system

Abstract

The system according to this embodiment aims to automatically generate and efficiently manage various internal company documents. [Solution] The system according to the embodiment comprises a generation unit, a classification unit, an update unit, and a search unit. The generation unit automatically generates documents. The classification unit classifies and tags the documents generated by the generation unit. The update unit updates the documents classified by the classification unit. The search unit searches for documents updated by the update unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the generation and management of various in-house documents are performed manually, resulting in low efficiency and problems in searchability.

[0005] The system according to the embodiment aims to automatically generate various in-house documents and manage them efficiently.

Means for Solving the Problems

[0006] The system according to the embodiment includes a generation unit, a classification unit, an update unit, and a search unit. The generation unit automatically generates documents. The classification unit classifies and tags the documents generated by the generation unit. The update unit updates the documents classified by the classification unit. The search unit searches for the documents updated by the update unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate and efficiently manage various internal company documents. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The document management system according to an embodiment of the present invention is a system in which an AI agent automatically generates and manages various internal company documents (manuals, reports, meeting minutes, etc.). First, the AI ​​agent automatically generates documents based on the input content. Next, the generated documents are automatically classified and tagged. Furthermore, existing documents are automatically updated to maintain their up-to-date status. Finally, an advanced search function is provided that allows users to search for documents in the form of questions, utilizing natural language processing technology. This mechanism reduces the effort required for document creation and updating, eliminates the complexities of document management, and enables efficient access to necessary information. For example, the AI ​​agent automatically generates documents based on the input content. This includes, for example, meeting minutes, automatic report generation, and automatic creation of standard operating procedures (SOPs). When a user inputs the content of a meeting, the AI ​​agent analyzes the content and automatically generates meeting minutes. In addition, periodic report generation is automated, and reports are generated simply by the user inputting the necessary data. Next, the generated documents are automatically classified and tagged. For example, documents are automatically tagged by project name or assigned person's name, making it easy to search for related documents. This allows for efficient document organization and quick retrieval of necessary information. Furthermore, existing documents are automatically updated. For instance, documents are automatically revised to reflect changes in laws and policies, ensuring they are always up-to-date. This eliminates the need for manual updates and improves document accuracy. Finally, advanced search functionality is provided, utilizing natural language processing technology to allow users to search documents in question format. For example, asking "Where is the latest sales report?" will return relevant documents. This feature allows users to quickly obtain necessary information, improving work efficiency. This system reduces the effort required for document creation and updating, eliminates the complexities of document management, and enables efficient access to necessary information.For example, a centralized management system accessible to all employees enables unified document management, leading to increased operational efficiency. Furthermore, in today's world where paperless operations and the importance of digital documents are growing, its adoption by many companies is anticipated. This allows the document management system to efficiently handle the automatic generation, classification, updating, and searching of documents.

[0029] The document management system according to this embodiment comprises a generation unit, a classification unit, an update unit, and a search unit. The generation unit automatically generates documents. For example, the generation unit automatically generates meeting minutes. For example, when a user inputs the contents of a meeting, the generation unit can analyze the contents and automatically generate meeting minutes. For example, the generation unit automatically generates periodic reports. For example, when a user inputs the necessary data, the generation unit can generate reports. For example, the generation unit automatically generates standard operating procedures (SOPs). For example, when a user inputs the contents of an SOP, the generation unit can analyze the contents and automatically generate an SOP. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without using a generation AI. The classification unit classifies and tags the documents generated by the generation unit. For example, the classification unit tags documents by project name. For example, the classification unit can automatically tag generated documents with project names. For example, the classification unit tags documents by the name of the person in charge. The classification unit can, for example, automatically tag generated documents with the names of the responsible persons. The classification unit can also, for example, tag documents based on their content. The classification unit can, for example, analyze the content of documents, extract relevant keywords, and tag them. Some or all of the above processes in the classification unit may be performed using AI or not. The update unit updates documents classified by the classification unit. The update unit modifies documents in accordance with changes in laws and regulations. The update unit can, for example, automatically modify documents based on the content of new laws and regulations when they come into effect. The update unit modifies documents in accordance with changes in company policies. The update unit can, for example, automatically modify documents based on the content of changes in company policies when they are changed. The update unit modifies documents in accordance with changes in industry regulations. The update unit can, for example, automatically modify documents based on the content of changes in industry regulations when they are changed.Some or all of the processing described above in the update unit may be performed using AI or not. The search unit searches for documents updated by the update unit. The search unit allows users to search for documents in the form of questions, for example. For example, if a user asks, "What is the latest sales report?", the search unit can return relevant documents. For example, the search unit provides search results by utilizing natural language processing technology. For example, the search unit can analyze a user's question, search for relevant documents, and return them. For example, when displaying search results, the search unit can prioritize displaying documents that are highly relevant. Some or all of the processing described above in the search unit may be performed using AI or not. As a result, the document management system according to the embodiment can efficiently perform automatic generation, classification, updating, and searching of documents.

[0030] The generation unit automatically generates documents. For example, the generation unit automatically generates meeting minutes. For example, when a user inputs the content of a meeting, the generation unit can analyze the content and automatically generate the minutes. For example, the generation unit automatically generates periodic reports. For example, when a user inputs the necessary data, the generation unit can generate a report. For example, the generation unit automatically generates standard operating procedures (SOPs). For example, when a user inputs the content of an SOP, the generation unit can analyze the content and automatically generate the SOP. Some or all of the above processes in the generation unit may be performed using generation AI, or they may not be performed using generation AI. The generation unit utilizes natural language processing technology to analyze text data input by the user and generates documents in an appropriate format. For example, when generating meeting minutes, it automatically extracts meeting participants, agenda items, and content of discussions, and organizes them chronologically. This allows the user to easily understand the content of the meeting. When generating periodic reports, it automatically creates graphs and tables based on the data input by the user, generating visually easy-to-understand reports. This allows users to quickly review data analysis results. In generating Standard Operating Procedures (SOPs), the system analyzes user-entered procedures and organizes them according to the appropriate procedure format. This enables users to easily create and share procedures. The generation unit utilizes generation AI to analyze user input more advancedly and generate more accurate documents. For example, the generation AI can learn from past document data and propose the optimal document structure based on user input. This allows users to efficiently generate high-quality documents.

[0031] The classification unit classifies and tags the documents generated by the generation unit. For example, the classification unit tags documents by project name. For example, the classification unit can automatically tag generated documents by project name. For example, the classification unit tags documents by the name of the person in charge. For example, the classification unit can automatically tag generated documents by the name of the person in charge. The classification unit can also tag documents based on their content. For example, the classification unit can analyze the content of documents, extract relevant keywords, and tag them. Some or all of the above processes in the classification unit may be performed using AI or not. The classification unit utilizes natural language processing technology to analyze the content of documents and automatically assign appropriate tags. For example, when extracting project names or names of people in charge, it detects specific keywords or phrases within the document and tags them accordingly. This allows users to easily search and manage documents. Furthermore, the classification unit can extract relevant keywords based on the content of documents and tag them. For example, in meeting minutes, topics and important statements are extracted as keywords, and tags are assigned based on these keywords. This allows users to quickly find documents related to specific topics. The classification unit can use AI to analyze document content more advancedly and perform more accurate tagging. For example, the AI ​​can learn from past document data and suggest the most suitable tags according to the user's needs. This allows users to classify and manage documents efficiently.

