system

Abstract

The system according to this embodiment aims to efficiently manage and utilize a company's document assets. [Solution] The system according to the embodiment comprises an analysis unit, a classification unit, a management unit, a generation unit, and an update unit. The analysis unit analyzes existing specifications and technical documents. The classification unit automatically organizes and classifies the documents analyzed by the analysis unit. The management unit collaboratively edits and version-controls documents created and updated by users in real time. The generation unit automatically generates the latest specifications and technical documents from source code. The update unit immediately reflects changes to the documents 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 is difficult to efficiently manage and utilize a company's document assets, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently manage and utilize a company's document assets.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a classification unit, a management unit, a generation unit, and an update unit. The analysis unit analyzes existing specifications and technical documents. The classification unit automatically organizes and classifies the documents analyzed by the analysis unit. The management unit collaboratively edits and version-controls documents created and updated by users in real time. The generation unit automatically generates the latest specifications and technical documents from source code. The update unit immediately reflects changes to the documents generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage and utilize a company's document assets. [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 multiple computers. Examples of communication standards applicable 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 document management system according to an embodiment of the present invention is an innovative solution that comprehensively manages and utilizes a company's document assets by leveraging generative AI. This document management system improves searchability and accessibility by analyzing, integrating, automatically organizing, and classifying existing specifications and technical documents. Documents created and updated by users can also be collaboratively edited and version-controlled in real time, and high-quality document creation is supported by automatic completion and improvement suggestions from the generative AI. It also features a real-time update function that automatically generates the latest specifications and technical documents from source code and reflects changes immediately. Furthermore, the generative AI automatically maintains consistency in terminology and cross-references, realizing consistent information management. Advanced natural language search functions and summary generation by the generative AI enable rapid access to necessary information. This achieves improved development efficiency and product quality, reduced man-hours, and improved information access, thereby strengthening business competitiveness. By making full use of the high-precision data analysis and automation functions of the generative AI, this document management system provides a clear return on investment for adopting companies and comprehensively solves information management challenges. This allows document management systems to efficiently manage and utilize a company's document assets, comprehensively solving information management challenges.

[0029] The document management system according to this embodiment comprises an analysis unit, a classification unit, a management unit, a generation unit, and an update unit. The analysis unit analyzes existing specifications and technical documents. The analysis unit analyzes the content of documents using, for example, text analysis technology. The analysis unit can also analyze the structure of documents using, for example, structural analysis technology. The analysis unit can also analyze the meaning of documents using, for example, natural language processing technology. The classification unit automatically organizes and classifies the documents analyzed by the analysis unit. The classification unit classifies documents using, for example, categorization technology. The classification unit can also tag documents using, for example, tagging technology. The classification unit can also automatically classify documents using, for example, machine learning technology. The management unit collaboratively edits and version-controls documents created and updated by users in real time. The management unit enables multiple users to edit documents simultaneously using, for example, simultaneous editing technology. The management unit can also manage document versions using, for example, version control technology. The management unit can also manage document access rights using, for example, access rights management technology. The generation unit automatically generates the latest specifications and technical documents from source code. The generation unit generates documents from source code using, for example, code comment analysis technology. The generation unit can also generate documents from source code using, for example, document template technology. The generation unit can also generate documents from source code using, for example, natural language generation technology. The update unit immediately reflects changes to the documents generated by the generation unit. The update unit detects changes to documents using, for example, change detection technology. The update unit can also immediately reflect changes to documents using, for example, real-time update technology. The update unit can also manage changes to documents using, for example, version control technology. As a result, the document management system according to this embodiment can efficiently manage and utilize a company's document assets and comprehensively solve information management challenges.

[0030] The analysis unit analyzes existing specifications and technical documents. For example, it uses text analysis techniques to analyze the content of documents. Specifically, text analysis techniques extract keywords, summarize documents, and understand context. This allows for quick identification of important parts and highly relevant information within the document. The analysis unit can also analyze the structure of documents using structural analysis techniques. Structural analysis techniques analyze the relationships between chapters and sections, clarifying the overall structure of the document. This facilitates document navigation and allows for quick access to necessary information. The analysis unit can also analyze the meaning of documents using natural language processing techniques. Natural language processing techniques understand the context within documents and extract meaningful information. For example, it can analyze the meaning of specific technical terms and jargon, leading to a deeper understanding of the document's content. This allows the analysis unit to comprehensively analyze the content, structure, and meaning of documents, contributing to improved document quality and efficient management. Furthermore, based on these analysis results, the analysis unit can identify areas for improvement and missing information, contributing to improved document quality.

[0031] The classification unit automatically organizes and classifies documents analyzed by the analysis unit. For example, the classification unit classifies documents using categorization technology. Categorization technology automatically classifies documents into appropriate categories based on their content. This improves document searchability, allowing for quick access to necessary information. The classification unit can also tag documents using tagging technology. Tagging technology automatically assigns appropriate tags based on document content and specific keywords. This further improves document searchability, making it easier to find related documents. The classification unit can also automatically classify documents using machine learning technology. Machine learning technology learns from past classification data and appropriately classifies new documents. This improves classification accuracy and streamlines document management. Furthermore, the classification unit can continuously improve its classification algorithm based on user feedback. This allows the classification unit to organize and classify documents efficiently and accurately, contributing to more efficient document management.

[0032] The management department collaboratively edits and version-controls documents created and updated by users in real time. For example, the management department can use concurrent editing technology to enable multiple users to edit a document simultaneously. Concurrent editing technology ensures that edits by multiple users simultaneously do not conflict, allowing the entire team to efficiently create and update documents. The management department can also manage document versions using version control technology. Version control technology saves the document's change history and allows users to revert to previous versions. This allows tracking the document's change history and referencing past versions as needed. The management department can also manage document access rights using access rights management technology. Access rights management technology sets viewing and editing permissions for each user, protecting confidential information. This improves document security and reduces the risk of information leakage. Furthermore, the management department can record user operation logs to understand document usage. This allows for analysis of document usage, helping to improve management efficiency and identify areas for improvement.

[0033] The generation unit automatically generates the latest specifications and technical documents from source code. For example, the generation unit generates documents from source code using code comment analysis technology. Code comment analysis technology analyzes comments within the source code and automatically generates the content of the document. This ensures consistency between the source code and the document, and allows for the creation of documents that reflect the latest information. The generation unit can also generate documents from source code using document template technology. Document template technology reflects the content of the source code into the document based on a predefined template. This ensures document consistency and allows for efficient document creation. The generation unit can also generate documents from source code using natural language generation technology. Natural language generation technology converts the content of the source code into natural language, generating easy-to-read documents. This provides documents that are easy to understand not only for engineers but also for non-engineers. Furthermore, the generation unit can evaluate the quality of the generated documents and make corrections as needed. This allows the generation unit to consistently provide high-quality documents and contribute to the efficiency of document management.

