system

Abstract

The system according to this embodiment aims to clarify application requirements based on user responses and to quickly create prototypes. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and an output unit. The reception unit receives responses from the user. The analysis unit analyzes the responses received by the reception unit and clarifies the application requirements. The generation unit creates a prototype application based on the requirements clarified by the analysis unit. The output unit outputs a requirements definition document and a design document based on the prototype application created by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003] <所

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 labor to define requirements and create prototypes of applications.

[0005] The system according to the embodiment aims to clarify the requirements of an application based on a user's answer and quickly create a prototype.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an output unit. The reception unit receives user responses. The analysis unit analyzes the responses received by the reception unit and clarifies the application requirements. The generation unit creates a prototype application based on the requirements clarified by the analysis unit. The output unit outputs a requirements definition document and a design document based on the prototype application created by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can clarify application requirements based on user responses and quickly create prototypes. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The application prototype automatic generation system according to an embodiment of the present invention is a system that automatically generates application prototypes using generative AI for the purpose of improving internal business operations within a company. Many companies have numerous applications used at the business unit or department level, and these applications often have a limited number of users. Although the data handled and the details differ, most applications are based on CRUD (Create, Read, Update, Delete) operations and share a common basic structure. Recently, the technology of creating simple applications (e.g., calculators, roulette, etc.) using generative AI has attracted attention. This technology is further developed to create a system in which the user answers questions from the AI ​​to clarify the requirements of the application they want to realize, and the AI ​​creates a prototype application based on those answers. The user can then proceed with development based on this prototype. Furthermore, it is expected that requirements definition documents and design documents can also be output, significantly reducing the man-hours required in the initial stages of development. For example, the user answers questions from the AI. For example, the user answers questions such as "What functions are needed?" and "What kind of data will be handled?". These answers are input into the generative AI. Next, the generative AI analyzes the input answers to clarify the requirements of the application. The generating AI identifies the necessary functions and data structures based on the user's responses. For example, if a user responds that they want to create a "customer management system," the generating AI will identify functions such as registering, searching, updating, and deleting customer information. The generating AI then creates a prototype application based on the identified requirements. Based on the identified functions and data structures, the generating AI generates application code. For example, it generates code for performing CRUD operations on customer information. Furthermore, the generating AI also outputs documents such as requirements specifications and design documents. This allows users to proceed with development based on the prototype application and significantly reduces the initial development effort. This mechanism is expected to streamline internal business processes and drastically shorten the system development process.For example, the time between the start of a system development project and the actual development begins will be shortened, leading to smoother project progress. Furthermore, automatic generation by AI is expected to ensure the application of optimal technologies, improving system quality. As a result, application prototype automatic generation systems can streamline internal business processes and significantly shorten the system development process.

[0029] The application prototype automatic generation system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and an output unit. The reception unit receives user responses. User responses include, but are not limited to, text format, multiple-choice format, etc. The reception unit presents, for example, the types of questions the user should answer. For example, the reception unit can present the user with questions such as "What functions are needed?" or "What kind of data will be handled?" The analysis unit analyzes the responses received by the reception unit and clarifies the application requirements. The analysis is performed using, for example, techniques such as natural language processing or data mining, but is not limited to, such techniques. For example, the analysis unit identifies the necessary functions and data structures based on the user's responses. For example, if the user answers that they want to create a "customer management system," the analysis unit identifies functions such as registering, searching, updating, and deleting customer information. The generation unit creates a prototype application based on the requirements clarified by the analysis unit. The generation unit generates application code based on, for example, the identified functions and data structures. Code generation is performed based on, for example, the programming language to be used or a code template, but is not limited to, such examples. For example, the generation unit generates code for performing CRUD operations on customer information. The output unit outputs requirements specifications and design documents based on the prototype application created by the generation unit. Requirements specifications and design documents include, but are not limited to, items and formats to be included. For example, the output unit automatically generates requirements specifications and design documents based on the generated prototype application. As a result, the application prototype automatic generation system according to the embodiment can significantly reduce the man-hours required in the early stages of development by automatically generating an application prototype based on user responses and outputting requirements specifications and design documents. Some or all of the above-described processes in the reception unit, analysis unit, generation unit, and output unit may be performed using, for example, AI, or without AI. For example, when the reception unit receives user responses, it can use AI to analyze the user's responses and present appropriate questions.The analysis unit can analyze user responses using a generation AI to identify necessary functions and data structures. The generation unit can generate code using the generation AI when creating a prototype application based on the identified requirements. The output unit can generate documents using AI when outputting requirements definition documents and design documents based on the generated prototype application.

[0030] The reception desk receives user responses. User responses may include, but are not limited to, text format or multiple-choice format. The reception desk may, for example, present the types of questions the user can answer. For example, the reception desk may ask the user questions such as "What functions do you need?" or "What kind of data will you be handling?" Specifically, the reception desk displays the questions through the user interface and provides text boxes or multiple-choice options for the user to enter their answers. If the user answers in text format, the reception desk receives the text and stores it in the database. If the answer is in multiple-choice format, the reception desk records the option selected by the user and stores it in the database as well. Furthermore, the reception desk can dynamically generate subsequent questions based on the user's answers. For example, if the user requests a "customer management system," the reception desk can then present a more detailed question such as "What fields will be included in the customer information?" This allows the reception desk to provide a flexible question flow tailored to the user's needs and collect more specific requirements. The reception desk can also analyze user responses in real time and provide appropriate feedback. For example, if a user's answer is incorrect or unclear, it can immediately display a message prompting correction. This allows the reception unit to improve the accuracy of user responses and facilitate the subsequent processing by the analysis and generation units.

[0031] The analysis unit analyzes the responses received by the reception unit to clarify the application requirements. The analysis is performed using techniques such as natural language processing and data mining, but is not limited to these examples. Specifically, the analysis unit tokenizes the user's responses and extracts important keywords and phrases. For example, from the response "customer management system," it identifies keywords such as "customer information," "registration," "search," "update," and "delete." Next, based on these keywords, it identifies the necessary functions and data structures. For example, it analyzes that "customer information" includes items such as "name," "address," and "telephone number." Furthermore, the analysis unit can understand the user's responses in context and supplement related requirements. For example, if the user responds that "a customer information search function is needed," the analysis unit can also identify detailed requirements such as "how search results are displayed" and "how search conditions are set." Based on these analysis results, the analysis unit clarifies the application requirements and hands them over to the generation unit. The analysis unit can also perform more accurate analyses by utilizing knowledge gained from past data and similar projects. For example, by referring to the requirements of previously created customer management systems and comparing them with user responses, the necessary functions and data structures can be supplemented. This allows the analysis unit to quickly and accurately analyze user responses and clarify application requirements.

