system

Abstract

The system according to this embodiment aims to streamline the generation and management of business documents. [Solution] The system according to the embodiment comprises a document selection unit, a format setting unit, a content checking unit, and a version control unit. The document selection unit selects the type of document. The format setting unit sets the format based on the template of the document selected by the document selection unit. The content checking unit checks the content based on the format set by the format setting unit. The version control unit manages the versions of the documents checked by the content checking unit on the cloud.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that the generation and management of business documents were time-consuming and difficult to perform efficiently.

[0005] The system according to the embodiment aims to improve the efficiency of generating and managing business documents.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a document selection unit, a format setting unit, a content checking unit, and a version control unit. The document selection unit selects the type of document. The format setting unit sets the format based on the template of the document selected by the document selection unit. The content checking unit checks the content based on the format set by the format setting unit. The version control unit manages the versions of the documents checked by the content checking unit on the cloud. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the generation and management of business 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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The business document generation and management assistant system according to an embodiment of the present invention is a system for streamlining the generation and management of business documents. This system automatically generates documents using generation AI and supports formatting, content checking, and version control. This significantly reduces the effort required for document creation. By linking internal templates and training data, it achieves more accurate document generation. For example, the user selects the type of document they want to generate, such as a contract, report, or plan. Next, they input the necessary information based on the selected document template. For example, in the case of a contract, they input information such as the parties to the contract, the contract details, and the contract period. This information is input into the generation AI. The generation AI automatically generates the document based on the input information. The generated document is automatically formatted and its content is automatically checked. For example, the generation AI checks whether there are any errors or inconsistencies in the content of a contract. Furthermore, the generated documents are version-controlled on the cloud. This makes it easy to access past versions and simplifies the management of documents involving multiple people. For example, by version control of a report, it is possible to track who modified which part. Furthermore, by linking internal standard templates with training data, more accurate document generation becomes possible. For example, by training the AI ​​with data from past contracts, it can generate more appropriate contracts. This system can significantly reduce the time and cost involved in creating business documents. For example, creating a contract that previously took several hours can now be completed in just a few minutes. In addition, document quality improves, and compliance risks are reduced. For example, by detecting errors in contracts in advance, legal risks can be avoided. Moreover, by providing advanced search functionality, necessary documents can be quickly found and accessed. For example, when searching for a specific contract, simply entering the parties to the contract and the contract details will allow the relevant document to be found immediately. In this way, an assistant that streamlines the generation and management of business documents can significantly improve a company's operational efficiency. For example, it can reduce the time employees spend creating documents, allowing them to focus on more important tasks.Furthermore, improved document quality and reduced compliance risks enhance a company's credibility. This allows business document generation and management assistant systems to streamline the creation and management of business documents, significantly reducing the effort required for document creation.

[0029] The business document generation and management assistant system according to the embodiment comprises a document selection unit, a format setting unit, a content checking unit, and a version control unit. The document selection unit selects the type of document. The document selection unit selects, for example, a type of document such as a contract, report, or plan. The document selection unit provides an interface for the user to select the type of document they want to generate. For example, if the user selects a contract, the document selection unit displays a contract template and provides fields for entering the necessary information. The format setting unit sets the format based on the document template selected by the document selection unit. The format setting unit automatically sets, for example, the format application method and setting items based on the selected document template. The format setting unit automatically sets the document layout and style to maintain document consistency. The content checking unit checks the content based on the format set by the format setting unit. The content checking unit uses generation AI to check the document content and detect errors and inconsistencies. The content checking unit detects, for example, grammatical errors and content contradictions in the document and suggests corrections. The content checking unit analyzes the document content using generation AI to improve document quality. The version control unit manages the versions of the documents checked by the content checking unit on the cloud. The version control unit manages document versions and makes it easy to access past versions. For example, the version control unit assigns version numbers to documents and saves change history. The version control unit manages document versions on the cloud and simplifies the management of documents involving multiple people. As a result, the business document generation and management assistant system according to this embodiment can streamline document generation and management and significantly reduce the effort required for document creation.

[0030] The document selection section allows users to select the type of document. For example, it can select documents such as contracts, reports, and plans. The document selection section provides an interface for users to select the type of document they want to generate. For example, if a user selects a contract, the document selection section displays a contract template and provides fields for entering the necessary information. The document selection section is designed for intuitive operation and includes drop-down menus and search functions. This allows users to quickly select the desired document. Furthermore, the document selection section saves a history of previously used documents and prioritizes the display of frequently used documents. This allows users to efficiently select documents while referring to their past work history. The document selection section also has a document suggestion function based on the user's role and job responsibilities. For example, it prioritizes displaying contracts and proposals to sales representatives, and plans and progress reports to project managers. This allows users to quickly select the document best suited to their work. In addition, the document selection section uses AI to learn the user's selection history and job responsibilities, providing more accurate support for future document selections. This enables the document selection unit to perform flexible and efficient document selection according to the user's needs.

[0031] The formatting section sets the formatting based on the document template selected by the document selection section. For example, the formatting section automatically sets the formatting application method and settings items based on the selected document template. The formatting section automatically sets the document layout and style, maintaining document consistency. Specifically, it automatically applies font type and size, line spacing, paragraph indentation, header and footer content, etc. This allows users to concentrate on creating content without being bothered by the appearance or layout of the document. Furthermore, the formatting section supports custom templates based on the brand guidelines of companies and organizations, allowing for the maintenance of a consistent brand image. For example, it automatically applies the company logo and color scheme to give the entire document a sense of unity. The formatting section also has a function that allows users to customize specific formatting settings, enabling flexible settings to meet individual needs. For example, custom formats tailored to specific projects or clients can be saved and reused. In addition, the formatting section uses AI to learn the user's past setting history, enabling faster and more accurate formatting in the future. This significantly improves the user's work efficiency.

[0032] The content checking unit checks the content based on the format set by the format setting unit. The content checking unit uses generative AI to check the document content and detect errors and inconsistencies. For example, the content checking unit can detect grammatical errors and contradictions in a document and suggest corrections. Specifically, the generative AI uses natural language processing technology to detect grammatical errors and spelling mistakes in the document and suggests appropriate corrections. It also checks whether the document content is consistent and points out inconsistencies and unclear parts. For example, in a contract, it can detect contradictions and inconsistencies between clauses and suggest corrections. Furthermore, the content checking unit also checks for specialized terminology and industry-specific expressions to support the use of appropriate terminology. For example, it can verify the accurate use of specialized terminology in specific industries such as the medical or legal fields and suggest corrections as needed. In addition, the content checking unit also checks the tone and style of the document to support document creation in accordance with the guidelines of the company or organization. For example, in a document where a formal tone is required, it can detect casual expressions and suggest appropriate corrections. Furthermore, the content checking unit can use AI to learn from the user's past revision history and improve the accuracy of subsequent checks. This allows the content checking unit to enhance document quality and improve the user's work efficiency.

[0033] The Version Control Unit manages documents checked by the Content Checking Unit on the cloud. The Version Control Unit handles document versioning, making it easy to access past versions. For example, it assigns version numbers to documents and saves change history. Specifically, a new version number is automatically assigned each time a document is updated, and the changes are recorded in detail. This allows users to easily compare past and current versions and understand the changes. Furthermore, the Version Control Unit also has features to simplify the management of documents involving multiple users. For example, when team members edit a document simultaneously, changes are reflected in real time, and a feature is provided to prevent conflicts. The Version Control Unit also includes a restore function that allows users to revert to a specific version, enabling quick recovery in case of accidental changes or deletions. Moreover, because the Version Control Unit manages documents on the cloud, it can be accessed regardless of location or device. This allows for efficient document management even when working remotely or traveling. The Version Control Unit also has enhanced security measures, preventing the leakage of confidential information through access permission settings and data encryption. This allows the version control department to streamline document management and improve user work efficiency.

