system

Abstract

Provide a system. A means for a user to select a document type; A means for the server to collect relevant historical documents and analyze them using a natural language processing engine; a means for the server to generate a document template based on the analysis results; A means for the server to generate specific content using a text generation AI based on the generated template; a means for displaying the generated document to the user by the terminal, and for the user to confirm and modify the contents; a means for the server to store a final version of the document incorporating the user's modifications; A system including:

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern business, document preparation, procedure manual creation, and other document creation tasks require a lot of time and effort, reducing work efficiency. Furthermore, these document tasks require consistency and accuracy, making experience and knowledge important, and requiring a certain level of proficiency. As a result, it can be difficult for beginners or inexperienced employees to create appropriate documents. Therefore, there is a need for a way to automate and efficiently perform these document tasks. [Means for solving the problem]

[0005] The present invention provides a system in which a user selects a document type, a server collects and analyzes past related documents to generate a document template, and then automatically generates specific content using a text generation AI. This system includes the following means.

[0006] 1. A means for the user to select the document type.

[0007] 2. A means by which the server collects relevant historical documents and analyzes them using a natural language processing engine.

[0008] 3. A means for the server to generate a document template based on the analysis results.

[0009] 4. A means for the server to generate specific content using text generation AI based on the generated template.

[0010] 5. A means for the terminal to display the generated document to the user, allowing the user to check and modify the contents.

[0011] 6. A means for the server to store the final version of the document incorporating the user's modifications.

[0012] This significantly reduces the time and effort required to create documents, improving work efficiency. In addition, by incorporating user corrections, it becomes possible to automatically generate highly complete documents.

[0013] "User" refers to an individual who uses the system to create or modify documents or materials.

[0014] "Terminal" refers to a computer or device operated by a user, and refers to equipment that provides an interface with the system.

[0015] "Server" refers to a central processing unit that performs data collection, analysis, document generation and storage.

[0016] "Document" or "Document Type" means a particular type of document that a user wants to create, such as a procedure, presentation, or report.

[0017] "Past related documents" are documents that have been previously created or saved and are related to the selected document type.

[0018] A "natural language processing engine" refers to software that has the technology to analyze and understand natural language used by humans.

[0019] "Sentence generation AI" refers to an artificial intelligence model that automatically generates natural-sounding sentences based on input data.

[0020] "Template" means a template generated by the server that serves as a basic framework for document configuration.

[0021] "Modification" refers to the act of a user checking the automatically generated document content and making any necessary changes or additions.

[0022] "Document Database" means the storage system used by the Server to store and manage documents.

[0023] "Sharing" refers to the act of distributing and making accessible the final version of a document to stakeholders and other systems. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0025] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0026] First, the terms used in the following description will be explained.

[0027] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0028] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0029] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0030] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0031] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0032] [First embodiment]

[0033] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0034] 1, a 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.

[0035] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0036] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0037] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0038] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0039] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0040] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0042] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0043] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0044] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0045] This invention relates to a system that automates document creation and allows users to create high-quality documents simply by checking and correcting them. This system executes a series of steps, from the user's selection of the document type, to data collection and analysis by the server, automatic content generation using text generation AI, user corrections, and final saving and sharing.

[0046] System configuration

[0047] The system consists of the following main components:

[0048] User terminal: A device operated by a user, such as a PC or tablet.

[0049] Server: A central processing and data management device.

[0050] Document database: A storage system that stores and manages past related documents.

[0051] Natural language processing engine: Software that analyzes past related documents.

[0052] Text generation AI: An artificial intelligence model that generates text based on input data.

[0053] System Operation Overview

[0054] Start preparing the documents

[0055] A user logs into the system using a terminal and begins creating a document by selecting "Create a new document." The user then selects the type of document they want to create (procedure, presentation, report, etc.). The terminal sends the user's selection to the server.

[0056] Data collection and analysis

[0057] The server references a document database to gather past documents related to the document type selected by the user, then inputs the collected data into a natural language processing engine to extract elements such as titles, sections, and keywords, and then generates a template based on these elements.

[0058] Automatic sentence generation

[0059] The server uses text generation AI to automatically generate specific content based on the generated template. For example, in the case of a procedure manual, detailed explanations of each step are generated, allowing users to eliminate most of the manual work.

[0060] User confirmation and correction

[0061] The generated document is displayed on the device, where the user can review the contents. The user can then make any necessary corrections, such as adding more detail to a specific step or adding notes. The corrections are then sent from the device to the server.

[0062] Save and share the final version

[0063] Once the user has completed their modifications, the server generates and stores the final version of the document, which is then stored in a document database and can be shared with other systems or stakeholders as needed.

[0064] Specific examples

[0065] Example 1: Creating a procedure manual

[0066] 1. The user logs in and selects to create a new procedure.

[0067] User: Log in to the system and select "Create a new procedure manual."

[0068] Terminal: Sends user selection to server.

[0069] 2. The server collects and analyzes past procedures

[0070] Server: Collects past procedures from the document database.

[0071] Server: Analyzes using a natural language processing engine and generates templates.

