system

Abstract

The system according to this embodiment aims to streamline production work by automatically generating game world and dungeon designs. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives design requirements as input. The generation unit analyzes the requirements input by the reception unit and generates world and dungeon designs. The provision unit provides the designs generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, since the design of the game world and dungeon is performed manually, the production work requires time and labor, and there is room for efficiency improvement.

[0005] The system according to the embodiment aims to automatically generate the design of the game world and dungeon and improve the production work efficiency.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit inputs the design requirements. The generation unit analyzes the requirements input by the reception unit and generates the design of the world and dungeon. The provision unit provides the design generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate game world and dungeon designs, thereby streamlining production work. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The game design automatic generation system according to an embodiment of the present invention is a system that uses AI to automatically generate game worlds and dungeons, thereby streamlining the production work of creators. In this system, creators input design requirements corresponding to different elements and styles, and the AI ​​analyzes these requirements to automatically generate world and dungeon designs. The generated designs are provided so that creators can build diverse content in a short amount of time. This mechanism speeds up the production process, and players can enjoy new adventures. For example, when a creator inputs design requirements, they can specify elements such as theme, difficulty level, and play style. For example, they might input requirements such as "a fantasy-style dungeon with a medium difficulty level and an exploration-focused play style." This information is input to the AI. Next, the AI ​​analyzes the input requirements and automatically generates world and dungeon designs. The AI ​​has learned from past design data and patterns, and generates the optimal design based on the specified requirements. For example, for a fantasy-style dungeon, a design including elements such as stone walls, magical traps, and treasure chests will be generated. The generated designs are provided to the creator. Based on the generated designs, the creator can build diverse content in a short amount of time. For example, by incorporating generated dungeon designs into a game, new adventures can be offered to players. This mechanism speeds up the production process. Creators can generate diverse designs in a short time without running out of ideas. Players can also enjoy new adventures. For example, exploring dungeons with different designs each time can keep players engaged and prevent boredom. Furthermore, the AI ​​can provide a wide variety of designs that correspond to different themes, difficulty levels, and play styles. This allows creators to generate diverse content without being bound by specific themes or styles. For example, various designs can be generated, such as horror-themed dungeons or dungeons with puzzle elements.In this way, by using AI to automatically generate game worlds and dungeons, it is possible to streamline the production work of creators and improve the player experience. As a result, the automated game design generation system can streamline the production work of creators and provide players with new adventures.

[0029] The game design automatic generation system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit is where creators input design requirements. Creators can specify elements such as theme, difficulty level, and play style. For example, the reception unit can input requirements such as a fantasy-style dungeon, medium difficulty level, and exploration-focused play style. The generation unit analyzes the requirements input by the reception unit and generates world and dungeon designs. The generation unit uses AI to learn from past design data and patterns and generates the optimal design based on the specified requirements. For example, if it is a fantasy-style dungeon, the generation unit will generate a design that includes elements such as stone walls, magical traps, and treasure chests. The provision unit provides the designs generated by the generation unit to the creator. The provision unit provides the generated designs so that creators can build diverse content in a short amount of time. For example, the provision unit can provide players with new adventures by incorporating the generated dungeon designs into the game. As a result, the game design automatic generation system according to this embodiment can streamline the creator's production work and provide players with new adventures.

[0030] The input section is where creators input design requirements. Creators can specify elements such as theme, difficulty level, and play style. Specifically, the input section has a user-friendly interface and is designed for intuitive operation by creators. For example, dropdown menus, sliders, and checkboxes are used to allow creators to easily select and input requirements. The input section also has a function to save and reuse requirements that creators have previously entered. This saves creators the trouble of re-entering requirements they have set once. Furthermore, the input section has a function to provide real-time feedback on the requirements entered by creators. For example, it displays a preview of the design that will be generated based on the specified requirements, allowing creators to make corrections and adjustments on the spot. This allows creators to efficiently create designs that are closest to their vision.

[0031] The generation unit analyzes the requirements entered by the reception unit and generates world and dungeon designs. Using AI, the generation unit learns from past design data and patterns to generate the optimal design based on the specified requirements. Specifically, the generation unit maintains a large database containing past game designs and patterns. Based on this database, the AI ​​generates the design best suited to the creator's specified requirements. For example, for a fantasy-style dungeon, it would generate a design including elements such as stone walls, magical traps, and treasure chests. The AI ​​uses natural language processing technology to analyze the creator's requirements and select appropriate design elements. Furthermore, the generation unit performs multiple simulations to determine the optimal placement and structure, ensuring the generated design does not compromise game balance or playability. This allows the generation unit to provide designs that are both faithful to the creator's requirements and engaging and challenging for players. Additionally, the generation unit provides editing tools to allow creators to customize the generated designs. This enables creators to further incorporate their own ideas into the generated designs.

[0032] The Delivery Unit is responsible for providing creators with the designs generated by the Generation Unit. The Delivery Unit enables creators to quickly build diverse content using these generated designs. Specifically, the Delivery Unit includes a preview function that allows creators to visually check the generated designs, making it easier for them to review the details. The Delivery Unit also has the ability to export generated designs in various formats. For example, it can export in a format that can be directly imported into game engines, or as 2D and 3D graphic data. This allows creators to immediately integrate the generated designs into their games. Furthermore, the Delivery Unit collects feedback on the generated designs and incorporates that feedback into the Generation Unit. This continuously improves the accuracy and quality of the entire system. The Delivery Unit also provides a function that allows creators to share generated designs with other team members. For example, it can use cloud storage to make the generated designs accessible to all project members. This enables efficient design review and modification by the entire team.

[0033] The generation unit includes a learning unit that learns from past design data. The generation unit may also include a learning unit that learns from past project data. For example, the generation unit learns design patterns and trends using past project data. The generation unit may also include a learning unit that learns from user feedback. For example, the generation unit analyzes user feedback and learns areas for design improvement. Furthermore, the generation unit can also evaluate the quality of designs using past design data. For example, the generation unit evaluates the quality of designs based on past design data and generates the optimal design. In this way, the quality of the generated designs improves by learning from past design data. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past design data into AI and have the AI ​​learn design patterns and trends.

[0034] The generation unit includes an evaluation unit that evaluates the quality of the generated design. The generation unit includes an evaluation unit that collects user feedback to evaluate the quality of the generated design. For example, the generation unit evaluates the quality of the design based on user feedback. The generation unit may also include an evaluation unit that evaluates the quality of the design based on test results. For example, the generation unit analyzes test results and evaluates the quality of the design. Furthermore, the generation unit may also include an evaluation unit that collects expert opinions to evaluate the quality of the generated design. For example, the generation unit evaluates the quality of the design based on expert opinions. This ensures that the quality of the design provided to creators is guaranteed by evaluating the quality of the generated design. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user feedback into AI and have AI perform the design quality evaluation.

