system

Abstract

The system according to the embodiment aims to efficiently identify bugs in a game and fix them quickly. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit accumulates bug reports from players in a database. The analysis unit analyzes the bug information accumulated by the collection unit and extracts bugs that have been reported frequently. The generation unit creates a fix patch based on the bug information extracted by the analysis unit.

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, the process of efficiently identifying and fixing bugs in games was complicated, making it difficult to respond quickly.

[0005] The system according to the embodiment aims to efficiently identify bugs in a game and fix them quickly. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit accumulates bug reports from players in a database. The analysis unit analyzes the bug information accumulated by the collection unit and extracts bugs that have been reported frequently. The generation unit creates a fix patch based on the bug information extracted by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently identify bugs in a game and fix them quickly. [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 bug fixing system according to an embodiment of the present invention is a system for efficiently fixing bugs reported by players. This bug fixing system accumulates bug information reported by players in a database and extracts bugs that are frequently reported. Next, the bug information is input into a generation AI tuned for specific applications, which identifies similar bugs lurking in existing game programs. Finally, the generation AI proposes a fix method and automatically creates a fix patch. This patch is reviewed by a game developer, and after necessary modifications are made, it is provided to players. This improves the quality of the game and increases player satisfaction. For example, the system includes a "collection unit" that collects bug information reported by players and stores that information in a database. Next, the system includes an "analysis unit" that extracts frequently reported bugs from the accumulated bug information and inputs it into a generation AI tuned for specific applications. The generation AI analyzes the input bug information and identifies similar bugs lurking in existing game programs. For the identified bugs, the generation AI proposes a fix method and automatically creates a patch to make the fix. This patch is reviewed by a game developer, and after necessary modifications are made, it is provided to players. This will improve the quality of the game and increase player satisfaction. This will enable the bug fixing system to efficiently collect and analyze bug reports from players and create fix patches.

[0029] A bug fixing system according to an embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit accumulates bug reports from players in a database. Bug reports from players may be in text, audio, or image formats, but are not limited to these examples. The collection unit, for example, provides an interface through which players can report bugs in a game. Players can enter details about the bug and attach screenshots and videos. The collection unit can also accept voice input from players and convert it into text data using voice recognition technology. For example, when a player reports a bug, the collection unit records the voice input with a microphone, and voice recognition software automatically analyzes the voice and saves it as text. The collection unit can also collect information about bugs mentioned by players on social media. For example, the collection unit analyzes social media posts and accumulates them in a database as bug reports. The analysis unit analyzes the bug information accumulated by the collection unit and extracts bugs with a high volume of reports. These bugs may be extracted based on criteria such as the number of reports within a certain period of time or the percentage of the total number of reports, but are not limited to these examples. For example, the analysis unit statistically analyzes bug information in the database to identify bugs that are frequently reported. The analysis unit can also evaluate the importance of bugs based on their frequency of occurrence and impact. For example, the analysis unit prioritizes extracting bugs that occur frequently or that have a significant impact on the progress of the game. The generation unit creates a fix patch based on the bug information extracted by the analysis unit. The generation unit uses a generation AI to suggest a fix method for the bug and automatically create a fix patch. For example, the generation unit inputs bug information into the generation AI and has it suggest a fix method. The generation AI identifies similar bugs lurking in existing game programs and suggests a fix method. For example, the generation AI identifies the part of the code that causes the bug and suggests a fix method. The generation unit also automatically creates a fix patch based on the fix method suggested by the generation AI. For example, the generation unit executes the fix method suggested by the generation AI and generates a fix patch.This allows the bug fixing system according to the embodiment to efficiently collect and analyze bug reports from players and create fix patches.

[0030] The collection unit can analyze the player's past bug reporting history and select an appropriate reporting method. For example, if the player has previously submitted detailed bug reports in text, the collection unit can provide a similar text input form. Furthermore, if the player has previously submitted bug reports using many screenshots, the collection unit can also provide an interface that encourages the player to attach screenshots. Furthermore, if the player has previously submitted bug reports using voice, the collection unit can also prioritize voice input. This allows the optimal reporting method to be provided based on the player's past reporting history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the player's past bug reporting history data into the generation AI and have the generation AI select the optimal reporting method.

