posted by the player on social media and make suggestions based on them. The suggestion unit can also analyze the player's behavioral patterns on social media and make suggestions by associating them with in-game behavioral data. For example, the suggestion unit can analyze the player's social media activities and compare them with in-game behavioral data. The suggestion unit can also analyze trends on social media and use this information to suggest new content in the game. For example, the suggestion unit can identify popular topics on social media and suggest new content based on them. In this way, by analyzing social media activities, suggestions can be made based on the player's interests.

[0094] The adjustment unit can estimate the player's emotions and adjust the frequency of enemy appearances and levels based on the results. For example, if the player is feeling frustrated, the adjustment unit uses the emotion engine to estimate this and reduce the frequency of enemy appearances. For example, the emotion engine analyzes the player's facial expressions and voice data to detect frustration. The adjustment unit can also estimate if the player is enjoying the game and appropriately increase the enemy levels. For example, the emotion engine analyzes the player's behavior data to detect enjoyment. The adjustment unit can also estimate if the player is feeling dissatisfied and adjust the frequency of enemy appearances. For example, the emotion engine analyzes the player's feedback data to detect dissatisfaction. This allows the player to enjoy the game at an appropriate level of difficulty by adjusting the frequency of enemy appearances and levels according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The adjustment unit can analyze the player's past battle records and set optimal enemy appearance frequencies and levels. The adjustment unit, for example, analyzes the player's past battle records and causes enemies of an appropriate difficulty level to appear. For example, the adjustment unit analyzes the player's past battle record data and sets appropriate enemy appearance frequencies and levels. The adjustment unit can also adjust the enemy appearance frequency based on the player's win rate. For example, the adjustment unit increases the enemy appearance frequency if the player's win rate is high. The adjustment unit can also set enemy levels based on the player's past battle data. For example, the adjustment unit analyzes data on enemies the player has fought in the past and sets appropriate enemy levels. In this way, the analysis of past battle records can set optimal enemy appearance frequencies and levels for the player.

[0096] The adjustment unit can adjust the appearance frequency and level of enemies based on specific events in the game. For example, the adjustment unit increases the appearance frequency of enemies during major events in the game. For example, the adjustment unit increases the appearance frequency of enemies during the event period. The adjustment unit can also adjust the level of enemies during specific quests. For example, the adjustment unit increases the level of enemies during specific quests. The adjustment unit can also adjust the appearance frequency and level of enemies based on seasonal events. For example, the adjustment unit adjusts the appearance frequency and level of enemies during seasonal events. In this way, by adjusting the appearance frequency and level of enemies based on specific events, it is possible to provide an appropriate level of difficulty according to the event.

[0097] The adjustment unit can estimate the player's emotions and adjust the enemy spawn locations based on the results. For example, if the player is feeling frustrated, the adjustment unit uses the emotion engine to estimate and reduce the number of enemy spawn locations. For example, the emotion engine analyzes the player's facial expressions and voice data to detect frustration. The adjustment unit can also estimate and increase the number of enemy spawn locations if the player is enjoying themselves. For example, the emotion engine analyzes the player's behavior data to detect enjoyment. The adjustment unit can also estimate and adjust the enemy spawn locations if the player is feeling dissatisfied. For example, the emotion engine analyzes the player's feedback data to detect dissatisfaction. This allows for an appropriate game experience by adjusting the enemy spawn locations according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The adjustment unit can set different enemy appearance frequencies and levels for each region, taking into account the player's geographical location information. The adjustment unit, for example, adjusts the enemy appearance frequency in a specific region based on the player's geographical location information. For example, the adjustment unit records which region the player is playing in and adjusts the enemy appearance frequency for that region. The adjustment unit can also analyze player behavior patterns for each region and set region-specific enemy levels. For example, the adjustment unit can analyze player behavior patterns for each region and set region-specific enemy levels. The adjustment unit can also analyze trends for each region and set region-specific enemy appearance frequencies. For example, the adjustment unit can analyze player behavior patterns for each region and set region-specific enemy appearance frequencies. In this way, by setting different enemy appearance frequencies and levels for each region, it is possible to provide a region-specific game experience.