[0035] The service provider includes an interface for providing the generated designs to creators. The service provider includes, for example, a user interface for providing the generated designs to creators. For example, the service provider provides the generated designs to creators through a web-based interface. The service provider may also include an interface for providing the generated designs via an API. For example, the service provider integrates the generated designs with other systems via an API. Furthermore, the service provider may include a mobile application for providing the generated designs to creators. For example, the service provider provides the generated designs to creators through a mobile application. By providing an interface for providing the generated designs to creators, creators can easily utilize the designs. Some or all of the above-described processes in the interface may be performed using, for example, AI, or not using AI. For example, the interface can input the generated designs into an AI and have the AI ​​execute the optimal method for providing them to creators.

[0036] The input section includes input fields for specifying elements such as theme, difficulty, and play style. For example, the input section may include an input field for specifying a theme, such as fantasy, science fiction, or horror. The input section may also include an input field for specifying a difficulty level, such as beginner, intermediate, or advanced. Furthermore, the input section may also include an input field for specifying a play style, such as solo play, multiplayer, exploration-focused, or combat-focused. By including input fields for specifying elements such as theme, difficulty, and play style, creators can input detailed requirements. Some or all of the above processing in the input section may be performed using AI, for example, or not. For example, the input section can input the requirements specified by the creator into the AI ​​and have the AI ​​generate the optimal design.

[0037] The reception desk can analyze the user's past input history of design requirements and suggest the optimal input method. For example, the reception desk can automatically display design requirements that the user has frequently entered in the past as suggestions. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest design requirements that will be used during specific time periods based on the user's past input history. In this way, by analyzing the user's past input history of design requirements, the reception desk can suggest the optimal input method. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past input history data into AI and have the AI ​​suggest the optimal input method.

[0038] The reception desk can filter design requirements based on the user's current projects and areas of interest when they are entered. For example, the reception desk can prioritize displaying requirements related to the user's current ongoing projects. For example, the reception desk can suggest highly relevant design requirements based on the user's areas of interest. The reception desk can also analyze the user's past project history and suggest optimal design requirements. This allows for the suggestion of highly relevant design requirements by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's project data into AI and have the AI ​​perform the filtering of optimal design requirements.

[0039] The reception desk can prioritize the input of highly relevant design requirements by considering the user's geographical location when design requirements are entered. For example, if the user is in a specific region, the reception desk can prioritize displaying design requirements related to that region. For example, the reception desk can suggest highly relevant design requirements based on the user's geographical location. Furthermore, if the user is on the move, the reception desk can suggest the most suitable design requirements based on their current location. This allows for the priority input of highly relevant design requirements by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into AI and have the AI ​​suggest the most suitable design requirements.

[0040] The reception desk can analyze the user's social media activity and input relevant requirements when design requirements are entered. For example, the reception desk can analyze the user's social media activity and suggest relevant design requirements. For example, the reception desk can input optimal design requirements based on information shared by the user on social media. The reception desk can also analyze the user's interests on social media and suggest relevant design requirements. In this way, relevant design requirements can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into AI and have the AI ​​suggest optimal design requirements.

[0041] The generation unit can adjust the level of detail of the generated design based on the importance of the requirements. For example, the generation unit can generate a detailed design based on important requirements. For example, the generation unit can generate a simplified design based on lower-priority requirements. The generation unit can also dynamically adjust the level of detail of the design according to the importance of the requirements. This allows for the generation of detailed designs for important requirements by adjusting the level of detail based on the importance of the requirements. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input requirement importance data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the generated design.

[0042] The generation unit can apply different generation algorithms depending on the category of the requirements when generating designs. For example, the generation unit can apply a fantasy-specific generation algorithm to fantasy-themed requirements. For example, the generation unit can apply a horror-specific generation algorithm to horror-themed requirements. Furthermore, the generation unit can apply a puzzle-specific generation algorithm to requirements that include puzzle elements. By applying different generation algorithms depending on the category of the requirements, the optimal design for each category can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category data of the requirements into the AI ​​and have the AI ​​execute the application of the optimal generation algorithm.

[0043] The generation unit can determine the generation priority based on the requirements submission timing when generating designs. For example, the generation unit can generate designs with the highest priority based on urgent requirements. For example, the generation unit can generate designs with priority based on requirements with an approaching submission deadline. Furthermore, the generation unit can dynamically adjust the design generation priority according to the submission timing. This allows for a quick response to urgent requirements by determining the generation priority based on the requirements submission timing. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input requirement submission timing data into AI and have the AI ​​determine the generation priority.

[0044] The generation unit can adjust the generation order based on the relevance of requirements during design generation. For example, the generation unit can prioritize generating designs based on highly relevant requirements. For example, the generation unit can postpone generating designs based on less relevant requirements. Furthermore, the generation unit can dynamically adjust the design generation order according to the relevance of requirements. This allows for priority addressing of highly relevant requirements by adjusting the generation order based on the relevance of requirements. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input requirement relevance data into AI and have AI perform the adjustment of the generation order.

[0045] The service provider can analyze the user's past usage history to select the optimal service provider method when providing designs. For example, the service provider can select the optimal service provider method based on the service provider method the user has used in the past. For example, the service provider can propose the most efficient service provider method based on the user's past usage history. Furthermore, the service provider can analyze the user's past usage history and dynamically adjust the service provider method. This allows the service provider to select the optimal service provider method by analyzing the user's past usage history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user usage history data into AI and have the AI ​​select the optimal service provider method.

[0046] The delivery unit can customize the delivery method based on the user's current project status when providing designs. For example, the delivery unit can prioritize providing designs related to the user's current ongoing projects. For example, the delivery unit can select the optimal delivery method based on the user's project status. Furthermore, the delivery unit can analyze the user's project status and dynamically customize the delivery method. This allows the design to be provided in the most optimal way by customizing the delivery method based on the user's current project status. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the user's project status data into AI and have the AI ​​perform the customization of the delivery method.

[0047] The service provider can select the optimal delivery method when providing designs, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can prioritize providing designs related to that region. For example, the service provider can select the optimal delivery method based on the user's geographical location information. Furthermore, if the user is on the move, the service provider can select the optimal delivery method based on their current location. In this way, the service provider can select the optimal delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into AI and have the AI ​​select the optimal delivery method.