[0031] When acquiring a bug report, the collection unit can filter the bug reports based on the player's current game progress and play style. For example, if the player is fighting a boss, the collection unit temporarily suspends the bug report and prompts the player to report it after the battle is over. Furthermore, if the player is exploring, the collection unit can prompt the player to report the bug at a timing that does not interfere with the progress of the exploration. Furthermore, if the player is playing in multiplayer mode, the collection unit can prompt the player to report the bug at a timing that does not affect other players. This allows bug reports to be acquired at an appropriate timing depending on the player's game progress and play style. Some or all of the above-described processing by the collection unit may be performed using, or without, an AI. For example, the collection unit can input the player's game progress data to a generation AI and have the generation AI perform filtering.

[0032] When acquiring bug reports, the collection unit can prioritize acquiring highly relevant reports based on the player's geographical location information. For example, if a player is playing in a specific region, the collection unit can prioritize acquiring bug reports related to that region. Furthermore, if a player is playing on a specific server, the collection unit can prioritize acquiring bug reports related to that server. Furthermore, if a player is playing during a specific event, the collection unit can prioritize acquiring bug reports related to that event. This makes it possible to prioritize acquiring highly relevant bug reports based on the player's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the player's geographical location information data to the generation AI and cause the generation AI to acquire highly relevant reports.

[0033] When acquiring a bug report, the collection unit can analyze the player's social media activity and acquire related reports. For example, if a player mentions a specific bug on social media, the collection unit can prioritize acquiring the bug report. Furthermore, if a player discusses a bug with other players on social media, the collection unit can acquire the bug report by referring to the discussion. Furthermore, if a player shares a screenshot of a bug on social media, the collection unit can acquire the bug report by referring to the screenshot. This allows acquisition of related bug reports based on the player's social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media posting data into a generation AI and cause the generation AI to acquire related bug reports.

[0034] The analysis unit can adjust the level of detail of the analysis based on the severity of the bug. For example, in the case of a serious bug, the analysis unit performs a detailed analysis to identify the root cause. In addition, in the case of a minor bug, the analysis unit can perform a simple analysis to quickly fix the bug. Furthermore, in the case of a medium-level bug, the analysis unit can perform an analysis with a moderate level of detail to efficiently fix the bug. This allows the level of detail of the analysis to be adjusted according to the severity of the bug. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input bug severity data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0035] The analysis unit can apply different analysis algorithms depending on the bug category. For example, in the case of a graphics-related bug, the analysis unit can apply an image analysis algorithm. In addition, in the case of a network-related bug, the analysis unit can also apply a communication analysis algorithm. Furthermore, in the case of a game logic-related bug, the analysis unit can also apply a code analysis algorithm. This makes it possible to apply the optimal analysis algorithm depending on the bug category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input bug category data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0036] The analysis unit can determine the analysis priority based on the time when the bug occurred. For example, the analysis unit prioritizes analysis for a recently occurred bug. The analysis unit can also analyze a bug that occurred in the past but is still unresolved at the next priority level. Furthermore, the analysis unit can also analyze a bug that has been left unresolved for a long time at the normal priority level. This makes it possible to determine the analysis priority level based on the time when the bug occurred. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the bug occurred to the generation AI and have the generation AI determine the analysis priority level.

[0037] The analysis unit can adjust the order of analysis based on the relevance of bugs. For example, the analysis unit prioritizes analysis of a bug that is highly related to other bugs. In addition, the analysis unit can also analyze a bug that has occurred alone in the next order of priority. Furthermore, the analysis unit can analyze a bug that is less related to other bugs in the normal order of priority. This makes it possible to adjust the order of analysis based on the relevance of bugs. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input bug relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0038] When generating a fix patch, the generation unit can improve the accuracy of generation based on the interrelationships between bugs. For example, if multiple bugs are related, the generation unit generates a patch that fixes them all at once. Furthermore, if a bug fix affects other functions, the generation unit can also generate a patch that minimizes the impact. Furthermore, if a bug fix has the potential to cause other bugs, the generation unit can generate a patch taking into account that risk. This improves the accuracy of fix patches by taking into account the interrelationships between bugs. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit may input bug interrelationship data into the generation AI and cause the generation AI to generate a fix patch.

