system

Abstract

The system according to this embodiment aims to automatically generate a program configuration based on user input and to verify its quality and accuracy. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a provision unit, and a verification unit. The reception unit receives input from the user. The generation unit generates a program configuration based on the information received by the reception unit. The provision unit provides the program configuration generated by the generation unit as a diagram. The verification unit verifies the quality and accuracy of the program configuration 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 prior art, the process of automatically generating a program configuration and checking its quality and accuracy is not sufficiently efficient, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically generate a program configuration based on an input from a user and check its quality and accuracy.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a verification unit. The reception unit receives input from the user. The generation unit generates a program configuration based on the information received by the reception unit. The provision unit provides the program configuration generated by the generation unit as a diagram. The verification unit verifies the quality and accuracy of the program configuration generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate a program configuration based on user input and verify its quality and accuracy. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 system according to an embodiment of the present invention is a system that enables the use of a game engine by allowing the user to input the functions they want to create using a generation AI, which then automatically constructs the program and provides it as a diagram. This system begins with the user inputting the functions they want to create using a game engine into the generation AI while looking at the screen of their laptop. Next, the generation AI analyzes the input and automatically generates the necessary program configuration. The generated program configuration is provided to the user as a diagram. This makes it easier for the user to understand visually, enabling them to use the game engine with expertise. For example, if the user inputs, "Tell me how to make a character run in the game engine," the generation AI analyzes the instruction and automatically generates a program configuration for making the character run. The generated program configuration is provided as a diagram, and the user can use this diagram to configure the settings for making the character run. This system is extremely useful for people who want to make games but don't know how, people who are studying the Unreal Game Engine, people who are having trouble making games, and people who have a structure in their head but can't realize it. By having the generation AI provide the entire production process as a diagram, game production can proceed based on visual information, enabling efficient learning and production. Furthermore, the current number of game players worldwide is 3.7 billion, and in Japan it is 55.35 million, with further increases expected. By using this system, it is possible to expand the customer base through the provision of game content. The system will automatically generate program configurations based on user input and provide them as diagrams, enabling expert use of the game engine.

[0029] The system according to the embodiment comprises a reception unit, a generation unit, a provision unit, and a verification unit. The reception unit receives input from the user. The reception unit can receive information from the user in the form of, for example, text input, voice input, or image input. The reception unit may also include a guide unit that guides the format and content of the information entered by the user. The generation unit generates a program configuration based on the information received by the reception unit. The generation unit automatically generates the program configuration using a generation AI. For example, the generation AI analyzes the user's input and generates the necessary program configuration. The generation unit may also include a verification unit that checks the quality and accuracy of the generated program configuration. The provision unit provides the program configuration generated by the generation unit as a diagram. For example, the provision unit provides the generated program configuration as a diagram in the form of a PDF, SVG, or interactive graph. The provision unit may also include a verification unit that checks whether the generated program configuration actually works. The verification unit checks the quality and accuracy of the program configuration generated by the generation unit. For example, the verification unit checks whether the generated program configuration actually works using methods such as unit testing, system testing, or user testing. This allows the system to automatically generate a program configuration based on user input and provide it as a diagram, enabling expert use of the game engine. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit inputs user input into the generation AI, and the generation AI generates the program configuration. Some or all of the above-described processes in the provision unit may be performed using a generation AI or not. For example, the provision unit inputs the generated program configuration into the generation AI, and the generation AI provides it as a diagram. Some or all of the above-described processes in the verification unit may be performed using a generation AI or not. For example, the verification unit inputs the generated program configuration into the generation AI, and the generation AI verifies its quality and accuracy.

[0030] The reception unit receives input from users. The reception unit can accept information from users in various formats, such as text input, voice input, and image input. Specifically, for text input, users can input program requirements and specifications using a keyboard. For voice input, users give voice instructions through a microphone, and this voice is converted into text using speech recognition technology. For image input, users can take pictures of handwritten diagrams or sketches with a camera and upload the images to the system. The reception unit may also include a guide unit that guides users on the format and content of the information they input. The guide unit presents the appropriate format and necessary information when users are inputting, improving the accuracy and efficiency of input. For example, for text input, the guide unit displays placeholders in the input form to indicate what information the user should input. For voice input, the guide unit provides examples of voice commands to show how the user should give instructions. For image input, the guide unit provides instructions regarding the resolution and format of the images to be uploaded. This allows the reception unit to support diverse input formats, enabling users to provide information smoothly. Furthermore, the reception unit can also be equipped with a function to temporarily store the entered information and allow the user to correct the entered content as needed. For example, after the user completes the input, a confirmation screen can be displayed, providing an opportunity to review and correct the entered content. This allows the reception unit to receive user input accurately and efficiently.

[0031] The generation unit generates program configurations based on information received by the reception unit. The generation unit automatically generates program configurations using a generation AI. Specifically, the generation AI analyzes user input and generates the necessary program configurations. For example, it analyzes user-inputted requirements and specifications using natural language processing technology and designs the program's module and class structure. The generation AI learns from past program configurations and design patterns, enabling it to propose the optimal program configuration. The generation unit can also include a verification unit to check the quality and accuracy of the generated program configurations. The generation unit internally simulates the program configurations generated by the generation AI and evaluates their accuracy and efficiency. For example, it tests whether the generated program configuration produces the correct output for a specific input. The generation unit also performs interface tests to verify that the generated program configurations can properly integrate with existing systems and other modules. This allows the generation unit to automatically generate high-quality program configurations based on user input, improving the overall system efficiency and reliability. Furthermore, the generation unit can also provide feedback on the generated program configurations to the user, enabling modifications and improvements as needed. For example, if part of the generated program configuration does not meet the user's requirements, the user can propose modifications, and the generation unit will reflect those modifications and generate a new program configuration. This allows the generation unit to flexibly respond to user needs and provide the optimal program configuration.

[0032] The provider unit provides the program configuration generated by the generation unit as a diagram. Specifically, the generated program configuration is provided as a diagram in formats such as PDF, SVG, and interactive graphs. For example, in PDF format, the generated program configuration is saved as a document, allowing users to easily view and print it. In SVG format, it is saved as vector graphics, providing a diagram that does not degrade in quality even when enlarged or reduced. In interactive graphs, users can click or hover over the diagram to display detailed information. The provider unit can also include a verification unit to check whether the generated program configuration actually works. The provider unit runs the generated program configuration in a simulation environment and checks its operation. For example, it checks whether the generated program configuration produces the correct output for a specific input. The provider unit also performs interface tests to check whether the generated program configuration can properly integrate with existing systems and other modules. This allows the provider unit to provide users with high-quality diagrams and help them understand the program configuration. Furthermore, the provider unit can also include a function to allow users to easily share the generated diagrams. For example, the generated diagrams can be saved to cloud storage and a sharing link can be generated, allowing users to easily share the diagrams with other members. Furthermore, the service provider offers a function that allows users to add comments and feedback to the generated diagrams, facilitating smooth communication within the team. This enables the service provider to deliver high-quality diagrams to users, supporting their understanding and sharing of the program structure.

