system

Abstract

The system according to the embodiment aims to efficiently generate characters and create their movements, voices, and conversations. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a movement generation unit, a voice generation unit, and a conversation generation unit. The reception unit allows a user to input characteristics of a character. The generation unit generates a character based on the characteristics input by the reception unit. The movement generation unit generates movements of the character generated by the generation unit. The voice generation unit generates voices for the character generated by the generation unit. The conversation generation unit generates conversations and stories between the characters generated by the generation 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, creating characters and creating their movements, voices, and dialogue during game development was time-consuming and labor-intensive, making it difficult to do this efficiently.

[0005] The system according to the embodiment aims to efficiently generate characters and create their movements, voices, and conversations. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a movement generation unit, a voice generation unit, and a conversation generation unit. The reception unit allows a user to input characteristics of a character. The generation unit generates a character based on the characteristics input by the reception unit. The movement generation unit generates movements of the character generated by the generation unit. The voice generation unit generates voices for the characters generated by the generation unit. The conversation generation unit generates conversations and stories between the characters generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate characters and create their movements, voices, and conversations. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A game development support system according to an embodiment of the present invention utilizes a generation AI in all phases of game development, testing, and execution, thereby improving work efficiency and creativity. This game development support system first uses StableDiffusion for character generation in the development phase. When a user inputs a character's characteristics, the generation AI generates the character based on those characteristics. Next, motion generation uses MotionDiffuse, and the generation AI generates character movements. Furthermore, voice generation uses LLM and a voice synthesis AI, and the generation AI generates character voices. Furthermore, conversation and story generation uses a generation AI and NLP, and the generation AI generates character conversations and stories. Next, in the testing phase, a gameplay AI is used for bug detection. The generation AI plays the game and detects bugs. Finally, in the execution phase, the generation AI is used to optimize difficulty and reward settings. The generation AI analyzes player play data and sets optimal difficulty and reward settings. This system improves work efficiency and creativity in all phases of game development, testing, and execution. For example, in the development phase, a reception unit is provided where a user inputs character characteristics, and a generation unit is provided where a character is generated based on those characteristics. The generation unit is assumed to include the processing of the generation AI. Next, regarding motion generation, a generation unit is provided that generates character movements, and this generation unit includes processing by the generation AI. Furthermore, regarding voice generation, a generation unit is provided that generates character voices, and this generation unit includes processing by the generation AI. Furthermore, regarding conversation and story generation, a generation unit is provided that generates conversations between characters and stories, and this generation unit includes processing by the generation AI. Next, in the test phase, a detection unit is provided that detects bugs, and this detection unit includes processing by the generation AI. Specific examples of bugs and how the generation AI detects them are explained in detail. Finally, in the execution phase, an optimization unit is provided that analyzes player data and optimizes difficulty and reward settings, and this optimization unit includes processing by the generation AI. This section specifically describes how the generation AI analyzes player data and what algorithms are used for optimization.This system enables the game development support system to improve work efficiency and creativity in all phases of game development, testing, and execution.

[0029] A game development support system according to an embodiment includes a reception unit, a generation unit, a movement generation unit, a voice generation unit, and a conversation generation unit. The reception unit receives input of a character's characteristics from a user. The character's characteristics input by the user include, but are not limited to, appearance, personality, and abilities. The reception unit provides, for example, an interface for the user to input the character's appearance. The reception unit can also provide options for the user to input the character's personality and abilities. The generation unit generates a character based on the characteristics input by the reception unit using a generation AI. The generation unit generates the character's appearance using, for example, Stable Diffusion. The generation unit can also generate the character's movement using Motion Diffuse. The generation unit can also generate the character's voice using LLM or a voice synthesis AI. For example, the generation unit generates the character's appearance based on the characteristics input by the user. The generation unit generates the character's appearance based on the characteristics input by the user using, for example, Stable Diffusion. The generation unit can also generate the character's movement using Motion Diffuse. The generation unit can also generate the character's voice using, for example, LLM or a voice synthesis AI. The motion generation unit generates motion of the character generated by the generation unit. The motion generation unit generates motion of the character using, for example, MotionDiffuse. The motion generation unit generates, for example, a walking motion of the character. The motion generation unit can also generate a jumping motion of the character. The motion generation unit can also generate an attacking motion of the character. For example, the motion generation unit generates a walking motion of the character using MotionDiffuse. The motion generation unit can also generate a jumping motion of the character using MotionDiffuse. The motion generation unit can also generate an attacking motion of the character using MotionDiffuse. The voice generation unit generates a voice of the character generated by the generation unit. The voice generation unit generates the voice of the character using, for example, LLM or voice synthesis AI.The voice generation unit generates, for example, lines for a character. The voice generation unit can also generate emotional expressions for a character. The voice generation unit can also generate a singing voice for a character. For example, the voice generation unit generates lines for a character using an LLM or a voice synthesis AI. The voice generation unit can also generate emotional expressions for a character using an LLM or a voice synthesis AI. The voice generation unit can also generate a singing voice for a character using an LLM or a voice synthesis AI. The conversation generation unit generates conversations and stories between characters generated by the generation unit. The conversation generation unit generates conversations and stories between characters using, for example, a generation AI or NLP. The conversation generation unit generates dialogues between characters. The conversation generation unit can also generate stories for characters. The conversation generation unit can also generate backstories for characters. For example, the conversation generation unit generates dialogues between characters using a generation AI or NLP. The conversation generation unit can also generate stories for characters using a generation AI or NLP. Furthermore, the conversation generation unit can generate a character's backstory using generative AI or NLP. This allows the game development support system according to the embodiment to generate a character based on the user's input of the character's characteristics, and generate the character's movements, voice, conversation, and story based on the input characteristics.

[0030] The detection unit can detect bugs. The detection unit detects bugs using a generation AI. For example, the detection unit plays a game using a gameplay AI and detects bugs. For example, the detection unit detects program errors that occur during operation of the game. The detection unit can also detect defects in the graphic display of the game. Furthermore, the detection unit can detect abnormalities in the audio output of the game. For example, the detection unit detects program errors that occur during operation of the game using a gameplay AI. The detection unit can also detect defects in the graphic display of the game using a gameplay AI. Furthermore, the detection unit can detect abnormalities in the audio output of the game. In this way, the detection unit can detect bugs. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the detection unit plays a game using a gameplay AI and detects bugs. The detection unit executes an algorithm for the gameplay AI to detect program errors that occur during operation of the game. The detection unit can also execute an algorithm for the gameplay AI to detect abnormalities in the graphic display of the game. Additionally, the detector may also run algorithms for the gameplay AI to detect anomalies in the game's audio output, thereby enabling the detector to detect bugs.

[0031] The optimization unit can analyze player data and adjust the difficulty level and reward settings. The optimization unit uses a generation AI to analyze player data and adjust the difficulty level and reward settings. The optimization unit analyzes data such as the player's play time, score, and behavioral history. The optimization unit can adjust the difficulty level based on the player's play time, for example. The optimization unit can also adjust the reward settings based on the player's score. The optimization unit can also adjust the difficulty level and reward settings based on the player's behavioral history. For example, the optimization unit adjusts the difficulty level based on the player's play time. For example, the optimization unit can increase the difficulty level if the player's play time is long. For example, the optimization unit can decrease the difficulty level if the player's play time is short. The optimization unit can also adjust the reward settings based on the player's score. For example, the optimization unit can increase the reward level if the player's score is high. For example, the optimization unit can decrease the reward level if the player's score is low. The optimization unit can also adjust the difficulty level and reward settings based on the player's behavioral history. For example, if a player frequently performs a specific behavior, the optimization unit can adjust the difficulty level associated with that behavior. The optimization unit can also adjust reward settings related to a particular behavior if the player does not frequently perform that behavior. This allows the optimization unit to analyze player data and adjust the difficulty level and reward settings. Some or all of the above-described processing in the optimization unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the optimization unit inputs the player's play data into the generation AI, which then analyzes the play data and executes an algorithm that adjusts the difficulty level and reward settings.