[0039] When generating a patch, the generation unit can generate the patch based on attribute information of the person who caused the bug. For example, if the person who caused the bug is using a specific device, the generation unit can generate a patch optimized for that device. Furthermore, if the person who caused the bug is in a specific region, the generation unit can generate a patch optimized for the network environment of that region. Furthermore, if the person who caused the bug is playing in a specific game mode, the generation unit can generate a patch optimized for that mode. In this way, the optimal patch can be generated by taking into account the attribute information of the person who caused the bug. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input attribute information data of the person who caused the bug into the generation AI and cause the generation AI to generate a patch.

[0040] When generating a patch, the generation unit can generate the patch based on the geographical distribution of bugs. For example, for a bug that frequently occurs in a specific region, the generation unit generates a patch optimized for that region. The generation unit can also generate a common patch for bugs that occur in multiple regions. Furthermore, the generation unit can generate an optimal patch by taking into account the network environment of each region. In this way, an optimal patch can be generated by taking into account the geographical distribution of bugs. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input geographical distribution data of bugs into the generation AI and cause the generation AI to generate a patch.

[0041] When generating a fix patch, the generation unit can improve the accuracy of the generation by referring to literature related to the bug. For example, the generation unit can refer to literature related to past bug fixes and apply similar techniques. The generation unit can also refer to bug fix cases in other games and introduce the optimal fix method. Furthermore, the generation unit can refer to academic papers and apply the latest fix techniques. In this way, the accuracy of the fix patch can be improved by referring to literature related to the bug. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input bug-related literature data into the generation AI and cause the generation AI to generate a fix patch.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The collection unit can monitor a player's gameplay data in real time and automatically detect abnormal behavior. For example, if a player repeatedly performs a specific action or an unexpected error occurs in the game, the data is collected and stored in a database as a bug report. The collection unit can also analyze a player's operation log and detect abnormal patterns. Furthermore, the collection unit can analyze a player's in-game chat log and find signs of bugs in conversations with other players. This allows the system to automatically detect abnormalities and respond quickly before a player reports a bug.

[0044] The collection unit can analyze a player's past gameplay data and evaluate the reliability of a bug report. For example, if a player has made accurate bug reports in the past, the report can be evaluated as having high reliability. On the other hand, if a player has made incorrect bug reports in the past, the report can be evaluated as having low reliability. Furthermore, the collection unit can analyze a player's in-game behavior history to supplement the reliability of a bug report. This allows reliable bug reports to be processed preferentially.

[0045] When acquiring bug reports, the collection unit can filter them based on the player's role in the game and the characteristics of the character. For example, if the player is a healer, bug reports related to that role can be acquired preferentially. Also, if the player is using a specific character, bug reports related to that character can be acquired preferentially. Furthermore, if the player is using a specific skill or item, bug reports related to the usage of that skill or item can be acquired preferentially. This makes it possible to acquire appropriate bug reports according to the player's role in the game and the characteristics of the character.

[0046] When acquiring bug reports, the collection unit can prioritize acquiring highly relevant reports based on the player's device information. For example, if the player is playing on a specific device, bug reports related to that device can be prioritized. Also, if the player is using a specific OS version, bug reports related to that version can be prioritized. Furthermore, if the player is using specific graphics settings, bug reports related to those settings can be prioritized. This allows highly relevant bug reports to be prioritized based on the player's device information.

[0047] The collection unit can analyze the player's social media activity to evaluate the reliability of the bug report. For example, if a player discusses a bug with other players on social media, the content of that discussion can be used as a reference to evaluate the reliability of the bug report. Also, if a player shares a screenshot of the bug on social media, the screenshot can be used as a reference to evaluate the reliability of the bug report. Furthermore, if a player reports a bug in another game on social media, the content of that report can be used as a reference to evaluate the reliability. In this way, the reliability of the bug report can be evaluated based on the player's social media activity.

[0048] The analysis unit can adjust the allocation of analysis resources based on the severity of the bug. For example, in the case of a serious bug, the analysis unit can allocate more resources and perform a detailed analysis. In addition, in the case of a minor bug, the analysis unit can perform a simple analysis with fewer resources. Furthermore, in the case of a medium-serious bug, the analysis unit can allocate appropriate resources and perform an efficient analysis. This allows the allocation of analysis resources to be optimized according to the severity of the bug.