[0034] The integration unit integrates internal standard templates with training data to achieve more accurate document generation. For example, the integration unit trains the AI ​​with past document data based on internal standard templates. The integration unit uses a generation AI to analyze past document data and improve the accuracy of document generation. For example, by training the AI ​​with data from past contracts, the integration unit can generate more appropriate contracts. The integration unit improves the accuracy of document generation by integrating internal standard templates with training data. This improves the accuracy of document generation and enables the generation of more appropriate documents. Some or all of the above processes in the integration unit may be performed using a generation AI, or not. For example, the integration unit can input past document data into the generation AI and have the generation AI perform training to improve the accuracy of document generation.

[0035] The search unit provides advanced search functionality, enabling quick searching and access to necessary documents. For example, the search unit uses search algorithms to search for documents. The search unit uses filtering methods to refine search results. For example, when searching for a specific contract, the search unit allows users to quickly find the relevant document simply by entering the contracting parties and contract details. By providing advanced search functionality, the search unit enables quick searching and access to necessary documents. This allows for quick searching and access to necessary documents. Some or all of the above-described processes in the search unit may be performed using, for example, generative AI, or without generative AI. For example, the search unit can input a search algorithm into a generative AI and have the generative AI perform processes to improve the accuracy of the search results.

[0036] The document selection unit selects the type of document, such as a contract, report, or plan. The document selection unit provides, for example, an interface for the user to select the type of document they want to generate. If the user selects a contract, the document selection unit displays a contract template and provides fields for entering the necessary information. This provides a function for selecting the type of document. Some or all of the above processing in the document selection unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the document selection unit can input the user's selection history into the generation AI and cause the generation AI to perform processing to suggest document types.

[0037] The formatting unit performs formatting based on the selected document template. For example, the formatting unit automatically sets the formatting application method and settings based on the selected document template. The formatting unit automatically sets the document layout and style to maintain document consistency. This automates the formatting of the document. Some or all of the above processes in the formatting unit may be performed using, for example, a generation AI, or without a generation AI. For example, the formatting unit can input the document template into a generation AI and have the generation AI perform the formatting process.

[0038] The content checking unit uses a generation AI to check the content of the document and detect errors and inconsistencies. For example, the content checking unit detects grammatical errors and contradictions in the document and suggests corrections. The content checking unit uses the generation AI to analyze the content of the document and improve its quality. As a result, the document's content is checked automatically, and errors and inconsistencies are detected. Some or all of the above-described processes in the content checking unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the content checking unit can input the document's content into a generation AI and have the generation AI perform the content checking process.

[0039] The version control unit manages document versions on the cloud, making it easy to access past versions. For example, the version control unit assigns version numbers to documents and saves change history. The version control unit simplifies the management of documents involving multiple people by managing document versions on the cloud. This makes document version control easy. Some or all of the above processes in the version control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the version control unit can input document version control into a generative AI and have the generative AI execute the version control process.

[0040] The document selection unit analyzes the user's past document selection history and automatically selects the most suitable document type. For example, the document selection unit automatically suggests document types that the user has frequently selected in the past. The document selection unit can also predict and suggest document types that will be selected during specific time periods based on the user's past selection history. The document selection unit can also suggest highly relevant document types based on the user's past selection history. This ensures that the most suitable document type is automatically selected based on the user's past selection history. Some or all of the above-described processes in the document selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the document selection unit can input the user's past selection history data into a generative AI and have the generative AI perform the processing necessary to automatically select document types.

[0041] The document selection unit filters documents based on the user's current projects and work content during the document selection process. For example, the document selection unit prioritizes suggesting document types related to the user's current ongoing projects. The document selection unit can also suggest the most relevant document types based on the user's work content. The document selection unit can also suggest necessary document types according to the progress of the user's projects. This filters the document types based on the user's projects and work content. Some or all of the above processing in the document selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the document selection unit can input the user's project data into a generative AI and have the generative AI perform the processing to filter document types.

[0042] The document selection unit prioritizes suggesting documents that are highly relevant to the user's geographical location when selecting documents. For example, if the user is in a specific region, the document selection unit suggests document types related to that region. The document selection unit can also suggest the most relevant document types based on the user's geographical location. If the user is on the move, the document selection unit can also suggest necessary document types based on the user's current location. This ensures that highly relevant documents are suggested based on the user's geographical location. Some or all of the above processing in the document selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the document selection unit can input the user's geographical location information into a generative AI and have the generative AI perform the processing to suggest document types.

[0043] The document selection unit analyzes the user's social media activity and suggests relevant documents when selecting documents. For example, the document selection unit suggests the type of relevant document based on the user's social media activity. The document selection unit can also suggest the necessary document type based on what the user has mentioned on social media. The document selection unit can also analyze the user's social media activity and suggest the most relevant document type. As a result, relevant documents are suggested based on the user's social media activity. Some or all of the above processing in the document selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the document selection unit can input the user's social media data into a generative AI and have the generative AI perform the processing to suggest document types.

[0044] The formatting unit adjusts the level of detail in the format based on the importance of the document during formatting. For example, the formatting unit provides a detailed format for important documents. For general documents, it can also provide a simple format. For urgent documents, it can provide a format that can be created quickly. This ensures that the level of detail in the formatting unit is adjusted based on the importance of the document. Some or all of the above processing in the formatting unit may be performed using, for example, a generation AI, or without a generation AI. For example, the formatting unit can input document importance data into a generation AI and have the generation AI perform processing to adjust the level of detail in the format.

[0045] The formatting unit applies different formatting algorithms depending on the document category when formatting. For example, in the case of a contract, the formatting unit provides a format based on legal requirements. In the case of a report, the formatting unit may also provide a format that includes statistical data. In the case of a plan, the formatting unit may also provide a format suitable for project management. This ensures that different formatting algorithms are applied depending on the document category. Some or all of the above processing in the formatting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the formatting unit can input document category data into a generative AI and have the generative AI perform the processing to apply the formatting algorithm.

[0046] The format setting unit determines the priority of formats based on the document submission date when setting the format. For example, in the case of an urgent document, the format setting unit prioritizes a format that can be created quickly. For documents with an approaching submission deadline, the format setting unit may also prioritize a more detailed format. For documents with a distant submission deadline, the format setting unit may also provide a customizable format. This ensures that the format priority is determined based on the document submission date. Some or all of the above processing in the format setting unit may be performed using, for example, a generation AI, or without a generation AI. For example, the format setting unit can input document submission date data into a generation AI and have the generation AI perform the processing to determine the format priority.

[0047] The formatting unit adjusts the order of formats based on the relevance of the documents when formatting. For example, the formatting unit prioritizes detailed formatting for important documents. For general documents, the formatting unit may also prioritize simple formatting. For highly relevant documents, the formatting unit may also provide customizable formats. This adjusts the order of formats based on the relevance of the documents. Some or all of the above processing in the formatting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the formatting unit can input document relevance data into a generative AI and have the generative AI perform processing to adjust the order of formats.

[0048] The content checking unit improves the accuracy of its checks by considering the interrelationships between documents during content checking. For example, the content checking unit checks for consistency in content by referring to related documents. The content checking unit can also detect errors and inconsistencies by considering the interrelationships between documents. The content checking unit can also improve the accuracy of its checks based on the interrelationships between documents. As a result, the accuracy of the checks is improved by considering the interrelationships between documents. Some or all of the above-described processes in the content checking unit may be performed using, for example, a generating AI, or without a generating AI. For example, the content checking unit can input interrelationship data between documents into a generating AI and have the generating AI execute processes to improve the accuracy of the checks.