[0032] The update unit updates documents classified by the classification unit. The update unit, for example, revises documents in response to changes in laws and regulations. For example, if new laws and regulations come into effect, the update unit can automatically revise documents based on their content. The update unit, for example, revises documents in response to changes in company policies. For example, if company policies change, the update unit can automatically revise documents based on their content. The update unit, for example, revises documents in response to changes in industry regulations. For example, if industry regulations change, the update unit can automatically revise documents based on their content. Some or all of the above processes in the update unit may be performed using AI or not. The update unit constantly monitors for the latest information and automatically makes necessary corrections to respond quickly to changes in laws, company policies, and industry regulations. For example, if new laws and regulations come into effect, it analyzes their content and automatically updates the relevant documents. This ensures that users always have access to documents based on the latest information. Similarly, if company policies change, it analyzes their content and automatically updates the relevant documents. This ensures that users have access to documents based on the company's latest policies. Similarly, if industry regulations change, the system analyzes the changes and automatically updates the relevant documents. This ensures users have access to documents that are up-to-date with industry regulations. The update function utilizes AI to respond quickly and accurately to changes in laws, company policies, and industry regulations. For example, the AI ​​can learn from historical document data and suggest the best correction methods based on the changes. This allows users to efficiently update and manage their documents.

[0033] The search unit searches for documents that have been updated by the update unit. The search unit allows users to search for documents in the form of questions, for example. For example, if a user asks, "What is the latest sales report?", the search unit can return relevant documents. The search unit provides search results using, for example, natural language processing technology. For example, the search unit can analyze a user's question and search for and return relevant documents. For example, when displaying search results, the search unit can prioritize displaying documents that are highly relevant. Some or all of the above processing in the search unit may be performed using AI or not. The search unit uses natural language processing technology to analyze user questions and provide appropriate search results. For example, if a user asks, "What is the latest sales report?", the search unit analyzes the question and searches for and returns relevant documents. This allows the user to quickly obtain the necessary information. Furthermore, when displaying search results, the search unit prioritizes displaying documents that are highly relevant. This allows the user to quickly find the most important information. By using AI, the search unit can analyze user questions more advancedly and provide more accurate search results. For example, AI can learn from past search history and user behavior patterns to suggest optimal search results tailored to the user's needs. This allows users to efficiently search for and utilize the information they need.

[0034] The generation unit can generate meeting minutes, automatic reports, and automatic standard operating procedures. For example, the generation unit can automatically generate meeting minutes. For example, when a user inputs the content of a meeting, the generation unit can analyze the content and automatically generate the minutes. For example, the generation unit can automatically generate periodic reports. For example, when a user inputs the necessary data, the generation unit can generate reports. For example, the generation unit can automatically generate standard operating procedures (SOPs). For example, when a user inputs the content of an SOP, the generation unit can analyze the content and automatically generate the SOP. This makes it possible to automatically create meeting minutes, reports, and standard operating procedures. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the content of a meeting into a generation AI and have the generation AI generate the meeting minutes.

[0035] The classification unit can tag documents by project name or assigned name. For example, the classification unit can tag documents by project name. For example, the classification unit can automatically tag generated documents with project name. For example, the classification unit can tag documents by assigned name. For example, the classification unit can automatically tag generated documents with assigned name. This allows for efficient document organization. Some or all of the above processes in the classification unit may be performed using AI or not. For example, the classification unit can input generated documents into AI and have the AI ​​perform the tagging.

[0036] The update unit can revise documents in response to changes in laws and policies. For example, the update unit can revise documents in response to changes in laws. For example, the update unit can automatically revise documents based on the content of new laws when they come into effect. The update unit can revise documents in response to changes in company policies. For example, the update unit can automatically revise documents based on the content of changes to company policies. This improves the accuracy of the documents. Some or all of the above processes in the update unit may be performed using AI or not. For example, the update unit can input the changes in laws into AI and have AI perform the document revisions.

[0037] The search unit can enable users to search for documents in the form of questions. For example, if a user asks, "What is the latest sales report?", the search unit can return relevant documents. This allows users to quickly obtain the information they need. Some or all of the above processing in the search unit may or may not be performed using AI. For example, the search unit can input a user's question into the AI ​​and have the AI ​​perform a search for relevant documents.

[0038] The search unit can provide search results by utilizing natural language processing technology. For example, the search unit can provide search results by utilizing natural language processing technology. For example, the search unit can analyze a user's question and search for and return relevant documents. This provides advanced search functionality. Some or all of the above-described processes in the search unit may be performed using AI or not. For example, the search unit may use the user's AI or not. For example, the search unit can input a user's question into the AI ​​and have the AI ​​perform a search for relevant documents.

[0039] The generation unit can analyze the user's past document creation history and select the optimal generation method when generating a document. For example, the generation unit can analyze the style of documents the user has created in the past and generate a new document in a similar style. For example, the generation unit can select the optimal template based on templates the user has used in the past and generate a document. For example, the generation unit can incorporate phrases and expressions that the user has frequently used in the past to generate a document. This makes it possible to generate optimal documents based on the user's past history. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's past document creation history into a generation AI and have the generation AI select the optimal generation method.

[0040] The generation unit can customize the content of documents based on the user's current projects and areas of interest during document generation. For example, the generation unit can prioritize information related to the user's current projects when generating documents. For example, the generation unit can generate documents that include relevant topics and data based on the user's areas of interest. For example, the generation unit can customize the content of documents based on keywords specified by the user. This enables document generation tailored to the user's projects and areas of interest. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input information about the user's current projects and areas of interest into the generation AI and have the generation AI perform document customization.

[0041] The generation unit can prioritize generating highly relevant content by considering the user's geographical location information when generating documents. For example, if the user is in a specific region, the generation unit can prioritize incorporating information related to that region when generating documents. For example, if the user is on a business trip, the generation unit can generate documents that include data and information related to the destination. For example, if the user is overseas, the generation unit can generate documents that include content based on the laws and regulations of that country. This makes it possible to generate highly relevant documents based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI perform the generation of highly relevant content.

[0042] The generation unit can analyze a user's social media activity and generate relevant content when generating documents. For example, the generation unit can generate documents containing relevant topics based on information shared by the user on social media. For example, the generation unit can generate documents by incorporating information about accounts followed by the user on social media. For example, the generation unit can generate documents containing relevant content based on information about groups and communities the user participates in on social media. This enables the generation of highly relevant documents based on the user's social media activity. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input data on the user's social media activity into a generation AI and have the generation AI perform the generation of relevant content.