[0049] The analysis unit can combine different analysis methods depending on the bug category. For example, for graphics-related bugs, image analysis and code analysis can be combined for analysis. For network-related bugs, communication analysis and log analysis can be combined for analysis. Furthermore, for game logic-related bugs, code analysis and simulation analysis can be combined for analysis. This allows the analysis accuracy to be improved by combining the optimal analysis methods depending on the bug category.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The collection unit accumulates bug reports from players in a database. Bug reports from players include text, audio, and image formats. The collection unit provides an interface for players to report bugs in the game, allowing players to enter details of the bug and attach screenshots and videos. The collection unit can also accept voice input and convert it into text data using voice recognition technology. The collection unit also collects bug reports from social media. Step 2: The analysis unit analyzes the bug information accumulated by the collection unit and extracts bugs that have been reported frequently. These bugs are extracted based on criteria such as the number of reports within a certain period of time or the percentage of the total number of reports. The analysis unit can also statistically analyze the bug information in the database and evaluate the importance of bugs based on their frequency of occurrence and impact. Step 3: The generation unit creates a fix patch based on the bug information extracted by the analysis unit. The generation unit uses the generation AI to propose a fix method for the bug and automatically create a fix patch. The generation AI identifies the part of the code that causes the bug and proposes a fix method. The generation unit executes the fix method proposed by the generation AI and generates a fix patch.

[0052] (Example 2) A bug fixing system according to an embodiment of the present invention is a system for efficiently fixing bugs reported by players. This bug fixing system accumulates bug information reported by players in a database and extracts bugs that are frequently reported. Next, the bug information is input into a generation AI tuned for specific applications, which identifies similar bugs lurking in existing game programs. Finally, the generation AI proposes a fix method and automatically creates a fix patch. This patch is reviewed by a game developer, and after necessary modifications are made, it is provided to players. This improves the quality of the game and increases player satisfaction. For example, the system includes a "collection unit" that collects bug information reported by players and stores that information in a database. Next, the system includes an "analysis unit" that extracts frequently reported bugs from the accumulated bug information and inputs it into a generation AI tuned for specific applications. The generation AI analyzes the input bug information and identifies similar bugs lurking in existing game programs. For the identified bugs, the generation AI proposes a fix method and automatically creates a patch to make the fix. This patch is reviewed by a game developer, and after necessary modifications are made, it is provided to players. This will improve the quality of the game and increase player satisfaction. This will enable the bug fixing system to efficiently collect and analyze bug reports from players and create fix patches.

[0053] A bug fixing system according to an embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit accumulates bug reports from players in a database. Bug reports from players may be in text, audio, or image formats, but are not limited to these examples. The collection unit, for example, provides an interface through which players can report bugs in a game. Players can enter details about the bug and attach screenshots and videos. The collection unit can also accept voice input from players and convert it into text data using voice recognition technology. For example, when a player reports a bug, the collection unit records the voice input with a microphone, and voice recognition software automatically analyzes the voice and saves it as text. The collection unit can also collect information about bugs mentioned by players on social media. For example, the collection unit analyzes social media posts and accumulates them in a database as bug reports. The analysis unit analyzes the bug information accumulated by the collection unit and extracts bugs with a high volume of reports. These bugs may be extracted based on criteria such as the number of reports within a certain period of time or the percentage of the total number of reports, but are not limited to these examples. For example, the analysis unit statistically analyzes bug information in the database to identify bugs that are frequently reported. The analysis unit can also evaluate the importance of bugs based on their frequency of occurrence and impact. For example, the analysis unit prioritizes extracting bugs that occur frequently or that have a significant impact on the progress of the game. The generation unit creates a fix patch based on the bug information extracted by the analysis unit. The generation unit uses a generation AI to suggest a fix method for the bug and automatically create a fix patch. For example, the generation unit inputs bug information into the generation AI and has it suggest a fix method. The generation AI identifies similar bugs lurking in existing game programs and suggests a fix method. For example, the generation AI identifies the part of the code that causes the bug and suggests a fix method. The generation unit also automatically creates a fix patch based on the fix method suggested by the generation AI. For example, the generation unit executes the fix method suggested by the generation AI and generates a fix patch.This allows the bug fixing system according to the embodiment to efficiently collect and analyze bug reports from players and create fix patches.

