System

Abstract

An object of a system according to an embodiment is to quickly generate and modify a design of an environment or a stage in a game.SOLUTION: A system includes a reception unit, a generation unit, a confirmation unit, and a provision unit. The reception unit receives design requirements from a creator. The generation unit generates a design of an environment or a stage in the game on the basis of the requirements received by the reception unit. The confirmation unit confirms and corrects the design generated by the generation unit. The provision unit incorporates the design confirmed by the confirmation unit into the game.SELECTED DRAWING: Figure 1

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] Previous technology had the problem that the process of quickly generating and modifying in-game environment and stage designs was complex and time-consuming.

[0005] The system according to the embodiment aims to rapidly create and modify the design of environments and stages in a game. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, a verifying unit, and a providing unit. The receiving unit receives design requirements from a creator. The generating unit generates designs for environments and stages within the game based on the requirements received by the receiving unit. The verifying unit checks and modifies the designs generated by the generating unit. The providing unit incorporates the designs confirmed by the verifying unit into the game. [Effects of the Invention]

[0007] Systems according to embodiments allow for the rapid creation and modification of in-game environment and stage designs. [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 generation system according to an embodiment of the present invention is a system that instantly generates worlds and dungeons for games and RPGs. The generation system accepts design requirements from creators, and a generation AI generates world and dungeon designs based on those requirements. The creator can then review and modify the designs and ultimately incorporate them into the game. For example, the creator inputs requirements specifying the world or dungeon's theme, difficulty, size, and specific elements (e.g., placement of traps and treasure chests). The generation system then uses a generation AI to generate a design that meets the specified requirements. The generated world or dungeon is then reviewed by the creator and modified as necessary. This allows creators to build diverse content in a short period of time. This speeds up the production process and allows players to enjoy new adventures. This significantly reduces the time creators spend on manual design. For example, it allows more content to be provided in a shorter period of time. Furthermore, players can enjoy new adventures. Because the generated worlds and dungeons have different designs each time, players can always enjoy a fresh experience.

[0029] A generation system according to an embodiment includes a receiving unit, a generating unit, a verifying unit, and a providing unit. The receiving unit receives design requirements from a creator. The design requirements include, but are not limited to, the theme, difficulty, size, and specific elements (e.g., placement of traps and treasure chests) of the world or dungeon. The receiving unit receives, for example, a creator's input requirement, such as "a medium-sized fantasy dungeon with many traps and few treasure chests." The generating unit uses a generation AI to generate a world or dungeon design based on the requirements received by the receiving unit. The generation AI generates a design that meets the specified requirements based on data learned in advance. For example, the generation AI generates a design for a medium-sized fantasy dungeon with many traps and few treasure chests. The verifying unit checks the design generated by the generating unit and makes modifications as necessary. For example, the creator can make changes to parts of the generated dungeon or insert additional elements. The providing unit incorporates the design confirmed by the verifying unit into a game. For example, the generated design can be incorporated as a game stage. As a result, the generation system according to the embodiment allows creators to create a variety of content in a short time, thereby speeding up the production process. 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 requirements specified by a creator into the generation AI, and the generation AI generates a design. As a result, the generation system allows creators to create a variety of content in a short time, thereby speeding up the production process.

[0030] The generation unit can use a generation AI to generate a design that meets the specified requirements. The generation unit, for example, uses a generation AI to generate a design that meets the specified requirements. The generation AI generates a design that meets the specified requirements based on pre-learned data. For example, the generation AI generates a design for a fantasy-style medium-sized dungeon with many traps and few treasure chests. The generation unit can also use the generation AI to generate a design that meets the specified requirements. For example, the generation AI generates a design for a large-scale science fiction-style world with many enemy characters and an abundance of items. This makes it possible to quickly generate a design that meets the specified requirements by using the generation AI. Some or all of the above-mentioned 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 requirements specified by the creator into the generation AI, and the generation AI generates a design.

[0031] The reception unit can accept requirements specifying the theme, difficulty, size, and specific elements of a world or dungeon. For example, the reception unit accepts requirements from a creator specifying the theme, difficulty, size, and specific elements of a world or dungeon. Design requirements include, but are not limited to, themes such as fantasy, science fiction, and horror; difficulty levels such as beginner, intermediate, and advanced; sizes such as small, medium, and large; and specific elements such as traps, treasure chests, and enemy characters. For example, the reception unit accepts requirements input by a creator such as "a large horror-style world with many enemy characters and an abundance of items." The reception unit can also save and reuse requirements specified by a creator. For example, a creator can create new design requirements based on requirements previously input. This allows a creator to specify detailed design requirements. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit inputs the requirements specified by the creator into AI, which then analyzes the requirements.

[0032] The verification unit may modify specific parts of the generated design or insert additional elements. For example, the verification unit may modify specific parts of the generated design or insert additional elements. Specific parts include, but are not limited to, backgrounds, characters, objects, etc. For example, the verification unit may change the background of the generated dungeon or add a new character. The verification unit may also delete or modify parts of the generated design. For example, the verification unit may delete unnecessary objects or change the position of characters. This allows creators to flexibly modify the generated design. Some or all of the above-described processing in the verification unit may be performed using, or without, AI. For example, the verification unit may input the generated design into AI, which then suggests modifications.

[0033] The providing unit can incorporate the generated design into the game. For example, the providing unit incorporates the generated design into the game. Incorporation includes, but is not limited to, an import procedure, an integration method, and the like. For example, the providing unit imports the generated world as a game stage and integrates it with other game elements. The providing unit can also reflect the generated design into the game in real time. For example, the providing unit instantly displays the generated dungeon in the game so that the player can explore it. This allows the generated design to be quickly reflected in the game. Some or all of the above-described processing by the providing unit may be performed using, or without, an AI. For example, the providing unit inputs the generated design into an AI, and the AI ​​executes the integration procedure.

[0034] The generation unit can generate a design based on pre-learned data. The generation unit generates a design based on, for example, pre-learned data. Pre-learned data includes, for example, past design data, user feedback, etc., but is not limited to these examples. For example, the generation unit generates a new fantasy-style dungeon based on a fantasy-style dungeon design generated in the past. The generation unit can also improve the accuracy of the design based on user feedback. For example, the generation unit learns feedback provided by the user and reflects it in the next design generation. This makes it possible to generate a more accurate design by utilizing the pre-learned data. 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 pre-learned data into the generation AI, and the generation AI generates a design.

[0035] The reception unit can analyze the history of past design requirements and provide the user with an optimal guide for inputting requirements. The reception unit, for example, analyzes the history of past design requirements and provides the user with an optimal guide for inputting requirements. The history of past design requirements can include, but is not limited to, a database, a log file, etc. For example, the reception unit displays an optimal input guide based on design requirements previously input by the user. The reception unit can also automatically display requirements frequently used by the user as candidates to simplify input. The reception unit can also extract specific patterns from the user's past input history and suggest an optimal input method. This makes it possible to provide the user with an optimal input guide by utilizing the past history. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the history of past design requirements into AI, which then provides the optimal input guide.

