system

Abstract

We provide the system. [Solution] A means of extracting text data from documents stored in cloud storage, A means of automatically generating metadata from text data extracted using a generative model, A means of classifying documents based on metadata and providing search results according to user queries, A means of authenticating user access rights and, if necessary, executing an access authorization process, A means of generating document templates based on user operations and supplementing their content, A means to manage access restrictions based on server security protocols and prevent information leakage, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] There is a problem that it is difficult to efficiently manage and search a huge amount of document information within an enterprise, and thus it is hindered to quickly and accurately utilize the information. In addition, due to the lack of proper management of access rights, there may be a risk of information leakage. Furthermore, there is also a problem that the document creation work is inefficient and consumes a large amount of time and labor.

Means for Solving the Problems

[0005] This system extracts text data from documents stored in cloud storage and automatically generates metadata using a generative model. Based on the metadata, the system classifies documents and provides search results corresponding to user queries. Furthermore, it ensures information security by authenticating user access rights and executing access approval processes as needed. It also improves document creation efficiency by generating document templates and supplementing their content based on user actions. Finally, it enhances the reliability of overall information management by managing access restrictions based on server security protocols and preventing information leaks.

[0006] "Cloud storage" is a system that stores and manages data on remote servers accessible via the internet.

[0007] A "document" is an electronic or physical file containing text information, and is a unit of information with specific content.

[0008] A "generative model" is an algorithm or software that uses natural language processing or machine learning to generate specific output data from input data.

[0009] Metadata is auxiliary data that represents information about the attributes, structure, and content of data, and is used to streamline data management and retrieval.

[0010] "Classification" is the process of grouping and organizing different pieces of information or data based on specific criteria.

[0011] A "search result" is a list of relevant data and information that the system responds to in response to a user's query.

[0012] "Access permissions" are management rules that define the scope and restrictions to which users and systems can access specific data or functions.

[0013] An "approval process" is the procedure for obtaining the official permission necessary to use a particular action or access right.

[0014] A "document template" is a pre-defined document model that incorporates a set format and content to improve the efficiency of document creation.

[0015] A "security protocol" is a set of procedures and rules established to protect the integrity and confidentiality of information.

[0016] "Information leakage" refers to the unintended disclosure or release of confidential or personal information to external parties. [Brief explanation of the drawing]

[0017] [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]Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the language used in the following description will be explained.

[0020] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, 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), and the like.

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] 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.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 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.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] The 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.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] To implement this invention, a system integrating multiple functions is used. First, a server operates continuously to manage documents stored in the company's cloud storage, monitoring new and updated documents within the storage. This monitoring utilizes a storage API, extracting the document's text data whenever an update is detected.

[0039] Next, the server uses a generative model to automatically generate metadata from the extracted text data. The generative model used here includes applications of natural language processing technology, accurately extracting attribute information such as document type, author, and creation date to construct the metadata.

[0040] The server then classifies the documents based on the generated metadata. The classified documents are indexed for search engines, allowing them to instantly provide relevant documents in response to user search requests. This enables users to find the information they need quickly and effectively.

[0041] Furthermore, when a user attempts to access a specific document, the server checks the user's access rights and, if necessary, initiates the appropriate access authorization process. This feature enhances information security and simplifies permission management by administrators.

[0042] Furthermore, when a user creates a new document, the device uses a generative model to provide a document template and completes the content based on the user's input. This process streamlines document creation and reduces the burden on the user.

[0043] Finally, the server adheres to security protocols to properly manage access restrictions while preventing information leaks. These protocols are essential for ensuring information security and are regularly reviewed and updated.

[0044] As a concrete example, consider the case of creating monthly reports within a company. By using this system, users can easily search past reports and utilize the information automatically embedded in templates. Furthermore, it is possible to significantly reduce the effort required to create new reports. In this way, the present invention contributes to improved information management and security, as well as increased efficiency in document creation.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The server establishes access to cloud storage and periodically retrieves a list of documents within the storage. When new and updated documents are detected, text data is extracted from those documents.

[0048] Step 2:

[0049] The server analyzes the extracted text data and generates metadata using natural language processing techniques. This metadata includes the document title, creation date, author, and a summary of its content.

[0050] Step 3:

[0051] The server classifies documents based on the generated metadata. The classified documents are indexed for search engines, allowing users to quickly find relevant documents in response to their queries.

[0052] Step 4:

[0053] When a user searches for documents, they enter a query using a dedicated interface. The server receives this query, searches the indexed data, and returns a list of relevant documents to the user.

[0054] Step 5:

[0055] When a user requests access to a specific document, the server verifies the user's credentials. If necessary, the server automatically initiates an access authorization process, and once authorized, grants access to the document.

[0056] Step 6:

[0057] When a user creates a new document, the terminal uses a generative model to suggest a document template. The user enters information according to the template, and the generative model then completes and adjusts the document to ensure overall consistency.

[0058] Step 7:

[0059] To maintain the security of the entire system, the server records access logs and monitors for abnormal access. The server updates security policies as needed.

[0060] (Example 1)

[0061] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0062] Modern information management systems are required to efficiently and securely manage vast amounts of documents and to provide appropriate information quickly according to user needs. However, existing technologies still have challenges in terms of document classification and search accuracy, security, and the efficiency of user template generation. In particular, the lack of automation in document metadata generation and access rights management, and the thorough prevention of information leaks are problematic.

[0063] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0064] In this invention, the server includes means for retrieving information from documents stored in an information recording device, means for automatically generating related information from the retrieved information using a generation AI model, and means for classifying documents based on the related information and providing search results in response to user inquiries. This enables efficient document management, improved search accuracy, and enhanced security.

[0065] "Information recording device" refers to a storage medium or device used to store and manage documents and data.

[0066] A "generative AI model" refers to a mathematical model based on artificial intelligence technology that automatically generates useful related information and metadata from information such as text data.

[0067] "Related information" refers to attribute information extracted and added to documents by a generative AI model, and is used for classification and searching.

[0068] "User inquiries" refer to searches or requests made by users to the system in order to obtain specific information.

[0069] "Search results" refers to a collection of relevant documents and information provided based on user inquiries.

[0070] "Document basics" refers to templates and templates for documents that users create, and is intended to support efficient document creation.

[0071] An "information processing device" refers to a computer system used for processing, managing, and controlling information.

[0072] "Security regulations" refer to policies and procedures established to ensure the confidentiality, integrity, and availability of information.

[0073] To implement this invention, a cloud-based document management system is used as the foundation, with a server, terminals, and users each fulfilling their respective roles. Specific embodiments are shown below.

[0074] The server is the central unit that manages documents stored in the information recording device. As software that monitors and utilizes documents stored in cloud storage, the server has the functionality to check for document updates using an API. When a document is updated, it extracts the text data and automatically generates metadata using a generative AI model. This generative AI model is based on natural language processing and possesses the ability to accurately extract relevant information from documents.

[0075] When a user attempts to access a specific document, the server verifies the user's access rights and, if necessary, implements an access authorization process. This feature ensures the security of information and restricts access to unauthorized information. In addition, security protocols based on the Information Processing Equipment Security Code minimize the risk of information leakage.

[0076] The device leverages a generative AI model to provide the document foundation when a user creates a new document. Based on these prompts, it can quickly complete the information and formatting the user needs. For example, when creating a sales report, the user inputs sales data into a pre-designed template, and the AI ​​automatically generates graphs and summaries based on that content. This reduces the burden of document creation for the user and significantly improves efficiency.

[0077] As an example of a prompt, you could enter the instruction, "Use this generative AI model to extract important metadata from past sales reports and use it in a new monthly report template." This instruction will cause the AI ​​model to process the specified data appropriately and provide the user with the most suitable document foundation.

[0078] Thus, the present invention constructs a system that efficiently manages documents using cloud storage and provides secure and rapid information access. This system enables users to effectively perform their daily tasks.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The server connects to cloud storage to detect new or modified documents. It periodically polls using the storage API to check for updates. The input is document metadata from the cloud storage, and the output is a list of documents for which updates have been detected. The server then uses this information to identify which documents need to be processed.

[0082] Step 2:

[0083] The server extracts text data from the documents detected in step 1. It uses the storage API to retrieve the contents of the specified documents in text format and uses this as input. The output is text data, which is used in subsequent processing. The server then prepares this text data for further processing.

[0084] Step 3:

[0085] The server applies a generative AI model to the extracted text data to automatically generate metadata. The input is the text data obtained in step 2, and the output is the generated metadata. The generative AI model uses natural language processing to extract information such as the document type, author, and creation date, and outputs this as metadata.

[0086] Step 4:

[0087] The server categorizes documents based on the generated metadata and creates an index for the search engine. The input is the metadata generated in step 3, and the output is the index information. This index is used to quickly provide relevant documents when a user performs a search. The server applies a classification algorithm to efficiently organize the documents.

[0088] Step 5:

[0089] When a user attempts to access a specific document, the server verifies the user's access permissions. The input is the user's authentication information and the document they are requesting access to; the output is the result of granting or denying access. The server evaluates the security policy and, if necessary, seeks administrator approval.

[0090] Step 6:

[0091] The terminal utilizes a generative AI model to provide document templates when a user creates a new document. Based on this prompt, the terminal takes the user's initial input as input and outputs a completed document template. The terminal adds an auto-completion function to this template to improve the user's work efficiency.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] In business facilities, a large volume of documents are generated daily, and it is essential to manage these documents efficiently and to be able to quickly search and use them when needed. However, conventional document management systems require considerable effort for document classification and searching, and access rights management is complex. Furthermore, document creation is often time-consuming and prone to errors, highlighting the need to improve the efficiency of document management and creation.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes means for extracting document data stored in cloud storage, means for automatically generating attribute information from the extracted data using a generative model, and means for managing restrictions based on an information security protocol and preventing information leakage. This makes it possible to efficiently manage large volumes of documents in business facilities and realize appropriate access rights and an efficient document creation process.

[0097] "Cloud storage" refers to a virtual storage system that stores data via the internet, enabling broad data access that is not dependent on physical storage devices.

[0098] A "generative model" is a program based on artificial intelligence technology used to derive relevant information and patterns from data input, and specifically includes machine learning algorithms.

[0099] "Attribute information" refers to metadata associated with documents and data, and includes information such as the type of document, the creator, and the creation date.

[0100] An "information security protocol" is a set of policies, procedures, and technologies that define how data is protected from unauthorized access and leakage, and is a means of ensuring the confidentiality and integrity of information.

[0101] "Business facilities" refer to places and equipment used to conduct business or operations, and include factories, offices, and logistics centers.

[0102] This invention is a system for streamlining the management of large volumes of documents in business facilities. The server utilizes cloud storage to extract document data from virtual storage accessible via the internet. By using a storage API, the system can monitor the document status in real time when detecting data updates or new creations.

[0103] The server automatically generates attribute information from the extracted data using a generative model. This generative model includes machine learning algorithms that extract information such as document type, author, and creation date as metadata, enriching the document's attribute information. This facilitates document classification and searching.

[0104] Documents stored in cloud storage are managed with access restrictions based on information security protocols. Specifically, authentication services such as OAuth are used to verify user permissions and grant access when appropriate. This ensures that only necessary information is provided to the appropriate users while maintaining the confidentiality of the documents.

[0105] Users can access the system using a communication terminal and, when creating new documents, can use document templates automatically completed using a generative model. This lowers the barrier to document creation and allows users to proceed with their work quickly. For example, when creating an inventory report, it is possible to generate a document template with the necessary information automatically completed through prompts such as, "Please create a weekly inventory report including last week's inventory records and this year's sales data."

[0106] This system is implemented using cloud services such as AWS®, Azure®, or Google® Cloud as a platform, and using a generative model such as OpenAI® GPT-3®. For data protection and access management, the system always adheres to the latest information security protocols to ensure the confidentiality and integrity of documents.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The server uses a storage API to monitor document data on cloud storage. The input is status information from the cloud storage, which is used to detect new or modified documents. This allows the server to extract data on updated documents.

[0110] Step 2:

[0111] The server uses a generative AI model to generate attribute information from extracted document data. The input is the extracted document data, and the output generates metadata such as document type, author, and creation date. The generative AI model uses natural language processing techniques to analyze document attributes and automatically construct the metadata.

