system

Abstract

The system according to this embodiment aims to read design documents and provide quick and accurate answers to user questions. [Solution] The system according to the embodiment comprises a reading unit, an analysis unit, a reception unit, a generation unit, and a provision unit. The reading unit reads the design document. The analysis unit analyzes the contents of the design document read by the reading unit. The reception unit receives questions from the user. The generation unit generates answers to the questions received by the reception unit. The provision unit provides the answers generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it takes time to conduct an impact survey of the system, and there is room for improvement in efficiency and prevention of survey omissions.

[0005] The system according to the embodiment aims to read a design document and answer questions from users quickly and accurately.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reading unit, an analysis unit, a reception unit, a generation unit, and a provision unit. The reading unit reads the design document. The analysis unit analyzes the contents of the design document read by the reading unit. The reception unit receives questions from the user. The generation unit generates answers to the questions received by the reception unit. The provision unit provides the answers generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can read design documents and respond quickly and accurately to user inquiries. [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 applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The impact assessment efficiency system according to an embodiment of the present invention is a system for building a chatbot to streamline system impact assessments and reduce the likelihood of missed impacts. The impact assessment efficiency system reads design documents, analyzes their contents, and provides appropriate answers to user questions. For example, the impact assessment efficiency system reads design documents. In this process, it analyzes each section of the design document in detail to understand each function and component of the system. For example, the impact assessment efficiency system analyzes the system's architecture, data flow, and detailed explanations of each function. This allows the impact assessment efficiency system to understand the overall picture of the system. Next, the impact assessment efficiency system understands the contents of the design document and provides appropriate answers to user questions. For example, if a user asks, "How will changing this function affect other functions?", the impact assessment efficiency system identifies the scope of the impact and explains the specific impact based on the contents of the design document. In this process, AI analyzes the contents of the design document and generates the optimal answer. This reduces the time required for impact assessments and reduces the likelihood of missed impacts. By having the impact assessment efficiency system answer questions that were previously asked of experts, the resources of experts can be freed up and allowed to concentrate on other important tasks. Furthermore, because the impact assessment efficiency system provides answers based on the design document, it can reduce the number of missed impact assessments. For example, by providing answers that take into account all functions and components described in the design document, the accuracy of the impact assessment can be improved. In this way, by building an impact assessment efficiency system using AI, it is possible to streamline the system's impact assessment and reduce the number of missed impact assessments. As a result, the impact assessment efficiency system can achieve both increased efficiency in impact assessments and a reduction in missed impact assessments.

[0029] The impact assessment efficiency system according to this embodiment comprises a reading unit, an analysis unit, a reception unit, a generation unit, and a provision unit. The reading unit reads design documents. Design documents include, but are not limited to, software design documents and hardware design documents. The reading unit digitizes and reads design documents using scanning technology, for example. The reading unit can also directly read design documents submitted in digital format. Furthermore, the reading unit can read printed design documents using OCR technology. For example, the reading unit scans the design document with a high-resolution scanner and converts it into text information using OCR technology. Design documents in digital format can be directly read if they are submitted in a specific file format. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The analysis unit analyzes the contents of the design documents read by the reading unit. The analysis is performed by, but is not limited to, methods such as text analysis and structural analysis. For example, the analysis unit analyzes the contents of the design document using text analysis technology. The analysis unit can also analyze each section of the design document in detail using structural analysis technology. Furthermore, the analysis unit can also analyze the contents of the design document using AI. For example, the analysis unit uses text analysis technology to analyze the contents of the design document and understand each function and component of the system. Structural analysis technology analyzes each section of the design document in detail to understand the system architecture and data flow. AI analyzes the contents of the design document and understands the overall picture of the system. The reception unit receives questions from users. These questions include, but are not limited to, technical questions and operational questions. The reception unit receives questions from users using, for example, online forms. The reception unit can also receive questions from users using a chatbot. Furthermore, the reception unit can also receive questions from users using voice input. For example, the reception unit receives questions from users using online forms. The chatbot receives questions through dialogue with the user. Voice input recognizes the user's voice and converts the question into text. The generation unit generates answers to the questions received by the reception unit.The generation is performed using, for example, AI, but is not limited to such examples. For example, the generation unit uses AI to generate the optimal answer to the user's question. The AI ​​analyzes the contents of the design document and generates the optimal answer to the user's question. The generation unit uses, for example, AI to generate the optimal answer to the user's question. The AI ​​analyzes the contents of the design document and generates the optimal answer to the user's question. The providing unit provides the answers generated by the generation unit. The provision is performed by, for example, email or dashboard, but is not limited to such examples. For example, the providing unit provides the generated answers to the user via email. The dashboard provides an interface that the user can access and check the answers. As a result, the impact assessment efficiency system according to the embodiment can achieve increased efficiency in impact assessments and a reduction in missed impact assessments. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can provide answers using an AI model that takes the answers generated by the generation unit as input and outputs answers.

[0030] The reading unit reads design documents. These design documents include, but are not limited to, software design documents and hardware design documents. The reading unit can, for example, digitize and read design documents using scanning technology. It can also directly read design documents submitted in digital format. Furthermore, the reading unit can read printed design documents using OCR technology. For example, the reading unit scans the design document with a high-resolution scanner and converts it into text information using OCR technology. Digital design documents submitted in specific file formats can be read directly. OCR technology recognizes printed characters with high accuracy and converts them into digital text. When digitizing paper design documents using scanning technology, the reading unit achieves efficient and highly accurate digitization by adjusting the resolution and scanning speed. For example, if the design document contains detailed information, scanning at high resolution allows for accurate digitization of even the finest details of text and diagrams. Adjusting the scanning speed also allows for the rapid digitization of large volumes of design documents. Design documents submitted digitally are compatible with common file formats such as PDF, Word, and Excel, and the effort required for digitization can be reduced by directly reading these files. Furthermore, by using OCR technology, the text information of printed design documents can be recognized with high accuracy and converted into digital text. OCR technology analyzes the shape and arrangement of characters and recognizes printed characters as digital data, so it can also handle handwritten characters and special fonts. As a result, the reading unit can efficiently digitize design documents in various formats and provide them to the analysis and generation units, creating a foundation for this process.

[0031] The analysis unit analyzes the contents of the design document read by the reading unit. The analysis is performed using methods such as text analysis and structural analysis, but is not limited to these examples. For example, the analysis unit can analyze the contents of the design document using text analysis technology. The analysis unit can also analyze each section of the design document in detail using structural analysis technology. Furthermore, the analysis unit can analyze the contents of the design document using AI. For example, the analysis unit uses text analysis technology to analyze the contents of the design document and understand each function and component of the system. Structural analysis technology analyzes each section of the design document in detail and understands the system architecture and data flow. AI analyzes the contents of the design document and understands the overall picture of the system. When the analysis unit analyzes the contents of the design document using text analysis technology, it utilizes natural language processing technology. Natural language processing technology analyzes the text of the design document, extracts keywords, and understands the context. This allows for an accurate understanding of the contents of the design document and the identification of each function and component of the system. Structural analysis technology analyzes sections and subsections of the design document and understands the system architecture and data flow in detail. For example, by analyzing the headings and paragraph structure of design documents, the AI ​​can clarify the system's hierarchical structure and data flow. The AI ​​uses machine learning algorithms to analyze the contents of design documents and understand the overall system. The AI ​​has the ability to learn from large amounts of design document data and automatically analyze the contents of design documents. This allows the analysis unit to quickly and accurately analyze the contents of design documents and grasp the overall system. Furthermore, the analysis unit can refer to past design document data and related technical documents to gain a deeper understanding of the contents of the design documents. This allows the analysis unit to analyze the contents of design documents in detail and provide a foundation for efficiently conducting impact assessments of the system.

[0032] The reception desk receives questions from users. These questions may include, but are not limited to, technical or operational questions. The reception desk may, for example, use online forms to receive user questions. It can also use a chatbot to receive user questions. Furthermore, it can use voice input to receive user questions. For example, the reception desk may use online forms to receive user questions. A chatbot receives questions through dialogue with the user. Voice input recognizes the user's voice and converts the question into text. When the reception desk receives user questions using online forms, it provides an interface that allows users to easily input questions. Online forms provide appropriate input fields depending on the type and content of the question, enabling users to quickly enter the necessary information. A chatbot uses natural language processing technology to interact with the user and receive questions. The chatbot provides appropriate responses to user questions and collects necessary information. Voice input uses speech recognition technology to convert the user's voice into text and receive questions. Voice input is highly convenient because it allows users to input questions without using their hands. This allows the reception department to receive user inquiries in various ways and collect information quickly and accurately. Furthermore, the reception department can categorize user inquiries and route them to the appropriate department or person in charge. As a result, the reception department can efficiently process user inquiries and support the streamlining of impact assessments.

[0033] The generation unit generates answers to questions received by the reception unit. Generation is performed using, for example, AI, but is not limited to such examples. For example, the generation unit uses AI to generate the best answer to a user's question. The AI ​​analyzes the contents of the design document and generates the best answer to the user's question. The generation unit uses, for example, AI to generate the best answer to a user's question. The AI ​​analyzes the contents of the design document and generates the best answer to the user's question. The generation unit utilizes natural language processing technology when generating the best answer to a user's question using AI. Natural language processing technology provides a foundation for analyzing the content of the user's question and generating an appropriate answer. The AI ​​analyzes the contents of the design document, extracts relevant information, and generates an answer. For example, the AI ​​analyzes each section and item of the design document and generates the best answer to the user's question. The AI ​​has the ability to learn from past question and answer data and generate the best answer to similar questions. This allows the generation unit to provide quick and accurate answers to user questions. Furthermore, the generation unit can evaluate the quality of the generated answers and make corrections or improvements as needed. This allows the generation unit to consistently provide high-quality responses and improve user satisfaction.