[0036] The reception unit can present recommended design requirements based on the user's current project or area of ​​interest when the user inputs design requirements. For example, the reception unit presents recommended requirements based on the user's current project or area of ​​interest when the user inputs design requirements. Examples of current projects include, but are not limited to, project names and progress statuses. Examples of areas of interest include, but are not limited to, game genres and design styles. For example, the reception unit analyzes the content of the project the user is currently working on and proposes related requirements. The reception unit can also automatically present appropriate design requirements based on the user's area of ​​interest. The reception unit can also display recommended requirements based on elements in which the user has previously shown interest. This allows appropriate requirements to be proposed based on the user's project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit inputs the user's project information into AI, which then presents recommended requirements.

[0037] The reception unit can select the optimal input means depending on the user's input method when inputting design requirements. For example, when inputting design requirements, the reception unit selects the optimal input means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the reception unit inputs the requirements using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can also extract the requirements using image analysis technology. This makes it possible to provide the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's input method to AI, which then selects the optimal input means.

[0038] The reception unit may prioritize the presentation of highly relevant design requirements by taking into consideration the user's geographical location information when inputting design requirements. For example, the reception unit prioritizes the presentation of highly relevant design requirements by taking into consideration the user's geographical location information when inputting design requirements. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the user is in a specific region, the reception unit prioritizes the presentation of design requirements related to that region. The reception unit may also automatically display region-specific requirements based on the user's location information. Furthermore, if the user is traveling, the reception unit may suggest optimal requirements based on the user's current location. This allows the presentation of highly relevant requirements based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's geographical location information into AI, which then presents highly relevant requirements.

[0039] The reception unit can analyze the user's social media activity when inputting design requirements and suggest related requirements. For example, when inputting design requirements, the reception unit analyzes the user's social media activity and suggests related requirements. Social media activity includes, but is not limited to, post content and follower reactions. For example, the reception unit suggests related design requirements based on content shared by the user on social media. The reception unit can also analyze the user's social media activity history and suggest appropriate requirements. The reception unit can also suggest related requirements based on the activity of the user's friends. This makes it possible to provide related requirements based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's social media data into AI, which then suggests related requirements.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting design requirements. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting design requirements. Past feedback includes, but is not limited to, user reviews, survey results, etc. For example, the reception unit suggests an optimal input method based on feedback previously provided by the user. The reception unit can also customize the input interface by reflecting the user's feedback. The reception unit can also analyze the user's past feedback and optimize the input procedure. This makes it possible to provide an optimal input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's feedback data into AI, which then customizes the input method.

[0041] The generation unit can adjust the level of detail of the generated design based on the importance of the design during generation. For example, the generation unit adjusts the level of detail of the generated design based on the importance of the design during generation. The importance of the design includes, but is not limited to, project priority, user requirements, etc. For example, the generation unit generates a detailed design for important design elements. The generation unit can also generate a simplified design for less important elements. The generation unit can also adjust the precision of the generated design based on the importance of the design. This allows for providing an optimal level of detail based on the importance of the design. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit inputs design importance data into the generation AI, which then adjusts the level of detail of the generated design.

[0042] The generation unit can apply different generation algorithms depending on the design category during generation. For example, the generation unit applies different generation algorithms depending on the design category during generation. Design categories include, but are not limited to, character design and background design. For example, the generation unit applies a specific generation algorithm to a fantasy-style design. The generation unit can also apply a different generation algorithm to a science fiction-style design. The generation unit can also apply an even different generation algorithm to a horror-style design. This makes it possible to provide an optimal generation algorithm depending on the design category. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit inputs design category data into the generation AI, which then applies an appropriate generation algorithm.

[0043] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. Past generation results include, but are not limited to, the history of generated designs and evaluation results. For example, the generation unit generates a highly accurate design based on designs previously generated by the user. The generation unit can also analyze the user's past generation results and propose an optimal generation method. The generation unit can also adjust the generation algorithm by referring to the user's past generation history. This allows optimal generation accuracy to be provided based on the user's past generation results. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI or without using a generation AI. For example, the generation unit inputs the user's past generation result data into the generation AI, which then improves the accuracy of generation.

[0044] The generation unit can determine the generation priority based on the design submission time during generation. The generation unit, for example, determines the generation priority based on the design submission time during generation. The submission time includes, but is not limited to, for example, a submission deadline, a project schedule, etc. For example, the generation unit prioritizes the generation of design requirements with an upcoming deadline. The generation unit can also postpone requirements with a more distant submission time. The generation unit can also adjust the order of generation according to the submission time. This makes it possible to provide an optimal generation priority according to the design submission time. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit inputs submission time data into the generation AI, and the generation AI determines the generation priority.

[0045] The generation unit can adjust the order of generation based on the relevance of the designs during generation. The generation unit, for example, adjusts the order of generation based on the relevance of the designs during generation. Design relevance includes, but is not limited to, for example, matching of themes and commonality of elements. For example, the generation unit prioritizes generation of highly relevant design requirements. The generation unit can also postpone requirements with low relevance. The generation unit can also adjust the order of generation based on the relevance of the designs. This makes it possible to provide an optimal generation order based on the relevance of the designs. 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 design relevance data into the generation AI, and the generation AI adjusts the order of generation.

[0046] The generation unit can adjust the use of technical terminology in the generated design according to the user's level of expertise during generation. For example, the generation unit adjusts the use of technical terminology in the generated design according to the user's level of expertise during generation. Expertise levels include, but are not limited to, beginner, intermediate, and expert levels. For example, if the user is a beginner, the generation unit can generate a design that avoids technical terminology. If the user is an intermediate user, the generation unit can also generate a design that uses appropriate technical terminology. If the user is an advanced user, the generation unit can also generate a design that uses a lot of technical terminology. This allows the use of optimal technical terminology according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit inputs the user's expertise level data into the generation AI, and the generation AI adjusts the use of technical terminology.

[0047] The verification unit can improve the accuracy of the verification by taking into account the interrelationships of the generated designs during verification. For example, the verification unit can improve the accuracy of the verification by taking into account the interrelationships of the generated designs during verification. Examples of interrelationships include, but are not limited to, the relevance and influence between design elements. For example, the verification unit can analyze the interrelationships of the generated designs and verify their consistency. The verification unit can also propose necessary modifications by taking into account the interrelationships of the designs. The verification unit can also provide an optimal verification method based on the interrelationships of the designs. This improves the accuracy of the verification by taking into account the interrelationships of the generated designs. Some or all of the above-described processing in the verification unit can be performed using, for example, AI, or can be performed without using AI. For example, the verification unit inputs design interrelation data into AI, which then improves the accuracy of the verification.