[0032] The generation unit creates a prototype application based on the requirements clarified by the analysis unit. For example, the generation unit generates application code based on identified functions and data structures. Code generation is performed based on, for example, the programming language and code templates to be used, but is not limited to these examples. Specifically, the generation unit selects an appropriate programming language and applies code templates based on the requirements received from the analysis unit. For example, when generating code to perform CRUD operations on customer information, it automatically generates code for the database schema definition, data access layer, business logic layer, and user interface layer. The generation unit integrates this code to create a working prototype application. Furthermore, the generation unit can perform automated testing after code generation to ensure code quality. For example, it can run unit tests and integration tests on the generated code to verify that it functions correctly. In addition, the generation unit can provide customizable code according to user requirements. For example, if a user desires a specific design or layout, customization can be performed to meet those requirements. This allows the generation unit to quickly create high-quality prototype applications based on user requirements.

[0033] The output unit outputs requirements specifications and design documents based on the prototype application created by the generation unit. These documents include, but are not limited to, items and formats. Specifically, the output unit analyzes the code and database schema of the generated prototype application and automatically generates the requirements specifications and design documents based on them. The requirements specifications detail the application's purpose, functional requirements, non-functional requirements, and data requirements. The design documents include the system architecture, database design, interface design, and flowcharts. The output unit outputs these documents in the appropriate format and provides them to the user. For example, it can generate documents in PDF or Word format for users to download. The output unit can also save the generated documents to cloud storage and share them with team members. Furthermore, the output unit can receive user feedback and modify / update the document content. For example, if a user wants to make changes to the requirements specifications, the output unit reflects those changes and regenerates the latest document. This allows the output unit to provide users with accurate and up-to-date requirements specifications and design documents, facilitating a smooth development process.

[0034] The reception desk can present the types of questions the user should answer. For example, the reception desk might ask the user questions such as "What functions do you need?" or "What kind of data will you be handling?". By presenting the types of questions the user should answer, the reception desk can efficiently provide the user with the information they need. The types of questions include, but are not limited to, technical questions or business questions. This allows the reception desk to efficiently provide the user with the information they need by presenting the types of questions the user should answer. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, when presenting the types of questions the user should answer, the reception desk can use AI to analyze the user's answers and present appropriate questions.

[0035] The analysis unit can identify the necessary functions and data structures based on the user's responses. For example, if the user responds that they want to create a "customer management system," the analysis unit will identify functions such as registering, searching, updating, and deleting customer information. This clarifies the application requirements by identifying the necessary functions and data structures based on the user's responses. The necessary functions and data structures include, but are not limited to, a list of functions and a database schema. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not. For example, when analyzing the user's responses, the analysis unit can use generative AI to analyze the content of the responses and identify the necessary functions and data structures.

[0036] The generation unit can generate application code based on specified functions and data structures. For example, the generation unit generates code to perform CRUD operations on customer information. This allows for the rapid creation of prototype applications by generating application code based on specified functions and data structures. Code generation includes, but is not limited to, the programming language to be used and code templates. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can use a generation AI to generate code when creating a prototype application based on specified requirements.

[0037] The output unit can output requirements specifications and design documents based on the generated prototype application. For example, the output unit can output requirements specifications and design documents based on the generated prototype application. For example, the output unit can automatically generate requirements specifications and design documents based on the generated prototype application. This significantly reduces the man-hours required in the early stages of development by outputting requirements specifications and design documents based on the generated prototype application. Requirements specifications and design documents include, but are not limited to, items to be included and format. Some or all of the above-described processes in the output unit may be performed using AI, for example, or not using AI. For example, when outputting requirements specifications and design documents based on the generated prototype application, the output unit can use AI to generate the documents.

[0038] The generation unit can generate code for performing CRUD operations on customer information. For example, the generation unit generates code for performing CRUD operations on customer information. For example, the generation unit generates code that includes functions such as registering, searching, updating, and deleting customer information. This allows for the rapid creation of a prototype of a customer management system by generating code for performing CRUD operations on customer information. CRUD operations on customer information include, but are not limited to, procedures for creating, reading, updating, and deleting data. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, when generating code for performing CRUD operations on customer information, the generation unit can use a generation AI to generate the code.

[0039] The reception desk can analyze a user's past response history and dynamically generate the most appropriate questions. For example, the reception desk can automatically generate relevant questions based on the user's past responses. For example, the reception desk can analyze a user's past response patterns and prioritize presenting important unanswered questions. The reception desk can also efficiently collect information by omitting duplicate questions from the user's past response history. This allows for the automatic generation of relevant questions and efficient information collection by analyzing the user's past response history. Past response history includes, but is not limited to, the format in which responses are saved and the analysis algorithms used. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, when analyzing a user's past response history, the reception desk can use AI to analyze the content of the responses and generate the most appropriate questions.

[0040] The reception desk can customize the content of questions based on the user's current work situation when presenting them. For example, if the user is busy, the reception desk can present concise and to-the-point questions. For example, if the user has time, the reception desk can present detailed questions to gather more in-depth information. The reception desk can also prioritize presenting relevant questions according to the user's work content. This allows for efficient information gathering by customizing the content of questions according to the user's work situation. Current work situation includes, but is not limited to, the stage of a business process or the content of the work. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, when analyzing the user's current work situation, the reception desk can use AI to analyze the content of the work and present the most appropriate questions.

[0041] The reception desk can prioritize presenting highly relevant questions by considering the user's geographical location when presenting questions. For example, if the user is in a specific region, the reception desk can prioritize presenting questions related to that region. For example, if the user is on the move, the reception desk can present questions related to their destination. Furthermore, the reception desk can present questions that take into account region-specific requirements based on the user's location information. This allows for the efficient collection of information by prioritizing highly relevant questions and considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, when analyzing the user's geographical location information, the reception desk can use AI to analyze the location information and present the most appropriate questions.

[0042] The reception desk can analyze the user's social media activity and present relevant questions when presenting them. For example, the reception desk can analyze the content of the user's social media posts and present relevant questions. For example, the reception desk can present relevant questions based on the activity of the user's followers and friends. The reception desk can also analyze the user's interests on social media and present relevant questions. This allows for the efficient collection of information by presenting relevant questions through the analysis of the user's social media activity. Social media activity includes, but is not limited to, the analysis of post content and follower analysis. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, when analyzing the user's social media activity, the reception desk can use AI to analyze post content and follower activity and present the most appropriate questions.

[0043] The analysis unit can improve its analysis algorithm by referring to the user's past response data during analysis. For example, the analysis unit can adjust the analysis algorithm based on the user's past response data to improve accuracy. For example, the analysis unit can analyze the user's past response patterns and select the optimal analysis method. Furthermore, the analysis unit can refer to the user's past response data to omit redundant analysis and perform analysis efficiently. In this way, by referring to the user's past response data, the analysis algorithm can be optimized and accuracy can be improved. Past response data includes, but is not limited to, data storage format and database structure. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, when the analysis unit refers to the user's past response data, it can use generative AI to analyze the data and select the optimal analysis algorithm.