[0112] Step 3:

[0113] The server creates an index for classifying and searching documents based on the generated attribute information. The input is the generated attribute information, and the output is the document information indexed by the search engine. This allows the server to provide appropriate and relevant documents in response to user search requests.

[0114] Step 4:

[0115] The terminal receives queries from the user and sends them to the server. The input is the user's search query, and the output is the search request data sent to the server. Users can specify keywords and conditions to find the documents they need.

[0116] Step 5:

[0117] The server searches for relevant documents from its indexed document information based on the received search request and returns the results to the terminal. The input is the user's search request, and the output is the relevant document data. This allows the user to quickly obtain the information they need.

[0118] Step 6:

[0119] When a user attempts to access a document, the terminal requests access permission from the server. The input is the user's access request, and the output is the permission request data sent to the server. The server uses an authentication service to verify the user's permissions and grants access if appropriate.

[0120] Step 7:

[0121] When a user creates a new document, the device uses a generative AI model to provide an automatically completed document template. The input is the user's document creation request, and the output is the document template generated by the AI. This allows the user to create documents efficiently and with minimal effort.

[0122] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0123] This invention's system integrates sentiment recognition technology with document management. First, the server periodically detects new or modified documents in cloud storage and extracts the text data. Then, it automatically generates metadata from the extracted text data using a generative model. Based on the metadata, the server classifies the documents and performs efficient indexing using a search engine. This process enables fast and accurate document retrieval in response to user search queries.

[0124] Next, when a user uses the system to input data, the terminal activates an emotion engine to recognize emotions from the user's input and interactions. This emotion information interacts with other system functions, such as influencing the selection of document templates and content completion. By flexibly responding to emotions, the system improves the user's document creation experience.

[0125] As a concrete example, consider a scenario where a user is creating a presentation for a client. Not only does the server quickly provide relevant documents, but the terminal uses an emotion engine to sense the user's stress and confusion and provide appropriate support. This can be achieved, for example, by suggesting a simpler template if the user is struggling with a difficult part.

[0126] Furthermore, the server verifies user access rights and applies appropriate access restrictions based on security protocols. This feature ensures the overall security and reliability of the system.

[0127] Finally, upon completion of all tasks, the emotion engine summarizes the user's emotional state and provides feedback, suggesting improvements for future use. In this way, the present invention simultaneously achieves improved document management and user experience.

[0128] The following describes the processing flow.

[0129] Step 1:

[0130] The server connects to cloud storage and periodically searches for new and updated documents within the storage. For the detected documents, it extracts text data and converts it into a parseable data format.

[0131] Step 2:

[0132] The server uses a generative model to generate document metadata from the extracted text data. This metadata includes information such as the document title, creation date, and author, and is stored in a database.

[0133] Step 3:

[0134] The server automatically classifies documents based on metadata and creates an index for the search engine. This allows for the rapid delivery of search results in response to user queries.

[0135] Step 4:

[0136] The user enters a search query using a dedicated interface. The server receives this query, searches the indexed database, and provides the user with a list of relevant documents.

[0137] Step 5:

[0138] When a user requests to view a document, the server checks the user's access rights and, if necessary, performs an access authorization process. If authorized, the user is granted access to the document.

[0139] Step 6:

[0140] When a user creates or edits a document, the device activates an emotion engine that analyzes the input and actions to recognize the user's emotions. Based on this information, it provides optimal document template suggestions and supplementary features.

[0141] Step 7:

[0142] The device detects document consistency and linguistic errors based on emotions indicated by the emotion engine, and suggests necessary corrections. Feedback is collected during this process to improve the user experience.

[0143] Step 8:

[0144] After all tasks are completed, the server records access logs in accordance with security protocols and continues monitoring to prevent information leaks. In this way, the overall security and reliability of the system are maintained.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] Modern information management systems require efficient and accurate classification and retrieval of information, but conventional technologies do not adequately support document creation based on the user's emotional state. Furthermore, balancing information leakage prevention with flexible access control remains a challenge. Therefore, there is a need to improve the user experience while simultaneously strengthening system security.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes means for detecting and extracting character data from information stored in cloud storage, means for automatically generating attribute information from the extracted character data using a generative AI model, and means for classifying information based on the attribute information and providing search results in response to inquiries. This enables appropriate document creation support based on user sentiment and enhanced information security.

[0150] "Cloud storage" is a virtual storage device used to store and manage data via the internet.

[0151] "Character data" refers to the digital representation of information expressed through characters that make up documents and texts.

[0152] A "generative AI model" is an artificial intelligence technology that automatically understands patterns based on data and generates and analyzes new data.

[0153] "Attribute information" refers to metadata that represents the characteristics and features of data and documents, and is used for classification and searching.

[0154] A "formal template" is a standardized template that serves as the basis for documents and presentations, and is used by users to supplement the content.

[0155] "Emotion recognition" is the process of analyzing and identifying the emotional state a person is exhibiting based on their input and actions.

[0156] "Interaction" refers to the interaction of operations and communication between a user and a system.

[0157] "Information security" refers to measures taken to protect data and information from unauthorized access and leakage, and to ensure their proper safeguarding.

[0158] This document describes how a server, terminal, and user specifically utilize the system as an embodiment of this invention.

[0159] First, the server scans cloud storage to detect new or modified information. It uses OCR technology and text analysis software to extract text data from this information. Next, the server uses a generative AI model to generate attribute information from this text data. This process utilizes natural language processing techniques to extract necessary keywords and themes.

[0160] Subsequently, the server classifies the information based on attribute information generated through traditional search engine technologies, allowing users to efficiently search for relevant information. Software such as Apache® Solr or Elasticsearch® may be used at this stage.

[0161] Furthermore, when a user uses the system, the terminal uses emotion recognition technology to analyze the user's input and interactions to understand the user's emotional state. This emotion data is used in conjunction with a generative AI model to select document templates and complete content.

[0162] For example, a user might be creating a presentation for a client. In this case, the server quickly provides necessary reference materials, and the terminal senses the user's stress levels and supports them by suggesting simple templates when problems arise. An example of a prompt message might be, "Please help me create a product introduction presentation for our company. I'm feeling a little anxious right now."

[0163] This improves the convenience of document creation for users and allows them to work efficiently in a secure environment. The server verifies user access rights, applies appropriate restrictions based on security protocols, and prevents information leaks. This maintains the overall security and reliability of the system.

[0164] This system allows users to enjoy a more comfortable and effective service.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1: The server patrols the cloud storage and detects new or updated information. In this step, the server uses the cloud storage API to retrieve a list of files in the storage and extracts the relevant information based on metadata such as the modification date and time. The input requires cloud storage account information, and the output is a list of information.

[0167] Step 2: The server extracts text data from the detected information. In this process, OCR technology and text analysis tools are used to obtain text data from PDF and image files as well. The input is the information list obtained in Step 1, and the output is the extracted text data.

[0168] Step 3: The server uses a generative AI model to generate attribute information from the extracted text data. Here, natural language processing techniques are utilized to extract important keywords and themes and convert the content into metadata. The input is the text data obtained in Step 2, and the output is attribute information.

[0169] Step 4: The server classifies the information based on the generated attribute information and creates an index for the search engine. This step uses the indexing capabilities of search engines such as Apache Solr or Elasticsearch to enable rapid information retrieval. The input is the attribute information obtained in Step 3, and the output is the index data.

[0170] Step 5: When the user accesses the system, the terminal activates emotion recognition technology to acquire emotion data from the user's input and interactions during document creation. This process may utilize technologies such as voice tone and face capture. The input is user operation and interaction information, and the output is the user's emotion data.

[0171] Step 6: The device assists in selecting document templates and supplementing content based on the user's sentiment data. Here, a generative AI model is used to suggest the most suitable template for the user, efficiently assisting in document completion. The input is the sentiment data obtained in Step 5, and the output is the recommended template and document content.

[0172] Step 7: The server verifies the user's access rights and restricts access according to security procedures. Here, user rights are verified using LDAP or OAuth authentication systems to control access to sensitive information. The input is the user's authentication information, and the output is the result of the access rights.

[0173] (Application Example 2)

[0174] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0175] In recent years, in workplaces such as factories, where the volume of information handled has increased, there is a need for both efficient information management and support that takes into account the emotions of operators. However, conventional systems lack the efficiency of information retrieval and the ability to respond dynamically based on emotions, leading to problems such as decreased work efficiency and increased operator stress. Therefore, it is necessary to build a system that can efficiently manage information while considering the emotional state of workers and providing appropriate support.

[0176] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0177] In this invention, the server includes means for extracting information data stored in cloud storage, means for automatically generating characteristic data from the extracted information data using a generative model, and means for recognizing the emotions of users in the workplace and dynamically adjusting information provision and support based on those emotions. This streamlines information management in the workplace and enables flexible support that responds to the emotions of workers.

[0178] "Cloud storage" is a service that provides external data space for storing data via the internet.

[0179] "Information data" refers to a collection of various types of information related to the workplace, such as documents, records, and procedure manuals.

[0180] A "generative model" is an artificial intelligence technology that has a computational procedure for generating characteristic data from informational data.

[0181] "Characteristic data" refers to metadata generated from information data, which is attribute information useful for classifying and searching for information.

[0182] "Authentication" is the process of verifying whether a user has legitimate access rights.

[0183] A "template" is a model that provides a basic structure and format for creating documents and information.

[0184] A "server protection protocol" is a set of rules and procedures for implementing access restrictions and protecting information.

[0185] "Emotional recognition" is a technology that analyzes an operator's voice and actions to identify their emotional state.

[0186] "Dynamic adjustment" refers to flexibly changing the content or process according to the situation.

[0187] "Preventing information leaks" means taking measures to prevent unauthorized users from accessing information.

[0188] This invention constructs a dynamic system that manages information data held in cloud storage and provides appropriate support based on the operator's emotions. The server first extracts information data from cloud storage and automatically generates characteristic data using a generative model. This characteristic data allows for the classification of information and efficient results to be provided for user queries.

[0189] The system uses a speech recognition API to acquire voice data from the operator and an emotion recognition engine to analyze their emotions. Based on this emotion information, the server customizes templates and support information using a generative AI model to provide information tailored to the operator. For example, if the operator is feeling anxious, the server can display a step-by-step guide to help them calm down.

[0190] As a concrete example, consider a situation in a factory assembly line where an inexperienced operator is seeking instructions. In this case, the system senses the operator's anxiety and immediately provides specific advice using a generative AI model, such as, "Please calm down. Let's try steps 1 through 3 slowly."

[0191] An example of a prompt message is, "Generate helpful advice for workers when they are feeling anxious. This should include concise instructions and tips for relaxation." This system enables efficient work and maintains mental well-being.

[0192] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0193] Step 1:

[0194] The server extracts information data from cloud storage. It receives access permission information and a data identifier from the cloud storage as input, and downloads the relevant information data using the storage service API. This data is then formatted in text format to prepare it for subsequent data processing.

[0195] Step 2:

[0196] The server converts extracted informational data into characteristic data using a generative model. It receives the extracted informational data as input and generates metadata from it using natural language processing techniques. By using a generative model (e.g., GPT-3), attribute information based on the content of the informational data is automatically created and stored as characteristic data. This characteristic data is used for information classification.

[0197] Step 3:

[0198] The terminal acquires the operator's voice and analyzes the emotional data using an emotion recognition engine. It receives voice data from the microphone as input and converts it to text via a speech recognition API (e.g., Google Cloud Speech-to-Text). Based on this text, it identifies the emotional state using an emotion analysis API (e.g., IBM Watson® Tone Analyzer) and outputs it as emotional data.

[0199] Step 4:

[0200] The server utilizes a generative AI model based on emotional data to generate dynamically adjusted templates and support information. It receives emotional and characteristic data obtained in the previous step as input, prompting the generative AI model to generate support content. This content, adjusted according to the operator's stress and anxiety, is then sent to the terminal as output.

[0201] Step 5:

[0202] The user receives support information from the server and proceeds with the task. The generated templates and advice are displayed on the user's terminal screen, serving as a guide for specific actions. This process allows the user to work with confidence.