[0072] 3. The server automatically generates the contents of the procedure manual using AI

[0073] Server: Creates specific instructions for each step using text generation AI.

[0074] 4. The user checks and corrects the content

[0075] Terminal: View automatically generated instructions.

[0076] User: Check the content and make corrections such as "Explain step 3 in more detail."

[0077] 5. The server stores and shares the final version

[0078] Server: Generates the final version of the procedure manual that reflects the corrections, stores it in the document database, and shares it as needed.

[0079] By using the above means, users can efficiently create high-quality documents, thereby improving business efficiency.

[0080] The processing flow will be explained below.

[0081] Step 1:

[0082] The user begins creating a document.

[0083] A user logs into the system and selects the option "Create a new document."

[0084] The terminal sends this user selection to the server as a request.

[0085] Step 2:

[0086] The user selects a document type.

[0087] Users select a document type such as a procedure manual, presentation, or report.

[0088] The terminal transmits the user's selection information to the server.

[0089] Step 3:

[0090] The server collects past related documents.

[0091] The server retrieves historical documents related to the selected document type from the document database.

[0092] The server passes the collected data to a natural language processing engine.

[0093] Step 4:

[0094] The server analyzes the collected data.

[0095] The server uses a natural language processing engine to extract titles, sections, keywords, etc. from the collected documents.

[0096] The server generates a document template based on the analysis results.

[0097] Step 5:

[0098] The server generates the content using text generation AI.

[0099] The server applies a text generation AI to the generated template to generate specific content.

[0100] Through this process, the server automatically fills in the appropriate text for each section of the document.

[0101] Step 6:

[0102] The terminal displays the generated document to the user.

[0103] The terminal displays the completed document in a user interface.

[0104] The terminal provides an interface that allows the user to view and edit the content.

[0105] Step 7:

[0106] The user checks and corrects the content.

[0107] The user checks the displayed document in detail.

[0108] The user edits any parts that need to be corrected and inputs the corrections into the terminal.

[0109] Step 8:

[0110] The terminal transmits the user's modifications to the server.

[0111] The terminal transmits the modifications made by the user to the server.

[0112] Step 9:

[0113] The server reflects the changes.

[0114] The server reflects the modifications sent by the user and generates the final version of the document.

[0115] The server sends a preview of the final version to the device.

[0116] Step 10:

[0117] The device displays a preview of the final version to the user.

[0118] The terminal displays the final document to the user and provides an interface for final confirmation.

[0119] Step 11:

[0120] The user saves the document.

[0121] The user performs an operation to save the document after final confirmation.

[0122] The terminal sends a save request to the server.

[0123] Step 12:

[0124] The server stores the final document.

[0125] The server stores the final document in a document database.

[0126] The server shares the final document with other systems and stakeholders as needed.

[0127] Example 1

[0128] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0129] Modern business requires fast and accurate document creation, but this process is inefficient because it takes a lot of time and effort. Human error is especially likely to occur when checking and correcting document content, requiring multiple checks. To resolve this situation, a system is needed that automates the entire document creation process and reduces the burden on users.

[0130] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0131] In this invention, the server includes means for collecting past related documents and analyzing them using a natural language processing engine, means for generating document templates based on the analysis results, and means for generating specific content using a sentence generation software module based on the generated templates, thereby enabling the automatic generation of high-quality documents.

[0132] "Document type" refers to the category or type of document the user wants to create, including procedures, presentations, reports, and the like.

[0133] A "server" refers to a device that performs central processing and data management and controls the entire system.

[0134] "Past related documents" refers to documents of the same type that were created previously and are stored in a database, and are used as references when creating new documents.

[0135] A "natural language processing engine" is software that analyzes past related documents to extract elements and meanings of text. Specifically, it has the ability to automatically identify titles, sections, keywords, etc. from strings of characters.

[0136] A "template" is a framework that defines the structure and format of a document, and serves as the basis for generating specific documents based on this framework.

[0137] "Text generation software module" refers to an artificial intelligence program that automatically generates specific content based on a template.

[0138] "Terminal" refers to the device that a user uses to access and operate the system, such as a PC or tablet.

[0139] "User interface" refers to the function that provides the screen and operation method for the user to operate on the terminal, including the interface for checking and modifying the document content.

[0140] "Data storage system" refers to a storage device for securely storing generated documents for later access.

[0141] "Visual interface" refers to a graphical user interface (GUI) that allows users to intuitively view and modify document contents.

[0142] "Sharing with other systems or parties" refers to the ability to make the generated final document accessible between different systems or involved users.

[0143] The present invention is a system that automates document creation, allowing the user to create high-quality documents simply by checking and correcting the documents. A specific embodiment of this system will be described below.

[0144] System configuration

[0145] The system consists of the following main components:

[0146] User terminal: A device operated by a user, such as a PC or tablet. It provides a user interface and allows users to check and edit document contents.

[0147] Server: A central device that manages processing and data and controls the entire system. The server accesses the data storage system and is responsible for collecting, analyzing, and storing the necessary data.

[0148] Document database: A storage system that stores and manages past related documents. Previously created documents of the same type are accumulated here and used as reference for creating new documents.