[0033] The verification unit verifies the quality and accuracy of the program configuration generated by the generation unit. Specifically, it verifies whether the generated program configuration actually works using methods such as unit tests, system tests, and user tests. Unit tests verify that each module and class of the program works correctly individually. System tests verify that the entire program works correctly as an integrated unit. User tests involve actual users using the program and evaluating its usability and the accuracy of its functions. Based on these test results, the verification unit evaluates the quality and accuracy of the generated program configuration and makes corrections and improvements as necessary. For example, if an error is detected in a unit test, the error is corrected and the test is repeated. Also, if a performance problem is found in a system test, optimization is performed to resolve the problem. Furthermore, feedback from users is collected during user tests, and improvements are made to improve the usability of the program. In this way, the verification unit can ensure the quality and accuracy of the generated program configuration and provide users with a high-quality program. In addition, the verification unit can record the test results in detail and use them for future improvements and troubleshooting. For example, test results can be saved in a database and compared with past test results to continuously evaluate improvements in program quality. Furthermore, the verification unit can efficiently conduct tests using automated test tools, improving both the accuracy and efficiency of the tests. This allows the verification unit to ensure the quality and accuracy of the generated program configuration, providing users with high-quality programs.

[0034] The reception unit includes a guide unit that guides the format and content of the information entered by the user. The guide unit guides the user by methods such as presenting an input format, providing input examples, and providing real-time feedback. The guide unit can improve the accuracy of input by appropriately guiding the format and content of the information entered by the user. For example, the guide unit specifies the format of the information to be entered by the user and presents appropriate input examples. The guide unit can also provide real-time feedback on the content of the information entered by the user to encourage appropriate input. In this way, the reception unit can improve the accuracy of input by guiding the format and content of the information entered by the user. Some or all of the above processing in the guide unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the guide unit inputs the user's input into the generation AI, and the generation AI presents appropriate input examples.

[0035] The generation unit generates program configurations using a generation AI. For example, the generation unit uses the generation AI to analyze user input and generate the necessary program configuration. By using the generation AI, the generation unit automates the generation of program configurations. For example, the generation unit inputs "Tell me how to make a character run in the game engine" to the generation AI, which analyzes the instruction and generates a program configuration for making the character run. The generation unit can then pass the generated program configuration to a verification unit to check its quality and accuracy. Thus, by using the generation AI, the generation unit automates the generation of program configurations. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without using the generation AI. For example, the generation unit inputs user input to the generation AI, which then generates a program configuration.

[0036] The service provider provides the generated program configuration as a diagram. The service provider provides the generated program configuration as a diagram in formats such as PDF, SVG, or interactive graph. By providing the generated program configuration as a diagram, the service provider makes it easier for users to visually understand it. For example, the service provider provides the generated program configuration in PDF format, allowing users to understand the program configuration based on that diagram. Alternatively, the service provider provides the generated program configuration in SVG format, allowing users to zoom in and out to examine the details. Furthermore, the service provider provides the generated program configuration as an interactive graph, allowing users to manipulate the graph to understand the program configuration. Thus, by providing the generated program configuration as a diagram, the service provider makes it easier for users to visually understand it. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without using a generation AI. For example, the service provider inputs the generated program configuration into a generation AI, and the generation AI provides it as a diagram.

[0037] The verification unit verifies whether the generated program configuration actually works. The verification unit verifies whether the generated program configuration actually works using methods such as unit tests, system tests, and user tests. The quality and accuracy are guaranteed by the verification unit verifying whether the generated program configuration actually works. For example, the verification unit verifies the generated program configuration with unit tests to confirm that each module works correctly. The verification unit also verifies the generated program configuration with system tests to confirm that the overall operation is correct. Furthermore, the verification unit verifies the generated program configuration with user tests to confirm its operation when actually used by a user. In this way, the quality and accuracy are guaranteed by the verification unit verifying whether the generated program configuration actually works. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the verification unit inputs the generated program configuration into a generation AI, and the generation AI verifies the quality and accuracy.

[0038] The reception desk analyzes the user's past input history and selects the optimal input method. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, the reception desk can suggest relevant input methods based on the content the user has entered in the past. In this way, the reception desk can provide the user with the optimal input method by analyzing past input history. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk inputs the user's past input history into a generative AI, and the generative AI selects the optimal input method.

[0039] The reception unit filters input based on the user's current projects and areas of interest. For example, the reception unit prioritizes receiving information related to the user's current projects. The reception unit can filter relevant input based on the user's areas of interest. It can also filter input based on areas the user has shown interest in in the past. This allows the reception unit to provide highly relevant information by filtering based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit inputs data on the user's projects and areas of interest into a generative AI, and the generative AI performs the filtering.

[0040] The reception unit prioritizes accepting highly relevant inputs, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit prioritizes accepting information related to that region. If the user is traveling, the reception unit can prioritize accepting information related to the travel destination. Also, if the user is at home, the reception unit can prioritize accepting information about the area around their home. In this way, the reception unit can prioritize accepting highly relevant information by taking into account the user's geographical location. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit inputs the user's geographical location information into a generative AI, and the generative AI prioritizes accepting highly relevant inputs.

[0041] The reception unit analyzes the user's social media activity during input and accepts relevant input. For example, the reception unit can accept relevant input based on information the user has shared on social media. The reception unit can also accept relevant input based on accounts the user follows on social media. Furthermore, the reception unit can accept relevant input based on topics the user has shown interest in on social media. This allows the reception unit to efficiently receive relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit inputs the user's social media activity data into a generative AI, and the generative AI accepts relevant input.

[0042] The generation unit adjusts the level of detail of the generated program based on its importance. For example, in the case of an important program configuration, the generation unit can generate a detailed explanation. In the case of a general program configuration, the generation unit can generate a concise explanation. In the case of an auxiliary program configuration, the generation unit can generate a summary containing only the essential points. In this way, the generation unit can provide a program configuration with an appropriate level of detail by adjusting the level of detail of the generated program based on its importance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs program importance data into the generation AI, and the generation AI adjusts the level of detail of the generated program.

[0043] The generation unit applies different generation algorithms depending on the program category during generation. For example, the generation unit applies a specific algorithm for programs related to game logic. For programs related to graphics, it may apply a different algorithm. Furthermore, for programs related to user interfaces, it may apply yet another algorithm. In this way, the generation unit can provide an appropriate program structure by applying different generation algorithms depending on the program category. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs program category data into the generation AI, and the generation AI applies a different generation algorithm.

[0044] The generation unit determines the generation priority based on the program submission dates during generation. For example, the generation unit prioritizes generating program configurations with approaching deadlines. It can postpone generating program configurations with distant submission dates. Furthermore, the generation unit can generate program configurations with unknown submission dates after the generation of other program configurations is complete. In this way, the generation unit can provide program configurations in the appropriate order by determining the generation priority based on the program submission dates. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs program submission date data into the generation AI, and the generation AI determines the generation priority.

