system

Abstract

The system according to this embodiment aims to streamline the external design document creation process and quickly generate consistent, high-quality design documents. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, and a creation unit. The analysis unit analyzes the requirements. The generation unit generates a UI / UX design based on the requirements analyzed by the analysis unit. The creation unit creates an external design document based on the design generated by the generation unit.

Description

Technical Field

[0006] , ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes a great deal of time and cost to create an external design document, and it is difficult to maintain consistency.

[0005] < The system according to the embodiment aims to streamline the process of creating an external design document and quickly generate a high-quality and consistent design document.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a generation unit, and a creation unit. The analysis unit analyzes requirements. The generation unit generates a UI / UX design based on the requirements analyzed by the analysis unit. The creation unit creates an external design document based on the design generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the process of creating external design documents and quickly generate consistent, high-quality design documents. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The automated solution system according to an embodiment of the present invention is a system for solving the problem of creating external design documents. This system analyzes requirements using natural language processing, generates UI / UX designs using deep learning, and creates design documents using generative AI. This significantly streamlines the design process, reduces human error, and enables the rapid generation of consistent, high-quality design documents. For example, the system first analyzes system requirements using natural language processing. For example, it analyzes requirements such as displaying data A in the AA list when the login button is pressed, displaying benefits B on the home screen, and moving to the terms and conditions screen when the details button is pressed. This analysis uses natural language processing technologies such as large-scale language models. Next, the system generates UI / UX designs using deep learning. Specifically, it prepares design components using a Custom Layout Engine, automatically lays out the components, and optimizes the size of the components. This enables the automatic generation of user-friendly UI / UX designs. Finally, the system automatically generates external design documents using generative AI. For example, it uses a large-scale language model to create external design documents based on the analyzed requirements and the generated UI / UX designs. This significantly reduces the time required to create design documents and lowers costs. This leads to a more efficient design process, reduced human error, and the rapid generation of consistent, high-quality design documents. It also reduces the burden on specialists, allowing them to focus on more creative tasks. For example, with the current expansion of system development demand, the ability to instantly generate external design documents from system requirements anywhere with internet access improves the efficiency of system development. Thus, automated solution systems streamline the creation of external design documents and reduce the burden on specialists.

[0029] The automated solution system according to this embodiment comprises an analysis unit, a generation unit, and a creation unit. The analysis unit analyzes the requirements. The analysis unit analyzes the requirements using, for example, natural language processing technology. The analysis unit analyzes the content of the requirements in detail and clarifies the system requirements. The generation unit generates a UI / UX design based on the requirements analyzed by the analysis unit. The generation unit generates the UI / UX design using, for example, deep learning technology. The generation unit automatically lays out the design components and optimizes the size of the components. The generation unit can generate a user-friendly UI / UX design. The creation unit creates an external design document based on the design generated by the generation unit. The creation unit automatically generates the external design document using, for example, generation AI. The creation unit can significantly reduce the time required to create the design document and reduce costs. As a result, the automated solution system according to this embodiment can efficiently perform requirements analysis, UI / UX design generation, and external design document creation.

[0030] The analysis unit analyzes the requirements. For example, the analysis unit analyzes the requirements using natural language processing technology. Specifically, the analysis unit receives text data such as requirements documents and specifications as input and uses natural language processing technology to analyze its contents in detail. Natural language processing technology includes morphological analysis, syntactic analysis, and semantic analysis, and these are combined to accurately understand the content of the requirements. For example, morphological analysis is used to divide the text into words, syntactic analysis is used to analyze the structure of sentences, and semantic analysis is used to understand the meaning of sentences. Furthermore, the analysis unit extracts important keywords and phrases from the requirements and clarifies the system requirements based on them. For example, from the requirement statement "Users must log in from the login screen," the keywords "user," "login screen," and "login" are extracted, and the system requirements are defined based on these. The analysis unit saves these analysis results in a database so that subsequent generation and creation units can use them. In this way, the analysis unit can accurately and efficiently analyze the content of the requirements and clarify the system requirements.

[0031] The generation unit generates UI / UX designs based on the requirements analyzed by the analysis unit. The generation unit generates UI / UX designs using, for example, deep learning technology. Specifically, the generation unit receives requirement data provided by the analysis unit as input and generates UI / UX designs using a deep learning model. Deep learning models include convolutional neural networks (CNNs) and generative opposing networks (GANs), which are used to automatically lay out design components and optimize their sizes. For example, a CNN is used to learn design patterns based on requirements, and a GAN is used to generate new designs. The generation unit evaluates the generated designs and makes necessary modifications to create user-friendly UI / UX designs. For example, usability testing is conducted, and the design is improved based on user feedback. The generation unit saves the generated designs to a database, making them available to subsequent creation units. This allows the generation unit to efficiently generate high-quality UI / UX designs based on requirements.