[0149] Natural language processing engine: A software engine that analyzes past related documents and extracts elements such as titles, sections, keywords, etc. Specific examples include spaCy and NLTK.

[0150] Text generation software module: An artificial intelligence program that generates specific content based on templates. Specifically, this applies to generative AI models such as GPT-3 (registered trademark).

[0151] System Operation Overview

[0152] Start preparing the documents

[0153] A user logs into the system using a terminal and selects "Create a new document." The user then selects the type of document they want to create (e.g., procedure manual, presentation, report, etc.). The selection information is sent from the terminal to the server.

[0154] Data collection and analysis

[0155] The server references a document database to collect past documents related to the document type selected by the user, then inputs the collected data into a natural language processing engine to extract elements such as titles, sections, and keywords.The server then generates a template based on the extracted elements.

[0156] Automatic sentence generation

[0157] The server uses a text generation software module to automatically generate specific content based on the generated template. For example, in the case of a procedure manual, detailed explanations of each step are generated, eliminating most of the manual work required by the user.

[0158] Example prompt:

[0159] Please create a procedure titled "System Setup" with sections "Preparation," "Installation," and "Configuration."

[0160] User confirmation and correction

[0161] The generated document is displayed on the terminal, and the user can review the contents. The user can then make appropriate corrections, such as adding more detail to a specific step or adding notes. The corrections are then sent from the terminal to the server.

[0162] Save and share the final version

[0163] Once the user's modifications are complete, the server generates the final document and stores it in a data storage system, where it can be shared with other systems or stakeholders as needed.

[0164] As a concrete example, the operation procedure for "creating a new procedure manual" is shown below:

[0165] 1. The user logs in and selects to create a new procedure.

[0166] 2. The server collects past procedure manuals, analyzes them using a natural language processing engine, and generates templates.

[0167] 3. The server automatically generates a procedure manual using a text generation software module based on the template.

[0168] 4. The terminal displays the procedure manual, and the user checks and corrects it.

[0169] 5. The server stores the final version and shares it as needed.

[0170] This allows users to efficiently create high-quality documents, improving business efficiency.

[0171] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0172] Step 1:

[0173] The user logs in to the terminal and accesses the system's web application. The user selects "Create a new document" from the main menu. The user selects the type of document they want to create and sends it to the server. Specifically, the user opens a browser, accesses the system URL, and logs in by entering their user ID and password. They click the "Create a new document" button and select "Procedure" from the drop-down menu. The input is the user's selection information (create a new document and document type), and the output is sending this selection information to the server.

[0174] Step 2:

[0175] The server queries the document database to collect past documents related to the document type selected by the user. The collected data is input into a natural language processing engine to extract elements such as title, section, and keywords. Specifically, the server executes an SQL query to retrieve all documents of the "procedure" type. The retrieved documents are passed to the natural language processing engine, which extracts key elements (title, section, keywords). The input is past procedure documents retrieved from the database, and the output is elements such as title, section, and keywords.

[0176] Step 3:

[0177] The server generates a template based on the extracted elements. Specifically, it passes the elements obtained from the natural language processing engine to the template generation module, which automatically generates a procedure template. The template includes main sections. The input is the extracted element data, and the output is the procedure template.

[0178] Step 4:

[0179] The server uses a text generation software module to generate specific content based on the generated template. The server sends the template and necessary keywords to the generative AI model. The server receives the text automatically generated by the generative AI model and sends it to the user's device. As a specific operation, the following prompt sentence is sent to the generative AI model: "Please create a procedure manual. The title is 'System Setup' and the sections are 'Preparation', 'Installation', and 'Configuration'." The input is the template and keywords, and the output is the automatically generated document.

[0180] Step 5:

[0181] The terminal displays the generated document on the user interface. The user checks the contents and makes corrections as necessary. Specifically, the document generated on the terminal is displayed, and the user selects the parts that need correction and enters edit mode. The corrections are then sent to the server. The input is the automatically generated document, and the output is the user's corrections.

[0182] Step 6:

[0183] The server generates a final version of the document that reflects the user's modifications and saves it in the data storage system. The server generates a final version of the document and saves it in the document database. It also prepares it for sharing with other systems and related parties as needed. Specifically, the server saves the final version of the procedure manual in the database. The user clicks the "Share" button and sends the shared link generated by the system via email or chat. The input is the document that reflects the user's modifications, and the output is the final version of the procedure manual and the shared link.

[0184] (Application example 1)

[0185] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0186] Logistics centers are required to create work instructions and procedures efficiently and accurately. Conventional methods require a lot of manual work, which leads to problems such as errors and reduced work efficiency. Furthermore, it is difficult to generate appropriate instructions based on past data, and it is an issue to quickly provide instructions that are easy for workers to understand.

[0187] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0188] In this invention, the server includes a means for a user to select a document type, a means for collecting past related documents and analyzing them using a natural language processing engine, a means for generating a document template based on the analysis results, a means for generating specific content using a sentence generation AI, a means for a terminal to display the generated document to the user so that the user can confirm and modify the content, and a means for saving the final document that reflects the user's modifications and sharing it with related departments or other processing systems as necessary. This enables efficient and accurate creation of work instructions at a logistics center.