[0034] The update unit immediately reflects changes to documents generated by the generation unit. The update unit detects document changes using, for example, change detection technology. Change detection technology detects document changes in real time and immediately reflects those changes. This ensures that documents are always up-to-date. The update unit can also immediately reflect document changes using, for example, real-time update technology. Real-time update technology immediately reflects document changes, ensuring users always have access to the latest information. This maintains document integrity and facilitates smooth information sharing. The update unit can also manage document changes using, for example, version control technology. Version control technology saves document change history and allows users to revert to previous versions. This allows tracking document change history and referencing past versions as needed. Furthermore, the update unit can identify areas for improvement based on user feedback and continuously enhance quality. This enables the update unit to consistently provide the latest and highest-quality documents, contributing to more efficient document management.

[0035] The analysis unit can analyze existing specifications and technical documents and integrate them into the system. For example, the analysis unit can analyze the content of documents using text analysis technology and integrate it into the system. The analysis unit can also analyze the structure of documents using structural analysis technology and integrate it into the system. The analysis unit can also analyze the meaning of documents using natural language processing technology and integrate it into the system. This enables centralized management of documents by analyzing existing documents and integrating them into the system. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input existing specifications and technical documents into a generation AI and have the generation AI perform the document analysis.

[0036] The classification unit can automatically classify and tag imported documents, thereby improving search functionality. For example, the classification unit can classify documents using categorization techniques. The classification unit can also tag documents using tagging techniques. Furthermore, the classification unit can automatically classify documents using machine learning techniques. This automatic classification and tagging of documents improves searchability. Some or all of the above processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input imported documents into a generating AI and have the generating AI perform document classification and tagging.

[0037] The management unit can share and manage documents created and updated by users in real time. The management unit can enable multiple users to edit documents simultaneously using, for example, concurrent editing technology. The management unit can also manage document versions using, for example, version control technology. The management unit can also manage document access rights using, for example, access rights management technology. This streamlines collaborative editing by sharing and managing user-created and updated documents in real time. Some or all of the above processes in the management unit may be performed using, for example, AI, or not using AI. For example, the management unit can input user-created and updated documents into a generating AI and have the generating AI perform document sharing and management.

[0038] The generation unit can automatically generate the latest specifications and technical documents from source code. The generation unit can generate documents from source code using, for example, code comment analysis technology. The generation unit can also generate documents from source code using, for example, document template technology. The generation unit can also generate documents from source code using, for example, natural language generation technology. This makes document updates more efficient by automatically generating the latest documents from source code. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input source code into a generation AI and have the generation AI perform the document generation.

[0039] The update unit can detect changes in code and documentation and immediately update the relevant documentation. The update unit can detect changes in documentation using, for example, change detection technology. The update unit can also immediately reflect changes in documentation using, for example, real-time update technology. The update unit can also manage changes in documentation using, for example, version control technology. This ensures the consistency of documentation by immediately reflecting changes in code and documentation. Some or all of the above processes in the update unit may be performed using, for example, AI, or not using AI. For example, the update unit can input changes in code and documentation into a generating AI and have the generating AI perform the document update.

[0040] The management department can support the creation of high-quality documents by providing automatic completion and improvement suggestions using generative AI. For example, the management department can use natural language processing technology to automatically complete documents. The management department can also use machine learning algorithms to suggest improvements to documents. The management department can also use generative AI to analyze the content of documents and suggest areas for improvement. In this way, the creation of high-quality documents is supported by automatic completion and improvement suggestions using generative AI. Some or all of the above processes in the management department may be performed using generative AI, or not. For example, the management department can input documents created by users into the generative AI and have the generative AI perform automatic completion and improvement suggestions for the documents.

[0041] The update unit enables consistent information management by having the generating AI automatically maintain terminology consistency and cross-reference consistency. For example, the update unit can use a glossary to standardize terminology within a document. The update unit can also use link management technology to maintain the consistency of cross-references within a document. For example, the update unit can use the generating AI to analyze the content of a document and maintain terminology consistency and cross-reference consistency. This enables consistent information management by having the generating AI automatically maintain terminology consistency and cross-reference consistency. Some or all of the above processes in the update unit may be performed using the generating AI, or not. For example, the update unit can input the content of a document into the generating AI and have the generating AI perform terminology consistency and maintain cross-reference consistency.

[0042] The management unit can provide advanced natural language search capabilities and summary generation using generative AI, enabling rapid access to necessary information. For example, the management unit can provide document search functionality using natural language processing technology. The management unit can also generate document summaries using generative AI. Furthermore, the management unit can improve document search functionality using search algorithms. This enables rapid access to necessary information by providing advanced natural language search capabilities and summary generation using generative AI. Some or all of the above-described processes in the management unit may be performed using generative AI, or not. For example, the management unit can input documents into a generative AI and have the generative AI perform the search function and summary generation.

[0043] The analysis unit can adjust the level of detail in its analysis based on the importance of the documents. For example, it can analyze highly important documents in detail and extract information down to the smallest detail. It can also analyze less important documents simply and extract only the main information. It can also analyze documents of moderate importance appropriately and extract the necessary information in a balanced way. By adjusting the level of detail in the analysis based on the importance of the documents, efficient analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the documents into a generating AI and have the generating AI adjust the level of detail in the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the document category during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical documents to extract technical details. For example, the analysis unit can apply a structured analysis algorithm to specifications to extract the key points of the specifications. For example, the analysis unit can apply a user-friendly analysis algorithm to manuals to extract operating procedures. This allows for appropriate analysis by applying different analysis algorithms depending on the document category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the document category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the submission date of the documents during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted documents. The analysis unit may also postpone the analysis of older documents. The analysis unit may also moderately analyze documents with a moderate submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the documents. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date of the documents into a generating AI and have the generating AI determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the documents during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant documents. For example, the analysis unit may postpone the analysis of less relevant documents. For example, the analysis unit may moderately analyze documents of moderate relevance. By adjusting the order of analysis based on the relevance of the documents, efficient analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the documents into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The classification unit can improve the accuracy of classification by considering the interrelationships between documents during the classification process. For example, the classification unit can group related documents together and classify them. The classification unit can also classify by considering links between documents. The classification unit can also classify based on cross-references between documents. This improves the accuracy of classification by considering the interrelationships between documents. Some or all of the above processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the interrelationships between documents into a generating AI and have the generating AI perform the task of improving the accuracy of classification.

[0048] The classification unit can classify documents while considering the attribute information of the document submitter. For example, the classification unit can classify documents based on the submitter's job title. The classification unit can also classify documents based on the submitter's department. The classification unit can also classify documents based on the submitter's field of expertise. This allows for appropriate classification by considering the attribute information of the document submitter. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the submitter's attribute information into a generating AI and have the generating AI perform the classification.

[0049] The classification unit can perform classification while considering the geographical distribution of documents. For example, the classification unit can classify based on the location where the documents were created. The classification unit can also classify based on the target region of the documents. The classification unit can also classify based on the distribution region of the documents. This allows for appropriate classification by considering the geographical distribution of documents. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the geographical distribution of documents into a generating AI and have the generating AI perform the classification.