[0099] The adjustment unit can analyze a player's social media activity and adjust the frequency and level of related enemy appearances. For example, the adjustment unit can analyze a player's social media posts and adjust the frequency of enemy appearances based on feedback about the game. For example, the adjustment unit can collect comments and ratings about the game posted by players on social media and adjust the frequency of enemy appearances based on the comments and ratings. The adjustment unit can also analyze a player's behavioral patterns on social media and adjust the enemy levels by correlating them with in-game behavioral data. For example, the adjustment unit can analyze a player's social media activity and compare it with in-game behavioral data. The adjustment unit can also analyze trends on social media and adjust the frequency of new enemy appearances in the game. For example, the adjustment unit can identify popular topics on social media and adjust the frequency of new enemy appearances based on the identified topics. In this way, the analysis of social media activity can adjust the frequency and level of enemy appearances based on a player's interests.

[0100] The generation unit can estimate the player's emotions and adjust the content of the quest based on the results. For example, if the player is feeling frustrated, the generation unit uses an emotion engine to estimate this and generate a quest with a low level of difficulty. For example, the emotion engine analyzes the player's facial expressions and voice data to detect frustration. The generation unit can also use the emotion engine to estimate if the player is enjoying themselves and generate a challenging quest. For example, the emotion engine analyzes the player's behavioral data to detect enjoyment. The generation unit can also use the emotion engine to estimate if the player is feeling dissatisfied and generate a quest with a high reward. For example, the emotion engine analyzes the player's feedback data to detect dissatisfaction. This makes it possible to provide an appropriate quest based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0101] The generation unit can analyze the player's past quest history and generate an optimal quest. The generation unit, for example, analyzes the player's past quest history and generates a similar quest. For example, the generation unit analyzes data on quests that the player has previously completed and generates a similar quest. The generation unit can also generate a quest of appropriate difficulty based on the player's past quest completion rate. For example, if the player has a high quest completion rate, the generation unit generates a quest with a high difficulty. The generation unit can also analyze the player's past quest reward history and generate a quest with a high reward. For example, the generation unit analyzes data on rewards the player has previously obtained and generates a quest with a high reward. In this way, the generation unit can provide the player with the optimal quest by analyzing the past quest history.

[0102] The generation unit can generate a quest based on a specific event in the game. For example, the generation unit generates a special quest during a major event in the game. For example, the generation unit generates a limited quest during the event period. The generation unit can also generate a time-limited quest based on a specific seasonal event. For example, the generation unit generates a special quest during the seasonal event period. The generation unit can also generate an area-limited quest based on an event in a specific area in the game. For example, the generation unit generates a quest based on an event in a specific area. In this way, by generating a quest based on a specific event, it is possible to provide a new challenge in accordance with the event.

[0103] The generation unit can estimate the player's emotions and adjust the difficulty of the quest based on the results. For example, if the player is feeling frustrated, the generation unit uses an emotion engine to estimate this and generate a quest with a low level of difficulty. For example, the emotion engine analyzes the player's facial expressions and voice data to detect frustration. The generation unit can also estimate if the player is enjoying themselves and generate a quest with a high level of difficulty. For example, the emotion engine analyzes the player's behavioral data to detect enjoyment. The generation unit can also estimate if the player is feeling dissatisfied and generate a quest with a high reward. For example, the emotion engine analyzes the player's feedback data to detect dissatisfaction. This makes it possible to provide a quest with an appropriate level of difficulty depending on the player's emotions. Emotion estimation is achieved, for example, using an emotion estimation function using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] The generation unit can generate different quests for each region, taking into account the geographical location information of the player. The generation unit, for example, generates region-specific quests based on the geographical location information of the player. For example, the generation unit records which region the player is playing in and generates a quest for that region. The generation unit can also analyze player behavior patterns for each region and generate region-specific quests. For example, the generation unit can analyze player behavior patterns for each region and generate region-specific quests. The generation unit can also analyze trends for each region and generate region-specific quests. For example, the generation unit can analyze player behavior patterns for each region and generate region-specific quests. In this way, by providing different quests for each region, it is possible to provide a region-specific gaming experience.