[0045] The generation unit adjusts the generation order based on the relevance of the programs during generation. For example, the generation unit prioritizes generating program configurations with high relevance. The generation unit can postpone generating program configurations with low relevance. Furthermore, the generation unit can generate program configurations whose relevance is unknown after the generation of other program configurations is complete. In this way, the generation unit can provide program configurations in an appropriate order by adjusting the generation order based on the relevance of the programs. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs program relevance data into the generation AI, and the generation AI adjusts the generation order.

[0046] The provider adjusts the level of detail of the diagrams based on the importance of the generated program configurations at the time of provision. For example, the provider provides detailed diagrams for important program configurations. For general program configurations, the provider can provide concise diagrams. Furthermore, for auxiliary program configurations, the provider can provide diagrams containing only the essentials. In this way, the provider can provide diagrams with appropriate levels of detail by adjusting the level of detail of the diagrams based on the importance of the program configurations. Some or all of the above processing in the provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the provider inputs program configuration importance data into a generation AI, and the generation AI adjusts the level of detail of the diagrams.

[0047] The provider applies different diagram formats depending on the program category at the time of provision. For example, the provider applies a specific diagram format for programs related to game logic. The provider may apply a different diagram format for programs related to graphics. Furthermore, the provider may apply yet another diagram format for programs related to user interfaces. In this way, the provider can provide appropriate diagrams by applying different diagram formats depending on the program category. Some or all of the above processing in the provider may be performed using a generation AI, or not using a generation AI. For example, the provider inputs program category data into a generation AI, and the generation AI applies different diagram formats.

[0048] The provisioning department determines the priority of the diagrams based on the submission dates of the generated program configurations at the time of provision. For example, the provisioning department will provide diagrams of program configurations with approaching deadlines first. The provisioning department may postpone providing diagrams of program configurations with distant submission dates. Furthermore, the provisioning department may provide diagrams of program configurations with unknown submission dates after the provision of other diagrams has been completed. In this way, the provisioning department can provide diagrams in the appropriate order by determining the priority of the diagrams based on the submission dates of the program configurations. Some or all of the above processing in the provisioning department may be performed using a generation AI, or it may be performed without a generation AI. For example, the provisioning department inputs program configuration submission date data into a generation AI, and the generation AI determines the priority of the diagrams.

[0049] The provider adjusts the order of the diagrams based on the relationships between the generated program configurations when providing them. For example, the provider prioritizes providing diagrams of highly relevant program configurations. The provider may postpone providing diagrams of less relevant program configurations. Furthermore, the provider may provide diagrams of program configurations whose relationships are unknown after the provision of other diagrams has been completed. In this way, the provider can provide the diagrams in the appropriate order by adjusting the order of the diagrams based on the relationships between the program configurations. Some or all of the above processing in the provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the provider inputs program configuration relationship data into a generation AI, and the generation AI adjusts the order of the diagrams.

[0050] The verification unit, during verification, selects the optimal verification method by referring to the past operation history of the generated program configuration. For example, the verification unit selects the optimal verification method based on program configurations that have operated successfully in the past. The verification unit can adjust the verification method based on program configurations that have produced errors in the past. Furthermore, the verification unit can analyze the past operation history and select the most efficient verification method. In this way, the verification unit can provide the optimal verification method by referring to the past operation history. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the verification unit inputs the past operation history data of the generated program configuration into the generation AI, and the generation AI selects the optimal verification method.

[0051] The verification unit applies different verification methods depending on the category of the generated program configuration during verification. For example, the verification unit applies a specific verification method for programs related to game logic. For programs related to graphics, the verification unit may apply a different verification method. Furthermore, for programs related to user interfaces, the verification unit may apply yet another verification method. In this way, the verification unit can provide an appropriate verification method by applying different verification methods depending on the category of the program configuration. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the verification unit inputs program configuration category data into the generation AI, and the generation AI applies different verification methods.

[0052] The verification unit selects the optimal verification method during verification, taking into account the geographical location information of the generated program configuration. For example, if the user is in a specific region, the verification unit applies a verification method related to that region. If the user is traveling, the verification unit can apply a verification method related to the travel destination. Furthermore, if the user is at home, the verification unit can select a verification method based on information about the area around their home. In this way, the verification unit can provide the optimal verification method by taking geographical location information into consideration. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the verification unit inputs the geographical location data of the generated program configuration into a generation AI, and the generation AI selects the optimal verification method.

[0053] The verification unit improves the accuracy of the verification by referring to relevant literature for the generated program configuration during the verification process. For example, the verification unit verifies the accuracy of the generated program configuration based on the relevant literature. The verification unit can identify areas for improvement in the generated program configuration by referring to the relevant literature. Furthermore, the verification unit can optimize the generated program configuration based on the relevant literature. In this way, the verification unit can improve the accuracy of the verification by referring to the relevant literature. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the verification unit inputs the relevant literature data for the generated program configuration into the generation AI, and the generation AI improves the accuracy of the verification.

[0054] The guide unit, when displaying a guide, selects the optimal guide method by referring to the user's past operation history. For example, the guide unit selects the optimal guide method based on the operation methods the user has frequently used in the past. The guide unit can prioritize guiding users to specific operation methods based on the user's past operation history. The guide unit can also provide relevant guide methods based on operations the user has performed in the past. In this way, the guide unit can provide the optimal guide method by referring to the past operation history. Some or all of the above processing in the guide unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the guide unit inputs the user's past operation history data into a generation AI, and the generation AI selects the optimal guide method.

[0055] The guide unit selects the optimal guide method when displaying a guide, taking into account the user's device information. For example, if the user is using a smartphone, the guide unit can provide a guide method that matches the screen size. If the user is using a tablet, the guide unit can provide a guide method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the guide unit can provide a concise and highly visible guide method. In this way, the guide unit can provide the optimal guide method by taking device information into account. Some or all of the above processing in the guide unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the guide unit inputs the user's device information data into a generation AI, and the generation AI selects the optimal guide method.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The reception desk can analyze user input and infer the user's intent. For example, if a user inputs "Tell me how to make a character run in the game engine," the reception desk will analyze the intent and provide detailed information about the character's movements. Furthermore, even if the user provides ambiguous input, the reception desk can infer and provide relevant information. In addition, the reception desk can refer to the user's past input history and make inferences based on similar input. This allows the reception desk to accurately grasp the user's intent and provide appropriate information.

[0058] The generation unit can optimize the generated program configuration. For example, it can remove unnecessary code to improve the performance of the generated program configuration. It can also add comments and documentation to improve the readability of the generated program configuration. Furthermore, it can detect and fix vulnerabilities to enhance the security of the generated program configuration. In this way, the generation unit can improve the quality of the generated program configuration.

[0059] The service provider can customize the generated program configuration according to the user's skill level. For example, beginner users can be provided with diagrams that include detailed explanations and tutorials. Intermediate users can be provided with diagrams that highlight key points, and advanced users can be provided with concise diagrams. Furthermore, the service provider can adjust the content of the diagrams provided based on user feedback. This allows the service provider to provide diagrams that are appropriate for the user's skill level.