[0034] The providing unit provides the answers generated by the generating unit. The provision may be, but is not limited to, methods such as email or a dashboard. For example, the providing unit may provide the generated answers to the user via email. The dashboard provides an interface that the user can access to review the answers. This enables the impact assessment efficiency system according to the embodiment to improve the efficiency of impact assessments and reduce the number of missed impact assessments. Some or all of the processing described above in the providing unit may be performed using, for example, AI, or not. For example, the providing unit can provide answers using an AI model that takes the answers generated by the generating unit as input and outputs the answers. When providing the answers generated by the generating unit to the user, the providing unit selects a method that considers user convenience. For example, email delivery allows the user to receive the email directly and review the answers, thus enabling quick and reliable information transmission. The dashboard provides an interface that the user can access to review the answers and also allows them to refer to a history of past questions and answers. This enables the user to centrally manage the information they need. The providing unit can collect user feedback and use it to improve the delivery method and the quality of the answers. For example, by allowing users to rate and comment on the responses provided, the service provider can use that feedback to improve the service. This enables the service provider to provide information to users quickly and reliably, and to improve the efficiency of impact assessments and reduce the number of missed impacts.

[0035] The learning unit learns the contents of the design document. The learning unit learns the contents of the design document using methods such as machine learning or deep learning. For example, the learning unit learns the contents of the design document using a machine learning algorithm. The machine learning algorithm analyzes the contents of the design document and understands each function and component of the system. The learning unit learns the contents of the design document using a deep learning algorithm. The deep learning algorithm analyzes the contents of the design document and understands the overall picture of the system. As a result, the accuracy of the impact assessment efficiency system is improved by learning the contents of the design document. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can learn the contents of the design document using an AI model that takes the contents of the design document as input and outputs training data.

[0036] The feedback collection unit collects feedback from users. For example, the feedback collection unit collects feedback such as user opinions and suggestions for improvement. For example, the feedback collection unit collects feedback from users in the form of a questionnaire. The questionnaire format allows for easy user response. The feedback collection unit also collects feedback from users in the form of an interview. The interview format allows for detailed feedback through dialogue with the user. The feedback collection unit also collects feedback from users using an online form. The online form provides an interface that allows users to easily provide feedback. This enables improvements to the impact assessment efficiency system by collecting user feedback. Some or all of the above-described processes in the feedback collection unit may be performed using AI, or not. For example, the feedback collection unit can collect feedback using an AI model that takes user feedback data as input and outputs feedback.

[0037] The improvement unit improves the accuracy of the impact assessment efficiency system based on feedback. The improvement unit improves the accuracy of the impact assessment efficiency system based on user feedback, for example. The improvement unit analyzes feedback data to improve the accuracy of the impact assessment efficiency system. The improvement unit analyzes feedback data using AI to improve the accuracy of the impact assessment efficiency system, for example. The AI ​​analyzes feedback data to improve the accuracy of the impact assessment efficiency system. As a result, more accurate answers are provided by improving the accuracy of the impact assessment efficiency system based on feedback. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can improve the accuracy of the impact assessment efficiency system using an AI model that takes feedback data as input and outputs an improvement algorithm.

[0038] The analysis unit can analyze each section of the design document in detail and understand each function and component of the system. For example, the analysis unit can analyze each section of the design document in detail. For example, the analysis unit can analyze each section of the design document item by item. For example, the analysis unit can analyze each section of the design document section by section. By doing so, by analyzing each section of the design document in detail, the overall picture of the system can be grasped. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze each section of the design document in detail using an AI model that takes each section of the design document as input and outputs analysis results.

[0039] The generation unit can generate the optimal answer to a user's question. The generation unit can, for example, generate the optimal answer to a user's question. For example, the generation unit can use AI to generate the optimal answer to a user's question. The AI ​​analyzes the contents of the design document and generates the optimal answer to the user's question. The generation unit can, for example, use AI to generate the optimal answer to a user's question. The AI ​​analyzes the contents of the design document and generates the optimal answer to the user's question. This improves the accuracy of impact assessments by generating the optimal answer to a user's question. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate the optimal answer to a user's question using an AI model that takes a user's question as input and outputs the optimal answer.

[0040] The service provider can provide the user with the generated response. The service provider can, for example, provide the user with the generated response. For example, the service provider can use email to provide the user with the generated response. The service provider can, for example, use a dashboard to provide the user with the generated response. The service provider can, for example, use a chatbot to provide the user with the generated response. This makes impact assessments more efficient by providing the user with the generated response. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide the user with the response using an AI model that takes the response generated by the generation unit as input and outputs the response.

[0041] The reading unit can analyze the past reading history of design documents and select the optimal reading method. For example, the reading unit analyzes the past reading history of design documents to identify frequently read sections. For example, the reading unit analyzes the past reading history of design documents to identify parts that users frequently refer to. For example, the reading unit analyzes the past reading history of design documents to suggest a reading order tailored to the user's preferences. This enables efficient reading of design documents by analyzing the past reading history. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can select the optimal reading method using an AI model that takes the past reading history of design documents as input and outputs the optimal reading method.

[0042] The reading unit can prioritize reading important sections of the system when reading the design document. For example, the reading unit might prioritize reading sections related to the system's architecture first. For example, the reading unit might prioritize reading sections related to data flow. For example, the reading unit might prioritize reading sections that provide detailed descriptions of each function. This allows for efficient information acquisition by prioritizing the reading of important sections of the system. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can prioritize reading important sections of the design document using an AI model that takes important sections of the design document as input and outputs the order in which to prioritize reading them.

[0043] The loading unit can prioritize loading sections that are highly relevant when loading design documents, taking into account the user's project progress. For example, the loading unit considers the user's project progress. For instance, in the early stages of a project, it prioritizes loading sections that provide an overview of the system. For instance, in the middle stages of a project, it prioritizes loading sections that contain detailed descriptions of each function. For instance, in the final stages of a project, it prioritizes loading sections related to testing and debugging. This allows for the efficient acquisition of highly relevant information by considering the project progress. Some or all of the above processing in the loading unit may be performed using AI, for example, or without AI. For example, the loading unit can prioritize loading highly relevant sections using an AI model that takes the user's project progress as input and outputs highly relevant sections.

[0044] The reading unit can read relevant sections by referring to the user's past question history when reading design documents. For example, the reading unit can refer to the user's past question history. For example, the reading unit can prioritize reading sections related to what the user has asked in the past. For example, the reading unit can identify frequently referenced sections from the user's past question history. For example, the reading unit can analyze the user's past question history and efficiently read highly relevant sections. This allows for the efficient acquisition of highly relevant information by referring to past question history. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can read relevant sections using an AI model that takes the user's past question history as input and outputs relevant sections.

[0045] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of the system when analyzing each section of the design document. For example, the analysis unit considers the interrelationships of the system when analyzing each section of the design document. For example, the analysis unit performs analysis considering the interdependencies of each function. For example, the analysis unit performs analysis considering the interrelationships of data flows. For example, the analysis unit performs analysis considering the overall system architecture. By considering the interrelationships of the system, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can improve the accuracy of its analysis by using an AI model that takes each section of the design document as input and outputs analysis results that consider the interrelationships.

[0046] The analysis unit can perform analysis by referring to the system's change history when analyzing design documents. For example, the analysis unit can refer to the system's change history. For example, the analysis unit can perform analysis that reflects the latest information based on the system's change history. For example, the analysis unit can refer to past change history and perform analysis that takes changes into account. For example, the analysis unit can identify the scope of impact based on the change history and perform a detailed analysis. This makes it possible to perform analysis that reflects the latest information by referring to the system's change history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can perform analysis using an AI model that takes the system's change history as input and outputs analysis results that take changes into account.

[0047] The analysis unit can perform analysis while considering the geographical distribution of the system when analyzing the design document. For example, the analysis unit considers the geographical distribution of the system. For example, the analysis unit performs analysis that considers the characteristics of each region based on the geographical distribution of the system. For example, the analysis unit analyzes the data flow of each region based on the geographical distribution. For example, the analysis unit analyzes the interrelationships of functions in each region, taking the geographical distribution into consideration. This makes it possible to perform analysis that reflects the characteristics of each region by considering the geographical distribution of the system. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can perform analysis using an AI model that takes the geographical distribution of the system as input and outputs analysis results that consider the characteristics of each region.

[0048] The analysis unit can improve the accuracy of its analysis by referring to relevant technical documents when analyzing design documents. For example, the analysis unit may refer to relevant technical documents. For example, the analysis unit may perform an analysis that reflects the latest technical information based on relevant technical documents. For example, the analysis unit may refer to technical documents and perform an analysis that complements the content of the design document. For example, the analysis unit may perform a detailed analysis of each section of the design document based on technical documents. As a result, the accuracy of the analysis is improved by referring to relevant technical documents. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may perform an analysis using an AI model that takes relevant technical documents as input and outputs analysis results that reflect the technical information.

[0049] The reception unit can select the optimal reception method when receiving a question by referring to the user's past question history. For example, the reception unit may refer to the user's past question history. For example, the reception unit may prioritize suggesting question formats that the user has frequently used in the past. For example, the reception unit may automatically suggest relevant questions from the user's past question history. For example, the reception unit may analyze the user's past question history and select the optimal question reception method. This allows the reception unit to select the optimal question reception method by referring to the past question history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may select the optimal question reception method using an AI model that takes the user's past question history as input and outputs the optimal question reception method.

[0050] The reception desk can filter questions when they are received, taking into account the user's current project status. For example, the reception desk considers the user's current project status. For example, the reception desk prioritizes receiving highly relevant questions based on the current project status. For example, the reception desk prioritizes receiving highly important questions, taking into account the progress of the project. For example, the reception desk filters appropriate questions according to the project phase. This allows for the efficient reception of highly relevant questions by considering the project status. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can filter questions using an AI model that takes the user's project status as input and outputs the filtered question results.

[0051] The reception unit can prioritize receiving questions that are highly relevant, taking into account the user's geographical location information. For example, the reception unit considers the user's geographical location information. For example, the reception unit prioritizes receiving questions that are highly relevant based on the user's current location. For example, the reception unit prioritizes receiving region-specific questions, taking into account geographical location information. For example, the reception unit filters the most relevant questions based on the user's location information. This allows for the efficient reception of highly relevant questions by considering geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can receive questions using an AI model that takes the user's geographical location information as input and outputs highly relevant questions.