[0189] "Users" are the workers and other related parties who operate the system.

[0190] "Documents" refers to work instructions and procedures at logistics centers.

[0191] A "server" is a central control unit that processes and manages data.

[0192] A "template" refers to a format or framework that serves as a framework for generating a document.

[0193] "Text generation AI" is an artificial intelligence model that automatically generates documents using natural language processing technology.

[0194] A "natural language processing engine" is a software engine that analyzes documents and extracts elements.

[0195] A "terminal" is a device that a user uses to operate the system, and includes a smartphone, tablet, etc.

[0196] A "document database" is a storage system that stores and manages past documents.

[0197] A "prompt sentence" is the initial input given to a text generation AI, and determines the direction of the generated document.

[0198] "Related departments" refers to departments or groups that carry out other operations inside or outside the logistics center.

[0199] "Other processing systems" refers to other systems or applications for sharing generated documents.

[0200] The system for implementing this invention automatically generates work instructions and procedures within a logistics center, providing efficient and accurate instructions. The system is composed of the following main components:

[0201] Key Components

[0202] User terminal: A device operated by a user, such as a smartphone or tablet.

[0203] Server: A cloud server (e.g., AWS (registered trademark), Google (registered trademark) Cloud, Microsoft (registered trademark) Azure (registered trademark), etc.) that processes and manages data.

[0204] Document database: A cloud storage (e.g., Amazon S3, Google Cloud Storage, etc.) that stores and manages historical documents.

[0205] Natural language processing engine: A software engine for parsing documents and extracting elements (e.g., AWS Comprehend, Google Cloud Natural Language API, SpaCy, etc.).

[0206] Text generation AI: An artificial intelligence model that automatically generates documents using natural language processing technology (e.g., OpenAI (registered trademark) GPT-3, GPT-4 (registered trademark), etc.).

[0207] System Operation Overview

[0208] 1. Log in and create a new instruction

[0209] The user logs into the app using their smartphone and selects "Create new instructions." The selection is sent to the server.

[0210] 2. Data collection and template generation

[0211] The server collects relevant past documents from cloud storage and analyzes the data using a natural language processing engine, generating templates based on the analysis results.

[0212] 3. Automatic document generation

[0213] Based on the generated template, the server uses text generation AI to automatically generate specific content.

[0214] 4. User confirmation and correction

[0215] The generated document is displayed on the user's terminal, and the user can check the content and make corrections as necessary. The corrections are then sent to the server and reflected.

[0216] 5. Save and share the final version

[0217] The server generates a final version of the document that reflects the changes, stores it in cloud storage, and shares it with related departments and other processing systems as needed.

[0218] Specific examples

[0219] For example, when a distribution center worker creates a new inventory work order, they might use the following prompt:

[0220] Prompt Text: "Logistics Directive Template: Title: Shelf Inventory\nSections: Preparation, Process, Completion\nKeywords: inventory, checklist, items, count"

[0221] Based on this prompt, the AI ​​generates specific work instructions like this:

[0222] Title: Shelf Inventory

[0223] Sections:

[0224] 1. Preparation

[0225] - Verify the inventory list and necessary tools.

[0226] - Ensure all team members are briefed on the process.

[0227] 2. Process

[0228] - Follow the checklist to count each item on the shelves.

[0229] - Record discrepancies with existing records.

[0230] 3. Completion

[0231] - Review the collected data for accuracy.

[0232] - Report any major issues to the supervisor.

[0233] Keywords: inventory, checklist, items, count

[0234] In this way, the system supports the efficient and accurate creation of work instructions in logistics centers.

[0235] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0236] Step 1:

[0237] The user logs into the app using their smartphone and selects "Create new instruction." The login and selection information is entered and sent to the server, which receives the data and proceeds to the next processing step.

[0238] Step 2:

[0239] The server accesses the document database to collect past related documents. Past instruction data is input, and the collected data is sent to the natural language processing engine. The server uses the natural language processing engine to analyze the collected documents and extract elements such as titles, sections, and keywords. The extracted elements are output and the process proceeds to the next processing step.

[0240] Step 3:

[0241] The server generates a document template based on the analysis results. The analyzed data is used as input to generate the template. The generated template is sent to a text generation AI, which automatically generates a specific document. The generated document is output and proceeds to the next processing step.

[0242] Step 4:

[0243] The generated document is sent to the user's terminal. The terminal displays this automatically generated document to the user. The user checks the document contents and makes corrections as necessary (for example, "Please explain step 3 in detail"). The corrected content is sent from the terminal to the server as input.

[0244] Step 5:

[0245] The server receives the submitted corrections and generates the final document. Using the correction data as input, the server generates the final document and stores it in the document database. The stored final document is shared with related departments and other processing systems as needed. Sharing the output final document with other related systems allows work within the logistics center to be carried out efficiently and accurately.