[0203] 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.

[0204] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0205] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0206] [Second Embodiment]

[0207] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0210] 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.

[0211] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0212] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0213] 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.

[0214] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0215] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0216] The 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.

[0217] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0218] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0219] To implement this invention, a system integrating multiple functions is used. First, a server operates continuously to manage documents stored in the company's cloud storage, monitoring new and updated documents within the storage. This monitoring utilizes a storage API, extracting the document's text data whenever an update is detected.

[0220] Next, the server uses a generative model to automatically generate metadata from the extracted text data. The generative model used here includes applications of natural language processing technology, accurately extracting attribute information such as document type, author, and creation date to construct the metadata.

[0221] The server then classifies the documents based on the generated metadata. The classified documents are indexed for search engines, allowing them to instantly provide relevant documents in response to user search requests. This enables users to find the information they need quickly and effectively.

[0222] Furthermore, when a user attempts to access a specific document, the server checks the user's access rights and, if necessary, initiates the appropriate access authorization process. This feature enhances information security and simplifies permission management by administrators.

[0223] Furthermore, when a user creates a new document, the device uses a generative model to provide a document template and completes the content based on the user's input. This process streamlines document creation and reduces the burden on the user.

[0224] Finally, the server adheres to security protocols to properly manage access restrictions while preventing information leaks. These protocols are essential for ensuring information security and are regularly reviewed and updated.

[0225] As a concrete example, consider the case of creating monthly reports within a company. By using this system, users can easily search past reports and utilize the information automatically embedded in templates. Furthermore, it is possible to significantly reduce the effort required to create new reports. In this way, the present invention contributes to improved information management and security, as well as increased efficiency in document creation.

[0226] The following describes the processing flow.

[0227] Step 1:

[0228] The server establishes access to cloud storage and periodically retrieves a list of documents within the storage. When new and updated documents are detected, text data is extracted from those documents.

[0229] Step 2:

[0230] The server analyzes the extracted text data and generates metadata using natural language processing techniques. This metadata includes the document title, creation date, author, and a summary of its content.

[0231] Step 3:

[0232] The server classifies documents based on the generated metadata. The classified documents are indexed for search engines, allowing users to quickly find relevant documents in response to their queries.

[0233] Step 4:

[0234] When a user searches for documents, they enter a query using a dedicated interface. The server receives this query, searches the indexed data, and returns a list of relevant documents to the user.

[0235] Step 5:

[0236] When a user requests access to a specific document, the server verifies the user's credentials. If necessary, the server automatically initiates an access authorization process, and once authorized, grants access to the document.

[0237] Step 6:

[0238] When a user creates a new document, the terminal uses a generative model to suggest a document template. The user enters information according to the template, and the generative model then completes and adjusts the document to ensure overall consistency.

[0239] Step 7:

[0240] To maintain the security of the entire system, the server records access logs and monitors for abnormal access. The server updates security policies as needed.

[0241] (Example 1)

[0242] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0243] Modern information management systems are required to efficiently and securely manage vast amounts of documents and to provide appropriate information quickly according to user needs. However, existing technologies still have challenges in terms of document classification and search accuracy, security, and the efficiency of user template generation. In particular, the lack of automation in document metadata generation and access rights management, and the thorough prevention of information leaks are problematic.

[0244] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0245] In this invention, the server includes means for retrieving information from documents stored in an information recording device, means for automatically generating related information from the retrieved information using a generation AI model, and means for classifying documents based on the related information and providing search results in response to user inquiries. This enables efficient document management, improved search accuracy, and enhanced security.

[0246] "Information recording device" refers to a storage medium or device used to store and manage documents and data.

[0247] A "generative AI model" refers to a mathematical model based on artificial intelligence technology that automatically generates useful related information and metadata from information such as text data.

[0248] "Related information" refers to attribute information extracted and added to documents by a generative AI model, and is used for classification and searching.

[0249] "User inquiries" refer to searches or requests made by users to the system in order to obtain specific information.

[0250] "Search results" refers to a collection of relevant documents and information provided based on user inquiries.

[0251] "Document basics" refers to templates and templates for documents that users create, and is intended to support efficient document creation.

[0252] An "information processing device" refers to a computer system used for processing, managing, and controlling information.

[0253] "Security regulations" refer to policies and procedures established to ensure the confidentiality, integrity, and availability of information.

[0254] To implement this invention, a cloud-based document management system is used as the foundation, with a server, terminals, and users each fulfilling their respective roles. Specific embodiments are shown below.

[0255] The server is the central unit that manages documents stored in the information recording device. As software that monitors and utilizes documents stored in cloud storage, the server has the functionality to check for document updates using an API. When a document is updated, it extracts the text data and automatically generates metadata using a generative AI model. This generative AI model is based on natural language processing and possesses the ability to accurately extract relevant information from documents.

[0256] When a user attempts to access a specific document, the server verifies the user's access rights and, if necessary, implements an access authorization process. This feature ensures the security of information and restricts access to unauthorized information. In addition, security protocols based on the Information Processing Equipment Security Code minimize the risk of information leakage.

[0257] The device leverages a generative AI model to provide the document foundation when a user creates a new document. Based on these prompts, it can quickly complete the information and formatting the user needs. For example, when creating a sales report, the user inputs sales data into a pre-designed template, and the AI ​​automatically generates graphs and summaries based on that content. This reduces the burden of document creation for the user and significantly improves efficiency.

[0258] As an example of a prompt, you could enter the instruction, "Use this generative AI model to extract important metadata from past sales reports and use it in a new monthly report template." This instruction will cause the AI ​​model to process the specified data appropriately and provide the user with the most suitable document foundation.

[0259] Thus, the present invention constructs a system that efficiently manages documents using cloud storage and provides secure and rapid information access. This system enables users to effectively perform their daily tasks.

[0260] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0261] Step 1:

[0262] The server connects to cloud storage to detect new or modified documents. It periodically polls using the storage API to check for updates. The input is document metadata from the cloud storage, and the output is a list of documents for which updates have been detected. The server then uses this information to identify which documents need to be processed.

[0263] Step 2:

[0264] The server extracts text data from the documents detected in step 1. It uses the storage API to retrieve the contents of the specified documents in text format and uses this as input. The output is text data, which is used in subsequent processing. The server then prepares this text data for further processing.

[0265] Step 3:

[0266] The server applies a generative AI model to the extracted text data to automatically generate metadata. The input is the text data obtained in step 2, and the output is the generated metadata. The generative AI model uses natural language processing to extract information such as the document type, author, and creation date, and outputs this as metadata.

[0267] Step 4:

[0268] The server categorizes documents based on the generated metadata and creates an index for the search engine. The input is the metadata generated in step 3, and the output is the index information. This index is used to quickly provide relevant documents when a user performs a search. The server applies a classification algorithm to efficiently organize the documents.

[0269] Step 5:

[0270] When a user attempts to access a specific document, the server verifies the user's access permissions. The input is the user's authentication information and the document they are requesting access to; the output is the result of granting or denying access. The server evaluates the security policy and, if necessary, seeks administrator approval.

[0271] Step 6:

[0272] The terminal utilizes a generative AI model to provide document templates when a user creates a new document. Based on this prompt, the terminal takes the user's initial input as input and outputs a completed document template. The terminal adds an auto-completion function to this template to improve the user's work efficiency.

[0273] (Application Example 1)

[0274] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0275] In business facilities, a large volume of documents are generated daily, and it is essential to manage these documents efficiently and to be able to quickly search and use them when needed. However, conventional document management systems require considerable effort for document classification and searching, and access rights management is complex. Furthermore, document creation is often time-consuming and prone to errors, highlighting the need to improve the efficiency of document management and creation.

[0276] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0277] In this invention, the server includes means for extracting document data stored in cloud storage, means for automatically generating attribute information from the extracted data using a generative model, and means for managing restrictions based on an information security protocol and preventing information leakage. This makes it possible to efficiently manage large volumes of documents in business facilities and realize appropriate access rights and an efficient document creation process.

[0278] "Cloud storage" refers to a virtual storage system that stores data via the internet, enabling broad data access that is not dependent on physical storage devices.

[0279] A "generative model" is a program based on artificial intelligence technology used to derive relevant information and patterns from data input, and specifically includes machine learning algorithms.

[0280] "Attribute information" refers to metadata associated with documents and data, and includes information such as the type of document, the creator, and the creation date.

[0281] An "information security protocol" is a set of policies, procedures, and technologies that define how data is protected from unauthorized access and leakage, and is a means of ensuring the confidentiality and integrity of information.

[0282] "Business facilities" refer to places and equipment used to conduct business or operations, and include factories, offices, and logistics centers.

[0283] This invention is a system for streamlining the management of large volumes of documents in business facilities. The server utilizes cloud storage to extract document data from virtual storage accessible via the internet. By using a storage API, the system can monitor the document status in real time when detecting data updates or new creations.

[0284] The server automatically generates attribute information from the extracted data using a generation model. The generation model includes a machine learning algorithm, which extracts information such as the document type, author, creation date, etc. as metadata, enriching the attribute information of the document. This facilitates document classification and retrieval.

[0285] Documents on the cloud storage device are managed with access restrictions based on information security protocols. Specifically, authentication services such as OAuth are used to verify the user's permissions and grant access when appropriate. This ensures that only the necessary information is provided to the appropriate users while maintaining the confidentiality of the documents.

[0286] When a user accesses the system using a communication terminal and creates a new document, they can use a document template automatically completed using the generation model. This lowers the hurdle for the user to create documents and enables them to proceed with their work quickly. As a specific example, when creating an inventory report, it is possible to generate a document template with the necessary information automatically completed through a prompt such as "Please create a weekly inventory report including last week's inventory records and this year's sales data."

[0287] This system is implemented by leveraging cloud services such as AWS, Azure, or Google Cloud as the platform and using, for example, OpenAI GPT-3 as the generation model. In data protection and access management, it always adheres to the latest information security protocols to ensure the confidentiality and integrity of the documents.

[0288] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0289] Step 1:

[0290] The server uses a storage API to monitor document data on cloud storage. The input is status information from the cloud storage, which is used to detect new or modified documents. This allows the server to extract data on updated documents.

[0291] Step 2:

[0292] The server uses a generative AI model to generate attribute information from extracted document data. The input is the extracted document data, and the output generates metadata such as document type, author, and creation date. The generative AI model uses natural language processing techniques to analyze document attributes and automatically construct the metadata.

[0293] Step 3:

[0294] The server creates an index for classifying and searching documents based on the generated attribute information. The input is the generated attribute information, and the output is the document information indexed by the search engine. This allows the server to provide appropriate and relevant documents in response to user search requests.

[0295] Step 4:

[0296] The terminal receives queries from the user and sends them to the server. The input is the user's search query, and the output is the search request data sent to the server. Users can specify keywords and conditions to find the documents they need.

[0297] Step 5:

[0298] The server searches for relevant documents from its indexed document information based on the received search request and returns the results to the terminal. The input is the user's search request, and the output is the relevant document data. This allows the user to quickly obtain the information they need.

[0299] Step 6:

[0300] When a user attempts to access a document, the terminal requests access permission from the server. The input is the user's access request, and the output is the permission request data sent to the server. The server uses an authentication service to verify the user's permissions and grants access if appropriate.

[0301] Step 7:

[0302] When a user creates a new document, the device uses a generative AI model to provide an automatically completed document template. The input is the user's document creation request, and the output is the document template generated by the AI. This allows the user to create documents efficiently and with minimal effort.

[0303] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0304] This invention's system integrates sentiment recognition technology with document management. First, the server periodically detects new or modified documents in cloud storage and extracts the text data. Then, it automatically generates metadata from the extracted text data using a generative model. Based on the metadata, the server classifies the documents and performs efficient indexing using a search engine. This process enables fast and accurate document retrieval in response to user search queries.

[0305] Next, when a user uses the system to input data, the terminal activates an emotion engine to recognize emotions from the user's input and interactions. This emotion information interacts with other system functions, such as influencing the selection of document templates and content completion. By flexibly responding to emotions, the system improves the user's document creation experience.