[0052] The reception unit can analyze the user's social media activity when receiving a question and accept relevant questions. For example, the reception unit can analyze the user's social media activity. For example, the reception unit can automatically suggest relevant questions based on the user's social media activity. For example, the reception unit can analyze the content of social media posts and filter for the most relevant questions. For example, the reception unit can prioritize accepting highly relevant questions based on the user's social media activity. This allows for the efficient acceptance of highly relevant questions by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can accept questions using an AI model that takes the user's social media activity as input and outputs relevant questions.

[0053] The generation unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the generation unit adjusts the level of detail in the answer based on the importance of the question. For example, the generation unit provides a detailed answer for a high-importance question. For example, the generation unit provides a concise answer for a low-importance question. The generation unit dynamically adjusts the level of detail in the answer according to the importance of the question. This ensures that an appropriate answer is provided by adjusting the level of detail in the answer according to the importance of the question. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can adjust the level of detail in the answer using an AI model that takes the importance of the question as input and outputs the level of detail in the answer.

[0054] The generation unit can apply different generation algorithms depending on the question category when generating answers. For example, the generation unit can apply different generation algorithms depending on the question category. For example, for technical questions, the generation unit generates answers that refer to technical literature. For example, for business-related questions, the generation unit generates answers that refer to business literature. The generation unit can improve the accuracy of answers by applying the most suitable generation algorithm for each category. This improves the accuracy of answers by applying the most suitable generation algorithm depending on the question category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate answers using an AI model that takes the question category as input and outputs the most suitable generation algorithm.

[0055] The generation unit can determine the priority of answers based on when the questions were submitted when generating answers. For example, the generation unit determines the priority of answers based on when the questions were submitted. For example, the generation unit determines the priority of answers based on the time period in which the questions were submitted. For example, the generation unit dynamically adjusts the order of answers according to when the questions were submitted. For example, the generation unit provides the optimal answer considering when the questions were submitted. In this way, appropriate answers are provided by determining the priority of answers based on when the questions were submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can determine the priority of answers using an AI model that takes the time the questions were submitted as input and outputs the priority of answers.

[0056] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the generation unit adjusts the order of answers based on the relevance of the questions. For example, the generation unit prioritizes providing the most relevant answers based on the relevance of the questions. For example, the generation unit dynamically adjusts the order of answers considering the relevance of the questions. For example, the generation unit provides the optimal answer according to the relevance of the questions. In this way, appropriate answers are provided by adjusting the order of answers based on the relevance of the questions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can adjust the order of answers using an AI model that takes the relevance of questions as input and outputs the order of answers.

[0057] The service provider can select the optimal service provider method by referring to the user's past question history when providing answers. For example, the service provider can refer to the user's past question history. For example, the service provider can prioritize suggesting service provider methods that the user has frequently used in the past. For example, the service provider can automatically suggest relevant answers from the user's past question history. For example, the service provider can analyze the user's past question history and select the optimal service provider method. This allows the service provider to select the optimal answer provider method by referring to the past question history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select a service provider method using an AI model that takes the user's past question history as input and outputs the optimal service provider method.

[0058] The service provider can customize its responses when providing answers, taking into account the user's current project status. For example, the service provider considers the user's current project status. For example, the service provider prioritizes providing highly relevant answers based on the current project status. For example, the service provider prioritizes providing highly important answers by considering the progress of the project. For example, the service provider customizes appropriate answers according to the project phase. This allows the service provider to provide highly relevant answers by considering the project status. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can customize responses using an AI model that takes the user's project status as input and outputs customized responses.

[0059] The service provider can select the optimal service delivery method when providing answers, taking into account the user's geographical location information. For example, the service provider may consider the user's geographical location information. For example, the service provider may prioritize providing highly relevant answers based on the user's current location. For example, the service provider may prioritize providing region-specific answers, taking into account geographical location information. For example, the service provider may provide the optimal answer based on the user's location information. This allows for the provision of highly relevant answers by considering geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may select a service delivery method using an AI model that takes the user's geographical location information as input and outputs the optimal service delivery method.

[0060] The service provider can analyze the user's social media activity and adjust the method of providing answers when providing responses. For example, the service provider can analyze the user's social media activity. For example, the service provider can automatically suggest relevant answers based on the user's social media activity. For example, the service provider can analyze the content of social media posts and provide the most appropriate answers. For example, the service provider can prioritize providing highly relevant answers based on the user's social media activity. In this way, highly relevant answers can be provided by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can adjust the method of providing answers using an AI model that takes the user's social media activity as input and outputs the most appropriate answer.

[0061] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit refers to past learning data. For example, the learning unit selects the optimal learning algorithm based on past learning data. For example, the learning unit improves the accuracy of the learning algorithm by referring to past learning data. For example, the learning unit analyzes past learning data and optimizes the learning algorithm. As a result, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can optimize the learning algorithm using an AI model that takes past learning data as input and outputs the optimal learning algorithm.

[0062] The learning unit can weight the training data based on the update history of the design document during training. For example, the learning unit refers to the update history of the design document. For example, the learning unit weights the training data based on the latest update history of the design document. For example, the learning unit refers to past update history and adjusts the weighting of the training data. For example, the learning unit optimizes the weighting of the training data based on the update history. This improves the accuracy of training by weighting the training data based on the update history of the design document. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can weight the training data using an AI model that takes the update history of the design document as input and outputs the result of weighting the training data.

[0063] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback collection unit may refer to the user's past feedback history. For example, the feedback collection unit may preferentially suggest feedback formats that the user has frequently provided in the past. For example, the feedback collection unit may automatically suggest relevant feedback from the user's past feedback history. For example, the feedback collection unit may analyze the user's past feedback history and select the optimal collection method. This allows the optimal feedback collection method to be selected by referring to past feedback history. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit may select a collection method using an AI model that takes the user's past feedback history as input and outputs the optimal collection method.

[0064] The feedback collection unit can prioritize collecting highly relevant feedback by considering the user's geographical location information during feedback collection. For example, the feedback collection unit considers the user's geographical location information. For example, the feedback collection unit prioritizes collecting highly relevant feedback based on the user's current location. For example, the feedback collection unit prioritizes collecting region-specific feedback by considering geographical location information. For example, the feedback collection unit collects optimal feedback based on the user's location information. This allows for the efficient collection of highly relevant feedback by considering geographical location information. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can collect feedback using an AI model that takes the user's geographical location information as input and outputs highly relevant feedback.

[0065] The improvement unit can optimize the improvement algorithm by referring to past feedback data during the improvement process. For example, the improvement unit refers to past feedback data. For example, the improvement unit selects the optimal improvement algorithm based on past feedback data. For example, the improvement unit improves the accuracy of the improvement algorithm by referring to past feedback data. For example, the improvement unit analyzes past feedback data and optimizes the improvement algorithm. As a result, the accuracy of the improvement algorithm is improved by referring to past feedback data. Some or all of the above processes in the improvement unit may be performed using AI, for example, or without using AI. For example, the improvement unit can optimize the improvement algorithm using an AI model that takes past feedback data as input and outputs the optimal improvement algorithm.

[0066] The improvement unit can weight improvements based on the design document's update history. For example, the improvement unit refers to the design document's update history. For example, the improvement unit weights improvements based on the latest design document's update history. For example, the improvement unit refers to past update history and adjusts the improvement weighting. For example, the improvement unit optimizes the improvement weighting based on the update history. This improves the accuracy of improvements by weighting improvements based on the design document's update history. Some or all of the above processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can weight improvements using an AI model that takes the design document's update history as input and outputs the improvement weighting results.

[0067] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0068] The impact assessment efficiency system can retrieve relevant sections by referencing the user's past question history. For example, it can prioritize retrieving sections related to questions the user has asked in the past. It can identify frequently referenced sections from the user's past question history. By analyzing the user's past question history, it can efficiently retrieve highly relevant sections. This allows for the efficient acquisition of highly relevant information by referencing past question history.

[0069] The impact assessment efficiency system can prioritize reading critical sections of a system when reviewing design documents. For example, it can prioritize reading sections related to the system architecture first, sections related to data flow, and sections containing detailed descriptions of each function. This allows for efficient information retrieval by prioritizing the reading of critical sections of the system.

[0070] The impact assessment efficiency system allows for analysis of design documents by referencing the system's change history. For example, it can perform analyses that reflect the latest information based on the system's change history. It can refer to past change history and perform analyses that take changes into account. Based on the change history, it can identify the scope of impact and perform detailed analyses. As a result, by referring to the system's change history, it becomes possible to perform analyses that reflect the latest information.

[0071] The impact assessment efficiency system can adjust the level of detail in responses based on the importance of the question during response generation. For example, it can provide detailed answers to high-importance questions and concise answers to low-importance questions. The level of detail in responses can be dynamically adjusted according to the importance of the question. This ensures that appropriate responses are provided by adjusting the level of detail according to the importance of the question.

[0072] The impact assessment efficiency system can select the optimal response delivery method by referring to the user's past question history. For example, it can prioritize suggesting delivery methods that the user has frequently used in the past. It can automatically suggest relevant answers from the user's past question history. It can analyze the user's past question history and select the optimal delivery method. This allows the system to select the optimal response delivery method by referring to past question history.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The reading unit reads the design documents. Design documents include software design documents and hardware design documents. The reading unit can digitize and read design documents using scanning technology. It can also directly read design documents submitted in digital format, and can read printed design documents using OCR technology. For example, the reading unit scans the design document with a high-resolution scanner and converts it into text information using OCR technology. Step 2: The analysis unit analyzes the contents of the design document read by the reading unit. The analysis is performed using methods such as text analysis and structural analysis. For example, the analysis unit uses text analysis technology to analyze the contents of the design document and understand each function and component of the system. It also uses structural analysis technology to analyze each section of the design document in detail and understand the system architecture and data flow. Furthermore, it is possible to use AI to analyze the contents of the design document and understand the overall picture of the system. Step 3: The reception desk receives questions from users. These questions may include technical questions or questions about operation. The reception desk can receive questions from users using online forms, chatbots, voice input, etc. For example, questions can be received using online forms, chatbots can receive questions through dialogue with users, and voice input can recognize the user's voice and convert the question into text. Step 4: The generation unit generates answers to the questions received by the reception unit. Generation is performed using AI. For example, the generation unit uses AI to generate the optimal answer to the user's question. The AI ​​analyzes the contents of the design document and generates the optimal answer to the user's question. Step 5: The delivery unit provides the answers generated by the generation unit. Delivery can be done via email or through a dashboard. For example, the delivery unit provides the generated answers to the user via email. The dashboard provides an interface that the user can access to review the answers.