[0246] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0247] This invention relates to a system that automates document creation, recognizing the user's emotions and creating high-quality documents simply by confirming and correcting them. This system executes a series of processes, from the user selecting the document type, to the server recognizing emotions using an emotion engine, collecting and analyzing data, automatically generating content using a text generation AI, and finally saving and sharing the document after the user has made corrections.

[0248] System configuration

[0249] The system consists of the following main components:

[0250] User terminal: A device operated by a user, such as a PC or tablet.

[0251] Server: A central processing and data management device.

[0252] Document database: A storage system that stores and manages past related documents.

[0253] Natural language processing engine: Software that analyzes past related documents.

[0254] Text generation AI: An artificial intelligence model that generates text based on input data.

[0255] Emotion engine: Software that recognizes the user's emotions and provides feedback to the system.

[0256] System Operation Overview

[0257] Start preparing the documents

[0258] A user logs into the system using a terminal and begins creating a document by selecting "Create a new document." The user then selects the type of document they want to create (procedure, presentation, report, etc.). The terminal sends the user's selection to the server.

[0259] Data collection and analysis

[0260] The server references a document database to gather past documents related to the document type selected by the user, then inputs the collected data into a natural language processing engine to extract elements such as titles, sections, and keywords, and then generates a template based on these elements.

[0261] Emotion recognition

[0262] In addition, the server uses an emotion engine to recognize the user's emotions, which detects the user's current emotions based on their inputs and operating situations, and optimizes the document generation process accordingly.

[0263] Automatic sentence generation

[0264] The server uses text generation AI to automatically generate specific content based on the generated template. For example, in the case of a procedure manual, detailed explanations of each step are generated. The text generation AI also adjusts tone and style as needed, taking into account the recognition results of the emotion engine.

[0265] User confirmation and correction

[0266] The generated document is displayed on the device, where the user can review the contents. The user can then make any necessary corrections, such as adding more detail to a specific step or adding notes. The corrections are then sent from the device to the server.

[0267] Save and share the final version

[0268] Once the user has completed their modifications, the server generates and stores the final version of the document, which is then stored in a document database and can be shared with other systems or stakeholders as needed.

[0269] Specific examples

[0270] Example 1: Creating a procedure manual

[0271] 1. The user logs in and selects to create a new procedure.

[0272] User: Log in to the system and select "Create a new procedure manual."

[0273] Terminal: Sends user selection to server.

[0274] 2. The server collects and analyzes past procedures

[0275] Server: Collects past procedures from the document database.

[0276] Server: Analyzes using a natural language processing engine and generates templates.

[0277] 3. The server recognizes the user's emotions and provides feedback

[0278] Server: Recognizes user emotions using an emotion engine.

[0279] Server: Based on the recognition results, the tone and style of the text generation AI are adjusted.

[0280] 4. The server automatically generates the contents of the procedure manual using AI

[0281] Server: Creates specific instructions for each step using text generation AI.

[0282] 5. The user checks and corrects the content

[0283] Terminal: View automatically generated instructions.

[0284] User: Check the content and make corrections such as "Explain step 3 in more detail."

[0285] 6. The server stores and shares the final version

[0286] Server: Generates the final version of the procedure manual that reflects the corrections, stores it in the document database, and shares it as needed.

[0287] By using the above means, users can efficiently create high-quality documents, which improves business efficiency and also allows for individual responses that reflect the user's feelings.

[0288] The processing flow will be explained below.

[0289] Step 1:

[0290] The user begins creating a document.

[0291] The user logs into the system and selects "Create a new document."

[0292] The terminal sends this user selection to the server as a request.

[0293] Step 2:

[0294] The user selects a document type.

[0295] Users select a document type such as a procedure manual, presentation, or report.

[0296] The terminal transmits the user's selection information to the server.

[0297] Step 3:

[0298] The server collects past related documents.

[0299] The server collects historical documents related to the selected document type from a document database.

[0300] The server passes the collected data to a natural language processing engine.

[0301] Step 4:

[0302] The server analyzes the collected data.

[0303] The server uses a natural language processing engine to extract titles, sections, keywords, etc. from the collected documents.

[0304] The server generates a document template based on the analysis results.

[0305] Step 5:

[0306] The server uses an emotion engine to recognize the user's emotions.

[0307] The server receives the user's operation status and input contents from the terminal and sends them to the emotion engine.

[0308] The emotion engine analyzes the user's emotions and feeds the results back to the server.

[0309] Step 6:

[0310] The server generates the content using text generation AI.

[0311] Based on the generated template, the server applies text generation AI to generate specific content.

[0312] Taking into account the results of the emotion engine, the text generation AI adjusts tone and style as needed.

[0313] Step 7:

[0314] The terminal displays the generated document to the user.

[0315] The terminal displays the completed document in a user interface.

[0316] The terminal provides an interface that allows the user to view and edit the content.

[0317] Step 8:

[0318] The user checks and corrects the content.

[0319] The user checks the displayed document in detail.

[0320] The user edits any parts that need to be corrected and inputs the corrections into the terminal.

[0321] Step 9:

[0322] The terminal transmits the user's modifications to the server.

[0323] The terminal transmits the modifications made by the user to the server.