[0043] The classification unit can improve the accuracy of document classification by considering the interrelationships of ideas. For example, the classification unit can analyze the co-occurrence relationships of keywords within documents and classify highly relevant documents into the same category. For example, the classification unit can group related documents by considering citation relationships between documents. For example, the classification unit can analyze the content of documents and classify them by theme or topic. This enables highly accurate classification that considers the interrelationships of ideas. Some or all of the above processes in the classification unit may be performed using generative AI, or they may be performed without generative AI. For example, the classification unit can input the content of documents into generative AI and have the generative AI perform an analysis of the interrelationships of ideas.

[0044] 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 or department. For example, the classification unit can classify documents based on the submitter's field of expertise or skill set. For example, the classification unit can classify documents by referring to the submitter's past submission history. This enables appropriate classification based on the submitter's attribute information. Some or all of the above processing in the classification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the classification unit can input the document submitter's attribute information into a generative AI and have the generative AI perform the classification.

[0045] The classification unit can classify documents while considering their geographical distribution. For example, the classification unit can classify documents based on where they were created. For example, the classification unit can classify documents based on the region or country to which the content relates. For example, the classification unit can classify documents while considering the location of the document submitter. This enables appropriate classification based on geographical distribution. Some or all of the above processing in the classification unit may be performed using or without a generative AI. For example, the classification unit can input data on the geographical distribution of documents into a generative AI and have the generative AI perform the classification.

[0046] The classification unit can improve the accuracy of its classification by referring to related literature when classifying documents. For example, the classification unit can classify documents by referring to related academic papers and technical literature. For example, the classification unit can classify documents based on related industry reports and market research. For example, the classification unit can classify documents by referring to related patent documents. This improves the accuracy of the classification by referring to related literature. Some or all of the above processing in the classification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the classification unit can input data of related literature into a generative AI and have the generative AI perform the classification accuracy improvement.

[0047] The update unit can select the optimal update method by referring to past update history when updating a document. For example, the update unit can analyze past update history and update the document in a similar manner. For example, the update unit can select and apply the most effective update method from past update history. For example, the update unit can adjust the frequency and timing of updates based on past update history. This enables optimal document updates based on past update history. Some or all of the above processes in the update unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the update unit can input data from past update history into a generation AI and have the generation AI select the optimal update method.

[0048] The update unit can automatically generate updated content based on changes in laws and policies when a document is updated. For example, if a new law comes into effect, the update unit can automatically update the document based on its content. For example, if a company's policy changes, the update unit can automatically update the document based on its content. For example, if industry regulations change, the update unit can automatically update the document based on its content. This enables automatic document updates based on changes in laws and policies. Some or all of the above-described processes in the update unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the update unit can input the changes in laws and policies into a generation AI and have the generation AI generate the updated content.

[0049] The update unit can prioritize updating highly relevant content when updating documents, taking into account the user's geographical location. For example, if the user is in a specific region, the update unit can prioritize updating information related to that region. For example, if the user is on a business trip, the update unit can prioritize updating data and information related to the destination. For example, if the user is overseas, the update unit can prioritize updating content based on the laws and regulations of that country. This enables highly relevant document updates based on the user's geographical location. Some or all of the above processing in the update unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the update unit can input the user's geographical location information into a generation AI and have the generation AI perform the update of highly relevant content.

[0050] The update unit can analyze the user's social media activity and update relevant content when updating a document. For example, the update unit can update a document to include relevant topics based on information shared by the user on social media. For example, the update unit can update a document by incorporating information about accounts the user follows on social media. For example, the update unit can update a document to include relevant content based on information about groups and communities the user participates in on social media. This enables highly relevant document updates based on the user's social media activity. Some or all of the above processing in the update unit may be performed using a generative AI, or not. For example, the update unit can input data on the user's social media activity into a generative AI and have the generative AI perform the update of relevant content.

[0051] The search unit can apply the most suitable search algorithm by referring to past search history during a search. For example, the search unit can prioritize displaying relevant search results based on keywords the user has previously searched for. For example, the search unit can prioritize displaying the most frequently searched content from the user's past search history. For example, the search unit can analyze the user's past search history and provide search results by applying the most suitable search algorithm. This allows the search unit to provide the best possible search results based on past search history. Some or all of the above-described processes in the search unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the search unit can input past search history data into a generative AI and have the generative AI execute the application of the most suitable search algorithm.

[0052] The search unit can apply different search methods to each document category during a search. For example, the search unit can apply a search method specialized for procedures and operating instructions to documents in the manual category. For example, the search unit can apply a search method specialized for data and analysis results to documents in the report category. For example, the search unit can apply a search method specialized for meeting content and decisions to documents in the meeting minutes category. This allows the search unit to apply the most appropriate search method according to the document category. Some or all of the above processing in the search unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the search unit can input document category information into a generative AI and have the generative AI execute the application of different search methods.

[0053] The search unit can adjust the display order of search results based on the submission date of the documents during a search. For example, the search unit can prioritize displaying the most recent documents. For example, the search unit can prioritize displaying documents within a period specified by the user. For example, the search unit can postpone the display of older documents. This allows the search results to be provided in an appropriate display order based on the submission date of the documents. Some or all of the above processing in the search unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the search unit can input data on the submission date of documents into a generation AI and have the generation AI perform the adjustment of the display order.

[0054] The search unit can provide search results by referencing relevant market data during a search. For example, the search unit can provide search results based on relevant market reports and research data. For example, the search unit can provide search results based on relevant industry news and trend information. For example, the search unit can provide search results based on relevant competitor information. This enables the provision of appropriate search results based on relevant market data. Some or all of the above processing in the search unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the search unit can input relevant market data into a generation AI and have the generation AI perform the task of providing search results.

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

[0056] The generation unit can analyze the user's past document creation history and select the optimal generation method when generating a document. For example, the generation unit can analyze the style of documents the user has created in the past and generate a new document in a similar style. For example, the generation unit can select the optimal template based on templates the user has used in the past and generate a document. For example, the generation unit can incorporate phrases and expressions that the user has frequently used in the past to generate a document. This makes it possible to generate optimal documents based on the user's past history. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's past document creation history into a generation AI and have the generation AI select the optimal generation method.

[0057] The generation unit can customize the content of documents based on the user's current projects and areas of interest during document generation. For example, the generation unit can prioritize information related to the user's current projects when generating documents. For example, the generation unit can generate documents that include relevant topics and data based on the user's areas of interest. For example, the generation unit can customize the content of documents based on keywords specified by the user. This enables document generation tailored to the user's projects and areas of interest. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input information about the user's current projects and areas of interest into the generation AI and have the generation AI perform document customization.

[0058] The generation unit can prioritize generating highly relevant content by considering the user's geographical location information when generating documents. For example, if the user is in a specific region, the generation unit can prioritize incorporating information related to that region when generating documents. For example, if the user is on a business trip, the generation unit can generate documents that include data and information related to the destination. For example, if the user is overseas, the generation unit can generate documents that include content based on the laws and regulations of that country. This makes it possible to generate highly relevant documents based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI perform the generation of highly relevant content.