[0075] (Example of form 2) The impact assessment efficiency system according to an embodiment of the present invention is a system for building a chatbot to streamline system impact assessments and reduce the likelihood of missed impacts. The impact assessment efficiency system reads design documents, analyzes their contents, and provides appropriate answers to user questions. For example, the impact assessment efficiency system reads design documents. In this process, it analyzes each section of the design document in detail to understand each function and component of the system. For example, the impact assessment efficiency system analyzes the system's architecture, data flow, and detailed explanations of each function. This allows the impact assessment efficiency system to understand the overall picture of the system. Next, the impact assessment efficiency system understands the contents of the design document and provides appropriate answers to user questions. For example, if a user asks, "How will changing this function affect other functions?", the impact assessment efficiency system identifies the scope of the impact and explains the specific impact based on the contents of the design document. In this process, AI analyzes the contents of the design document and generates the optimal answer. This reduces the time required for impact assessments and reduces the likelihood of missed impacts. By having the impact assessment efficiency system answer questions that were previously asked of experts, the resources of experts can be freed up and allowed to concentrate on other important tasks. Furthermore, because the impact assessment efficiency system provides answers based on the design document, it can reduce the number of missed impact assessments. For example, by providing answers that take into account all functions and components described in the design document, the accuracy of the impact assessment can be improved. In this way, by building an impact assessment efficiency system using AI, it is possible to streamline the system's impact assessment and reduce the number of missed impact assessments. As a result, the impact assessment efficiency system can achieve both increased efficiency in impact assessments and a reduction in missed impact assessments.

[0076] The impact assessment efficiency system according to this embodiment comprises a reading unit, an analysis unit, a reception unit, a generation unit, and a provision unit. The reading unit reads design documents. Design documents include, but are not limited to, software design documents and hardware design documents. The reading unit digitizes and reads design documents using scanning technology, for example. The reading unit can also directly read design documents submitted in digital format. Furthermore, the reading unit can read printed design documents using OCR technology. For example, the reading unit scans the design document with a high-resolution scanner and converts it into text information using OCR technology. Design documents in digital format can be directly read if they are submitted in a specific file format. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The analysis unit analyzes the contents of the design documents read by the reading unit. The analysis is performed by, but is not limited to, methods such as text analysis and structural analysis. For example, the analysis unit analyzes the contents of the design document using text analysis technology. The analysis unit can also analyze each section of the design document in detail using structural analysis technology. Furthermore, the analysis unit can also analyze the contents of the design document using AI. For example, the analysis unit uses text analysis technology to analyze the contents of the design document and understand each function and component of the system. Structural analysis technology analyzes each section of the design document in detail to understand the system architecture and data flow. AI analyzes the contents of the design document and understands the overall picture of the system. The reception unit receives questions from users. These questions include, but are not limited to, technical questions and operational questions. The reception unit receives questions from users using, for example, online forms. The reception unit can also receive questions from users using a chatbot. Furthermore, the reception unit can also receive questions from users using voice input. For example, the reception unit receives questions from users using online forms. The chatbot receives questions through dialogue with the user. Voice input recognizes the user's voice and converts the question into text. The generation unit generates answers to the questions received by the reception unit.The generation is performed using, for example, AI, but is not limited to such examples. For example, the generation unit uses AI to generate the optimal answer to the user's question. The AI ​​analyzes the contents of the design document and generates the optimal answer to the user's question. The generation unit uses, for example, AI to generate the optimal answer to the user's question. The AI ​​analyzes the contents of the design document and generates the optimal answer to the user's question. The providing unit provides the answers generated by the generation unit. The provision is performed by, for example, email or dashboard, but is not limited to such examples. For example, the providing unit provides the generated answers to the user via email. The dashboard provides an interface that the user can access and check the answers. As a result, the impact assessment efficiency system according to the embodiment can achieve increased efficiency in impact assessments and a reduction in missed impact assessments. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can provide answers using an AI model that takes the answers generated by the generation unit as input and outputs answers.

[0077] The reading unit reads design documents. These design documents include, but are not limited to, software design documents and hardware design documents. The reading unit can, for example, digitize and read design documents using scanning technology. It can also directly read design documents submitted in digital format. Furthermore, the reading unit can read printed design documents using OCR technology. For example, the reading unit scans the design document with a high-resolution scanner and converts it into text information using OCR technology. Digital design documents submitted in specific file formats can be read directly. OCR technology recognizes printed characters with high accuracy and converts them into digital text. When digitizing paper design documents using scanning technology, the reading unit achieves efficient and highly accurate digitization by adjusting the resolution and scanning speed. For example, if the design document contains detailed information, scanning at high resolution allows for accurate digitization of even the finest details of text and diagrams. Adjusting the scanning speed also allows for the rapid digitization of large volumes of design documents. Design documents submitted digitally are compatible with common file formats such as PDF, Word, and Excel, and the effort required for digitization can be reduced by directly reading these files. Furthermore, by using OCR technology, the text information of printed design documents can be recognized with high accuracy and converted into digital text. OCR technology analyzes the shape and arrangement of characters and recognizes printed characters as digital data, so it can also handle handwritten characters and special fonts. As a result, the reading unit can efficiently digitize design documents in various formats and provide them to the analysis and generation units, creating a foundation for this process.

[0078] The analysis unit analyzes the contents of the design document read by the reading unit. The analysis is performed using methods such as text analysis and structural analysis, but is not limited to these examples. For example, the analysis unit can analyze the contents of the design document using text analysis technology. The analysis unit can also analyze each section of the design document in detail using structural analysis technology. Furthermore, the analysis unit can analyze the contents of the design document using AI. For example, the analysis unit uses text analysis technology to analyze the contents of the design document and understand each function and component of the system. Structural analysis technology analyzes each section of the design document in detail and understands the system architecture and data flow. AI analyzes the contents of the design document and understands the overall picture of the system. When the analysis unit analyzes the contents of the design document using text analysis technology, it utilizes natural language processing technology. Natural language processing technology analyzes the text of the design document, extracts keywords, and understands the context. This allows for an accurate understanding of the contents of the design document and the identification of each function and component of the system. Structural analysis technology analyzes sections and subsections of the design document and understands the system architecture and data flow in detail. For example, by analyzing the headings and paragraph structure of design documents, the AI ​​can clarify the system's hierarchical structure and data flow. The AI ​​uses machine learning algorithms to analyze the contents of design documents and understand the overall system. The AI ​​has the ability to learn from large amounts of design document data and automatically analyze the contents of design documents. This allows the analysis unit to quickly and accurately analyze the contents of design documents and grasp the overall system. Furthermore, the analysis unit can refer to past design document data and related technical documents to gain a deeper understanding of the contents of the design documents. This allows the analysis unit to analyze the contents of design documents in detail and provide a foundation for efficiently conducting impact assessments of the system.

[0079] The reception desk receives questions from users. These questions may include, but are not limited to, technical or operational questions. The reception desk may, for example, use online forms to receive user questions. It can also use a chatbot to receive user questions. Furthermore, it can use voice input to receive user questions. For example, the reception desk may use online forms to receive user questions. A chatbot receives questions through dialogue with the user. Voice input recognizes the user's voice and converts the question into text. When the reception desk receives user questions using online forms, it provides an interface that allows users to easily input questions. Online forms provide appropriate input fields depending on the type and content of the question, enabling users to quickly enter the necessary information. A chatbot uses natural language processing technology to interact with the user and receive questions. The chatbot provides appropriate responses to user questions and collects necessary information. Voice input uses speech recognition technology to convert the user's voice into text and receive questions. Voice input is highly convenient because it allows users to input questions without using their hands. This allows the reception department to receive user inquiries in various ways and collect information quickly and accurately. Furthermore, the reception department can categorize user inquiries and route them to the appropriate department or person in charge. As a result, the reception department can efficiently process user inquiries and support the streamlining of impact assessments.

[0080] The generation unit generates answers to questions received by the reception unit. Generation is performed using, for example, AI, but is not limited to such examples. For example, the generation unit uses AI to generate the best answer to a user's question. The AI ​​analyzes the contents of the design document and generates the best answer to the user's question. The generation unit uses, for example, AI to generate the best answer to a user's question. The AI ​​analyzes the contents of the design document and generates the best answer to the user's question. The generation unit utilizes natural language processing technology when generating the best answer to a user's question using AI. Natural language processing technology provides a foundation for analyzing the content of the user's question and generating an appropriate answer. The AI ​​analyzes the contents of the design document, extracts relevant information, and generates an answer. For example, the AI ​​analyzes each section and item of the design document and generates the best answer to the user's question. The AI ​​has the ability to learn from past question and answer data and generate the best answer to similar questions. This allows the generation unit to provide quick and accurate answers to user questions. Furthermore, the generation unit can evaluate the quality of the generated answers and make corrections or improvements as needed. This allows the generation unit to consistently provide high-quality responses and improve user satisfaction.

[0081] The providing unit provides the answers generated by the generating unit. The provision may be, but is not limited to, methods such as email or a dashboard. For example, the providing unit may provide the generated answers to the user via email. The dashboard provides an interface that the user can access to review the answers. This enables the impact assessment efficiency system according to the embodiment to improve the efficiency of impact assessments and reduce the number of missed impact assessments. Some or all of the processing described above in the providing unit may be performed using, for example, AI, or not. For example, the providing unit can provide answers using an AI model that takes the answers generated by the generating unit as input and outputs the answers. When providing the answers generated by the generating unit to the user, the providing unit selects a method that considers user convenience. For example, email delivery allows the user to receive the email directly and review the answers, thus enabling quick and reliable information transmission. The dashboard provides an interface that the user can access to review the answers and also allows them to refer to a history of past questions and answers. This enables the user to centrally manage the information they need. The providing unit can collect user feedback and use it to improve the delivery method and the quality of the answers. For example, by allowing users to rate and comment on the responses provided, the service provider can use that feedback to improve the service. This enables the service provider to provide information to users quickly and reliably, and to improve the efficiency of impact assessments and reduce the number of missed impacts.