[0324] Step 10:

[0325] The server reflects the changes.

[0326] The server reflects the modifications sent by the user and generates the final version of the document.

[0327] The server sends a preview of the final version to the device.

[0328] Step 11:

[0329] The device displays a preview of the final version to the user.

[0330] The terminal displays the final document to the user and provides an interface for final confirmation.

[0331] Step 12:

[0332] The user saves the document.

[0333] The user performs an operation to save the document after final confirmation.

[0334] The terminal sends a save request to the server.

[0335] Step 13:

[0336] The server stores the final document.

[0337] The server stores the final document in a document database.

[0338] The server shares the final document with other systems and stakeholders as needed.

[0339] Specific examples

[0340] Example 1: Creating a procedure manual

[0341] 1. The user logs in and selects to create a new procedure.

[0342] User: Log in to the system and select "Create a new procedure manual."

[0343] Terminal: Sends user selection to server.

[0344] 2. The server collects and analyzes past procedures

[0345] Server: Collects past procedures from a document database, analyzes them using a natural language processing engine, and generates templates.

[0346] 3. The server uses the emotion engine to recognize the user's emotions.

[0347] Server: The server receives the user's operation status and input information from the device and sends it to the emotion engine to analyze the user's emotions.

[0348] Server: Receives the results of the emotion engine and reflects them in the sentence generation AI.

[0349] 4. The server automatically generates the contents of the procedure manual using AI

[0350] Server: Using text generation AI, we create detailed instructions for each step and adjust the tone and style.

[0351] 5. The user checks and corrects the content

[0352] Terminal: View automatically generated instructions.

[0353] User: Check the content and make corrections such as "Explain step 3 in more detail."

[0354] 6. The server stores and shares the final version

[0355] Server: Generates the final version of the procedure manual that reflects the corrections, stores it in the document database, and shares it as needed.

[0356] By using the above means, users can efficiently create high-quality documents, which improves business efficiency and also allows for individual responses that reflect the user's feelings.

[0357] Example 2

[0358] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0359] In conventional document creation systems, the process of users manually creating and editing documents is time-consuming and laborious, making it inefficient. Furthermore, it is difficult to create documents that take into account the user's emotions and circumstances, making it difficult to create high-quality documents tailored to individual users. Therefore, there is a demand for a system that reduces user labor and allows for the rapid creation of high-quality documents that can be tailored to individual users.

[0360] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0361] In this invention, the server includes means for collecting past related documents and analyzing them using a natural language processing engine, means for generating document templates based on the analysis results, means for recognizing user emotions using an emotion engine, and means for adjusting tone and style using a sentence generation AI to generate specific content. This reduces the user's workload and enables the rapid creation of high-quality documents that can be tailored to individual needs.

[0362] A "user" is an entity that uses the system to create, check, and modify documents.

[0363] A "terminal" is a device operated by a user, and includes devices such as PCs and tablets.

[0364] A "server" is a device that centrally manages and processes data.

[0365] "Document type" refers to the specific type of document the user wants to create, including procedures, presentation materials, reports, and the like.

[0366] "Related historical documents" are existing documents stored in the document database that are related to the document type selected by the user.

[0367] A "natural language processing engine" is software for analyzing text data and extracting necessary information.

[0368] A "document template" is a format that includes the basic structure and main items of a document, and is generated based on collected document data.

[0369] An "emotion engine" is software that recognizes a user's emotions based on the user's input and operational status, and feeds that emotional information back into the system.

[0370] "Sentence generation AI" is an artificial intelligence model that automatically generates sentences based on specified data and templates.

[0371] "Tone and style" refers to the atmosphere and expression of the generated text, and is adjusted according to the user's emotions and situation.

[0372] The "final document" is the document that is completed by reflecting the user's modifications and is finally saved.

[0373] A "document database" is a storage system for storing and managing past related documents, final versions of documents, and the like.

[0374] The present invention relates to a system that enables users to efficiently create high-quality documents. This system comprises a means for the user to select a document type, a means for a server to recognize emotions using an emotion engine, a means for collecting past related documents and analyzing them with a natural language processing engine, a means for generating templates, a means for generating content using a sentence generation AI, and a means for a terminal to display the generated document to the user and allow them to make corrections. The specific configuration and operation of this system are described below.

[0375] System configuration

[0376] The system consists of the following main components:

[0377] User terminal: A device operated by a user, such as a PC or tablet.

[0378] Server: A central device that manages and processes data.

[0379] Document database: A storage system that stores and manages past related documents.

[0380] Natural language processing engine: Software that analyzes past related documents.

[0381] Text generation AI: An artificial intelligence model that generates text based on input data.

[0382] Emotion engine: Software that recognizes the user's emotions and provides feedback to the system.

[0383] Operation overview

[0384] Start preparing the documents

[0385] A user logs into the system using a terminal and selects "Create a new document." The user then selects the type of document they want to create (procedure, presentation, report, etc.). The terminal sends this selection to the server.

[0386] Data collection and analysis

[0387] The server accesses a document database to gather past documents related to the document type selected by the user, then inputs the collected data into a natural language processing engine to extract elements such as titles, sections, and keywords, and then generates a template based on these elements.