[0032] The reception unit can analyze the user's past input history and suggest an appropriate input method. The reception unit can analyze the user's past input history using a generation AI and suggest an appropriate input method. The reception unit, for example, automatically displays, as candidates, the characteristics of characters that the user has frequently input in the past. The reception unit can also, for example, prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the characteristics of characters to be used in a specific time period based on the user's past input history. For example, the reception unit can automatically display, as candidates, the characteristics of characters that the user has frequently input in the past. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the characteristics of characters to be used in a specific time period based on the user's past input history. In this way, the reception unit can suggest the optimal input method by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the user's past input history data into the generation AI, which then analyzes the input history and executes an algorithm that suggests an appropriate input method.

[0033] The reception unit can customize input fields based on the user's current project or areas of interest when inputting character characteristics. The reception unit customizes input fields based on the user's current project or areas of interest using a generation AI when inputting character characteristics. The reception unit, for example, preferentially displays character characteristics related to a project currently being carried out by the user. The reception unit can also suggest related character characteristics based on the user's areas of interest, for example. Furthermore, the reception unit can customize character characteristics based on areas in which the user has previously shown interest. For example, the reception unit preferentially displays character characteristics related to a project currently being carried out by the user. The reception unit can also suggest related character characteristics based on the user's areas of interest. Furthermore, the reception unit can customize character characteristics based on areas in which the user has previously shown interest. In this way, the reception unit customizes input fields based on the user's current project or areas of interest, enabling more appropriate character characteristic input. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs data about the user's projects and areas of interest into the generation AI, which then analyzes the data and runs an algorithm to customize the input fields.

[0034] The reception unit can prioritize input of highly relevant features in consideration of the user's geographical location information when inputting character features. The reception unit can prioritize input of highly relevant features in consideration of the user's geographical location information when inputting character features using the generation AI. For example, when the user is in a specific region, the reception unit suggests character features related to the region. The reception unit can also prioritize input of region-specific features based on the user's geographical location information. Furthermore, when the user is traveling, the reception unit can suggest features related to the culture and customs of the destination. For example, when the user is in a specific region, the reception unit suggests character features related to the region. Furthermore, the reception unit can also prioritize input of region-specific features based on the user's geographical location information. Furthermore, when the user is traveling, the reception unit can suggest features related to the culture and customs of the destination. In this way, the reception unit can prioritize input of highly relevant character features by considering the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed, for example, using the generation AI or without using the generation AI. For example, the reception unit inputs the user's geographical location information into the generation AI, which then analyzes the geographical location information and executes an algorithm that prioritizes input of highly relevant features.

[0035] The reception unit can analyze the user's social media activity when inputting the character's characteristics and suggest related characteristics. The reception unit can use the generation AI to analyze the user's social media activity when inputting the character's characteristics and suggest related characteristics. The reception unit can, for example, suggest the characteristics of characters frequently mentioned by the user on social media. The reception unit can also extract and suggest characteristics of interest from the user's social media activity. Furthermore, the reception unit can suggest related characteristics based on the content of posts of accounts the user follows. For example, the reception unit can suggest the characteristics of characters frequently mentioned by the user on social media. The reception unit can also extract and suggest characteristics of interest from the user's social media activity. Furthermore, the reception unit can suggest related characteristics based on the content of posts of accounts the user follows. In this way, the reception unit can suggest related character characteristics by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the reception unit inputs the user's social media activity data to the generation AI, and the generation AI analyzes the data and executes an algorithm to suggest related characteristics.

[0036] When generating a character, the generation unit can select an optimal generation algorithm by referring to the user's past character generation history. When generating a character using a generation AI, the generation unit selects an optimal generation algorithm by referring to the user's past character generation history. The generation unit, for example, selects an optimal generation algorithm based on the characteristics of characters the user has previously generated. The generation unit can also generate a character that reflects a preferred style from the user's past generation history. Furthermore, the generation unit can select an optimal generation algorithm by referring to successful examples of characters the user has previously generated. For example, the generation unit selects an optimal generation algorithm based on the characteristics of characters the user has previously generated. The generation unit can also generate a character that reflects a preferred style from the user's past generation history. Furthermore, the generation unit can select an optimal generation algorithm by referring to successful examples of characters the user has previously generated. In this way, the generation unit can select an optimal generation algorithm by referring to the user's past character generation history. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the user's past character generation history data into the generation AI, which then analyzes the data and executes an algorithm to select the optimal generation algorithm.

[0037] The generation unit can customize the generated content based on the user's current project or area of ​​interest when generating a character. The generation unit customizes the generated content based on the user's current project or area of ​​interest when generating a character using a generation AI. The generation unit, for example, generates a character related to a project currently being undertaken by the user. The generation unit can also generate a related character based on the user's area of ​​interest, for example. The generation unit can also customize the character based on areas in which the user has previously shown interest. For example, the generation unit generates a character related to a project currently being undertaken by the user. The generation unit can also generate a related character based on the user's area of ​​interest. The generation unit can also customize the character based on areas in which the user has previously shown interest. In this way, the generation unit can generate a more appropriate character by customizing the generated content based on the user's current project or area of ​​interest. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs data related to the user's project or area of ​​interest into the generation AI, which analyzes the data and executes an algorithm to customize the generated content.

[0038] When generating a character, the generation unit can prioritize generating a highly relevant character by taking into account the user's geographical location information. When generating a character using a generation AI, the generation unit prioritizes generating a highly relevant character by taking into account the user's geographical location information. For example, when the user is in a specific region, the generation unit generates a character related to that region. For example, the generation unit can also prioritize generating a character unique to that region based on the user's geographical location information. Furthermore, when the user is traveling, the generation unit can generate a character related to the culture or customs of a destination visited. For example, when the user is in a specific region, the generation unit generates a character related to that region. Furthermore, the generation unit can also prioritize generating a character unique to that region based on the user's geographical location information. Furthermore, when the user is traveling, the generation unit can generate a character related to the culture or customs of a destination visited. In this way, the generation unit can prioritize generating a highly relevant character by taking into account the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the user's geographical location information into the generation AI, which then analyzes the geographical location information and executes an algorithm that prioritizes generating highly relevant characters.

[0039] The generation unit can analyze the user's social media activities and generate related characters when generating characters. The generation unit uses a generation AI to analyze the user's social media activities and generate related characters when generating characters. For example, the generation unit generates characters that the user frequently mentions on social media. The generation unit can also extract and generate characters of interest from the user's social media activities. Furthermore, the generation unit can generate related characters based on the content posted by accounts the user follows. For example, the generation unit generates characters that the user frequently mentions on social media. The generation unit can also extract and generate characters of interest from the user's social media activities. Furthermore, the generation unit can generate related characters based on the content posted by accounts the user follows. In this way, the generation unit can generate related characters by analyzing the user's social media activities. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the user's social media activity data into the generation AI, which analyzes the data and executes an algorithm to generate related characters.

[0040] The detection unit can optimize the detection algorithm by referring to past bug data when detecting a bug. The detection unit can optimize the detection algorithm by referring to past bug data when detecting a bug using the generation AI. The detection unit, for example, selects an optimal detection algorithm based on data of bugs that have occurred in the past. The detection unit can also, for example, prioritize detecting frequently occurring bugs from the past bug data. Furthermore, the detection unit can analyze the past bug data and select the most efficient detection algorithm. For example, the detection unit selects an optimal detection algorithm based on data of bugs that have occurred in the past. The detection unit can also prioritize detecting frequently occurring bugs from the past bug data. Furthermore, the detection unit can analyze the past bug data and select the most efficient detection algorithm. In this way, the detection unit can optimize the detection algorithm by referring to the past bug data. Some or all of the above-described processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit inputs past bug data to the generation AI, which analyzes the data and executes an algorithm to select the optimal detection algorithm.