[0048] The service provider can analyze the user's social media activity and propose a delivery method when providing a design. For example, the service provider can analyze the user's social media activity and propose the most suitable delivery method. For example, the service provider can propose the most suitable delivery method based on information shared by the user on social media. Furthermore, the service provider can analyze the user's interests on social media and propose the most suitable delivery method. In this way, the service provider can propose the most suitable delivery method by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into AI and have the AI ​​propose the most suitable delivery method.

[0049] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and adjust the parameters of the learning algorithm. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. Thus, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into AI and have the AI ​​perform the optimization of the learning algorithm.

[0050] The learning unit can weight the training data based on the submission dates of design requirements during training. For example, the learning unit can weight the training data based on design requirements with upcoming submission dates. For example, the learning unit can weight the training data based on urgent design requirements. Furthermore, the learning unit can dynamically adjust the weighting of the training data according to the submission dates. This improves the accuracy of training by weighting the training data based on the submission dates of design requirements. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input design requirement submission date data into the AI ​​and have the AI ​​perform the weighting of the training data.

[0051] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data during design evaluation. For example, the evaluation unit can select the optimal evaluation algorithm based on past evaluation data. For example, the evaluation unit can analyze past evaluation data and adjust the parameters of the evaluation algorithm. Furthermore, the evaluation unit can improve the accuracy of the evaluation algorithm by referring to past evaluation data. As a result, the accuracy of the evaluation algorithm is improved by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input past evaluation data into AI and have the AI ​​perform the optimization of the evaluation algorithm.

[0052] The evaluation unit can weight the evaluation data based on the submission timing of the design requirements during the design evaluation. For example, the evaluation unit can weight the evaluation data based on design requirements with upcoming submission deadlines. For example, the evaluation unit can weight the evaluation data based on urgent design requirements. Furthermore, the evaluation unit can dynamically adjust the weighting of the evaluation data according to the submission timing. This improves the accuracy of the evaluation by weighting the evaluation data based on the submission timing of the design requirements. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the design requirement submission timing data into the AI ​​and have the AI ​​perform the weighting of the evaluation data.

[0053] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit can select the optimal display method based on the display method the user has used in the past. For example, the interface unit can suggest the most efficient display method from the user's past operation history. Furthermore, the interface unit can analyze the user's past operation history and dynamically adjust the display method. This allows the user to select the optimal display method by referring to the user's past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input user operation history data into AI and have the AI ​​select the optimal display method.

[0054] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the interface unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the interface unit can provide a concise and highly visible display method. In this way, the optimal display method can be selected by taking into account the user's device information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without using AI. For example, the interface unit can input the user's device information into the AI ​​and have the AI ​​select the optimal display method.

[0055] The input unit can suggest the optimal input method by referring to the user's past input history during input. For example, the input unit can suggest the optimal input method based on input methods the user has used in the past. For example, the input unit can suggest the most efficient input method from the user's past input history. Furthermore, the input unit can analyze the user's past input history and dynamically adjust the input method. This allows it to suggest the optimal input method by referring to the user's past input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's input history data into AI and have the AI ​​suggest the optimal input method.

[0056] The input unit can select the optimal input method when inputting data, taking into account the user's geographical location. For example, if the user is in a specific region, the input unit can prioritize providing input methods related to that region. For example, the input unit can select the optimal input method based on the user's geographical location. Furthermore, if the user is on the move, the input unit can select the optimal input method based on their current location. In this way, the optimal input method can be selected by considering the user's geographical location. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's geographical location information into AI and have AI select the optimal input method.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The reception desk can analyze the user's past input history of design requirements and suggest the optimal input method. For example, the reception desk can automatically display design requirements that the user has frequently entered in the past as suggestions. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest design requirements to be used during specific time periods based on the user's past input history. In this way, by analyzing the user's past input history of design requirements, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input past input history data into AI and have the AI ​​suggest the optimal input method.

[0059] The generation unit may include an evaluation unit that evaluates the quality of the generated design. For example, the generation unit can evaluate the quality of the design based on user feedback. The generation unit may also include an evaluation unit that evaluates the quality of the design based on test results. For example, the generation unit analyzes test results and evaluates the quality of the design. Furthermore, the generation unit may include an evaluation unit that collects expert opinions in order to evaluate the quality of the generated design. For example, the generation unit evaluates the quality of the design based on expert opinions. This ensures that the quality of the design provided to creators is guaranteed by evaluating the quality of the generated design. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user feedback into AI and have AI perform the evaluation of the design quality.

[0060] The service provider may include an interface for providing the generated designs to creators. For example, the service provider may provide the generated designs to creators through a web-based interface. The service provider may also include an interface for providing the generated designs via an API. For example, the service provider may integrate the generated designs with other systems via an API. Furthermore, the service provider may include a mobile application for providing the generated designs to creators. For example, the service provider may provide the generated designs to creators through a mobile application. By providing an interface for providing the generated designs to creators, creators can easily utilize the designs. Some or all of the above-described processing in the interface may be performed using AI, for example, or without AI. For example, the interface may input the generated designs into an AI and have the AI ​​execute the optimal method for providing them to creators.

[0061] The generation unit can adjust the level of detail of the generated design based on the importance of the requirements. For example, it can generate a detailed design based on important requirements. For example, it can generate a simplified design based on lower-priority requirements. The generation unit can also dynamically adjust the level of detail of the design according to the importance of the requirements. This allows for the generation of detailed designs for important requirements by adjusting the level of detail based on the importance of the requirements. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input requirement importance data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the generated design.

[0062] The generation unit can apply different generation algorithms depending on the category of the requirements when generating designs. For example, a fantasy-themed requirement can be given a fantasy-specific generation algorithm. For example, a horror-themed requirement can be given a horror-specific generation algorithm. Furthermore, a requirement that includes puzzle elements can be given a puzzle-specific generation algorithm. By applying different generation algorithms depending on the category of the requirements, the optimal design for each category can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category data of the requirements into the AI ​​and have the AI ​​execute the application of the optimal generation algorithm.