[0054] The collection unit can estimate the player's emotions and adjust the timing of acquiring bug reports based on the estimated player's emotions. For example, if the player is feeling frustrated, the collection unit can prompt the player to report a bug when the player takes a break to avoid interrupting gameplay. Furthermore, if the player is relaxed, the collection unit can prompt the player to report a bug while playing the game and collect detailed information. Furthermore, if the player is excited, the collection unit can prompt the player to report a bug after gameplay has ended and collect detailed information when the player is calm. This allows bug reports to be acquired at an appropriate time depending on the player's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using AI, or without AI. For example, the collection unit can input the player's facial expression data into the generation AI and cause the generation AI to estimate the player's emotions.

[0055] The collection unit can analyze the player's past bug reporting history and select an appropriate reporting method. For example, if the player has previously submitted detailed bug reports in text, the collection unit can provide a similar text input form. Furthermore, if the player has previously submitted bug reports using many screenshots, the collection unit can also provide an interface that encourages the player to attach screenshots. Furthermore, if the player has previously submitted bug reports using voice, the collection unit can also prioritize voice input. This allows the optimal reporting method to be provided based on the player's past reporting history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the player's past bug reporting history data into the generation AI and have the generation AI select the optimal reporting method.

[0056] When acquiring a bug report, the collection unit can filter the bug reports based on the player's current game progress and play style. For example, if the player is fighting a boss, the collection unit temporarily suspends the bug report and prompts the player to report it after the battle is over. Furthermore, if the player is exploring, the collection unit can prompt the player to report the bug at a timing that does not interfere with the progress of the exploration. Furthermore, if the player is playing in multiplayer mode, the collection unit can prompt the player to report the bug at a timing that does not affect other players. This allows bug reports to be acquired at an appropriate timing depending on the player's game progress and play style. Some or all of the above-described processing by the collection unit may be performed using, or without, an AI. For example, the collection unit can input the player's game progress data to a generation AI and have the generation AI perform filtering.

[0057] The collection unit can estimate the player's emotions and determine the priority of bug reports to be acquired based on the estimated player's emotions. For example, if the player is feeling very frustrated, the collection unit can process the bug report with a higher priority. If the player is relaxed, the collection unit can also process the bug report with a normal priority. If the player is excited, the collection unit can also process the bug report with a lower priority. This allows the priority of bug reports to be determined according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using an AI, for example, or without an AI. For example, the collection unit can input the player's emotion data into the generation AI and have the generation AI determine the priority of bug reports.

[0058] When acquiring bug reports, the collection unit can prioritize acquiring highly relevant reports based on the player's geographical location information. For example, if a player is playing in a specific region, the collection unit can prioritize acquiring bug reports related to that region. Furthermore, if a player is playing on a specific server, the collection unit can prioritize acquiring bug reports related to that server. Furthermore, if a player is playing during a specific event, the collection unit can prioritize acquiring bug reports related to that event. This makes it possible to prioritize acquiring highly relevant bug reports based on the player's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the player's geographical location information data to the generation AI and cause the generation AI to acquire highly relevant reports.

[0059] When acquiring a bug report, the collection unit can analyze the player's social media activity and acquire related reports. For example, if a player mentions a specific bug on social media, the collection unit can prioritize acquiring the bug report. Furthermore, if a player discusses a bug with other players on social media, the collection unit can acquire the bug report by referring to the discussion. Furthermore, if a player shares a screenshot of a bug on social media, the collection unit can acquire the bug report by referring to the screenshot. This allows acquisition of related bug reports based on the player's social media activity. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media posting data into a generation AI and cause the generation AI to acquire related bug reports.

[0060] The analysis unit can estimate the player's emotions and adjust the bug analysis method based on the estimated player's emotions. For example, if the player is feeling very frustrated, the analysis unit can analyze the bug in detail and quickly fix it. If the player is relaxed, the analysis unit can process the bug using the normal analysis procedure. If the player is excited, the analysis unit can analyze the bug in the next priority order. This allows the bug analysis method to be adjusted according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the player's emotion data into the generation AI and have the generation AI adjust the bug analysis method.