[0044] The analysis unit can customize the analysis method based on the user's business process during analysis. For example, the analysis unit can select the optimal analysis method according to the user's business process. For example, the analysis unit can customize the analysis method based on the user's work content to improve accuracy. The analysis unit can also analyze the user's business process and propose an efficient analysis method. This allows for efficient analysis by customizing the analysis method based on the user's business process. Business processes include, but are not limited to, stages and content of business flows. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, when analyzing a user's business process, the analysis unit can use generative AI to analyze the content of the business and select the optimal analysis method.

[0045] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user is in a specific region, the analysis unit will prioritize analyzing data related to that region. For example, if the user is on the move, the analysis unit can analyze data related to the user's destination. Furthermore, the analysis unit can perform analysis that takes into account region-specific requirements based on the user's location information. This allows for the efficient provision of information by prioritizing the analysis of highly relevant data by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above-described processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, when analyzing the user's geographical location information, the analysis unit can use generative AI to analyze the location information and select the optimal analysis method.

[0046] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can adjust the analysis algorithm based on the relevant literature provided by the user to improve accuracy. For example, the analysis unit can refer to literature related to the user's work and select the optimal analysis method. Furthermore, the analysis unit can analyze the user's relevant literature, omit redundant analyses, and perform analyses efficiently. In this way, the accuracy of the analysis can be improved by referring to the user's relevant literature. Relevant literature includes, but is not limited to, literature databases and citation methods. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, when the analysis unit refers to the user's relevant literature, it can use generative AI to analyze the literature and select the optimal analysis method.

[0047] The generation unit can select the optimal generation method by referring to the user's past prototype usage history during generation. For example, the generation unit can select the optimal generation method based on the user's past prototype usage history. For example, the generation unit can analyze the user's past usage patterns and propose an efficient generation method. Furthermore, the generation unit can refer to the user's past prototype usage history to omit redundant functions and perform generation efficiently. In this way, by referring to the user's past prototype usage history, the optimal generation method can be selected and prototypes can be generated efficiently. Past prototype usage history includes, but is not limited to, the format in which usage history is saved and the structure of the database. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, when the generation unit refers to the user's past prototype usage history, it can use a generation AI to analyze the data and select the optimal generation method.

[0048] The generation unit can customize the prototype structure based on the user's workflow during generation. For example, the generation unit can select the optimal prototype structure according to the user's workflow. For example, the generation unit can customize the prototype structure based on the user's work content to improve usability. The generation unit can also analyze the user's workflow and propose an efficient prototype structure. This allows for the creation of user-friendly prototypes by customizing the prototype structure based on the user's workflow. The workflow includes, but is not limited to, the stages and content of a business process. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, when analyzing the user's workflow, the generation unit can use a generation AI to analyze the content and select the optimal prototype structure.

[0049] The generation unit can generate an optimal prototype by considering the user's geographical location information during the generation process. For example, if the user is in a specific region, the generation unit can generate a prototype that includes functions related to that region. For example, if the user is on the move, the generation unit can generate a prototype that includes functions related to the user's destination. Furthermore, the generation unit can generate a prototype that takes into account region-specific requirements based on the user's location information. This allows for the generation of highly relevant prototypes and efficient information provision by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, when analyzing the user's geographical location information, the generation unit can use a generation AI to analyze the location information and generate an optimal prototype.

[0050] The generation unit can analyze the user's social media activity during generation and propose prototype functions. For example, the generation unit can analyze the content of the user's social media posts and propose relevant functions. For example, the generation unit can propose relevant functions based on the activity of the user's followers and friends. The generation unit can also analyze the user's interests on social media and propose relevant functions. In this way, by analyzing the user's social media activity, relevant functions can be proposed and prototypes can be generated efficiently. Social media activity includes, but is not limited to, analyzing post content and follower analysis. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, when analyzing the user's social media activity, the generation unit can use a generation AI to analyze post content and follower activity and propose optimal functions.

[0051] The output unit can select the optimal output method by referring to the user's past document usage history when outputting. For example, the output unit can select the optimal output method based on the user's past document usage history. For example, the output unit can analyze the user's past usage patterns and propose an efficient output method. Furthermore, the output unit can refer to the user's past document usage history to omit redundant information and output efficiently. In this way, by referring to the user's past document usage history, the optimal output method can be selected and information can be provided efficiently. Past document usage history includes, but is not limited to, the format in which the usage history is saved and the structure of the database. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, when the output unit refers to the user's past document usage history, it may use AI to analyze the data and select the optimal output method.

[0052] The output unit can customize the document content based on the user's business process at the time of output. For example, the output unit can select the optimal document content according to the user's business process. For example, the output unit can customize the document content based on the user's work content to improve usability. The output unit can also analyze the user's business process and propose efficient document content. In this way, by customizing the document content based on the user's business process, it is possible to provide user-friendly documents. Business processes include, but are not limited to, stages and work content of a business flow. Some or all of the above processing in the output unit may be performed using, for example, AI, or not using AI. For example, when analyzing the user's business process, the output unit can use AI to analyze the work content and select the optimal document content.

[0053] The output unit can output the most suitable document at the time of output, taking into account the user's geographical location information. For example, if the user is in a specific region, the output unit can output a document containing information related to that region. For example, if the user is on the move, the output unit can output a document containing information related to the destination. Furthermore, the output unit can output a document that takes into account region-specific requirements based on the user's location information. This allows for the provision of highly relevant documents and efficient information delivery by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the output unit may be performed using, for example, AI, or not using AI. For example, when analyzing the user's geographical location information, the output unit can use AI to analyze the location information and output the most suitable document.

[0054] The output unit can analyze the user's social media activity and suggest document content at the time of output. For example, the output unit can analyze the user's social media posts and suggest a document containing relevant information. For example, the output unit can suggest a document containing relevant information based on the activity of the user's followers and friends. The output unit can also analyze the user's interests on social media and suggest a document containing relevant information. In this way, by analyzing the user's social media activity, it is possible to provide documents containing relevant information and to provide information efficiently. Social media activity includes, but is not limited to, analyzing post content and follower analysis. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, when analyzing the user's social media activity, the output unit can use AI to analyze post content and follower activity and suggest the optimal document content.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception system can automatically generate relevant questions by referring to the user's past response history when receiving user responses. For example, by presenting relevant questions based on the user's past answers, consistency in the user's responses can be maintained. The reception system can also analyze the user's past response patterns and prioritize presenting important unanswered questions. Furthermore, the reception system can efficiently collect information by omitting duplicate questions from the user's past response history. In this way, by referring to the user's past response history, relevant questions can be automatically generated and information can be collected efficiently.

[0057] The reception desk can customize the questions based on the user's current work situation. For example, if the user is busy, concise and to-the-point questions can be presented. If the user has time, more detailed questions can be presented to gather more in-depth information. Furthermore, relevant questions can be prioritized according to the user's work content. This allows for efficient information gathering by customizing the questions according to the user's work situation.