[0063] The service provider can analyze the user's past usage history to select the optimal service delivery method when providing designs. For example, the service provider can select the optimal delivery method based on the delivery methods the user has used in the past. For example, the service provider can propose the most efficient delivery method based on the user's past usage history. Furthermore, the service provider can analyze the user's past usage history and dynamically adjust the delivery method. This allows the service provider to select the optimal delivery method by analyzing the user's past usage history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user usage history data into AI and have the AI ​​select the optimal delivery method.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception area is where creators input design requirements. Creators can specify elements such as theme, difficulty level, and play style. For example, the reception area can be used to input requirements such as a fantasy-themed dungeon, medium difficulty level, and exploration-focused play style. Step 2: The generation unit analyzes the requirements entered by the reception unit and generates world and dungeon designs. The generation unit uses AI to learn from past design data and patterns and generates the optimal design based on the specified requirements. For example, if it's a fantasy-style dungeon, the generation unit will generate a design that includes elements such as stone walls, magical traps, and treasure chests. Step 3: The provisioning section is the part that provides the designs generated by the generation section to the creator. The provisioning section provides the generated designs so that creators can build diverse content in a short amount of time. For example, the provisioning section can provide players with new adventures by incorporating the generated dungeon designs into the game.

[0066] (Example of form 2) The game design automatic generation system according to an embodiment of the present invention is a system that uses AI to automatically generate game worlds and dungeons, thereby streamlining the production work of creators. In this system, creators input design requirements corresponding to different elements and styles, and the AI ​​analyzes these requirements to automatically generate world and dungeon designs. The generated designs are provided so that creators can build diverse content in a short amount of time. This mechanism speeds up the production process, and players can enjoy new adventures. For example, when a creator inputs design requirements, they can specify elements such as theme, difficulty level, and play style. For example, they might input requirements such as "a fantasy-style dungeon with a medium difficulty level and an exploration-focused play style." This information is input to the AI. Next, the AI ​​analyzes the input requirements and automatically generates world and dungeon designs. The AI ​​has learned from past design data and patterns, and generates the optimal design based on the specified requirements. For example, for a fantasy-style dungeon, a design including elements such as stone walls, magical traps, and treasure chests will be generated. The generated designs are provided to the creator. Based on the generated designs, the creator can build diverse content in a short amount of time. For example, by incorporating generated dungeon designs into a game, new adventures can be offered to players. This mechanism speeds up the production process. Creators can generate diverse designs in a short time without running out of ideas. Players can also enjoy new adventures. For example, exploring dungeons with different designs each time can keep players engaged and prevent boredom. Furthermore, the AI ​​can provide a wide variety of designs that correspond to different themes, difficulty levels, and play styles. This allows creators to generate diverse content without being bound by specific themes or styles. For example, various designs can be generated, such as horror-themed dungeons or dungeons with puzzle elements.In this way, by using AI to automatically generate game worlds and dungeons, it is possible to streamline the production work of creators and improve the player experience. As a result, the automated game design generation system can streamline the production work of creators and provide players with new adventures.

[0067] The game design automatic generation system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit is where creators input design requirements. Creators can specify elements such as theme, difficulty level, and play style. For example, the reception unit can input requirements such as a fantasy-style dungeon, medium difficulty level, and exploration-focused play style. The generation unit analyzes the requirements input by the reception unit and generates world and dungeon designs. The generation unit uses AI to learn from past design data and patterns and generates the optimal design based on the specified requirements. For example, if it is a fantasy-style dungeon, the generation unit will generate a design that includes elements such as stone walls, magical traps, and treasure chests. The provision unit provides the designs generated by the generation unit to the creator. The provision unit provides the generated designs so that creators can build diverse content in a short amount of time. For example, the provision unit can provide players with new adventures by incorporating the generated dungeon designs into the game. As a result, the game design automatic generation system according to this embodiment can streamline the creator's production work and provide players with new adventures.

[0068] The input section is where creators input design requirements. Creators can specify elements such as theme, difficulty level, and play style. Specifically, the input section has a user-friendly interface and is designed for intuitive operation by creators. For example, dropdown menus, sliders, and checkboxes are used to allow creators to easily select and input requirements. The input section also has a function to save and reuse requirements that creators have previously entered. This saves creators the trouble of re-entering requirements they have set once. Furthermore, the input section has a function to provide real-time feedback on the requirements entered by creators. For example, it displays a preview of the design that will be generated based on the specified requirements, allowing creators to make corrections and adjustments on the spot. This allows creators to efficiently create designs that are closest to their vision.

[0069] The generation unit analyzes the requirements entered by the reception unit and generates world and dungeon designs. Using AI, the generation unit learns from past design data and patterns to generate the optimal design based on the specified requirements. Specifically, the generation unit maintains a large database containing past game designs and patterns. Based on this database, the AI ​​generates the design best suited to the creator's specified requirements. For example, for a fantasy-style dungeon, it would generate a design including elements such as stone walls, magical traps, and treasure chests. The AI ​​uses natural language processing technology to analyze the creator's requirements and select appropriate design elements. Furthermore, the generation unit performs multiple simulations to determine the optimal placement and structure, ensuring the generated design does not compromise game balance or playability. This allows the generation unit to provide designs that are both faithful to the creator's requirements and engaging and challenging for players. Additionally, the generation unit provides editing tools to allow creators to customize the generated designs. This enables creators to further incorporate their own ideas into the generated designs.

[0070] The Delivery Unit is responsible for providing creators with the designs generated by the Generation Unit. The Delivery Unit enables creators to quickly build diverse content using these generated designs. Specifically, the Delivery Unit includes a preview function that allows creators to visually check the generated designs, making it easier for them to review the details. The Delivery Unit also has the ability to export generated designs in various formats. For example, it can export in a format that can be directly imported into game engines, or as 2D and 3D graphic data. This allows creators to immediately integrate the generated designs into their games. Furthermore, the Delivery Unit collects feedback on the generated designs and incorporates that feedback into the Generation Unit. This continuously improves the accuracy and quality of the entire system. The Delivery Unit also provides a function that allows creators to share generated designs with other team members. For example, it can use cloud storage to make the generated designs accessible to all project members. This enables efficient design review and modification by the entire team.

[0071] The generation unit includes a learning unit that learns from past design data. The generation unit may also include a learning unit that learns from past project data. For example, the generation unit learns design patterns and trends using past project data. The generation unit may also include a learning unit that learns from user feedback. For example, the generation unit analyzes user feedback and learns areas for design improvement. Furthermore, the generation unit can also evaluate the quality of designs using past design data. For example, the generation unit evaluates the quality of designs based on past design data and generates the optimal design. In this way, the quality of the generated designs improves by learning from past design data. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past design data into AI and have the AI ​​learn design patterns and trends.