[0059] The generation unit can analyze a user's social media activity and generate relevant content when generating documents. For example, the generation unit can generate documents containing relevant topics based on information shared by the user on social media. For example, the generation unit can generate documents by incorporating information about accounts followed by the user on social media. For example, the generation unit can generate documents containing relevant content based on information about groups and communities the user participates in on social media. This enables the generation of highly relevant documents based on the user's social media activity. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input data on the user's social media activity into a generation AI and have the generation AI perform the generation of relevant content.

[0060] The classification unit can improve the accuracy of document classification by considering the interrelationships of ideas. For example, the classification unit can analyze the co-occurrence relationships of keywords within documents and classify highly relevant documents into the same category. For example, the classification unit can group related documents by considering citation relationships between documents. For example, the classification unit can analyze the content of documents and classify them by theme or topic. This enables highly accurate classification that considers the interrelationships of ideas. Some or all of the above processes in the classification unit may be performed using generative AI, or they may be performed without generative AI. For example, the classification unit can input the content of documents into generative AI and have the generative AI perform an analysis of the interrelationships of ideas.

[0061] 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 or department. For example, the classification unit can classify documents based on the submitter's field of expertise or skill set. For example, the classification unit can classify documents by referring to the submitter's past submission history. This enables appropriate classification based on the submitter's attribute information. Some or all of the above processing in the classification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the classification unit can input the document submitter's attribute information into a generative AI and have the generative AI perform the classification.

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

[0063] Step 1: The generation unit automatically generates documents. For example, it automatically generates meeting minutes, periodic reports, and standard operating procedures (SOPs). When a user inputs the content of a meeting, necessary data, or procedure manual, the unit analyzes the input and automatically generates the document. The processing in the generation unit may be performed using a generation AI or not. Step 2: The classification unit classifies and tags the documents generated by the generation unit. For example, tags can be assigned based on the project name, the name of the person in charge, and the content of the document. The classification unit can automatically tag the generated documents with the project name and the name of the person in charge, or it can analyze the content of the document to extract relevant keywords and assign tags to them. The processing in the classification unit may or may not be performed using AI. Step 3: The update unit updates the documents classified by the classification unit. For example, it revises documents in response to changes in laws and regulations, changes in company policies, and changes in industry regulations. When new laws, company policies, or industry regulations change, the documents can be automatically revised based on the content. The processing in the update unit may or may not be performed using AI. Step 4: The search unit searches for documents updated by the update unit. For example, the user can search for documents in the form of a question. If the user asks, "What is the latest sales report?", the search unit can return relevant documents. The search unit can provide search results using natural language processing technology, analyze the user's question, and search for and return relevant documents. When displaying search results, highly relevant documents can be displayed preferentially. The processing in the search unit may be performed using AI or not.

[0064] (Example of form 2) The document management system according to an embodiment of the present invention is a system in which an AI agent automatically generates and manages various internal company documents (manuals, reports, meeting minutes, etc.). First, the AI ​​agent automatically generates documents based on the input content. Next, the generated documents are automatically classified and tagged. Furthermore, existing documents are automatically updated to maintain their up-to-date status. Finally, an advanced search function is provided that allows users to search for documents in the form of questions, utilizing natural language processing technology. This mechanism reduces the effort required for document creation and updating, eliminates the complexities of document management, and enables efficient access to necessary information. For example, the AI ​​agent automatically generates documents based on the input content. This includes, for example, meeting minutes, automatic report generation, and automatic creation of standard operating procedures (SOPs). When a user inputs the content of a meeting, the AI ​​agent analyzes the content and automatically generates meeting minutes. In addition, periodic report generation is automated, and reports are generated simply by the user inputting the necessary data. Next, the generated documents are automatically classified and tagged. For example, documents are automatically tagged by project name or assigned person's name, making it easy to search for related documents. This allows for efficient document organization and quick retrieval of necessary information. Furthermore, existing documents are automatically updated. For instance, documents are automatically revised to reflect changes in laws and policies, ensuring they are always up-to-date. This eliminates the need for manual updates and improves document accuracy. Finally, advanced search functionality is provided, utilizing natural language processing technology to allow users to search documents in question format. For example, asking "Where is the latest sales report?" will return relevant documents. This feature allows users to quickly obtain necessary information, improving work efficiency. This system reduces the effort required for document creation and updating, eliminates the complexities of document management, and enables efficient access to necessary information.For example, a centralized management system accessible to all employees enables unified document management, leading to increased operational efficiency. Furthermore, in today's world where paperless operations and the importance of digital documents are growing, its adoption by many companies is anticipated. This allows the document management system to efficiently handle the automatic generation, classification, updating, and searching of documents.

[0065] The document management system according to this embodiment comprises a generation unit, a classification unit, an update unit, and a search unit. The generation unit automatically generates documents. For example, the generation unit automatically generates meeting minutes. For example, when a user inputs the contents of a meeting, the generation unit can analyze the contents and automatically generate meeting minutes. For example, the generation unit automatically generates periodic reports. For example, when a user inputs the necessary data, the generation unit can generate reports. For example, the generation unit automatically generates standard operating procedures (SOPs). For example, when a user inputs the contents of an SOP, the generation unit can analyze the contents and automatically generate an SOP. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without using a generation AI. The classification unit classifies and tags the documents generated by the generation unit. For example, the classification unit tags documents by project name. For example, the classification unit can automatically tag generated documents with project names. For example, the classification unit tags documents by the name of the person in charge. The classification unit can, for example, automatically tag generated documents with the names of the responsible persons. The classification unit can also, for example, tag documents based on their content. The classification unit can, for example, analyze the content of documents, extract relevant keywords, and tag them. Some or all of the above processes in the classification unit may be performed using AI or not. The update unit updates documents classified by the classification unit. The update unit modifies documents in accordance with changes in laws and regulations. The update unit can, for example, automatically modify documents based on the content of new laws and regulations when they come into effect. The update unit modifies documents in accordance with changes in company policies. The update unit can, for example, automatically modify documents based on the content of changes in company policies when they are changed. The update unit modifies documents in accordance with changes in industry regulations. The update unit can, for example, automatically modify documents based on the content of changes in industry regulations when they are changed.Some or all of the processing described above in the update unit may be performed using AI or not. The search unit searches for documents updated by the update unit. The search unit allows users to search for documents in the form of questions, for example. For example, if a user asks, "What is the latest sales report?", the search unit can return relevant documents. For example, the search unit provides search results by utilizing natural language processing technology. For example, the search unit can analyze a user's question, search for relevant documents, and return them. For example, when displaying search results, the search unit can prioritize displaying documents that are highly relevant. Some or all of the processing described above in the search unit may be performed using AI or not. As a result, the document management system according to the embodiment can efficiently perform automatic generation, classification, updating, and searching of documents.