[0032] The creation department creates external design documents based on the designs generated by the generation department. The creation department can, for example, automatically generate external design documents using generational AI. Specifically, the creation department receives design data provided by the generation department as input and automatically generates external design documents using generational AI. The generational AI includes natural language generation (NLG) and template-based generation technologies, which are used to automatically create each section of the design document. For example, NLG is used to generate design descriptions, and template-based generation technologies are used to format the design document. The creation department evaluates the generated design document and makes corrections as needed. For example, it reviews the content of the design document and corrects errors and ambiguities. The creation department saves the final design document to a database, making it accessible to the project team and clients. This allows the creation department to significantly reduce the time and cost of creating external design documents. Furthermore, the creation department can continuously update the generational AI's training data to improve the quality of the design documents. This enables the creation department to efficiently produce high-quality external design documents and contribute to the success of the project.

[0033] The analysis unit can analyze requirements using natural language processing. For example, the analysis unit analyzes requirements using natural language processing techniques. The analysis unit analyzes the content of the requirements in detail and clarifies the system requirements. This improves the accuracy of requirement analysis by using natural language processing. Natural language processing includes techniques such as morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the requirements text data into a generating AI and have the generating AI perform the requirements analysis.

[0034] The generation unit can generate UI / UX designs using deep learning. For example, the generation unit generates UI / UX designs using deep learning technology. The generation unit automatically lays out design components and optimizes their sizes. The generation unit can generate user-friendly UI / UX designs. This improves the accuracy of UI / UX design generation through the use of deep learning. Deep learning includes technologies such as neural networks and convolutional neural networks. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input requirement text data into a generation AI and have the generation AI perform the UI / UX design generation.

[0035] The generation unit can automatically lay out design components and optimize their sizes. For example, the generation unit can automatically lay out design components and optimize their sizes. The generation unit can generate user-friendly UI / UX designs. This allows for the generation of user-friendly UI / UX designs through the optimization of the layout and size of design components. Design components include, for example, buttons, text fields, and images. Optimization includes, for example, methods for adjusting sizes and layout criteria. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not. For example, the generation unit can input design component data into a generation AI and have the generation AI perform layout and size optimization.

[0036] The creation unit can automatically generate external design documents using a generation AI. For example, the creation unit can automatically generate external design documents using a generation AI. The creation unit can significantly reduce the time required to create design documents and lower costs. Thus, by using a generation AI, the time required to create external design documents can be reduced and costs can be lowered. Generation AI includes, for example, text generation AI and image generation AI. Some or all of the above-described processes in the creation unit may be performed using AI, or not using AI. For example, the creation unit can input generated UI / UX design data into a generation AI and have the generation AI create the external design documents.

[0037] The analysis unit can improve the accuracy of its analysis by referring to past project data. For example, the analysis unit can extract similar requirements from past project data and use them as a reference for the analysis. Based on past project data, the analysis unit can determine the priority of requirements and perform the analysis efficiently. The analysis unit analyzes past project data and finds common patterns in the analysis of requirements. This improves the accuracy of the analysis by referring to past project data. Past project data includes, for example, project history data and past requirements definition documents. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input past project data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0038] The analysis unit can determine the priority of analyses based on the importance of the requirements. For example, the analysis unit can evaluate the importance of the requirements and start the analysis from the most important requirements. The analysis unit can optimally allocate analysis resources based on the importance of the requirements. The analysis unit can adjust the analysis schedule taking into account the importance of the requirements. This enables efficient analysis by determining the priority of analyses based on the importance of the requirements. The importance of requirements includes, for example, business impact and technical difficulty. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input requirement importance data into a generating AI and have the generating AI perform the analysis priority determination.

[0039] The analysis unit can perform analysis while taking into account the user's industry-specific terminology. For example, the analysis unit can register the user's industry-specific terminology in a dictionary and consider it during analysis. The analysis unit prioritizes the analysis of requirements that include industry-specific terminology. The analysis unit reflects industry-specific terminology in the analysis results and provides them in a way that is easy for the user to understand. This makes the analysis results easier for the user to understand by taking industry-specific terminology into account. Industry-specific terminology includes, for example, medical terms, financial terms, and technical terms. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input industry-specific terminology data into a generating AI and have the generating AI perform the analysis.

[0040] The analysis unit can perform analysis while taking the user's geographical background into consideration. For example, the analysis unit can determine the priority of requirements based on the user's geographical background. The analysis unit can customize the analysis results while taking the geographical background into consideration. The analysis unit can adjust the method of analyzing requirements based on the geographical background. This makes the analysis results more appropriate for the user by taking the geographical background into consideration. Geographical background includes, for example, region-specific culture, language, and regulations. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical background data into a generating AI and have the generating AI perform the analysis.

[0041] The generation unit can improve the accuracy of generation by referring to past design data. For example, the generation unit can extract similar designs from past design data and use them as a reference for generation. Based on past design data, the generation unit determines design priorities and generates efficiently. The generation unit analyzes past design data and finds common patterns in design generation. As a result, the accuracy of generation is improved by referring to past design data. Past design data includes, for example, design history and user feedback. Some or all of the above processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input past design data into a generation AI and have the generation AI perform the task of improving the accuracy of generation.

[0042] The generation unit can optimize the design based on the user's device characteristics. For example, if the user is using a smartphone, the generation unit provides a design that matches the screen size. If the user is using a tablet, the generation unit provides a design optimized for a larger screen. If the user is using a desktop, the generation unit provides a design optimized for a larger screen. In this way, by optimizing the design based on device characteristics, a user-friendly design can be provided. Device characteristics include, for example, screen size, resolution, and input method. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input device characteristic data into a generation AI and have the generation AI perform the design optimization.