[0050] The classification unit can improve the accuracy of its classification by referring to related literature for the documents during the classification process. For example, the classification unit classifies documents based on related literature. The classification unit can also classify documents by considering citations in related literature. The classification unit can also classify documents by analyzing the content of related literature. This improves the accuracy of classification by classifying documents by referring to related literature. Some or all of the above processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input related literature into a generating AI and have the generating AI perform the task of improving the accuracy of the classification.

[0051] The management unit can select the optimal management method by referring to the user's past operation history during management. For example, the management unit may prioritize suggesting management methods that the user has used in the past. The management unit can also learn and suggest the optimal management method from the user's operation history. The management unit can also provide a customized management method based on the user's operation history. This enables optimal management by selecting a management method by referring to the user's past operation history. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's operation history into a generating AI and have the generating AI select the optimal management method.

[0052] The management department can determine management priorities based on the importance of documents during the management process. For example, the management department can prioritize the management of highly important documents. For example, the management department can postpone the management of less important documents. For example, the management department can manage documents of moderate importance appropriately. This enables efficient management by determining management priorities based on the importance of documents. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the importance of documents into a generating AI and have the generating AI perform the determination of management priorities.

[0053] The management unit can select the optimal management method during management, taking into account the user's device information. For example, if the user is using a smartphone, the management unit can provide a management method adapted to the screen size. For example, if the user is using a tablet, the management unit can also provide a management method optimized for a larger screen. For example, if the user is using a desktop, the management unit can also provide a detailed management method. This enables optimal management by selecting a management method that takes the user's device information into account. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's device information into a generating AI and have the generating AI select the optimal management method.

[0054] The management department can analyze users' social media activity and propose management methods during management. For example, the management department can propose the optimal management method based on users' social media activity. The management department can also provide customized management methods based on users' social media activity. The management department can also analyze users' social media activity and optimize management methods. This enables optimal management by analyzing users' social media activity and proposing management methods. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input users' social media activity into a generating AI and have the generating AI execute proposals for management methods.

[0055] The generation unit can optimize the generation algorithm by referring to the source code change history during generation. For example, the generation unit can select the optimal generation algorithm based on the source code change history. The generation unit can also analyze the source code change history and adjust the generation algorithm. The generation unit can also optimize the generation algorithm by referring to the source code change history. This makes it possible to generate appropriate documents by optimizing the generation algorithm by referring to the source code change history. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the source code change history into a generation AI and have the generation AI perform the optimization of the generation algorithm.

[0056] The generation unit can apply different generation algorithms depending on the document category during generation. For example, the generation unit can apply a specialized generation algorithm to technical documents. For example, the generation unit can apply a structured generation algorithm to specifications. For example, the generation unit can apply a user-friendly generation algorithm to manuals. By applying different generation algorithms depending on the document category, appropriate documents can be generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the document category into the generation AI and have the generation AI execute the application of the generation algorithm.

[0057] The generation unit can determine the generation priority based on the submission date of the source code during generation. For example, the generation unit may prioritize generating recently submitted source code. The generation unit may also postpone generating older source code. For example, the generation unit may moderately generate source code with a moderate submission date. This enables efficient document generation by determining the generation priority based on the submission date of the source code. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the source code submission date into the generation AI and have the generation AI determine the generation priority.

[0058] The generation unit can adjust the generation order based on the relevance of the source code during generation. For example, the generation unit can prioritize generating highly relevant source code. For example, the generation unit can postpone generating less relevant source code. For example, the generation unit can moderately generate source code of moderate relevance. By adjusting the generation order based on the relevance of the source code, efficient document generation becomes possible. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the source code into the generation AI and have the generation AI perform the adjustment of the generation order.

[0059] The update unit can optimize the update algorithm by referring to the document's change history during the update process. For example, the update unit can select the optimal update algorithm based on the document's change history. The update unit can also analyze the document's change history and adjust the update algorithm. The update unit can also optimize the update algorithm by referring to the document's change history. This enables appropriate updates by optimizing the update algorithm by referring to the document's change history. Some or all of the above processes in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit can input the document's change history into a generative AI and have the generative AI perform the optimization of the update algorithm.

[0060] The update unit can apply different update algorithms depending on the document category during the update process. For example, the update unit can apply a specialized update algorithm to technical documents. For example, the update unit can apply a structured update algorithm to specifications. For example, the update unit can apply a user-friendly update algorithm to manuals. This allows for appropriate updates by applying different update algorithms depending on the document category. Some or all of the above-described processes in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit can input the document category into the generative AI and have the generative AI execute the application of the update algorithm.

[0061] The update unit can determine update priorities based on the document submission date during the update process. For example, the update unit may prioritize updating recently submitted documents. The update unit may also postpone updating older documents. The update unit may also moderately update documents with a moderate submission date. This allows for efficient updates by determining update priorities based on the document submission date. Some or all of the above processing in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit can input the document submission dates into a generative AI and have the generative AI determine the update priorities.

[0062] The update unit can adjust the order of updates based on the relevance of the documents during the update process. For example, the update unit may prioritize updating highly relevant documents. For example, the update unit may postpone updating less relevant documents. For example, the update unit may moderately update documents of moderate relevance. This allows for efficient updates by adjusting the order of updates based on the relevance of the documents. Some or all of the above processing in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit can input the relevance of the documents into a generative AI and have the generative AI perform the adjustment of the update order.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The analysis unit can adjust the level of detail in its analysis based on the importance of the documents. For example, it can analyze highly important documents in detail to extract information down to the smallest detail. Conversely, it can analyze less important documents simply to extract only the main information. Furthermore, it can analyze documents of moderate importance appropriately to extract necessary information in a balanced manner.

[0065] The classification unit can apply different classification algorithms depending on the document category. For example, a specialized classification algorithm can be applied to technical documents to extract technical details. A structured classification algorithm can be applied to specifications to extract the key points of the specifications. Furthermore, a user-friendly classification algorithm can be applied to manuals to extract operating procedures.

[0066] The management department can select the optimal management method by referring to the user's past operation history. For example, it can prioritize suggesting management methods that the user has used in the past. Furthermore, it can learn and suggest optimal management methods from the user's operation history. In addition, it can provide customized management methods based on the user's operation history.

[0067] The generation unit can optimize the generation algorithm by referring to the source code change history. For example, it can select the optimal generation algorithm based on the source code change history. It can also analyze the source code change history and adjust the generation algorithm. Furthermore, it can optimize the generation algorithm by referring to the source code change history.