[0388] Emotion recognition

[0389] In addition, the server uses an emotion engine to recognize the user's emotions, which detects the user's current emotions based on their inputs and operation situations, and optimizes the document generation process accordingly.

[0390] Automatic sentence generation

[0391] The server uses text generation AI to automatically generate specific content based on the generated template. For example, in the case of a procedure manual, detailed explanations of each step are generated. The server also adjusts the tone and style of writing based on the recognition results of the emotion engine.

[0392] User confirmation and correction

[0393] The generated document is displayed on the device, where the user can review the contents and make any necessary corrections, such as adding detailed explanations for specific steps or adding notes. The corrections are then sent from the device to the server.

[0394] Save and share the final version

[0395] Once the user has completed their modifications, the server generates and stores the final version of the document, which is stored in a document database and can be shared with other systems or stakeholders as needed.

[0396] Specific examples

[0397] Example 1: Creating a procedure manual

[0398] 1. The user logs in and selects to create a new procedure.

[0399] User: Log in to the system and select "Create a new procedure manual."

[0400] Terminal: Sends the user's selection to the server.

[0401] 2. The server collects and analyzes past procedures

[0402] Server: Collects past procedures from a document database.

[0403] Server: Analyzes using a natural language processing engine and generates templates.

[0404] 3. The server recognizes the user's emotions and provides feedback

[0405] Server: Recognizes user emotions using an emotion engine.

[0406] Server: Based on the recognition results, adjust the tone and style of the text generation AI.

[0407] 4. The server automatically generates the contents of the procedure manual using AI

[0408] Server: Uses text generation AI to create specific instructions for each step in the procedure manual.

[0409] 5. The user checks and corrects the content

[0410] Terminal: View the automatically generated instructions.

[0411] User: Check the content and make corrections such as "Add more detail to the explanation of Step 3."

[0412] 6. The server stores and shares the final version

[0413] Server: Generates the final version of the procedure manual that reflects the modifications and stores it in the document database.

[0414] Server: Generate a link to share with other departments or stakeholders as needed.

[0415] Examples of prompt statements

[0416] 1. Specify the document type: "Start creating a new procedure. First, enter the procedure title."

[0417] 2. Key Element Analysis: "Based on the procedure title entered, extract key sections and keywords from related historical documents."

[0418] 3. Emotion detection: "Recognize the user's current emotion and adjust the tone and style of the document appropriately. If the user is relaxed, use a friendly tone."

[0419] 4. Auto-generated prompt: "Draw each step of the procedure in detail using the following format. For example, Step 1: Prepare the necessary tools and materials. Step 2: Explain the procedure. Step 3: Notes."

[0420] The above is a detailed description of an embodiment of the present invention. This system allows users to quickly and efficiently create high-quality documents, and also provides personalized responses that take into account the user's emotions.

[0421] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0422] System program processing flow

[0423] Step 1: User logs in and selects Create New Document

[0424] A user logs in to the system using a terminal.

[0425] Input: User ID and password

[0426] Output: Authentication status of the user

[0427] The server validates the credentials and displays the dashboard if authentication is successful.

[0428] Input: User ID and password

[0429] Output: Dashboard screen

[0430] The user selects "Create a new document" from the dashboard.

[0431] Input: User's document type selection

[0432] Output: Document type data

[0433] The terminal transmits the user's selection to the server.

[0434] Step 2: The server collects and analyzes past document data

[0435] The server accesses a document database to collect past documents related to the document type selected by the user.

[0436] Input: Document type data

[0437] Output: Dataset of collected historical documents

[0438] The server uses a natural language processing engine to extract elements such as titles, sections, and keywords from the collected documents.

[0439] Input: Dataset of historical documents

[0440] Output: Extracted elements (title, sections, keywords, etc.)

[0441] The server generates a template based on these extracted elements.

[0442] Input: Extracted elements

[0443] Output: Generated template

[0444] Step 3: The server recognizes the user's emotion

[0445] The server starts the emotion engine and analyzes the user's input and operation status.

[0446] Input: User operation log, input text

[0447] Output: Emotion recognition result

[0448] The server determines the user's current emotion (e.g., stress, relaxation, or impatience) based on the results of the emotion engine.

[0449] Input: Emotion recognition results

[0450] Output: User's emotional state data

[0451] The server feeds this emotional information back into the sentence generation process.

[0452] Step 4: The server automatically generates the content using text generation AI

[0453] The server uses a sentence generation AI to automatically generate sentences based on the generated template and the results of the emotion engine.

[0454] Input: Template, emotional state data

[0455] Output: The specific document content generated

[0456] The server generates the specific document content, for example in the case of a procedure manual, creating a detailed description of each step.

[0457] Input: Template, emotional state data

[0458] Output: Specific procedure manual content

[0459] The server takes emotional information into account and adjusts tone and style.

[0460] Step 5: User confirms and modifies the content

[0461] The terminal displays the generated document to the user.