[0306] As a specific example, consider the case where a user creates a customer-oriented presentation. The server not only provides relevant documents quickly, but the terminal uses an emotion engine to sense the user's stress and confusion and provide corresponding support. This can be achieved, for example, by proposing a simpler template when the user is stuck on a difficult part.

[0307] Furthermore, the server checks the user's access rights and applies appropriate access restrictions based on security protocols. This function ensures the security and reliability of the entire system.

[0308] Finally, when all tasks are completed, the emotion engine summarizes the user's emotional state and provides feedback to propose improvement plans for future use. In this way, the present invention simultaneously realizes document management and improvement of the user experience.

[0309] The processing flow will be described below.

[0310] Step 1:

[0311] The server connects to cloud storage and periodically searches for new and updated documents in the storage. For the detected documents, the text data is extracted and converted into an analyzable data format.

[0312] Step 2:

[0313] The server uses a generation model to generate document metadata from the extracted text data. This metadata includes the document title, creation date, author information, and is saved in a database.

[0314] Step 3:

[0315] The server automatically classifies the documents based on the metadata and creates indexes for the search engine. This enables quick provision of search results for queries from users.

[0316] Step 4:

[0317] The user enters a search query using a dedicated interface. The server receives this query, searches the indexed database, and provides the user with a list of relevant documents.

[0318] Step 5:

[0319] When a user requests to view a document, the server checks the user's access rights and, if necessary, performs an access authorization process. If authorized, the user is granted access to the document.

[0320] Step 6:

[0321] When a user creates or edits a document, the device activates an emotion engine that analyzes the input and actions to recognize the user's emotions. Based on this information, it provides optimal document template suggestions and supplementary features.

[0322] Step 7:

[0323] The device detects document consistency and linguistic errors based on emotions indicated by the emotion engine, and suggests necessary corrections. Feedback is collected during this process to improve the user experience.

[0324] Step 8:

[0325] After all tasks are completed, the server records access logs in accordance with security protocols and continues monitoring to prevent information leaks. In this way, the overall security and reliability of the system are maintained.

[0326] (Example 2)

[0327] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0328] Modern information management systems require efficient and accurate classification and retrieval of information, but conventional technologies do not adequately support document creation based on the user's emotional state. Furthermore, balancing information leakage prevention with flexible access control remains a challenge. Therefore, there is a need to improve the user experience while simultaneously strengthening system security.

[0329] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0330] In this invention, the server includes means for detecting and extracting character data from information stored in cloud storage, means for automatically generating attribute information from the extracted character data using a generative AI model, and means for classifying information based on the attribute information and providing search results in response to inquiries. This enables appropriate document creation support based on user sentiment and enhanced information security.

[0331] "Cloud storage" is a virtual storage device used to store and manage data via the internet.

[0332] "Character data" refers to the digital representation of information expressed through characters that make up documents and texts.

[0333] A "generative AI model" is an artificial intelligence technology that automatically understands patterns based on data and generates and analyzes new data.

[0334] "Attribute information" refers to metadata that represents the characteristics and features of data and documents, and is used for classification and searching.

[0335] A "formal template" is a standardized template that serves as the basis for documents and presentations, and is used by users to supplement the content.

[0336] "Emotion recognition" is the process of analyzing and identifying the emotional state a person is exhibiting based on their input and actions.

[0337] "Interaction" refers to the interaction of operations and communication between a user and a system.

[0338] "Information security" refers to measures taken to protect data and information from unauthorized access and leakage, and to ensure their proper safeguarding.

[0339] This document describes how a server, terminal, and user specifically utilize the system as an embodiment of this invention.

[0340] First, the server scans cloud storage to detect new or modified information. It uses OCR technology and text analysis software to extract text data from this information. Next, the server uses a generative AI model to generate attribute information from this text data. This process utilizes natural language processing techniques to extract necessary keywords and themes.

[0341] Subsequently, the server classifies the information based on attribute information generated through traditional search engine technologies, allowing users to efficiently search for relevant information. Software such as Apache Solr or Elasticsearch may be used at this stage.

[0342] Furthermore, when a user uses the system, the terminal uses emotion recognition technology to analyze the user's input and interactions to understand the user's emotional state. This emotion data is used in conjunction with a generative AI model to select document templates and complete content.

[0343] For example, a user might be creating a presentation for a client. In this case, the server quickly provides necessary reference materials, and the terminal senses the user's stress levels and supports them by suggesting simple templates when problems arise. An example of a prompt message might be, "Please help me create a product introduction presentation for our company. I'm feeling a little anxious right now."

[0344] This improves the convenience of document creation for users and allows them to work efficiently in a secure environment. The server verifies user access rights, applies appropriate restrictions based on security protocols, and prevents information leaks. This maintains the overall security and reliability of the system.

[0345] This system allows users to enjoy a more comfortable and effective service.

[0346] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0347] Step 1: The server patrols the cloud storage and detects new or updated information. In this step, the server uses the cloud storage API to retrieve a list of files in the storage and extracts the relevant information based on metadata such as the modification date and time. The input requires cloud storage account information, and the output is a list of information.

[0348] Step 2: The server extracts text data from the detected information. In this process, OCR technology and text analysis tools are used to obtain text data from PDF and image files as well. The input is the information list obtained in Step 1, and the output is the extracted text data.

[0349] Step 3: The server uses a generative AI model to generate attribute information from the extracted text data. Here, natural language processing techniques are utilized to extract important keywords and themes and convert the content into metadata. The input is the text data obtained in Step 2, and the output is attribute information.

[0350] Step 4: The server classifies the information based on the generated attribute information and creates an index for the search engine. This step uses the indexing capabilities of search engines such as Apache Solr or Elasticsearch to enable rapid information retrieval. The input is the attribute information obtained in Step 3, and the output is the index data.

[0351] Step 5: When the user accesses the system, the terminal activates emotion recognition technology to acquire emotion data from the user's input and interactions during document creation. This process may utilize technologies such as voice tone and face capture. The input is user operation and interaction information, and the output is the user's emotion data.

[0352] Step 6: The device assists in selecting document templates and supplementing content based on the user's sentiment data. Here, a generative AI model is used to suggest the most suitable template for the user, efficiently assisting in document completion. The input is the sentiment data obtained in Step 5, and the output is the recommended template and document content.

[0353] Step 7: The server verifies the user's access rights and restricts access according to security procedures. Here, user rights are verified using LDAP or OAuth authentication systems to control access to sensitive information. The input is the user's authentication information, and the output is the result of the access rights.

[0354] (Application Example 2)

[0355] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0356] In recent years, in workplaces such as factories, where the volume of information handled has increased, there is a need for both efficient information management and support that takes into account the emotions of operators. However, conventional systems lack the efficiency of information retrieval and the ability to respond dynamically based on emotions, leading to problems such as decreased work efficiency and increased operator stress. Therefore, it is necessary to build a system that can efficiently manage information while considering the emotional state of workers and providing appropriate support.

[0357] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0358] In this invention, the server includes means for extracting information data stored in cloud storage, means for automatically generating characteristic data from the extracted information data using a generative model, and means for recognizing the emotions of users in the workplace and dynamically adjusting information provision and support based on those emotions. This streamlines information management in the workplace and enables flexible support that responds to the emotions of workers.

[0359] "Cloud storage" is a service that provides external data space for storing data via the internet.

[0360] "Information data" refers to a collection of various types of information related to the workplace, such as documents, records, and procedure manuals.

[0361] A "generative model" is an artificial intelligence technology that has a computational procedure for generating characteristic data from informational data.

[0362] "Characteristic data" refers to metadata generated from information data, which is attribute information useful for classifying and searching for information.

[0363] "Authentication" is the process of verifying whether a user has legitimate access rights.

[0364] A "template" is a model that provides a basic structure and format for creating documents and information.

[0365] A "server protection protocol" is a set of rules and procedures for implementing access restrictions and protecting information.

[0366] "Emotional recognition" is a technology that analyzes an operator's voice and actions to identify their emotional state.

[0367] "Dynamic adjustment" refers to flexibly changing the content or process according to the situation.

[0368] "Preventing information leaks" means taking measures to prevent unauthorized users from accessing information.

[0369] This invention constructs a dynamic system that manages information data held in cloud storage and provides appropriate support based on the operator's emotions. The server first extracts information data from cloud storage and automatically generates characteristic data using a generative model. This characteristic data allows for the classification of information and efficient results to be provided for user queries.

[0370] The system uses a speech recognition API to acquire voice data from the operator and an emotion recognition engine to analyze their emotions. Based on this emotion information, the server customizes templates and support information using a generative AI model to provide information tailored to the operator. For example, if the operator is feeling anxious, the server can display a step-by-step guide to help them calm down.

[0371] As a concrete example, consider a situation in a factory assembly line where an inexperienced operator is seeking instructions. In this case, the system senses the operator's anxiety and immediately provides specific advice using a generative AI model, such as, "Please calm down. Let's try steps 1 through 3 slowly."

[0372] An example of a prompt message is, "Generate helpful advice for workers when they are feeling anxious. This should include concise instructions and tips for relaxation." This system enables efficient work and maintains mental well-being.

[0373] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0374] Step 1:

[0375] The server extracts information data from cloud storage. It receives access permission information and a data identifier from the cloud storage as input, and downloads the relevant information data using the storage service API. This data is then formatted in text format to prepare it for subsequent data processing.

[0376] Step 2:

[0377] The server converts extracted informational data into characteristic data using a generative model. It receives the extracted informational data as input and generates metadata from it using natural language processing techniques. By using a generative model (e.g., GPT-3), attribute information based on the content of the informational data is automatically created and stored as characteristic data. This characteristic data is used for information classification.

[0378] Step 3:

[0379] The terminal acquires the operator's voice and analyzes the emotional data using an emotion recognition engine. It receives voice data from the microphone as input and converts it to text via a speech recognition API (e.g., Google Cloud Speech-to-Text). Based on this text, it identifies the emotional state using an emotion analysis API (e.g., IBM Watson Tone Analyzer) and outputs it as emotional data.

[0380] Step 4:

[0381] The server utilizes a generative AI model based on emotional data to generate dynamically adjusted templates and support information. It receives emotional and characteristic data obtained in the previous step as input, prompting the generative AI model to generate support content. This content, adjusted according to the operator's stress and anxiety, is then sent to the terminal as output.

[0382] Step 5:

[0383] The user receives support information from the server and proceeds with the task. The generated templates and advice are displayed on the user's terminal screen, serving as a guide for specific actions. This process allows the user to work with confidence.

[0384] 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.

[0385] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0386] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0387] [Third Embodiment]

[0388] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0389] 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.

[0390] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0391] 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.

[0392] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0393] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0394] 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.

[0395] 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.

[0396] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0397] The 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.

[0398] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0399] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0400] To implement this invention, a system integrating multiple functions is used. First, a server operates continuously to manage documents stored in the company's cloud storage, monitoring new and updated documents within the storage. This monitoring utilizes a storage API, extracting the document's text data whenever an update is detected.

[0401] Next, the server uses a generative model to automatically generate metadata from the extracted text data. The generative model used here includes applications of natural language processing technology, accurately extracting attribute information such as document type, author, and creation date to construct the metadata.

[0402] The server then classifies the documents based on the generated metadata. The classified documents are indexed for search engines, allowing them to instantly provide relevant documents in response to user search requests. This enables users to find the information they need quickly and effectively.

[0403] Furthermore, when a user attempts to access a specific document, the server checks the user's access rights and, if necessary, initiates the appropriate access authorization process. This feature enhances information security and simplifies permission management by administrators.

[0404] Furthermore, when a user creates a new document, the device uses a generative model to provide a document template and completes the content based on the user's input. This process streamlines document creation and reduces the burden on the user.

[0405] Finally, the server adheres to security protocols to properly manage access restrictions while preventing information leaks. These protocols are essential for ensuring information security and are regularly reviewed and updated.

[0406] As a concrete example, consider the case of creating monthly reports within a company. By using this system, users can easily search past reports and utilize the information automatically embedded in templates. Furthermore, it is possible to significantly reduce the effort required to create new reports. In this way, the present invention contributes to improved information management and security, as well as increased efficiency in document creation.