[0049] The content checking unit performs checks while considering the attribute information of the document submitter. The content checking unit adjusts the checking criteria based on, for example, the submitter's expertise. The content checking unit can also adjust the level of detail of the check based on the submitter's job title. The content checking unit can also improve the accuracy of the check by considering the submitter's past submission history. As a result, the check is performed based on the attribute information of the document submitter. Some or all of the above processes in the content checking unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the content checking unit can input the submitter's attribute information into a generating AI and have the generating AI perform the process of adjusting the checking criteria.

[0050] The content checking unit performs content checks while considering the geographical distribution of the documents. For example, the content checking unit performs checks based on regional regulations, taking into account the geographical distribution of the documents. The content checking unit can also check the content of the documents while considering the geographical distribution. The content checking unit can also improve the accuracy of the checks by considering the characteristics of each region. As a result, checks are performed based on the geographical distribution of the documents. Some or all of the above-described processes in the content checking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the content checking unit can input geographical distribution data of the documents into a generative AI and have the generative AI execute processes to improve the accuracy of the checks.

[0051] The content checking unit improves the accuracy of the check by referring to related literature in the document during the content check. For example, the content checking unit checks the content of the document by referring to related literature. The content checking unit can also detect errors and inconsistencies based on the related literature. The content checking unit can also improve the accuracy of the check by referring to related literature. As a result, the accuracy of the check is improved by referring to related literature in the document. Some or all of the above processing in the content checking unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the content checking unit can input related literature data into a generating AI and have the generating AI execute processing to improve the accuracy of the check.

[0052] The version control unit optimizes the current version by referring to past version data during version control. For example, the version control unit optimizes the current version based on past version data. The version control unit can also correct errors and inconsistencies by referring to past version data. The version control unit can also provide an optimal version control method based on past version data. As a result, the current version is optimized based on past version data. Some or all of the above processes in the version control unit may be performed using, for example, a generation AI, or without a generation AI. For example, the version control unit can input past version data into a generation AI and have the generation AI execute the process to optimize the current version.

[0053] The version control unit applies different version control methods to each document category during version control. For example, in the case of a contract, the version control unit provides a version control method based on legal requirements. In the case of a report, the version control unit may also provide a version control method that includes statistical data. In the case of a plan, the version control unit may also provide a version control method suitable for project management. This ensures that different version control methods are applied depending on the document category. Some or all of the above processing in the version control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the version control unit can input document category data into a generative AI and have the generative AI perform the processing to apply the version control method.

[0054] The version control unit assigns version weights based on the document submission date during version control. For example, the version control unit assigns a higher importance to documents with an approaching submission deadline. It can also assign a lower importance to documents with a distant submission deadline. The version control unit can also adjust the version weighting based on the submission date. This ensures that versions are weighted according to the document submission date. Some or all of the above processing in the version control unit may be performed using, for example, a generation AI, or without a generation AI. For example, the version control unit can input document submission date data into a generation AI and have the generation AI perform the processing for assigning version weights.

[0055] The version control unit performs version control by referring to relevant market data for the document. The version control unit optimizes document version control based on market data, for example. The version control unit can also perform document version control by referring to relevant market data. The version control unit can also improve the accuracy of version control based on market data. As a result, version control is performed based on relevant market data for the document. Some or all of the above processing in the version control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the version control unit can input relevant market data into a generative AI and have the generative AI execute the processing for version control.

[0056] The collaboration unit optimizes the collaboration algorithm by referring to past collaboration data during collaboration. For example, the collaboration unit provides an optimal collaboration algorithm based on past collaboration data. The collaboration unit can also improve the accuracy of collaboration by referring to past collaboration data. The collaboration unit can also optimize the collaboration algorithm based on past collaboration data. As a result, the collaboration algorithm is optimized based on past collaboration data. Some or all of the above processing in the collaboration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collaboration unit can input past collaboration data into a generative AI and have the generative AI execute processing to optimize the collaboration algorithm.

[0057] The integration unit weights the integration data based on the document submission date during integration. For example, the integration unit may set a higher importance for documents with an approaching submission deadline, and a lower importance for documents with a distant submission deadline. The integration unit can also adjust the weighting of the integration data based on the submission date. This ensures that the integration data is weighted according to the document submission date. Some or all of the above processing in the integration unit may be performed using, for example, a generating AI, or without a generating AI. For example, the integration unit can input document submission date data into a generating AI and have the generating AI perform the processing to weight the integration data.

[0058] The search unit provides the most relevant search results by referencing the user's past search history during a search. For example, the search unit provides the most relevant search results based on the user's past search history. The search unit can also provide highly relevant search results from the user's past search history. The search unit can also improve the accuracy of search results by referencing the user's past search history. This ensures that the most relevant search results are provided based on the user's past search history. Some or all of the above-described processes in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the user's past search history data into a generative AI and have the generative AI perform the processing necessary to provide the most relevant search results.

[0059] The search unit provides optimal search results by considering the user's device information during a search. For example, if the user is using a smartphone, the search unit provides search results that are adapted to the screen size. If the user is using a tablet, the search unit can also provide search results optimized for a larger screen. If the user is using a smartwatch, the search unit can also provide concise and highly visible search results. This ensures that optimal search results are provided based on the user's device information. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the user's device information into a generative AI and have the generative AI perform the processing necessary to provide optimal search results.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The document selection unit can analyze the user's past selection history and suggest the most suitable document type. For example, it can automatically suggest document types that the user has frequently selected in the past. Furthermore, it can predict and suggest document types that will be selected during specific time periods based on the user's selection history. This ensures that the most suitable document type is suggested based on the user's past selection history. The document selection unit can input the user's selection history data into a generating AI and have the generating AI perform the processing necessary to suggest document types.

[0062] The formatting unit can adjust the level of detail in the format based on the importance of the document. For example, it can provide a detailed format for important documents and a simple format for general documents. Furthermore, it can provide a format that can be created quickly for urgent documents. In this way, the level of detail in the format is adjusted based on the importance of the document. The formatting unit inputs document importance data into the generation AI and causes the generation AI to perform processing to adjust the level of detail in the format.

[0063] The content checking unit can improve the accuracy of its checks by considering the interrelationships between documents. For example, it can refer to related documents and check for consistency in content. Furthermore, it can detect errors and inconsistencies by considering the interrelationships between documents. This improves the accuracy of the checks by considering the interrelationships between documents. The content checking unit can input interrelationship data between documents into the generating AI and have the generating AI perform processing to improve the accuracy of the checks.

[0064] The version control unit can optimize the current version by referring to past version data. For example, it can optimize the current version based on past version data. Furthermore, it can correct errors and inconsistencies by referring to past version data. This optimizes the current version based on past version data. The version control unit can input past version data into the generation AI and have the generation AI execute the process to optimize the current version.

[0065] The search unit can provide optimal search results by referring to the user's past search history. For example, it can provide optimal search results based on the user's past search history. Furthermore, it can also provide highly relevant search results from the user's past search history. This ensures that optimal search results are provided based on the user's past search history. The search unit can input the user's past search history data into a generating AI and have the generating AI execute the processing necessary to provide optimal search results.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The document selection section allows you to select the type of document. For example, the document selection section allows you to select the type of document, such as a contract, report, or plan. The document selection section provides an interface for the user to select the type of document they want to generate. For example, if the user selects a contract, the document selection section displays a contract template and provides fields for entering the necessary information. Step 2: The formatting unit sets the format based on the document template selected by the document selection unit. For example, the formatting unit automatically sets the formatting application method and settings items based on the selected document template. The formatting unit automatically sets the document layout and style to maintain document consistency. Step 3: The content checking unit checks the content based on the format set by the format setting unit. The content checking unit uses generation AI to check the document content and detect errors and inconsistencies. For example, the content checking unit detects grammatical errors and content contradictions in the document and suggests corrections. The content checking unit uses generation AI to analyze the document content and improve the document quality. Step 4: The Version Control Department manages the versions of documents checked by the Content Checking Department on the cloud. The Version Control Department manages document versions and makes it easy to access past versions. For example, the Version Control Department assigns version numbers to documents and saves change history. The Version Control Department manages document versions on the cloud, simplifying the management of documents involving multiple people.