[0048] The verification unit may perform verification taking into consideration attribute information of the person who submitted the design. For example, the verification unit may perform verification taking into consideration attribute information of the person who submitted the design. Attribute information may include, but is not limited to, age, gender, and occupation. For example, the verification unit may adjust the accuracy of verification taking into consideration the level of expertise of the person who submitted the design. The verification unit may also determine verification priorities based on the submitter's past performance. The verification unit may also provide an optimal verification method based on the submitter's attribute information. This allows for the provision of an optimal verification method based on the attribute information of the design submitter. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit may input the submitter's attribute information into AI, which then performs verification.

[0049] The confirmation unit can weight the confirmation based on the frequency of design submissions during confirmation. For example, the confirmation unit weights the confirmation based on the frequency of design submissions during confirmation. The submission frequency includes, but is not limited to, for example, the number of submissions per month or the number of submissions per year. For example, the confirmation unit prioritizes checking design requirements that are submitted more frequently. The confirmation unit can also postpone requirements that are submitted less frequently. The confirmation unit can also adjust the confirmation weights according to the submission frequency. This makes it possible to provide optimal confirmation weights according to the frequency of design submissions. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit inputs submission frequency data into AI, and the AI ​​weights the confirmation.

[0050] The verification unit may perform verification taking into account the geographical distribution of the design. For example, the verification unit may perform verification taking into account the geographical distribution of the design. Geographical distribution includes, but is not limited to, distribution by region or by country. For example, if the design is related to a specific region, the verification unit may perform verification taking into account the characteristics of that region. The verification unit may also evaluate the applicability of the design based on the geographical distribution. The verification unit may also suggest modifications to the design taking geographical factors into account. This provides an optimal verification method based on the geographical distribution of the design. Some or all of the above-described processing in the verification unit may be performed using, or without, AI. For example, the verification unit inputs geographical distribution data into AI, which then performs verification.

[0051] The verification unit may improve the accuracy of the verification by referring to related literature of the design during verification. For example, the verification unit may improve the accuracy of the verification by referring to related literature of the design during verification. Related literature includes, but is not limited to, academic papers, technical reports, etc. For example, the verification unit may automatically search for literature related to the design and use it as reference for verification. The verification unit may also evaluate the consistency of the design based on the related literature. The verification unit may also utilize literature information to suggest design modifications. This allows for providing an optimal verification method based on the related literature of the design. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without AI. For example, the verification unit may input related literature data into AI, which then improves the accuracy of the verification.

[0052] The confirmation unit may perform confirmation taking into consideration the market value of the design. For example, the confirmation unit may perform confirmation taking into consideration the market value of the design. Market value includes, but is not limited to, sales forecasts and demand forecasts. For example, the confirmation unit may evaluate the market value of the design and determine a priority for confirmation. The confirmation unit may also suggest design modifications based on the market value. The confirmation unit may also evaluate the applicability of the design taking into consideration the market value. This makes it possible to provide an optimal confirmation method based on the market value of the design. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit inputs market value data into AI, which then performs the confirmation.

[0053] The providing unit can improve the accuracy of the provided design by taking into account the interrelationships between the generated designs when providing the design. For example, the providing unit improves the accuracy of the provided design by taking into account the interrelationships between the generated designs when providing the design. Interrelationships include, but are not limited to, the relevance and influence between design elements. For example, the providing unit analyzes the interrelationships between the generated designs and checks their consistency. The providing unit can also propose necessary modifications by taking into account the interrelationships between the designs. The providing unit can also provide an optimal providing method based on the interrelationships between the designs. This improves the accuracy of the provided design by taking into account the interrelationships between the generated designs. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs design interrelation data into AI, which improves the accuracy of the provided design.

[0054] The providing unit may provide the design while taking into consideration the attribute information of the person who submitted the design. For example, the providing unit may provide the design while taking into consideration the attribute information of the person who submitted the design. The attribute information may include, but is not limited to, age, gender, occupation, etc. For example, the providing unit may adjust the accuracy of the provision while taking into consideration the level of expertise of the person who submitted the design. The providing unit may also determine the priority of provision based on the submitter's past performance. The providing unit may also provide the optimal provision method based on the attribute information of the submitter. This allows the optimal provision method to be provided based on the attribute information of the person who submitted the design. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may input the submitter's attribute information into AI, and the AI ​​may provide the information.

[0055] The providing unit can weight the provision based on the frequency of design submissions at the time of provision. For example, the providing unit weights the provision based on the frequency of design submissions at the time of provision. The submission frequency includes, but is not limited to, for example, the number of submissions per month or the number of submissions per year. For example, the providing unit prioritizes the provision of design requirements that are submitted more frequently. The providing unit can also postpone requirements that are submitted less frequently. The providing unit can also adjust the provision weights according to the submission frequency. This makes it possible to provide optimal provision weights according to the frequency of design submissions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs submission frequency data into AI, and the AI ​​weights the provision.

[0056] The providing unit may provide the design taking into consideration the geographical distribution of the design when providing the design. For example, the providing unit may provide the design taking into consideration the geographical distribution of the design when providing the design. Geographical distribution includes, but is not limited to, distribution by region or by country. For example, if the design is related to a specific region, the providing unit may provide the design taking into consideration the characteristics of that region. The providing unit may also evaluate the applicability of the design based on the geographical distribution. The providing unit may also suggest modifications to the design taking geographical factors into consideration. This makes it possible to provide an optimal provision method based on the geographical distribution of the design. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs geographical distribution data into AI, and the AI ​​performs the provision.

[0057] The providing unit can improve the accuracy of the provided design by referring to related literature of the design when providing the design. For example, the providing unit can improve the accuracy of the provided design by referring to related literature of the design when providing the design. Related literature includes, but is not limited to, academic papers, technical reports, etc. For example, the providing unit can automatically search for literature related to the design and use it as reference for providing the design. The providing unit can also evaluate the consistency of the design based on the related literature. The providing unit can also utilize literature information to suggest design modifications. This allows for the provision of an optimal providing method based on the related literature of the design. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit inputs related literature data into AI, which then improves the accuracy of the provided design.

[0058] The provision unit may provide the design taking into consideration the market value of the design when providing it. For example, the provision unit provides the design taking into consideration the market value of the design when providing it. Market value includes, but is not limited to, sales forecasts, demand forecasts, etc. For example, the provision unit evaluates the market value of the design and determines the priority of provision. The provision unit may also suggest design modifications based on the market value. The provision unit may also evaluate the applicability of the design taking into consideration the market value. This makes it possible to provide an optimal provision method based on the market value of the design. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit inputs market value data into AI, and the AI ​​provides the data.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The generation system can also analyze the user's past play style and reflect this in the design of the world or dungeon it generates. For example, if the user has previously preferred an exploration-based play style, the generation unit can generate a design that includes many exploration elements. If the user prefers combat, the generation unit can generate a design with many combat scenes. If the user prefers puzzles, the generation unit can generate a design that includes many puzzle elements. This makes it possible to provide the optimal design according to the user's play style.