[0041] The detection unit can apply different detection methods to each game category when detecting bugs. The detection unit can apply different detection methods to each game category when detecting bugs using the generation AI. For example, in the case of an action game, the detection unit prioritizes detecting bugs related to real-time actions. For example, in the case of a puzzle game, the detection unit can also prioritize detecting logical errors and inconsistencies. Furthermore, in the case of an RPG game, the detection unit can also prioritize detecting bugs related to story progression. For example, in the case of an action game, the detection unit prioritizes detecting bugs related to real-time actions. Furthermore, in the case of a puzzle game, the detection unit can also prioritize detecting logical errors and inconsistencies. Furthermore, in the case of an RPG game, the detection unit can also prioritize detecting bugs related to story progression. This allows the detection unit to apply different detection methods to each game category, enabling more appropriate bug detection. Some or all of the above-described processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit inputs data related to game categories to the generation AI, which analyzes the data and executes an algorithm that applies different detection methods to each category.

[0042] The detection unit can detect bugs by taking into account the geographical distribution of the game. The detection unit can detect bugs by using the generation AI. For example, the detection unit prioritizes detecting bugs that are likely to occur in a specific region. The detection unit can also detect region-specific bugs based on the geographical distribution. Furthermore, the detection unit can analyze and detect bug occurrence rates in different regions by taking the geographical distribution into account. For example, the detection unit prioritizes detecting bugs that are likely to occur in a specific region. The detection unit can also detect region-specific bugs based on the geographical distribution. Furthermore, the detection unit can analyze and detect bug occurrence rates in different regions by taking the geographical distribution into account. In this way, the detection unit can detect region-specific bugs by taking the geographical distribution of the game into account. Some or all of the above-described processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit inputs data regarding the geographical distribution of the game into the generation AI, and the generation AI analyzes the data and executes an algorithm that detects bugs by taking the geographical distribution into account.

[0043] The detection unit can improve detection accuracy by referring to literature related to the game when detecting bugs. The detection unit can improve detection accuracy by referring to literature related to the game when detecting bugs using the generation AI. The detection unit, for example, prioritizes detecting bugs reported in the past based on the relevant literature. The detection unit can also extract and detect frequently occurring bug patterns from the related literature. Furthermore, the detection unit can apply the latest bug detection techniques by referring to the related literature. For example, the detection unit prioritizes detecting bugs reported in the past based on the related literature. The detection unit can also extract and detect frequently occurring bug patterns from the related literature. Furthermore, the detection unit can apply the latest bug detection techniques by referring to the related literature. In this way, the detection unit can improve detection accuracy by referring to literature related to the game. Some or all of the above-described processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit inputs data related to literature related to the game into the generation AI, which analyzes the data and executes an algorithm to detect bugs by referring to the related literature.

[0044] The optimization unit can select the optimal setting method when setting the difficulty level and rewards by referring to the user's past play data. The optimization unit can select the optimal setting method when setting the difficulty level and rewards using the generation AI by referring to the user's past play data. The optimization unit, for example, sets the optimal difficulty level based on the user's past play data. The optimization unit can also determine the optimal distribution of rewards from the user's past play data. Furthermore, the optimization unit can analyze the user's past play data and set the difficulty level and rewards according to the user's play style. For example, the optimization unit can set the optimal difficulty level based on the user's past play data. The optimization unit can also determine the optimal distribution of rewards from the user's past play data. Furthermore, the optimization unit can analyze the user's past play data and set the difficulty level and rewards according to the user's play style. In this way, the optimization unit can set the optimal difficulty level and rewards by referring to the user's past play data. Some or all of the above-described processing in the optimization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the optimization unit inputs the user's past play data into the generation AI, which then analyzes the data and executes an algorithm to select the optimal setting method.

[0045] The optimization unit can apply different optimization methods for each game category when setting the difficulty level and rewards. The optimization unit uses the generation AI to apply different optimization methods for each game category when setting the difficulty level and rewards. For example, in the case of an action game, the optimization unit sets the difficulty level with an emphasis on real-time reactions. For example, in the case of a puzzle game, the optimization unit can also set the rewards with an emphasis on logical thinking. Furthermore, in the case of an RPG game, the optimization unit can set the difficulty level and rewards in accordance with the progress of the story. For example, in the case of an action game, the optimization unit sets the difficulty level with an emphasis on real-time reactions. Furthermore, in the case of a puzzle game, the optimization unit can also set the rewards with an emphasis on logical thinking. Furthermore, in the case of an RPG game, the optimization unit can set the difficulty level and rewards in accordance with the progress of the story. In this way, the optimization unit can apply different optimization methods for each game category, enabling more appropriate difficulty and reward settings. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the optimization unit inputs data regarding the game category into the generation AI, which analyzes the data and executes an algorithm that applies different optimization methods to each category.

[0046] The optimization unit can select the optimal setting method for difficulty and reward settings by taking into account the user's geographical location information. The optimization unit uses the generation AI to select the optimal setting method for difficulty and reward settings by taking into account the user's geographical location information. For example, if the user is in a specific region, the optimization unit sets difficulty and reward settings that match the tendencies of players in that region. The optimization unit can also perform region-specific settings based on the user's geographical location information. Furthermore, if the user is traveling, the optimization unit can set difficulty and reward settings that match the culture and customs of the destination. For example, if the user is in a specific region, the optimization unit sets difficulty and reward settings that match the tendencies of players in that region. Furthermore, the optimization unit can perform region-specific settings based on the user's geographical location information. Furthermore, if the user is traveling, the optimization unit can set difficulty and reward settings that match the culture and customs of the destination. In this way, the optimization unit can perform region-specific difficulty and reward settings by taking into account the user's geographical location information. Some or all of the above-described processing in the optimization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the optimization unit inputs the user's geographic location information into the generation AI, which then analyzes the data and runs an algorithm to select the optimal setting method.

[0047] The optimization unit can analyze the user's social media activity to suggest optimal settings when setting the difficulty level and rewards. The optimization unit uses the generation AI to analyze the user's social media activity to suggest optimal settings when setting the difficulty level and rewards. The optimization unit, for example, sets the difficulty level and rewards based on game features frequently mentioned by the user on social media. The optimization unit can also extract and suggest settings for games of interest from the user's social media activity. Furthermore, the optimization unit can set relevant difficulty levels and rewards based on the content posted by accounts the user follows. For example, the optimization unit sets the difficulty level and rewards based on game features frequently mentioned by the user on social media. The optimization unit can also extract and suggest settings for games of interest from the user's social media activity. Furthermore, the optimization unit can set relevant difficulty levels and rewards based on the content posted by accounts the user follows. In this way, the optimization unit can suggest optimal difficulty and reward settings by analyzing the user's social media activity. Some or all of the above-described processing in the optimization unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the optimization unit inputs a user's social media activity data into the generation AI, which then analyzes the data and runs an algorithm that suggests the optimal settings.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The reception unit can monitor the user's input speed in real time and adjust the complexity of the interface according to the input speed. For example, if the user is inputting quickly, the reception unit can provide a simple interface and reduce the number of input items. Alternatively, if the user is inputting slowly, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, the reception unit can suggest voice input or gesture input based on the user's input speed. In this way, the reception unit can adjust the interface according to the user's input speed, enabling more appropriate character feature input.

[0050] When detecting bugs, the detection unit can analyze the user's play style and apply a bug detection algorithm appropriate to the play style. For example, for a user with an aggressive play style, it can prioritize detecting bugs in battle scenes and action scenes. For a user with an exploration-based play style, it can also prioritize detecting bugs related to maps and items. Furthermore, for a user with a story-focused play style, it can also prioritize detecting bugs related to story progression. This allows the detection unit to adjust the bug detection algorithm according to the user's play style, enabling more appropriate bug detection.