[0058] The analysis unit can improve its analysis algorithm by referring to the user's past response data. For example, it can adjust the analysis algorithm based on the user's past response data to improve accuracy. It can also analyze the user's past response patterns and select the optimal analysis method. Furthermore, by referring to the user's past response data, it can omit redundant analyses and perform analyses efficiently. In this way, by referring to the user's past response data, the analysis algorithm can be optimized and accuracy can be improved.

[0059] The generation unit can select the optimal generation method by referring to the user's past prototype usage history. For example, it can select the optimal generation method based on the user's past prototype usage history. It can also analyze the user's past usage patterns and propose an efficient generation method. Furthermore, by referring to the user's past prototype usage history, it can omit redundant functions and generate prototypes efficiently. In this way, by referring to the user's past prototype usage history, the optimal generation method can be selected and prototypes can be generated efficiently.

[0060] The output unit can customize the document content based on the user's business processes. For example, it can select the most suitable document content according to the user's business processes. Furthermore, it can customize the document content based on the user's work content to improve usability. It can also analyze the user's business processes and suggest efficient document content. In this way, by customizing the document content based on the user's business processes, it can provide user-friendly documents.

[0061] The generation unit can create an optimal prototype by considering the user's geographical location. For example, if the user is in a specific region, it can generate a prototype that includes features related to that region. Furthermore, if the user is on the move, it can generate a prototype that includes features related to their destination. In addition, based on the user's location information, it can generate a prototype that takes region-specific requirements into account. This allows for the generation of highly relevant prototypes and efficient information delivery by considering the user's geographical location.

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

[0063] Step 1: The reception desk receives user responses. User responses may include, but are not limited to, text format, multiple-choice format, etc. The reception desk may, for example, present the types of questions the user should answer. For example, the reception desk may present the user with questions such as "What functions do you need?" or "What kind of data will you be handling?" Step 2: The analysis unit analyzes the responses received by the reception unit to clarify the application requirements. The analysis is performed using techniques such as natural language processing and data mining, but is not limited to these examples. For example, the analysis unit identifies the necessary functions and data structures based on the user's responses. For example, if the user responds that they want to create a "customer management system," the analysis unit will identify functions such as registering, searching, updating, and deleting customer information. Step 3: The generation unit creates a prototype application based on the requirements clarified by the analysis unit. The generation unit generates the application code based on, for example, identified functions and data structures. Code generation is performed based on, for example, the programming language to be used and the code template, but is not limited to such examples. For example, the generation unit generates code to perform CRUD operations on customer information. Step 4: The output unit outputs requirements specifications and design documents based on the prototype application created by the generation unit. Requirements specifications and design documents include, but are not limited to, items and formats to be included. For example, the output unit automatically generates requirements specifications and design documents based on the generated prototype application.

[0064] (Example of form 2) The application prototype automatic generation system according to an embodiment of the present invention is a system that automatically generates application prototypes using generative AI for the purpose of improving internal business operations within a company. Many companies have numerous applications used at the business unit or department level, and these applications often have a limited number of users. Although the data handled and the details differ, most applications are based on CRUD (Create, Read, Update, Delete) operations and share a common basic structure. Recently, the technology of creating simple applications (e.g., calculators, roulette, etc.) using generative AI has attracted attention. This technology is further developed to create a system in which the user answers questions from the AI ​​to clarify the requirements of the application they want to realize, and the AI ​​creates a prototype application based on those answers. The user can then proceed with development based on this prototype. Furthermore, it is expected that requirements definition documents and design documents can also be output, significantly reducing the man-hours required in the initial stages of development. For example, the user answers questions from the AI. For example, the user answers questions such as "What functions are needed?" and "What kind of data will be handled?". These answers are input into the generative AI. Next, the generative AI analyzes the input answers to clarify the requirements of the application. The generating AI identifies the necessary functions and data structures based on the user's responses. For example, if a user responds that they want to create a "customer management system," the generating AI will identify functions such as registering, searching, updating, and deleting customer information. The generating AI then creates a prototype application based on the identified requirements. Based on the identified functions and data structures, the generating AI generates application code. For example, it generates code for performing CRUD operations on customer information. Furthermore, the generating AI also outputs documents such as requirements specifications and design documents. This allows users to proceed with development based on the prototype application and significantly reduces the initial development effort. This mechanism is expected to streamline internal business processes and drastically shorten the system development process.For example, the time between the start of a system development project and the actual development begins will be shortened, leading to smoother project progress. Furthermore, automatic generation by AI is expected to ensure the application of optimal technologies, improving system quality. As a result, application prototype automatic generation systems can streamline internal business processes and significantly shorten the system development process.

[0065] The application prototype automatic generation system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and an output unit. The reception unit receives user responses. User responses include, but are not limited to, text format, multiple-choice format, etc. The reception unit presents, for example, the types of questions the user should answer. For example, the reception unit can present the user with questions such as "What functions are needed?" or "What kind of data will be handled?" The analysis unit analyzes the responses received by the reception unit and clarifies the application requirements. The analysis is performed using, for example, techniques such as natural language processing or data mining, but is not limited to, such techniques. For example, the analysis unit identifies the necessary functions and data structures based on the user's responses. For example, if the user answers that they want to create a "customer management system," the analysis unit identifies functions such as registering, searching, updating, and deleting customer information. The generation unit creates a prototype application based on the requirements clarified by the analysis unit. The generation unit generates application code based on, for example, the identified functions and data structures. Code generation is performed based on, for example, the programming language to be used or a code template, but is not limited to, such examples. For example, the generation unit generates code for performing CRUD operations on customer information. The output unit outputs requirements specifications and design documents based on the prototype application created by the generation unit. Requirements specifications and design documents include, but are not limited to, items and formats to be included. For example, the output unit automatically generates requirements specifications and design documents based on the generated prototype application. As a result, the application prototype automatic generation system according to the embodiment can significantly reduce the man-hours required in the early stages of development by automatically generating an application prototype based on user responses and outputting requirements specifications and design documents. Some or all of the above-described processes in the reception unit, analysis unit, generation unit, and output unit may be performed using, for example, AI, or without AI. For example, when the reception unit receives user responses, it can use AI to analyze the user's responses and present appropriate questions.The analysis unit can analyze user responses using a generation AI to identify necessary functions and data structures. The generation unit can generate code using the generation AI when creating a prototype application based on the identified requirements. The output unit can generate documents using AI when outputting requirements definition documents and design documents based on the generated prototype application.

[0066] The reception desk receives user responses. User responses may include, but are not limited to, text format or multiple-choice format. The reception desk may, for example, present the types of questions the user can answer. For example, the reception desk may ask the user questions such as "What functions do you need?" or "What kind of data will you be handling?" Specifically, the reception desk displays the questions through the user interface and provides text boxes or multiple-choice options for the user to enter their answers. If the user answers in text format, the reception desk receives the text and stores it in the database. If the answer is in multiple-choice format, the reception desk records the option selected by the user and stores it in the database as well. Furthermore, the reception desk can dynamically generate subsequent questions based on the user's answers. For example, if the user requests a "customer management system," the reception desk can then present a more detailed question such as "What fields will be included in the customer information?" This allows the reception desk to provide a flexible question flow tailored to the user's needs and collect more specific requirements. The reception desk can also analyze user responses in real time and provide appropriate feedback. For example, if a user's answer is incorrect or unclear, it can immediately display a message prompting correction. This allows the reception unit to improve the accuracy of user responses and facilitate the subsequent processing by the analysis and generation units.