[0061] The generation system can also receive real-time feedback from the user and immediately reflect it in the generated design. For example, if the user provides feedback such as "I want it to be more difficult" for a generated design, the generation unit will regenerate a design with a higher level of difficulty. Also, if the user provides feedback such as "I want more treasure chests," a design with more treasure chests can be generated. Furthermore, if the user provides feedback such as "I want this part to be wider," a design with that part wider can be generated. This makes it possible to provide optimal designs in response to the user's real-time feedback.

[0062] The generation system can also learn from the user's past design requirements and make optimal suggestions the next time they input design requirements. For example, if the user has frequently specified a "medium-sized fantasy dungeon" in the past, the system will suggest similar requirements the next time they input requirements. Also, if the user frequently specifies the requirement "many traps and few treasure chests," the system can automatically suggest those requirements. Furthermore, if the user prefers the requirement "many enemy characters and an abundance of items," the system can suggest those requirements. This allows the system to provide optimal suggestions based on the user's past requirements.

[0063] The generation system can further adjust the length of the design to be generated taking into account the user's play time. For example, if the user can only play for a short time, a short design can be generated. Alternatively, if the user can play for a long time, a long design can be generated. Furthermore, if the user has a medium amount of play time, a design of medium length can be generated. This makes it possible to provide the optimal design length according to the user's play time.

[0064] The generation system can also analyze the user's past feedback to improve the accuracy of the generated design. For example, if a user previously provided feedback such as "I want more traps," the system can add more traps when generating the next design. Also, if a user provided feedback such as "The placement of the treasure chests was good," the system can use that placement as a reference. Furthermore, if a user provided feedback such as "There are too many enemy characters," the system can reduce the number of enemy characters when generating the next design. This allows the system to provide optimal designs based on the user's past feedback.

[0065] The generation system can further adjust the level of detail of the generated design taking into account the capabilities of the user's device. For example, if the user is using a high-performance device, a high-detail design can be generated. If the user is using a low-performance device, a low-detail design can be generated. Furthermore, if the user is using a medium-performance device, a medium-detail design can be generated. This allows the system to provide the optimal level of detail of the design according to the capabilities of the user's device.

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

[0067] Step 1: The reception department accepts design requirements from creators. Design requirements include the world and dungeon theme, difficulty, size, and specific elements (such as the placement of traps and treasure chests). For example, a creator may input requirements such as "a medium-sized fantasy dungeon with many traps and few treasure chests." Step 2: The generator uses the AI ​​to generate world and dungeon designs based on the requirements received by the reception unit. The AI ​​generates designs that meet the specified requirements based on pre-trained data. For example, the AI ​​generates a fantasy-style medium-sized dungeon design with many traps and few treasure chests. Step 3: The verification unit checks the design generated by the generation unit and makes any necessary modifications. For example, the creator can make changes to parts of the generated dungeon or insert additional elements. Step 4: The providing unit incorporates the design confirmed by the confirming unit into the game. For example, the generated design can be incorporated as a stage in the game.

[0068] (Example 2) A generation system according to an embodiment of the present invention is a system that instantly generates worlds and dungeons for games and RPGs. The generation system accepts design requirements from creators, and a generation AI generates world and dungeon designs based on those requirements. The creator can then review and modify the designs and ultimately incorporate them into the game. For example, the creator inputs requirements specifying the world or dungeon's theme, difficulty, size, and specific elements (e.g., placement of traps and treasure chests). The generation system then uses a generation AI to generate a design that meets the specified requirements. The generated world or dungeon is then reviewed by the creator and modified as necessary. This allows creators to build diverse content in a short period of time. This speeds up the production process and allows players to enjoy new adventures. This significantly reduces the time creators spend on manual design. For example, it allows more content to be provided in a shorter period of time. Furthermore, players can enjoy new adventures. Because the generated worlds and dungeons have different designs each time, players can always enjoy a fresh experience.

[0069] A generation system according to an embodiment includes a receiving unit, a generating unit, a verifying unit, and a providing unit. The receiving unit receives design requirements from a creator. The design requirements include, but are not limited to, the theme, difficulty, size, and specific elements (e.g., placement of traps and treasure chests) of the world or dungeon. The receiving unit receives, for example, a creator's input requirement, such as "a medium-sized fantasy dungeon with many traps and few treasure chests." The generating unit uses a generation AI to generate a world or dungeon design based on the requirements received by the receiving unit. The generation AI generates a design that meets the specified requirements based on data learned in advance. For example, the generation AI generates a design for a medium-sized fantasy dungeon with many traps and few treasure chests. The verifying unit checks the design generated by the generating unit and makes modifications as necessary. For example, the creator can make changes to parts of the generated dungeon or insert additional elements. The providing unit incorporates the design confirmed by the verifying unit into a game. For example, the generated design can be incorporated as a game stage. As a result, the generation system according to the embodiment allows creators to create a variety of content in a short time, thereby speeding up the production process. 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 requirements specified by a creator into the generation AI, and the generation AI generates a design. As a result, the generation system allows creators to create a variety of content in a short time, thereby speeding up the production process.

[0070] The generation unit can use a generation AI to generate a design that meets the specified requirements. The generation unit, for example, uses a generation AI to generate a design that meets the specified requirements. The generation AI generates a design that meets the specified requirements based on pre-learned data. For example, the generation AI generates a design for a fantasy-style medium-sized dungeon with many traps and few treasure chests. The generation unit can also use the generation AI to generate a design that meets the specified requirements. For example, the generation AI generates a design for a large-scale science fiction-style world with many enemy characters and an abundance of items. This makes it possible to quickly generate a design that meets the specified requirements by using the generation AI. Some or all of the above-mentioned 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 requirements specified by the creator into the generation AI, and the generation AI generates a design.

[0071] The reception unit can accept requirements specifying the theme, difficulty, size, and specific elements of a world or dungeon. For example, the reception unit accepts requirements from a creator specifying the theme, difficulty, size, and specific elements of a world or dungeon. Design requirements include, but are not limited to, themes such as fantasy, science fiction, and horror; difficulty levels such as beginner, intermediate, and advanced; sizes such as small, medium, and large; and specific elements such as traps, treasure chests, and enemy characters. For example, the reception unit accepts requirements input by a creator such as "a large horror-style world with many enemy characters and an abundance of items." The reception unit can also save and reuse requirements specified by a creator. For example, a creator can create new design requirements based on requirements previously input. This allows a creator to specify detailed design requirements. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit inputs the requirements specified by the creator into AI, which then analyzes the requirements.