[0060] The verification unit can simulate the operating environment of the generated program configuration. For example, the verification unit can simulate operation in different hardware and software environments to verify the compatibility of the program configuration. It can also simulate operation in different network environments to verify the performance of the program configuration. Furthermore, the verification unit can simulate different user scenarios to verify the usability of the program configuration. This allows the verification unit to pre-verify the operating environment of the generated program configuration and prevent problems before they occur.

[0061] The guidance unit can analyze the user's past operation history and provide the optimal guidance method. For example, it can provide the optimal guidance based on the operation methods the user has frequently used in the past. It can also prioritize guiding users through specific operation methods based on the user's past operation history. Furthermore, it can provide relevant guidance methods based on operations the user has performed in the past. In this way, the guidance unit can provide the user with the most optimal guidance method by analyzing past operation history.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception unit receives input from the user. The reception unit can receive information from the user in various formats, such as text input, voice input, and image input. The reception unit may also include a guide unit that guides the user on the format and content of the information they input. Step 2: The generation unit generates a program configuration based on the information received by the reception unit. The generation unit automatically generates the program configuration using a generation AI. For example, the generation AI analyzes the user's input and generates the necessary program configuration. Step 3: The provider unit provides the program configuration generated by the generator unit as a diagram. The provider unit provides the generated program configuration as a diagram in formats such as PDF, SVG, or interactive graph. Step 4: The verification unit checks the quality and accuracy of the program configuration generated by the generation unit. For example, the verification unit checks whether the generated program configuration actually works using methods such as unit tests, system tests, and user tests.

[0064] (Example of form 2) The system according to an embodiment of the present invention is a system that enables the use of a game engine by allowing the user to input the functions they want to create using a generation AI, which then automatically constructs the program and provides it as a diagram. This system begins with the user inputting the functions they want to create using a game engine into the generation AI while looking at the screen of their laptop. Next, the generation AI analyzes the input and automatically generates the necessary program configuration. The generated program configuration is provided to the user as a diagram. This makes it easier for the user to understand visually, enabling them to use the game engine with expertise. For example, if the user inputs, "Tell me how to make a character run in the game engine," the generation AI analyzes the instruction and automatically generates a program configuration for making the character run. The generated program configuration is provided as a diagram, and the user can use this diagram to configure the settings for making the character run. This system is extremely useful for people who want to make games but don't know how, people who are studying game engines, people who are having trouble making games, and people who have a structure in their head but can't realize it. By having the generation AI provide the entire production process as a diagram, game production can proceed based on visual information, enabling efficient learning and production. Furthermore, the current number of game players worldwide is 3.7 billion, and in Japan it is 55.35 million, with further increases expected. By using this system, it is possible to expand the customer base through the provision of game content. The system will automatically generate program configurations based on user input and provide them as diagrams, enabling expert use of the game engine.

[0065] The system according to the embodiment comprises a reception unit, a generation unit, a provision unit, and a verification unit. The reception unit receives input from the user. The reception unit can receive information from the user in the form of, for example, text input, voice input, or image input. The reception unit may also include a guide unit that guides the format and content of the information entered by the user. The generation unit generates a program configuration based on the information received by the reception unit. The generation unit automatically generates the program configuration using a generation AI. For example, the generation AI analyzes the user's input and generates the necessary program configuration. The generation unit may also include a verification unit that checks the quality and accuracy of the generated program configuration. The provision unit provides the program configuration generated by the generation unit as a diagram. For example, the provision unit provides the generated program configuration as a diagram in the form of a PDF, SVG, or interactive graph. The provision unit may also include a verification unit that checks whether the generated program configuration actually works. The verification unit checks the quality and accuracy of the program configuration generated by the generation unit. For example, the verification unit checks whether the generated program configuration actually works using methods such as unit testing, system testing, or user testing. This allows the system to automatically generate a program configuration based on user input and provide it as a diagram, enabling expert use of the game engine. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit inputs user input into the generation AI, and the generation AI generates the program configuration. Some or all of the above-described processes in the provision unit may be performed using a generation AI or not. For example, the provision unit inputs the generated program configuration into the generation AI, and the generation AI provides it as a diagram. Some or all of the above-described processes in the verification unit may be performed using a generation AI or not. For example, the verification unit inputs the generated program configuration into the generation AI, and the generation AI verifies its quality and accuracy.

[0066] The reception unit receives input from users. The reception unit can accept information from users in various formats, such as text input, voice input, and image input. Specifically, for text input, users can input program requirements and specifications using a keyboard. For voice input, users give voice instructions through a microphone, and this voice is converted into text using speech recognition technology. For image input, users can take pictures of handwritten diagrams or sketches with a camera and upload the images to the system. The reception unit may also include a guide unit that guides users on the format and content of the information they input. The guide unit presents the appropriate format and necessary information when users are inputting, improving the accuracy and efficiency of input. For example, for text input, the guide unit displays placeholders in the input form to indicate what information the user should input. For voice input, the guide unit provides examples of voice commands to show how the user should give instructions. For image input, the guide unit provides instructions regarding the resolution and format of the images to be uploaded. This allows the reception unit to support diverse input formats, enabling users to provide information smoothly. Furthermore, the reception unit can also be equipped with a function to temporarily store the entered information and allow the user to correct the entered content as needed. For example, after the user completes the input, a confirmation screen can be displayed, providing an opportunity to review and correct the entered content. This allows the reception unit to receive user input accurately and efficiently.

[0067] The generation unit generates program configurations based on information received by the reception unit. The generation unit automatically generates program configurations using a generation AI. Specifically, the generation AI analyzes user input and generates the necessary program configurations. For example, it analyzes user-inputted requirements and specifications using natural language processing technology and designs the program's module and class structure. The generation AI learns from past program configurations and design patterns, enabling it to propose the optimal program configuration. The generation unit can also include a verification unit to check the quality and accuracy of the generated program configurations. The generation unit internally simulates the program configurations generated by the generation AI and evaluates their accuracy and efficiency. For example, it tests whether the generated program configuration produces the correct output for a specific input. The generation unit also performs interface tests to verify that the generated program configurations can properly integrate with existing systems and other modules. This allows the generation unit to automatically generate high-quality program configurations based on user input, improving the overall system efficiency and reliability. Furthermore, the generation unit can also provide feedback on the generated program configurations to the user, enabling modifications and improvements as needed. For example, if part of the generated program configuration does not meet the user's requirements, the user can propose modifications, and the generation unit will reflect those modifications and generate a new program configuration. This allows the generation unit to flexibly respond to user needs and provide the optimal program configuration.