[0067] The analysis unit analyzes the responses received by the reception unit to clarify the application requirements. The analysis is performed using techniques such as natural language processing and data mining, but is not limited to these examples. Specifically, the analysis unit tokenizes the user's responses and extracts important keywords and phrases. For example, from the response "customer management system," it identifies keywords such as "customer information," "registration," "search," "update," and "delete." Next, based on these keywords, it identifies the necessary functions and data structures. For example, it analyzes that "customer information" includes items such as "name," "address," and "telephone number." Furthermore, the analysis unit can understand the user's responses in context and supplement related requirements. For example, if the user responds that "a customer information search function is needed," the analysis unit can also identify detailed requirements such as "how search results are displayed" and "how search conditions are set." Based on these analysis results, the analysis unit clarifies the application requirements and hands them over to the generation unit. The analysis unit can also perform more accurate analyses by utilizing knowledge gained from past data and similar projects. For example, by referring to the requirements of previously created customer management systems and comparing them with user responses, the necessary functions and data structures can be supplemented. This allows the analysis unit to quickly and accurately analyze user responses and clarify application requirements.

[0068] The generation unit creates a prototype application based on the requirements clarified by the analysis unit. For example, the generation unit generates application code based on identified functions and data structures. Code generation is performed based on, for example, the programming language and code templates to be used, but is not limited to these examples. Specifically, the generation unit selects an appropriate programming language and applies code templates based on the requirements received from the analysis unit. For example, when generating code to perform CRUD operations on customer information, it automatically generates code for the database schema definition, data access layer, business logic layer, and user interface layer. The generation unit integrates this code to create a working prototype application. Furthermore, the generation unit can perform automated testing after code generation to ensure code quality. For example, it can run unit tests and integration tests on the generated code to verify that it functions correctly. In addition, the generation unit can provide customizable code according to user requirements. For example, if a user desires a specific design or layout, customization can be performed to meet those requirements. This allows the generation unit to quickly create high-quality prototype applications based on user requirements.

[0069] The output unit outputs requirements specifications and design documents based on the prototype application created by the generation unit. These documents include, but are not limited to, items and formats. Specifically, the output unit analyzes the code and database schema of the generated prototype application and automatically generates the requirements specifications and design documents based on them. The requirements specifications detail the application's purpose, functional requirements, non-functional requirements, and data requirements. The design documents include the system architecture, database design, interface design, and flowcharts. The output unit outputs these documents in the appropriate format and provides them to the user. For example, it can generate documents in PDF or Word format for users to download. The output unit can also save the generated documents to cloud storage and share them with team members. Furthermore, the output unit can receive user feedback and modify / update the document content. For example, if a user wants to make changes to the requirements specifications, the output unit reflects those changes and regenerates the latest document. This allows the output unit to provide users with accurate and up-to-date requirements specifications and design documents, facilitating a smooth development process.

[0070] The reception desk can present the types of questions the user should answer. For example, the reception desk might ask the user questions such as "What functions do you need?" or "What kind of data will you be handling?". By presenting the types of questions the user should answer, the reception desk can efficiently provide the user with the information they need. The types of questions include, but are not limited to, technical questions or business questions. This allows the reception desk to efficiently provide the user with the information they need by presenting the types of questions the user should answer. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, when presenting the types of questions the user should answer, the reception desk can use AI to analyze the user's answers and present appropriate questions.

[0071] The analysis unit can identify the necessary functions and data structures based on the user's responses. For example, if the user responds that they want to create a "customer management system," the analysis unit will identify functions such as registering, searching, updating, and deleting customer information. This clarifies the application requirements by identifying the necessary functions and data structures based on the user's responses. The necessary functions and data structures include, but are not limited to, a list of functions and a database schema. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not. For example, when analyzing the user's responses, the analysis unit can use generative AI to analyze the content of the responses and identify the necessary functions and data structures.

[0072] The generation unit can generate application code based on specified functions and data structures. For example, the generation unit generates code to perform CRUD operations on customer information. This allows for the rapid creation of prototype applications by generating application code based on specified functions and data structures. Code generation includes, but is not limited to, the programming language to be used and code templates. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can use a generation AI to generate code when creating a prototype application based on specified requirements.

[0073] The output unit can output requirements specifications and design documents based on the generated prototype application. For example, the output unit can output requirements specifications and design documents based on the generated prototype application. For example, the output unit can automatically generate requirements specifications and design documents based on the generated prototype application. This significantly reduces the man-hours required in the early stages of development by outputting requirements specifications and design documents based on the generated prototype application. Requirements specifications and design documents include, but are not limited to, items to be included and format. Some or all of the above-described processes in the output unit may be performed using AI, for example, or not using AI. For example, when outputting requirements specifications and design documents based on the generated prototype application, the output unit can use AI to generate the documents.

[0074] The generation unit can generate code for performing CRUD operations on customer information. For example, the generation unit generates code for performing CRUD operations on customer information. For example, the generation unit generates code that includes functions such as registering, searching, updating, and deleting customer information. This allows for the rapid creation of a prototype of a customer management system by generating code for performing CRUD operations on customer information. CRUD operations on customer information include, but are not limited to, procedures for creating, reading, updating, and deleting data. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, when generating code for performing CRUD operations on customer information, the generation unit can use a generation AI to generate the code.

[0075] The reception desk can estimate the user's emotions and adjust the order in which questions are presented based on the estimated emotions. For example, if the user is stressed, the reception desk can start with simple questions and gradually increase the difficulty. For example, if the user is relaxed, the reception desk can present detailed questions first to efficiently gather information. Also, if the user is in a hurry, the reception desk can prioritize important questions to quickly clarify requirements. In this way, by adjusting the order in which questions are presented based on the user's emotions, the user's stress can be reduced and information can be gathered efficiently. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 reception desk may be performed using AI, for example, or not using AI. For example, when estimating the user's emotions, the reception desk can use AI to analyze the user's facial expressions and voice to estimate their emotions.

[0076] The reception desk can analyze a user's past response history and dynamically generate the most appropriate questions. For example, the reception desk can automatically generate relevant questions based on the user's past responses. For example, the reception desk can analyze a user's past response patterns and prioritize presenting important unanswered questions. The reception desk can also efficiently collect information by omitting duplicate questions from the user's past response history. This allows for the automatic generation of relevant questions and efficient information collection by analyzing the user's past response history. Past response history includes, but is not limited to, the format in which responses are saved and the analysis algorithms used. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, when analyzing a user's past response history, the reception desk can use AI to analyze the content of the responses and generate the most appropriate questions.