[0068] The update department can prioritize updates based on when the documents were submitted. For example, recently submitted documents can be updated first, while older documents can be postponed. Furthermore, documents with a moderate submission date can be updated at a moderate pace.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The analysis unit analyzes existing specifications and technical documents. The analysis unit uses, for example, text analysis technology, structural analysis technology, and natural language processing technology to analyze the content, structure, and meaning of the documents. Step 2: The classification unit automatically organizes and classifies the documents analyzed by the analysis unit. The classification unit classifies and tags documents using, for example, categorization technology, tagging technology, and machine learning technology. Step 3: The administration department collaboratively edits and version-controls documents created and updated by users in real time. The administration department uses technologies such as simultaneous editing, version control, and access control to allow multiple users to edit documents simultaneously and manage versions and access permissions. Step 4: The generation unit automatically generates the latest specifications and technical documents from the source code. The generation unit generates documents from the source code using, for example, code comment analysis technology, document template technology, and natural language generation technology. Step 5: The update unit immediately reflects changes to the document generated by the generation unit. The update unit detects changes to the document using, for example, change detection technology, real-time update technology, and version control technology, and immediately reflects them.

[0071] (Example of form 2) The document management system according to an embodiment of the present invention is an innovative solution that comprehensively manages and utilizes a company's document assets by leveraging generative AI. This document management system improves searchability and accessibility by analyzing, integrating, automatically organizing, and classifying existing specifications and technical documents. Documents created and updated by users can also be collaboratively edited and version-controlled in real time, and high-quality document creation is supported by automatic completion and improvement suggestions from the generative AI. It also features a real-time update function that automatically generates the latest specifications and technical documents from source code and reflects changes immediately. Furthermore, the generative AI automatically maintains consistency in terminology and cross-references, realizing consistent information management. Advanced natural language search functions and summary generation by the generative AI enable rapid access to necessary information. This achieves improved development efficiency and product quality, reduced man-hours, and improved information access, thereby strengthening business competitiveness. By making full use of the high-precision data analysis and automation functions of the generative AI, this document management system provides a clear return on investment for adopting companies and comprehensively solves information management challenges. This allows document management systems to efficiently manage and utilize a company's document assets, comprehensively solving information management challenges.

[0072] The document management system according to this embodiment comprises an analysis unit, a classification unit, a management unit, a generation unit, and an update unit. The analysis unit analyzes existing specifications and technical documents. The analysis unit analyzes the content of documents using, for example, text analysis technology. The analysis unit can also analyze the structure of documents using, for example, structural analysis technology. The analysis unit can also analyze the meaning of documents using, for example, natural language processing technology. The classification unit automatically organizes and classifies the documents analyzed by the analysis unit. The classification unit classifies documents using, for example, categorization technology. The classification unit can also tag documents using, for example, tagging technology. The classification unit can also automatically classify documents using, for example, machine learning technology. The management unit collaboratively edits and version-controls documents created and updated by users in real time. The management unit enables multiple users to edit documents simultaneously using, for example, simultaneous editing technology. The management unit can also manage document versions using, for example, version control technology. The management unit can also manage document access rights using, for example, access rights management technology. The generation unit automatically generates the latest specifications and technical documents from source code. The generation unit generates documents from source code using, for example, code comment analysis technology. The generation unit can also generate documents from source code using, for example, document template technology. The generation unit can also generate documents from source code using, for example, natural language generation technology. The update unit immediately reflects changes to the documents generated by the generation unit. The update unit detects changes to documents using, for example, change detection technology. The update unit can also immediately reflect changes to documents using, for example, real-time update technology. The update unit can also manage changes to documents using, for example, version control technology. As a result, the document management system according to this embodiment can efficiently manage and utilize a company's document assets and comprehensively solve information management challenges.

[0073] The analysis unit analyzes existing specifications and technical documents. For example, it uses text analysis techniques to analyze the content of documents. Specifically, text analysis techniques extract keywords, summarize documents, and understand context. This allows for quick identification of important parts and highly relevant information within the document. The analysis unit can also analyze the structure of documents using structural analysis techniques. Structural analysis techniques analyze the relationships between chapters and sections, clarifying the overall structure of the document. This facilitates document navigation and allows for quick access to necessary information. The analysis unit can also analyze the meaning of documents using natural language processing techniques. Natural language processing techniques understand the context within documents and extract meaningful information. For example, it can analyze the meaning of specific technical terms and jargon, leading to a deeper understanding of the document's content. This allows the analysis unit to comprehensively analyze the content, structure, and meaning of documents, contributing to improved document quality and efficient management. Furthermore, based on these analysis results, the analysis unit can identify areas for improvement and missing information, contributing to improved document quality.

[0074] The classification unit automatically organizes and classifies documents analyzed by the analysis unit. For example, the classification unit classifies documents using categorization technology. Categorization technology automatically classifies documents into appropriate categories based on their content. This improves document searchability, allowing for quick access to necessary information. The classification unit can also tag documents using tagging technology. Tagging technology automatically assigns appropriate tags based on document content and specific keywords. This further improves document searchability, making it easier to find related documents. The classification unit can also automatically classify documents using machine learning technology. Machine learning technology learns from past classification data and appropriately classifies new documents. This improves classification accuracy and streamlines document management. Furthermore, the classification unit can continuously improve its classification algorithm based on user feedback. This allows the classification unit to organize and classify documents efficiently and accurately, contributing to more efficient document management.

[0075] The management department collaboratively edits and version-controls documents created and updated by users in real time. For example, the management department can use concurrent editing technology to enable multiple users to edit a document simultaneously. Concurrent editing technology ensures that edits by multiple users simultaneously do not conflict, allowing the entire team to efficiently create and update documents. The management department can also manage document versions using version control technology. Version control technology saves the document's change history and allows users to revert to previous versions. This allows tracking the document's change history and referencing past versions as needed. The management department can also manage document access rights using access rights management technology. Access rights management technology sets viewing and editing permissions for each user, protecting confidential information. This improves document security and reduces the risk of information leakage. Furthermore, the management department can record user operation logs to understand document usage. This allows for analysis of document usage, helping to improve management efficiency and identify areas for improvement.

[0076] The generation unit automatically generates the latest specifications and technical documents from source code. For example, the generation unit generates documents from source code using code comment analysis technology. Code comment analysis technology analyzes comments within the source code and automatically generates the content of the document. This ensures consistency between the source code and the document, and allows for the creation of documents that reflect the latest information. The generation unit can also generate documents from source code using document template technology. Document template technology reflects the content of the source code into the document based on a predefined template. This ensures document consistency and allows for efficient document creation. The generation unit can also generate documents from source code using natural language generation technology. Natural language generation technology converts the content of the source code into natural language, generating easy-to-read documents. This provides documents that are easy to understand not only for engineers but also for non-engineers. Furthermore, the generation unit can evaluate the quality of the generated documents and make corrections as needed. This allows the generation unit to consistently provide high-quality documents and contribute to the efficiency of document management.

[0077] The update unit immediately reflects changes to documents generated by the generation unit. The update unit detects document changes using, for example, change detection technology. Change detection technology detects document changes in real time and immediately reflects those changes. This ensures that documents are always up-to-date. The update unit can also immediately reflect document changes using, for example, real-time update technology. Real-time update technology immediately reflects document changes, ensuring users always have access to the latest information. This maintains document integrity and facilitates smooth information sharing. The update unit can also manage document changes using, for example, version control technology. Version control technology saves document change history and allows users to revert to previous versions. This allows tracking document change history and referencing past versions as needed. Furthermore, the update unit can identify areas for improvement based on user feedback and continuously enhance quality. This enables the update unit to consistently provide the latest and highest-quality documents, contributing to more efficient document management.