[0068] (Example of form 2) The business document generation and management assistant system according to an embodiment of the present invention is a system for streamlining the generation and management of business documents. This system automatically generates documents using generation AI and supports formatting, content checking, and version control. This significantly reduces the effort required for document creation. By linking internal templates and training data, it achieves more accurate document generation. For example, the user selects the type of document they want to generate, such as a contract, report, or plan. Next, they input the necessary information based on the selected document template. For example, in the case of a contract, they input information such as the parties to the contract, the contract details, and the contract period. This information is input into the generation AI. The generation AI automatically generates the document based on the input information. The generated document is automatically formatted and its content is automatically checked. For example, the generation AI checks whether there are any errors or inconsistencies in the content of a contract. Furthermore, the generated documents are version-controlled on the cloud. This makes it easy to access past versions and simplifies the management of documents involving multiple people. For example, by version control of a report, it is possible to track who modified which part. Furthermore, by linking internal standard templates with training data, more accurate document generation becomes possible. For example, by training the AI ​​with data from past contracts, it can generate more appropriate contracts. This system can significantly reduce the time and cost involved in creating business documents. For example, creating a contract that previously took several hours can now be completed in just a few minutes. In addition, document quality improves, and compliance risks are reduced. For example, by detecting errors in contracts in advance, legal risks can be avoided. Moreover, by providing advanced search functionality, necessary documents can be quickly found and accessed. For example, when searching for a specific contract, simply entering the parties to the contract and the contract details will allow the relevant document to be found immediately. In this way, an assistant that streamlines the generation and management of business documents can significantly improve a company's operational efficiency. For example, it can reduce the time employees spend creating documents, allowing them to focus on more important tasks.Furthermore, improved document quality and reduced compliance risks enhance a company's credibility. This allows business document generation and management assistant systems to streamline the creation and management of business documents, significantly reducing the effort required for document creation.

[0069] The business document generation and management assistant system according to the embodiment comprises a document selection unit, a format setting unit, a content checking unit, and a version control unit. The document selection unit selects the type of document. The document selection unit selects, for example, a type of document such as a contract, report, or plan. The document selection unit provides an interface for the user to select the type of document they want to generate. For example, if the user selects a contract, the document selection unit displays a contract template and provides fields for entering the necessary information. The format setting unit sets the format based on the document template selected by the document selection unit. The format setting unit automatically sets, for example, the format application method and setting items based on the selected document template. The format setting unit automatically sets the document layout and style to maintain document consistency. The content checking unit checks the content based on the format set by the format setting unit. The content checking unit uses generation AI to check the document content and detect errors and inconsistencies. The content checking unit detects, for example, grammatical errors and content contradictions in the document and suggests corrections. The content checking unit analyzes the document content using generation AI to improve document quality. The version control unit manages the versions of the documents checked by the content checking unit on the cloud. The version control unit manages document versions and makes it easy to access past versions. For example, the version control unit assigns version numbers to documents and saves change history. The version control unit manages document versions on the cloud and simplifies the management of documents involving multiple people. As a result, the business document generation and management assistant system according to this embodiment can streamline document generation and management and significantly reduce the effort required for document creation.

[0070] The document selection section allows users to select the type of document. For example, it can select documents such as contracts, reports, and plans. The document selection section provides an interface for users to select the type of document they want to generate. For example, if a user selects a contract, the document selection section displays a contract template and provides fields for entering the necessary information. The document selection section is designed for intuitive operation and includes drop-down menus and search functions. This allows users to quickly select the desired document. Furthermore, the document selection section saves a history of previously used documents and prioritizes the display of frequently used documents. This allows users to efficiently select documents while referring to their past work history. The document selection section also has a document suggestion function based on the user's role and job responsibilities. For example, it prioritizes displaying contracts and proposals to sales representatives, and plans and progress reports to project managers. This allows users to quickly select the document best suited to their work. In addition, the document selection section uses AI to learn the user's selection history and job responsibilities, providing more accurate support for future document selections. This enables the document selection unit to perform flexible and efficient document selection according to the user's needs.

[0071] The formatting section sets the formatting based on the document template selected by the document selection section. For example, the formatting section automatically sets the formatting application method and settings items based on the selected document template. The formatting section automatically sets the document layout and style, maintaining document consistency. Specifically, it automatically applies font type and size, line spacing, paragraph indentation, header and footer content, etc. This allows users to concentrate on creating content without being bothered by the appearance or layout of the document. Furthermore, the formatting section supports custom templates based on the brand guidelines of companies and organizations, allowing for the maintenance of a consistent brand image. For example, it automatically applies the company logo and color scheme to give the entire document a sense of unity. The formatting section also has a function that allows users to customize specific formatting settings, enabling flexible settings to meet individual needs. For example, custom formats tailored to specific projects or clients can be saved and reused. In addition, the formatting section uses AI to learn the user's past setting history, enabling faster and more accurate formatting in the future. This significantly improves the user's work efficiency.

[0072] The content checking unit checks the content based on the format set by the format setting unit. The content checking unit uses generative AI to check the document content and detect errors and inconsistencies. For example, the content checking unit can detect grammatical errors and contradictions in a document and suggest corrections. Specifically, the generative AI uses natural language processing technology to detect grammatical errors and spelling mistakes in the document and suggests appropriate corrections. It also checks whether the document content is consistent and points out inconsistencies and unclear parts. For example, in a contract, it can detect contradictions and inconsistencies between clauses and suggest corrections. Furthermore, the content checking unit also checks for specialized terminology and industry-specific expressions to support the use of appropriate terminology. For example, it can verify the accurate use of specialized terminology in specific industries such as the medical or legal fields and suggest corrections as needed. In addition, the content checking unit also checks the tone and style of the document to support document creation in accordance with the guidelines of the company or organization. For example, in a document where a formal tone is required, it can detect casual expressions and suggest appropriate corrections. Furthermore, the content checking unit can use AI to learn from the user's past revision history and improve the accuracy of subsequent checks. This allows the content checking unit to enhance document quality and improve the user's work efficiency.

[0073] The Version Control Unit manages documents checked by the Content Checking Unit on the cloud. The Version Control Unit handles document versioning, making it easy to access past versions. For example, it assigns version numbers to documents and saves change history. Specifically, a new version number is automatically assigned each time a document is updated, and the changes are recorded in detail. This allows users to easily compare past and current versions and understand the changes. Furthermore, the Version Control Unit also has features to simplify the management of documents involving multiple users. For example, when team members edit a document simultaneously, changes are reflected in real time, and a feature is provided to prevent conflicts. The Version Control Unit also includes a restore function that allows users to revert to a specific version, enabling quick recovery in case of accidental changes or deletions. Moreover, because the Version Control Unit manages documents on the cloud, it can be accessed regardless of location or device. This allows for efficient document management even when working remotely or traveling. The Version Control Unit also has enhanced security measures, preventing the leakage of confidential information through access permission settings and data encryption. This allows the version control department to streamline document management and improve user work efficiency.

[0074] The integration unit integrates internal standard templates with training data to achieve more accurate document generation. For example, the integration unit trains the AI ​​with past document data based on internal standard templates. The integration unit uses a generation AI to analyze past document data and improve the accuracy of document generation. For example, by training the AI ​​with data from past contracts, the integration unit can generate more appropriate contracts. The integration unit improves the accuracy of document generation by integrating internal standard templates with training data. This improves the accuracy of document generation and enables the generation of more appropriate documents. Some or all of the above processes in the integration unit may be performed using a generation AI, or not. For example, the integration unit can input past document data into the generation AI and have the generation AI perform training to improve the accuracy of document generation.