[0051] The reception unit can analyze the user's input content in real time and dynamically change the interface based on the input content. For example, if the user inputs a specific keyword, input items related to that keyword can be automatically displayed. Also, related options can be suggested based on the content entered by the user. Furthermore, if the user changes the input content, the interface can be updated immediately. In this way, the reception unit can dynamically change the interface according to the user's input content, allowing for more appropriate character characteristic input.

[0052] The reception unit can analyze the user's past input history and predict input content. For example, it can automatically display the characteristics of characters that the user has frequently input in the past as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest the characteristics of characters to be used in a specific time period from the user's past input history. In this way, the reception unit can suggest the optimal input method by analyzing the user's past input history.

[0053] The reception unit can suggest regional character characteristics by taking into account the user's geographical location information. For example, if the user is in a specific region, character characteristics related to that region are preferentially displayed. The reception unit can also preferentially input regional characteristics based on the user's geographical location information. Furthermore, if the user is traveling, it can suggest characteristics related to the culture and customs of the place the user is visiting. In this way, the reception unit can preferentially input highly relevant character characteristics by taking into account the user's geographical location information.

[0054] The reception unit can analyze the user's social media activity and suggest related character characteristics. For example, it can suggest the characteristics of characters that the user frequently mentions on social media. It can also extract and suggest characteristics of interest from the user's social media activity. It can also suggest related characteristics based on the content of posts from accounts the user follows. In this way, the reception unit can suggest related character characteristics by analyzing the user's social media activity.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The reception unit allows the user to input the characteristics of the character. The characteristics of the character input by the user include appearance, personality, abilities, etc. The reception unit provides an interface for the user to input the character's appearance and options for inputting personality and abilities. Step 2: The generator generates a character based on the characteristics input by the receiver. For example, the generator generates the character's appearance using StableDiffusion, the character's movements using MotionDiffuse, and the character's voice using LLM or voice synthesis AI. Step 3: The motion generator generates the motion of the character generated by the generator. The motion generator uses MotionDiffuse to generate the character's walking motion, jumping motion, attack motion, etc. Step 4: The voice generation unit generates the voice of the character generated by the generation unit. The voice generation unit uses LLM and voice synthesis AI to generate the character's lines, emotional expressions, singing voice, etc. Step 5: The conversation generation unit generates conversations and stories between the characters generated by the generation unit. The conversation generation unit uses generative AI and NLP to generate dialogues between characters, stories, backstories, etc.

[0057] (Example 2) A game development support system according to an embodiment of the present invention utilizes a generation AI in all phases of game development, testing, and execution, thereby improving work efficiency and creativity. This game development support system first uses StableDiffusion for character generation in the development phase. When a user inputs a character's characteristics, the generation AI generates the character based on those characteristics. Next, motion generation uses MotionDiffuse, and the generation AI generates character movements. Furthermore, voice generation uses LLM and a voice synthesis AI, and the generation AI generates character voices. Furthermore, conversation and story generation uses a generation AI and NLP, and the generation AI generates character conversations and stories. Next, in the testing phase, a gameplay AI is used for bug detection. The generation AI plays the game and detects bugs. Finally, in the execution phase, the generation AI is used to optimize difficulty and reward settings. The generation AI analyzes player play data and sets optimal difficulty and reward settings. This system improves work efficiency and creativity in all phases of game development, testing, and execution. For example, in the development phase, a reception unit is provided where a user inputs character characteristics, and a generation unit is provided where a character is generated based on those characteristics. The generation unit is assumed to include the processing of the generation AI. Next, regarding motion generation, a generation unit is provided that generates character movements, and this generation unit includes processing by the generation AI. Furthermore, regarding voice generation, a generation unit is provided that generates character voices, and this generation unit includes processing by the generation AI. Furthermore, regarding conversation and story generation, a generation unit is provided that generates conversations between characters and stories, and this generation unit includes processing by the generation AI. Next, in the test phase, a detection unit is provided that detects bugs, and this detection unit includes processing by the generation AI. Specific examples of bugs and how the generation AI detects them are explained in detail. Finally, in the execution phase, an optimization unit is provided that analyzes player data and optimizes difficulty and reward settings, and this optimization unit includes processing by the generation AI. This section specifically describes how the generation AI analyzes player data and what algorithms are used for optimization.This system enables the game development support system to improve work efficiency and creativity in all phases of game development, testing, and execution.

[0058] A game development support system according to an embodiment includes a reception unit, a generation unit, a movement generation unit, a voice generation unit, and a conversation generation unit. The reception unit receives input of a character's characteristics from a user. The character's characteristics input by the user include, but are not limited to, appearance, personality, and abilities. The reception unit provides, for example, an interface for the user to input the character's appearance. The reception unit can also provide options for the user to input the character's personality and abilities. The generation unit generates a character based on the characteristics input by the reception unit using a generation AI. The generation unit generates the character's appearance using, for example, Stable Diffusion. The generation unit can also generate the character's movement using Motion Diffuse. The generation unit can also generate the character's voice using LLM or a voice synthesis AI. For example, the generation unit generates the character's appearance based on the characteristics input by the user. The generation unit generates the character's appearance based on the characteristics input by the user using, for example, Stable Diffusion. The generation unit can also generate the character's movement using Motion Diffuse. The generation unit can also generate the character's voice using, for example, LLM or a voice synthesis AI. The motion generation unit generates motion of the character generated by the generation unit. The motion generation unit generates motion of the character using, for example, MotionDiffuse. The motion generation unit generates, for example, a walking motion of the character. The motion generation unit can also generate a jumping motion of the character. The motion generation unit can also generate an attacking motion of the character. For example, the motion generation unit generates a walking motion of the character using MotionDiffuse. The motion generation unit can also generate a jumping motion of the character using MotionDiffuse. The motion generation unit can also generate an attacking motion of the character using MotionDiffuse. The voice generation unit generates a voice of the character generated by the generation unit. The voice generation unit generates the voice of the character using, for example, LLM or voice synthesis AI.The voice generation unit generates, for example, lines for a character. The voice generation unit can also generate emotional expressions for a character. The voice generation unit can also generate a singing voice for a character. For example, the voice generation unit generates lines for a character using an LLM or a voice synthesis AI. The voice generation unit can also generate emotional expressions for a character using an LLM or a voice synthesis AI. The voice generation unit can also generate a singing voice for a character using an LLM or a voice synthesis AI. The conversation generation unit generates conversations and stories between characters generated by the generation unit. The conversation generation unit generates conversations and stories between characters using, for example, a generation AI or NLP. The conversation generation unit generates dialogues between characters. The conversation generation unit can also generate stories for characters. The conversation generation unit can also generate backstories for characters. For example, the conversation generation unit generates dialogues between characters using a generation AI or NLP. The conversation generation unit can also generate stories for characters using a generation AI or NLP. Furthermore, the conversation generation unit can generate a character's backstory using generative AI or NLP. This allows the game development support system according to the embodiment to generate a character based on the user's input of the character's characteristics, and generate the character's movements, voice, conversation, and story based on the input characteristics.

[0059] The detection unit can detect bugs. The detection unit detects bugs using a generation AI. For example, the detection unit plays a game using a gameplay AI and detects bugs. For example, the detection unit detects program errors that occur during operation of the game. The detection unit can also detect defects in the graphic display of the game. Furthermore, the detection unit can detect abnormalities in the audio output of the game. For example, the detection unit detects program errors that occur during operation of the game using a gameplay AI. The detection unit can also detect defects in the graphic display of the game using a gameplay AI. Furthermore, the detection unit can detect abnormalities in the audio output of the game. In this way, the detection unit can detect bugs. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the detection unit plays a game using a gameplay AI and detects bugs. The detection unit executes an algorithm for the gameplay AI to detect program errors that occur during operation of the game. The detection unit can also execute an algorithm for the gameplay AI to detect abnormalities in the graphic display of the game. Additionally, the detector may also run algorithms for the gameplay AI to detect anomalies in the game's audio output, thereby enabling the detector to detect bugs.