[0078] The analysis unit can analyze existing specifications and technical documents and integrate them into the system. For example, the analysis unit can analyze the content of documents using text analysis technology and integrate it into the system. The analysis unit can also analyze the structure of documents using structural analysis technology and integrate it into the system. The analysis unit can also analyze the meaning of documents using natural language processing technology and integrate it into the system. This enables centralized management of documents by analyzing existing documents and integrating them into the system. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input existing specifications and technical documents into a generation AI and have the generation AI perform the document analysis.

[0079] The classification unit can automatically classify and tag imported documents, thereby improving search functionality. For example, the classification unit can classify documents using categorization techniques. The classification unit can also tag documents using tagging techniques. Furthermore, the classification unit can automatically classify documents using machine learning techniques. This automatic classification and tagging of documents improves searchability. Some or all of the above processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input imported documents into a generating AI and have the generating AI perform document classification and tagging.

[0080] The management unit can share and manage documents created and updated by users in real time. The management unit can enable multiple users to edit documents simultaneously using, for example, concurrent editing technology. The management unit can also manage document versions using, for example, version control technology. The management unit can also manage document access rights using, for example, access rights management technology. This streamlines collaborative editing by sharing and managing user-created and updated documents in real time. Some or all of the above processes in the management unit may be performed using, for example, AI, or not using AI. For example, the management unit can input user-created and updated documents into a generating AI and have the generating AI perform document sharing and management.

[0081] The generation unit can automatically generate the latest specifications and technical documents from source code. The generation unit can generate documents from source code using, for example, code comment analysis technology. The generation unit can also generate documents from source code using, for example, document template technology. The generation unit can also generate documents from source code using, for example, natural language generation technology. This makes document updates more efficient by automatically generating the latest documents from source code. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input source code into a generation AI and have the generation AI perform the document generation.

[0082] The update unit can detect changes in code and documentation and immediately update the relevant documentation. The update unit can detect changes in documentation using, for example, change detection technology. The update unit can also immediately reflect changes in documentation using, for example, real-time update technology. The update unit can also manage changes in documentation using, for example, version control technology. This ensures the consistency of documentation by immediately reflecting changes in code and documentation. Some or all of the above processes in the update unit may be performed using, for example, AI, or not using AI. For example, the update unit can input changes in code and documentation into a generating AI and have the generating AI perform the document update.

[0083] The management department can support the creation of high-quality documents by providing automatic completion and improvement suggestions using generative AI. For example, the management department can use natural language processing technology to automatically complete documents. The management department can also use machine learning algorithms to suggest improvements to documents. The management department can also use generative AI to analyze the content of documents and suggest areas for improvement. In this way, the creation of high-quality documents is supported by automatic completion and improvement suggestions using generative AI. Some or all of the above processes in the management department may be performed using generative AI, or not. For example, the management department can input documents created by users into the generative AI and have the generative AI perform automatic completion and improvement suggestions for the documents.

[0084] The update unit enables consistent information management by having the generating AI automatically maintain terminology consistency and cross-reference consistency. For example, the update unit can use a glossary to standardize terminology within a document. The update unit can also use link management technology to maintain the consistency of cross-references within a document. For example, the update unit can use the generating AI to analyze the content of a document and maintain terminology consistency and cross-reference consistency. This enables consistent information management by having the generating AI automatically maintain terminology consistency and cross-reference consistency. Some or all of the above processes in the update unit may be performed using the generating AI, or not. For example, the update unit can input the content of a document into the generating AI and have the generating AI perform terminology consistency and maintain cross-reference consistency.

[0085] The management unit can provide advanced natural language search capabilities and summary generation using generative AI, enabling rapid access to necessary information. For example, the management unit can provide document search functionality using natural language processing technology. The management unit can also generate document summaries using generative AI. Furthermore, the management unit can improve document search functionality using search algorithms. This enables rapid access to necessary information by providing advanced natural language search capabilities and summary generation using generative AI. Some or all of the above-described processes in the management unit may be performed using generative AI, or not. For example, the management unit can input documents into a generative AI and have the generative AI perform the search function and summary generation.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, emotion analysis technology. The analysis unit can also estimate emotions by analyzing, for example, user behavior data. The analysis unit can also estimate the user's emotions and adjust the analysis priority using, for example, generative AI. This allows for analysis tailored to the user's situation by adjusting the analysis priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user behavior data into the generative AI and have the generative AI perform emotion estimation and adjustment of analysis priority.

[0087] The analysis unit can adjust the level of detail in its analysis based on the importance of the documents. For example, it can analyze highly important documents in detail and extract information down to the smallest detail. It can also analyze less important documents simply and extract only the main information. It can also analyze documents of moderate importance appropriately and extract the necessary information in a balanced way. By adjusting the level of detail in the analysis based on the importance of the documents, efficient analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the documents into a generating AI and have the generating AI adjust the level of detail in the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the document category during analysis. For example, the analysis unit can apply a specialized analysis algorithm to technical documents to extract technical details. For example, the analysis unit can apply a structured analysis algorithm to specifications to extract the key points of the specifications. For example, the analysis unit can apply a user-friendly analysis algorithm to manuals to extract operating procedures. This allows for appropriate analysis by applying different analysis algorithms depending on the document category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the document category into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, emotion analysis technology. The analysis unit can also estimate emotions by analyzing the user's behavioral data. The analysis unit can also estimate the user's emotions using, for example, generative AI and adjust the display method of the analysis results. This makes it possible to display the analysis results in a way that is easy for the user to understand by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user behavioral data into the generative AI and have the generative AI perform emotion estimation and adjustment of the display method of the analysis results.

[0090] The analysis unit can determine the priority of analysis based on the submission date of the documents during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted documents. The analysis unit may also postpone the analysis of older documents. The analysis unit may also moderately analyze documents with a moderate submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the documents. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission date of the documents into a generating AI and have the generating AI determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the documents during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant documents. For example, the analysis unit may postpone the analysis of less relevant documents. For example, the analysis unit may moderately analyze documents of moderate relevance. By adjusting the order of analysis based on the relevance of the documents, efficient analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the documents into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0092] The classification unit can estimate the user's emotions and adjust the classification criteria based on the estimated user emotions. The classification unit can estimate the user's emotions using, for example, emotion analysis techniques. The classification unit can also estimate emotions by analyzing, for example, user behavior data. The classification unit can also estimate the user's emotions and adjust the classification criteria using, for example, generative AI. This allows for appropriate classification for the user by adjusting the classification criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using, for example, AI, or not using AI. For example, the classification unit can input user behavior data into the generative AI and have the generative AI perform emotion estimation and adjustment of classification criteria.