[0082] The learning unit learns the contents of the design document. The learning unit learns the contents of the design document using methods such as machine learning or deep learning. For example, the learning unit learns the contents of the design document using a machine learning algorithm. The machine learning algorithm analyzes the contents of the design document and understands each function and component of the system. The learning unit learns the contents of the design document using a deep learning algorithm. The deep learning algorithm analyzes the contents of the design document and understands the overall picture of the system. As a result, the accuracy of the impact assessment efficiency system is improved by learning the contents of the design document. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can learn the contents of the design document using an AI model that takes the contents of the design document as input and outputs training data.

[0083] The feedback collection unit collects feedback from users. For example, the feedback collection unit collects feedback such as user opinions and suggestions for improvement. For example, the feedback collection unit collects feedback from users in the form of a questionnaire. The questionnaire format allows for easy user response. The feedback collection unit also collects feedback from users in the form of an interview. The interview format allows for detailed feedback through dialogue with the user. The feedback collection unit also collects feedback from users using an online form. The online form provides an interface that allows users to easily provide feedback. This enables improvements to the impact assessment efficiency system by collecting user feedback. Some or all of the above-described processes in the feedback collection unit may be performed using AI, or not. For example, the feedback collection unit can collect feedback using an AI model that takes user feedback data as input and outputs feedback.

[0084] The improvement unit improves the accuracy of the impact assessment efficiency system based on feedback. The improvement unit improves the accuracy of the impact assessment efficiency system based on user feedback, for example. The improvement unit analyzes feedback data to improve the accuracy of the impact assessment efficiency system. The improvement unit analyzes feedback data using AI to improve the accuracy of the impact assessment efficiency system, for example. The AI ​​analyzes feedback data to improve the accuracy of the impact assessment efficiency system. As a result, more accurate answers are provided by improving the accuracy of the impact assessment efficiency system based on feedback. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can improve the accuracy of the impact assessment efficiency system using an AI model that takes feedback data as input and outputs an improvement algorithm.

[0085] The analysis unit can analyze each section of the design document in detail and understand each function and component of the system. For example, the analysis unit can analyze each section of the design document in detail. For example, the analysis unit can analyze each section of the design document item by item. For example, the analysis unit can analyze each section of the design document section by section. By doing so, by analyzing each section of the design document in detail, the overall picture of the system can be grasped. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze each section of the design document in detail using an AI model that takes each section of the design document as input and outputs analysis results.

[0086] The generation unit can generate the optimal answer to a user's question. The generation unit can, for example, generate the optimal answer to a user's question. For example, the generation unit can use AI to generate the optimal answer to a user's question. The AI ​​analyzes the contents of the design document and generates the optimal answer to the user's question. The generation unit can, for example, use AI to generate the optimal answer to a user's question. The AI ​​analyzes the contents of the design document and generates the optimal answer to the user's question. This improves the accuracy of impact assessments by generating the optimal answer to a user's question. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate the optimal answer to a user's question using an AI model that takes a user's question as input and outputs the optimal answer.

[0087] The service provider can provide the user with the generated response. The service provider can, for example, provide the user with the generated response. For example, the service provider can use email to provide the user with the generated response. The service provider can, for example, use a dashboard to provide the user with the generated response. The service provider can, for example, use a chatbot to provide the user with the generated response. This makes impact assessments more efficient by providing the user with the generated response. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide the user with the response using an AI model that takes the response generated by the generation unit as input and outputs the response.

[0088] The reading unit can estimate the user's emotions and adjust the timing of the design document reading based on the estimated user emotions. For example, the reading unit can use facial recognition technology to estimate the user's emotions. For example, the reading unit can use voice analysis technology to estimate the user's emotions. For example, the reading unit can use biosensors to estimate the user's emotions. For example, the reading unit can estimate the user's emotions and adjust the timing of the design document reading based on the estimated user emotions. For example, if the user is stressed, the reading of the design document is delayed and waited until the user is relaxed. For example, if the user is focused, the reading of the design document is started immediately to efficiently acquire information. For example, if the user is tired, the reading of the design document is paused to allow the user time to rest. This makes it possible to efficiently acquire information by adjusting the timing of the design document reading according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the reading unit may be performed using AI, or not using AI. For example, the reading unit can adjust the timing of reading the design document using an AI model that takes user emotion data as input and outputs the timing of reading the design document.

[0089] The reading unit can analyze the past reading history of design documents and select the optimal reading method. For example, the reading unit analyzes the past reading history of design documents to identify frequently read sections. For example, the reading unit analyzes the past reading history of design documents to identify parts that users frequently refer to. For example, the reading unit analyzes the past reading history of design documents to suggest a reading order tailored to the user's preferences. This enables efficient reading of design documents by analyzing the past reading history. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can select the optimal reading method using an AI model that takes the past reading history of design documents as input and outputs the optimal reading method.

[0090] The reading unit can prioritize reading important sections of the system when reading the design document. For example, the reading unit might prioritize reading sections related to the system's architecture first. For example, the reading unit might prioritize reading sections related to data flow. For example, the reading unit might prioritize reading sections that provide detailed descriptions of each function. This allows for efficient information acquisition by prioritizing the reading of important sections of the system. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can prioritize reading important sections of the design document using an AI model that takes important sections of the design document as input and outputs the order in which to prioritize reading them.

[0091] The reading unit can estimate the user's emotions and determine the priority of the design documents to read based on the estimated user emotions. For example, the reading unit can use facial recognition technology to estimate the user's emotions. For example, the reading unit can use voice analysis technology to estimate the user's emotions. For example, the reading unit can use biosensors to estimate the user's emotions. For example, the reading unit can estimate the user's emotions and determine the priority of the design documents to read based on the estimated user emotions. For example, if the user is nervous, it will start reading from the less important sections. For example, if the user is relaxed, it will start reading from the more important sections. For example, if the user is in a hurry, it will prioritize reading the most important sections. This enables efficient information acquisition by determining the priority of design documents according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can determine the priority of design documents using an AI model that takes user emotion data as input and outputs the priority of design documents.

[0092] The loading unit can prioritize loading sections that are highly relevant when loading design documents, taking into account the user's project progress. For example, the loading unit considers the user's project progress. For instance, in the early stages of a project, it prioritizes loading sections that provide an overview of the system. For instance, in the middle stages of a project, it prioritizes loading sections that contain detailed descriptions of each function. For instance, in the final stages of a project, it prioritizes loading sections related to testing and debugging. This allows for the efficient acquisition of highly relevant information by considering the project progress. Some or all of the above processing in the loading unit may be performed using AI, for example, or without AI. For example, the loading unit can prioritize loading highly relevant sections using an AI model that takes the user's project progress as input and outputs highly relevant sections.

[0093] The reading unit can read relevant sections by referring to the user's past question history when reading design documents. For example, the reading unit can refer to the user's past question history. For example, the reading unit can prioritize reading sections related to what the user has asked in the past. For example, the reading unit can identify frequently referenced sections from the user's past question history. For example, the reading unit can analyze the user's past question history and efficiently read highly relevant sections. This allows for the efficient acquisition of highly relevant information by referring to past question history. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can read relevant sections using an AI model that takes the user's past question history as input and outputs relevant sections.

[0094] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated emotions. For example, the analysis unit can estimate the user's emotions. For example, the analysis unit can use facial recognition technology to estimate the user's emotions. For example, the analysis unit can use voice analysis technology to estimate the user's emotions. For example, the analysis unit can use biosensors to estimate the user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated emotions. For example, if the user is relaxed, a detailed analysis is performed. For example, if the user is in a hurry, a concise analysis is performed. For example, if the user is stressed, the depth of the analysis is reduced, and only the important points are analyzed. This allows for efficient analysis by adjusting the depth of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the depth of the analysis using an AI model that takes user emotion data as input and outputs the depth of the analysis.

[0095] The analysis unit can improve the accuracy of its analysis by considering the interrelationships of the system when analyzing each section of the design document. For example, the analysis unit considers the interrelationships of the system when analyzing each section of the design document. For example, the analysis unit performs analysis considering the interdependencies of each function. For example, the analysis unit performs analysis considering the interrelationships of data flows. For example, the analysis unit performs analysis considering the overall system architecture. By considering the interrelationships of the system, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can improve the accuracy of its analysis by using an AI model that takes each section of the design document as input and outputs analysis results that consider the interrelationships.

[0096] The analysis unit can perform analysis by referring to the system's change history when analyzing design documents. For example, the analysis unit can refer to the system's change history. For example, the analysis unit can perform analysis that reflects the latest information based on the system's change history. For example, the analysis unit can refer to past change history and perform analysis that takes changes into account. For example, the analysis unit can identify the scope of impact based on the change history and perform a detailed analysis. This makes it possible to perform analysis that reflects the latest information by referring to the system's change history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can perform analysis using an AI model that takes the system's change history as input and outputs analysis results that take changes into account.

[0097] 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, the analysis unit can estimate the user's emotions. For example, the analysis unit can use facial recognition technology to estimate the user's emotions. For example, the analysis unit can use voice analysis technology to estimate the user's emotions. For example, the analysis unit can use biosensors to estimate the user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, it provides a simple and highly visible display method. For example, if the user is relaxed, it provides a display method that includes detailed information. For example, if the user is in a hurry, it provides a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a highly visible display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the display method of the analysis results using an AI model that takes user emotion data as input and outputs a method for displaying the analysis results.

[0098] The analysis unit can perform analysis while considering the geographical distribution of the system when analyzing the design document. For example, the analysis unit considers the geographical distribution of the system. For example, the analysis unit performs analysis that considers the characteristics of each region based on the geographical distribution of the system. For example, the analysis unit analyzes the data flow of each region based on the geographical distribution. For example, the analysis unit analyzes the interrelationships of functions in each region, taking the geographical distribution into consideration. This makes it possible to perform analysis that reflects the characteristics of each region by considering the geographical distribution of the system. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can perform analysis using an AI model that takes the geographical distribution of the system as input and outputs analysis results that consider the characteristics of each region.