[0043] The generation unit can perform generation while considering the user's industry-specific design requirements. For example, the generation unit can register the user's industry-specific design requirements in a dictionary and consider them during generation. The generation unit prioritizes generating designs that include industry-specific design requirements. The generation unit reflects industry-specific design requirements in the generation results and provides them to the user in an easy-to-understand format. In this way, by considering industry-specific design requirements, it is possible to provide designs that are easy for users to understand. Industry-specific design requirements include, for example, design standards for the medical industry and design requirements for the financial industry. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input industry-specific design requirement data into a generation AI and have the generation AI perform the generation.

[0044] The generation unit can generate designs while taking the user's cultural background into consideration. For example, the generation unit adjusts the colors and layout of the design based on the user's cultural background. The generation unit customizes the elements of the design while taking the cultural background into consideration. The generation unit adjusts the style of the design based on the cultural background. This allows for the provision of designs that are more appropriate for the user by taking cultural background into consideration. Cultural background includes, for example, regional culture, customs, and values. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input cultural background data into a generation AI and have the generation AI perform the design generation.

[0045] The creation unit can improve the accuracy of its creation by referring to past design document data. For example, the creation unit can extract similar design documents from past design document data and use them as a reference for creation. Based on past design document data, the creation unit can determine the priority of design documents and create them efficiently. The creation unit can analyze past design document data and find common patterns in design document creation. As a result, the accuracy of creation is improved by referring to past design document data. Past design document data includes, for example, design document history data and past project data. Some or all of the above processes in the creation unit may be performed using, for example, AI, or not using AI. For example, the creation unit can input past design document data into a generation AI and have the generation AI perform the task of improving the accuracy of creation.

[0046] The creation unit can prioritize the content of the design document based on the importance of the requirements. For example, the creation unit evaluates the importance of the requirements and includes the most important requirements in the design document first. The creation unit optimally allocates resources for the design document based on the importance of the requirements. The creation unit adjusts the schedule for the design document, taking into account the importance of the requirements. This enables efficient design document creation by prioritizing the content of the design document based on the importance of the requirements. The content of the design document includes, for example, the order in which important functions are described and whether or not detailed explanations are included. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input requirement importance data into a generation AI and have the generation AI prioritize the content of the design document.

[0047] The creation unit can create documents while considering the user's industry-specific format. For example, the creation unit can register the user's industry-specific format in a dictionary and consider it during creation. The creation unit prioritizes creating design documents that include industry-specific formats. The creation unit reflects the industry-specific format in the creation results and provides them to the user in an easy-to-understand format. In this way, by considering industry-specific formats, it is possible to provide design documents that are easy for the user to understand. Industry-specific formats include, for example, formats for the medical industry, formats for the financial industry, etc. Some or all of the above processing in the creation unit may be performed using, for example, AI, or not using AI. For example, the creation unit can input industry-specific format data into a generation AI and have the generation AI perform the creation.

[0048] The creation unit can create documents while taking the user's geographical background into consideration. For example, the creation unit can determine the priority of design documents based on the user's geographical background. The creation unit can customize the content of design documents while taking geographical background into consideration. The creation unit can adjust the style of design documents while taking geographical background into consideration. This allows for the provision of design documents that are more appropriate for the user by considering geographical background. Geographical background includes, for example, region-specific regulations, culture, and language. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input geographical background data into a generation AI and have the generation AI perform the creation.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The analysis unit can improve the accuracy of its analysis by referring to past project data. For example, it can extract similar requirements from past project data and use them as a reference for analysis. Based on past project data, it can determine the priority of requirements and perform analysis efficiently. By analyzing past project data, it can find common patterns in requirements analysis. In this way, the accuracy of the analysis is improved by referring to past project data. Past project data includes, for example, project history data and past requirements definition documents. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past project data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0051] The generation unit can improve the accuracy of generation by referring to past design data. For example, it can extract similar designs from past design data and use them as a reference for generation. Based on past design data, it can determine design priorities and generate designs efficiently. It can analyze past design data to find common patterns in design generation. As a result, the accuracy of generation is improved by referring to past design data. Past design data includes, for example, design history and user feedback. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past design data into a generation AI and have the generation AI perform the task of improving the accuracy of generation.

[0052] The creation unit can improve the accuracy of its creation by referring to past design document data. For example, it can extract similar design documents from past design document data and use them as a reference for creation. Based on past design document data, it can determine the priority of design documents and create them efficiently. It can analyze past design document data to find common patterns in design document creation. As a result, the accuracy of creation is improved by referring to past design document data. Past design document data includes, for example, design document history data and past project data. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input past design document data into a generation AI and have the generation AI perform the task of improving the accuracy of creation.

[0053] The analysis unit can perform analysis while considering the user's industry-specific terminology. For example, it can register the user's industry-specific terminology in a dictionary and consider it during analysis. It prioritizes the analysis of requirements that include industry-specific terminology. It reflects industry-specific terminology in the analysis results and provides them in a way that is easy for the user to understand. In this way, considering industry-specific terminology makes the analysis results easier for the user to understand. Industry-specific terminology includes, for example, medical terms, financial terms, and technical terms. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input industry-specific terminology data into a generating AI and have the generating AI perform the analysis.