[0105] The generation unit can analyze a player's social media activity and generate a related quest. For example, the generation unit can analyze a player's social media posts and generate a quest based on feedback about the game. For example, the generation unit can collect comments and ratings about the game posted by the player on social media and generate a quest based on the comments and ratings. The generation unit can also analyze a player's behavioral patterns on social media and generate a quest by associating them with in-game behavioral data. For example, the generation unit can analyze a player's social media activity and compare it with in-game behavioral data. The generation unit can also analyze trends on social media and use the results to generate new in-game quests. For example, the generation unit can identify popular topics on social media and generate new quests based on those trends. In this way, analyzing social media activity can provide quests based on a player's interests.

[0106] The management unit can estimate the player's emotions and adjust the rare item drop rate based on the results. For example, if the player is feeling frustrated, the management unit uses an emotion engine to estimate this and increase the rare item drop rate. For example, the emotion engine analyzes the player's facial expressions and voice data to detect frustration. The management unit can also estimate if the player is enjoying themselves and adjust the rare item drop rate appropriately. For example, the emotion engine analyzes the player's behavioral data to detect enjoyment. The management unit can also estimate if the player is feeling dissatisfied and adjust the rare item drop rate. For example, the emotion engine analyzes the player's feedback data to detect dissatisfaction. This allows the player to obtain rare items at an appropriate frequency by adjusting the rare item drop rate according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI.

[0107] The management unit can analyze the player's past item acquisition history and set an optimal drop rate. The management unit, for example, analyzes the player's past item acquisition history and sets an appropriate drop rate. For example, the management unit analyzes data on items previously acquired by the player and sets an optimal drop rate. The management unit can also adjust the drop rate based on the player's past item acquisition frequency. For example, the management unit reduces the drop rate if the player frequently acquires items. The management unit can also analyze the player's past item acquisition patterns and set an optimal drop rate. For example, the management unit analyzes the types and frequency of items previously acquired by the player and sets an optimal drop rate. In this way, the optimal drop rate can be set for the player by analyzing the player's past item acquisition history.

[0108] The management unit can adjust the drop rate of rare items based on specific events within the game. For example, the management unit increases the drop rate of rare items during major events within the game. For example, the management unit increases the drop rate of rare items during the event period. The management unit can also adjust the drop rate of rare items based on specific seasonal events. For example, the management unit adjusts the drop rate of rare items during the seasonal event period. The management unit can also adjust the drop rate of rare items based on events in specific areas within the game. For example, the management unit adjusts the drop rate of rare items based on events in specific areas. In this way, by adjusting the drop rate of rare items based on specific events, rare items can be obtained at an appropriate frequency depending on the event.

[0109] The management unit can estimate the player's emotions and adjust the drop locations of rare items based on the results. For example, if the player is feeling frustrated, the management unit uses an emotion engine to estimate this and increase the number of drop locations for rare items. For example, the emotion engine analyzes the player's facial expressions and voice data to detect frustration. The management unit can also estimate if the player is enjoying themselves and adjust the drop locations of rare items appropriately. For example, the emotion engine analyzes the player's behavioral data to detect enjoyment. The management unit can also estimate if the player is feeling dissatisfied and adjust the drop locations of rare items. For example, the emotion engine analyzes the player's feedback data to detect dissatisfaction. This allows the player to obtain rare items in appropriate locations by adjusting the drop locations of rare items according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI.

[0110] The management unit can set different rare item drop rates for each region, taking into account the player's geographical location information. The management unit, for example, adjusts the rare item drop rate in a specific region based on the player's geographical location information. For example, the management unit records which region the player is playing in and adjusts the rare item drop rate for that region. The management unit can also analyze player behavior patterns for each region and set region-specific rare item drop rates. For example, the management unit can analyze player behavior patterns for each region and set region-specific rare item drop rates. The management unit can also analyze trends for each region and set region-specific rare item drop rates. For example, the management unit can analyze player behavior patterns for each region and set region-specific rare item drop rates. In this way, by setting different rare item drop rates for each region, it is possible to provide a region-specific gaming experience.