[0099] The analysis unit can improve the accuracy of its analysis by referring to relevant technical documents when analyzing design documents. For example, the analysis unit may refer to relevant technical documents. For example, the analysis unit may perform an analysis that reflects the latest technical information based on relevant technical documents. For example, the analysis unit may refer to technical documents and perform an analysis that complements the content of the design document. For example, the analysis unit may perform a detailed analysis of each section of the design document based on technical documents. As a result, the accuracy of the analysis is improved by referring to relevant technical documents. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may perform an analysis using an AI model that takes relevant technical documents as input and outputs analysis results that reflect the technical information.

[0100] The reception unit can estimate the user's emotions and adjust the question reception method based on the estimated emotions. For example, the reception unit can estimate the user's emotions. For example, the reception unit can use facial recognition technology to estimate the user's emotions. For example, the reception unit can use voice analysis technology to estimate the user's emotions. For example, the reception unit can use biosensors to estimate the user's emotions. For example, the reception unit can estimate the user's emotions and adjust the question reception method based on the estimated emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. For example, if the user is relaxed, it can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, it can prioritize voice input and quickly receive questions. This enables efficient question reception by adjusting the question reception method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can adjust the question reception method using an AI model that takes user emotion data as input and outputs a question reception method.

[0101] The reception unit can select the optimal reception method when receiving a question by referring to the user's past question history. For example, the reception unit may refer to the user's past question history. For example, the reception unit may prioritize suggesting question formats that the user has frequently used in the past. For example, the reception unit may automatically suggest relevant questions from the user's past question history. For example, the reception unit may analyze the user's past question history and select the optimal question reception method. This allows the reception unit to select the optimal question reception method by referring to the past question history. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit may select the optimal question reception method using an AI model that takes the user's past question history as input and outputs the optimal question reception method.

[0102] The reception desk can filter questions when they are received, taking into account the user's current project status. For example, the reception desk considers the user's current project status. For example, the reception desk prioritizes receiving highly relevant questions based on the current project status. For example, the reception desk prioritizes receiving highly important questions, taking into account the progress of the project. For example, the reception desk filters appropriate questions according to the project phase. This allows for the efficient reception of highly relevant questions by considering the project status. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can filter questions using an AI model that takes the user's project status as input and outputs the filtered question results.

[0103] The reception desk can estimate the user's emotions and prioritize questions based on the estimated emotions. For example, the reception desk can use facial recognition technology to estimate the user's emotions. For example, the reception desk can use voice analysis technology to estimate the user's emotions. For example, the reception desk can use biosensors to estimate the user's emotions. For example, the reception desk can estimate the user's emotions and prioritize questions based on the estimated emotions. For example, if the user is nervous, less important questions will be given priority. For example, if the user is relaxed, more important questions will be given priority. For example, if the user is in a hurry, the most important questions will be given priority. This enables efficient question reception by prioritizing questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can determine the priority of questions using an AI model that takes user emotion data as input and outputs the priority of questions.

[0104] The reception unit can prioritize receiving questions that are highly relevant, taking into account the user's geographical location information. For example, the reception unit considers the user's geographical location information. For example, the reception unit prioritizes receiving questions that are highly relevant based on the user's current location. For example, the reception unit prioritizes receiving region-specific questions, taking into account geographical location information. For example, the reception unit filters the most relevant questions based on the user's location information. This allows for the efficient reception of highly relevant questions by considering geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can receive questions using an AI model that takes the user's geographical location information as input and outputs highly relevant questions.

[0105] The reception unit can analyze the user's social media activity when receiving a question and accept relevant questions. For example, the reception unit can analyze the user's social media activity. For example, the reception unit can automatically suggest relevant questions based on the user's social media activity. For example, the reception unit can analyze the content of social media posts and filter for the most relevant questions. For example, the reception unit can prioritize accepting highly relevant questions based on the user's social media activity. This allows for the efficient acceptance of highly relevant questions by analyzing social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can accept questions using an AI model that takes the user's social media activity as input and outputs relevant questions.

[0106] The generation unit can estimate the user's emotions and adjust the way the response is expressed based on the estimated emotions. For example, the generation unit can estimate the user's emotions. For example, the generation unit can use facial recognition technology to estimate the user's emotions. For example, the generation unit can use voice analysis technology to estimate the user's emotions. For example, the generation unit can use biosensors to estimate the user's emotions. For example, the generation unit can estimate the user's emotions and adjust the way the response is expressed based on the estimated emotions. For example, if the user is relaxed, it will provide a response that includes a detailed explanation. For example, if the user is in a hurry, it will provide a concise and to-the-point response. For example, if the user is stressed, it will provide a response in a gentle tone. In this way, an appropriate response is provided by adjusting the way the response is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can adjust the way the response is expressed using an AI model that takes user emotion data as input and outputs a way of expressing the response.

[0107] The generation unit can adjust the level of detail in the answer based on the importance of the question when generating the answer. For example, the generation unit adjusts the level of detail in the answer based on the importance of the question. For example, the generation unit provides a detailed answer for a high-importance question. For example, the generation unit provides a concise answer for a low-importance question. The generation unit dynamically adjusts the level of detail in the answer according to the importance of the question. This ensures that an appropriate answer is provided by adjusting the level of detail in the answer according to the importance of the question. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can adjust the level of detail in the answer using an AI model that takes the importance of the question as input and outputs the level of detail in the answer.

[0108] The generation unit can apply different generation algorithms depending on the question category when generating answers. For example, the generation unit can apply different generation algorithms depending on the question category. For example, for technical questions, the generation unit generates answers that refer to technical literature. For example, for business-related questions, the generation unit generates answers that refer to business literature. The generation unit can improve the accuracy of answers by applying the most suitable generation algorithm for each category. This improves the accuracy of answers by applying the most suitable generation algorithm depending on the question category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate answers using an AI model that takes the question category as input and outputs the most suitable generation algorithm.

[0109] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, the generation unit can estimate the user's emotions. For example, the generation unit can use facial recognition technology to estimate the user's emotions. For example, the generation unit can use voice analysis technology to estimate the user's emotions. For example, the generation unit can use biosensors to estimate the user's emotions. For example, the generation unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is in a hurry, it will provide a short, to-the-point response. For example, if the user is relaxed, it will provide a longer response that includes a detailed explanation. For example, if the user is stressed, it will provide a concise and easy-to-understand response. In this way, an appropriate response is provided by adjusting the length of the response according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can adjust the length of the response using an AI model that takes user emotion data as input and outputs the length of the response.

[0110] The generation unit can determine the priority of answers based on when the questions were submitted when generating answers. For example, the generation unit determines the priority of answers based on when the questions were submitted. For example, the generation unit determines the priority of answers based on the time period in which the questions were submitted. For example, the generation unit dynamically adjusts the order of answers according to when the questions were submitted. For example, the generation unit provides the optimal answer considering when the questions were submitted. In this way, appropriate answers are provided by determining the priority of answers based on when the questions were submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can determine the priority of answers using an AI model that takes the time the questions were submitted as input and outputs the priority of answers.

[0111] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the generation unit adjusts the order of answers based on the relevance of the questions. For example, the generation unit prioritizes providing the most relevant answers based on the relevance of the questions. For example, the generation unit dynamically adjusts the order of answers considering the relevance of the questions. For example, the generation unit provides the optimal answer according to the relevance of the questions. In this way, appropriate answers are provided by adjusting the order of answers based on the relevance of the questions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can adjust the order of answers using an AI model that takes the relevance of questions as input and outputs the order of answers.

[0112] The service provider can estimate the user's emotions and adjust the method of providing responses based on the estimated emotions. For example, the service provider can estimate the user's emotions. For example, the service provider can use facial recognition technology to estimate the user's emotions. For example, the service provider can use voice analysis technology to estimate the user's emotions. For example, the service provider can use biosensors to estimate the user's emotions. For example, the service provider can estimate the user's emotions and adjust the method of providing responses based on the estimated emotions. For example, if the user is relaxed, it will provide a response that includes a detailed explanation. For example, if the user is in a hurry, it will provide a concise and to-the-point response. For example, if the user is stressed, it will provide a response in a gentle tone. In this way, appropriate responses are provided by adjusting the method of providing responses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can adjust the method of providing responses using an AI model that takes user emotion data as input and outputs a method for providing responses.

[0113] The service provider can select the optimal service provider method by referring to the user's past question history when providing answers. For example, the service provider can refer to the user's past question history. For example, the service provider can prioritize suggesting service provider methods that the user has frequently used in the past. For example, the service provider can automatically suggest relevant answers from the user's past question history. For example, the service provider can analyze the user's past question history and select the optimal service provider method. This allows the service provider to select the optimal answer provider method by referring to the past question history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select a service provider method using an AI model that takes the user's past question history as input and outputs the optimal service provider method.

[0114] The service provider can customize its responses when providing answers, taking into account the user's current project status. For example, the service provider considers the user's current project status. For example, the service provider prioritizes providing highly relevant answers based on the current project status. For example, the service provider prioritizes providing highly important answers by considering the progress of the project. For example, the service provider customizes appropriate answers according to the project phase. This allows the service provider to provide highly relevant answers by considering the project status. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can customize responses using an AI model that takes the user's project status as input and outputs customized responses.

[0115] The service provider can estimate the user's emotions and adjust the timing of response delivery based on the estimated emotions. For example, the service provider can estimate the user's emotions. For example, the service provider can use facial recognition technology to estimate the user's emotions. For example, the service provider can use voice analysis technology to estimate the user's emotions. For example, the service provider can use biosensors to estimate the user's emotions. For example, the service provider can estimate the user's emotions and adjust the timing of response delivery based on the estimated emotions. For example, if the user is relaxed, it will provide a response that includes a detailed explanation. For example, if the user is in a hurry, it will provide a concise and to-the-point response. For example, if the user is stressed, it will provide a response in a gentle tone. In this way, by adjusting the timing of response delivery according to the user's emotions, an appropriate response is 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 processing described above in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can adjust the timing of response delivery using an AI model that takes user emotion data as input and outputs the timing of response delivery.