[0072] The verification unit may modify specific parts of the generated design or insert additional elements. For example, the verification unit may modify specific parts of the generated design or insert additional elements. Specific parts include, but are not limited to, backgrounds, characters, objects, etc. For example, the verification unit may change the background of the generated dungeon or add a new character. The verification unit may also delete or modify parts of the generated design. For example, the verification unit may delete unnecessary objects or change the position of characters. This allows creators to flexibly modify the generated design. Some or all of the above-described processing in the verification unit may be performed using, or without, AI. For example, the verification unit may input the generated design into AI, which then suggests modifications.

[0073] The providing unit can incorporate the generated design into the game. For example, the providing unit incorporates the generated design into the game. Incorporation includes, but is not limited to, an import procedure, an integration method, and the like. For example, the providing unit imports the generated world as a game stage and integrates it with other game elements. The providing unit can also reflect the generated design into the game in real time. For example, the providing unit instantly displays the generated dungeon in the game so that the player can explore it. This allows the generated design to be quickly reflected in the game. Some or all of the above-described processing by the providing unit may be performed using, or without, an AI. For example, the providing unit inputs the generated design into an AI, and the AI ​​executes the integration procedure.

[0074] The generation unit can generate a design based on pre-learned data. The generation unit generates a design based on, for example, pre-learned data. Pre-learned data includes, for example, past design data, user feedback, etc., but is not limited to these examples. For example, the generation unit generates a new fantasy-style dungeon based on a fantasy-style dungeon design generated in the past. The generation unit can also improve the accuracy of the design based on user feedback. For example, the generation unit learns feedback provided by the user and reflects it in the next design generation. This makes it possible to generate a more accurate design by utilizing the pre-learned data. 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 pre-learned data into the generation AI, and the generation AI generates a design.

[0075] The reception unit can analyze the user's emotions and adjust the input interface for design requirements based on the analyzed user emotions. The reception unit, for example, analyzes the user's emotions and adjusts the input interface for design requirements based on the analyzed user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception unit can prioritize voice input to enable quick input of design requirements. This allows for an optimal input interface to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI 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 can be performed using, for example, AI, or without AI. For example, the reception unit inputs the user's emotional data into the AI, which then adjusts the input interface.

[0076] The reception unit can analyze the history of past design requirements and provide the user with an optimal guide for inputting requirements. The reception unit, for example, analyzes the history of past design requirements and provides the user with an optimal guide for inputting requirements. The history of past design requirements can include, but is not limited to, a database, a log file, etc. For example, the reception unit displays an optimal input guide based on design requirements previously input by the user. The reception unit can also automatically display requirements frequently used by the user as candidates to simplify input. The reception unit can also extract specific patterns from the user's past input history and suggest an optimal input method. This makes it possible to provide the user with an optimal input guide by utilizing the past history. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the history of past design requirements into AI, which then provides the optimal input guide.

[0077] The reception unit can present recommended design requirements based on the user's current project or area of ​​interest when the user inputs design requirements. For example, the reception unit presents recommended requirements based on the user's current project or area of ​​interest when the user inputs design requirements. Examples of current projects include, but are not limited to, project names and progress statuses. Examples of areas of interest include, but are not limited to, game genres and design styles. For example, the reception unit analyzes the content of the project the user is currently working on and proposes related requirements. The reception unit can also automatically present appropriate design requirements based on the user's area of ​​interest. The reception unit can also display recommended requirements based on elements in which the user has previously shown interest. This allows appropriate requirements to be proposed based on the user's project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit inputs the user's project information into AI, which then presents recommended requirements.

[0078] The reception unit can select the optimal input means depending on the user's input method when inputting design requirements. For example, when inputting design requirements, the reception unit selects the optimal input means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the reception unit inputs the requirements using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can also extract the requirements using image analysis technology. This makes it possible to provide the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's input method to AI, which then selects the optimal input means.

[0079] The reception unit can estimate the user's emotions and determine the priority of requirements based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of requirements based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is feeling stressed, the reception unit can prioritize input of important requirements. Also, if the user is relaxed, the reception unit can sequentially input detailed requirements. Also, if the user is in a hurry, the reception unit can input only the most important requirements. This makes it possible to provide an optimal priority of requirements according to the user's emotions. Emotion estimation is realized using an emotion estimation function, 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, an AI, or may be performed without using an AI. For example, the reception unit inputs the user's emotion data into an AI, which then determines the priority of requirements.

[0080] The reception unit may prioritize the presentation of highly relevant design requirements by taking into consideration the user's geographical location information when inputting design requirements. For example, the reception unit prioritizes the presentation of highly relevant design requirements by taking into consideration the user's geographical location information when inputting design requirements. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the user is in a specific region, the reception unit prioritizes the presentation of design requirements related to that region. The reception unit may also automatically display region-specific requirements based on the user's location information. Furthermore, if the user is traveling, the reception unit may suggest optimal requirements based on the user's current location. This allows the presentation of highly relevant requirements based on the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's geographical location information into AI, which then presents highly relevant requirements.

[0081] The reception unit can analyze the user's social media activity when inputting design requirements and suggest related requirements. For example, when inputting design requirements, the reception unit analyzes the user's social media activity and suggests related requirements. Social media activity includes, but is not limited to, post content and follower reactions. For example, the reception unit suggests related design requirements based on content shared by the user on social media. The reception unit can also analyze the user's social media activity history and suggest appropriate requirements. The reception unit can also suggest related requirements based on the activity of the user's friends. This makes it possible to provide related requirements based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's social media data into AI, which then suggests related requirements.

[0082] The reception unit can customize the input method by reflecting the user's past feedback when inputting design requirements. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting design requirements. Past feedback includes, but is not limited to, user reviews, survey results, etc. For example, the reception unit suggests an optimal input method based on feedback previously provided by the user. The reception unit can also customize the input interface by reflecting the user's feedback. The reception unit can also analyze the user's past feedback and optimize the input procedure. This makes it possible to provide an optimal input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit inputs the user's feedback data into AI, which then customizes the input method.

[0083] The generation unit can estimate the user's emotions and adjust the style of the generated design based on the estimated user's emotions. The generation unit, for example, estimates the user's emotions and adjusts the style of the generated design based on the estimated user's emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the generation unit generates a design with soft colors when the user is relaxed. The generation unit can also generate a design with bright colors when the user is excited. The generation unit can also generate a design with subdued colors when the user is stressed. This allows for providing an optimal design style according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using the generation AI, for example, or without the generation AI. For example, the generation unit inputs the user's emotion data into the generation AI, which then adjusts the design style.