[0407] The following describes the processing flow.

[0408] Step 1:

[0409] The server establishes access to cloud storage and periodically retrieves a list of documents within the storage. When new and updated documents are detected, text data is extracted from those documents.

[0410] Step 2:

[0411] The server analyzes the extracted text data and generates metadata using natural language processing techniques. This metadata includes the document title, creation date, author, and a summary of its content.

[0412] Step 3:

[0413] The server classifies documents based on the generated metadata. The classified documents are indexed for search engines, allowing users to quickly find relevant documents in response to their queries.

[0414] Step 4:

[0415] When a user searches for documents, they enter a query using a dedicated interface. The server receives this query, searches the indexed data, and returns a list of relevant documents to the user.

[0416] Step 5:

[0417] When a user requests access to a specific document, the server verifies the user's credentials. If necessary, the server automatically initiates an access authorization process, and once authorized, grants access to the document.

[0418] Step 6:

[0419] When a user creates a new document, the terminal uses a generative model to suggest a document template. The user enters information according to the template, and the generative model then completes and adjusts the document to ensure overall consistency.

[0420] Step 7:

[0421] To maintain the security of the entire system, the server records access logs and monitors for abnormal access. The server updates security policies as needed.

[0422] (Example 1)

[0423] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0424] Modern information management systems are required to efficiently and securely manage vast amounts of documents and to provide appropriate information quickly according to user needs. However, existing technologies still have challenges in terms of document classification and search accuracy, security, and the efficiency of user template generation. In particular, the lack of automation in document metadata generation and access rights management, and the thorough prevention of information leaks are problematic.

[0425] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0426] In this invention, the server includes means for retrieving information from documents stored in an information recording device, means for automatically generating related information from the retrieved information using a generation AI model, and means for classifying documents based on the related information and providing search results in response to user inquiries. This enables efficient document management, improved search accuracy, and enhanced security.

[0427] "Information recording device" refers to a storage medium or device used to store and manage documents and data.

[0428] A "generative AI model" refers to a mathematical model based on artificial intelligence technology that automatically generates useful related information and metadata from information such as text data.

[0429] "Related information" refers to attribute information extracted and added to documents by a generative AI model, and is used for classification and searching.

[0430] "User inquiries" refer to searches or requests made by users to the system in order to obtain specific information.

[0431] "Search results" refers to a collection of relevant documents and information provided based on user inquiries.

[0432] "Document basics" refers to templates and templates for documents that users create, and is intended to support efficient document creation.

[0433] An "information processing device" refers to a computer system used for processing, managing, and controlling information.

[0434] "Security regulations" refer to policies and procedures established to ensure the confidentiality, integrity, and availability of information.

[0435] To implement this invention, a cloud-based document management system is used as the foundation, with a server, terminals, and users each fulfilling their respective roles. Specific embodiments are shown below.

[0436] The server is the central unit that manages documents stored in the information recording device. As software that monitors and utilizes documents stored in cloud storage, the server has the functionality to check for document updates using an API. When a document is updated, it extracts the text data and automatically generates metadata using a generative AI model. This generative AI model is based on natural language processing and possesses the ability to accurately extract relevant information from documents.

[0437] When a user attempts to access a specific document, the server verifies the user's access rights and, if necessary, implements an access authorization process. This feature ensures the security of information and restricts access to unauthorized information. In addition, security protocols based on the Information Processing Equipment Security Code minimize the risk of information leakage.

[0438] The device leverages a generative AI model to provide the document foundation when a user creates a new document. Based on these prompts, it can quickly complete the information and formatting the user needs. For example, when creating a sales report, the user inputs sales data into a pre-designed template, and the AI ​​automatically generates graphs and summaries based on that content. This reduces the burden of document creation for the user and significantly improves efficiency.

[0439] As an example of a prompt, you could enter the instruction, "Use this generative AI model to extract important metadata from past sales reports and use it in a new monthly report template." This instruction will cause the AI ​​model to process the specified data appropriately and provide the user with the most suitable document foundation.

[0440] Thus, the present invention constructs a system that efficiently manages documents using cloud storage and provides secure and rapid information access. This system enables users to effectively perform their daily tasks.

[0441] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0442] Step 1:

[0443] The server connects to cloud storage to detect new or modified documents. It periodically polls using the storage API to check for updates. The input is document metadata from the cloud storage, and the output is a list of documents for which updates have been detected. The server then uses this information to identify which documents need to be processed.

[0444] Step 2:

[0445] The server extracts text data from the documents detected in step 1. It uses the storage API to retrieve the contents of the specified documents in text format and uses this as input. The output is text data, which is used in subsequent processing. The server then prepares this text data for further processing.

[0446] Step 3:

[0447] The server applies a generative AI model to the extracted text data to automatically generate metadata. The input is the text data obtained in step 2, and the output is the generated metadata. The generative AI model uses natural language processing to extract information such as the document type, author, and creation date, and outputs this as metadata.

[0448] Step 4:

[0449] The server categorizes documents based on the generated metadata and creates an index for the search engine. The input is the metadata generated in step 3, and the output is the index information. This index is used to quickly provide relevant documents when a user performs a search. The server applies a classification algorithm to efficiently organize the documents.

[0450] Step 5:

[0451] When a user attempts to access a specific document, the server verifies the user's access permissions. The input is the user's authentication information and the document they are requesting access to; the output is the result of granting or denying access. The server evaluates the security policy and, if necessary, seeks administrator approval.

[0452] Step 6:

[0453] The terminal utilizes a generative AI model to provide document templates when a user creates a new document. Based on this prompt, the terminal takes the user's initial input as input and outputs a completed document template. The terminal adds an auto-completion function to this template to improve the user's work efficiency.

[0454] (Application Example 1)

[0455] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0456] In business facilities, a large volume of documents are generated daily, and it is essential to manage these documents efficiently and to be able to quickly search and use them when needed. However, conventional document management systems require considerable effort for document classification and searching, and access rights management is complex. Furthermore, document creation is often time-consuming and prone to errors, highlighting the need to improve the efficiency of document management and creation.

[0457] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0458] In this invention, the server includes means for extracting document data stored in cloud storage, means for automatically generating attribute information from the extracted data using a generative model, and means for managing restrictions based on an information security protocol and preventing information leakage. This makes it possible to efficiently manage large volumes of documents in business facilities and realize appropriate access rights and an efficient document creation process.

[0459] "Cloud storage" refers to a virtual storage system that stores data via the internet, enabling broad data access that is not dependent on physical storage devices.

[0460] A "generative model" is a program based on artificial intelligence technology used to derive relevant information and patterns from data input, and specifically includes machine learning algorithms.

[0461] "Attribute information" refers to metadata associated with documents and data, and includes information such as the type of document, the creator, and the creation date.

[0462] An "information security protocol" is a set of policies, procedures, and technologies that define how data is protected from unauthorized access and leakage, and is a means of ensuring the confidentiality and integrity of information.

[0463] "Business facilities" refer to places and equipment used to conduct business or operations, and include factories, offices, and logistics centers.

[0464] This invention is a system for streamlining the management of large volumes of documents in business facilities. The server utilizes cloud storage to extract document data from virtual storage accessible via the internet. By using a storage API, the system can monitor the document status in real time when detecting data updates or new creations.

[0465] The server automatically generates attribute information from the extracted data using a generative model. This generative model includes machine learning algorithms that extract information such as document type, author, and creation date as metadata, enriching the document's attribute information. This facilitates document classification and searching.

[0466] Documents stored in cloud storage are managed with access restrictions based on information security protocols. Specifically, authentication services such as OAuth are used to verify user permissions and grant access when appropriate. This ensures that only necessary information is provided to the appropriate users while maintaining the confidentiality of the documents.

[0467] Users can access the system using a communication terminal and, when creating new documents, can use document templates automatically completed using a generative model. This lowers the barrier to document creation and allows users to proceed with their work quickly. For example, when creating an inventory report, it is possible to generate a document template with the necessary information automatically completed through prompts such as, "Please create a weekly inventory report including last week's inventory records and this year's sales data."

[0468] This system is implemented using cloud services such as AWS, Azure, or Google Cloud as its platform, and using a generative model such as OpenAI GPT-3. For data protection and access management, it adheres to the latest information security protocols at all times to ensure the confidentiality and integrity of documents.

[0469] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0470] Step 1:

[0471] The server uses a storage API to monitor document data on cloud storage. The input is status information from the cloud storage, which is used to detect new or modified documents. This allows the server to extract data on updated documents.

[0472] Step 2:

[0473] The server uses a generative AI model to generate attribute information from extracted document data. The input is the extracted document data, and the output generates metadata such as document type, author, and creation date. The generative AI model uses natural language processing techniques to analyze document attributes and automatically construct the metadata.

[0474] Step 3:

[0475] The server creates an index for classifying and searching documents based on the generated attribute information. The input is the generated attribute information, and the output is the document information indexed by the search engine. This allows the server to provide appropriate and relevant documents in response to user search requests.

[0476] Step 4:

[0477] The terminal receives queries from the user and sends them to the server. The input is the user's search query, and the output is the search request data sent to the server. Users can specify keywords and conditions to find the documents they need.

[0478] Step 5:

[0479] The server searches for relevant documents from its indexed document information based on the received search request and returns the results to the terminal. The input is the user's search request, and the output is the relevant document data. This allows the user to quickly obtain the information they need.

[0480] Step 6:

[0481] When a user attempts to access a document, the terminal requests access permission from the server. The input is the user's access request, and the output is the permission request data sent to the server. The server uses an authentication service to verify the user's permissions and grants access if appropriate.

[0482] Step 7:

[0483] When a user creates a new document, the device uses a generative AI model to provide an automatically completed document template. The input is the user's document creation request, and the output is the document template generated by the AI. This allows the user to create documents efficiently and with minimal effort.

[0484] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0485] This invention's system integrates sentiment recognition technology with document management. First, the server periodically detects new or modified documents in cloud storage and extracts the text data. Then, it automatically generates metadata from the extracted text data using a generative model. Based on the metadata, the server classifies the documents and performs efficient indexing using a search engine. This process enables fast and accurate document retrieval in response to user search queries.

[0486] Next, when a user uses the system to input data, the terminal activates an emotion engine to recognize emotions from the user's input and interactions. This emotion information interacts with other system functions, such as influencing the selection of document templates and content completion. By flexibly responding to emotions, the system improves the user's document creation experience.

[0487] As a concrete example, consider a scenario where a user is creating a presentation for a client. Not only does the server quickly provide relevant documents, but the terminal uses an emotion engine to sense the user's stress and confusion and provide appropriate support. This can be achieved, for example, by suggesting a simpler template if the user is struggling with a difficult part.

[0488] Furthermore, the server verifies user access rights and applies appropriate access restrictions based on security protocols. This feature ensures the overall security and reliability of the system.

[0489] Finally, upon completion of all tasks, the emotion engine summarizes the user's emotional state and provides feedback, suggesting improvements for future use. In this way, the present invention simultaneously achieves improved document management and user experience.

[0490] The following describes the processing flow.

[0491] Step 1:

[0492] The server connects to cloud storage and periodically searches for new and updated documents within the storage. For the detected documents, it extracts text data and converts it into a parseable data format.

[0493] Step 2:

[0494] The server uses a generative model to generate document metadata from the extracted text data. This metadata includes information such as the document title, creation date, and author, and is stored in a database.

[0495] Step 3:

[0496] The server automatically classifies documents based on metadata and creates an index for the search engine. This allows for the rapid delivery of search results in response to user queries.

[0497] Step 4:

[0498] The user enters a search query using a dedicated interface. The server receives this query, searches the indexed database, and provides the user with a list of relevant documents.

[0499] Step 5:

[0500] When a user requests to view a document, the server checks the user's access rights and, if necessary, performs an access authorization process. If authorized, the user is granted access to the document.

[0501] Step 6:

[0502] When a user creates or edits a document, the device activates an emotion engine that analyzes the input and actions to recognize the user's emotions. Based on this information, it provides optimal document template suggestions and supplementary features.