[0054] The generation unit can optimize the design based on the user's device characteristics. For example, if the user is using a smartphone, it provides a design that matches the screen size. If the user is using a tablet, it provides a design optimized for a larger screen. If the user is using a desktop, it provides a design optimized for a larger screen. By optimizing the design based on device characteristics, it is possible to provide a user-friendly design. Device characteristics include, for example, screen size, resolution, and input method. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input device characteristic data into a generation AI and have the generation AI perform the design optimization.

[0055] The creation unit can create documents while considering the user's industry-specific format. For example, it can register the user's industry-specific format in a dictionary and consider it during creation. It can prioritize the creation of design documents that include industry-specific formats. It can reflect the industry-specific format in the creation results and provide them in an easy-to-understand format for the user. In this way, by considering industry-specific formats, it is possible to provide design documents that are easy for the user to understand. Industry-specific formats include, for example, formats for the medical industry, formats for the financial industry, etc. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input industry-specific format data into a generation AI and have the generation AI perform the creation.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The analysis unit analyzes the requirements. The analysis unit analyzes the requirements using, for example, natural language processing technology, and analyzes the content of the requirements in detail to clarify the system requirements. Step 2: The generation unit generates a UI / UX design based on the requirements analyzed by the analysis unit. For example, the generation unit generates the UI / UX design using deep learning technology, automatically lays out the design components, and optimizes the size of the components. This enables the generation of a user-friendly UI / UX design. Step 3: The creation unit creates external design documents based on the designs generated by the generation unit. The creation unit can, for example, use generation AI to automatically generate external design documents, significantly reducing the time required to create design documents and lowering costs.

[0058] (Example of form 2) The automated solution system according to an embodiment of the present invention is a system for solving the problem of creating external design documents. This system analyzes requirements using natural language processing, generates UI / UX designs using deep learning, and creates design documents using generative AI. This significantly streamlines the design process, reduces human error, and enables the rapid generation of consistent, high-quality design documents. For example, the system first analyzes system requirements using natural language processing. For example, it analyzes requirements such as displaying data A in list AA when the login button is pressed, displaying benefits B on the home screen, and moving to the terms and conditions screen when the details button is pressed. This analysis uses natural language processing technologies such as large-scale language models. Next, the system generates UI / UX designs using deep learning. Specifically, it prepares design components using a Custom Layout Engine, automatically lays out the components, and optimizes the size of the components. This enables the automatic generation of user-friendly UI / UX designs. Finally, the system automatically generates external design documents using generative AI. For example, it uses a large-scale language model to create external design documents based on the analyzed requirements and the generated UI / UX designs. This significantly reduces the time required to create design documents and lowers costs. This leads to a more efficient design process, reduced human error, and the rapid generation of consistent, high-quality design documents. It also reduces the burden on specialists, allowing them to focus on more creative tasks. For example, with the current expansion of system development demand, the ability to instantly generate external design documents from system requirements anywhere with internet access improves the efficiency of system development. Thus, automated solution systems streamline the creation of external design documents and reduce the burden on specialists.

[0059] The automated solution system according to this embodiment comprises an analysis unit, a generation unit, and a creation unit. The analysis unit analyzes the requirements. The analysis unit analyzes the requirements using, for example, natural language processing technology. The analysis unit analyzes the content of the requirements in detail and clarifies the system requirements. The generation unit generates a UI / UX design based on the requirements analyzed by the analysis unit. The generation unit generates the UI / UX design using, for example, deep learning technology. The generation unit automatically lays out the design components and optimizes the size of the components. The generation unit can generate a user-friendly UI / UX design. The creation unit creates an external design document based on the design generated by the generation unit. The creation unit automatically generates the external design document using, for example, generation AI. The creation unit can significantly reduce the time required to create the design document and reduce costs. As a result, the automated solution system according to this embodiment can efficiently perform requirements analysis, UI / UX design generation, and external design document creation.

[0060] The analysis unit analyzes the requirements. For example, the analysis unit analyzes the requirements using natural language processing technology. Specifically, the analysis unit receives text data such as requirements documents and specifications as input and uses natural language processing technology to analyze its contents in detail. Natural language processing technology includes morphological analysis, syntactic analysis, and semantic analysis, and these are combined to accurately understand the content of the requirements. For example, morphological analysis is used to divide the text into words, syntactic analysis is used to analyze the structure of sentences, and semantic analysis is used to understand the meaning of sentences. Furthermore, the analysis unit extracts important keywords and phrases from the requirements and clarifies the system requirements based on them. For example, from the requirement statement "Users must log in from the login screen," the keywords "user," "login screen," and "login" are extracted, and the system requirements are defined based on these. The analysis unit saves these analysis results in a database so that subsequent generation and creation units can use them. In this way, the analysis unit can accurately and efficiently analyze the content of the requirements and clarify the system requirements.