[0068] The provider unit provides the program configuration generated by the generation unit as a diagram. Specifically, the generated program configuration is provided as a diagram in formats such as PDF, SVG, and interactive graphs. For example, in PDF format, the generated program configuration is saved as a document, allowing users to easily view and print it. In SVG format, it is saved as vector graphics, providing a diagram that does not degrade in quality even when enlarged or reduced. In interactive graphs, users can click or hover over the diagram to display detailed information. The provider unit can also include a verification unit to check whether the generated program configuration actually works. The provider unit runs the generated program configuration in a simulation environment and checks its operation. For example, it checks whether the generated program configuration produces the correct output for a specific input. The provider unit also performs interface tests to check whether the generated program configuration can properly integrate with existing systems and other modules. This allows the provider unit to provide users with high-quality diagrams and help them understand the program configuration. Furthermore, the provider unit can also include a function to allow users to easily share the generated diagrams. For example, the generated diagrams can be saved to cloud storage and a sharing link can be generated, allowing users to easily share the diagrams with other members. Furthermore, the service provider offers a function that allows users to add comments and feedback to the generated diagrams, facilitating smooth communication within the team. This enables the service provider to deliver high-quality diagrams to users, supporting their understanding and sharing of the program structure.

[0069] The verification unit verifies the quality and accuracy of the program configuration generated by the generation unit. Specifically, it verifies whether the generated program configuration actually works using methods such as unit tests, system tests, and user tests. Unit tests verify that each module and class of the program works correctly individually. System tests verify that the entire program works correctly as an integrated unit. User tests involve actual users using the program and evaluating its usability and the accuracy of its functions. Based on these test results, the verification unit evaluates the quality and accuracy of the generated program configuration and makes corrections and improvements as necessary. For example, if an error is detected in a unit test, the error is corrected and the test is repeated. Also, if a performance problem is found in a system test, optimization is performed to resolve the problem. Furthermore, feedback from users is collected during user tests, and improvements are made to improve the usability of the program. In this way, the verification unit can ensure the quality and accuracy of the generated program configuration and provide users with a high-quality program. In addition, the verification unit can record the test results in detail and use them for future improvements and troubleshooting. For example, test results can be saved in a database and compared with past test results to continuously evaluate improvements in program quality. Furthermore, the verification unit can efficiently conduct tests using automated test tools, improving both the accuracy and efficiency of the tests. This allows the verification unit to ensure the quality and accuracy of the generated program configuration, providing users with high-quality programs.

[0070] The reception unit includes a guide unit that guides the format and content of the information entered by the user. The guide unit guides the user by methods such as presenting an input format, providing input examples, and providing real-time feedback. The guide unit can improve the accuracy of input by appropriately guiding the format and content of the information entered by the user. For example, the guide unit specifies the format of the information to be entered by the user and presents appropriate input examples. The guide unit can also provide real-time feedback on the content of the information entered by the user to encourage appropriate input. In this way, the reception unit can improve the accuracy of input by guiding the format and content of the information entered by the user. Some or all of the above processing in the guide unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the guide unit inputs the user's input into the generation AI, and the generation AI presents appropriate input examples.

[0071] The generation unit generates program configurations using a generation AI. For example, the generation unit uses the generation AI to analyze user input and generate the necessary program configuration. By using the generation AI, the generation unit automates the generation of program configurations. For example, the generation unit inputs "Tell me how to make a character run in the game engine" to the generation AI, which analyzes the instruction and generates a program configuration for making the character run. The generation unit can then pass the generated program configuration to a verification unit to check its quality and accuracy. Thus, by using the generation AI, the generation unit automates the generation of program configurations. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without using the generation AI. For example, the generation unit inputs user input to the generation AI, which then generates a program configuration.

[0072] The service provider provides the generated program configuration as a diagram. The service provider provides the generated program configuration as a diagram in formats such as PDF, SVG, or interactive graph. By providing the generated program configuration as a diagram, the service provider makes it easier for users to visually understand it. For example, the service provider provides the generated program configuration in PDF format, allowing users to understand the program configuration based on that diagram. Alternatively, the service provider provides the generated program configuration in SVG format, allowing users to zoom in and out to examine the details. Furthermore, the service provider provides the generated program configuration as an interactive graph, allowing users to manipulate the graph to understand the program configuration. Thus, by providing the generated program configuration as a diagram, the service provider makes it easier for users to visually understand it. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without using a generation AI. For example, the service provider inputs the generated program configuration into a generation AI, and the generation AI provides it as a diagram.

[0073] The verification unit verifies whether the generated program configuration actually works. The verification unit verifies whether the generated program configuration actually works using methods such as unit tests, system tests, and user tests. The quality and accuracy are guaranteed by the verification unit verifying whether the generated program configuration actually works. For example, the verification unit verifies the generated program configuration with unit tests to confirm that each module works correctly. The verification unit also verifies the generated program configuration with system tests to confirm that the overall operation is correct. Furthermore, the verification unit verifies the generated program configuration with user tests to confirm its operation when actually used by a user. In this way, the quality and accuracy are guaranteed by the verification unit verifying whether the generated program configuration actually works. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the verification unit inputs the generated program configuration into a generation AI, and the generation AI verifies the quality and accuracy.

[0074] The reception desk estimates the user's emotions and adjusts the timing of input based on the estimated emotions. For example, if the user is stressed, the reception desk delays the input timing to provide a relaxing environment. If the user is focused, the reception desk speeds up the input timing to efficiently receive information. Also, if the user is tired, the reception desk adjusts the input timing to allow for breaks while receiving information. In this way, the reception desk can provide a more appropriate input environment by adjusting the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function with 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 generative AI or not. For example, the reception desk inputs the user's emotion data into the generative AI, the generative AI estimates the emotion, and adjusts the input timing.

[0075] The reception desk analyzes the user's past input history and selects the optimal input method. For example, the reception desk prioritizes suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, the reception desk can suggest relevant input methods based on the content the user has entered in the past. In this way, the reception desk can provide the user with the optimal input method by analyzing past input history. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk inputs the user's past input history into a generative AI, and the generative AI selects the optimal input method.

[0076] The reception unit filters input based on the user's current projects and areas of interest. For example, the reception unit prioritizes receiving information related to the user's current projects. The reception unit can filter relevant input based on the user's areas of interest. It can also filter input based on areas the user has shown interest in in the past. This allows the reception unit to provide highly relevant information by filtering based on the user's projects and areas of interest. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit inputs data on the user's projects and areas of interest into a generative AI, and the generative AI performs the filtering.

[0077] The reception desk estimates the user's emotions and determines the priority of inputs based on the estimated emotions. For example, if the user is nervous, the reception desk may prioritize important inputs. If the user is relaxed, the reception desk may prioritize detailed inputs. Also, if the user is in a hurry, the reception desk may prioritize inputs that can be processed quickly. In this way, the reception desk can provide a more appropriate input order by determining the priority of inputs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 or without a generative AI. For example, the reception desk inputs the user's emotion data into a generative AI, which estimates the emotions and determines the priority of inputs.

[0078] The reception unit prioritizes accepting highly relevant inputs, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit prioritizes accepting information related to that region. If the user is traveling, the reception unit can prioritize accepting information related to the travel destination. Also, if the user is at home, the reception unit can prioritize accepting information about the area around their home. In this way, the reception unit can prioritize accepting highly relevant information by taking into account the user's geographical location. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit inputs the user's geographical location information into a generative AI, and the generative AI prioritizes accepting highly relevant inputs.