[0072] The generation unit includes an evaluation unit that evaluates the quality of the generated design. The generation unit includes an evaluation unit that collects user feedback to evaluate the quality of the generated design. For example, the generation unit evaluates the quality of the design based on user feedback. The generation unit may also include an evaluation unit that evaluates the quality of the design based on test results. For example, the generation unit analyzes test results and evaluates the quality of the design. Furthermore, the generation unit may also include an evaluation unit that collects expert opinions to evaluate the quality of the generated design. For example, the generation unit evaluates the quality of the design based on expert opinions. This ensures that the quality of the design provided to creators is guaranteed by evaluating the quality of the generated design. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user feedback into AI and have AI perform the design quality evaluation.

[0073] The service provider includes an interface for providing the generated designs to creators. The service provider includes, for example, a user interface for providing the generated designs to creators. For example, the service provider provides the generated designs to creators through a web-based interface. The service provider may also include an interface for providing the generated designs via an API. For example, the service provider integrates the generated designs with other systems via an API. Furthermore, the service provider may include a mobile application for providing the generated designs to creators. For example, the service provider provides the generated designs to creators through a mobile application. By providing an interface for providing the generated designs to creators, creators can easily utilize the designs. Some or all of the above-described processes in the interface may be performed using, for example, AI, or not using AI. For example, the interface can input the generated designs into an AI and have the AI ​​execute the optimal method for providing them to creators.

[0074] The input section includes input fields for specifying elements such as theme, difficulty, and play style. For example, the input section may include an input field for specifying a theme, such as fantasy, science fiction, or horror. The input section may also include an input field for specifying a difficulty level, such as beginner, intermediate, or advanced. Furthermore, the input section may also include an input field for specifying a play style, such as solo play, multiplayer, exploration-focused, or combat-focused. By including input fields for specifying elements such as theme, difficulty, and play style, creators can input detailed requirements. Some or all of the above processing in the input section may be performed using AI, for example, or not. For example, the input section can input the requirements specified by the creator into the AI ​​and have the AI ​​generate the optimal design.

[0075] The reception desk can estimate the user's emotions and adjust the timing of design requirement input based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. Also, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of design requirements. This reduces user stress and enables efficient input by adjusting the timing of design requirement input based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into AI and have the AI ​​adjust the timing of design requirement input.

[0076] The reception desk can analyze the user's past input history of design requirements and suggest the optimal input method. For example, the reception desk can automatically display design requirements that the user has frequently entered in the past as suggestions. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest design requirements that will be used during specific time periods based on the user's past input history. In this way, by analyzing the user's past input history of design requirements, the reception desk can suggest the optimal input method. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past input history data into AI and have the AI ​​suggest the optimal input method.

[0077] The reception desk can filter design requirements based on the user's current projects and areas of interest when they are entered. For example, the reception desk can prioritize displaying requirements related to the user's current ongoing projects. For example, the reception desk can suggest highly relevant design requirements based on the user's areas of interest. The reception desk can also analyze the user's past project history and suggest optimal design requirements. This allows for the suggestion of highly relevant design requirements by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's project data into AI and have the AI ​​perform the filtering of optimal design requirements.

[0078] The reception desk can estimate the user's emotions and determine the priority of the requirements to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize the input of important requirements. For example, if the user is relaxed, the reception desk can provide time to enter detailed requirements. Also, if the user is in a hurry, the reception desk can allow the user to quickly enter the most important requirements. This allows for the priority of important requirements to be entered by determining the priority of the requirements to be entered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into AI and have the AI ​​perform the determination of requirement priorities.

[0079] The reception desk can prioritize the input of highly relevant design requirements by considering the user's geographical location when design requirements are entered. For example, if the user is in a specific region, the reception desk can prioritize displaying design requirements related to that region. For example, the reception desk can suggest highly relevant design requirements based on the user's geographical location. Furthermore, if the user is on the move, the reception desk can suggest the most suitable design requirements based on their current location. This allows for the priority input of highly relevant design requirements by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into AI and have the AI ​​suggest the most suitable design requirements.

[0080] The reception desk can analyze the user's social media activity and input relevant requirements when design requirements are entered. For example, the reception desk can analyze the user's social media activity and suggest relevant design requirements. For example, the reception desk can input optimal design requirements based on information shared by the user on social media. The reception desk can also analyze the user's interests on social media and suggest relevant design requirements. In this way, relevant design requirements can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into AI and have the AI ​​suggest optimal design requirements.

[0081] The generation unit can estimate the user's emotions and adjust the design generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a design that progresses at a leisurely pace. For example, if the user is in a hurry, the generation unit can generate a design that emphasizes the shortest route. Also, if the user is excited, the generation unit can generate a design with visually stimulating effects. In this way, by adjusting the design generation method based on the user's emotions, a design suitable for the user can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is 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 processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the design generation method.

[0082] The generation unit can adjust the level of detail of the generated design based on the importance of the requirements. For example, the generation unit can generate a detailed design based on important requirements. For example, the generation unit can generate a simplified design based on lower-priority requirements. The generation unit can also dynamically adjust the level of detail of the design according to the importance of the requirements. This allows for the generation of detailed designs for important requirements by adjusting the level of detail based on the importance of the requirements. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input requirement importance data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the generated design.

[0083] The generation unit can apply different generation algorithms depending on the category of the requirements when generating designs. For example, the generation unit can apply a fantasy-specific generation algorithm to fantasy-themed requirements. For example, the generation unit can apply a horror-specific generation algorithm to horror-themed requirements. Furthermore, the generation unit can apply a puzzle-specific generation algorithm to requirements that include puzzle elements. By applying different generation algorithms depending on the category of the requirements, the optimal design for each category can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category data of the requirements into the AI ​​and have the AI ​​execute the application of the optimal generation algorithm.

[0084] The generation unit can estimate the user's emotions and adjust the length of the generated design based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise design. For example, if the user is relaxed, the generation unit can generate a longer design that includes detailed explanations. Furthermore, if the user is excited, the generation unit can generate a design with visually stimulating effects. In this way, by adjusting the length of the generated design based on the user's emotions, a design suitable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the length of the design.

[0085] The generation unit can determine the generation priority based on the requirements submission timing when generating designs. For example, the generation unit can generate designs with the highest priority based on urgent requirements. For example, the generation unit can generate designs with priority based on requirements with an approaching submission deadline. Furthermore, the generation unit can dynamically adjust the design generation priority according to the submission timing. This allows for a quick response to urgent requirements by determining the generation priority based on the requirements submission timing. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input requirement submission timing data into AI and have the AI ​​determine the generation priority.

[0086] The generation unit can adjust the generation order based on the relevance of requirements during design generation. For example, the generation unit can prioritize generating designs based on highly relevant requirements. For example, the generation unit can postpone generating designs based on less relevant requirements. Furthermore, the generation unit can dynamically adjust the design generation order according to the relevance of requirements. This allows for priority addressing of highly relevant requirements by adjusting the generation order based on the relevance of requirements. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input requirement relevance data into AI and have AI perform the adjustment of the generation order.