[0061] The generation unit generates UI / UX designs based on the requirements analyzed by the analysis unit. The generation unit generates UI / UX designs using, for example, deep learning technology. Specifically, the generation unit receives requirement data provided by the analysis unit as input and generates UI / UX designs using a deep learning model. Deep learning models include convolutional neural networks (CNNs) and generative opposing networks (GANs), which are used to automatically lay out design components and optimize their sizes. For example, a CNN is used to learn design patterns based on requirements, and a GAN is used to generate new designs. The generation unit evaluates the generated designs and makes necessary modifications to create user-friendly UI / UX designs. For example, usability testing is conducted, and the design is improved based on user feedback. The generation unit saves the generated designs to a database, making them available to subsequent creation units. This allows the generation unit to efficiently generate high-quality UI / UX designs based on requirements.

[0062] The creation department creates external design documents based on the designs generated by the generation department. The creation department can, for example, automatically generate external design documents using generational AI. Specifically, the creation department receives design data provided by the generation department as input and automatically generates external design documents using generational AI. The generational AI includes natural language generation (NLG) and template-based generation technologies, which are used to automatically create each section of the design document. For example, NLG is used to generate design descriptions, and template-based generation technologies are used to format the design document. The creation department evaluates the generated design document and makes corrections as needed. For example, it reviews the content of the design document and corrects errors and ambiguities. The creation department saves the final design document to a database, making it accessible to the project team and clients. This allows the creation department to significantly reduce the time and cost of creating external design documents. Furthermore, the creation department can continuously update the generational AI's training data to improve the quality of the design documents. This enables the creation department to efficiently produce high-quality external design documents and contribute to the success of the project.

[0063] The analysis unit can analyze requirements using natural language processing. For example, the analysis unit analyzes requirements using natural language processing techniques. The analysis unit analyzes the content of the requirements in detail and clarifies the system requirements. This improves the accuracy of requirement analysis by using natural language processing. Natural language processing includes techniques such as morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the requirements text data into a generating AI and have the generating AI perform the requirements analysis.

[0064] The generation unit can generate UI / UX designs using deep learning. For example, the generation unit generates UI / UX designs using deep learning technology. The generation unit automatically lays out design components and optimizes their sizes. The generation unit can generate user-friendly UI / UX designs. This improves the accuracy of UI / UX design generation through the use of deep learning. Deep learning includes technologies such as neural networks and convolutional neural networks. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input requirement text data into a generation AI and have the generation AI perform the UI / UX design generation.

[0065] The generation unit can automatically lay out design components and optimize their sizes. For example, the generation unit can automatically lay out design components and optimize their sizes. The generation unit can generate user-friendly UI / UX designs. This allows for the generation of user-friendly UI / UX designs through the optimization of the layout and size of design components. Design components include, for example, buttons, text fields, and images. Optimization includes, for example, methods for adjusting sizes and layout criteria. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not. For example, the generation unit can input design component data into a generation AI and have the generation AI perform layout and size optimization.

[0066] The creation unit can automatically generate external design documents using a generation AI. For example, the creation unit can automatically generate external design documents using a generation AI. The creation unit can significantly reduce the time required to create design documents and lower costs. Thus, by using a generation AI, the time required to create external design documents can be reduced and costs can be lowered. Generation AI includes, for example, text generation AI and image generation AI. Some or all of the above-described processes in the creation unit may be performed using AI, or not using AI. For example, the creation unit can input generated UI / UX design data into a generation AI and have the generation AI create the external design documents.

[0067] The analysis unit can estimate the user's emotions and adjust the requirements analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit simplifies the analysis process and provides results quickly. If the user is relaxed, the analysis unit provides detailed analysis results to allow the user to understand them more deeply. If the user is in a hurry, the analysis unit prioritizes analyzing important requirements and provides results quickly. This allows for more appropriate analysis results by adjusting the requirements analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the requirements analysis method.

[0068] The analysis unit can improve the accuracy of its analysis by referring to past project data. For example, the analysis unit can extract similar requirements from past project data and use them as a reference for the analysis. Based on past project data, the analysis unit can determine the priority of requirements and perform the analysis efficiently. The analysis unit analyzes past project data and finds common patterns in the analysis of requirements. This improves the accuracy of the analysis by referring to past project data. Past project data includes, for example, project history data and past requirements definition documents. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input past project data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0069] The analysis unit can determine the priority of analyses based on the importance of the requirements. For example, the analysis unit can evaluate the importance of the requirements and start the analysis from the most important requirements. The analysis unit can optimally allocate analysis resources based on the importance of the requirements. The analysis unit can adjust the analysis schedule taking into account the importance of the requirements. This enables efficient analysis by determining the priority of analyses based on the importance of the requirements. The importance of requirements includes, for example, business impact and technical difficulty. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input requirement importance data into a generating AI and have the generating AI perform the analysis priority determination.

[0070] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. If the user is relaxed, the analysis unit provides a display method that includes detailed information. If the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0071] The analysis unit can perform analysis while taking into account the user's industry-specific terminology. For example, the analysis unit can register the user's industry-specific terminology in a dictionary and consider it during analysis. The analysis unit prioritizes the analysis of requirements that include industry-specific terminology. The analysis unit reflects industry-specific terminology in the analysis results and provides them in a way that is easy for the user to understand. This makes the analysis results easier for the user to understand by taking industry-specific terminology into account. Industry-specific terminology includes, for example, medical terms, financial terms, and technical terms. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input industry-specific terminology data into a generating AI and have the generating AI perform the analysis.