[0060] The optimization unit can analyze player data and adjust the difficulty level and reward settings. The optimization unit uses a generation AI to analyze player data and adjust the difficulty level and reward settings. The optimization unit analyzes data such as the player's play time, score, and behavioral history. The optimization unit can adjust the difficulty level based on the player's play time, for example. The optimization unit can also adjust the reward settings based on the player's score. The optimization unit can also adjust the difficulty level and reward settings based on the player's behavioral history. For example, the optimization unit adjusts the difficulty level based on the player's play time. For example, the optimization unit can increase the difficulty level if the player's play time is long. For example, the optimization unit can decrease the difficulty level if the player's play time is short. The optimization unit can also adjust the reward settings based on the player's score. For example, the optimization unit can increase the reward level if the player's score is high. For example, the optimization unit can decrease the reward level if the player's score is low. The optimization unit can also adjust the difficulty level and reward settings based on the player's behavioral history. For example, if a player frequently performs a specific behavior, the optimization unit can adjust the difficulty level associated with that behavior. The optimization unit can also adjust reward settings related to a particular behavior if the player does not frequently perform that behavior. This allows the optimization unit to analyze player data and adjust the difficulty level and reward settings. Some or all of the above-described processing in the optimization unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the optimization unit inputs the player's play data into the generation AI, which then analyzes the play data and executes an algorithm that adjusts the difficulty level and reward settings.

[0061] The reception unit can estimate the user's emotions and adjust the character feature input interface based on the estimated user emotions. The reception unit can estimate the user's emotions using a generation AI and adjust the character feature input interface based on the estimated user emotions. For example, if the user is stressed, the reception unit can provide a simple interface and minimize input steps. For example, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of character features. For example, if the user is stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of character features. In this way, the reception unit can adjust the interface according to the user's emotions, enabling more appropriate character feature input. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, or without, the generation AI. For example, the reception unit inputs the user's facial expression data into the generation AI, which then infers the emotion and executes an algorithm to adjust the interface.

[0062] The reception unit can analyze the user's past input history and suggest an appropriate input method. The reception unit can analyze the user's past input history using a generation AI and suggest an appropriate input method. The reception unit, for example, automatically displays, as candidates, the characteristics of characters that the user has frequently input in the past. The reception unit can also, for example, prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the characteristics of characters to be used in a specific time period based on the user's past input history. For example, the reception unit can automatically display, as candidates, the characteristics of characters that the user has frequently input in the past. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest the characteristics of characters to be used in a specific time period based on the user's past input history. In this way, the reception unit can suggest the optimal input method by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the user's past input history data into the generation AI, which then analyzes the input history and executes an algorithm that suggests an appropriate input method.

[0063] The reception unit can customize input fields based on the user's current project or areas of interest when inputting character characteristics. The reception unit customizes input fields based on the user's current project or areas of interest using a generation AI when inputting character characteristics. The reception unit, for example, preferentially displays character characteristics related to a project currently being carried out by the user. The reception unit can also suggest related character characteristics based on the user's areas of interest, for example. Furthermore, the reception unit can customize character characteristics based on areas in which the user has previously shown interest. For example, the reception unit preferentially displays character characteristics related to a project currently being carried out by the user. The reception unit can also suggest related character characteristics based on the user's areas of interest. Furthermore, the reception unit can customize character characteristics based on areas in which the user has previously shown interest. In this way, the reception unit customizes input fields based on the user's current project or areas of interest, enabling more appropriate character characteristic input. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs data about the user's projects and areas of interest into the generation AI, which then analyzes the data and runs an algorithm to customize the input fields.

[0064] The reception unit can estimate the user's emotions and determine the priority of the character features to be input based on the estimated user emotions. The reception unit can estimate the user's emotions using a generation AI and determine the priority of the character features to be input based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize input of important features. For example, when the user is relaxed, the reception unit can also prioritize input of detailed features. Furthermore, when the user is in a hurry, the reception unit can prioritize input of basic features. For example, when the user is stressed, the reception unit can prioritize input of important features. Furthermore, when the user is relaxed, the reception unit can also prioritize input of detailed features. Furthermore, when the user is in a hurry, the reception unit can prioritize input of basic features. In this way, the reception unit can prioritize input of the character features according to the user's emotions, thereby enabling more appropriate character feature input. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI. For example, the reception unit inputs the user's facial expression data into the generation AI, and the generation AI executes an algorithm to estimate emotions and determine the priority of character features.

[0065] The reception unit can prioritize input of highly relevant features in consideration of the user's geographical location information when inputting character features. The reception unit can prioritize input of highly relevant features in consideration of the user's geographical location information when inputting character features using the generation AI. For example, when the user is in a specific region, the reception unit suggests character features related to the region. The reception unit can also prioritize input of region-specific features based on the user's geographical location information. Furthermore, when the user is traveling, the reception unit can suggest features related to the culture and customs of the destination. For example, when the user is in a specific region, the reception unit suggests character features related to the region. Furthermore, the reception unit can also prioritize input of region-specific features based on the user's geographical location information. Furthermore, when the user is traveling, the reception unit can suggest features related to the culture and customs of the destination. In this way, the reception unit can prioritize input of highly relevant character features by considering the user's geographical location information. Some or all of the above-described processing in the reception unit can be performed, for example, using the generation AI or without using the generation AI. For example, the reception unit inputs the user's geographical location information into the generation AI, which then analyzes the geographical location information and executes an algorithm that prioritizes input of highly relevant features.

[0066] The reception unit can analyze the user's social media activity when inputting the character's characteristics and suggest related characteristics. The reception unit can use the generation AI to analyze the user's social media activity when inputting the character's characteristics and suggest related characteristics. The reception unit can, for example, suggest the characteristics of characters frequently mentioned by the user on social media. The reception unit can also extract and suggest characteristics of interest from the user's social media activity. Furthermore, the reception unit can suggest related characteristics based on the content of posts of accounts the user follows. For example, the reception unit can suggest the characteristics of characters frequently mentioned by the user on social media. The reception unit can also extract and suggest characteristics of interest from the user's social media activity. Furthermore, the reception unit can suggest related characteristics based on the content of posts of accounts the user follows. In this way, the reception unit can suggest related character characteristics by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the reception unit inputs the user's social media activity data to the generation AI, and the generation AI analyzes the data and executes an algorithm to suggest related characteristics.

[0067] The generation unit can estimate the user's emotions and adjust the character generation method based on the estimated user emotions. The generation unit can estimate the user's emotions using a generation AI and adjust the character generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates a character that progresses at a leisurely pace. For example, if the user is in a hurry, the generation unit can provide a character that is generated quickly. Furthermore, if the user is excited, the generation unit can generate a visually stimulating character. For example, if the user is relaxed, the generation unit generates a character that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can provide a character that is generated quickly. Furthermore, if the user is excited, the generation unit can generate a visually stimulating character. In this way, the generation unit can generate a more appropriate character by adjusting the character generation method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit inputs the user's facial expression data into the generation AI, which then infers the emotion and executes an algorithm to adjust the character generation method.

[0068] When generating a character, the generation unit can select an optimal generation algorithm by referring to the user's past character generation history. When generating a character using a generation AI, the generation unit selects an optimal generation algorithm by referring to the user's past character generation history. The generation unit, for example, selects an optimal generation algorithm based on the characteristics of characters the user has previously generated. The generation unit can also generate a character that reflects a preferred style from the user's past generation history. Furthermore, the generation unit can select an optimal generation algorithm by referring to successful examples of characters the user has previously generated. For example, the generation unit selects an optimal generation algorithm based on the characteristics of characters the user has previously generated. The generation unit can also generate a character that reflects a preferred style from the user's past generation history. Furthermore, the generation unit can select an optimal generation algorithm by referring to successful examples of characters the user has previously generated. In this way, the generation unit can select an optimal generation algorithm by referring to the user's past character generation history. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the user's past character generation history data into the generation AI, which then analyzes the data and executes an algorithm to select the optimal generation algorithm.