[0066] The generation unit automatically generates documents. For example, the generation unit automatically generates meeting minutes. For example, when a user inputs the content of a meeting, the generation unit can analyze the content and automatically generate the minutes. For example, the generation unit automatically generates periodic reports. For example, when a user inputs the necessary data, the generation unit can generate a report. For example, the generation unit automatically generates standard operating procedures (SOPs). For example, when a user inputs the content of an SOP, the generation unit can analyze the content and automatically generate the SOP. Some or all of the above processes in the generation unit may be performed using generation AI, or they may not be performed using generation AI. The generation unit utilizes natural language processing technology to analyze text data input by the user and generates documents in an appropriate format. For example, when generating meeting minutes, it automatically extracts meeting participants, agenda items, and content of discussions, and organizes them chronologically. This allows the user to easily understand the content of the meeting. When generating periodic reports, it automatically creates graphs and tables based on the data input by the user, generating visually easy-to-understand reports. This allows users to quickly review data analysis results. In generating Standard Operating Procedures (SOPs), the system analyzes user-entered procedures and organizes them according to the appropriate procedure format. This enables users to easily create and share procedures. The generation unit utilizes generation AI to analyze user input more advancedly and generate more accurate documents. For example, the generation AI can learn from past document data and propose the optimal document structure based on user input. This allows users to efficiently generate high-quality documents.

[0067] The classification unit classifies and tags the documents generated by the generation unit. For example, the classification unit tags documents by project name. For example, the classification unit can automatically tag generated documents by project name. For example, the classification unit tags documents by the name of the person in charge. For example, the classification unit can automatically tag generated documents by the name of the person in charge. The classification unit can also tag documents based on their content. For example, the classification unit can analyze the content of documents, extract relevant keywords, and tag them. Some or all of the above processes in the classification unit may be performed using AI or not. The classification unit utilizes natural language processing technology to analyze the content of documents and automatically assign appropriate tags. For example, when extracting project names or names of people in charge, it detects specific keywords or phrases within the document and tags them accordingly. This allows users to easily search and manage documents. Furthermore, the classification unit can extract relevant keywords based on the content of documents and tag them. For example, in meeting minutes, topics and important statements are extracted as keywords, and tags are assigned based on these keywords. This allows users to quickly find documents related to specific topics. The classification unit can use AI to analyze document content more advancedly and perform more accurate tagging. For example, the AI ​​can learn from past document data and suggest the most suitable tags according to the user's needs. This allows users to classify and manage documents efficiently.

[0068] The update unit updates documents classified by the classification unit. The update unit, for example, revises documents in response to changes in laws and regulations. For example, if new laws and regulations come into effect, the update unit can automatically revise documents based on their content. The update unit, for example, revises documents in response to changes in company policies. For example, if company policies change, the update unit can automatically revise documents based on their content. The update unit, for example, revises documents in response to changes in industry regulations. For example, if industry regulations change, the update unit can automatically revise documents based on their content. Some or all of the above processes in the update unit may be performed using AI or not. The update unit constantly monitors for the latest information and automatically makes necessary corrections to respond quickly to changes in laws, company policies, and industry regulations. For example, if new laws and regulations come into effect, it analyzes their content and automatically updates the relevant documents. This ensures that users always have access to documents based on the latest information. Similarly, if company policies change, it analyzes their content and automatically updates the relevant documents. This ensures that users have access to documents based on the company's latest policies. Similarly, if industry regulations change, the system analyzes the changes and automatically updates the relevant documents. This ensures users have access to documents that are up-to-date with industry regulations. The update function utilizes AI to respond quickly and accurately to changes in laws, company policies, and industry regulations. For example, the AI ​​can learn from historical document data and suggest the best correction methods based on the changes. This allows users to efficiently update and manage their documents.

[0069] The search unit searches for documents that have been updated by the update unit. The search unit allows users to search for documents in the form of questions, for example. For example, if a user asks, "What is the latest sales report?", the search unit can return relevant documents. The search unit provides search results using, for example, natural language processing technology. For example, the search unit can analyze a user's question and search for and return relevant documents. For example, when displaying search results, the search unit can prioritize displaying documents that are highly relevant. Some or all of the above processing in the search unit may be performed using AI or not. The search unit uses natural language processing technology to analyze user questions and provide appropriate search results. For example, if a user asks, "What is the latest sales report?", the search unit analyzes the question and searches for and returns relevant documents. This allows the user to quickly obtain the necessary information. Furthermore, when displaying search results, the search unit prioritizes displaying documents that are highly relevant. This allows the user to quickly find the most important information. By using AI, the search unit can analyze user questions more advancedly and provide more accurate search results. For example, AI can learn from past search history and user behavior patterns to suggest optimal search results tailored to the user's needs. This allows users to efficiently search for and utilize the information they need.

[0070] The generation unit can generate meeting minutes, automatic reports, and automatic standard operating procedures. For example, the generation unit can automatically generate meeting minutes. For example, when a user inputs the content of a meeting, the generation unit can analyze the content and automatically generate the minutes. For example, the generation unit can automatically generate periodic reports. For example, when a user inputs the necessary data, the generation unit can generate reports. For example, the generation unit can automatically generate standard operating procedures (SOPs). For example, when a user inputs the content of an SOP, the generation unit can analyze the content and automatically generate the SOP. This makes it possible to automatically create meeting minutes, reports, and standard operating procedures. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the content of a meeting into a generation AI and have the generation AI generate the meeting minutes.

[0071] The classification unit can tag documents by project name or assigned name. For example, the classification unit can tag documents by project name. For example, the classification unit can automatically tag generated documents with project name. For example, the classification unit can tag documents by assigned name. For example, the classification unit can automatically tag generated documents with assigned name. This allows for efficient document organization. Some or all of the above processes in the classification unit may be performed using AI or not. For example, the classification unit can input generated documents into AI and have the AI ​​perform the tagging.

[0072] The update unit can revise documents in response to changes in laws and policies. For example, the update unit can revise documents in response to changes in laws. For example, the update unit can automatically revise documents based on the content of new laws when they come into effect. The update unit can revise documents in response to changes in company policies. For example, the update unit can automatically revise documents based on the content of changes to company policies. This improves the accuracy of the documents. Some or all of the above processes in the update unit may be performed using AI or not. For example, the update unit can input the changes in laws into AI and have AI perform the document revisions.

[0073] The search unit can enable users to search for documents in the form of questions. For example, if a user asks, "What is the latest sales report?", the search unit can return relevant documents. This allows users to quickly obtain the information they need. Some or all of the above processing in the search unit may or may not be performed using AI. For example, the search unit can input a user's question into the AI ​​and have the AI ​​perform a search for relevant documents.

[0074] The search unit can provide search results by utilizing natural language processing technology. For example, the search unit can provide search results by utilizing natural language processing technology. For example, the search unit can analyze a user's question and search for and return relevant documents. This provides advanced search functionality. Some or all of the above-described processes in the search unit may be performed using AI or not. For example, the search unit may use the user's AI or not. For example, the search unit can input a user's question into the AI ​​and have the AI ​​perform a search for relevant documents.

[0075] The generation unit can estimate the user's emotions and adjust the timing of document generation based on the estimated emotions. For example, if the user is stressed, the generation AI can delay document generation and start it when the user is relaxed. If the user is in a hurry, the generation AI can generate the document quickly and provide it immediately. If the user is focused, the generation AI can generate the document continuously without interruption. This allows for document generation at an appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the timing of document generation.