[0087] The delivery unit can estimate the user's emotions and adjust the design delivery method based on the estimated user emotions. For example, if the user is relaxed, the delivery unit can select a delivery method that includes detailed explanations. For example, if the user is in a hurry, the delivery unit can select a concise and quick delivery method. Also, if the user is excited, the delivery unit can select a visually stimulating delivery method. In this way, by adjusting the design delivery method based on the user's emotions, the design can be delivered in a way that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into AI and have the AI ​​perform the adjustment of the design delivery method.

[0088] The service provider can analyze the user's past usage history to select the optimal service provider method when providing designs. For example, the service provider can select the optimal service provider method based on the service provider method the user has used in the past. For example, the service provider can propose the most efficient service provider method based on the user's past usage history. Furthermore, the service provider can analyze the user's past usage history and dynamically adjust the service provider method. This allows the service provider to select the optimal service provider method by analyzing the user's past usage history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user usage history data into AI and have the AI ​​select the optimal service provider method.

[0089] The delivery unit can customize the delivery method based on the user's current project status when providing designs. For example, the delivery unit can prioritize providing designs related to the user's current ongoing projects. For example, the delivery unit can select the optimal delivery method based on the user's project status. Furthermore, the delivery unit can analyze the user's project status and dynamically customize the delivery method. This allows the design to be provided in the most optimal way by customizing the delivery method based on the user's current project status. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the user's project status data into AI and have the AI ​​perform the customization of the delivery method.

[0090] The service provider can estimate the user's emotions and determine the priority of the designs to be provided based on the estimated emotions. For example, if the user is stressed, the service provider can prioritize providing important designs. For example, if the user is relaxed, the service provider can provide more detailed designs. Also, if the user is in a hurry, the service provider can quickly provide the most important designs. This allows for the priority of important designs to be provided by determining the priority of the designs to be provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into AI and have the AI ​​perform the design priority determination.

[0091] The service provider can select the optimal delivery method when providing designs, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can prioritize providing designs related to that region. For example, the service provider can select the optimal delivery method based on the user's geographical location information. Furthermore, if the user is on the move, the service provider can select the optimal delivery method based on their current location. In this way, the service provider can select the optimal delivery method by considering the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into AI and have the AI ​​select the optimal delivery method.

[0092] The service provider can analyze the user's social media activity and propose a delivery method when providing a design. For example, the service provider can analyze the user's social media activity and propose the most suitable delivery method. For example, the service provider can propose the most suitable delivery method based on information shared by the user on social media. Furthermore, the service provider can analyze the user's interests on social media and propose the most suitable delivery method. In this way, the service provider can propose the most suitable delivery method by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media data into AI and have the AI ​​propose the most suitable delivery method.

[0093] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, the learning unit can select detailed training data. For example, if the user is in a hurry, the learning unit can select concise training data. Also, if the user is excited, the learning unit can select visually stimulating training data. This improves the efficiency of learning by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into an AI and have the AI ​​perform the selection of training data.

[0094] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and adjust the parameters of the learning algorithm. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. Thus, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into AI and have the AI ​​perform the optimization of the learning algorithm.

[0095] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can increase the learning frequency when the user is relaxed. For example, it can decrease the learning frequency when the user is in a hurry. Furthermore, the learning unit can dynamically adjust the learning frequency when the user is excited. This improves the efficiency of learning by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into AI and have the AI ​​adjust the learning frequency.

[0096] The learning unit can weight the training data based on the submission dates of design requirements during training. For example, the learning unit can weight the training data based on design requirements with upcoming submission dates. For example, the learning unit can weight the training data based on urgent design requirements. Furthermore, the learning unit can dynamically adjust the weighting of the training data according to the submission dates. This improves the accuracy of training by weighting the training data based on the submission dates of design requirements. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input design requirement submission date data into the AI ​​and have the AI ​​perform the weighting of the training data.

[0097] The evaluation unit can estimate the user's emotions and adjust the design evaluation criteria based on the estimated user emotions. For example, if the user is relaxed, the evaluation unit can apply detailed evaluation criteria. For example, if the user is in a hurry, the evaluation unit can apply concise evaluation criteria. Also, if the user is excited, the evaluation unit can apply visually stimulating evaluation criteria. This allows for appropriate evaluation by adjusting the design evaluation criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input user emotion data into AI and have the AI ​​perform the adjustment of evaluation criteria.

[0098] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data during design evaluation. For example, the evaluation unit can select the optimal evaluation algorithm based on past evaluation data. For example, the evaluation unit can analyze past evaluation data and adjust the parameters of the evaluation algorithm. Furthermore, the evaluation unit can improve the accuracy of the evaluation algorithm by referring to past evaluation data. As a result, the accuracy of the evaluation algorithm is improved by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input past evaluation data into AI and have the AI ​​perform the optimization of the evaluation algorithm.

[0099] The evaluation unit can estimate the user's emotions and adjust the frequency of evaluations based on the estimated emotions. For example, the evaluation unit can increase the frequency of evaluations when the user is relaxed. For example, the evaluation unit can decrease the frequency of evaluations when the user is in a hurry. Furthermore, the evaluation unit can dynamically adjust the frequency of evaluations when the user is excited. This allows for appropriate evaluations by adjusting the frequency of evaluations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, or not using AI. For example, the evaluation unit can input user emotion data into AI and have the AI ​​adjust the frequency of evaluations.

[0100] The evaluation unit can weight the evaluation data based on the submission timing of the design requirements during the design evaluation. For example, the evaluation unit can weight the evaluation data based on design requirements with upcoming submission deadlines. For example, the evaluation unit can weight the evaluation data based on urgent design requirements. Furthermore, the evaluation unit can dynamically adjust the weighting of the evaluation data according to the submission timing. This improves the accuracy of the evaluation by weighting the evaluation data based on the submission timing of the design requirements. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the design requirement submission timing data into the AI ​​and have the AI ​​perform the weighting of the evaluation data.

[0101] The interface unit can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, if the user is tense, the interface unit can provide an interface with calming colors to reduce visual stress. For example, if the user is having fun, the interface unit can provide an interface with bright colors to make the input process more enjoyable. Also, if the user is tired, the interface unit can provide a simple and highly visible interface to facilitate the input process. In this way, by adjusting the interface display method based on the user's emotions, a display suitable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input user emotion data into AI and have the AI ​​adjust the interface display method.