[0084] The generation unit can adjust the level of detail of the generated design based on the importance of the design during generation. For example, the generation unit adjusts the level of detail of the generated design based on the importance of the design during generation. The importance of the design includes, but is not limited to, project priority, user requirements, etc. For example, the generation unit generates a detailed design for important design elements. The generation unit can also generate a simplified design for less important elements. The generation unit can also adjust the precision of the generated design based on the importance of the design. This allows for providing an optimal level of detail based on the importance of the design. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit inputs design importance data into the generation AI, which then adjusts the level of detail of the generated design.

[0085] The generation unit can apply different generation algorithms depending on the design category during generation. For example, the generation unit applies different generation algorithms depending on the design category during generation. Design categories include, but are not limited to, character design and background design. For example, the generation unit applies a specific generation algorithm to a fantasy-style design. The generation unit can also apply a different generation algorithm to a science fiction-style design. The generation unit can also apply an even different generation algorithm to a horror-style design. This makes it possible to provide an optimal generation algorithm depending on the design category. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit inputs design category data into the generation AI, which then applies an appropriate generation algorithm.

[0086] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. Past generation results include, but are not limited to, the history of generated designs and evaluation results. For example, the generation unit generates a highly accurate design based on designs previously generated by the user. The generation unit can also analyze the user's past generation results and propose an optimal generation method. The generation unit can also adjust the generation algorithm by referring to the user's past generation history. This allows optimal generation accuracy to be provided based on the user's past generation results. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI or without using a generation AI. For example, the generation unit inputs the user's past generation result data into the generation AI, which then improves the accuracy of generation.

[0087] The generation unit can estimate the user's emotion and adjust the length of the generated design based on the estimated user emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the generated design based on the estimated user emotion. The user's emotion is estimated using technologies such as facial expression recognition and voice analysis. For example, the generation unit generates a short design when the user is in a hurry. The generation unit can also generate a long design when the user is relaxed. The generation unit can also generate a visually stimulating design when the user is excited. This allows the optimal design length to be provided according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the user's emotion data into the generation AI, and the generation AI adjusts the length of the design.

[0088] The generation unit can determine the generation priority based on the design submission time during generation. The generation unit, for example, determines the generation priority based on the design submission time during generation. The submission time includes, but is not limited to, for example, a submission deadline, a project schedule, etc. For example, the generation unit prioritizes the generation of design requirements with an upcoming deadline. The generation unit can also postpone requirements with a more distant submission time. The generation unit can also adjust the order of generation according to the submission time. This makes it possible to provide an optimal generation priority according to the design submission time. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit inputs submission time data into the generation AI, and the generation AI determines the generation priority.

[0089] The generation unit can adjust the order of generation based on the relevance of the designs during generation. The generation unit, for example, adjusts the order of generation based on the relevance of the designs during generation. Design relevance includes, but is not limited to, for example, matching of themes and commonality of elements. For example, the generation unit prioritizes generation of highly relevant design requirements. The generation unit can also postpone requirements with low relevance. The generation unit can also adjust the order of generation based on the relevance of the designs. This makes it possible to provide an optimal generation order based on the relevance of the designs. 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 design relevance data into the generation AI, and the generation AI adjusts the order of generation.

[0090] The generation unit can adjust the use of technical terminology in the generated design according to the user's level of expertise during generation. For example, the generation unit adjusts the use of technical terminology in the generated design according to the user's level of expertise during generation. Expertise levels include, but are not limited to, beginner, intermediate, and expert levels. For example, if the user is a beginner, the generation unit can generate a design that avoids technical terminology. If the user is an intermediate user, the generation unit can also generate a design that uses appropriate technical terminology. If the user is an advanced user, the generation unit can also generate a design that uses a lot of technical terminology. This allows the use of optimal technical terminology according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit inputs the user's expertise level data into the generation AI, and the generation AI adjusts the use of technical terminology.

[0091] The confirmation unit can estimate the user's emotions and adjust the confirmation display method based on the estimated user emotions. The confirmation unit, for example, estimates the user's emotions and adjusts the confirmation display method based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the confirmation unit provides a simple, highly visible display method. If the user is relaxed, the confirmation unit can provide a display method including detailed information. If the user is in a hurry, the confirmation unit can provide a display method that focuses on the main points. This makes it possible to provide an optimal confirmation display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the confirmation unit may be performed using AI, or may be performed without AI. For example, the confirmation unit inputs the user's emotion data into AI, and the AI ​​adjusts the confirmation display method.

[0092] The verification unit can improve the accuracy of the verification by taking into account the interrelationships of the generated designs during verification. For example, the verification unit can improve the accuracy of the verification by taking into account the interrelationships of the generated designs during verification. Examples of interrelationships include, but are not limited to, the relevance and influence between design elements. For example, the verification unit can analyze the interrelationships of the generated designs and verify their consistency. The verification unit can also propose necessary modifications by taking into account the interrelationships of the designs. The verification unit can also provide an optimal verification method based on the interrelationships of the designs. This improves the accuracy of the verification by taking into account the interrelationships of the generated designs. Some or all of the above-described processing in the verification unit can be performed using, for example, AI, or can be performed without using AI. For example, the verification unit inputs design interrelation data into AI, which then improves the accuracy of the verification.

[0093] The verification unit may perform verification taking into consideration attribute information of the person who submitted the design. For example, the verification unit may perform verification taking into consideration attribute information of the person who submitted the design. Attribute information may include, but is not limited to, age, gender, and occupation. For example, the verification unit may adjust the accuracy of verification taking into consideration the level of expertise of the person who submitted the design. The verification unit may also determine verification priorities based on the submitter's past performance. The verification unit may also provide an optimal verification method based on the submitter's attribute information. This allows for the provision of an optimal verification method based on the attribute information of the design submitter. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit may input the submitter's attribute information into AI, which then performs verification.

[0094] The confirmation unit can weight the confirmation based on the frequency of design submissions during confirmation. For example, the confirmation unit weights the confirmation based on the frequency of design submissions during confirmation. The submission frequency includes, but is not limited to, for example, the number of submissions per month or the number of submissions per year. For example, the confirmation unit prioritizes checking design requirements that are submitted more frequently. The confirmation unit can also postpone requirements that are submitted less frequently. The confirmation unit can also adjust the confirmation weights according to the submission frequency. This makes it possible to provide optimal confirmation weights according to the frequency of design submissions. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit inputs submission frequency data into AI, and the AI ​​weights the confirmation.

[0095] The confirmation unit can estimate the user's emotions and adjust the order in which the confirmation results are displayed based on the estimated user emotions. The confirmation unit, for example, estimates the user's emotions and adjusts the order in which the confirmation results are displayed based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the confirmation unit prioritizes displaying important results. Furthermore, if the user is relaxed, the confirmation unit can sequentially display detailed results. Furthermore, if the user is in a hurry, the confirmation unit can first display results that highlight the main points. This allows for an optimal display order of the confirmation results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the confirmation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the confirmation unit inputs the user's emotion data into an AI, and the AI ​​adjusts the order in which the confirmation results are displayed.