[0077] The reception desk can customize the content of questions based on the user's current work situation when presenting them. For example, if the user is busy, the reception desk can present concise and to-the-point questions. For example, if the user has time, the reception desk can present detailed questions to gather more in-depth information. The reception desk can also prioritize presenting relevant questions according to the user's work content. This allows for efficient information gathering by customizing the content of questions according to the user's work situation. Current work situation includes, but is not limited to, the stage of a business process or the content of the work. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, when analyzing the user's current work situation, the reception desk can use AI to analyze the content of the work and present the most appropriate questions.

[0078] The reception desk can estimate the user's emotions and adjust the difficulty of questions based on the estimated emotions. For example, if the user is nervous, the reception desk can start with easy questions and gradually increase the difficulty. For example, if the user is relaxed, the reception desk can present detailed questions first to efficiently gather information. Also, if the user is in a hurry, the reception desk can prioritize presenting important questions to quickly clarify requirements. By adjusting the difficulty of questions based on the user's emotions, the reception desk can reduce user stress and efficiently gather information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, when estimating the user's emotions, the reception desk can use AI to analyze the user's facial expressions and voice to estimate their emotions.

[0079] The reception desk can prioritize presenting highly relevant questions by considering the user's geographical location when presenting questions. For example, if the user is in a specific region, the reception desk can prioritize presenting questions related to that region. For example, if the user is on the move, the reception desk can present questions related to their destination. Furthermore, the reception desk can present questions that take into account region-specific requirements based on the user's location information. This allows for the efficient collection of information by prioritizing highly relevant questions and considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, when analyzing the user's geographical location information, the reception desk can use AI to analyze the location information and present the most appropriate questions.

[0080] The reception desk can analyze the user's social media activity and present relevant questions when presenting them. For example, the reception desk can analyze the content of the user's social media posts and present relevant questions. For example, the reception desk can present relevant questions based on the activity of the user's followers and friends. The reception desk can also analyze the user's interests on social media and present relevant questions. This allows for the efficient collection of information by presenting relevant questions through the analysis of the user's social media activity. Social media activity includes, but is not limited to, the analysis of post content and follower analysis. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, when analyzing the user's social media activity, the reception desk can use AI to analyze post content and follower activity and present the most appropriate questions.

[0081] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis to improve accuracy. For example, if the user is in a hurry, the analysis unit can perform a rapid analysis to ensure the minimum necessary accuracy. Also, if the user is stressed, the analysis unit can perform a concise analysis to reduce the user's burden. In this way, by adjusting the accuracy of the analysis based on the user's emotions, the user's burden is reduced and the analysis can be performed efficiently. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 a generative AI, or not using a generative AI. For example, when estimating the user's emotions, the analysis unit can use a generative AI to analyze the user's facial expressions and voice to estimate emotions.

[0082] The analysis unit can improve its analysis algorithm by referring to the user's past response data during analysis. For example, the analysis unit can adjust the analysis algorithm based on the user's past response data to improve accuracy. For example, the analysis unit can analyze the user's past response patterns and select the optimal analysis method. Furthermore, the analysis unit can refer to the user's past response data to omit redundant analysis and perform analysis efficiently. In this way, by referring to the user's past response data, the analysis algorithm can be optimized and accuracy can be improved. Past response data includes, but is not limited to, data storage format and database structure. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, when the analysis unit refers to the user's past response data, it can use generative AI to analyze the data and select the optimal analysis algorithm.

[0083] The analysis unit can customize the analysis method based on the user's business process during analysis. For example, the analysis unit can select the optimal analysis method according to the user's business process. For example, the analysis unit can customize the analysis method based on the user's work content to improve accuracy. The analysis unit can also analyze the user's business process and propose an efficient analysis method. This allows for efficient analysis by customizing the analysis method based on the user's business process. Business processes include, but are not limited to, stages and content of business flows. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, when analyzing a user's business process, the analysis unit can use generative AI to analyze the content of the business and select the optimal analysis method.

[0084] 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 tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, the burden on the user can be reduced and information can be provided efficiently. 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, for example, generative AI, or not using generative AI. For example, when estimating the user's emotions, the analysis unit can use generative AI to analyze the user's facial expressions and voice and estimate the emotions.

[0085] The analysis unit can perform analysis while considering the user's geographical location information. For example, if the user is in a specific region, the analysis unit will prioritize analyzing data related to that region. For example, if the user is on the move, the analysis unit can analyze data related to the user's destination. Furthermore, the analysis unit can perform analysis that takes into account region-specific requirements based on the user's location information. This allows for the efficient provision of information by prioritizing the analysis of highly relevant data by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above-described processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, when analyzing the user's geographical location information, the analysis unit can use generative AI to analyze the location information and select the optimal analysis method.

[0086] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can adjust the analysis algorithm based on the relevant literature provided by the user to improve accuracy. For example, the analysis unit can refer to literature related to the user's work and select the optimal analysis method. Furthermore, the analysis unit can analyze the user's relevant literature, omit redundant analyses, and perform analyses efficiently. In this way, the accuracy of the analysis can be improved by referring to the user's relevant literature. Relevant literature includes, but is not limited to, literature databases and citation methods. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, when the analysis unit refers to the user's relevant literature, it can use generative AI to analyze the literature and select the optimal analysis method.

[0087] The generation unit can estimate the user's emotions and adjust the functionality of the prototype it generates based on those emotions. For example, if the user is relaxed, the generation unit can generate a prototype with detailed functionality. For example, if the user is in a hurry, the generation unit can generate a prototype with only basic functionality. Also, if the user is stressed, the generation unit can generate a simple and easy-to-use prototype. By adjusting the prototype's functionality based on the user's emotions, the user's burden is reduced and prototypes can be generated efficiently. 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI, or not. For example, when estimating the user's emotions, the generation unit can use a generative AI to analyze the user's facial expressions and voice to estimate their emotions.

[0088] The generation unit can select the optimal generation method by referring to the user's past prototype usage history during generation. For example, the generation unit can select the optimal generation method based on the user's past prototype usage history. For example, the generation unit can analyze the user's past usage patterns and propose an efficient generation method. Furthermore, the generation unit can refer to the user's past prototype usage history to omit redundant functions and perform generation efficiently. In this way, by referring to the user's past prototype usage history, the optimal generation method can be selected and prototypes can be generated efficiently. Past prototype usage history includes, but is not limited to, the format in which usage history is saved and the structure of the database. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, when the generation unit refers to the user's past prototype usage history, it can use a generation AI to analyze the data and select the optimal generation method.

[0089] The generation unit can customize the prototype structure based on the user's workflow during generation. For example, the generation unit can select the optimal prototype structure according to the user's workflow. For example, the generation unit can customize the prototype structure based on the user's work content to improve usability. The generation unit can also analyze the user's workflow and propose an efficient prototype structure. This allows for the creation of user-friendly prototypes by customizing the prototype structure based on the user's workflow. The workflow includes, but is not limited to, the stages and content of a business process. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, when analyzing the user's workflow, the generation unit can use a generation AI to analyze the content and select the optimal prototype structure.