[0111] The management unit can analyze players' social media activities and adjust the drop rate of related rare items. For example, the management unit can analyze players' social media posts and adjust the drop rate of rare items based on feedback about the game. For example, the management unit can collect comments and ratings about the game posted by players on social media and adjust the drop rate of rare items based on the comments and ratings. The management unit can also analyze players' behavioral patterns on social media and adjust the drop rate of rare items by correlating them with in-game behavioral data. For example, the management unit can analyze players' social media activities and compare them with in-game behavioral data. The management unit can also analyze trends on social media and adjust the drop rate of new rare items in the game. For example, the management unit can identify popular topics on social media and adjust the drop rate of new rare items based on the identified topics. In this way, the analysis of social media activities can adjust the drop rate of rare items based on players' interests. === Hard Collateral 1-1 === Each of the multiple elements, including the detection unit, collection unit, suggestion unit, adjustment unit, generation unit, and management unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the detection unit detects the player's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14, and identifies and fixes bugs using the control unit 46A. The collection unit collects player behavior data using the control unit 46A of the smart device 14, which is analyzed by the identification processing unit 290 of the data processing device 12. The suggestion unit proposes the implementation of new maps, enemies, and items using the identification processing unit 290 of the data processing device 12. The adjustment unit adjusts the enemy appearance frequency and level using the identification processing unit 290 of the data processing device 12. The generation unit generates new quests using the identification processing unit 290 of the data processing device 12. The management unit adjusts the drop rate of rare items using the identification processing unit 290 of the data processing device 12. The emotion engine estimates the player's emotions using the identification processing unit 290 of the data processing device 12 and determines the priority of bugs. === Hard Collateral 1-2 === Each of the multiple elements, including the detection unit, collection unit, suggestion unit, adjustment unit, generation unit, and management unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the detection unit detects the player's facial expressions and voice using the camera 42 and microphone 238 of the smart glasses 214, and identifies and fixes bugs using the control unit 46A. The collection unit collects player behavior data using the control unit 46A of the smart glasses 214, which is analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit suggests the implementation of new maps, enemies, and items using the specific processing unit 290 of the data processing device 12. The adjustment unit adjusts the appearance frequency and level of enemies using the specific processing unit 290 of the data processing device 12. The generation unit generates new quests using the specific processing unit 290 of the data processing device 12. The management unit adjusts the drop rate of rare items using the specific processing unit 290 of the data processing device 12. The emotion engine estimates the player's emotions using the specific processing unit 290 of the data processing device 12 and determines the priority of the bugs. === Hard Collateral 1-3 === Each of the multiple elements, including the detection unit, collection unit, suggestion unit, adjustment unit, generation unit, and management unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the detection unit detects the player's facial expressions and voice using the camera 42 and microphone 238 of the headset-type terminal 314, and identifies and fixes bugs using the control unit 46A. The collection unit collects player behavior data using the control unit 46A of the headset-type terminal 314, which is analyzed by the specific processing unit 290 of the data processing device 12. The suggestion unit suggests the implementation of new maps, enemies, and items using the specific processing unit 290 of the data processing device 12. The adjustment unit adjusts the appearance frequency and level of enemies using the specific processing unit 290 of the data processing device 12. The generation unit generates new quests using the specific processing unit 290 of the data processing device 12. The management unit adjusts the drop rate of rare items using the specific processing unit 290 of the data processing device 12. The emotion engine estimates the player's emotions using the specific processing unit 290 of the data processing device 12 and determines the priority of the bugs. === Hard Collateral 1-4 === Each of the multiple elements, including the detection unit, collection unit, suggestion unit, adjustment unit, generation unit, and management unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the detection unit detects the player's facial expressions and voice using the camera 42 and microphone 238 of the robot 414, and identifies and fixes bugs using the control unit 46A. The collection unit collects player behavior data using the control unit 46A of the robot 414, which is analyzed by the identification processing unit 290 of the data processing device 12. The suggestion unit proposes the implementation of new maps, enemies, and items using the identification processing unit 290 of the data processing device 12. The adjustment unit adjusts the enemy appearance frequency and level using the identification processing unit 290 of the data processing device 12. The generation unit generates new quests using the identification processing unit 290 of the data processing device 12. The management unit adjusts the drop rate of rare items using the identification processing unit 290 of the data processing device 12. The emotion engine estimates the player's emotions using the identification processing unit 290 of the data processing device 12 and determines the priority of bugs.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The suggestion unit can estimate the player's emotions and suggest the implementation of new maps, enemies, and items based on the results. For example, if the player is excited, the emotion engine can estimate this and suggest more challenging enemies and complex maps. Alternatively, if the player is relaxed, the emotion engine can estimate this and suggest vast maps suitable for exploration and collectible items. Furthermore, if the player is feeling frustrated, the emotion engine can estimate this and suggest less difficult enemies and easier-to-obtain items. This makes it possible to provide optimal content according to the player's emotions.