[0096] The verification unit may perform verification taking into account the geographical distribution of the design. For example, the verification unit may perform verification taking into account the geographical distribution of the design. Geographical distribution includes, but is not limited to, distribution by region or by country. For example, if the design is related to a specific region, the verification unit may perform verification taking into account the characteristics of that region. The verification unit may also evaluate the applicability of the design based on the geographical distribution. The verification unit may also suggest modifications to the design taking geographical factors into account. This provides an optimal verification method based on the geographical distribution of the design. Some or all of the above-described processing in the verification unit may be performed using, or without, AI. For example, the verification unit inputs geographical distribution data into AI, which then performs verification.

[0097] The verification unit may improve the accuracy of the verification by referring to related literature of the design during verification. For example, the verification unit may improve the accuracy of the verification by referring to related literature of the design during verification. Related literature includes, but is not limited to, academic papers, technical reports, etc. For example, the verification unit may automatically search for literature related to the design and use it as reference for verification. The verification unit may also evaluate the consistency of the design based on the related literature. The verification unit may also utilize literature information to suggest design modifications. This allows for providing an optimal verification method based on the related literature of the design. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without AI. For example, the verification unit may input related literature data into AI, which then improves the accuracy of the verification.

[0098] The confirmation unit may perform confirmation taking into consideration the market value of the design. For example, the confirmation unit may perform confirmation taking into consideration the market value of the design. Market value includes, but is not limited to, sales forecasts and demand forecasts. For example, the confirmation unit may evaluate the market value of the design and determine a priority for confirmation. The confirmation unit may also suggest design modifications based on the market value. The confirmation unit may also evaluate the applicability of the design taking into consideration the market value. This makes it possible to provide an optimal confirmation method based on the market value of the design. Some or all of the above-described processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit inputs market value data into AI, which then performs the confirmation.

[0099] The providing unit can estimate the user's emotions and adjust the display method of the design to be provided based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions and adjusts the display method of the design to be provided based on the estimated user's emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the providing unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This makes it possible to provide an optimal design display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit inputs the user's emotion data into AI, which then adjusts the display method of the design.

[0100] The providing unit can improve the accuracy of the provided design by taking into account the interrelationships between the generated designs when providing the design. For example, the providing unit improves the accuracy of the provided design by taking into account the interrelationships between the generated designs when providing the design. Interrelationships include, but are not limited to, the relevance and influence between design elements. For example, the providing unit analyzes the interrelationships between the generated designs and checks their consistency. The providing unit can also propose necessary modifications by taking into account the interrelationships between the designs. The providing unit can also provide an optimal providing method based on the interrelationships between the designs. This improves the accuracy of the provided design by taking into account the interrelationships between the generated designs. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs design interrelation data into AI, which improves the accuracy of the provided design.

[0101] The providing unit may provide the design while taking into consideration the attribute information of the person who submitted the design. For example, the providing unit may provide the design while taking into consideration the attribute information of the person who submitted the design. The attribute information may include, but is not limited to, age, gender, occupation, etc. For example, the providing unit may adjust the accuracy of the provision while taking into consideration the level of expertise of the person who submitted the design. The providing unit may also determine the priority of provision based on the submitter's past performance. The providing unit may also provide the optimal provision method based on the attribute information of the submitter. This allows the optimal provision method to be provided based on the attribute information of the person who submitted the design. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit may input the submitter's attribute information into AI, and the AI ​​may provide the information.

[0102] The providing unit can weight the provision based on the frequency of design submissions at the time of provision. For example, the providing unit weights the provision based on the frequency of design submissions at the time of provision. The submission frequency includes, but is not limited to, for example, the number of submissions per month or the number of submissions per year. For example, the providing unit prioritizes the provision of design requirements that are submitted more frequently. The providing unit can also postpone requirements that are submitted less frequently. The providing unit can also adjust the provision weights according to the submission frequency. This makes it possible to provide optimal provision weights according to the frequency of design submissions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs submission frequency data into AI, and the AI ​​weights the provision.

[0103] The providing unit can estimate the user's emotions and determine the priority of designs to provide based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of designs to provide based on the estimated user emotions. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, if the user is nervous, the providing unit can prioritize providing important designs. If the user is relaxed, the providing unit can sequentially provide detailed designs. If the user is in a hurry, the providing unit can first provide designs that focus on the main points. This allows the optimal design priority to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, 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 providing unit may be performed using AI, or may be performed without AI. For example, the providing unit inputs the user's emotion data into AI, which then determines the design priority.

[0104] The providing unit may provide the design taking into consideration the geographical distribution of the design when providing the design. For example, the providing unit may provide the design taking into consideration the geographical distribution of the design when providing the design. Geographical distribution includes, but is not limited to, distribution by region or by country. For example, if the design is related to a specific region, the providing unit may provide the design taking into consideration the characteristics of that region. The providing unit may also evaluate the applicability of the design based on the geographical distribution. The providing unit may also suggest modifications to the design taking geographical factors into consideration. This makes it possible to provide an optimal provision method based on the geographical distribution of the design. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit inputs geographical distribution data into AI, and the AI ​​performs the provision.

[0105] The providing unit can improve the accuracy of the provided design by referring to related literature of the design when providing the design. For example, the providing unit can improve the accuracy of the provided design by referring to related literature of the design when providing the design. Related literature includes, but is not limited to, academic papers, technical reports, etc. For example, the providing unit can automatically search for literature related to the design and use it as reference for providing the design. The providing unit can also evaluate the consistency of the design based on the related literature. The providing unit can also utilize literature information to suggest design modifications. This allows for the provision of an optimal providing method based on the related literature of the design. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit inputs related literature data into AI, which then improves the accuracy of the provided design.