[0079] The reception unit analyzes the user's social media activity during input and accepts relevant input. For example, the reception unit can accept relevant input based on information the user has shared on social media. The reception unit can also accept relevant input based on accounts the user follows on social media. Furthermore, the reception unit can accept relevant input based on topics the user has shown interest in on social media. This allows the reception unit to efficiently receive relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit inputs the user's social media activity data into a generative AI, and the generative AI accepts relevant input.

[0080] The generation unit estimates the user's emotions and adjusts the presentation of the generated program structure based on the estimated user emotions. For example, if the user is relaxed, the generation unit uses a visually calm presentation. If the user is in a hurry, the generation unit can use a concise and to-the-point presentation. If the user is excited, the generation unit can use a visually stimulating presentation. In this way, the generation unit can provide a more appropriate presentation by adjusting the presentation of the program structure according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 these examples. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit inputs user emotion data into the generation AI, the generation AI estimates the emotions, and adjusts the presentation of the program structure.

[0081] The generation unit adjusts the level of detail of the generated program based on its importance. For example, in the case of an important program configuration, the generation unit can generate a detailed explanation. In the case of a general program configuration, the generation unit can generate a concise explanation. In the case of an auxiliary program configuration, the generation unit can generate a summary containing only the essential points. In this way, the generation unit can provide a program configuration with an appropriate level of detail by adjusting the level of detail of the generated program based on its importance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs program importance data into the generation AI, and the generation AI adjusts the level of detail of the generated program.

[0082] The generation unit applies different generation algorithms depending on the program category during generation. For example, the generation unit applies a specific algorithm for programs related to game logic. For programs related to graphics, it may apply a different algorithm. Furthermore, for programs related to user interfaces, it may apply yet another algorithm. In this way, the generation unit can provide an appropriate program structure by applying different generation algorithms depending on the program category. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs program category data into the generation AI, and the generation AI applies a different generation algorithm.

[0083] The generation unit estimates the user's emotions and adjusts the length of the generated program structure based on the estimated emotions. For example, if the user is in a hurry, the generation unit generates a short, concise program structure. If the user is relaxed, the generation unit can generate a longer program structure that includes detailed explanations. If the user is excited, the generation unit can generate a program structure with visually stimulating effects. In this way, the generation unit can provide a program structure of appropriate length by adjusting its length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit inputs user emotion data into the generation AI, the generation AI estimates the emotions, and adjusts the length of the program structure.

[0084] The generation unit determines the generation priority based on the program submission dates during generation. For example, the generation unit prioritizes generating program configurations with approaching deadlines. It can postpone generating program configurations with distant submission dates. Furthermore, the generation unit can generate program configurations with unknown submission dates after the generation of other program configurations is complete. In this way, the generation unit can provide program configurations in the appropriate order by determining the generation priority based on the program submission dates. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs program submission date data into the generation AI, and the generation AI determines the generation priority.

[0085] The generation unit adjusts the generation order based on the relevance of the programs during generation. For example, the generation unit prioritizes generating program configurations with high relevance. The generation unit can postpone generating program configurations with low relevance. Furthermore, the generation unit can generate program configurations whose relevance is unknown after the generation of other program configurations is complete. In this way, the generation unit can provide program configurations in an appropriate order by adjusting the generation order based on the relevance of the programs. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit inputs program relevance data into the generation AI, and the generation AI adjusts the generation order.

[0086] The service provider estimates the user's emotions and adjusts the display method of the provided diagrams based on the estimated emotions. For example, if the user is nervous, the service provider may provide a simple and highly visible display method. If the user is relaxed, the service provider may provide a display method that includes detailed information. If the user is in a hurry, the service provider may provide a display method that gets straight to the point. In this way, the service provider can provide an appropriate display method by adjusting the display method of the diagrams according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using the generative AI or not. For example, the service provider inputs the user's emotion data into the generative AI, the generative AI estimates the emotion, and adjusts the display method of the diagrams.

[0087] The provider adjusts the level of detail of the diagrams based on the importance of the generated program configurations at the time of provision. For example, the provider provides detailed diagrams for important program configurations. For general program configurations, the provider can provide concise diagrams. Furthermore, for auxiliary program configurations, the provider can provide diagrams containing only the essentials. In this way, the provider can provide diagrams with appropriate levels of detail by adjusting the level of detail of the diagrams based on the importance of the program configurations. Some or all of the above processing in the provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the provider inputs program configuration importance data into a generation AI, and the generation AI adjusts the level of detail of the diagrams.

[0088] The provider applies different diagram formats depending on the program category at the time of provision. For example, the provider applies a specific diagram format for programs related to game logic. The provider may apply a different diagram format for programs related to graphics. Furthermore, the provider may apply yet another diagram format for programs related to user interfaces. In this way, the provider can provide appropriate diagrams by applying different diagram formats depending on the program category. Some or all of the above processing in the provider may be performed using a generation AI, or not using a generation AI. For example, the provider inputs program category data into a generation AI, and the generation AI applies different diagram formats.

[0089] The service provider estimates the user's emotions and adjusts the length of the provided diagrams based on the estimated emotions. For example, if the user is in a hurry, the service provider may provide a short, concise diagram. If the user is relaxed, the service provider may provide a longer diagram with detailed explanations. If the user is excited, the service provider may provide a diagram with visually stimulating effects. In this way, the service provider can provide diagrams of appropriate length by adjusting the length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 or without a generative AI. For example, the service provider inputs user emotion data into a generative AI, which estimates the emotion and adjusts the length of the diagram.

[0090] The provisioning department determines the priority of the diagrams based on the submission dates of the generated program configurations at the time of provision. For example, the provisioning department will provide diagrams of program configurations with approaching deadlines first. The provisioning department may postpone providing diagrams of program configurations with distant submission dates. Furthermore, the provisioning department may provide diagrams of program configurations with unknown submission dates after the provision of other diagrams has been completed. In this way, the provisioning department can provide diagrams in the appropriate order by determining the priority of the diagrams based on the submission dates of the program configurations. Some or all of the above processing in the provisioning department may be performed using a generation AI, or it may be performed without a generation AI. For example, the provisioning department inputs program configuration submission date data into a generation AI, and the generation AI determines the priority of the diagrams.

[0091] The provider adjusts the order of the diagrams based on the relationships between the generated program configurations when providing them. For example, the provider prioritizes providing diagrams of highly relevant program configurations. The provider may postpone providing diagrams of less relevant program configurations. Furthermore, the provider may provide diagrams of program configurations whose relationships are unknown after the provision of other diagrams has been completed. In this way, the provider can provide the diagrams in the appropriate order by adjusting the order of the diagrams based on the relationships between the program configurations. Some or all of the above processing in the provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the provider inputs program configuration relationship data into a generation AI, and the generation AI adjusts the order of the diagrams.