[0069] The generation unit can customize the generated content based on the user's current project or area of ​​interest when generating a character. The generation unit customizes the generated content based on the user's current project or area of ​​interest when generating a character using a generation AI. The generation unit, for example, generates a character related to a project currently being undertaken by the user. The generation unit can also generate a related character based on the user's area of ​​interest, for example. The generation unit can also customize the character based on areas in which the user has previously shown interest. For example, the generation unit generates a character related to a project currently being undertaken by the user. The generation unit can also generate a related character based on the user's area of ​​interest. The generation unit can also customize the character based on areas in which the user has previously shown interest. In this way, the generation unit can generate a more appropriate character by customizing the generated content based on the user's current project or area of ​​interest. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs data related to the user's project or area of ​​interest into the generation AI, which analyzes the data and executes an algorithm to customize the generated content.

[0070] The generation unit can estimate the user's emotions and determine the priority of characters to be generated based on the estimated user emotions. The generation unit can estimate the user's emotions using a generation AI and determine the priority of characters to be generated based on the estimated user emotions. For example, when the user is stressed, the generation unit can prioritize generating important characters. For example, when the user is relaxed, the generation unit can also generate detailed characters. Furthermore, when the user is in a hurry, the generation unit can prioritize generating basic characters. For example, when the user is stressed, the generation unit can prioritize generating important characters. Furthermore, when the user is relaxed, the generation unit can also generate detailed characters. Furthermore, when the user is in a hurry, the generation unit can prioritize generating basic characters. In this way, the generation unit can generate more appropriate characters by determining the priority of characters to be generated according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the user's facial expression data into the generation AI, which then infers the emotions and executes an algorithm to determine the priority of the characters to be generated.

[0071] When generating a character, the generation unit can prioritize generating a highly relevant character by taking into account the user's geographical location information. When generating a character using a generation AI, the generation unit prioritizes generating a highly relevant character by taking into account the user's geographical location information. For example, when the user is in a specific region, the generation unit generates a character related to that region. For example, the generation unit can also prioritize generating a character unique to that region based on the user's geographical location information. Furthermore, when the user is traveling, the generation unit can generate a character related to the culture or customs of a destination visited. For example, when the user is in a specific region, the generation unit generates a character related to that region. Furthermore, the generation unit can also prioritize generating a character unique to that region based on the user's geographical location information. Furthermore, when the user is traveling, the generation unit can generate a character related to the culture or customs of a destination visited. In this way, the generation unit can prioritize generating a highly relevant character by taking into account the user's geographical location information. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the user's geographical location information into the generation AI, which then analyzes the geographical location information and executes an algorithm that prioritizes generating highly relevant characters.

[0072] The generation unit can analyze the user's social media activities and generate related characters when generating characters. The generation unit uses a generation AI to analyze the user's social media activities and generate related characters when generating characters. For example, the generation unit generates characters that the user frequently mentions on social media. The generation unit can also extract and generate characters of interest from the user's social media activities. Furthermore, the generation unit can generate related characters based on the content posted by accounts the user follows. For example, the generation unit generates characters that the user frequently mentions on social media. The generation unit can also extract and generate characters of interest from the user's social media activities. Furthermore, the generation unit can generate related characters based on the content posted by accounts the user follows. In this way, the generation unit can generate related characters by analyzing the user's social media activities. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the user's social media activity data into the generation AI, which analyzes the data and executes an algorithm to generate related characters.

[0073] The detection unit can estimate the user's emotions and adjust the bug detection criteria based on the estimated user emotions. The detection unit can estimate the user's emotions using the generation AI and adjust the bug detection criteria based on the estimated user emotions. For example, the detection unit can prioritize detecting important bugs when the user is stressed. For example, the detection unit can detect detailed bugs when the user is relaxed. Furthermore, the detection unit can prioritize detecting basic bugs when the user is in a hurry. For example, the detection unit can prioritize detecting important bugs when the user is stressed. Furthermore, the detection unit can detect detailed bugs when the user is relaxed. Furthermore, the detection unit can prioritize detecting basic bugs when the user is in a hurry. This allows the detection unit to adjust the bug detection criteria according to the user's emotions, thereby enabling more appropriate bug detection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI. For example, the detection unit inputs user facial expression data into the generation AI, which then infers the emotion and executes an algorithm to adjust the criteria for bug detection.

[0074] The detection unit can optimize the detection algorithm by referring to past bug data when detecting a bug. The detection unit can optimize the detection algorithm by referring to past bug data when detecting a bug using the generation AI. The detection unit, for example, selects an optimal detection algorithm based on data of bugs that have occurred in the past. The detection unit can also, for example, prioritize detecting frequently occurring bugs from the past bug data. Furthermore, the detection unit can analyze the past bug data and select the most efficient detection algorithm. For example, the detection unit selects an optimal detection algorithm based on data of bugs that have occurred in the past. The detection unit can also prioritize detecting frequently occurring bugs from the past bug data. Furthermore, the detection unit can analyze the past bug data and select the most efficient detection algorithm. In this way, the detection unit can optimize the detection algorithm by referring to the past bug data. Some or all of the above-described processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit inputs past bug data to the generation AI, which analyzes the data and executes an algorithm to select the optimal detection algorithm.

[0075] The detection unit can apply different detection methods to each game category when detecting bugs. The detection unit can apply different detection methods to each game category when detecting bugs using the generation AI. For example, in the case of an action game, the detection unit prioritizes detecting bugs related to real-time actions. For example, in the case of a puzzle game, the detection unit can also prioritize detecting logical errors and inconsistencies. Furthermore, in the case of an RPG game, the detection unit can also prioritize detecting bugs related to story progression. For example, in the case of an action game, the detection unit prioritizes detecting bugs related to real-time actions. Furthermore, in the case of a puzzle game, the detection unit can also prioritize detecting logical errors and inconsistencies. Furthermore, in the case of an RPG game, the detection unit can also prioritize detecting bugs related to story progression. This allows the detection unit to apply different detection methods to each game category, enabling more appropriate bug detection. Some or all of the above-described processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit inputs data related to game categories to the generation AI, which analyzes the data and executes an algorithm that applies different detection methods to each category.

[0076] The detection unit can estimate the user's emotions and determine the priority of bug detection based on the estimated user emotions. The detection unit can estimate the user's emotions using the generation AI and determine the priority of bug detection based on the estimated user emotions. For example, the detection unit can prioritize detecting serious bugs when the user is stressed. For example, the detection unit can detect detailed bugs when the user is relaxed. Furthermore, the detection unit can prioritize detecting basic bugs when the user is in a hurry. For example, the detection unit can prioritize detecting serious bugs when the user is stressed. Furthermore, the detection unit can detect detailed bugs when the user is relaxed. Furthermore, the detection unit can prioritize detecting basic bugs when the user is in a hurry. This allows the detection unit to prioritize bug detection based on the user's emotions, enabling more appropriate bug detection. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI. For example, the detection unit inputs user facial expression data into the generation AI, which then infers the emotion and executes an algorithm to determine the priority of bug detection.

[0077] The detection unit can detect bugs by taking into account the geographical distribution of the game. The detection unit can detect bugs by using the generation AI. For example, the detection unit prioritizes detecting bugs that are likely to occur in a specific region. The detection unit can also detect region-specific bugs based on the geographical distribution. Furthermore, the detection unit can analyze and detect bug occurrence rates in different regions by taking the geographical distribution into account. For example, the detection unit prioritizes detecting bugs that are likely to occur in a specific region. The detection unit can also detect region-specific bugs based on the geographical distribution. Furthermore, the detection unit can analyze and detect bug occurrence rates in different regions by taking the geographical distribution into account. In this way, the detection unit can detect region-specific bugs by taking the geographical distribution of the game into account. Some or all of the above-described processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit inputs data regarding the geographical distribution of the game into the generation AI, and the generation AI analyzes the data and executes an algorithm that detects bugs by taking the geographical distribution into account.