[0072] The analysis unit can perform analysis while taking the user's geographical background into consideration. For example, the analysis unit can determine the priority of requirements based on the user's geographical background. The analysis unit can customize the analysis results while taking the geographical background into consideration. The analysis unit can adjust the method of analyzing requirements based on the geographical background. This makes the analysis results more appropriate for the user by taking the geographical background into consideration. Geographical background includes, for example, region-specific culture, language, and regulations. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical background data into a generating AI and have the generating AI perform the analysis.

[0073] The generation unit can estimate the user's emotions and adjust the UI / UX design generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit will generate a design that progresses at a relaxed pace. If the user is in a hurry, the generation unit will generate a design that emphasizes the shortest route. If the user is excited, the generation unit will generate a design with visually stimulating effects. In this way, by adjusting the UI / UX design generation method according to the user's emotions, a more appropriate design can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the UI / UX design generation method.

[0074] The generation unit can improve the accuracy of generation by referring to past design data. For example, the generation unit can extract similar designs from past design data and use them as a reference for generation. Based on past design data, the generation unit determines design priorities and generates efficiently. The generation unit analyzes past design data and finds common patterns in design generation. As a result, the accuracy of generation is improved by referring to past design data. Past design data includes, for example, design history and user feedback. Some or all of the above processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input past design data into a generation AI and have the generation AI perform the task of improving the accuracy of generation.

[0075] The generation unit can optimize the design based on the user's device characteristics. For example, if the user is using a smartphone, the generation unit provides a design that matches the screen size. If the user is using a tablet, the generation unit provides a design optimized for a larger screen. If the user is using a desktop, the generation unit provides a design optimized for a larger screen. In this way, by optimizing the design based on device characteristics, a user-friendly design can be provided. Device characteristics include, for example, screen size, resolution, and input method. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input device characteristic data into a generation AI and have the generation AI perform the design optimization.

[0076] The generation unit can estimate the user's emotions and adjust the design's colors and layout based on those emotions. For example, if the user is relaxed, the generation unit provides calm colors and a simple layout. If the user is excited, the generation unit provides bright colors and a dynamic layout. If the user is tired, the generation unit provides highly visible colors and an intuitive layout. This allows for the provision of a more appropriate design by adjusting the design's colors and layout 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-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the design's colors and layout.

[0077] The generation unit can perform generation while considering the user's industry-specific design requirements. For example, the generation unit can register the user's industry-specific design requirements in a dictionary and consider them during generation. The generation unit prioritizes generating designs that include industry-specific design requirements. The generation unit reflects industry-specific design requirements in the generation results and provides them to the user in an easy-to-understand format. In this way, by considering industry-specific design requirements, it is possible to provide designs that are easy for users to understand. Industry-specific design requirements include, for example, design standards for the medical industry and design requirements for the financial industry. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input industry-specific design requirement data into a generation AI and have the generation AI perform the generation.

[0078] The generation unit can generate designs while taking the user's cultural background into consideration. For example, the generation unit adjusts the colors and layout of the design based on the user's cultural background. The generation unit customizes the elements of the design while taking the cultural background into consideration. The generation unit adjusts the style of the design based on the cultural background. This allows for the provision of designs that are more appropriate for the user by taking cultural background into consideration. Cultural background includes, for example, regional culture, customs, and values. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input cultural background data into a generation AI and have the generation AI perform the design generation.

[0079] The creation unit can estimate the user's emotions and adjust the method of creating the design document based on the estimated user emotions. For example, if the user is relaxed, the creation unit will generate a design document with detailed explanations. If the user is in a hurry, the creation unit will generate a concise design document that gets straight to the point. If the user is excited, the creation unit will generate a design document with visually stimulating effects. In this way, by adjusting the method of creating the design document according to the user's emotions, a more appropriate design document can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the method of creating the design document.

[0080] The creation unit can improve the accuracy of its creation by referring to past design document data. For example, the creation unit can extract similar design documents from past design document data and use them as a reference for creation. Based on past design document data, the creation unit can determine the priority of design documents and create them efficiently. The creation unit can analyze past design document data and find common patterns in design document creation. As a result, the accuracy of creation is improved by referring to past design document data. Past design document data includes, for example, design document history data and past project data. Some or all of the above processes in the creation unit may be performed using, for example, AI, or not using AI. For example, the creation unit can input past design document data into a generation AI and have the generation AI perform the task of improving the accuracy of creation.

[0081] The creation unit can prioritize the content of the design document based on the importance of the requirements. For example, the creation unit evaluates the importance of the requirements and includes the most important requirements in the design document first. The creation unit optimally allocates resources for the design document based on the importance of the requirements. The creation unit adjusts the schedule for the design document, taking into account the importance of the requirements. This enables efficient design document creation by prioritizing the content of the design document based on the importance of the requirements. The content of the design document includes, for example, the order in which important functions are described and whether or not detailed explanations are included. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input requirement importance data into a generation AI and have the generation AI prioritize the content of the design document.