[0092] The verification unit estimates the user's emotions and adjusts the verification method based on the estimated emotions. For example, if the user is nervous, the verification unit can provide a simple and highly visible verification method. If the user is relaxed, the verification unit can provide a verification method that includes detailed information. If the user is in a hurry, the verification unit can provide a concise verification method. In this way, the verification unit can provide an appropriate verification method by adjusting it according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the verification unit may be performed using the generative AI or not. For example, the verification unit inputs the user's emotion data into the generative AI, the generative AI estimates the emotion, and adjusts the verification method.

[0093] The verification unit, during verification, selects the optimal verification method by referring to the past operation history of the generated program configuration. For example, the verification unit selects the optimal verification method based on program configurations that have operated successfully in the past. The verification unit can adjust the verification method based on program configurations that have produced errors in the past. Furthermore, the verification unit can analyze the past operation history and select the most efficient verification method. In this way, the verification unit can provide the optimal verification method by referring to the past operation history. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the verification unit inputs the past operation history data of the generated program configuration into the generation AI, and the generation AI selects the optimal verification method.

[0094] The verification unit applies different verification methods depending on the category of the generated program configuration during verification. For example, the verification unit applies a specific verification method for programs related to game logic. For programs related to graphics, the verification unit may apply a different verification method. Furthermore, for programs related to user interfaces, the verification unit may apply yet another verification method. In this way, the verification unit can provide an appropriate verification method by applying different verification methods depending on the category of the program configuration. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the verification unit inputs program configuration category data into the generation AI, and the generation AI applies different verification methods.

[0095] The verification unit estimates the user's emotions and determines the priority of verification based on the estimated emotions. For example, if the user is nervous, the verification unit will prioritize important verifications. If the user is relaxed, the verification unit will prioritize detailed verifications. Also, if the user is in a hurry, the verification unit will prioritize items that can be checked quickly. In this way, the verification unit can perform verifications in the appropriate order by determining the priority of verifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 verification unit may be performed using or without a generative AI. For example, the verification unit inputs the user's emotion data into a generative AI, the generative AI estimates the emotions, and determines the priority of verifications.

[0096] The verification unit selects the optimal verification method during verification, taking into account the geographical location information of the generated program configuration. For example, if the user is in a specific region, the verification unit applies a verification method related to that region. If the user is traveling, the verification unit can apply a verification method related to the travel destination. Furthermore, if the user is at home, the verification unit can select a verification method based on information about the area around their home. In this way, the verification unit can provide the optimal verification method by taking geographical location information into consideration. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the verification unit inputs the geographical location data of the generated program configuration into a generation AI, and the generation AI selects the optimal verification method.

[0097] The verification unit improves the accuracy of the verification by referring to relevant literature for the generated program configuration during the verification process. For example, the verification unit verifies the accuracy of the generated program configuration based on the relevant literature. The verification unit can identify areas for improvement in the generated program configuration by referring to the relevant literature. Furthermore, the verification unit can optimize the generated program configuration based on the relevant literature. In this way, the verification unit can improve the accuracy of the verification by referring to the relevant literature. Some or all of the above processing in the verification unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the verification unit inputs the relevant literature data for the generated program configuration into the generation AI, and the generation AI improves the accuracy of the verification.

[0098] The guide unit estimates the user's emotions and adjusts the guide's display method based on the estimated emotions. For example, if the user is nervous, the guide unit provides a simple and highly visible display method. If the user is relaxed, the guide unit can provide a display method that includes detailed information. If the user is in a hurry, the guide unit can provide a concise display method. In this way, the guide unit can provide appropriate guidance by adjusting the guide's display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the guide unit may be performed using the generative AI or not. For example, the guide unit inputs the user's emotion data into the generative AI, the generative AI estimates the emotion, and adjusts the guide's display method.

[0099] The guide unit, when displaying a guide, selects the optimal guide method by referring to the user's past operation history. For example, the guide unit selects the optimal guide method based on the operation methods the user has frequently used in the past. The guide unit can prioritize guiding users to specific operation methods based on the user's past operation history. The guide unit can also provide relevant guide methods based on operations the user has performed in the past. In this way, the guide unit can provide the optimal guide method by referring to the past operation history. Some or all of the above processing in the guide unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the guide unit inputs the user's past operation history data into a generation AI, and the generation AI selects the optimal guide method.

[0100] The guide unit estimates the user's emotions and adjusts the guide's operating procedures based on the estimated emotions. For example, if the user is nervous, the guide unit can provide simple and highly visible operating procedures. If the user is relaxed, the guide unit can provide operating procedures that include detailed information. Also, if the user is in a hurry, the guide unit can provide operating procedures that get straight to the point. In this way, the guide unit can provide appropriate operating procedures by adjusting the guide's operating procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 guide unit may be performed using or without a generative AI. For example, the guide unit inputs the user's emotion data into a generative AI, the generative AI estimates the emotion, and adjusts the guide's operating procedures.

[0101] The guide unit selects the optimal guide method when displaying a guide, taking into account the user's device information. For example, if the user is using a smartphone, the guide unit can provide a guide method that matches the screen size. If the user is using a tablet, the guide unit can provide a guide method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the guide unit can provide a concise and highly visible guide method. In this way, the guide unit can provide the optimal guide method by taking device information into account. Some or all of the above processing in the guide unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the guide unit inputs the user's device information data into a generation AI, and the generation AI selects the optimal guide method.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The reception desk can analyze user input and infer the user's intent. For example, if a user inputs "Tell me how to make a character run in the game engine," the reception desk will analyze the intent and provide detailed information about the character's movements. Furthermore, even if the user provides ambiguous input, the reception desk can infer and provide relevant information. In addition, the reception desk can refer to the user's past input history and make inferences based on similar input. This allows the reception desk to accurately grasp the user's intent and provide appropriate information.

[0104] The generation unit can optimize the generated program configuration. For example, it can remove unnecessary code to improve the performance of the generated program configuration. It can also add comments and documentation to improve the readability of the generated program configuration. Furthermore, it can detect and fix vulnerabilities to enhance the security of the generated program configuration. In this way, the generation unit can improve the quality of the generated program configuration.

[0105] The service provider can customize the generated program configuration according to the user's skill level. For example, beginner users can be provided with diagrams that include detailed explanations and tutorials. Intermediate users can be provided with diagrams that highlight key points, and advanced users can be provided with concise diagrams. Furthermore, the service provider can adjust the content of the diagrams provided based on user feedback. This allows the service provider to provide diagrams that are appropriate for the user's skill level.

[0106] The verification unit can simulate the operating environment of the generated program configuration. For example, the verification unit can simulate operation in different hardware and software environments to verify the compatibility of the program configuration. It can also simulate operation in different network environments to verify the performance of the program configuration. Furthermore, the verification unit can simulate different user scenarios to verify the usability of the program configuration. This allows the verification unit to pre-verify the operating environment of the generated program configuration and prevent problems before they occur.