[0075] The search unit provides advanced search functionality, enabling quick searching and access to necessary documents. For example, the search unit uses search algorithms to search for documents. The search unit uses filtering methods to refine search results. For example, when searching for a specific contract, the search unit allows users to quickly find the relevant document simply by entering the contracting parties and contract details. By providing advanced search functionality, the search unit enables quick searching and access to necessary documents. This allows for quick searching and access to necessary documents. Some or all of the above-described processes in the search unit may be performed using, for example, generative AI, or without generative AI. For example, the search unit can input a search algorithm into a generative AI and have the generative AI perform processes to improve the accuracy of the search results.

[0076] The document selection unit selects the type of document, such as a contract, report, or plan. The document selection unit provides, for example, an interface for the user to select the type of document they want to generate. If the user selects a contract, the document selection unit displays a contract template and provides fields for entering the necessary information. This provides a function for selecting the type of document. Some or all of the above processing in the document selection unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the document selection unit can input the user's selection history into the generation AI and cause the generation AI to perform processing to suggest document types.

[0077] The formatting unit performs formatting based on the selected document template. For example, the formatting unit automatically sets the formatting application method and settings based on the selected document template. The formatting unit automatically sets the document layout and style to maintain document consistency. This automates the formatting of the document. Some or all of the above processes in the formatting unit may be performed using, for example, a generation AI, or without a generation AI. For example, the formatting unit can input the document template into a generation AI and have the generation AI perform the formatting process.

[0078] The content checking unit uses a generation AI to check the content of the document and detect errors and inconsistencies. For example, the content checking unit detects grammatical errors and contradictions in the document and suggests corrections. The content checking unit uses the generation AI to analyze the content of the document and improve its quality. As a result, the document's content is checked automatically, and errors and inconsistencies are detected. Some or all of the above-described processes in the content checking unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the content checking unit can input the document's content into a generation AI and have the generation AI perform the content checking process.

[0079] The version control unit manages document versions on the cloud, making it easy to access past versions. For example, the version control unit assigns version numbers to documents and saves change history. The version control unit simplifies the management of documents involving multiple people by managing document versions on the cloud. This makes document version control easy. Some or all of the above processes in the version control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the version control unit can input document version control into a generative AI and have the generative AI execute the version control process.

[0080] The document selection unit estimates the user's emotions and suggests document types based on the estimated emotions. For example, if the user is stressed, the document selection unit will prioritize suggesting simple document types. If the user is relaxed, the document selection unit may also suggest detailed document types. If the user is in a hurry, the document selection unit may also suggest the type of document that can be created most quickly. In this way, document types are suggested based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the document selection unit may be performed using a generative AI, or not using a generative AI. For example, the document selection unit can input user emotion data into a generative AI and have the generative AI perform the processing to suggest document types.

[0081] The document selection unit analyzes the user's past document selection history and automatically selects the most suitable document type. For example, the document selection unit automatically suggests document types that the user has frequently selected in the past. The document selection unit can also predict and suggest document types that will be selected during specific time periods based on the user's past selection history. The document selection unit can also suggest highly relevant document types based on the user's past selection history. This ensures that the most suitable document type is automatically selected based on the user's past selection history. Some or all of the above-described processes in the document selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the document selection unit can input the user's past selection history data into a generative AI and have the generative AI perform the processing necessary to automatically select document types.

[0082] The document selection unit filters documents based on the user's current projects and work content during the document selection process. For example, the document selection unit prioritizes suggesting document types related to the user's current ongoing projects. The document selection unit can also suggest the most relevant document types based on the user's work content. The document selection unit can also suggest necessary document types according to the progress of the user's projects. This filters the document types based on the user's projects and work content. Some or all of the above processing in the document selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the document selection unit can input the user's project data into a generative AI and have the generative AI perform the processing to filter document types.

[0083] The document selection unit estimates the user's emotions and determines the priority of document selection based on the estimated emotions. For example, if the user is stressed, the document selection unit may prioritize displaying simpler document types. If the user is relaxed, the document selection unit may also prioritize displaying more detailed document types. If the user is in a hurry, the document selection unit may also prioritize displaying the type of document that can be created most quickly. In this way, the priority of document selection is determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 document selection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the document selection unit may input user emotion data into a generative AI and have the generative AI perform the processing to determine the priority of document selection.

[0084] The document selection unit prioritizes suggesting documents that are highly relevant to the user's geographical location when selecting documents. For example, if the user is in a specific region, the document selection unit suggests document types related to that region. The document selection unit can also suggest the most relevant document types based on the user's geographical location. If the user is on the move, the document selection unit can also suggest necessary document types based on the user's current location. This ensures that highly relevant documents are suggested based on the user's geographical location. Some or all of the above processing in the document selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the document selection unit can input the user's geographical location information into a generative AI and have the generative AI perform the processing to suggest document types.

[0085] The document selection unit analyzes the user's social media activity and suggests relevant documents when selecting documents. For example, the document selection unit suggests the type of relevant document based on the user's social media activity. The document selection unit can also suggest the necessary document type based on what the user has mentioned on social media. The document selection unit can also analyze the user's social media activity and suggest the most relevant document type. As a result, relevant documents are suggested based on the user's social media activity. Some or all of the above processing in the document selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the document selection unit can input the user's social media data into a generative AI and have the generative AI perform the processing to suggest document types.

[0086] The formatting unit estimates the user's emotions and adjusts the format style based on the estimated emotions. For example, if the user is stressed, the formatting unit provides a simple format style. If the user is relaxed, the formatting unit can also provide a more detailed format style. If the user is in a hurry, the formatting unit can also provide a format style that can be created quickly. This ensures that the format style is adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the formatting unit may be performed using a generative AI, or not. For example, the formatting unit can input user emotion data into a generative AI and have the generative AI perform the processing to adjust the format style.

[0087] The formatting unit adjusts the level of detail in the format based on the importance of the document during formatting. For example, the formatting unit provides a detailed format for important documents. For general documents, it can also provide a simple format. For urgent documents, it can provide a format that can be created quickly. This ensures that the level of detail in the formatting unit is adjusted based on the importance of the document. Some or all of the above processing in the formatting unit may be performed using, for example, a generation AI, or without a generation AI. For example, the formatting unit can input document importance data into a generation AI and have the generation AI perform processing to adjust the level of detail in the format.

[0088] The formatting unit applies different formatting algorithms depending on the document category when formatting. For example, in the case of a contract, the formatting unit provides a format based on legal requirements. In the case of a report, the formatting unit may also provide a format that includes statistical data. In the case of a plan, the formatting unit may also provide a format suitable for project management. This ensures that different formatting algorithms are applied depending on the document category. Some or all of the above processing in the formatting unit may be performed using, for example, a generative AI, or without a generative AI. For example, the formatting unit can input document category data into a generative AI and have the generative AI perform the processing to apply the formatting algorithm.

[0089] The formatting unit estimates the user's emotions and adjusts the length of the format based on the estimated emotions. For example, if the user is stressed, the formatting unit provides a short format. If the user is relaxed, the formatting unit may also provide a detailed format. If the user is in a hurry, the formatting unit may also provide a short format that can be created quickly. This adjusts the length of the format based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the formatting unit may be performed using a generative AI, or not using a generative AI. For example, the formatting unit can input user emotion data into a generative AI and have the generative AI perform the processing to adjust the length of the format.