[0093] The classification unit can improve the accuracy of classification by considering the interrelationships between documents during the classification process. For example, the classification unit can group related documents together and classify them. The classification unit can also classify by considering links between documents. The classification unit can also classify based on cross-references between documents. This improves the accuracy of classification by considering the interrelationships between documents. Some or all of the above processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the interrelationships between documents into a generating AI and have the generating AI perform the task of improving the accuracy of classification.

[0094] The classification unit can classify documents while considering the attribute information of the document submitter. For example, the classification unit can classify documents based on the submitter's job title. The classification unit can also classify documents based on the submitter's department. The classification unit can also classify documents based on the submitter's field of expertise. This allows for appropriate classification by considering the attribute information of the document submitter. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the submitter's attribute information into a generating AI and have the generating AI perform the classification.

[0095] The classification unit can estimate the user's emotions and adjust the display order of the classification results based on the estimated user emotions. The classification unit can estimate the user's emotions using, for example, emotion analysis technology. The classification unit can also estimate emotions by analyzing, for example, user behavior data. The classification unit can also estimate the user's emotions using, for example, generative AI and adjust the display order of the classification results. This makes it possible to display the results in a way that is easy for the user to understand by adjusting the display order of the classification results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using, for example, AI, or not using AI. For example, the classification unit can input user behavior data into the generative AI and have the generative AI perform emotion estimation and adjustment of the display order of the classification results.

[0096] The classification unit can perform classification while considering the geographical distribution of documents. For example, the classification unit can classify based on the location where the documents were created. The classification unit can also classify based on the target region of the documents. The classification unit can also classify based on the distribution region of the documents. This allows for appropriate classification by considering the geographical distribution of documents. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input the geographical distribution of documents into a generating AI and have the generating AI perform the classification.

[0097] The classification unit can improve the accuracy of its classification by referring to related literature for the documents during the classification process. For example, the classification unit classifies documents based on related literature. The classification unit can also classify documents by considering citations in related literature. The classification unit can also classify documents by analyzing the content of related literature. This improves the accuracy of classification by classifying documents by referring to related literature. Some or all of the above processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input related literature into a generating AI and have the generating AI perform the task of improving the accuracy of the classification.

[0098] The management department can estimate the user's emotions and adjust management methods based on the estimated user emotions. For example, the management department can estimate the user's emotions using emotion analysis technology. For example, the management department can also estimate emotions by analyzing user behavior data. For example, the management department can estimate user emotions using generative AI and adjust management methods. This allows for appropriate management for the user by adjusting management methods based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not using AI. For example, the management department can input user behavior data into a generative AI and have the generative AI perform emotion estimation and adjustment of management methods.

[0099] The management unit can select the optimal management method by referring to the user's past operation history during management. For example, the management unit may prioritize suggesting management methods that the user has used in the past. The management unit can also learn and suggest the optimal management method from the user's operation history. The management unit can also provide a customized management method based on the user's operation history. This enables optimal management by selecting a management method by referring to the user's past operation history. Some or all of the above processes in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's operation history into a generating AI and have the generating AI select the optimal management method.

[0100] The management department can determine management priorities based on the importance of documents during the management process. For example, the management department can prioritize the management of highly important documents. For example, the management department can postpone the management of less important documents. For example, the management department can manage documents of moderate importance appropriately. This enables efficient management by determining management priorities based on the importance of documents. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the importance of documents into a generating AI and have the generating AI perform the determination of management priorities.

[0101] The management unit can estimate the user's emotions and adjust the frequency of management based on the estimated emotions. The management unit can estimate the user's emotions, for example, using emotion analysis technology. The management unit can also estimate emotions by analyzing user behavior data, for example. The management unit can also estimate the user's emotions and adjust the frequency of management using generative AI, for example. This allows for appropriate management for the user by adjusting the frequency of management based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can input user behavior data into a generative AI and have the generative AI perform emotion estimation and adjustment of the frequency of management.

[0102] The management unit can select the optimal management method during management, taking into account the user's device information. For example, if the user is using a smartphone, the management unit can provide a management method adapted to the screen size. For example, if the user is using a tablet, the management unit can also provide a management method optimized for a larger screen. For example, if the user is using a desktop, the management unit can also provide a detailed management method. This enables optimal management by selecting a management method that takes the user's device information into account. Some or all of the above processing in the management unit may be performed using AI, for example, or without AI. For example, the management unit can input the user's device information into a generating AI and have the generating AI select the optimal management method.

[0103] The management department can analyze users' social media activity and propose management methods during management. For example, the management department can propose the optimal management method based on users' social media activity. The management department can also provide customized management methods based on users' social media activity. The management department can also analyze users' social media activity and optimize management methods. This enables optimal management by analyzing users' social media activity and proposing management methods. Some or all of the above processes in the management department may be performed using AI, for example, or without AI. For example, the management department can input users' social media activity into a generating AI and have the generating AI execute proposals for management methods.

[0104] The generation unit can estimate the user's emotions and determine the priority of documents to generate based on the estimated user emotions. The generation unit can estimate the user's emotions using, for example, emotion analysis technology. The generation unit can also estimate emotions by analyzing, for example, user behavior data. The generation unit can also estimate the user's emotions and determine the priority of documents to generate using, for example, a generation AI. This makes it possible to generate documents appropriate for the user by determining the priority of documents to generate based on 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 processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user behavior data into the generation AI and have the generation AI perform emotion estimation and determine the priority of documents to generate.

[0105] The generation unit can optimize the generation algorithm by referring to the source code change history during generation. For example, the generation unit can select the optimal generation algorithm based on the source code change history. The generation unit can also analyze the source code change history and adjust the generation algorithm. The generation unit can also optimize the generation algorithm by referring to the source code change history. This makes it possible to generate appropriate documents by optimizing the generation algorithm by referring to the source code change history. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the source code change history into a generation AI and have the generation AI perform the optimization of the generation algorithm.

[0106] The generation unit can apply different generation algorithms depending on the document category during generation. For example, the generation unit can apply a specialized generation algorithm to technical documents. For example, the generation unit can apply a structured generation algorithm to specifications. For example, the generation unit can apply a user-friendly generation algorithm to manuals. By applying different generation algorithms depending on the document category, appropriate documents can be generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the document category into the generation AI and have the generation AI execute the application of the generation algorithm.

[0107] The generation unit can estimate the user's emotions and adjust the display method of the generated document based on the estimated user emotions. The generation unit can estimate the user's emotions, for example, using emotion analysis technology. The generation unit can also estimate emotions by analyzing user behavior data, for example. The generation unit can also estimate the user's emotions using a generation AI and adjust the display method of the generated document. By adjusting the display method of the generated document based on the user's emotions, it becomes possible to display it in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input user behavior data into the generation AI and have the generation AI perform emotion estimation and adjustment of the display method of the generated document.

[0108] The generation unit can determine the generation priority based on the submission date of the source code during generation. For example, the generation unit may prioritize generating recently submitted source code. The generation unit may also postpone generating older source code. For example, the generation unit may moderately generate source code with a moderate submission date. This enables efficient document generation by determining the generation priority based on the submission date of the source code. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the source code submission date into the generation AI and have the generation AI determine the generation priority.