[0503] Step 7:

[0504] The device detects document consistency and linguistic errors based on emotions indicated by the emotion engine, and suggests necessary corrections. Feedback is collected during this process to improve the user experience.

[0505] Step 8:

[0506] After all tasks are completed, the server records access logs in accordance with security protocols and continues monitoring to prevent information leaks. In this way, the overall security and reliability of the system are maintained.

[0507] (Example 2)

[0508] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0509] Modern information management systems require efficient and accurate classification and retrieval of information, but conventional technologies do not adequately support document creation based on the user's emotional state. Furthermore, balancing information leakage prevention with flexible access control remains a challenge. Therefore, there is a need to improve the user experience while simultaneously strengthening system security.

[0510] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0511] In this invention, the server includes means for detecting and extracting character data from information stored in cloud storage, means for automatically generating attribute information from the extracted character data using a generative AI model, and means for classifying information based on the attribute information and providing search results in response to inquiries. This enables appropriate document creation support based on user sentiment and enhanced information security.

[0512] "Cloud storage" is a virtual storage device used to store and manage data via the internet.

[0513] "Character data" refers to the digital representation of information expressed through characters that make up documents and texts.

[0514] A "generative AI model" is an artificial intelligence technology that automatically understands patterns based on data and generates and analyzes new data.

[0515] "Attribute information" refers to metadata that represents the characteristics and features of data and documents, and is used for classification and searching.

[0516] A "formal template" is a standardized template that serves as the basis for documents and presentations, and is used by users to supplement the content.

[0517] "Emotion recognition" is the process of analyzing and identifying the emotional state a person is exhibiting based on their input and actions.

[0518] "Interaction" refers to the interaction of operations and communication between a user and a system.

[0519] "Information security" refers to measures taken to protect data and information from unauthorized access and leakage, and to ensure their proper safeguarding.

[0520] This document describes how a server, terminal, and user specifically utilize the system as an embodiment of this invention.

[0521] First, the server scans cloud storage to detect new or modified information. It uses OCR technology and text analysis software to extract text data from this information. Next, the server uses a generative AI model to generate attribute information from this text data. This process utilizes natural language processing techniques to extract necessary keywords and themes.

[0522] Subsequently, the server classifies the information based on attribute information generated through traditional search engine technologies, allowing users to efficiently search for relevant information. Software such as Apache Solr or Elasticsearch may be used at this stage.

[0523] Furthermore, when a user uses the system, the terminal uses emotion recognition technology to analyze the user's input and interactions to understand the user's emotional state. This emotion data is used in conjunction with a generative AI model to select document templates and complete content.

[0524] For example, a user might be creating a presentation for a client. In this case, the server quickly provides necessary reference materials, and the terminal senses the user's stress levels and supports them by suggesting simple templates when problems arise. An example of a prompt message might be, "Please help me create a product introduction presentation for our company. I'm feeling a little anxious right now."

[0525] This improves the convenience of document creation for users and allows them to work efficiently in a secure environment. The server verifies user access rights, applies appropriate restrictions based on security protocols, and prevents information leaks. This maintains the overall security and reliability of the system.

[0526] This system allows users to enjoy a more comfortable and effective service.

[0527] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0528] Step 1: The server patrols the cloud storage and detects new or updated information. In this step, the server uses the cloud storage API to retrieve a list of files in the storage and extracts the relevant information based on metadata such as the modification date and time. The input requires cloud storage account information, and the output is a list of information.

[0529] Step 2: The server extracts text data from the detected information. In this process, OCR technology and text analysis tools are used to obtain text data from PDF and image files as well. The input is the information list obtained in Step 1, and the output is the extracted text data.

[0530] Step 3: The server uses a generative AI model to generate attribute information from the extracted text data. Here, natural language processing techniques are utilized to extract important keywords and themes and convert the content into metadata. The input is the text data obtained in Step 2, and the output is attribute information.

[0531] Step 4: The server classifies the information based on the generated attribute information and creates an index for the search engine. This step uses the indexing capabilities of search engines such as Apache Solr or Elasticsearch to enable rapid information retrieval. The input is the attribute information obtained in Step 3, and the output is the index data.

[0532] Step 5: When the user accesses the system, the terminal activates emotion recognition technology to acquire emotion data from the user's input and interactions during document creation. This process may utilize technologies such as voice tone and face capture. The input is user operation and interaction information, and the output is the user's emotion data.

[0533] Step 6: The device assists in selecting document templates and supplementing content based on the user's sentiment data. Here, a generative AI model is used to suggest the most suitable template for the user, efficiently assisting in document completion. The input is the sentiment data obtained in Step 5, and the output is the recommended template and document content.

[0534] Step 7: The server verifies the user's access rights and restricts access according to security procedures. Here, user rights are verified using LDAP or OAuth authentication systems to control access to sensitive information. The input is the user's authentication information, and the output is the result of the access rights.

[0535] (Application Example 2)

[0536] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0537] In recent years, in workplaces such as factories, where the volume of information handled has increased, there is a need for both efficient information management and support that takes into account the emotions of operators. However, conventional systems lack the efficiency of information retrieval and the ability to respond dynamically based on emotions, leading to problems such as decreased work efficiency and increased operator stress. Therefore, it is necessary to build a system that can efficiently manage information while considering the emotional state of workers and providing appropriate support.

[0538] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0539] In this invention, the server includes means for extracting information data stored in cloud storage, means for automatically generating characteristic data from the extracted information data using a generative model, and means for recognizing the emotions of users in the workplace and dynamically adjusting information provision and support based on those emotions. This streamlines information management in the workplace and enables flexible support that responds to the emotions of workers.

[0540] "Cloud storage" is a service that provides external data space for storing data via the internet.

[0541] "Information data" refers to a collection of various types of information related to the workplace, such as documents, records, and procedure manuals.

[0542] A "generative model" is an artificial intelligence technology that has a computational procedure for generating characteristic data from informational data.

[0543] "Characteristic data" refers to metadata generated from information data, which is attribute information useful for classifying and searching for information.

[0544] "Authentication" is the process of verifying whether a user has legitimate access rights.

[0545] A "template" is a model that provides a basic structure and format for creating documents and information.

[0546] A "server protection protocol" is a set of rules and procedures for implementing access restrictions and protecting information.

[0547] "Emotional recognition" is a technology that analyzes an operator's voice and actions to identify their emotional state.

[0548] "Dynamic adjustment" refers to flexibly changing the content or process according to the situation.

[0549] "Preventing information leaks" means taking measures to prevent unauthorized users from accessing information.

[0550] This invention constructs a dynamic system that manages information data held in cloud storage and provides appropriate support based on the operator's emotions. The server first extracts information data from cloud storage and automatically generates characteristic data using a generative model. This characteristic data allows for the classification of information and efficient results to be provided for user queries.

[0551] The system uses a speech recognition API to acquire voice data from the operator and an emotion recognition engine to analyze their emotions. Based on this emotion information, the server customizes templates and support information using a generative AI model to provide information tailored to the operator. For example, if the operator is feeling anxious, the server can display a step-by-step guide to help them calm down.

[0552] As a concrete example, consider a situation in a factory assembly line where an inexperienced operator is seeking instructions. In this case, the system senses the operator's anxiety and immediately provides specific advice using a generative AI model, such as, "Please calm down. Let's try steps 1 through 3 slowly."

[0553] An example of a prompt message is, "Generate helpful advice for workers when they are feeling anxious. This should include concise instructions and tips for relaxation." This system enables efficient work and maintains mental well-being.

[0554] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0555] Step 1:

[0556] The server extracts information data from cloud storage. It receives access permission information and a data identifier from the cloud storage as input, and downloads the relevant information data using the storage service API. This data is then formatted in text format to prepare it for subsequent data processing.

[0557] Step 2:

[0558] The server converts extracted informational data into characteristic data using a generative model. It receives the extracted informational data as input and generates metadata from it using natural language processing techniques. By using a generative model (e.g., GPT-3), attribute information based on the content of the informational data is automatically created and stored as characteristic data. This characteristic data is used for information classification.

[0559] Step 3:

[0560] The terminal acquires the operator's voice and analyzes the emotional data using an emotion recognition engine. It receives voice data from the microphone as input and converts it to text via a speech recognition API (e.g., Google Cloud Speech-to-Text). Based on this text, it identifies the emotional state using an emotion analysis API (e.g., IBM Watson Tone Analyzer) and outputs it as emotional data.

[0561] Step 4:

[0562] The server utilizes a generative AI model based on emotional data to generate dynamically adjusted templates and support information. It receives emotional and characteristic data obtained in the previous step as input, prompting the generative AI model to generate support content. This content, adjusted according to the operator's stress and anxiety, is then sent to the terminal as output.

[0563] Step 5:

[0564] The user receives support information from the server and proceeds with the task. The generated templates and advice are displayed on the user's terminal screen, serving as a guide for specific actions. This process allows the user to work with confidence.

[0565] 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.

[0566] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0567] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0568] [Fourth Embodiment]

[0569] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0570] 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.

[0571] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0572] 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.

[0573] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0574] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0575] 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.

[0576] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0577] 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.

[0578] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0579] The 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.

[0580] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0581] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0582] To implement this invention, a system integrating multiple functions is used. First, a server operates continuously to manage documents stored in the company's cloud storage, monitoring new and updated documents within the storage. This monitoring utilizes a storage API, extracting the document's text data whenever an update is detected.

[0583] Next, the server uses a generative model to automatically generate metadata from the extracted text data. The generative model used here includes applications of natural language processing technology, accurately extracting attribute information such as document type, author, and creation date to construct the metadata.

[0584] The server then classifies the documents based on the generated metadata. The classified documents are indexed for search engines, allowing them to instantly provide relevant documents in response to user search requests. This enables users to find the information they need quickly and effectively.

[0585] Furthermore, when a user attempts to access a specific document, the server checks the user's access rights and, if necessary, initiates the appropriate access authorization process. This feature enhances information security and simplifies permission management by administrators.

[0586] Furthermore, when a user creates a new document, the device uses a generative model to provide a document template and completes the content based on the user's input. This process streamlines document creation and reduces the burden on the user.

[0587] Finally, the server adheres to security protocols to properly manage access restrictions while preventing information leaks. These protocols are essential for ensuring information security and are regularly reviewed and updated.

[0588] As a concrete example, consider the case of creating monthly reports within a company. By using this system, users can easily search past reports and utilize the information automatically embedded in templates. Furthermore, it is possible to significantly reduce the effort required to create new reports. In this way, the present invention contributes to improved information management and security, as well as increased efficiency in document creation.

[0589] The following describes the processing flow.

[0590] Step 1:

[0591] The server establishes access to cloud storage and periodically retrieves a list of documents within the storage. When new and updated documents are detected, text data is extracted from those documents.

[0592] Step 2:

[0593] The server analyzes the extracted text data and generates metadata using natural language processing techniques. This metadata includes the document title, creation date, author, and a summary of its content.

[0594] Step 3:

[0595] The server classifies documents based on the generated metadata. The classified documents are indexed for search engines, allowing users to quickly find relevant documents in response to their queries.

[0596] Step 4:

[0597] When a user searches for documents, they enter a query using a dedicated interface. The server receives this query, searches the indexed data, and returns a list of relevant documents to the user.

[0598] Step 5:

[0599] When a user requests access to a specific document, the server verifies the user's credentials. If necessary, the server automatically initiates an access authorization process, and once authorized, grants access to the document.

[0600] Step 6:

[0601] When a user creates a new document, the terminal uses a generative model to suggest a document template. The user enters information according to the template, and the generative model then completes and adjusts the document to ensure overall consistency.

[0602] Step 7:

[0603] To maintain the security of the entire system, the server records access logs and monitors for abnormal access. The server updates security policies as needed.

[0604] (Example 1)

[0605] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0606] Modern information management systems are required to efficiently and securely manage vast amounts of documents and to provide appropriate information quickly according to user needs. However, existing technologies still have challenges in terms of document classification and search accuracy, security, and the efficiency of user template generation. In particular, the lack of automation in document metadata generation and access rights management, and the thorough prevention of information leaks are problematic.