[0076] The generation unit can analyze the user's past document creation history and select the optimal generation method when generating a document. For example, the generation unit can analyze the style of documents the user has created in the past and generate a new document in a similar style. For example, the generation unit can select the optimal template based on templates the user has used in the past and generate a document. For example, the generation unit can incorporate phrases and expressions that the user has frequently used in the past to generate a document. This makes it possible to generate optimal documents based on the user's past history. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's past document creation history into a generation AI and have the generation AI select the optimal generation method.

[0077] The generation unit can customize the content of documents based on the user's current projects and areas of interest during document generation. For example, the generation unit can prioritize information related to the user's current projects when generating documents. For example, the generation unit can generate documents that include relevant topics and data based on the user's areas of interest. For example, the generation unit can customize the content of documents based on keywords specified by the user. This enables document generation tailored to the user's projects and areas of interest. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input information about the user's current projects and areas of interest into the generation AI and have the generation AI perform document customization.

[0078] The generation unit can estimate the user's emotions and determine the priority of documents to generate based on the estimated emotions. For example, if the user is stressed, the generation AI can postpone the generation of less important documents. For example, if the user is relaxed, the generation AI can prioritize the generation of more important documents. For example, if the user is in a hurry, the generation AI can prioritize the generation of urgent documents. This allows documents to be generated with priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI determine the document priorities.

[0079] The generation unit can prioritize generating highly relevant content by considering the user's geographical location information when generating documents. For example, if the user is in a specific region, the generation unit can prioritize incorporating information related to that region when generating documents. For example, if the user is on a business trip, the generation unit can generate documents that include data and information related to the destination. For example, if the user is overseas, the generation unit can generate documents that include content based on the laws and regulations of that country. This makes it possible to generate highly relevant documents based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI perform the generation of highly relevant content.

[0080] The generation unit can analyze a user's social media activity and generate relevant content when generating documents. For example, the generation unit can generate documents containing relevant topics based on information shared by the user on social media. For example, the generation unit can generate documents by incorporating information about accounts followed by the user on social media. For example, the generation unit can generate documents containing relevant content based on information about groups and communities the user participates in on social media. This enables the generation of highly relevant documents based on the user's social media activity. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input data on the user's social media activity into a generation AI and have the generation AI perform the generation of relevant content.

[0081] The classification unit can estimate the user's emotions and adjust the document classification criteria based on the estimated user emotions. For example, if the user is stressed, the classification unit can apply simple classification criteria to simplify document classification. For example, if the user is relaxed, the classification unit can apply detailed classification criteria to further classify documents. For example, if the user is in a hurry, the classification unit can prioritize classifying high-priority documents. This allows documents to be classified using appropriate classification criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of classification criteria.

[0082] The classification unit can improve the accuracy of document classification by considering the interrelationships of ideas. For example, the classification unit can analyze the co-occurrence relationships of keywords within documents and classify highly relevant documents into the same category. For example, the classification unit can group related documents by considering citation relationships between documents. For example, the classification unit can analyze the content of documents and classify them by theme or topic. This enables highly accurate classification that considers the interrelationships of ideas. Some or all of the above processes in the classification unit may be performed using generative AI, or they may be performed without generative AI. For example, the classification unit can input the content of documents into generative AI and have the generative AI perform an analysis of the interrelationships of ideas.

[0083] 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 or department. For example, the classification unit can classify documents based on the submitter's field of expertise or skill set. For example, the classification unit can classify documents by referring to the submitter's past submission history. This enables appropriate classification based on the submitter's attribute information. Some or all of the above processing in the classification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the classification unit can input the document submitter's attribute information into a generative AI and have the generative AI perform the classification.

[0084] The classification unit can estimate the user's emotions and adjust the display order of documents classified based on the estimated user emotions. For example, if the user is stressed, the classification unit can display less important documents later. For example, if the user is relaxed, the classification unit can prioritize displaying documents containing detailed information. For example, if the user is in a hurry, the classification unit can prioritize displaying urgent documents. This allows documents to be displayed in an appropriate order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display order.

[0085] The classification unit can classify documents while considering their geographical distribution. For example, the classification unit can classify documents based on where they were created. For example, the classification unit can classify documents based on the region or country to which the content relates. For example, the classification unit can classify documents while considering the location of the document submitter. This enables appropriate classification based on geographical distribution. Some or all of the above processing in the classification unit may be performed using or without a generative AI. For example, the classification unit can input data on the geographical distribution of documents into a generative AI and have the generative AI perform the classification.

[0086] The classification unit can improve the accuracy of its classification by referring to related literature when classifying documents. For example, the classification unit can classify documents by referring to related academic papers and technical literature. For example, the classification unit can classify documents based on related industry reports and market research. For example, the classification unit can classify documents by referring to related patent documents. This improves the accuracy of the classification by referring to related literature. Some or all of the above processing in the classification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the classification unit can input data of related literature into a generative AI and have the generative AI perform the classification accuracy improvement.

[0087] The update unit can estimate the user's emotions and adjust the timing of document updates based on the estimated emotions. For example, if the user is stressed, the update unit can delay the update and perform the update when the user is relaxed. For example, if the user is in a hurry, the update unit can update the document immediately. For example, if the user is focused, the update unit can perform continuous document updates. This allows the document to be updated at an appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the update timing.

[0088] The update unit can select the optimal update method by referring to past update history when updating a document. For example, the update unit can analyze past update history and update the document in a similar manner. For example, the update unit can select and apply the most effective update method from past update history. For example, the update unit can adjust the frequency and timing of updates based on past update history. This enables optimal document updates based on past update history. Some or all of the above processes in the update unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the update unit can input data from past update history into a generation AI and have the generation AI select the optimal update method.

[0089] The update unit can automatically generate updated content based on changes in laws and policies when a document is updated. For example, if a new law comes into effect, the update unit can automatically update the document based on its content. For example, if a company's policy changes, the update unit can automatically update the document based on its content. For example, if industry regulations change, the update unit can automatically update the document based on its content. This enables automatic document updates based on changes in laws and policies. Some or all of the above-described processes in the update unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the update unit can input the changes in laws and policies into a generation AI and have the generation AI generate the updated content.

[0090] The update unit can estimate the user's emotions and determine the priority of documents to update based on the estimated emotions. For example, if the user is stressed, the update unit may postpone updating less important documents. For example, if the user is relaxed, the update unit may prioritize updating more important documents. For example, if the user is in a hurry, the update unit may prioritize updating urgent documents. This allows documents to be updated with priorities that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can input user emotion data into a generative AI and have the generative AI determine the priority of documents to update.

[0091] The update unit can prioritize updating highly relevant content when updating documents, taking into account the user's geographical location. For example, if the user is in a specific region, the update unit can prioritize updating information related to that region. For example, if the user is on a business trip, the update unit can prioritize updating data and information related to the destination. For example, if the user is overseas, the update unit can prioritize updating content based on the laws and regulations of that country. This enables highly relevant document updates based on the user's geographical location. Some or all of the above processing in the update unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the update unit can input the user's geographical location information into a generation AI and have the generation AI perform the update of highly relevant content.