[0082] The creation unit can estimate the user's emotions and adjust the layout of the design document based on the estimated emotions. For example, if the user is relaxed, the creation unit provides calm colors and a simple layout. If the user is excited, the creation unit provides bright colors and a dynamic layout. If the user is tired, the creation unit provides highly visible colors and an intuitive layout. This allows for the provision of more appropriate design documents by adjusting the layout according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the design document layout.

[0083] The creation unit can create documents while considering the user's industry-specific format. For example, the creation unit can register the user's industry-specific format in a dictionary and consider it during creation. The creation unit prioritizes creating design documents that include industry-specific formats. The creation unit reflects the industry-specific format in the creation results and provides them to the user in an easy-to-understand format. In this way, by considering industry-specific formats, it is possible to provide design documents that are easy for the user to understand. Industry-specific formats include, for example, formats for the medical industry, formats for the financial industry, etc. Some or all of the above processing in the creation unit may be performed using, for example, AI, or not using AI. For example, the creation unit can input industry-specific format data into a generation AI and have the generation AI perform the creation.

[0084] The creation unit can create documents while taking the user's geographical background into consideration. For example, the creation unit can determine the priority of design documents based on the user's geographical background. The creation unit can customize the content of design documents while taking geographical background into consideration. The creation unit can adjust the style of design documents while taking geographical background into consideration. This allows for the provision of design documents that are more appropriate for the user by considering geographical background. Geographical background includes, for example, region-specific regulations, culture, and language. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input geographical background data into a generation AI and have the generation AI perform the creation.

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

[0086] The analysis unit can estimate the user's emotions and adjust the requirements analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis process can be simplified and results can be provided quickly. If the user is relaxed, detailed analysis results can be provided to allow the user to understand more deeply. If the user is in a hurry, important requirements can be prioritized in the analysis and results can be provided quickly. In this way, more appropriate analysis results can be provided by adjusting the requirements analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the requirements analysis method.

[0087] The generation unit can estimate the user's emotions and adjust the UI / UX design generation method based on the estimated user emotions. For example, if the user is relaxed, it can generate a design that progresses at a leisurely pace. If the user is in a hurry, it can generate a design that emphasizes the shortest route. If the user is excited, it can generate a design with visually stimulating effects. In this way, by adjusting the UI / UX design generation method according to the user's emotions, a more appropriate design can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the UI / UX design generation method.

[0088] The creation unit can estimate the user's emotions and adjust the method of creating the design document based on the estimated emotions. For example, if the user is relaxed, it can generate a design document with detailed explanations. If the user is in a hurry, it can generate a concise design document that gets straight to the point. If the user is excited, it can generate a design document with visually stimulating effects. This allows for the provision of more appropriate design documents by adjusting the method of creating the design document 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-described processes in the creation unit may be performed using AI or not. For example, the creation unit can input user emotion data into a generative AI and have the generative AI adjust the method of creating the design document.

[0089] The analysis unit can improve the accuracy of its analysis by referring to past project data. For example, it can extract similar requirements from past project data and use them as a reference for analysis. Based on past project data, it can determine the priority of requirements and perform analysis efficiently. By analyzing past project data, it can find common patterns in requirements analysis. In this way, the accuracy of the analysis is improved by referring to past project data. Past project data includes, for example, project history data and past requirements definition documents. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past project data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0090] The generation unit can improve the accuracy of generation by referring to past design data. For example, it can extract similar designs from past design data and use them as a reference for generation. Based on past design data, it can determine design priorities and generate designs efficiently. It can analyze past design data to find common patterns in design generation. As a result, the accuracy of generation is improved by referring to past design data. Past design data includes, for example, design history and user feedback. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past design data into a generation AI and have the generation AI perform the task of improving the accuracy of generation.

[0091] The creation unit can improve the accuracy of its creation by referring to past design document data. For example, it can extract similar design documents from past design document data and use them as a reference for creation. Based on past design document data, it can determine the priority of design documents and create them efficiently. It can analyze past design document data to find common patterns in design document creation. As a result, the accuracy of creation is improved by referring to past design document data. Past design document data includes, for example, design document history data and past project data. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input past design document data into a generation AI and have the generation AI perform the task of improving the accuracy of creation.

[0092] The analysis unit can perform analysis while considering the user's industry-specific terminology. For example, it can register the user's industry-specific terminology in a dictionary and consider it during analysis. It prioritizes the analysis of requirements that include industry-specific terminology. It reflects industry-specific terminology in the analysis results and provides them in a way that is easy for the user to understand. In this way, considering industry-specific terminology makes the analysis results easier for the user to understand. Industry-specific terminology includes, for example, medical terms, financial terms, and technical terms. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input industry-specific terminology data into a generating AI and have the generating AI perform the analysis.

[0093] The generation unit can optimize the design based on the user's device characteristics. For example, if the user is using a smartphone, it provides a design that matches the screen size. If the user is using a tablet, it provides a design optimized for a larger screen. If the user is using a desktop, it provides a design optimized for a larger screen. By optimizing the design based on device characteristics, it is possible to provide a user-friendly design. Device characteristics include, for example, screen size, resolution, and input method. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input device characteristic data into a generation AI and have the generation AI perform the design optimization.