[0114] The collection unit can analyze players' social media activities and collect data by associating them with in-game behavioral data. For example, it can collect comments and ratings about the game posted by players on social media and analyze players' behavioral patterns based on them. It can also analyze players' activities on social media and compare them with in-game behavioral data. It can also analyze trends on social media and use the results to suggest new content in the game. In this way, analyzing social media activities can help understand players' behavioral patterns and feedback.

[0115] The adjustment unit can estimate the player's emotions and adjust the frequency of enemy appearances and levels based on the results. For example, if the player is feeling frustrated, the emotion engine can estimate this and reduce the frequency of enemy appearances. Alternatively, if the player is enjoying the game, the emotion engine can estimate this and appropriately increase the enemy levels. Furthermore, if the player is feeling dissatisfied, the emotion engine can estimate this and adjust the frequency of enemy appearances. In this way, by adjusting the frequency of enemy appearances and levels according to the player's emotions, the game can be enjoyed at an appropriate level of difficulty.

[0116] The generation unit can analyze the player's past quest history and generate the most suitable quest. For example, it can analyze the player's past quest history and generate a similar quest. It can also generate a quest of appropriate difficulty based on the player's past quest completion rate. It can also analyze the player's past quest reward history and generate a quest with a high reward. In this way, it is possible to provide the player with the most suitable quest by analyzing their past quest history.

[0117] The management unit can estimate the player's emotions and adjust the drop rate of rare items based on the results. For example, if the player is feeling frustrated, the emotion engine can estimate this and increase the drop rate of rare items. Also, if the player is enjoying the game, the emotion engine can estimate this and adjust the drop rate of rare items appropriately. Furthermore, if the player is feeling dissatisfied, the emotion engine can estimate this and adjust the drop rate of rare items. In this way, by adjusting the drop rate of rare items according to the player's emotions, rare items can be obtained at an appropriate frequency.

[0118] The suggestion unit can analyze the player's past play history and make optimal suggestions. For example, it can analyze the player's past quest history and suggest similar quests. It can also analyze the player's past item acquisition history and suggest related items. It can also analyze the player's past battle history and suggest appropriate enemies. In this way, it is possible to make optimal suggestions to the player by analyzing the player's past play history.

[0119] The collection unit can collect data for each region taking into account the geographical location information of the player. For example, play data for a specific region can be collected based on the geographical location information of the player. It can also collect bug occurrence data for each region and identify problems for each region. It can also collect player behavior patterns for each region and analyze trends specific to that region. In this way, by collecting data for each region, it is possible to identify problems and trends specific to that region.

[0120] The generation unit can estimate the player's emotions and adjust the content of the quest based on the results. For example, if the player is feeling frustrated, the emotion engine can estimate this and generate a quest with a low level of difficulty. Alternatively, if the player is enjoying themselves, the emotion engine can estimate this and generate a challenging quest. Furthermore, if the player is feeling dissatisfied, the emotion engine can estimate this and generate a quest with a high reward. This makes it possible to provide appropriate quests according to the player's emotions.