[0061] The analysis unit can adjust the level of detail of the analysis based on the severity of the bug. For example, in the case of a serious bug, the analysis unit performs a detailed analysis to identify the root cause. In addition, in the case of a minor bug, the analysis unit can perform a simple analysis to quickly fix the bug. Furthermore, in the case of a medium-level bug, the analysis unit can perform an analysis with a moderate level of detail to efficiently fix the bug. This allows the level of detail of the analysis to be adjusted according to the severity of the bug. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input bug severity data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0062] The analysis unit can apply different analysis algorithms depending on the bug category. For example, in the case of a graphics-related bug, the analysis unit can apply an image analysis algorithm. In addition, in the case of a network-related bug, the analysis unit can also apply a communication analysis algorithm. Furthermore, in the case of a game logic-related bug, the analysis unit can also apply a code analysis algorithm. This makes it possible to apply the optimal analysis algorithm depending on the bug category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input bug category data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0063] The analysis unit can estimate the player's emotions and determine the analysis priority based on the estimated player's emotions. For example, if the player is feeling very frustrated, the analysis unit can analyze that bug as the highest priority. Furthermore, if the player is relaxed, the analysis unit can analyze that bug with normal priority. Furthermore, if the player is excited, the analysis unit can analyze that bug with the next highest priority. This allows the analysis priority to be determined according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the player's emotion data into the generation AI and have the generation AI determine the analysis priority.

[0064] The analysis unit can determine the analysis priority based on the time when the bug occurred. For example, the analysis unit prioritizes analysis for a recently occurred bug. The analysis unit can also analyze a bug that occurred in the past but is still unresolved at the next priority level. Furthermore, the analysis unit can also analyze a bug that has been left unresolved for a long time at the normal priority level. This makes it possible to determine the analysis priority level based on the time when the bug occurred. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the bug occurred to the generation AI and have the generation AI determine the analysis priority level.

[0065] The analysis unit can adjust the order of analysis based on the relevance of bugs. For example, the analysis unit prioritizes analysis of a bug that is highly related to other bugs. In addition, the analysis unit can also analyze a bug that has occurred alone in the next order of priority. Furthermore, the analysis unit can analyze a bug that is less related to other bugs in the normal order of priority. This makes it possible to adjust the order of analysis based on the relevance of bugs. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input bug relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0066] The generation unit can estimate the player's emotions and adjust the method for generating a patch based on the estimated player's emotions. For example, if the player is feeling very frustrated, the generation unit can quickly generate a patch. Furthermore, if the player is relaxed, the generation unit can also generate a patch using a normal procedure. Furthermore, if the player is excited, the generation unit can also generate a patch in the following order of priority. This allows the method for generating a patch to be adjusted according to the player's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the method for generating a patch.

[0067] When generating a fix patch, the generation unit can improve the accuracy of generation based on the interrelationships between bugs. For example, if multiple bugs are related, the generation unit generates a patch that fixes them all at once. Furthermore, if a bug fix affects other functions, the generation unit can also generate a patch that minimizes the impact. Furthermore, if a bug fix has the potential to cause other bugs, the generation unit can generate a patch taking into account that risk. This improves the accuracy of fix patches by taking into account the interrelationships between bugs. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit may input bug interrelationship data into the generation AI and cause the generation AI to generate a fix patch.

[0068] When generating a patch, the generation unit can generate the patch based on attribute information of the person who caused the bug. For example, if the person who caused the bug is using a specific device, the generation unit can generate a patch optimized for that device. Furthermore, if the person who caused the bug is in a specific region, the generation unit can generate a patch optimized for the network environment of that region. Furthermore, if the person who caused the bug is playing in a specific game mode, the generation unit can generate a patch optimized for that mode. In this way, the optimal patch can be generated by taking into account the attribute information of the person who caused the bug. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input attribute information data of the person who caused the bug into the generation AI and cause the generation AI to generate a patch.

[0069] The generation unit can estimate the player's emotions and adjust the display method of the patch based on the estimated player's emotions. For example, if the player is feeling very frustrated, the generation unit can briefly display how to apply the patch. If the player is relaxed, the generation unit can provide a display method including detailed instructions. If the player is excited, the generation unit can provide a visually easy-to-understand display method. This allows the display method of the patch to be adjusted according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using an AI, for example, or without an AI. For example, the generation unit can input the player's emotion data into the generation AI and cause the generation AI to adjust the display method of the patch.