[0090] The format setting unit determines the priority of formats based on the document submission date when setting the format. For example, in the case of an urgent document, the format setting unit prioritizes a format that can be created quickly. For documents with an approaching submission deadline, the format setting unit may also prioritize a more detailed format. For documents with a distant submission deadline, the format setting unit may also provide a customizable format. This ensures that the format priority is determined based on the document submission date. Some or all of the above processing in the format setting unit may be performed using, for example, a generation AI, or without a generation AI. For example, the format setting unit can input document submission date data into a generation AI and have the generation AI perform the processing to determine the format priority.

[0091] The formatting unit adjusts the order of formats based on the relevance of the documents when formatting. For example, the formatting unit prioritizes detailed formatting for important documents. For general documents, the formatting unit may also prioritize simple formatting. For highly relevant documents, the formatting unit may also provide customizable formats. This adjusts the order of formats based on the relevance of the documents. Some or all of the above processing in the formatting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the formatting unit can input document relevance data into a generative AI and have the generative AI perform processing to adjust the order of formats.

[0092] The content checking unit estimates the user's emotions and adjusts the content checking criteria based on the estimated emotions. For example, if the user is stressed, the content checking unit provides simple checking criteria. If the user is relaxed, the content checking unit may also provide detailed checking criteria. If the user is in a hurry, the content checking unit may also provide criteria that can be checked quickly. In this way, the content checking criteria are adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the content checking unit may be performed using a generative AI, or not using a generative AI. For example, the content checking unit can input user emotion data into a generative AI and have the generative AI perform processing to adjust the content checking criteria.

[0093] The content checking unit improves the accuracy of its checks by considering the interrelationships between documents during content checking. For example, the content checking unit checks for consistency in content by referring to related documents. The content checking unit can also detect errors and inconsistencies by considering the interrelationships between documents. The content checking unit can also improve the accuracy of its checks based on the interrelationships between documents. As a result, the accuracy of the checks is improved by considering the interrelationships between documents. Some or all of the above-described processes in the content checking unit may be performed using, for example, a generating AI, or without a generating AI. For example, the content checking unit can input interrelationship data between documents into a generating AI and have the generating AI execute processes to improve the accuracy of the checks.

[0094] The content checking unit performs checks while considering the attribute information of the document submitter. The content checking unit adjusts the checking criteria based on, for example, the submitter's expertise. The content checking unit can also adjust the level of detail of the check based on the submitter's job title. The content checking unit can also improve the accuracy of the check by considering the submitter's past submission history. As a result, the check is performed based on the attribute information of the document submitter. Some or all of the above processes in the content checking unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the content checking unit can input the submitter's attribute information into a generating AI and have the generating AI perform the process of adjusting the checking criteria.

[0095] The content checking unit estimates the user's emotions and adjusts the order in which the content checking results are displayed based on the estimated emotions. For example, if the user is stressed, the content checking unit prioritizes displaying important check results. If the user is relaxed, the content checking unit may also display detailed check results. If the user is in a hurry, the content checking unit may also prioritize displaying check results that can be quickly reviewed. In this way, the order in which the content checking results are displayed is adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the content checking unit may be performed using a generative AI, or not using a generative AI. For example, the content checking unit can input user emotion data into a generative AI and have the generative AI perform processing to adjust the order in which the content checking results are displayed.

[0096] The content checking unit performs content checks while considering the geographical distribution of the documents. For example, the content checking unit performs checks based on regional regulations, taking into account the geographical distribution of the documents. The content checking unit can also check the content of the documents while considering the geographical distribution. The content checking unit can also improve the accuracy of the checks by considering the characteristics of each region. As a result, checks are performed based on the geographical distribution of the documents. Some or all of the above-described processes in the content checking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the content checking unit can input geographical distribution data of the documents into a generative AI and have the generative AI execute processes to improve the accuracy of the checks.

[0097] The content checking unit improves the accuracy of the check by referring to related literature in the document during the content check. For example, the content checking unit checks the content of the document by referring to related literature. The content checking unit can also detect errors and inconsistencies based on the related literature. The content checking unit can also improve the accuracy of the check by referring to related literature. As a result, the accuracy of the check is improved by referring to related literature in the document. Some or all of the above processing in the content checking unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the content checking unit can input related literature data into a generating AI and have the generating AI execute processing to improve the accuracy of the check.

[0098] The version control unit estimates the user's emotions and adjusts the version control method based on the estimated emotions. For example, if the user is stressed, the version control unit provides a simple version control method. If the user is relaxed, the version control unit can also provide a more detailed version control method. If the user is in a hurry, the version control unit can also provide a version control method that allows for quick management. In this way, the version control method is adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the version control unit may be performed using a generative AI, or not using a generative AI. For example, the version control unit can input user emotion data into a generative AI and have the generative AI perform processing to adjust the version control method.

[0099] The version control unit optimizes the current version by referring to past version data during version control. For example, the version control unit optimizes the current version based on past version data. The version control unit can also correct errors and inconsistencies by referring to past version data. The version control unit can also provide an optimal version control method based on past version data. As a result, the current version is optimized based on past version data. Some or all of the above processes in the version control unit may be performed using, for example, a generation AI, or without a generation AI. For example, the version control unit can input past version data into a generation AI and have the generation AI execute the process to optimize the current version.

[0100] The version control unit applies different version control methods to each document category during version control. For example, in the case of a contract, the version control unit provides a version control method based on legal requirements. In the case of a report, the version control unit may also provide a version control method that includes statistical data. In the case of a plan, the version control unit may also provide a version control method suitable for project management. This ensures that different version control methods are applied depending on the document category. Some or all of the above processing in the version control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the version control unit can input document category data into a generative AI and have the generative AI perform the processing to apply the version control method.

[0101] The version control unit estimates the user's emotions and determines version control priorities based on the estimated emotions. For example, if the user is stressed, the version control unit prioritizes managing important versions. If the user is relaxed, the version control unit may also provide detailed version control. If the user is in a hurry, the version control unit may also prioritize providing versions that can be managed quickly. In this way, version control priorities are determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the version control unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the version control unit can input user emotion data into a generative AI and have the generative AI perform the processing to determine version control priorities.

[0102] The version control unit assigns version weights based on the document submission date during version control. For example, the version control unit assigns a higher importance to documents with an approaching submission deadline. It can also assign a lower importance to documents with a distant submission deadline. The version control unit can also adjust the version weighting based on the submission date. This ensures that versions are weighted according to the document submission date. Some or all of the above processing in the version control unit may be performed using, for example, a generation AI, or without a generation AI. For example, the version control unit can input document submission date data into a generation AI and have the generation AI perform the processing for assigning version weights.

[0103] The version control unit performs version control by referring to relevant market data for the document. The version control unit optimizes document version control based on market data, for example. The version control unit can also perform document version control by referring to relevant market data. The version control unit can also improve the accuracy of version control based on market data. As a result, version control is performed based on relevant market data for the document. Some or all of the above processing in the version control unit may be performed using, for example, a generative AI, or without a generative AI. For example, the version control unit can input relevant market data into a generative AI and have the generative AI execute the processing for version control.

[0104] The integration unit estimates the user's emotions and selects data to integrate based on the estimated emotions. For example, if the user is stressed, the integration unit prioritizes integrating simple data. If the user is relaxed, the integration unit can also integrate more detailed data. If the user is in a hurry, the integration unit can prioritize selecting data that can be quickly integrated. This ensures that data is selected based on 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-described processes in the integration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the integration unit can input user emotion data into a generative AI and have the generative AI perform the process of selecting data to integrate.

[0105] The collaboration unit optimizes the collaboration algorithm by referring to past collaboration data during collaboration. For example, the collaboration unit provides an optimal collaboration algorithm based on past collaboration data. The collaboration unit can also improve the accuracy of collaboration by referring to past collaboration data. The collaboration unit can also optimize the collaboration algorithm based on past collaboration data. As a result, the collaboration algorithm is optimized based on past collaboration data. Some or all of the above processing in the collaboration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collaboration unit can input past collaboration data into a generative AI and have the generative AI execute processing to optimize the collaboration algorithm.