[0092] The update unit can analyze the user's social media activity and update relevant content when updating a document. For example, the update unit can update a document to include relevant topics based on information shared by the user on social media. For example, the update unit can update a document by incorporating information about accounts the user follows on social media. For example, the update unit can update a document to include relevant content based on information about groups and communities the user participates in on social media. This enables highly relevant document updates based on the user's social media activity. Some or all of the above processing in the update unit may be performed using a generative AI, or not. For example, the update unit can input data on the user's social media activity into a generative AI and have the generative AI perform the update of relevant content.

[0093] The search unit can estimate the user's emotions and adjust the display method of search results based on the estimated emotions. For example, if the user is stressed, the search unit can provide a simple and highly visible display method. For example, if the user is relaxed, the search unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the search unit can provide a display method that gets straight to the point. This allows the search results to be provided in an appropriate display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI or not. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0094] The search unit can apply the most suitable search algorithm by referring to past search history during a search. For example, the search unit can prioritize displaying relevant search results based on keywords the user has previously searched for. For example, the search unit can prioritize displaying the most frequently searched content from the user's past search history. For example, the search unit can analyze the user's past search history and provide search results by applying the most suitable search algorithm. This allows the search unit to provide the best possible search results based on past search history. Some or all of the above-described processes in the search unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the search unit can input past search history data into a generative AI and have the generative AI execute the application of the most suitable search algorithm.

[0095] The search unit can apply different search methods to each document category during a search. For example, the search unit can apply a search method specialized for procedures and operating instructions to documents in the manual category. For example, the search unit can apply a search method specialized for data and analysis results to documents in the report category. For example, the search unit can apply a search method specialized for meeting content and decisions to documents in the meeting minutes category. This allows the search unit to apply the most appropriate search method according to the document category. Some or all of the above processing in the search unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the search unit can input document category information into a generative AI and have the generative AI execute the application of different search methods.

[0096] The search unit can estimate the user's emotions and determine the priority of search results based on the estimated emotions. For example, if the user is stressed, the search unit may display less important search results later. If the user is relaxed, the search unit may prioritize displaying search results containing detailed information. If the user is in a hurry, the search unit may prioritize displaying urgent search results. This allows the search results to be provided with priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using AI or not. For example, the search unit can input user emotion data into a generative AI and have the generative AI determine the priority of search results.

[0097] The search unit can adjust the display order of search results based on the submission date of the documents during a search. For example, the search unit can prioritize displaying the most recent documents. For example, the search unit can prioritize displaying documents within a period specified by the user. For example, the search unit can postpone the display of older documents. This allows the search results to be provided in an appropriate display order based on the submission date of the documents. Some or all of the above processing in the search unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the search unit can input data on the submission date of documents into a generation AI and have the generation AI perform the adjustment of the display order.

[0098] The search unit can provide search results by referencing relevant market data during a search. For example, the search unit can provide search results based on relevant market reports and research data. For example, the search unit can provide search results based on relevant industry news and trend information. For example, the search unit can provide search results based on relevant competitor information. This enables the provision of appropriate search results based on relevant market data. Some or all of the above processing in the search unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the search unit can input relevant market data into a generation AI and have the generation AI perform the task of providing search results.

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

[0100] The generation unit can estimate the user's emotions and adjust the timing of document generation based on the estimated emotions. For example, if the user is stressed, the generation AI can delay document generation and start it when the user is relaxed. If the user is in a hurry, the generation AI can generate the document quickly and provide it immediately. If the user is focused, the generation AI can generate the document continuously without interruption. This allows for document generation at an appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the timing of document generation.

[0101] The generation unit can analyze the user's past document creation history and select the optimal generation method when generating a document. For example, the generation unit can analyze the style of documents the user has created in the past and generate a new document in a similar style. For example, the generation unit can select the optimal template based on templates the user has used in the past and generate a document. For example, the generation unit can incorporate phrases and expressions that the user has frequently used in the past to generate a document. This makes it possible to generate optimal documents based on the user's past history. Some or all of the above processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the user's past document creation history into a generation AI and have the generation AI select the optimal generation method.

[0102] The generation unit can customize the content of documents based on the user's current projects and areas of interest during document generation. For example, the generation unit can prioritize information related to the user's current projects when generating documents. For example, the generation unit can generate documents that include relevant topics and data based on the user's areas of interest. For example, the generation unit can customize the content of documents based on keywords specified by the user. This enables document generation tailored to the user's projects and areas of interest. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input information about the user's current projects and areas of interest into the generation AI and have the generation AI perform document customization.

[0103] The generation unit can estimate the user's emotions and determine the priority of documents to generate based on the estimated emotions. For example, if the user is stressed, the generation AI can postpone the generation of less important documents. For example, if the user is relaxed, the generation AI can prioritize the generation of more important documents. For example, if the user is in a hurry, the generation AI can prioritize the generation of urgent documents. This allows documents to be generated with priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI determine the document priorities.

[0104] The generation unit can prioritize generating highly relevant content by considering the user's geographical location information when generating documents. For example, if the user is in a specific region, the generation unit can prioritize incorporating information related to that region when generating documents. For example, if the user is on a business trip, the generation unit can generate documents that include data and information related to the destination. For example, if the user is overseas, the generation unit can generate documents that include content based on the laws and regulations of that country. This makes it possible to generate highly relevant documents based on the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI perform the generation of highly relevant content.

[0105] The generation unit can analyze a user's social media activity and generate relevant content when generating documents. For example, the generation unit can generate documents containing relevant topics based on information shared by the user on social media. For example, the generation unit can generate documents by incorporating information about accounts followed by the user on social media. For example, the generation unit can generate documents containing relevant content based on information about groups and communities the user participates in on social media. This enables the generation of highly relevant documents based on the user's social media activity. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input data on the user's social media activity into a generation AI and have the generation AI perform the generation of relevant content.

[0106] The classification unit can estimate the user's emotions and adjust the document classification criteria based on the estimated user emotions. For example, if the user is stressed, the classification unit can apply simple classification criteria to simplify document classification. For example, if the user is relaxed, the classification unit can apply detailed classification criteria to further classify documents. For example, if the user is in a hurry, the classification unit can prioritize classifying high-priority documents. This allows documents to be classified using appropriate classification criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of classification criteria.

[0107] The classification unit can improve the accuracy of document classification by considering the interrelationships of ideas. For example, the classification unit can analyze the co-occurrence relationships of keywords within documents and classify highly relevant documents into the same category. For example, the classification unit can group related documents by considering citation relationships between documents. For example, the classification unit can analyze the content of documents and classify them by theme or topic. This enables highly accurate classification that considers the interrelationships of ideas. Some or all of the above processes in the classification unit may be performed using generative AI, or they may be performed without generative AI. For example, the classification unit can input the content of documents into generative AI and have the generative AI perform an analysis of the interrelationships of ideas.