[0121] The management unit can adjust the drop rate of rare items based on specific events within the game. For example, the drop rate of rare items can be increased during major events within the game. The management unit can also adjust the drop rate of rare items based on specific seasonal events. Furthermore, the management unit can also adjust the drop rate of rare items based on events in specific areas within the game. In this way, by adjusting the drop rate of rare items based on specific events, rare items can be obtained at an appropriate frequency depending on the event.

[0122] The suggestion unit can estimate the player's emotions and prioritize suggestions based on the results. For example, if the player is feeling frustrated, the emotion engine can estimate this and prioritize the most relaxing suggestion. Alternatively, if the player is enjoying themselves, the emotion engine can estimate this and prioritize the most challenging suggestion. Furthermore, if the player is feeling dissatisfied, the emotion engine can estimate this and prioritize the suggestion with the highest reward. This allows for more appropriate suggestions to be made by prioritizing suggestions according to the player's emotions.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The detection unit automatically detects and fixes bugs that occur in the game. For example, the detection unit analyzes in-game log data and identifies bugs. The detection unit can also collect and fix bug reports from players. Furthermore, the detection unit can use AI to learn and predict bug occurrence patterns. Step 2: The collection unit collects player behavior data and in-game statistical data. For example, the collection unit records the player's movement patterns. The collection unit can also collect items used by the player and battle history. Furthermore, the collection unit can use AI to analyze the player's behavior patterns and collect data. Step 3: The proposal unit uses the generation AI to propose new maps, enemies, and items based on the collected data. For example, the proposal unit analyzes player behavior data and proposes new map designs. The proposal unit can also propose new enemy implementations based on the player's battle history. Furthermore, the proposal unit can use AI to propose the implementation of items that will attract the player's interest. Step 4: The adjustment unit automatically adjusts the enemy appearance frequency and level according to the player's skill level and party composition. For example, the adjustment unit analyzes the player's skill level and adjusts the enemy appearance frequency. The adjustment unit can also adjust the enemy level based on the party composition. Furthermore, the adjustment unit can use AI to learn the player's skill level and party composition and adjust the enemy appearance frequency and level. Step 5: The generator uses the AI ​​to automatically generate new quests based on the player's actions and the situation in the game. For example, the generator analyzes the player's behavior data and generates new quests. The generator can also generate new quests based on the situation in the game. Furthermore, the generator can use AI to generate quests that will attract the player's interest. Step 6: The management unit automatically adjusts the drop rate of rare items according to the player's behavior and the situation in the game. For example, the management unit analyzes the player's behavior data and adjusts the drop rate of rare items. The management unit can also adjust the drop rate of rare items based on the situation in the game. Furthermore, the management unit can use AI to learn the player's behavior patterns and adjust the drop rate of rare items.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0127] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0165] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0167] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0168] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0169] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0170] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0171] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0172] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0173] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0175] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0180] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0181] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0182] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0186] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0187] 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.

[0188] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0189] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0190] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0191] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0193] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0194] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0195] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0196] [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a detection part that automatically detects and fixes bugs; a collection unit that collects player behavior data; a suggestion unit that proposes the implementation of a new map, enemy, or item based on the data collected by the collection unit; an adjustment unit that adjusts the enemy appearance frequency or level according to the player's skill level or party composition; a generator that generates new quests in response to the player's actions or the situation in the game; A management unit that adjusts the drop rate of rare items according to the player's actions or the situation in the game. A system characterized by:

2. The detection unit Automatically detect and fix bugs that occur in your games 2. The system of claim 1.

3. The collecting unit Collect player behavioral data or in-game statistics 2. The system of claim 1.

4. The proposal unit Based on the collected data, the generative AI will suggest new maps, enemies, and items to implement.

2. The system of claim 1.

5. The adjustment unit Automatically adjust enemy appearance frequency or level based on player skill level or party composition 2. The system of claim 1.

6. The generation unit Automatically generate new quests based on player actions and in-game situations 2. The system of claim 1.

7. The management unit Automatically adjust rare item drop rates based on player behavior or in-game conditions 2. The system of claim 1.

8. The detection unit Estimate player sentiment and prioritize and fix bugs based on the results 2. The system of claim 1.

Images