[0116] The service provider can select the optimal service delivery method when providing answers, taking into account the user's geographical location information. For example, the service provider may consider the user's geographical location information. For example, the service provider may prioritize providing highly relevant answers based on the user's current location. For example, the service provider may prioritize providing region-specific answers, taking into account geographical location information. For example, the service provider may provide the optimal answer based on the user's location information. This allows for the provision of highly relevant answers by considering geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may select a service delivery method using an AI model that takes the user's geographical location information as input and outputs the optimal service delivery method.

[0117] The service provider can analyze the user's social media activity and adjust the method of providing answers when providing responses. For example, the service provider can analyze the user's social media activity. For example, the service provider can automatically suggest relevant answers based on the user's social media activity. For example, the service provider can analyze the content of social media posts and provide the most appropriate answers. For example, the service provider can prioritize providing highly relevant answers based on the user's social media activity. In this way, highly relevant answers can be provided by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can adjust the method of providing answers using an AI model that takes the user's social media activity as input and outputs the most appropriate answer.

[0118] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, the learning unit can estimate the user's emotions. For example, the learning unit can use facial recognition technology to estimate the user's emotions. For example, the learning unit can use voice analysis technology to estimate the user's emotions. For example, the learning unit can use biosensors to estimate the user's emotions. For example, the learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is relaxed, it selects detailed training data. For example, if the user is in a hurry, it selects concise training data. For example, if the user is stressed, it selects easy-to-understand training data. This enables efficient learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can select training data using an AI model that takes user emotion data as input and outputs the results of training data selection.

[0119] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit refers to past learning data. For example, the learning unit selects the optimal learning algorithm based on past learning data. For example, the learning unit improves the accuracy of the learning algorithm by referring to past learning data. For example, the learning unit analyzes past learning data and optimizes the learning algorithm. As a result, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can optimize the learning algorithm using an AI model that takes past learning data as input and outputs the optimal learning algorithm.

[0120] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can estimate the user's emotions. For example, the learning unit can use facial recognition technology to estimate the user's emotions. For example, the learning unit can use voice analysis technology to estimate the user's emotions. For example, the learning unit can use biosensors to estimate the user's emotions. For example, the learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is relaxed, it will learn more frequently. For example, if the user is in a hurry, it will learn less frequently. For example, if the user is stressed, it will learn more frequently. This allows for efficient learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can adjust the learning frequency using an AI model that takes user emotion data as input and outputs a learning frequency.

[0121] The learning unit can weight the training data based on the update history of the design document during training. For example, the learning unit refers to the update history of the design document. For example, the learning unit weights the training data based on the latest update history of the design document. For example, the learning unit refers to past update history and adjusts the weighting of the training data. For example, the learning unit optimizes the weighting of the training data based on the update history. This improves the accuracy of training by weighting the training data based on the update history of the design document. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can weight the training data using an AI model that takes the update history of the design document as input and outputs the result of weighting the training data.

[0122] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. For example, the feedback collection unit can estimate the user's emotions. For example, the feedback collection unit can use facial recognition technology to estimate the user's emotions. For example, the feedback collection unit can use voice analysis technology to estimate the user's emotions. For example, the feedback collection unit can use biosensors to estimate the user's emotions. For example, the feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. For example, if the user is relaxed, detailed feedback is collected. For example, if the user is in a hurry, concise feedback is collected. For example, if the user is stressed, easy-to-understand feedback is collected. This allows for efficient feedback collection by adjusting the feedback collection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can adjust the feedback collection method using an AI model that takes user emotion data as input and outputs a feedback collection method.

[0123] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, the feedback collection unit may refer to the user's past feedback history. For example, the feedback collection unit may preferentially suggest feedback formats that the user has frequently provided in the past. For example, the feedback collection unit may automatically suggest relevant feedback from the user's past feedback history. For example, the feedback collection unit may analyze the user's past feedback history and select the optimal collection method. This allows the optimal feedback collection method to be selected by referring to past feedback history. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit may select a collection method using an AI model that takes the user's past feedback history as input and outputs the optimal collection method.

[0124] The feedback collection unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, the feedback collection unit can use facial recognition technology to estimate the user's emotions. For example, the feedback collection unit can use voice analysis technology to estimate the user's emotions. For example, the feedback collection unit can use biosensors to estimate the user's emotions. For example, the feedback collection unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is nervous, it will prioritize collecting feedback of low importance. For example, if the user is relaxed, it will prioritize collecting feedback of high importance. For example, if the user is in a hurry, it will prioritize collecting the most important feedback. This enables efficient feedback collection by determining the priority of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can determine the priority of feedback using an AI model that takes user emotion data as input and outputs the priority of feedback.

[0125] The feedback collection unit can prioritize collecting highly relevant feedback by considering the user's geographical location information during feedback collection. For example, the feedback collection unit considers the user's geographical location information. For example, the feedback collection unit prioritizes collecting highly relevant feedback based on the user's current location. For example, the feedback collection unit prioritizes collecting region-specific feedback by considering geographical location information. For example, the feedback collection unit collects optimal feedback based on the user's location information. This allows for the efficient collection of highly relevant feedback by considering geographical location information. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can collect feedback using an AI model that takes the user's geographical location information as input and outputs highly relevant feedback.

[0126] The improvement unit can estimate the user's emotions and adjust the improvement method based on the estimated user emotions. For example, the improvement unit can estimate the user's emotions. For example, the improvement unit can use facial recognition technology to estimate the user's emotions. For example, the improvement unit can use voice analysis technology to estimate the user's emotions. For example, the improvement unit can use biosensors to estimate the user's emotions. For example, the improvement unit estimates the user's emotions and adjusts the improvement method based on the estimated user emotions. For example, if the user is relaxed, it suggests a detailed improvement method. For example, if the user is in a hurry, it suggests a concise improvement method. For example, if the user is stressed, it suggests an easy-to-understand improvement method. This makes efficient improvement possible by adjusting the improvement method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can adjust the improvement method using an AI model that takes user emotion data as input and outputs improvement methods.

[0127] The improvement unit can optimize the improvement algorithm by referring to past feedback data during the improvement process. For example, the improvement unit refers to past feedback data. For example, the improvement unit selects the optimal improvement algorithm based on past feedback data. For example, the improvement unit improves the accuracy of the improvement algorithm by referring to past feedback data. For example, the improvement unit analyzes past feedback data and optimizes the improvement algorithm. As a result, the accuracy of the improvement algorithm is improved by referring to past feedback data. Some or all of the above processes in the improvement unit may be performed using AI, for example, or without using AI. For example, the improvement unit can optimize the improvement algorithm using an AI model that takes past feedback data as input and outputs the optimal improvement algorithm.

[0128] The improvement unit can estimate the user's emotions and determine the priority of improvements based on the estimated user emotions. For example, the improvement unit can estimate the user's emotions. For example, the improvement unit can use facial recognition technology to estimate the user's emotions. For example, the improvement unit can use voice analysis technology to estimate the user's emotions. For example, the improvement unit can use biosensors to estimate the user's emotions. For example, the improvement unit estimates the user's emotions and determines the priority of improvements based on the estimated user emotions. For example, if the user is nervous, improvements of low importance will be prioritized. For example, if the user is relaxed, improvements of high importance will be prioritized. For example, if the user is in a hurry, the most important improvements will be prioritized. This makes efficient improvement possible by determining the priority of improvements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can determine the priority of improvements using an AI model that takes user emotion data as input and outputs the priority of improvements.

[0129] The improvement unit can weight improvements based on the design document's update history. For example, the improvement unit refers to the design document's update history. For example, the improvement unit weights improvements based on the latest design document's update history. For example, the improvement unit refers to past update history and adjusts the improvement weighting. For example, the improvement unit optimizes the improvement weighting based on the update history. This improves the accuracy of improvements by weighting improvements based on the design document's update history. Some or all of the above processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can weight improvements using an AI model that takes the design document's update history as input and outputs the improvement weighting results.

[0130] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0131] The impact assessment efficiency system can estimate the user's emotions and prioritize questions based on those emotions. For example, if the user is nervous, less important questions can be prioritized. If the user is relaxed, more important questions can be prioritized. If the user is in a hurry, the most important questions can be prioritized. This allows for efficient question processing by prioritizing questions according to the user's emotions.

[0132] The impact assessment efficiency system can retrieve relevant sections by referencing the user's past question history. For example, it can prioritize retrieving sections related to questions the user has asked in the past. It can identify frequently referenced sections from the user's past question history. By analyzing the user's past question history, it can efficiently retrieve highly relevant sections. This allows for the efficient acquisition of highly relevant information by referencing past question history.

[0133] The impact assessment efficiency system can estimate the user's emotions and adjust the timing of reading design documents based on those emotions. For example, if the user is stressed, the system can delay reading the design documents and wait until the user is relaxed. If the user is focused, the system can immediately begin reading the design documents to efficiently acquire information. If the user is tired, the system can pause reading the design documents to allow the user time to rest. By adjusting the timing of design document reading according to the user's emotions, efficient information acquisition becomes possible.

[0134] The impact assessment efficiency system can prioritize reading critical sections of a system when reviewing design documents. For example, it can prioritize reading sections related to the system architecture first, sections related to data flow, and sections containing detailed descriptions of each function. This allows for efficient information retrieval by prioritizing the reading of critical sections of the system.

[0135] The impact assessment efficiency system can estimate the user's emotions and adjust the depth of analysis based on those emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is in a hurry, a concise analysis can be performed. If the user is stressed, the depth of analysis can be reduced, focusing only on the most important points. This allows for efficient analysis by adjusting the depth of analysis according to the user's emotions.

[0136] The impact assessment efficiency system allows for analysis of design documents by referencing the system's change history. For example, it can perform analyses that reflect the latest information based on the system's change history. It can refer to past change history and perform analyses that take changes into account. Based on the change history, it can identify the scope of impact and perform detailed analyses. As a result, by referring to the system's change history, it becomes possible to perform analyses that reflect the latest information.

[0137] The impact assessment efficiency system can estimate the user's emotions and adjust the way responses are expressed based on those emotions. For example, if the user is relaxed, it can provide a detailed explanation. If the user is in a hurry, it can provide a concise and to-the-point response. If the user is stressed, it can provide a gentle tone. In this way, by adjusting the way responses are expressed according to the user's emotions, appropriate responses are provided.