[0607] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0608] In this invention, the server includes means for retrieving information from documents stored in an information recording device, means for automatically generating related information from the retrieved information using a generation AI model, and means for classifying documents based on the related information and providing search results in response to user inquiries. This enables efficient document management, improved search accuracy, and enhanced security.

[0609] "Information recording device" refers to a storage medium or device used to store and manage documents and data.

[0610] A "generative AI model" refers to a mathematical model based on artificial intelligence technology that automatically generates useful related information and metadata from information such as text data.

[0611] "Related information" refers to attribute information extracted and added to documents by a generative AI model, and is used for classification and searching.

[0612] "User inquiries" refer to searches or requests made by users to the system in order to obtain specific information.

[0613] "Search results" refers to a collection of relevant documents and information provided based on user inquiries.

[0614] "Document basics" refers to templates and templates for documents that users create, and is intended to support efficient document creation.

[0615] An "information processing device" refers to a computer system used for processing, managing, and controlling information.

[0616] "Security regulations" refer to policies and procedures established to ensure the confidentiality, integrity, and availability of information.

[0617] To implement this invention, a cloud-based document management system is used as the foundation, with a server, terminals, and users each fulfilling their respective roles. Specific embodiments are shown below.

[0618] The server is the central unit that manages documents stored in the information recording device. As software that monitors and utilizes documents stored in cloud storage, the server has the functionality to check for document updates using an API. When a document is updated, it extracts the text data and automatically generates metadata using a generative AI model. This generative AI model is based on natural language processing and possesses the ability to accurately extract relevant information from documents.

[0619] When a user attempts to access a specific document, the server verifies the user's access rights and, if necessary, implements an access authorization process. This feature ensures the security of information and restricts access to unauthorized information. In addition, security protocols based on the Information Processing Equipment Security Code minimize the risk of information leakage.

[0620] The device leverages a generative AI model to provide the document foundation when a user creates a new document. Based on these prompts, it can quickly complete the information and formatting the user needs. For example, when creating a sales report, the user inputs sales data into a pre-designed template, and the AI ​​automatically generates graphs and summaries based on that content. This reduces the burden of document creation for the user and significantly improves efficiency.

[0621] As an example of a prompt, you could enter the instruction, "Use this generative AI model to extract important metadata from past sales reports and use it in a new monthly report template." This instruction will cause the AI ​​model to process the specified data appropriately and provide the user with the most suitable document foundation.

[0622] Thus, the present invention constructs a system that efficiently manages documents using cloud storage and provides secure and rapid information access. This system enables users to effectively perform their daily tasks.

[0623] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0624] Step 1:

[0625] The server connects to cloud storage to detect new or modified documents. It periodically polls using the storage API to check for updates. The input is document metadata from the cloud storage, and the output is a list of documents for which updates have been detected. The server then uses this information to identify which documents need to be processed.

[0626] Step 2:

[0627] The server extracts text data from the documents detected in step 1. It uses the storage API to retrieve the contents of the specified documents in text format and uses this as input. The output is text data, which is used in subsequent processing. The server then prepares this text data for further processing.

[0628] Step 3:

[0629] The server applies a generative AI model to the extracted text data to automatically generate metadata. The input is the text data obtained in step 2, and the output is the generated metadata. The generative AI model uses natural language processing to extract information such as the document type, author, and creation date, and outputs this as metadata.

[0630] Step 4:

[0631] The server categorizes documents based on the generated metadata and creates an index for the search engine. The input is the metadata generated in step 3, and the output is the index information. This index is used to quickly provide relevant documents when a user performs a search. The server applies a classification algorithm to efficiently organize the documents.

[0632] Step 5:

[0633] When a user attempts to access a specific document, the server verifies the user's access permissions. The input is the user's authentication information and the document they are requesting access to; the output is the result of granting or denying access. The server evaluates the security policy and, if necessary, seeks administrator approval.

[0634] Step 6:

[0635] The terminal utilizes a generative AI model to provide document templates when a user creates a new document. Based on this prompt, the terminal takes the user's initial input as input and outputs a completed document template. The terminal adds an auto-completion function to this template to improve the user's work efficiency.

[0636] (Application Example 1)

[0637] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0638] In business facilities, a large volume of documents are generated daily, and it is essential to manage these documents efficiently and to be able to quickly search and use them when needed. However, conventional document management systems require considerable effort for document classification and searching, and access rights management is complex. Furthermore, document creation is often time-consuming and prone to errors, highlighting the need to improve the efficiency of document management and creation.

[0639] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0640] In this invention, the server includes means for extracting document data stored in cloud storage, means for automatically generating attribute information from the extracted data using a generative model, and means for managing restrictions based on an information security protocol and preventing information leakage. This makes it possible to efficiently manage large volumes of documents in business facilities and realize appropriate access rights and an efficient document creation process.

[0641] "Cloud storage" refers to a virtual storage system that stores data via the internet, enabling broad data access that is not dependent on physical storage devices.

[0642] A "generative model" is a program based on artificial intelligence technology used to derive relevant information and patterns from data input, and specifically includes machine learning algorithms.

[0643] "Attribute information" refers to metadata associated with documents and data, and includes information such as the type of document, the creator, and the creation date.

[0644] An "information security protocol" is a set of policies, procedures, and technologies that define how data is protected from unauthorized access and leakage, and is a means of ensuring the confidentiality and integrity of information.

[0645] "Business facilities" refer to places and equipment used to conduct business or operations, and include factories, offices, and logistics centers.

[0646] This invention is a system for streamlining the management of large volumes of documents in business facilities. The server utilizes cloud storage to extract document data from virtual storage accessible via the internet. By using a storage API, the system can monitor the document status in real time when detecting data updates or new creations.

[0647] The server automatically generates attribute information from the extracted data using a generative model. This generative model includes machine learning algorithms that extract information such as document type, author, and creation date as metadata, enriching the document's attribute information. This facilitates document classification and searching.

[0648] Documents stored in cloud storage are managed with access restrictions based on information security protocols. Specifically, authentication services such as OAuth are used to verify user permissions and grant access when appropriate. This ensures that only necessary information is provided to the appropriate users while maintaining the confidentiality of the documents.

[0649] Users can access the system using a communication terminal and, when creating new documents, can use document templates automatically completed using a generative model. This lowers the barrier to document creation and allows users to proceed with their work quickly. For example, when creating an inventory report, it is possible to generate a document template with the necessary information automatically completed through prompts such as, "Please create a weekly inventory report including last week's inventory records and this year's sales data."

[0650] This system is implemented using cloud services such as AWS, Azure, or Google Cloud as its platform, and using a generative model such as OpenAI GPT-3. For data protection and access management, it adheres to the latest information security protocols at all times to ensure the confidentiality and integrity of documents.

[0651] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0652] Step 1:

[0653] The server uses a storage API to monitor document data on cloud storage. The input is status information from the cloud storage, which is used to detect new or modified documents. This allows the server to extract data on updated documents.

[0654] Step 2:

[0655] The server uses a generative AI model to generate attribute information from extracted document data. The input is the extracted document data, and the output generates metadata such as document type, author, and creation date. The generative AI model uses natural language processing techniques to analyze document attributes and automatically construct the metadata.

[0656] Step 3:

[0657] The server creates an index for classifying and searching documents based on the generated attribute information. The input is the generated attribute information, and the output is the document information indexed by the search engine. This allows the server to provide appropriate and relevant documents in response to user search requests.

[0658] Step 4:

[0659] The terminal receives queries from the user and sends them to the server. The input is the user's search query, and the output is the search request data sent to the server. Users can specify keywords and conditions to find the documents they need.

[0660] Step 5:

[0661] The server searches for relevant documents from its indexed document information based on the received search request and returns the results to the terminal. The input is the user's search request, and the output is the relevant document data. This allows the user to quickly obtain the information they need.

[0662] Step 6:

[0663] When a user attempts to access a document, the terminal requests access permission from the server. The input is the user's access request, and the output is the permission request data sent to the server. The server uses an authentication service to verify the user's permissions and grants access if appropriate.

[0664] Step 7:

[0665] When a user creates a new document, the device uses a generative AI model to provide an automatically completed document template. The input is the user's document creation request, and the output is the document template generated by the AI. This allows the user to create documents efficiently and with minimal effort.

[0666] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0667] This invention's system integrates sentiment recognition technology with document management. First, the server periodically detects new or modified documents in cloud storage and extracts the text data. Then, it automatically generates metadata from the extracted text data using a generative model. Based on the metadata, the server classifies the documents and performs efficient indexing using a search engine. This process enables fast and accurate document retrieval in response to user search queries.

[0668] Next, when a user uses the system to input data, the terminal activates an emotion engine to recognize emotions from the user's input and interactions. This emotion information interacts with other system functions, such as influencing the selection of document templates and content completion. By flexibly responding to emotions, the system improves the user's document creation experience.

[0669] As a concrete example, consider a scenario where a user is creating a presentation for a client. Not only does the server quickly provide relevant documents, but the terminal uses an emotion engine to sense the user's stress and confusion and provide appropriate support. This can be achieved, for example, by suggesting a simpler template if the user is struggling with a difficult part.

[0670] Furthermore, the server verifies user access rights and applies appropriate access restrictions based on security protocols. This feature ensures the overall security and reliability of the system.

[0671] Finally, upon completion of all tasks, the emotion engine summarizes the user's emotional state and provides feedback, suggesting improvements for future use. In this way, the present invention simultaneously achieves improved document management and user experience.

[0672] The following describes the processing flow.

[0673] Step 1:

[0674] The server connects to cloud storage and periodically searches for new and updated documents within the storage. For the detected documents, it extracts text data and converts it into a parseable data format.

[0675] Step 2:

[0676] The server uses a generative model to generate document metadata from the extracted text data. This metadata includes information such as the document title, creation date, and author, and is stored in a database.

[0677] Step 3:

[0678] The server automatically classifies documents based on metadata and creates an index for the search engine. This allows for the rapid delivery of search results in response to user queries.

[0679] Step 4:

[0680] The user enters a search query using a dedicated interface. The server receives this query, searches the indexed database, and provides the user with a list of relevant documents.

[0681] Step 5:

[0682] When a user requests to view a document, the server checks the user's access rights and, if necessary, performs an access authorization process. If authorized, the user is granted access to the document.

[0683] Step 6:

[0684] When a user creates or edits a document, the device activates an emotion engine that analyzes the input and actions to recognize the user's emotions. Based on this information, it provides optimal document template suggestions and supplementary features.

[0685] Step 7:

[0686] The device detects document consistency and linguistic errors based on emotions indicated by the emotion engine, and suggests necessary corrections. Feedback is collected during this process to improve the user experience.

[0687] Step 8:

[0688] After all tasks are completed, the server records access logs in accordance with security protocols and continues monitoring to prevent information leaks. In this way, the overall security and reliability of the system are maintained.

[0689] (Example 2)

[0690] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0691] Modern information management systems require efficient and accurate classification and retrieval of information, but conventional technologies do not adequately support document creation based on the user's emotional state. Furthermore, balancing information leakage prevention with flexible access control remains a challenge. Therefore, there is a need to improve the user experience while simultaneously strengthening system security.

[0692] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0693] In this invention, the server includes means for detecting and extracting character data from information stored in cloud storage, means for automatically generating attribute information from the extracted character data using a generative AI model, and means for classifying information based on the attribute information and providing search results in response to inquiries. This enables appropriate document creation support based on user sentiment and enhanced information security.

[0694] "Cloud storage" is a virtual storage device used to store and manage data via the internet.

[0695] "Character data" refers to the digital representation of information expressed through characters that make up documents and texts.

[0696] A "generative AI model" is an artificial intelligence technology that automatically understands patterns based on data and generates and analyzes new data.

[0697] "Attribute information" refers to metadata that represents the characteristics and features of data and documents, and is used for classification and searching.

[0698] A "formal template" is a standardized template that serves as the basis for documents and presentations, and is used by users to supplement the content.

[0699] "Emotion recognition" is the process of analyzing and identifying the emotional state a person is exhibiting based on their input and actions.

[0700] "Interaction" refers to the interaction of operations and communication between a user and a system.

[0701] "Information security" refers to measures taken to protect data and information from unauthorized access and leakage, and to ensure their proper safeguarding.