[0109] The generation unit can adjust the generation order based on the relevance of the source code during generation. For example, the generation unit can prioritize generating highly relevant source code. For example, the generation unit can postpone generating less relevant source code. For example, the generation unit can moderately generate source code of moderate relevance. By adjusting the generation order based on the relevance of the source code, efficient document generation becomes possible. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the source code into the generation AI and have the generation AI perform the adjustment of the generation order.

[0110] The update unit can estimate the user's emotions and adjust the update method based on the estimated user emotions. The update unit can estimate the user's emotions, for example, using emotion analysis technology. The update unit can also estimate emotions by analyzing user behavior data, for example. The update unit can also estimate the user's emotions and adjust the update method using generative AI, for example. This makes it possible to provide updates that are appropriate for the user by adjusting the update method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input user behavior data into a generative AI and have the generative AI perform emotion estimation and adjustment of the update method.

[0111] The update unit can optimize the update algorithm by referring to the document's change history during the update process. For example, the update unit can select the optimal update algorithm based on the document's change history. The update unit can also analyze the document's change history and adjust the update algorithm. The update unit can also optimize the update algorithm by referring to the document's change history. This enables appropriate updates by optimizing the update algorithm by referring to the document's change history. Some or all of the above processes in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit can input the document's change history into a generative AI and have the generative AI perform the optimization of the update algorithm.

[0112] The update unit can apply different update algorithms depending on the document category during the update process. For example, the update unit can apply a specialized update algorithm to technical documents. For example, the update unit can apply a structured update algorithm to specifications. For example, the update unit can apply a user-friendly update algorithm to manuals. This allows for appropriate updates by applying different update algorithms depending on the document category. Some or all of the above-described processes in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit can input the document category into the generative AI and have the generative AI execute the application of the update algorithm.

[0113] The update unit can estimate the user's emotions and adjust the update frequency based on the estimated emotions. The update unit can estimate the user's emotions using, for example, emotion analysis technology. The update unit can also estimate emotions by analyzing, for example, user behavior data. The update unit can also estimate the user's emotions and adjust the update frequency using, for example, generative AI. This allows for updates that are appropriate for the user by adjusting the update frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using, for example, AI, or not using AI. For example, the update unit can input user behavior data into a generative AI and have the generative AI perform emotion estimation and adjustment of the update frequency.

[0114] The update unit can determine update priorities based on the document submission date during the update process. For example, the update unit may prioritize updating recently submitted documents. The update unit may also postpone updating older documents. The update unit may also moderately update documents with a moderate submission date. This allows for efficient updates by determining update priorities based on the document submission date. Some or all of the above processing in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit can input the document submission dates into a generative AI and have the generative AI determine the update priorities.

[0115] The update unit can adjust the order of updates based on the relevance of the documents during the update process. For example, the update unit may prioritize updating highly relevant documents. For example, the update unit may postpone updating less relevant documents. For example, the update unit may moderately update documents of moderate relevance. This allows for efficient updates by adjusting the order of updates based on the relevance of the documents. Some or all of the above processing in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit can input the relevance of the documents into a generative AI and have the generative AI perform the adjustment of the update order.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The analysis unit can estimate the user's emotions and adjust the analysis priority based on those emotions. For example, if the user is stressed, the analysis unit can prioritize analyzing high-priority documents to reduce the user's burden. If the user is relaxed, the analysis unit can perform a more detailed analysis and provide more information. Furthermore, if the user is in a hurry, the analysis unit can perform a rapid analysis and provide the necessary information quickly.

[0118] The classification unit can estimate the user's emotions and adjust the classification criteria based on those emotions. For example, if the user is confused, the classification unit can simplify the categorization to make it easier for the user to find information. If the user is focused, the classification unit can provide more detailed categorization to allow the user to delve deeper into the information they need. Furthermore, if the user is tired, the classification unit can prioritize displaying important information to reduce the user's burden.

[0119] The management department can estimate the user's emotions and adjust management methods based on those estimates. For example, if the user is anxious, the management department can provide an interface that supports quick operation, allowing the user to perform the necessary actions quickly. If the user is relaxed, the management department can provide detailed operation guides to make it easier for the user to understand the operations. Furthermore, if the user is tired, the management department can simplify the operations to reduce the user's burden.

[0120] The generation unit can estimate the user's emotions and determine the priority of the documents to generate based on those emotions. For example, if the user is in a hurry, the generation unit can prioritize generating important documents so that the user can quickly obtain the information they need. If the user is relaxed, the generation unit can generate detailed documents so that the user can deeply understand the information. Furthermore, if the user is stressed, the generation unit can generate concise documents to reduce the user's burden.

[0121] The update unit can estimate the user's emotions and adjust the update method based on those emotions. For example, if the user is anxious, the update unit can perform a rapid update to allow the user to quickly obtain the necessary information. If the user is relaxed, the update unit can perform a detailed update to allow the user to deeply understand the information. Furthermore, if the user is tired, the update unit can perform a simplified update to reduce the user's burden.

[0122] The analysis unit can adjust the level of detail in its analysis based on the importance of the documents. For example, it can analyze highly important documents in detail to extract information down to the smallest detail. Conversely, it can analyze less important documents simply to extract only the main information. Furthermore, it can analyze documents of moderate importance appropriately to extract necessary information in a balanced manner.

[0123] The classification unit can apply different classification algorithms depending on the document category. For example, a specialized classification algorithm can be applied to technical documents to extract technical details. A structured classification algorithm can be applied to specifications to extract the key points of the specifications. Furthermore, a user-friendly classification algorithm can be applied to manuals to extract operating procedures.

[0124] The management department can select the optimal management method by referring to the user's past operation history. For example, it can prioritize suggesting management methods that the user has used in the past. Furthermore, it can learn and suggest optimal management methods from the user's operation history. In addition, it can provide customized management methods based on the user's operation history.

[0125] The generation unit can optimize the generation algorithm by referring to the source code change history. For example, it can select the optimal generation algorithm based on the source code change history. It can also analyze the source code change history and adjust the generation algorithm. Furthermore, it can optimize the generation algorithm by referring to the source code change history.