[0106] The provision unit may provide the design taking into consideration the market value of the design when providing it. For example, the provision unit provides the design taking into consideration the market value of the design when providing it. Market value includes, but is not limited to, sales forecasts, demand forecasts, etc. For example, the provision unit evaluates the market value of the design and determines the priority of provision. The provision unit may also suggest design modifications based on the market value. The provision unit may also evaluate the applicability of the design taking into consideration the market value. This makes it possible to provide an optimal provision method based on the market value of the design. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit inputs market value data into AI, and the AI ​​provides the data. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, confirmation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the camera 42 and microphone 38B of the smart device 14 are used to detect the user's facial expressions and voice, and the control unit 46A analyzes the emotions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a design using a generation AI. The confirmation unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and confirms and modifies the generated design. The provision unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and incorporates the confirmed design into the game. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, generation unit, confirmation unit, and provision unit, 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 control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the camera 42 and the microphone 238 of the smart glasses 214 are used to detect the user's facial expressions and voice, and the control unit 46A analyzes the emotions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a design using a generation AI. The confirmation unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and confirms and modifies the generated design. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and incorporates the confirmed design into the game. === Hard Collateral 1-3 === Each of the multiple elements, including the above-described reception unit, generation unit, confirmation unit, and provision unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the camera 42 and the microphone 238 of the headset-type terminal 314 are used to detect the user's facial expressions and voice, and the control unit 46A analyzes the emotions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a design using a generation AI. The confirmation unit is realized, for example, by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12, and confirms and modifies the generated design. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12, and incorporates the confirmed design into the game. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, confirmation unit, and provision unit, 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 control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the camera 42 and microphone 238 of the robot 414 are used to detect the user's facial expressions and voice, and the control unit 46A analyzes the emotions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a design using a generation AI. The confirmation unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and confirms and modifies the generated design. The provision unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and incorporates the confirmed design into the game.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The generation system can also analyze the user's past play style and reflect this in the design of the world or dungeon it generates. For example, if the user has previously preferred an exploration-based play style, the generation unit can generate a design that includes many exploration elements. If the user prefers combat, the generation unit can generate a design with many combat scenes. If the user prefers puzzles, the generation unit can generate a design that includes many puzzle elements. This makes it possible to provide the optimal design according to the user's play style.

[0109] The generation system can also receive real-time feedback from the user and immediately reflect it in the generated design. For example, if the user provides feedback such as "I want it to be more difficult" for a generated design, the generation unit will regenerate a design with a higher level of difficulty. Also, if the user provides feedback such as "I want more treasure chests," a design with more treasure chests can be generated. Furthermore, if the user provides feedback such as "I want this part to be wider," a design with that part wider can be generated. This makes it possible to provide optimal designs in response to the user's real-time feedback.

[0110] The generation system can further estimate the user's emotions and adjust the difficulty of the generated design based on the estimated user's emotions. For example, if the user is feeling stressed, a design with a lower difficulty level can be generated. Alternatively, if the user is relaxed, a design with a higher difficulty level can be generated. Furthermore, if the user is excited, a design with a medium level of difficulty can be generated. This makes it possible to provide a design with the optimum level of difficulty according to the user's emotions.

[0111] The generation system can also learn from the user's past design requirements and make optimal suggestions the next time they input design requirements. For example, if the user has frequently specified a "medium-sized fantasy dungeon" in the past, the system will suggest similar requirements the next time they input requirements. Also, if the user frequently specifies the requirement "many traps and few treasure chests," the system can automatically suggest those requirements. Furthermore, if the user prefers the requirement "many enemy characters and an abundance of items," the system can suggest those requirements. This allows the system to provide optimal suggestions based on the user's past requirements.

[0112] The generation system can further estimate the user's emotions and adjust the theme of the generated design based on the estimated user emotions. For example, if the user is relaxed, a calm-themed design can be generated. If the user is excited, a stimulating-themed design can be generated. Furthermore, if the user is stressed, a soothing-themed design can be generated. This makes it possible to provide the optimal theme design according to the user's emotions.

[0113] The generation system can further adjust the length of the design to be generated taking into account the user's play time. For example, if the user can only play for a short time, a short design can be generated. Alternatively, if the user can play for a long time, a long design can be generated. Furthermore, if the user has a medium amount of play time, a design of medium length can be generated. This makes it possible to provide the optimal design length according to the user's play time.

[0114] The generation system can further estimate the user's emotions and adjust the color tones of the generated design based on the estimated user emotions. For example, if the user is relaxed, a design with soft colors can be generated. If the user is excited, a design with vivid colors can be generated. Furthermore, if the user is stressed, a design with subdued colors can be generated. This makes it possible to provide a design with optimal colors according to the user's emotions.

[0115] The generation system can also analyze the user's past feedback to improve the accuracy of the generated design. For example, if a user previously provided feedback such as "I want more traps," the system can add more traps when generating the next design. Also, if a user provided feedback such as "The placement of the treasure chests was good," the system can use that placement as a reference. Furthermore, if a user provided feedback such as "There are too many enemy characters," the system can reduce the number of enemy characters when generating the next design. This allows the system to provide optimal designs based on the user's past feedback.

[0116] The generation system can further estimate the user's emotions and adjust the music design to be generated based on the estimated user emotions. For example, if the user is relaxed, calm music can be generated. If the user is excited, up-tempo music can be generated. Furthermore, if the user is stressed, soothing music can be generated. This allows the system to provide optimal music according to the user's emotions.

[0117] The generation system can further adjust the level of detail of the generated design taking into account the capabilities of the user's device. For example, if the user is using a high-performance device, a high-detail design can be generated. If the user is using a low-performance device, a low-detail design can be generated. Furthermore, if the user is using a medium-performance device, a medium-detail design can be generated. This allows the system to provide the optimal level of detail of the design according to the capabilities of the user's device.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception department accepts design requirements from creators. Design requirements include the world and dungeon theme, difficulty, size, and specific elements (such as the placement of traps and treasure chests). For example, a creator may input requirements such as "a medium-sized fantasy dungeon with many traps and few treasure chests." Step 2: The generator uses the AI ​​to generate world and dungeon designs based on the requirements received by the reception unit. The AI ​​generates designs that meet the specified requirements based on pre-trained data. For example, the AI ​​generates a fantasy-style medium-sized dungeon design with many traps and few treasure chests. Step 3: The verification unit checks the design generated by the generation unit and makes any necessary modifications. For example, the creator can make changes to parts of the generated dungeon or insert additional elements. Step 4: The providing unit incorporates the design confirmed by the confirming unit into the game. For example, the generated design can be incorporated as a stage in the game.

[0120] 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.

[0121] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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, in order to avoid confusion and to 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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 department that accepts design requirements from creators, a generation unit that generates a design of an environment or stage in the game based on the requirements received by the reception unit; a verification unit that verifies and modifies the design generated by the generation unit; a providing unit that incorporates the design confirmed by the confirmation unit into the game. A system characterized by:

2. The generation unit Use generative AI to generate designs that meet specified requirements 2. The system of claim 1.

3. The reception unit Accepts requirements specifying the theme, difficulty, size, and specific features of worlds and dungeons 2. The system of claim 1.

4. The confirmation unit Modifying specific parts of the generated design or inserting additional elements 2. The system of claim 1.

5. The providing unit Incorporating generated designs into the game 2. The system of claim 1.

6. The generation unit Generate designs based on pre-trained data 2. The system of claim 1.

7. The reception unit Analyze user emotions and adjust the input interface for design requirements based on the analyzed user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the history of past design requirements and provides users with guidance on optimal requirement input 2. The system of claim 1.

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