[0107] The reception desk can estimate the user's emotions and adjust the input content based on those estimates. For example, if the user is stressed, the reception desk can simplify the input content to help the user relax. If the user is excited, the reception desk can provide detailed information to keep the user interested. Furthermore, if the user is tired, the reception desk can divide the input content and accept information in stages with breaks in between. In this way, the reception desk can adjust the input content according to the user's emotions and provide a more appropriate input environment.

[0108] The generation unit can estimate the user's emotions and adjust the difficulty of the program configuration it generates based on those emotions. For example, if the user is relaxed, the generation unit can generate a program configuration with a high difficulty level. If the user is tense, the generation unit can generate a program configuration with a low difficulty level. Furthermore, if the user is excited, the generation unit can generate a challenging program configuration. In this way, the generation unit can adjust the difficulty of the program configuration according to the user's emotions and provide an appropriate program configuration.

[0109] The service provider can estimate the user's emotions and adjust the colors of the images it provides based on those emotions. For example, if the user is relaxed, the service provider can provide images with calming colors. If the user is excited, the service provider can provide images with vibrant colors. Furthermore, if the user is tired, the service provider can provide images with eye-friendly colors. In this way, the service provider can adjust the colors of the images according to the user's emotions and provide visually comfortable images.

[0110] The verification unit can estimate the user's emotions and adjust the frequency of verification based on those emotions. For example, if the user is nervous, the verification unit will perform verifications more frequently to increase the user's sense of security. Conversely, if the user is relaxed, the verification unit can reduce the frequency of verifications to avoid interrupting the user's work. Furthermore, if the user is in a hurry, the verification unit can perform only essential verifications, allowing the work to proceed quickly. In this way, the verification unit can adjust the frequency of verifications according to the user's emotions and provide an appropriate verification method.

[0111] The guide unit can estimate the user's emotions and adjust the guide content based on those emotions. For example, if the user is nervous, the guide unit can provide a simple and easy-to-understand guide. If the user is relaxed, the guide unit can provide a guide with more detailed information. Furthermore, if the user is excited, the guide unit can provide a visually appealing guide. In this way, the guide unit can adjust the guide content according to the user's emotions and provide an appropriate guide.

[0112] The guidance unit can analyze the user's past operation history and provide the optimal guidance method. For example, it can provide the optimal guidance based on the operation methods the user has frequently used in the past. It can also prioritize guiding users through specific operation methods based on the user's past operation history. Furthermore, it can provide relevant guidance methods based on operations the user has performed in the past. In this way, the guidance unit can provide the user with the most optimal guidance method by analyzing past operation history.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The reception unit receives input from the user. The reception unit can receive information from the user in various formats, such as text input, voice input, and image input. The reception unit may also include a guide unit that guides the user on the format and content of the information they input. Step 2: The generation unit generates a program configuration based on the information received by the reception unit. The generation unit automatically generates the program configuration using a generation AI. For example, the generation AI analyzes the user's input and generates the necessary program configuration. Step 3: The provider unit provides the program configuration generated by the generator unit as a diagram. The provider unit provides the generated program configuration as a diagram in formats such as PDF, SVG, or interactive graph. Step 4: The verification unit checks the quality and accuracy of the program configuration generated by the generation unit. For example, the verification unit checks whether the generated program configuration actually works using methods such as unit tests, system tests, and user tests.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and verification unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and automatically generates a program configuration using generation AI. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated program configuration as a diagram. The verification unit is implemented by the specific processing unit 290 of the data processing device 12 and verifies the quality and accuracy of the generated program configuration. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and verification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates a program configuration using a generation AI. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated program configuration as a diagram. The verification unit is implemented by the specific processing unit 290 of the data processing unit 12 and verifies the quality and accuracy of the generated program configuration. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and verification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates a program configuration using a generation AI. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated program configuration as a diagram. The verification unit is implemented by the specific processing unit 290 of the data processing unit 12 and verifies the quality and accuracy of the generated program configuration. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and verification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates a program configuration using a generation AI. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated program configuration as a diagram. The verification unit is implemented by the specific processing unit 290 of the data processing unit 12 and verifies the quality and accuracy of the generated program configuration. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) A reception area that receives input from users, A generation unit that generates a program configuration based on the information received by the reception unit, A providing unit that provides the program configuration generated by the generation unit as a diagram, The system includes a verification unit that verifies the quality and accuracy of the program configuration generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It includes a guide section that provides guidance on the format and content of the information entered by the user. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate program configuration using generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, The generated program configuration is provided as a diagram. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned verification unit is Verify whether the generated program configuration actually works. 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 input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When inputting information, 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 inputs 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 users input data, the system prioritizes accepting input that is highly relevant, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During input, the system analyzes the user's social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is This program estimates the user's emotions and adjusts the way it expresses the generated program structure based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, adjust the level of detail based on the importance of the program. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, different generation algorithms are applied depending on the program category. 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 program structure generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, the generation priority is determined based on the program submission date. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the generation order is adjusted based on the program's relevance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the provided diagrams are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the data, adjust the level of detail in the diagrams based on the importance of the generated program configuration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the content, different diagram formats will be applied depending on the program category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the provided diagrams based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the data, the priority of the diagrams will be determined based on the submission timing of the generated program configuration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the software, adjust the order of the diagrams based on the relationships between the generated program configurations. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned verification unit is We estimate the user's emotions and adjust the confirmation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned verification unit is During verification, the system selects the optimal verification method by referring to the past operation history of the generated program configuration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned verification unit is During verification, different verification methods are applied depending on the category of the generated program configuration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned verification unit is The system estimates the user's emotions and determines the priority of confirmations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned verification unit is During verification, the optimal verification method is selected, taking into account the geographical location information of the generated program configuration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned verification unit is During verification, we improve the accuracy of the verification by referring to relevant literature for the generated program configuration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned guide section is It estimates the user's emotions and adjusts how the guide is displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned guide section is When displaying a guide, the system selects the optimal guiding method by referring to the user's past operation history. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned guide section is The system estimates the user's emotions and adjusts the guide's steps based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned guide section is When displaying guides, the system selects the optimal guiding method considering the user's device information. The system described in Appendix 2, characterized by the features described herein. [Explanation of symbols]

[0187] 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 area that receives input from users, A generation unit that generates a program configuration based on the information received by the reception unit, A providing unit that provides the program configuration generated by the generation unit as a diagram, The system includes a verification unit that verifies the quality and accuracy of the program configuration generated by the generation unit. A system characterized by the following features.

2. The aforementioned reception unit is It includes a guide section that provides guidance on the format and content of the information entered by the user. The system according to feature 1.

3. The generating unit is Generate program configuration using generative AI. The system according to feature 1.

4. The aforementioned supply unit is, The generated program configuration is provided as a diagram. The system according to feature 1.

5. The aforementioned verification unit is Verify whether the generated program configuration actually works. The system according to feature 1.

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

7. The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When inputting information, 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 inputs based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When users input data, the system prioritizes accepting input that is highly relevant, taking into account their geographical location. The system according to feature 1.

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