[0102] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit can select the optimal display method based on the display method the user has used in the past. For example, the interface unit can suggest the most efficient display method from the user's past operation history. Furthermore, the interface unit can analyze the user's past operation history and dynamically adjust the display method. This allows the user to select the optimal display method by referring to the user's past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input user operation history data into AI and have the AI ​​select the optimal display method.

[0103] The interface unit can estimate the user's emotions and adjust the interface's operating procedures based on the estimated emotions. For example, if the user is tense, the interface unit can provide simple and highly visible operating procedures. For example, if the user is enjoying themselves, the interface unit can provide detailed operating procedures to make the operation more enjoyable. Also, if the user is tired, the interface unit can provide concise and highly visible operating procedures to make the operation easier. In this way, by adjusting the interface's operating procedures based on the user's emotions, an operation suitable for the user can be performed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input user emotion data into AI and have the AI ​​perform the adjustment of the operating procedures.

[0104] The interface unit can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit can provide a display method that matches the screen size. For example, if the user is using a tablet, the interface unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the interface unit can provide a concise and highly visible display method. In this way, the optimal display method can be selected by taking into account the user's device information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without using AI. For example, the interface unit can input the user's device information into the AI ​​and have the AI ​​select the optimal display method.

[0105] The input unit can estimate the user's emotions and adjust the input method based on the estimated emotions. For example, if the user is nervous, the input unit can provide a simple and highly visible input method. For example, if the user is having fun, the input unit can provide a detailed input method to make the input process enjoyable. Also, if the user is tired, the input unit can provide a concise and highly visible input method to make the input process easier. In this way, by adjusting the input method based on the user's emotions, the user can perform input in a manner appropriate to their needs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the input unit may be performed using AI, or not using AI. For example, the input unit can input user emotion data into AI and have the AI ​​perform the adjustment of the input method.

[0106] The input unit can suggest the optimal input method by referring to the user's past input history during input. For example, the input unit can suggest the optimal input method based on input methods the user has used in the past. For example, the input unit can suggest the most efficient input method from the user's past input history. Furthermore, the input unit can analyze the user's past input history and dynamically adjust the input method. This allows it to suggest the optimal input method by referring to the user's past input history. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's input history data into AI and have the AI ​​suggest the optimal input method.

[0107] The input unit can estimate the user's emotions and determine the priority of inputs based on the estimated emotions. For example, if the user is stressed, the input unit can prioritize important inputs. For example, if the user is relaxed, the input unit can provide time for detailed inputs. Also, if the user is in a hurry, the input unit can allow the most important inputs to be completed quickly. In this way, important inputs can be prioritized by determining the priority of inputs based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using AI or not using AI. For example, the input unit can input user emotion data into AI and have the AI ​​perform the determination of input priorities.

[0108] The input unit can select the optimal input method when inputting data, taking into account the user's geographical location. For example, if the user is in a specific region, the input unit can prioritize providing input methods related to that region. For example, the input unit can select the optimal input method based on the user's geographical location. Furthermore, if the user is on the move, the input unit can select the optimal input method based on their current location. In this way, the optimal input method can be selected by considering the user's geographical location. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's geographical location information into AI and have AI select the optimal input method.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The reception desk can analyze the user's past input history of design requirements and suggest the optimal input method. For example, the reception desk can automatically display design requirements that the user has frequently entered in the past as suggestions. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest design requirements to be used during specific time periods based on the user's past input history. In this way, by analyzing the user's past input history of design requirements, the reception desk can suggest the optimal input method for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input past input history data into AI and have the AI ​​suggest the optimal input method.

[0111] The generation unit may include an evaluation unit that evaluates the quality of the generated design. For example, the generation unit can evaluate the quality of the design based on user feedback. The generation unit may also include an evaluation unit that evaluates the quality of the design based on test results. For example, the generation unit analyzes test results and evaluates the quality of the design. Furthermore, the generation unit may include an evaluation unit that collects expert opinions in order to evaluate the quality of the generated design. For example, the generation unit evaluates the quality of the design based on expert opinions. This ensures that the quality of the design provided to creators is guaranteed by evaluating the quality of the generated design. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user feedback into AI and have AI perform the evaluation of the design quality.

[0112] The service provider may include an interface for providing the generated designs to creators. For example, the service provider may provide the generated designs to creators through a web-based interface. The service provider may also include an interface for providing the generated designs via an API. For example, the service provider may integrate the generated designs with other systems via an API. Furthermore, the service provider may include a mobile application for providing the generated designs to creators. For example, the service provider may provide the generated designs to creators through a mobile application. By providing an interface for providing the generated designs to creators, creators can easily utilize the designs. Some or all of the above-described processing in the interface may be performed using AI, for example, or without AI. For example, the interface may input the generated designs into an AI and have the AI ​​execute the optimal method for providing them to creators.

[0113] The reception desk can estimate the user's emotions and adjust the timing of design requirement input based on the estimated emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. For example, if the user is relaxed, it can provide detailed input options and suggest a customizable input method. Also, if the user is in a hurry, it can prioritize voice input to allow for quick input of design requirements. This reduces user stress and enables efficient input by adjusting the timing of design requirement input based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into AI and have the AI ​​adjust the timing of design requirement input.

[0114] The generation unit can estimate the user's emotions and adjust the design generation method based on the estimated user emotions. For example, if the user is relaxed, it can generate a design that progresses at a leisurely pace. For example, if the user is in a hurry, the generation unit can generate a design that emphasizes the shortest route. Also, if the user is excited, the generation unit can generate a design with visually stimulating effects. In this way, by adjusting the design generation method based on the user's emotions, a design suitable for the user can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is 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 processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the design generation method.

[0115] The delivery unit can estimate the user's emotions and adjust the design delivery method based on the estimated emotions. For example, if the user is relaxed, it can select a delivery method that includes detailed explanations. For example, if the user is in a hurry, the delivery unit can select a concise and quick delivery method. Also, if the user is excited, the delivery unit can select a visually stimulating delivery method. In this way, by adjusting the design delivery method based on the user's emotions, the design can be delivered in a way that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into AI and have the AI ​​perform the adjustment of the design delivery method.

[0116] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, detailed training data can be selected. For example, if the user is in a hurry, the learning unit can select concise training data. Also, if the user is excited, the learning unit can select visually stimulating training data. This improves the efficiency of learning by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into an AI and have the AI ​​perform the selection of training data.