[0090] The generation unit can estimate the user's emotions and determine the priority of prototypes to generate based on the estimated emotions. For example, if the user is relaxed, the generation unit may prioritize generating prototypes with detailed features. For example, if the user is in a hurry, the generation unit may prioritize generating prototypes with only basic features. Also, if the user is stressed, the generation unit may prioritize generating simple and easy-to-use prototypes. This allows for the efficient generation of prototypes that meet the user's needs by prioritizing prototypes based on 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI, or not. For example, when estimating the user's emotions, the generation unit may use a generative AI to analyze the user's facial expressions and voice to estimate their emotions.

[0091] The generation unit can generate an optimal prototype by considering the user's geographical location information during the generation process. For example, if the user is in a specific region, the generation unit can generate a prototype that includes functions related to that region. For example, if the user is on the move, the generation unit can generate a prototype that includes functions related to the user's destination. Furthermore, the generation unit can generate a prototype that takes into account region-specific requirements based on the user's location information. This allows for the generation of highly relevant prototypes and efficient information provision by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, when analyzing the user's geographical location information, the generation unit can use a generation AI to analyze the location information and generate an optimal prototype.

[0092] The generation unit can analyze the user's social media activity during generation and propose prototype functions. For example, the generation unit can analyze the content of the user's social media posts and propose relevant functions. For example, the generation unit can propose relevant functions based on the activity of the user's followers and friends. The generation unit can also analyze the user's interests on social media and propose relevant functions. In this way, by analyzing the user's social media activity, relevant functions can be proposed and prototypes can be generated efficiently. Social media activity includes, but is not limited to, analyzing post content and follower analysis. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, when analyzing the user's social media activity, the generation unit can use a generation AI to analyze post content and follower activity and propose optimal functions.

[0093] The output unit can estimate the user's emotions and adjust the format of the output document based on the estimated emotions. For example, if the user is tense, the output unit can provide a simple and highly legible document format. For example, if the user is relaxed, the output unit can provide a document format containing detailed information. Furthermore, if the user is in a hurry, the output unit can provide a document format that gets straight to the point. By adjusting the document format based on the user's emotions, the burden on the user can be reduced and information can be provided efficiently. 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 output unit may be performed using AI, for example, or not using AI. For example, when estimating the user's emotions, the output unit can use AI to analyze the user's facial expressions and voice to estimate the emotions.

[0094] The output unit can select the optimal output method by referring to the user's past document usage history when outputting. For example, the output unit can select the optimal output method based on the user's past document usage history. For example, the output unit can analyze the user's past usage patterns and propose an efficient output method. Furthermore, the output unit can refer to the user's past document usage history to omit redundant information and output efficiently. In this way, by referring to the user's past document usage history, the optimal output method can be selected and information can be provided efficiently. Past document usage history includes, but is not limited to, the format in which the usage history is saved and the structure of the database. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, when the output unit refers to the user's past document usage history, it may use AI to analyze the data and select the optimal output method.

[0095] The output unit can customize the document content based on the user's business process at the time of output. For example, the output unit can select the optimal document content according to the user's business process. For example, the output unit can customize the document content based on the user's work content to improve usability. The output unit can also analyze the user's business process and propose efficient document content. In this way, by customizing the document content based on the user's business process, it is possible to provide user-friendly documents. Business processes include, but are not limited to, stages and work content of a business flow. Some or all of the above processing in the output unit may be performed using, for example, AI, or not using AI. For example, when analyzing the user's business process, the output unit can use AI to analyze the work content and select the optimal document content.

[0096] The output unit can estimate the user's emotions and determine the priority of documents to output based on the estimated emotions. For example, if the user is relaxed, the output unit may prioritize outputting documents containing detailed information. For example, if the user is in a hurry, the output unit may prioritize outputting documents containing only basic information. Also, if the user is stressed, the output unit may prioritize outputting simple and easy-to-use documents. In this way, by prioritizing documents based on the user's emotions, documents that meet the user's needs can be efficiently 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 output unit may be performed using AI, or not using AI. For example, when estimating the user's emotions, the output unit may use AI to analyze the user's facial expressions and voice to estimate their emotions.

[0097] The output unit can output the most suitable document at the time of output, taking into account the user's geographical location information. For example, if the user is in a specific region, the output unit can output a document containing information related to that region. For example, if the user is on the move, the output unit can output a document containing information related to the destination. Furthermore, the output unit can output a document that takes into account region-specific requirements based on the user's location information. This allows for the provision of highly relevant documents and efficient information delivery by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the output unit may be performed using, for example, AI, or not using AI. For example, when analyzing the user's geographical location information, the output unit can use AI to analyze the location information and output the most suitable document.

[0098] The output unit can analyze the user's social media activity and suggest document content at the time of output. For example, the output unit can analyze the user's social media posts and suggest a document containing relevant information. For example, the output unit can suggest a document containing relevant information based on the activity of the user's followers and friends. The output unit can also analyze the user's interests on social media and suggest a document containing relevant information. In this way, by analyzing the user's social media activity, it is possible to provide documents containing relevant information and to provide information efficiently. Social media activity includes, but is not limited to, analyzing post content and follower analysis. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, when analyzing the user's social media activity, the output unit can use AI to analyze post content and follower activity and suggest the optimal document content.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The reception system can automatically generate relevant questions by referring to the user's past response history when receiving user responses. For example, by presenting relevant questions based on the user's past answers, consistency in the user's responses can be maintained. The reception system can also analyze the user's past response patterns and prioritize presenting important unanswered questions. Furthermore, the reception system can efficiently collect information by omitting duplicate questions from the user's past response history. In this way, by referring to the user's past response history, relevant questions can be automatically generated and information can be collected efficiently.

[0101] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on those emotions. For example, if the user is relaxed, a detailed analysis can be performed to improve accuracy. If the user is in a hurry, a rapid analysis can be performed to ensure the minimum necessary accuracy. Furthermore, if the user is stressed, a concise analysis can be performed to reduce the user's burden. In this way, by adjusting the accuracy of the analysis based on the user's emotions, the user's burden is reduced and the analysis can be performed efficiently.

[0102] The generation unit can estimate the user's emotions and adjust the functionality of the prototype generated based on those emotions. For example, if the user is relaxed, it can generate a prototype with detailed features. If the user is in a hurry, it can generate a prototype with only basic features. Furthermore, if the user is stressed, it can generate a simple and easy-to-use prototype. By adjusting the prototype's functionality based on the user's emotions, the burden on the user is reduced, and prototypes can be generated efficiently.

[0103] The output unit can estimate the user's emotions and adjust the format of the output document based on those emotions. For example, if the user is stressed, a simple and highly legible document format can be provided. If the user is relaxed, a document format containing detailed information can be provided. Furthermore, if the user is in a hurry, a document format that gets straight to the point can be provided. By adjusting the document format based on the user's emotions, the burden on the user can be reduced and information can be delivered efficiently.