[0126] The update department can prioritize updates based on when the documents were submitted. For example, recently submitted documents can be updated first, while older documents can be postponed. Furthermore, documents with a moderate submission date can be updated at a moderate pace.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The analysis unit analyzes existing specifications and technical documents. The analysis unit uses, for example, text analysis technology, structural analysis technology, and natural language processing technology to analyze the content, structure, and meaning of the documents. Step 2: The classification unit automatically organizes and classifies the documents analyzed by the analysis unit. The classification unit classifies and tags documents using, for example, categorization technology, tagging technology, and machine learning technology. Step 3: The administration department collaboratively edits and version-controls documents created and updated by users in real time. The administration department uses technologies such as simultaneous editing, version control, and access control to allow multiple users to edit documents simultaneously and manage versions and access permissions. Step 4: The generation unit automatically generates the latest specifications and technical documents from the source code. The generation unit generates documents from the source code using, for example, code comment analysis technology, document template technology, and natural language generation technology. Step 5: The update unit immediately reflects changes to the document generated by the generation unit. The update unit detects changes to the document using, for example, change detection technology, real-time update technology, and version control technology, and immediately reflects them.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the analysis unit, classification unit, management unit, generation unit, and update unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes existing specifications and technical documents. The classification unit is implemented by the control unit 46A of the smart device 14 and automatically organizes and classifies the analyzed documents. The management unit is implemented by the specific processing unit 290 of the data processing device 12 and performs real-time collaborative editing and version control of documents created and updated by users. The generation unit is implemented by the control unit 46A of the smart device 14 and automatically generates the latest specifications and technical documents from source code. The update unit is implemented by the specific processing unit 290 of the data processing device 12 and immediately reflects changes to the generated documents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the analysis unit, classification unit, management unit, generation unit, and update unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes existing specifications and technical documents. The classification unit is implemented by the control unit 46A of the smart glasses 214 and automatically organizes and classifies the analyzed documents. The management unit is implemented by the specific processing unit 290 of the data processing device 12 and performs real-time collaborative editing and version control of documents created and updated by the user. The generation unit is implemented by the control unit 46A of the smart glasses 214 and automatically generates the latest specifications and technical documents from source code. The update unit is implemented by the specific processing unit 290 of the data processing device 12 and immediately reflects changes to the generated documents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In 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.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 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.

[0164] Each of the multiple elements described above, including the analysis unit, classification unit, management unit, generation unit, and update unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes existing specifications and technical documents. The classification unit is implemented by the control unit 46A of the headset terminal 314 and automatically organizes and classifies the analyzed documents. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs real-time collaborative editing and version control of documents created and updated by users. The generation unit is implemented by the control unit 46A of the headset terminal 314 and automatically generates the latest specifications and technical documents from source code. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and immediately reflects changes to the generated documents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the analysis unit, classification unit, management unit, generation unit, and update unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes existing specifications and technical documents. The classification unit is implemented by the control unit 46A of the robot 414 and automatically organizes and classifies the analyzed documents. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs real-time collaborative editing and version control of documents created and updated by the user. The generation unit is implemented by the control unit 46A of the robot 414 and automatically generates the latest specifications and technical documents from source code. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and immediately reflects changes to the generated documents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) The analysis unit analyzes existing specifications and technical documents, A classification unit that automatically organizes and classifies the documents analyzed by the aforementioned analysis unit, The management department collaborates and manages versions of user-created and updated documents in real time. A generation unit that automatically generates the latest specifications and technical documents from source code, The system includes an update unit that immediately reflects changes to the document generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze existing specifications and technical documents and integrate them into the system. The system described in Appendix 1, characterized by the features described herein. (Note 3) The system described in Appendix 1, characterized by automatically classifying and tagging imported documents and improving search functionality. (Note 4) The system described in Appendix 1, characterized by sharing and managing user-created and updated documents in real time. (Note 5) The generating unit is Automatically generate the latest specifications and technical documentation from source code. The system described in Appendix 1, characterized by the features described herein. (Note 6) The system described in Appendix 1, characterized by detecting changes in code and documentation and immediately updating related documentation. (Note 7) The system described in Appendix 1 is characterized by providing AI-powered automatic completion and improvement suggestions to support the creation of high-quality documents. (Note 8) The system described in Appendix 1 is characterized in that AI automatically maintains consistency in terminology and cross-references, thereby achieving consistent information management. (Note 9) The system described in Appendix 1 is characterized by providing advanced natural language search capabilities and AI-powered summary generation, enabling rapid access to necessary information. (Note 10) The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the priority of analysis based on the estimated user emotions. (Note 11) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the document. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the document category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the display method of the analysis results based on the estimated user emotions. (Note 14) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on the submission date of the documents. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the documents. The system described in Appendix 1, characterized by the features described herein. (Note 16) The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the classification criteria based on the estimated user emotions. (Note 17) The aforementioned classification unit is When classifying documents, consider their interrelationships to improve classification accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned classification unit is When classifying documents, the attribute information of the document submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the display order of the classification results based on the estimated user emotions. (Note 20) The aforementioned classification unit is When classifying documents, the geographical distribution of the documents should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned classification unit is When classifying documents, referencing related literature improves the accuracy of the classification. The system described in Appendix 1, characterized by the features described herein. (Note 22) The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the management method based on the estimated user emotions. (Note 23) The aforementioned management department, During management, the system selects the optimal management method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, When managing documents, prioritize management based on their importance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the frequency of management based on the estimated user emotions. (Note 26) The aforementioned management department, During management, the optimal management method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, During management, we analyze users' social media activity and propose management methods. The system described in Appendix 1, characterized by the features described herein. (Note 28) The system described in Appendix 1, characterized by estimating the user's emotions and determining the priority of documents to be generated based on the estimated user emotions. (Note 29) The generating unit is During generation, the generation algorithm is optimized by referring to the source code's change history. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is During generation, different generation algorithms are applied depending on the document category. The system described in Appendix 1, characterized by the features described herein. (Note 31) The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the display method of the generated document based on the estimated user emotions. (Note 32) The generating unit is During generation, the generation priority is determined based on the source code submission date. The system described in Appendix 1, characterized by the features described herein. (Note 33) The generating unit is During generation, the generation order is adjusted based on the relevance of the source code. The system described in Appendix 1, characterized by the features described herein. (Note 34) The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the update method based on the estimated user emotions. (Note 35) The aforementioned update unit is During updates, the update algorithm is optimized by referring to the document's change history. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned update unit is When updating, different update algorithms are applied depending on the document category. The system described in Appendix 1, characterized by the features described herein. (Note 37) The system described in Appendix 1, characterized by estimating the user's emotions and adjusting the update frequency based on the estimated user emotions. (Note 38) The aforementioned update unit is When updating, prioritize updates based on when the documents were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned update unit is When updating, adjust the order of updates based on the relevance of the documents. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis unit analyzes existing specifications and technical documents, A classification unit that automatically organizes and classifies the documents analyzed by the aforementioned analysis unit, The management department collaborates and manages versions of user-created and updated documents in real time. A generation unit that automatically generates the latest specifications and technical documents from source code, The system includes an update unit that immediately reflects changes to the document generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze existing specifications and technical documents and integrate them into the system. The system according to feature 1.

3. The system according to claim 1, characterized by automatically classifying and tagging imported documents and improving search functionality.

4. The system according to claim 1, characterized by sharing and managing user-created and updated documents in real time.

5. The generating unit is Automatically generate the latest specifications and technical documentation from source code. The system according to feature 1.

6. The system according to claim 1, characterized by detecting changes in code or documentation and immediately updating related documentation.

7. The system according to claim 1, characterized by providing AI-powered automatic completion and improvement suggestions to support the creation of high-quality documents.

8. The system according to claim 1, characterized in that the AI ​​automatically maintains consistency in terminology and cross-references, thereby achieving consistent information management.

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