[0094] The creation unit can create documents while considering the user's industry-specific format. For example, it can register the user's industry-specific format in a dictionary and consider it during creation. It can prioritize the creation of design documents that include industry-specific formats. It can reflect the industry-specific format in the creation results and provide them in an easy-to-understand format for the user. In this way, by considering industry-specific formats, it is possible to provide design documents that are easy for the user to understand. Industry-specific formats include, for example, formats for the medical industry, formats for the financial industry, etc. Some or all of the above processes in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can input industry-specific format data into a generation AI and have the generation AI perform the creation.

[0095] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible display method is provided. If the user is relaxed, a display method including detailed information is provided. If the user is in a hurry, a display method that gets straight to the point is provided. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The analysis unit analyzes the requirements. The analysis unit analyzes the requirements using, for example, natural language processing technology, and analyzes the content of the requirements in detail to clarify the system requirements. Step 2: The generation unit generates a UI / UX design based on the requirements analyzed by the analysis unit. For example, the generation unit generates the UI / UX design using deep learning technology, automatically lays out the design components, and optimizes the size of the components. This enables the generation of a user-friendly UI / UX design. Step 3: The creation unit creates external design documents based on the designs generated by the generation unit. The creation unit can, for example, use generation AI to automatically generate external design documents, significantly reducing the time required to create design documents and lowering costs.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements, including the analysis unit, generation unit, and creation unit described above, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and analyzes requirements using natural language processing technology. The generation unit is implemented by the processor 46 of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and generates UI / UX designs using deep learning technology. The creation unit is implemented by the processor 46 of the smart device 14 and the specific processing unit 290 of the data processing unit 12, and automatically generates external design documents using generation AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0115] 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.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0117] Each of the multiple elements, including the analysis unit, generation unit, and creation unit described above, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and analyzes requirements using natural language processing technology. The generation unit is implemented by the processor 46 of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and generates UI / UX designs using deep learning technology. The creation unit is implemented by the processor 46 of the smart glasses 214 and the specific processing unit 290 of the data processing unit 12, and automatically generates external design documents using generation AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0133] Each of the multiple elements, including the analysis unit, generation unit, and creation unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and analyzes requirements using natural language processing technology. The generation unit is implemented by the processor 46 of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and generates UI / UX designs using deep learning technology. The creation unit is implemented by the processor 46 of the headset terminal 314 and the specific processing unit 290 of the data processing unit 12, and automatically generates external design documents using generation AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[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 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.

[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 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.

[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 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.

[0150] Each of the multiple elements, including the analysis unit, generation unit, and creation unit described above, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and the specific processing unit 290 of the data processing unit 12, and analyzes requirements using natural language processing technology. The generation unit is implemented by the processor 46 of the robot 414 and the specific processing unit 290 of the data processing unit 12, and generates UI / UX designs using deep learning technology. The creation unit is implemented by the processor 46 of the robot 414 and the specific processing unit 290 of the data processing unit 12, and automatically generates external design documents using generation AI. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) An analysis unit that analyzes the requirements, A generation unit that generates a UI / UX design based on the requirements analyzed by the aforementioned analysis unit, A creation unit that creates an external design document based on the design generated by the generation unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze requirements using natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generating UI / UX designs using deep learning The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Automatically lays out design components and optimizes component sizes. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned creation unit, Automatically generate external design documents using generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We estimate user emotions and adjust the requirements analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, Improve the accuracy of analysis by referring to past project data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, Prioritize the analysis based on the importance of the requirements. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The analysis takes into account the user's industry-specific terminology. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The analysis takes into account the user's geographical background. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates user emotions and adjusts the UI / UX design generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is Improve generation accuracy by referencing past design data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is Optimize the design based on the user's device characteristics. 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 design's colors and layout based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The system is generated while taking into account the user's industry-specific design requirements. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is Designs are generated considering the user's cultural background. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned creation unit, We estimate the user's emotions and adjust the design document creation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned creation unit, Improve the accuracy of creation by referring to past design document data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned creation unit, Prioritize the content of the design document based on the importance of the requirements. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned creation unit, It estimates the user's emotions and adjusts the layout of the design document based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned creation unit, Created taking into account the user's industry-specific format. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned creation unit, Created with the user's geographical background in mind. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 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. An analysis unit that analyzes the requirements, A generation unit that generates a UI / UX design based on the requirements analyzed by the aforementioned analysis unit, A creation unit that creates an external design document based on the design generated by the generation unit, Equipped with A system characterized by the following features.

2. The aforementioned analysis unit, Analyze requirements using natural language processing. The system according to feature 1.

3. The generating unit is Generating UI / UX designs using deep learning The system according to feature 1.

4. The generating unit is Automatically lays out design components and optimizes component sizes. The system according to feature 1.

5. The aforementioned creation unit, Automatically generate external design documents using generation AI. The system according to feature 1.

6. The aforementioned analysis unit, We estimate user emotions and adjust the requirements analysis method based on the estimated user emotions. The system according to feature 1.

7. The aforementioned analysis unit, Improve the accuracy of analysis by referring to past project data. The system according to feature 1.

8. The aforementioned analysis unit, Prioritize the analysis based on the importance of the requirements. The system according to feature 1.

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