[0104] The reception desk can customize the questions based on the user's current work situation. For example, if the user is busy, concise and to-the-point questions can be presented. If the user has time, more detailed questions can be presented to gather more in-depth information. Furthermore, relevant questions can be prioritized according to the user's work content. This allows for efficient information gathering by customizing the questions according to the user's work situation.

[0105] The analysis unit can improve its analysis algorithm by referring to the user's past response data. For example, it can adjust the analysis algorithm based on the user's past response data to improve accuracy. It can also analyze the user's past response patterns and select the optimal analysis method. Furthermore, by referring to the user's past response data, it can omit redundant analyses and perform analyses efficiently. In this way, by referring to the user's past response data, the analysis algorithm can be optimized and accuracy can be improved.

[0106] The generation unit can select the optimal generation method by referring to the user's past prototype usage history. For example, it can select the optimal generation method based on the user's past prototype usage history. It can also analyze the user's past usage patterns and propose an efficient generation method. Furthermore, by referring to the user's past prototype usage history, it can omit redundant functions and generate prototypes efficiently. In this way, by referring to the user's past prototype usage history, the optimal generation method can be selected and prototypes can be generated efficiently.

[0107] The output unit can customize the document content based on the user's business processes. For example, it can select the most suitable document content according to the user's business processes. Furthermore, it can customize the document content based on the user's work content to improve usability. It can also analyze the user's business processes and suggest efficient document content. In this way, by customizing the document content based on the user's business processes, it can provide user-friendly documents.

[0108] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the essentials can be provided. In this way, by adjusting the display method of the analysis results based on the user's emotions, the burden on the user can be reduced and information can be provided efficiently.

[0109] The generation unit can create an optimal prototype by considering the user's geographical location. For example, if the user is in a specific region, it can generate a prototype that includes features related to that region. Furthermore, if the user is on the move, it can generate a prototype that includes features related to their destination. In addition, based on the user's location information, it can generate a prototype that takes region-specific requirements into account. This allows for the generation of highly relevant prototypes and efficient information delivery by considering the user's geographical location.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk receives user responses. User responses may include, but are not limited to, text format, multiple-choice format, etc. The reception desk may, for example, present the types of questions the user should answer. For example, the reception desk may present the user with questions such as "What functions do you need?" or "What kind of data will you be handling?" Step 2: The analysis unit analyzes the responses received by the reception unit to clarify the application requirements. The analysis is performed using techniques such as natural language processing and data mining, but is not limited to these examples. For example, the analysis unit identifies the necessary functions and data structures based on the user's responses. For example, if the user responds that they want to create a "customer management system," the analysis unit will identify functions such as registering, searching, updating, and deleting customer information. Step 3: The generation unit creates a prototype application based on the requirements clarified by the analysis unit. The generation unit generates the application code based on, for example, identified functions and data structures. Code generation is performed based on, for example, the programming language to be used and the code template, but is not limited to such examples. For example, the generation unit generates code to perform CRUD operations on customer information. Step 4: The output unit outputs requirements specifications and design documents based on the prototype application created by the generation unit. Requirements specifications and design documents include, but are not limited to, items and formats to be included. For example, the output unit automatically generates requirements specifications and design documents based on the generated prototype application.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and output unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives the user's response. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received response. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a prototype application. The output unit is implemented by the output device 40 of the smart device 14 and outputs requirements definition documents and design documents. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and output unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives the user's response. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received response. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a prototype application. The output unit is implemented by the speaker 240 of the smart glasses 214 and outputs requirements specifications and design documents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and output unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives the user's response. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received response. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a prototype application. The output unit is implemented by the display 343 of the headset terminal 314 and outputs requirements definition documents and design documents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and output unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives the user's response. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received response. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a prototype application. The output unit is implemented by the speaker 240 of the robot 414 and outputs requirements definition documents and design documents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A reception desk that accepts user responses, An analysis unit analyzes the responses received by the reception unit and clarifies the application requirements, A generation unit that creates a prototype application based on the requirements clarified by the analysis unit, The system includes an output unit that outputs a requirements definition document and a design document based on the prototype application created by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Present the types of questions the user will answer. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Identify the necessary functions and data structures based on user responses. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate application code based on identified functions and data structures. The system described in Appendix 1, characterized by the features described herein. (Note 5) The output unit is, Based on the generated prototype application, requirements definition documents and design documents are output. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate code to perform CRUD operations on customer information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the order in which questions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past response history and dynamically generate relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When presenting questions, customize the questions based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and adjusts the difficulty of the questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When presenting questions, the system prioritizes and displays the most relevant questions by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When presenting questions, analyze the user's social media activity and present relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, we improve the analysis algorithm by referring to the user's past response data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the analysis method is customized based on the user's business processes. The system described in Appendix 1, characterized by the features described herein. (Note 16) 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 17) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the system references relevant literature from the user to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate user emotions and adjust the functionality of the prototype generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the system selects the optimal generation method by referring to the user's past prototype usage history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the prototype structure is customized based on the user's workflow. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates user emotions and determines the priority of prototypes to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the system considers the user's geographical location to create the optimal prototype. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the system analyzes the user's social media activity and proposes prototype features. The system described in Appendix 1, characterized by the features described herein. (Note 25) The output unit is, It estimates the user's emotions and adjusts the format of the output document based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The output unit is, During output, the system selects the optimal output method by referring to the user's past document usage history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The output unit is, When outputting, the document content is customized based on the user's business processes. The system described in Appendix 1, characterized by the features described herein. (Note 28) The output unit is, It estimates the user's emotions and determines the priority of documents to output based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The output unit is, When outputting, the system considers the user's geographical location to output the most suitable document. The system described in Appendix 1, characterized by the features described herein. (Note 30) The output unit is, When outputting, the system analyzes the user's social media activity and suggests document content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts user responses, An analysis unit analyzes the responses received by the reception unit and clarifies the application requirements, A generation unit that creates a prototype application based on the requirements clarified by the analysis unit, The system includes an output unit that outputs a requirements definition document and a design document based on the prototype application created by the generation unit. A system characterized by the following features.

2. The aforementioned reception unit is Present the types of questions the user will answer. The system according to feature 1.

3. The aforementioned analysis unit, Identify the necessary functions and data structures based on user responses. The system according to feature 1.

4. The generating unit is Generate application code based on identified functions and data structures. The system according to feature 1.

5. The output unit is, Based on the generated prototype application, requirements definition documents and design documents are output. The system according to feature 1.

6. The generating unit is Generate code to perform CRUD operations on customer information. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the order in which questions are presented based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past response history and dynamically generate relevant questions. The system according to feature 1.

9. The aforementioned reception unit is When presenting questions, customize the questions based on the user's current work situation. The system according to feature 1.

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