[0117] The generation unit can adjust the level of detail of the generated design based on the importance of the requirements. For example, it can generate a detailed design based on important requirements. For example, it can generate a simplified design based on lower-priority requirements. The generation unit can also dynamically adjust the level of detail of the design according to the importance of the requirements. This allows for the generation of detailed designs for important requirements by adjusting the level of detail based on the importance of the requirements. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input requirement importance data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the generated design.

[0118] The generation unit can apply different generation algorithms depending on the category of the requirements when generating designs. For example, a fantasy-themed requirement can be given a fantasy-specific generation algorithm. For example, a horror-themed requirement can be given a horror-specific generation algorithm. Furthermore, a requirement that includes puzzle elements can be given a puzzle-specific generation algorithm. By applying different generation algorithms depending on the category of the requirements, the optimal design for each category can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category data of the requirements into the AI ​​and have the AI ​​execute the application of the optimal generation algorithm.

[0119] The service provider can analyze the user's past usage history to select the optimal service delivery method when providing designs. For example, the service provider can select the optimal delivery method based on the delivery methods the user has used in the past. For example, the service provider can propose the most efficient delivery method based on the user's past usage history. Furthermore, the service provider can analyze the user's past usage history and dynamically adjust the delivery method. This allows the service provider to select the optimal delivery method by analyzing the user's past usage history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user usage history data into AI and have the AI ​​select the optimal delivery method.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The reception area is where creators input design requirements. Creators can specify elements such as theme, difficulty level, and play style. For example, the reception area can be used to input requirements such as a fantasy-themed dungeon, medium difficulty level, and exploration-focused play style. Step 2: The generation unit analyzes the requirements entered by the reception unit and generates world and dungeon designs. The generation unit uses AI to learn from past design data and patterns and generates the optimal design based on the specified requirements. For example, if it's a fantasy-style dungeon, the generation unit will generate a design that includes elements such as stone walls, magical traps, and treasure chests. Step 3: The provisioning section is the part that provides the designs generated by the generation section to the creator. The provisioning section provides the generated designs so that creators can build diverse content in a short amount of time. For example, the provisioning section can provide players with new adventures by incorporating the generated dungeon designs into the game.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] For example, the reception unit is implemented by either the data processing unit 12 or the smart device 14. For instance, when a creator inputs design requirements, they can specify elements such as theme, difficulty level, and play style using the reception device 38 of the smart device 14. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which uses AI to learn past design data and patterns and generates an optimal design based on the specified requirements. The provision unit provides the generated design to the creator through the output device 40 of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] For example, the reception unit is implemented by either the data processing unit 12 or the smart glasses 214. For instance, when a creator inputs design requirements, they can specify elements such as theme, difficulty level, and play style using the microphone 238 of the smart glasses 214. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses AI to learn past design data and patterns and generates the optimal design based on the specified requirements. The provision unit provides the generated design to the creator through the speaker 240 of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] For example, the reception unit is implemented by either the data processing unit 12 or the headset terminal 314. For instance, when a creator inputs design requirements, they can specify elements such as theme, difficulty level, and play style using the microphone 238 of the headset terminal 314. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses AI to learn past design data and patterns and generates an optimal design based on the specified requirements. The provision unit provides the generated design to the creator through the display 343 of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] For example, the reception unit is implemented by either the data processing unit 12 or the robot 414. For instance, when a creator inputs design requirements, they can specify elements such as theme, difficulty level, and play style using the microphone 238 of the robot 414. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses AI to learn past design data and patterns and generates an optimal design based on the specified requirements. The provision unit provides the generated design to the creator through the speaker 240 of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0175] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] 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.

[0185] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) A reception desk where design requirements are entered, The generation unit analyzes the requirements entered by the reception unit and generates world and dungeon designs, The system comprises a providing unit that provides the design generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is It includes a learning unit that learns from past design data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is It includes an evaluation unit that assesses the quality of the generated design. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, It includes an interface section for providing the generated design to the creator. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It features input fields for specifying elements such as theme, difficulty level, and play style. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of design requirement input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze past design requirement input history and propose the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering design requirements, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input requirements based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering design requirements, the system prioritizes highly relevant requirements based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering design requirements, analyze users' social media activity and enter relevant requirements. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the design generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During design generation, adjust the level of detail based on the importance of the requirements. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating designs, different generation algorithms are applied depending on the category of requirements. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of the generated design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating designs, prioritize the generation based on the requirements submission date. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During design generation, adjust the generation order based on the relevance of requirements. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, We estimate the user's emotions and adjust the design delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the design, we analyze the user's past usage history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the design, the delivery method will be customized based on the user's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, We estimate the user's emotions and prioritize the design offerings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the design, we will select the optimal delivery method considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing designs, we analyze users' social media activity and propose delivery methods. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned learning unit, During training, the training data is weighted based on the submission dates of the design requirements. The system described in Appendix 2, characterized by the features described herein. (Note 28) The evaluation unit, We estimate user emotions and adjust design evaluation criteria based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 29) The evaluation unit, During design evaluation, refer to past evaluation data to optimize the evaluation algorithm. The system described in Appendix 3, characterized by the features described herein. (Note 30) The evaluation unit, It estimates the user's emotions and adjusts the frequency of evaluations based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The evaluation unit, During the design evaluation, evaluation data is weighted based on the timing of the submission of design requirements. The system described in Appendix 3, characterized by the features described herein. (Note 32) The interface unit is It estimates the user's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 33) The interface unit is When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 4, characterized by the features described herein. (Note 34) The interface unit is It estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 35) The interface unit is When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned input unit is It estimates the user's emotions and adjusts the input method based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 37) The aforementioned input unit is During input, the system refers to the user's past input history to suggest the optimal input method. The system described in Appendix 5, characterized by the features described herein. (Note 38) The aforementioned input unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 39) The aforementioned input unit is When inputting data, the system selects the optimal input method by considering the user's geographical location. The system described in Appendix 5, characterized by the features described herein. [Explanation of Symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where design requirements are entered, The generation unit analyzes the requirements entered by the reception unit and generates world and dungeon designs, The system comprises a providing unit that provides the design generated by the generation unit. A system characterized by the following features.

2. The generating unit is It includes a learning unit that learns from past design data. The system according to feature 1.

3. The generating unit is It includes an evaluation unit that assesses the quality of the generated design. The system according to feature 1.

4. The aforementioned supply unit is, It includes an interface section for providing the generated design to the creator. The system according to feature 1.

5. The aforementioned reception unit is It features input fields for specifying elements such as theme, difficulty level, and play style. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of design requirement input based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is We analyze past design requirement input history and propose the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When entering design requirements, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input requirements based on the estimated user emotions. The system according to feature 1.

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