[0070] When generating a patch, the generation unit can generate the patch based on the geographical distribution of bugs. For example, for a bug that frequently occurs in a specific region, the generation unit generates a patch optimized for that region. The generation unit can also generate a common patch for bugs that occur in multiple regions. Furthermore, the generation unit can generate an optimal patch by taking into account the network environment of each region. In this way, an optimal patch can be generated by taking into account the geographical distribution of bugs. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input geographical distribution data of bugs into the generation AI and cause the generation AI to generate a patch.

[0071] When generating a fix patch, the generation unit can improve the accuracy of the generation by referring to literature related to the bug. For example, the generation unit can refer to literature related to past bug fixes and apply similar techniques. The generation unit can also refer to bug fix cases in other games and introduce the optimal fix method. Furthermore, the generation unit can refer to academic papers and apply the latest fix techniques. In this way, the accuracy of the fix patch can be improved by referring to literature related to the bug. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input bug-related literature data into the generation AI and cause the generation AI to generate a fix patch. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit receives bug reports from players using the reception device 38 of the smart device 14 and stores the reports in the database 24 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the stored bug information, and extracts bugs that have been reported frequently. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and uses a generation AI to propose a fix method and automatically create a fix patch. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit receives audio bug reports from players using the microphone 238 of the smart glasses 214 and stores them in the database 24 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the stored bug information, and extracts bugs that have been reported frequently. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and uses a generation AI to suggest a correction method and automatically create a correction patch. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit receives audio bug reports from players using the microphone 238 of the headset-type terminal 314 and stores the reports in the database 24 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the stored bug information, and extracts bugs that have been reported frequently. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and uses a generation AI to suggest a correction method and automatically create a correction patch. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit receives audio bug reports from players using the microphone 238 of the robot 414 and stores them in the database 24 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the stored bug information, and extracts bugs that have been reported frequently. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and uses a generation AI to propose a correction method and automatically create a correction patch.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The collection unit can monitor a player's gameplay data in real time and automatically detect abnormal behavior. For example, if a player repeatedly performs a specific action or an unexpected error occurs in the game, the data is collected and stored in a database as a bug report. The collection unit can also analyze a player's operation log and detect abnormal patterns. Furthermore, the collection unit can analyze a player's in-game chat log and find signs of bugs in conversations with other players. This allows the system to automatically detect abnormalities and respond quickly before a player reports a bug.

[0074] The collection unit can estimate the player's emotions and adjust the level of detail in the bug report based on the estimated player's emotions. For example, if the player is feeling very frustrated, a concise reporting form can be provided to allow the player to complete the report quickly. Alternatively, if the player is relaxed, a detailed reporting form can be provided to collect more information. Furthermore, if the player is excited, a visually easy-to-understand reporting form can be provided to improve the accuracy of the report. This makes it possible to provide the optimal reporting method according to the player's emotions.

[0075] The collection unit can analyze a player's past gameplay data and evaluate the reliability of a bug report. For example, if a player has made accurate bug reports in the past, the report can be evaluated as having high reliability. On the other hand, if a player has made incorrect bug reports in the past, the report can be evaluated as having low reliability. Furthermore, the collection unit can analyze a player's in-game behavior history to supplement the reliability of a bug report. This allows reliable bug reports to be processed preferentially.

[0076] When acquiring bug reports, the collection unit can filter them based on the player's role in the game and the characteristics of the character. For example, if the player is a healer, bug reports related to that role can be acquired preferentially. Also, if the player is using a specific character, bug reports related to that character can be acquired preferentially. Furthermore, if the player is using a specific skill or item, bug reports related to the usage of that skill or item can be acquired preferentially. This makes it possible to acquire appropriate bug reports according to the player's role in the game and the characteristics of the character.

[0077] The collection unit can estimate the player's emotions and adjust the method for acquiring bug reports based on the estimated player's emotions. For example, if the player is feeling very frustrated, voice input can be given priority to quickly complete the report. Alternatively, if the player is relaxed, text input can be given priority to collect more detailed information. Furthermore, if the player is excited, an interface can be provided that encourages the player to attach screenshots or videos, thereby collecting visual information. This makes it possible to provide the optimal reporting method depending on the player's emotions.