[0078] The detection unit can improve detection accuracy by referring to literature related to the game when detecting bugs. The detection unit can improve detection accuracy by referring to literature related to the game when detecting bugs using the generation AI. The detection unit, for example, prioritizes detecting bugs reported in the past based on the relevant literature. The detection unit can also extract and detect frequently occurring bug patterns from the related literature. Furthermore, the detection unit can apply the latest bug detection techniques by referring to the related literature. For example, the detection unit prioritizes detecting bugs reported in the past based on the related literature. The detection unit can also extract and detect frequently occurring bug patterns from the related literature. Furthermore, the detection unit can apply the latest bug detection techniques by referring to the related literature. In this way, the detection unit can improve detection accuracy by referring to literature related to the game. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit inputs data related to literature related to the game into the generation AI, which analyzes the data and executes an algorithm to detect bugs by referring to the related literature.

[0079] The optimization unit can estimate the user's emotions and adjust the difficulty level and reward setting method based on the estimated user's emotions. The optimization unit estimates the user's emotions using the generation AI and adjusts the difficulty level and reward setting method based on the estimated user's emotions. For example, if the user is stressed, the optimization unit can lower the difficulty level and increase the reward. For example, if the user is relaxed, the optimization unit can increase the difficulty level and decrease the reward. Furthermore, if the user is excited, the optimization unit can maintain the difficulty level and adjust the reward. For example, if the user is stressed, the optimization unit can lower the difficulty level and increase the reward. Furthermore, if the user is relaxed, the optimization unit can increase the difficulty level and decrease the reward. Furthermore, if the user is excited, the optimization unit can maintain the difficulty level and adjust the reward. In this way, the optimization unit can adjust the difficulty level and reward setting method according to the user's emotions, thereby providing a more appropriate gaming experience. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the optimization unit may be performed using, for example, a generation AI. For example, the optimization unit inputs the user's facial expression data into the generation AI, which then estimates the emotion and executes an algorithm to adjust the difficulty level and the method of setting rewards.

[0080] The optimization unit can select the optimal setting method when setting the difficulty level and rewards by referring to the user's past play data. The optimization unit can select the optimal setting method when setting the difficulty level and rewards using the generation AI by referring to the user's past play data. The optimization unit, for example, sets the optimal difficulty level based on the user's past play data. The optimization unit can also determine the optimal distribution of rewards from the user's past play data. Furthermore, the optimization unit can analyze the user's past play data and set the difficulty level and rewards according to the user's play style. For example, the optimization unit can set the optimal difficulty level based on the user's past play data. The optimization unit can also determine the optimal distribution of rewards from the user's past play data. Furthermore, the optimization unit can analyze the user's past play data and set the difficulty level and rewards according to the user's play style. In this way, the optimization unit can set the optimal difficulty level and rewards by referring to the user's past play data. Some or all of the above-described processing in the optimization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the optimization unit inputs the user's past play data into the generation AI, which then analyzes the data and executes an algorithm to select the optimal setting method.

[0081] The optimization unit can apply different optimization methods for each game category when setting the difficulty level and rewards. The optimization unit uses the generation AI to apply different optimization methods for each game category when setting the difficulty level and rewards. For example, in the case of an action game, the optimization unit sets the difficulty level with an emphasis on real-time reactions. For example, in the case of a puzzle game, the optimization unit can also set the rewards with an emphasis on logical thinking. Furthermore, in the case of an RPG game, the optimization unit can set the difficulty level and rewards in accordance with the progress of the story. For example, in the case of an action game, the optimization unit sets the difficulty level with an emphasis on real-time reactions. Furthermore, in the case of a puzzle game, the optimization unit can also set the rewards with an emphasis on logical thinking. Furthermore, in the case of an RPG game, the optimization unit can set the difficulty level and rewards in accordance with the progress of the story. In this way, the optimization unit can apply different optimization methods for each game category, enabling more appropriate difficulty and reward settings. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the optimization unit inputs data regarding the game category into the generation AI, which analyzes the data and executes an algorithm that applies different optimization methods to each category.

[0082] The optimization unit can estimate the user's emotions and determine the priority of difficulty and reward settings based on the estimated user emotions. The optimization unit can estimate the user's emotions using the generation AI and determine the priority of difficulty and reward settings based on the estimated user emotions. For example, the optimization unit prioritizes lowering the difficulty level when the user is stressed. For example, the optimization unit can prioritize reward settings when the user is relaxed. Furthermore, the optimization unit can prioritize adjusting the difficulty level when the user is in a hurry. For example, the optimization unit prioritizes lowering the difficulty level when the user is stressed. Furthermore, the optimization unit can prioritize reward settings when the user is relaxed. Furthermore, the optimization unit can prioritize adjusting the difficulty level when the user is in a hurry. In this way, the optimization unit can provide a more appropriate gaming experience by determining the priority of difficulty and reward settings according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the optimization unit may be performed using, for example, a generation AI. For example, the optimization unit inputs the user's facial expression data into the generation AI, which then infers the emotion and executes an algorithm to determine the priority of difficulty and reward settings.

[0083] The optimization unit can select the optimal setting method for difficulty and reward settings by taking into account the user's geographical location information. The optimization unit uses the generation AI to select the optimal setting method for difficulty and reward settings by taking into account the user's geographical location information. For example, if the user is in a specific region, the optimization unit sets difficulty and reward settings that match the tendencies of players in that region. The optimization unit can also perform region-specific settings based on the user's geographical location information. Furthermore, if the user is traveling, the optimization unit can set difficulty and reward settings that match the culture and customs of the destination. For example, if the user is in a specific region, the optimization unit sets difficulty and reward settings that match the tendencies of players in that region. Furthermore, the optimization unit can perform region-specific settings based on the user's geographical location information. Furthermore, if the user is traveling, the optimization unit can set difficulty and reward settings that match the culture and customs of the destination. In this way, the optimization unit can perform region-specific difficulty and reward settings by taking into account the user's geographical location information. Some or all of the above-described processing in the optimization unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the optimization unit inputs the user's geographic location information into the generation AI, which then analyzes the data and runs an algorithm to select the optimal setting method.