[0702] This document describes how a server, terminal, and user specifically utilize the system as an embodiment of this invention.

[0703] First, the server scans cloud storage to detect new or modified information. It uses OCR technology and text analysis software to extract text data from this information. Next, the server uses a generative AI model to generate attribute information from this text data. This process utilizes natural language processing techniques to extract necessary keywords and themes.

[0704] Subsequently, the server classifies the information based on attribute information generated through traditional search engine technologies, allowing users to efficiently search for relevant information. Software such as Apache Solr or Elasticsearch may be used at this stage.

[0705] Furthermore, when a user uses the system, the terminal uses emotion recognition technology to analyze the user's input and interactions to understand the user's emotional state. This emotion data is used in conjunction with a generative AI model to select document templates and complete content.

[0706] For example, a user might be creating a presentation for a client. In this case, the server quickly provides necessary reference materials, and the terminal senses the user's stress levels and supports them by suggesting simple templates when problems arise. An example of a prompt message might be, "Please help me create a product introduction presentation for our company. I'm feeling a little anxious right now."

[0707] This improves the convenience of document creation for users and allows them to work efficiently in a secure environment. The server verifies user access rights, applies appropriate restrictions based on security protocols, and prevents information leaks. This maintains the overall security and reliability of the system.

[0708] This system allows users to enjoy a more comfortable and effective service.

[0709] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0710] Step 1: The server patrols the cloud storage and detects new or updated information. In this step, the server uses the cloud storage API to retrieve a list of files in the storage and extracts the relevant information based on metadata such as the modification date and time. The input requires cloud storage account information, and the output is a list of information.

[0711] Step 2: The server extracts text data from the detected information. In this process, OCR technology and text analysis tools are used to obtain text data from PDF and image files as well. The input is the information list obtained in Step 1, and the output is the extracted text data.

[0712] Step 3: The server uses a generative AI model to generate attribute information from the extracted text data. Here, natural language processing techniques are utilized to extract important keywords and themes and convert the content into metadata. The input is the text data obtained in Step 2, and the output is attribute information.

[0713] Step 4: The server classifies the information based on the generated attribute information and creates an index for the search engine. This step uses the indexing capabilities of search engines such as Apache Solr or Elasticsearch to enable rapid information retrieval. The input is the attribute information obtained in Step 3, and the output is the index data.

[0714] Step 5: When the user accesses the system, the terminal activates emotion recognition technology to acquire emotion data from the user's input and interactions during document creation. This process may utilize technologies such as voice tone and face capture. The input is user operation and interaction information, and the output is the user's emotion data.

[0715] Step 6: The device assists in selecting document templates and supplementing content based on the user's sentiment data. Here, a generative AI model is used to suggest the most suitable template for the user, efficiently assisting in document completion. The input is the sentiment data obtained in Step 5, and the output is the recommended template and document content.

[0716] Step 7: The server verifies the user's access rights and restricts access according to security procedures. Here, user rights are verified using LDAP or OAuth authentication systems to control access to sensitive information. The input is the user's authentication information, and the output is the result of the access rights.

[0717] (Application Example 2)

[0718] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0719] In recent years, in workplaces such as factories, where the volume of information handled has increased, there is a need for both efficient information management and support that takes into account the emotions of operators. However, conventional systems lack the efficiency of information retrieval and the ability to respond dynamically based on emotions, leading to problems such as decreased work efficiency and increased operator stress. Therefore, it is necessary to build a system that can efficiently manage information while considering the emotional state of workers and providing appropriate support.

[0720] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0721] In this invention, the server includes means for extracting information data stored in cloud storage, means for automatically generating characteristic data from the extracted information data using a generative model, and means for recognizing the emotions of users in the workplace and dynamically adjusting information provision and support based on those emotions. This streamlines information management in the workplace and enables flexible support that responds to the emotions of workers.

[0722] "Cloud storage" is a service that provides external data space for storing data via the internet.

[0723] "Information data" refers to a collection of various types of information related to the workplace, such as documents, records, and procedure manuals.

[0724] A "generative model" is an artificial intelligence technology that has a computational procedure for generating characteristic data from informational data.

[0725] "Characteristic data" refers to metadata generated from information data, which is attribute information useful for classifying and searching for information.

[0726] "Authentication" is the process of verifying whether a user has legitimate access rights.

[0727] A "template" is a model that provides a basic structure and format for creating documents and information.

[0728] A "server protection protocol" is a set of rules and procedures for implementing access restrictions and protecting information.

[0729] "Emotional recognition" is a technology that analyzes an operator's voice and actions to identify their emotional state.

[0730] "Dynamic adjustment" refers to flexibly changing the content or process according to the situation.

[0731] "Preventing information leaks" means taking measures to prevent unauthorized users from accessing information.

[0732] This invention constructs a dynamic system that manages information data held in cloud storage and provides appropriate support based on the operator's emotions. The server first extracts information data from cloud storage and automatically generates characteristic data using a generative model. This characteristic data allows for the classification of information and efficient results to be provided for user queries.

[0733] The system uses a speech recognition API to acquire voice data from the operator and an emotion recognition engine to analyze their emotions. Based on this emotion information, the server customizes templates and support information using a generative AI model to provide information tailored to the operator. For example, if the operator is feeling anxious, the server can display a step-by-step guide to help them calm down.

[0734] As a concrete example, consider a situation in a factory assembly line where an inexperienced operator is seeking instructions. In this case, the system senses the operator's anxiety and immediately provides specific advice using a generative AI model, such as, "Please calm down. Let's try steps 1 through 3 slowly."

[0735] An example of a prompt message is, "Generate helpful advice for workers when they are feeling anxious. This should include concise instructions and tips for relaxation." This system enables efficient work and maintains mental well-being.

[0736] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0737] Step 1:

[0738] The server extracts information data from cloud storage. It receives access permission information and a data identifier from the cloud storage as input, and downloads the relevant information data using the storage service API. This data is then formatted in text format to prepare it for subsequent data processing.

[0739] Step 2:

[0740] The server converts extracted informational data into characteristic data using a generative model. It receives the extracted informational data as input and generates metadata from it using natural language processing techniques. By using a generative model (e.g., GPT-3), attribute information based on the content of the informational data is automatically created and stored as characteristic data. This characteristic data is used for information classification.

[0741] Step 3:

[0742] The terminal acquires the operator's voice and analyzes the emotional data using an emotion recognition engine. It receives voice data from the microphone as input and converts it to text via a speech recognition API (e.g., Google Cloud Speech-to-Text). Based on this text, it identifies the emotional state using an emotion analysis API (e.g., IBM Watson Tone Analyzer) and outputs it as emotional data.

[0743] Step 4:

[0744] The server utilizes a generative AI model based on emotional data to generate dynamically adjusted templates and support information. It receives emotional and characteristic data obtained in the previous step as input, prompting the generative AI model to generate support content. This content, adjusted according to the operator's stress and anxiety, is then sent to the terminal as output.

[0745] Step 5:

[0746] The user receives support information from the server and proceeds with the task. The generated templates and advice are displayed on the user's terminal screen, serving as a guide for specific actions. This process allows the user to work with confidence.

[0747] 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.

[0748] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0749] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0750] 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.

[0751] Figure 9 shows an 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.

[0752] 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.

[0753] 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.

[0754] 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, motorcycles, etc., 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, for example, based 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.

[0755] 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."

[0756] 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.

[0757] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0758] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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.

[0766] 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 the like 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.

[0767] 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 as being incorporated by reference.

[0768] The following is further disclosed regarding the embodiments described above.

[0769] (Claim 1)

[0770] A means of extracting text data from documents stored in cloud storage,

[0771] A means of automatically generating metadata from text data extracted using a generative model,

[0772] A means of classifying documents based on metadata and providing search results according to user queries,

[0773] A means of authenticating user access rights and, if necessary, executing an access authorization process,

[0774] A means of generating document templates based on user operations and supplementing their content,

[0775] A means to manage access restrictions based on server security protocols and prevent information leakage,

[0776] A system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1 for correcting the integrity and linguistic errors of a document template generated based on user input.

[0779] (Claim 3)

[0780] The system according to claim 1, wherein the server periodically detects new or modified documents on cloud storage and automatically updates the metadata.

[0781] "Example 1"

[0782] (Claim 1)

[0783] A means for retrieving information from a document stored in an information recording device,

[0784] A means for automatically generating related information from information extracted using a generative AI model,

[0785] A means for classifying documents based on related information and providing search results in response to user inquiries,

[0786] A means to verify the user's access rights and, if necessary, to implement access approval procedures,

[0787] A means of generating a document base based on user actions and supplementing its content,

[0788] In accordance with the Information Processing Equipment Security Regulations, usage restrictions are managed to prevent information leakage, and measures are taken to prevent information leakage.

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1 for correcting the consistency and linguistic errors of a document basis generated based on user input.

[0792] (Claim 3)

[0793] The system according to claim 1, wherein the information processing device periodically detects new or modified documents on the information recording device and automatically updates the related information.

[0794] "Application Example 1"

[0795] (Claim 1)

[0796] A means of extracting document data stored in cloud storage,

[0797] A means of automatically generating attribute information from data extracted using a generative model,

[0798] A means of classifying documents based on attribute information and providing information in accordance with user requests,

[0799] A means of authenticating user permissions and, if necessary, performing approval procedures,

[0800] A means of generating a document template based on user input and supplementing its content,

[0801] A means to manage restrictions based on information security protocols and prevent information leaks,

[0802] A means of managing large volumes of documents generated at business facilities using communication terminals,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1 for correcting the consistency and linguistic errors of a document template generated based on user input.

[0806] (Claim 3)

[0807] The system according to claim 1, wherein the server periodically detects new or modified documents on cloud storage and automatically updates attribute information.

[0808] "Example 2 of combining an emotion engine"

[0809] (Claim 1)

[0810] A means for detecting and extracting text data from information stored in cloud storage,

[0811] A means for automatically generating attribute information from character data extracted using a generative AI model,

[0812] A means for classifying information based on attribute information and providing search results in response to a query,

[0813] A means of verifying user permissions and implementing an execution permission process according to the conditions,

[0814] A means of generating a format template according to the user's actions and supplementing its content,

[0815] Means to manage restrictions and prevent leaks based on information security procedures,

[0816] A means of recognizing emotions from the user's actions and interactions, and providing appropriate assistance based on those emotions,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, which corrects the consistency and linguistic errors of a format template generated based on user input and performs optimization based on sentiment data.

[0820] (Claim 3)

[0821] The system according to claim 1, wherein the information processing device periodically detects new or modified information on cloud storage and automatically updates attribute information.

[0822] "Application example 2 when combining with an emotional engine"

[0823] (Claim 1)

[0824] A means of extracting information data stored in cloud storage,

[0825] A means for automatically generating characteristic data from information data extracted using a generative model,

[0826] A means of classifying information based on characteristic data and providing search results according to user queries,

[0827] A means of authenticating users and granting access permissions as needed,

[0828] A means of generating templates and supplementing information based on user interaction,

[0829] A means to manage access restrictions based on server protection protocols and prevent information leakage,

[0830] A means of recognizing the emotions of users in the workplace and dynamically adjusting information provision and support based on those emotions,

[0831] A system that includes this.

[0832] (Claim 2)

[0833] The system according to claim 1 for correcting the consistency and linguistic errors of templates generated based on user input.

[0834] (Claim 3)

[0835] The system according to claim 1, wherein the server periodically detects new or changed information on cloud storage and automatically updates characteristic data. [Explanation of Symbols]

[0836] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of extracting text data from documents stored in cloud storage, A means of automatically generating metadata from text data extracted using a generative model, A means of classifying documents based on metadata and providing search results according to user queries, A means of authenticating user access rights and, if necessary, executing an access authorization process, A means of generating document templates based on user operations and supplementing their content, A means to manage access restrictions based on server security protocols and prevent information leakage, A system that includes this.

2. The system according to claim 1 for correcting the integrity and linguistic errors of a document template generated based on user input.

3. The system according to claim 1, wherein the server periodically detects new or modified documents on cloud storage and automatically updates the metadata.

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