[0106] The interaction unit estimates the user's emotions and adjusts the frequency of interaction based on the estimated emotions. For example, if the user is stressed, the interaction unit may set a lower interaction frequency. If the user is relaxed, the interaction unit may also set a higher interaction frequency. If the user is in a hurry, the interaction unit may also provide a frequency that allows for quick interaction. In this way, the frequency of interaction is adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interaction unit may be performed using a generative AI, or not using a generative AI. For example, the interaction unit may input user emotion data into a generative AI and have the generative AI perform processing to adjust the frequency of interaction.

[0107] The integration unit weights the integration data based on the document submission date during integration. For example, the integration unit may set a higher importance for documents with an approaching submission deadline, and a lower importance for documents with a distant submission deadline. The integration unit can also adjust the weighting of the integration data based on the submission date. This ensures that the integration data is weighted according to the document submission date. Some or all of the above processing in the integration unit may be performed using, for example, a generating AI, or without a generating AI. For example, the integration unit can input document submission date data into a generating AI and have the generating AI perform the processing to weight the integration data.

[0108] The search unit estimates the user's emotions and adjusts how search results are displayed based on the estimated emotions. For example, if the user is stressed, the search unit may prioritize displaying simple search results. If the user is relaxed, the search unit may also display detailed search results. If the user is in a hurry, the search unit may also prioritize displaying search results that can be quickly reviewed. In this way, the display of search results is adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using, for example, generative AI, or not using generative AI. For example, the search unit may input user emotion data into a generative AI and have the generative AI perform processing to adjust how search results are displayed.

[0109] The search unit provides the most relevant search results by referencing the user's past search history during a search. For example, the search unit provides the most relevant search results based on the user's past search history. The search unit can also provide highly relevant search results from the user's past search history. The search unit can also improve the accuracy of search results by referencing the user's past search history. This ensures that the most relevant search results are provided based on the user's past search history. Some or all of the above-described processes in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the user's past search history data into a generative AI and have the generative AI perform the processing necessary to provide the most relevant search results.

[0110] The search unit estimates the user's emotions and prioritizes search results based on the estimated emotions. For example, if the user is stressed, the search unit will prioritize displaying important search results. If the user is relaxed, the search unit may also display detailed search results. If the user is in a hurry, the search unit may also prioritize displaying search results that can be quickly reviewed. In this way, the priority of search results is determined based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit may be performed using, for example, generative AI, or not using generative AI. For example, the search unit can input user emotion data into a generative AI and have the generative AI perform the processing to determine the priority of search results.

[0111] The search unit provides optimal search results by considering the user's device information during a search. For example, if the user is using a smartphone, the search unit provides search results that are adapted to the screen size. If the user is using a tablet, the search unit can also provide search results optimized for a larger screen. If the user is using a smartwatch, the search unit can also provide concise and highly visible search results. This ensures that optimal search results are provided based on the user's device information. Some or all of the above processing in the search unit may be performed using, for example, a generative AI, or without a generative AI. For example, the search unit can input the user's device information into a generative AI and have the generative AI perform the processing necessary to provide optimal search results.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The document selection unit can analyze the user's past selection history and suggest the most suitable document type. For example, it can automatically suggest document types that the user has frequently selected in the past. Furthermore, it can predict and suggest document types that will be selected during specific time periods based on the user's selection history. This ensures that the most suitable document type is suggested based on the user's past selection history. The document selection unit can input the user's selection history data into a generating AI and have the generating AI perform the processing necessary to suggest document types.

[0114] The formatting unit can adjust the level of detail in the format based on the importance of the document. For example, it can provide a detailed format for important documents and a simple format for general documents. Furthermore, it can provide a format that can be created quickly for urgent documents. In this way, the level of detail in the format is adjusted based on the importance of the document. The formatting unit inputs document importance data into the generation AI and causes the generation AI to perform processing to adjust the level of detail in the format.

[0115] The content checking unit can improve the accuracy of its checks by considering the interrelationships between documents. For example, it can refer to related documents and check for consistency in content. Furthermore, it can detect errors and inconsistencies by considering the interrelationships between documents. This improves the accuracy of the checks by considering the interrelationships between documents. The content checking unit can input interrelationship data between documents into the generating AI and have the generating AI perform processing to improve the accuracy of the checks.

[0116] The version control unit can optimize the current version by referring to past version data. For example, it can optimize the current version based on past version data. Furthermore, it can correct errors and inconsistencies by referring to past version data. This optimizes the current version based on past version data. The version control unit can input past version data into the generation AI and have the generation AI execute the process to optimize the current version.

[0117] The search unit can provide optimal search results by referring to the user's past search history. For example, it can provide optimal search results based on the user's past search history. Furthermore, it can also provide highly relevant search results from the user's past search history. This ensures that optimal search results are provided based on the user's past search history. The search unit can input the user's past search history data into a generating AI and have the generating AI execute the processing necessary to provide optimal search results.

[0118] The document selection unit can estimate the user's emotions and suggest document types based on those emotions. For example, if the user is stressed, it will prioritize suggesting simpler document types. Furthermore, if the user is relaxed, it can suggest more detailed document types. In this way, document types are suggested based on the user's emotions. The document selection unit inputs the user's emotion data into the generating AI and causes the generating AI to perform the processing necessary to suggest document types.

[0119] The formatting unit can estimate the user's emotions and adjust the format style based on those emotions. For example, if the user is stressed, it can provide a simple format style. Furthermore, if the user is relaxed, it can provide a more detailed format style. In this way, the format style is adjusted based on the user's emotions. The formatting unit can input the user's emotion data into the generating AI and have the generating AI perform the processing necessary to adjust the format style.

[0120] The content checking unit can estimate the user's emotions and adjust the content checking criteria based on those emotions. For example, if the user is stressed, it can provide simple checking criteria. Furthermore, if the user is relaxed, it can provide more detailed checking criteria. This ensures that the content checking criteria are adjusted based on the user's emotions. The content checking unit can input the user's emotion data into a generating AI and have the generating AI perform the necessary processing to adjust the content checking criteria.

[0121] The version control unit can estimate the user's emotions and adjust the version control method based on those emotions. For example, if the user is stressed, it can provide a simple version control method. Furthermore, if the user is relaxed, it can provide a more detailed version control method. In this way, the version control method is adjusted based on the user's emotions. The version control unit can input user emotion data into a generating AI and have the generating AI perform the processing necessary to adjust the version control method.