[0078] When acquiring bug reports, the collection unit can prioritize acquiring highly relevant reports based on the player's device information. For example, if the player is playing on a specific device, bug reports related to that device can be prioritized. Also, if the player is using a specific OS version, bug reports related to that version can be prioritized. Furthermore, if the player is using specific graphics settings, bug reports related to those settings can be prioritized. This allows highly relevant bug reports to be prioritized based on the player's device information.

[0079] The collection unit can analyze the player's social media activity to evaluate the reliability of the bug report. For example, if a player discusses a bug with other players on social media, the content of that discussion can be used as a reference to evaluate the reliability of the bug report. Also, if a player shares a screenshot of the bug on social media, the screenshot can be used as a reference to evaluate the reliability of the bug report. Furthermore, if a player reports a bug in another game on social media, the content of that report can be used as a reference to evaluate the reliability. In this way, the reliability of the bug report can be evaluated based on the player's social media activity.

[0080] The analysis unit can estimate the player's emotions and evaluate the impact of a bug based on the estimated player's emotions. For example, if the player is feeling very frustrated, the impact of the bug can be evaluated as high. If the player is relaxed, the impact of the bug can be evaluated using the normal evaluation criteria. Furthermore, if the player is excited, the impact of the bug can be evaluated using the following priority. This allows the impact of the bug to be evaluated appropriately according to the player's emotions.

[0081] The analysis unit can adjust the allocation of analysis resources based on the severity of the bug. For example, in the case of a serious bug, the analysis unit can allocate more resources and perform a detailed analysis. In addition, in the case of a minor bug, the analysis unit can perform a simple analysis with fewer resources. Furthermore, in the case of a medium-serious bug, the analysis unit can allocate appropriate resources and perform an efficient analysis. This allows the allocation of analysis resources to be optimized according to the severity of the bug.

[0082] The analysis unit can combine different analysis methods depending on the bug category. For example, for graphics-related bugs, image analysis and code analysis can be combined for analysis. For network-related bugs, communication analysis and log analysis can be combined for analysis. Furthermore, for game logic-related bugs, code analysis and simulation analysis can be combined for analysis. This allows the analysis accuracy to be improved by combining the optimal analysis methods depending on the bug category.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The collection unit accumulates bug reports from players in a database. Bug reports from players include text, audio, and image formats. The collection unit provides an interface for players to report bugs in the game, allowing players to enter details of the bug and attach screenshots and videos. The collection unit can also accept voice input and convert it into text data using voice recognition technology. The collection unit also collects bug reports from social media. Step 2: The analysis unit analyzes the bug information accumulated by the collection unit and extracts bugs that have been reported frequently. These bugs are extracted based on criteria such as the number of reports within a certain period of time or the percentage of the total number of reports. The analysis unit can also statistically analyze the bug information in the database and evaluate the importance of bugs based on their frequency of occurrence and impact. Step 3: The generation unit creates a fix patch based on the bug information extracted by the analysis unit. The generation unit uses the generation AI to propose a fix method for the bug and automatically create a fix patch. The generation AI identifies the part of the code that causes the bug and proposes a fix method. The generation unit executes the fix method proposed by the generation AI and generates a fix patch.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] [Explanation of symbols]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that accumulates bug reports from players in a database, an analysis unit that analyzes the bug information accumulated by the collection unit and extracts bugs that are frequently reported; a generation unit that generates a correction patch based on the bug information extracted by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Estimate player emotions and adjust the timing of bug reports based on the estimated player emotions. The system of claim 1 .

3. The collecting unit Analyze players' past bug reporting history and select the appropriate reporting method The system of claim 1 .

4. The collecting unit Filter bug reports based on the player's current game progress and play style The system of claim 1 .

5. The collecting unit Estimate player sentiment and prioritize bug reports based on the estimated player sentiment. The system of claim 1 .

6. The collecting unit When retrieving bug reports, prioritize the most relevant reports based on the player's geographic location. The system of claim 1 .

7. The collecting unit When a bug report is received, analyze the player's social media activity and retrieve related reports. The system of claim 1 .

8. The analysis unit Inferring player sentiment and adjusting bug analysis based on that sentiment The system of claim 1 .

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