[0108] 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 or department. For example, the classification unit can classify documents based on the submitter's field of expertise or skill set. For example, the classification unit can classify documents by referring to the submitter's past submission history. This enables appropriate classification based on the submitter's attribute information. Some or all of the above processing in the classification unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the classification unit can input the document submitter's attribute information into a generative AI and have the generative AI perform the classification.

[0109] The classification unit can estimate the user's emotions and adjust the display order of documents classified based on the estimated user emotions. For example, if the user is stressed, the classification unit can display less important documents later. For example, if the user is relaxed, the classification unit can prioritize displaying documents containing detailed information. For example, if the user is in a hurry, the classification unit can prioritize displaying urgent documents. This allows documents to be displayed in an appropriate order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI or not. For example, the classification unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display order.

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

[0111] Step 1: The generation unit automatically generates documents. For example, it automatically generates meeting minutes, periodic reports, and standard operating procedures (SOPs). When a user inputs the content of a meeting, necessary data, or procedure manual, the unit analyzes the input and automatically generates the document. The processing in the generation unit may be performed using a generation AI or not. Step 2: The classification unit classifies and tags the documents generated by the generation unit. For example, tags can be assigned based on the project name, the name of the person in charge, and the content of the document. The classification unit can automatically tag the generated documents with the project name and the name of the person in charge, or it can analyze the content of the document to extract relevant keywords and assign tags to them. The processing in the classification unit may or may not be performed using AI. Step 3: The update unit updates the documents classified by the classification unit. For example, it revises documents in response to changes in laws and regulations, changes in company policies, and changes in industry regulations. When new laws, company policies, or industry regulations change, the documents can be automatically revised based on the content. The processing in the update unit may or may not be performed using AI. Step 4: The search unit searches for documents updated by the update unit. For example, the user can search for documents in the form of a question. If the user asks, "What is the latest sales report?", the search unit can return relevant documents. The search unit can provide search results using natural language processing technology, analyze the user's question, and search for and return relevant documents. When displaying search results, highly relevant documents can be displayed preferentially. The processing in the search unit may be performed using AI or not.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the generation unit, classification unit, update unit, and search unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14, which analyzes the content when a user inputs the contents of a meeting and automatically generates meeting minutes. The classification unit is implemented by the identification processing unit 290 of the data processing device 12, which automatically tags the generated documents with project names and the names of the people in charge. The update unit is implemented by the identification processing unit 290 of the data processing device 12, which automatically modifies documents in accordance with changes in laws and policies. The search unit is implemented by the control unit 46A of the smart device 14, which allows the user to search for documents in a question format. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

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

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the generation unit, classification unit, update unit, and search unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214, which analyzes the content when a user inputs the contents of a meeting and automatically generates meeting minutes. The classification unit is implemented by the identification processing unit 290 of the data processing device 12, which automatically tags the generated documents with project names and the names of the people in charge. The update unit is implemented by the identification processing unit 290 of the data processing device 12, which automatically modifies documents in accordance with changes in laws and policies. The search unit is implemented by the control unit 46A of the smart glasses 214, which allows the user to search for documents in a question format. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

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

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the generation unit, classification unit, update unit, and search unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314, which analyzes the content when a user inputs the contents of a meeting and automatically generates meeting minutes. The classification unit is implemented by the identification processing unit 290 of the data processing unit 12, which automatically tags the generated documents with project names and the names of the people in charge. The update unit is implemented by the identification processing unit 290 of the data processing unit 12, which automatically modifies documents in accordance with changes in laws and policies. The search unit is implemented by the control unit 46A of the headset terminal 314, which allows the user to search for documents in a question format. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

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

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the generation unit, classification unit, update unit, and search unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414, which analyzes the content of a meeting input by the user and automatically generates meeting minutes. The classification unit is implemented by the identification processing unit 290 of the data processing unit 12, which automatically tags the generated documents with project names and names of the people in charge. The update unit is implemented by the identification processing unit 290 of the data processing unit 12, which automatically modifies documents in accordance with changes in laws and policies. The search unit is implemented by the control unit 46A of the robot 414, which allows the user to search for documents in a question format. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0165] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0175] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A generation unit that automatically generates documents, A classification unit that classifies and tags the documents generated by the generation unit, An update unit that updates documents classified by the aforementioned classification unit, The system includes a search unit that searches for documents updated by the update unit. A system characterized by the following features. (Note 2) The generating unit is This system generates meeting minutes, automatic reports, and automatic creation of standard operating procedures. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned classification unit is Tagging documents by project name or the name of the person in charge. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned update unit is Revise documents in accordance with changes in laws and policies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned search unit, Allows users to search documents using a question-based format. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned search unit, Providing search results using natural language processing technology The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is It estimates the user's emotions and adjusts the timing of document generation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is When generating a document, the system analyzes the user's past document creation history and selects the optimal generation method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating documents, customize the content based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and determines the priority of documents to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating documents, the system prioritizes generating highly relevant content by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During document generation, the system analyzes the user's social media activity and generates relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned classification unit is It estimates user sentiment and adjusts document classification criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned classification unit is When classifying documents, consider the interrelationships between ideas to improve the accuracy of the classification. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned classification unit is When classifying documents, the attribute information of the document's submitter should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned classification unit is It estimates the user's sentiment and adjusts the display order of documents categorized based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned classification unit is When classifying documents, consider their geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned classification unit is When classifying documents, refer to related literature to improve the accuracy of the classification. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned update unit is It estimates user sentiment and adjusts the timing of document updates based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned update unit is When updating a document, the system refers to past update history to select the most suitable update method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned update unit is When a document is updated, it automatically generates updates based on changes in laws and policies. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned update unit is It estimates user sentiment and determines the priority of documents to update based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned update unit is When updating documents, the system prioritizes updating relevant content by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned update unit is When updating documents, we analyze users' social media activity and update relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned search unit, When you perform a search, the system will refer to your past search history to apply the most suitable search algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned search unit, When searching, apply different search methods for each document category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned search unit, It estimates the user's emotions and determines the priority of search results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned search unit, When searching, adjust the display order of search results based on the submission date of the document. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned search unit, When you search, the search results are provided by referencing relevant market data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A generation unit that automatically generates documents, A classification unit that classifies and tags the documents generated by the generation unit, An update unit that updates documents classified by the aforementioned classification unit, The system includes a search unit that searches for documents updated by the update unit. A system characterized by the following features.

2. The generating unit is This system generates meeting minutes, automatic reports, and automatic creation of standard operating procedures. The system according to feature 1.

3. The aforementioned classification unit is Tagging documents by project name or the name of the person in charge. The system according to feature 1.

4. The aforementioned update unit is, Revise documents in accordance with changes in laws and policies. The system according to feature 1.

5. The aforementioned search unit, Allows users to search documents using a question-based format. The system according to feature 1.

6. The aforementioned search unit, Providing search results using natural language processing technology. The system according to feature 1.

7. The generating unit is It estimates the user's emotions and adjusts the timing of document generation based on the estimated user emotions. The system according to feature 1.

8. The generating unit is When generating a document, the system analyzes the user's past document creation history and selects the optimal generation method. The system according to feature 1.

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