[0138] The impact assessment efficiency system can adjust the level of detail in responses based on the importance of the question during response generation. For example, it can provide detailed answers to high-importance questions and concise answers to low-importance questions. The level of detail in responses can be dynamically adjusted according to the importance of the question. This ensures that appropriate responses are provided by adjusting the level of detail according to the importance of the question.

[0139] The impact assessment efficiency system can estimate the user's emotions and adjust the way responses are provided based on those estimates. For example, if the user is relaxed, it can provide a detailed explanation. If the user is in a hurry, it can provide a concise and to-the-point response. If the user is stressed, it can provide a gentle tone. By adjusting the response delivery method according to the user's emotions, the system ensures that appropriate responses are provided.

[0140] The impact assessment efficiency system can select the optimal response delivery method by referring to the user's past question history. For example, it can prioritize suggesting delivery methods that the user has frequently used in the past. It can automatically suggest relevant answers from the user's past question history. It can analyze the user's past question history and select the optimal delivery method. This allows the system to select the optimal response delivery method by referring to past question history.

[0141] The following briefly describes the processing flow for example form 2.

[0142] Step 1: The reading unit reads the design documents. Design documents include software design documents and hardware design documents. The reading unit can digitize and read design documents using scanning technology. It can also directly read design documents submitted in digital format, and can read printed design documents using OCR technology. For example, the reading unit scans the design document with a high-resolution scanner and converts it into text information using OCR technology. Step 2: The analysis unit analyzes the contents of the design document read by the reading unit. The analysis is performed using methods such as text analysis and structural analysis. For example, the analysis unit uses text analysis technology to analyze the contents of the design document and understand each function and component of the system. It also uses structural analysis technology to analyze each section of the design document in detail and understand the system architecture and data flow. Furthermore, it is possible to use AI to analyze the contents of the design document and understand the overall picture of the system. Step 3: The reception desk receives questions from users. These questions may include technical questions or questions about operation. The reception desk can receive questions from users using online forms, chatbots, voice input, etc. For example, questions can be received using online forms, chatbots can receive questions through dialogue with users, and voice input can recognize the user's voice and convert the question into text. Step 4: The generation unit generates answers to the questions received by the reception unit. Generation is performed using AI. For example, the generation unit uses AI to generate the optimal answer to the user's question. The AI ​​analyzes the contents of the design document and generates the optimal answer to the user's question. Step 5: The delivery unit provides the answers generated by the generation unit. Delivery can be done via email or through a dashboard. For example, the delivery unit provides the generated answers to the user via email. The dashboard provides an interface that the user can access to review the answers.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the reading unit, analysis unit, reception unit, generation unit, provision unit, learning unit, feedback collection unit, and improvement unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reading unit is implemented by the control unit 46A of the smart device 14 and reads the design document using scanning technology or OCR technology. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the contents of the design document using text analysis or structural analysis technology. The reception unit is implemented by the control unit 46A of the smart device 14 and receives questions from users using online forms or chatbots. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates the optimal answer to the user's question using AI. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated answer via email or dashboard. The learning unit is implemented by the specific processing unit 290 of the data processing device 12 and learns the contents of the design document using machine learning or deep learning. The feedback collection unit is implemented, for example, by the control unit 46A of the smart device 14, and collects user feedback using questionnaires or online forms. The improvement unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the feedback data to improve the accuracy of the impact assessment efficiency system. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0147] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the reading unit, analysis unit, reception unit, generation unit, provision unit, learning unit, feedback collection unit, and improvement unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reading unit is implemented by the control unit 46A of the smart glasses 214 and reads the design document using scanning technology or OCR technology. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the contents of the design document using text analysis or structural analysis technology. The reception unit is implemented by the control unit 46A of the smart glasses 214 and receives questions from users using online forms or chatbots. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal answer to the user's question using AI. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated answer via email or dashboard. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and learns the contents of the design document using machine learning or deep learning. The feedback collection unit is implemented, for example, by the control unit 46A of the smart glasses 214, and collects user feedback using questionnaires or online forms. The improvement unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the feedback data to improve the accuracy of the impact assessment efficiency system. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0163] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Each of the multiple elements described above, including the reading unit, analysis unit, reception unit, generation unit, provision unit, learning unit, feedback collection unit, and improvement unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reading unit is implemented by the control unit 46A of the headset terminal 314 and reads the design document using scanning technology or OCR technology. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the contents of the design document using text analysis or structural analysis technology. The reception unit is implemented by the control unit 46A of the headset terminal 314 and receives questions from users using online forms or chatbots. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal answer to the user's question using AI. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated answer via email or dashboard. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and learns the contents of the design document using machine learning or deep learning. The feedback collection unit is implemented, for example, by the control unit 46A of the headset terminal 314, and collects user feedback using questionnaires or online forms. The improvement unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the feedback data to improve the accuracy of the impact assessment efficiency system. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0179] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.).

[0192] 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.

[0193] 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.

[0194] 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.

[0195] Each of the multiple elements described above, including the reading unit, analysis unit, reception unit, generation unit, provision unit, learning unit, feedback collection unit, and improvement unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reading unit is implemented by the control unit 46A of the robot 414 and reads the design document using scanning technology or OCR technology. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the contents of the design document using text analysis or structural analysis technology. The reception unit is implemented by the control unit 46A of the robot 414 and receives questions from users using online forms or chatbots. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal answer to the user's question using AI. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated answer via email or dashboard. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the contents of the design document using machine learning or deep learning. The feedback collection unit is implemented, for example, by the control unit 46A of the robot 414, and collects user feedback using questionnaires or online forms. The improvement unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the feedback data to improve the accuracy of the impact assessment efficiency system. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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."

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] (Note 1) A reading unit that reads the design document, An analysis unit analyzes the contents of the design document read by the aforementioned reading unit, A reception desk that handles questions from users, A generation unit that generates answers to questions received by the reception unit, The system includes a providing unit that provides the answer generated by the generation unit. A system characterized by the following features. (Note 2) It includes a learning section for studying the contents of the design specifications. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a feedback collection unit for collecting user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes an improvement section that enhances the accuracy of the chatbot based on feedback. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze each section of the design document in detail to understand each function and component of the system. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate the best answer to the user's question. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Provides the user with a generated response. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reading unit, The system estimates the user's emotions and adjusts the timing of reading the design document based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reading unit, Analyze the history of past design document access and select the optimal access method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reading unit, When reading design documents, prioritize reading critical sections of the system. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reading unit, It estimates the user's emotions and determines the priority of design documents to read based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reading unit, When loading design documents, the system prioritizes loading sections that are most relevant to the user's project progress. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reading unit, When loading design documents, the system references the user's past question history to load relevant sections. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the depth of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing each section of the design document, consider the interrelationships between systems to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When analyzing design documents, the system's change history is referenced during the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) The aforementioned analysis unit, When analyzing design documents, the analysis should take into account the geographical distribution of the system. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, When analyzing design documents, refer to relevant technical literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned reception unit is The system estimates the user's emotions and adjusts how questions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned reception unit is When a question is submitted, the system will refer to the user's past question history to select the most appropriate submission method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned reception unit is When receiving a question, filter the questions considering the user's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned reception unit is The system estimates the user's emotions and prioritizes questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned reception unit is When receiving questions, the system prioritizes accepting questions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned reception unit is When receiving a question, the system analyzes the user's social media activity and accepts relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is When generating answers, adjust the level of detail in the answers based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is When generating answers, different generation algorithms are applied depending on the question category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is When generating answers, the system prioritizes answers based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The generating unit is When generating answers, the order of answers is adjusted based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, We estimate the user's emotions and adjust how we provide responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing an answer, the system will refer to the user's past question history to select the most appropriate method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing answers, customize the answers to take into account the user's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the timing of response delivery based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing responses, the optimal method of delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned supply unit is, When providing responses, we analyze users' social media activity and adjust the method of providing responses accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 41) The aforementioned learning unit, During training, the training data is weighted based on the update history of the design document. The system described in Appendix 2, characterized by the features described herein. (Note 42) The aforementioned feedback collection unit is We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned feedback collection unit is When collecting feedback, the system selects the optimal collection method by referring to the user's past feedback history. The system described in Appendix 3, characterized by the features described herein. (Note 44) The aforementioned feedback collection unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 45) The aforementioned feedback collection unit is When collecting feedback, the system prioritizes collecting highly relevant feedback by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 46) The aforementioned improvement unit is, It estimates user sentiment and adjusts improvement methods based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 47) The aforementioned improvement unit is, When making improvements, we optimize the improvement algorithm by referring to past feedback data. The system described in Appendix 4, characterized by the features described herein. (Note 48) The aforementioned improvement unit is, We estimate user emotions and determine improvement priorities based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 49) The aforementioned improvement unit is, When making improvements, weight the improvements based on the update history of the design document. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0215] 10, 210, 310, 410 data processing systems 12 data processing devices 14 smart devices 214 smart glasses 314 headset-type terminals 414 robots

Claims

1. A reading unit that reads the design document, An analysis unit analyzes the contents of the design document read by the aforementioned reading unit, A reception desk that handles questions from users, A generation unit that generates answers to questions received by the reception unit, The system includes a providing unit that provides the answer generated by the generation unit. A system characterized by the following features.

2. It includes a learning section for studying the contents of the design specifications. The system according to feature 1.

3. It includes a feedback collection unit for collecting user feedback. The system according to feature 1.

4. It includes an improvement section that enhances the accuracy of the chatbot based on feedback. The system according to feature 1.

5. The aforementioned analysis unit, Analyze each section of the design document in detail to understand each function and component of the system. The system according to feature 1.

6. The generating unit is Generate the best answer to the user's question. The system according to feature 1.

7. The aforementioned supply unit is, Provides the user with a generated response. The system according to feature 1.

8. The aforementioned reading unit, The system estimates the user's emotions and adjusts the timing of reading the design document based on those estimated emotions. The system according to feature 1.

9. The aforementioned reading unit, Analyze the history of past design document access and select the optimal access method. The system according to feature 1.

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