[0122] The search unit can estimate the user's emotions and adjust how search results are displayed based on those emotions. For example, if the user is stressed, simpler search results will be prioritized. Furthermore, if the user is relaxed, more detailed search results may be displayed. In this way, the display of search results is adjusted based on the user's emotions. The search unit can input user emotion data into a generating AI and have the generating AI perform processing to adjust how search results are displayed.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The document selection section allows you to select the type of document. For example, the document selection section allows you to select the type of document, such as a contract, report, or plan. The document selection section provides an interface for the user to select the type of document they want to generate. For example, if the user selects a contract, the document selection section displays a contract template and provides fields for entering the necessary information. Step 2: The formatting unit sets the format based on the document template selected by the document selection unit. For example, the formatting unit automatically sets the formatting application method and settings items based on the selected document template. The formatting unit automatically sets the document layout and style to maintain document consistency. Step 3: The content checking unit checks the content based on the format set by the format setting unit. The content checking unit uses generation AI to check the document content and detect errors and inconsistencies. For example, the content checking unit detects grammatical errors and content contradictions in the document and suggests corrections. The content checking unit uses generation AI to analyze the document content and improve the document quality. Step 4: The Version Control Department manages the versions of documents checked by the Content Checking Department on the cloud. The Version Control Department manages document versions and makes it easy to access past versions. For example, the Version Control Department assigns version numbers to documents and saves change history. The Version Control Department manages document versions on the cloud, simplifying the management of documents involving multiple people.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the document selection unit, format setting unit, content checking unit, version control unit, linking unit, search unit, and sentiment estimation function, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the document selection unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to select the type of document they want to generate. The format setting unit is implemented by the specific processing unit 290 of the data processing unit 12 and sets the format based on the template of the selected document. The content checking unit is implemented by the specific processing unit 290 of the data processing unit 12 and checks the content of the document using a generation AI. The version control unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs document version control on the cloud. The linking unit is implemented by the specific processing unit 290 of the data processing unit 12 and links the company's standard template with the learning data. The search unit is implemented by the control unit 46A of the smart device 14 and provides advanced search functionality. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which estimates the user's emotion and suggests the type of document. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the document selection unit, format setting unit, content checking unit, version control unit, linking unit, search unit, and sentiment estimation function, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the document selection unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to select the type of document they want to generate. The format setting unit is implemented by the specific processing unit 290 of the data processing unit 12 and sets the format based on the template of the selected document. The content checking unit is implemented by the specific processing unit 290 of the data processing unit 12 and checks the content of the document using a generation AI. The version control unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs document version control on the cloud. The linking unit is implemented by the specific processing unit 290 of the data processing unit 12 and links the company's standard template with the learning data. The search unit is implemented by the control unit 46A of the smart glasses 214 and provides advanced search functionality. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which estimates the user's emotion and suggests the type of document. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the document selection unit, format setting unit, content checking unit, version control unit, linking unit, search unit, and sentiment estimation function, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the document selection unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to select the type of document they want to generate. The format setting unit is implemented by the specific processing unit 290 of the data processing unit 12 and sets the format based on the template of the selected document. The content checking unit is implemented by the specific processing unit 290 of the data processing unit 12 and checks the content of the document using a generation AI. The version control unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs document version control on the cloud. The linking unit is implemented by the specific processing unit 290 of the data processing unit 12 and links the company's standard template with the learning data. The search unit is implemented by the control unit 46A of the headset terminal 314 and provides advanced search functionality. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which estimates the user's emotion and suggests the type of document. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the document selection unit, format setting unit, content checking unit, version control unit, linking unit, search unit, and sentiment estimation function, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the document selection unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to select the type of document they want to generate. The format setting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sets the format based on the template of the selected document. The content checking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and checks the content of the document using a generation AI. The version control unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs document version control on the cloud. The linking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and links the company's standard template with the learning data. The search unit is implemented by, for example, the control unit 46A of the robot 414 and provides an advanced search function. The emotion estimation function is implemented, for example, by the specific processing unit 290 of the data processing device 12, which estimates the user's emotion and suggests the type of document. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A document selection section for selecting the type of document, A formatting unit that sets the format based on the document template selected by the document selection unit, A content checking unit that checks the content based on the format set by the format setting unit, The system includes a version control unit that manages versions of documents checked by the content checking unit on the cloud. A system characterized by the following features. (Note 2) By linking the company's standard templates with the training data, It is equipped with a collaborative unit that enables more accurate document generation. The system described in Appendix 1, characterized by the features described herein. (Note 3) It provides advanced search functionality, It features a search unit for quickly searching and accessing necessary documents. The system described in Appendix 1, characterized by the features described herein. (Note 4) The document selection unit, Select the type of document, such as a contract, report, or plan. The system described in Appendix 1, characterized by the features described herein. (Note 5) The format setting unit is, Formatting is performed based on the selected document template. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned content checking unit, The AI ​​generates the document to check its content and detect errors and inconsistencies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned version control unit, Implement document version control in the cloud, making it easy to access past versions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The document selection unit, It estimates the user's emotions and suggests document types based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The document selection unit, The system analyzes the user's past document selection history and automatically selects the most suitable document type. The system described in Appendix 1, characterized by the features described herein. (Note 10) The document selection unit, When selecting documents, filtering is performed based on the user's current projects and work content. The system described in Appendix 1, characterized by the features described herein. (Note 11) The document selection unit, It estimates the user's emotions and determines the priority of document selection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The document selection unit, When selecting documents, the system prioritizes suggesting highly relevant documents based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The document selection unit, When selecting documents, the system analyzes the user's social media activity and suggests relevant documents. The system described in Appendix 1, characterized by the features described herein. (Note 14) The format setting unit is, It estimates the user's emotions and adjusts the format style based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The format setting unit is, When setting the format, adjust the level of detail in the formatting based on the importance of the document. The system described in Appendix 1, characterized by the features described herein. (Note 16) The format setting unit is, When formatting, different formatting algorithms are applied depending on the document category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The format setting unit is, It estimates the user's emotions and adjusts the format length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The format setting unit is, When setting the format, prioritize the format based on the document submission date. The system described in Appendix 1, characterized by the features described herein. (Note 19) The format setting unit is, When setting the format, adjust the order of formatting based on the relevance of the document. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned content checking unit, We estimate the user's emotions and adjust the content review criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned content checking unit, When checking the content, consider the interrelationships between documents to improve the accuracy of the check. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned content checking unit, When checking the content, the document submitter's attribute information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned content checking unit, The system estimates the user's emotions and adjusts the order in which the content review results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned content checking unit, When checking the content, the geographical distribution of the documents should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned content checking unit, When checking the content, refer to related literature to improve the accuracy of the check. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned version control unit, It estimates user sentiment and adjusts version control methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned version control unit, During version control, the current version is optimized by referencing past version data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned version control unit, When version control is performed, different version control methods are applied to each document category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned version control unit, It estimates user sentiment and determines version control priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned version control unit, During version control, weight versions based on when the document was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned version control unit, During version control, version control is performed by referencing relevant market data for the document. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, The system estimates the user's emotions and selects the data to link based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned linkage unit is, During integration, the integration algorithm is optimized by referring to past integration data. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the frequency of interaction based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned linkage unit is, During data transfer, the data is weighted based on the submission date of the documents. The system described in Appendix 2, characterized by the features described herein. (Note 36) 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 3, characterized by the features described herein. (Note 37) The aforementioned search unit, When a user searches, the system provides the most relevant search results by referencing their past search history. The system described in Appendix 3, characterized by the features described herein. (Note 38) 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 3, characterized by the features described herein. (Note 39) The aforementioned search unit, When searching, the system takes the user's device information into consideration to provide the most suitable search results. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0197] 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 document selection section for selecting the type of document, A formatting unit that sets the format based on the document template selected by the document selection unit, A content checking unit that checks the content based on the format set by the format setting unit, The system includes a version control unit that manages versions of documents checked by the content checking unit on the cloud. A system characterized by the following features.

2. By linking the company's standard templates with the training data, It is equipped with a collaborative unit that enables more accurate document generation. The system according to feature 1.

3. It provides advanced search functionality, It features a search unit for quickly searching and accessing necessary documents. The system according to feature 1.

4. The document selection unit, Select the type of document, such as a contract, report, or plan. The system according to feature 1.

5. The format setting unit is, Formatting is performed based on the selected document template. The system according to feature 1.

6. The aforementioned content checking unit, The AI ​​generates the document to check its content and detect errors and inconsistencies. The system according to feature 1.

7. The aforementioned version control unit, Implement document version control in the cloud, making it easy to access past versions. The system according to feature 1.

8. The document selection unit, It estimates the user's emotions and suggests document types based on those estimated emotions. The system according to feature 1.

9. The document selection unit, The system analyzes the user's past document selection history and automatically selects the most suitable document type. The system according to feature 1.

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