[0084] The optimization unit can analyze the user's social media activity to suggest optimal settings when setting the difficulty level and rewards. The optimization unit uses the generation AI to analyze the user's social media activity to suggest optimal settings when setting the difficulty level and rewards. The optimization unit, for example, sets the difficulty level and rewards based on game features frequently mentioned by the user on social media. The optimization unit can also extract and suggest settings for games of interest from the user's social media activity. Furthermore, the optimization unit can set relevant difficulty levels and rewards based on the content posted by accounts the user follows. For example, the optimization unit sets the difficulty level and rewards based on game features frequently mentioned by the user on social media. The optimization unit can also extract and suggest settings for games of interest from the user's social media activity. Furthermore, the optimization unit can set relevant difficulty levels and rewards based on the content posted by accounts the user follows. In this way, the optimization unit can suggest optimal difficulty and reward settings by analyzing the user's social media activity. Some or all of the above-described processing in the optimization unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the optimization unit inputs a user's social media activity data into the generation AI, which then analyzes the data and runs an algorithm that suggests the optimal settings. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, generation unit, movement generation unit, voice generation unit, and conversation generation unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for the user to input the character's characteristics. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates the character's appearance, movement, and voice using a generation AI. The movement generation unit is implemented, for example, by the control unit 46A of the smart device 14 and generates the character's movement using Motion Diffuse. The voice generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates the character's voice using LLM or speech synthesis AI. The conversation generation unit is implemented, for example, by the control unit 46A of the smart device 14 and generates conversations and stories between characters using a generation AI or NLP. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, generation unit, movement generation unit, voice generation unit, and conversation 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 reception unit is realized by the microphone 238 of the smart glasses 214 and provides an interface for the user to input the character's characteristics. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the character's appearance, movement, and voice using a generation AI. The movement generation unit is realized, for example, by the control unit 46A of the smart glasses 214 and generates the character's movement using Motion Diffuse. The voice generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the character's voice using LLM or speech synthesis AI. The conversation generation unit is realized, for example, by the control unit 46A of the smart glasses 214 and generates conversations and stories between characters using a generation AI or NLP. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, generation unit, movement generation unit, voice generation unit, and conversation generation unit, described above, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is implemented by the microphone 238 of the headset-type terminal 314 and provides an interface for the user to input the character's characteristics. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates the character's appearance, movement, and voice using a generation AI. The movement generation unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and generates the character's movement using Motion Diffuse. The voice generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates the character's voice using LLM or speech synthesis AI. The conversation generation unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and generates conversations and stories between characters using a generation AI or NLP. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, generation unit, movement generation unit, voice generation unit, and conversation 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 reception unit is realized by the microphone 238 of the robot 414 and provides an interface for the user to input the character's characteristics. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the character's appearance, movement, and voice using a generation AI. The movement generation unit is realized, for example, by the control unit 46A of the robot 414 and generates the character's movement using Motion Diffuse. The voice generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates the character's voice using LLM or speech synthesis AI. The conversation generation unit is realized, for example, by the control unit 46A of the robot 414 and generates conversations and stories between characters using a generation AI or NLP.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The reception unit can monitor the user's input speed in real time and adjust the complexity of the interface according to the input speed. For example, if the user is inputting quickly, the reception unit can provide a simple interface and reduce the number of input items. Alternatively, if the user is inputting slowly, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, the reception unit can suggest voice input or gesture input based on the user's input speed. In this way, the reception unit can adjust the interface according to the user's input speed, enabling more appropriate character feature input.

[0087] When detecting bugs, the detection unit can analyze the user's play style and apply a bug detection algorithm appropriate to the play style. For example, for a user with an aggressive play style, it can prioritize detecting bugs in battle scenes and action scenes. For a user with an exploration-based play style, it can also prioritize detecting bugs related to maps and items. Furthermore, for a user with a story-focused play style, it can also prioritize detecting bugs related to story progression. This allows the detection unit to adjust the bug detection algorithm according to the user's play style, enabling more appropriate bug detection.

[0088] The optimization unit can monitor the user's biometric information (heart rate, galvanic skin response, etc.) in real time while playing a game, and adjust the difficulty level and reward settings based on the information. For example, if the user's heart rate is high, the optimization unit can lower the difficulty level and increase the rewards. Alternatively, if the user's heart rate is low, the optimization unit can raise the difficulty level and decrease the rewards. Furthermore, if the user's galvanic skin response is high, the optimization unit can maintain the difficulty level and adjust the rewards. In this way, the optimization unit can provide a more appropriate gaming experience by adjusting the difficulty level and reward settings based on the user's biometric information.

[0089] The reception unit can analyze the user's input content in real time and dynamically change the interface based on the input content. For example, if the user inputs a specific keyword, input items related to that keyword can be automatically displayed. Also, related options can be suggested based on the content entered by the user. Furthermore, if the user changes the input content, the interface can be updated immediately. In this way, the reception unit can dynamically change the interface according to the user's input content, allowing for more appropriate character characteristic input.

[0090] The reception unit can estimate the user's emotions and provide feedback on the input content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can display an encouraging message to encourage the user to continue inputting. If the user is relaxed, the reception unit can provide detailed feedback and suggest improvements to the input content. Furthermore, if the user is in a hurry, the reception unit can provide concise feedback to help the user complete the input quickly. In this way, the reception unit can provide feedback according to the user's emotions, enabling the user to input more appropriate character characteristics.

[0091] The reception unit can analyze the user's past input history and predict input content. For example, it can automatically display the characteristics of characters that the user has frequently input in the past as candidates. It can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest the characteristics of characters to be used in a specific time period from the user's past input history. In this way, the reception unit can suggest the optimal input method by analyzing the user's past input history.

[0092] The reception unit can estimate the user's emotions and determine the priority of the character characteristics to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize input of important characteristics. If the user is relaxed, the reception unit can prioritize input of detailed characteristics. Furthermore, if the user is in a hurry, the reception unit can prioritize input of basic characteristics. In this way, the reception unit can prioritize input of character characteristics according to the user's emotions, thereby enabling more appropriate character characteristic input.

[0093] The reception unit can suggest regional character characteristics by taking into account the user's geographical location information. For example, if the user is in a specific region, character characteristics related to that region are preferentially displayed. The reception unit can also preferentially input regional characteristics based on the user's geographical location information. Furthermore, if the user is traveling, it can suggest characteristics related to the culture and customs of the place the user is visiting. In this way, the reception unit can preferentially input highly relevant character characteristics by taking into account the user's geographical location information.

[0094] The reception unit can analyze the user's social media activity and suggest related character characteristics. For example, it can suggest the characteristics of characters that the user frequently mentions on social media. It can also extract and suggest characteristics of interest from the user's social media activity. It can also suggest related characteristics based on the content of posts from accounts the user follows. In this way, the reception unit can suggest related character characteristics by analyzing the user's social media activity.

[0095] The generation unit can estimate the user's emotions and adjust the character generation method based on the estimated user's emotions. For example, if the user is relaxed, a character that progresses at a leisurely pace can be generated. If the user is in a hurry, a character that is generated quickly can be provided. Furthermore, if the user is excited, a visually stimulating character can be generated. In this way, the generation unit can generate a more appropriate character by adjusting the character generation method according to the user's emotions.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The reception unit allows the user to input the characteristics of the character. The characteristics of the character input by the user include appearance, personality, abilities, etc. The reception unit provides an interface for the user to input the character's appearance and options for inputting personality and abilities. Step 2: The generator generates a character based on the characteristics input by the receiver. For example, the generator generates the character's appearance using StableDiffusion, the character's movements using MotionDiffuse, and the character's voice using LLM or voice synthesis AI. Step 3: The motion generator generates the motion of the character generated by the generator. The motion generator uses MotionDiffuse to generate the character's walking motion, jumping motion, attack motion, etc. Step 4: The voice generation unit generates the voice of the character generated by the generation unit. The voice generation unit uses LLM and voice synthesis AI to generate the character's lines, emotional expressions, singing voice, etc. Step 5: The conversation generation unit generates conversations and stories between the characters generated by the generation unit. The conversation generation unit uses generative AI and NLP to generate dialogues between characters, stories, backstories, etc.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for allowing a user to input characteristics of a character; a generation unit that generates a character based on the characteristics input by the reception unit; a movement generation unit that generates a movement of the character generated by the generation unit; a voice generation unit that generates a voice of the character generated by the generation unit; a conversation generation unit that generates conversations and stories between the characters generated by the generation unit; Equipped with A system characterized by:

2. Equipped with a detection unit to detect bugs The system of claim 1 .

3. Equipped with an adjustment section that analyzes player data and adjusts difficulty and reward settings The system of claim 1 .

4. The reception unit Estimate the user's emotions and adjust the character feature input interface based on the estimated user emotions. The system of claim 1 .

5. The reception unit Analyzes the user's past input history and suggests appropriate input methods The system of claim 1 .

6. The reception unit When entering character characteristics, customize the input based on the user's current projects and interests. The system of claim 1 .

7. The reception unit Estimate the user's emotions and prioritize the input character's features based on the estimated user emotions. The system of claim 1 .

8. The reception unit When entering character characteristics, the system takes into account the user's geographical location information and prioritizes the most relevant characteristics. The system of claim 1 .

9. The reception unit When entering a character's characteristics, the app analyzes the user's social media activity and suggests related characteristics. The system of claim 1 .

Images