[0462] Input: Generated document content

[0463] Output: Displayed document

[0464] The user reviews the document and identifies any necessary revisions (e.g., "More details on step 3").

[0465] Input: Displayed document, user modifications

[0466] Output: Correction instruction data

[0467] The terminal transmits correction instruction data to the server.

[0468] Step 6: The server saves and shares the final version

[0469] The server generates the final document reflecting the user's modifications.

[0470] Input: Correction instruction data, initial generated document content

[0471] Output: Final document

[0472] The server stores the final version of the document in a document database.

[0473] Input: Final document

[0474] Output: Save status

[0475] The server generates links to share the document with other systems or stakeholders as needed.

[0476] This allows users to quickly and efficiently create high-quality documents, and the system also provides personalized responses that reflect the user's feelings.

[0477] (Application example 2)

[0478] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0479] Currently, creating product reviews and customer support emails on online shopping sites faces the challenge of generating high-quality text that reflects user emotions. This can lead to inconsistencies in the content of reviews and emails, resulting in a poor user experience. Another problem is the effort required for users to manually edit the content of reviews and emails. To solve these issues, a system is needed that automatically generates appropriate text that takes user emotions into account.

[0480] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0481] In this invention, the server includes a means for allowing a user to select a document type, a means for collecting past related documents and analyzing them using a natural language processing engine, and a means for generating a document template based on the analysis results, which allows the tone and style of writing to be adjusted based on the user's emotions, making it possible to automatically generate high-quality product reviews and customer support emails.

[0482] A "user terminal" is a device that a user uses to operate the system, and includes general computer devices such as smartphones and tablets.

[0483] A "server" is a central device for processing and managing large amounts of data, and oversees database operations and AI processing.

[0484] A "document database" is a storage system that stores and manages historical related documents and provides quick data access.

[0485] A "natural language processing engine" is a general term for software that analyzes input text and understands its meaning.

[0486] "Text generation AI" is an artificial intelligence model that automatically generates text based on input data, producing high-quality text according to specific rules and patterns.

[0487] An "emotion engine" is software that recognizes the user's emotions and provides feedback to the system based on those emotions.

[0488] A "template" is a format that serves as a basic framework for efficiently generating a variety of documents, and defines the components of each document.

[0489] "Tone of the text" refers to the emotional tone or style of the generated text, which is adjusted based on the user's emotions.

[0490] "Style" refers to the manner or style of writing, and can range from formal to casual.

[0491] A "visual interface" is a screen display mechanism that allows users to visually view and manipulate documents and data.

[0492] MODE FOR CARRYING OUT THE INVENTION

[0493] System Configuration

[0494] In practicing this invention, the system includes as major components:

[0495] 1. User terminal: A device operated by a user, such as a smartphone or tablet.

[0496] 2. Server: A central device for processing and managing large amounts of data, using AWS (Amazon Web Services) or Google Cloud Platform.

[0497] 3. Document database: A storage system that stores and manages historical related documents.

[0498] 4. Natural language processing engine: A general term for software that analyzes input text and understands its meaning. Examples include SpaCy and BERT.

[0499] 5. Text generation AI: An artificial intelligence model that automatically generates text based on input data. We will use OpenAI GPT-4 as a concrete example.

[0500] 6. Emotion Engine: Software that recognizes the user's emotions and provides feedback to the system based on those emotions. It uses the Microsoft Azure Cognitive Services Emotion API.

[0501] Processing flow

[0502] 1. User operation: The user logs in to the system using a terminal and selects the document type (product review or customer support email).

[0503] 2. Data collection and analysis: The server collects relevant documents from the document database and analyzes them using a natural language processing engine (e.g., SpaCy or BERT), extracting elements such as titles, sections, and keywords, and generating templates.

[0504] 3. Emotion Recognition: The emotion engine (Microsoft Azure Cognitive Services Emotion API) detects the current emotion based on the user's input and operation context. Based on the emotional state, the text generation AI adjusts the tone and style of writing.

[0505] 4. Automatic sentence generation: Based on the template generated in the previous step, the server uses sentence generation AI (OpenAI GPT-4) to automatically generate specific content, resulting in high-quality sentences that reflect emotions.

[0506] 5. Confirmation and correction: The terminal displays the generated text to the user. The user can confirm the content and make corrections as necessary. The corrected data is sent back to the server.

[0507] 6. Final saving and sharing: The server reflects the user's modifications, generates the final version of the document, and saves it in the document database. If necessary, the document can be shared with other systems or stakeholders.

[0508] Specific examples

[0509] Example 1: Creating a product review

[0510] 1. User logs in and selects to create a new review

[0511] User: Log in to the system and select "Create a new review."

[0512] Terminal: Sends user selection to server.

[0513] 2. The server collects and analyzes past reviews

[0514] Server: Collects past reviews from a document database.

[0515] Server: Analyzes using a natural language processing engine and generates templates.

[0516] 3. The server recognizes the user's emotions and provides feedback

[0517] Server: Recognizes user emotions using an emotion engine.

[0518] Server: Based on the recognition results, the tone and style of the sentence generation AI are adjusted.

[0519] 4. The server automatically generates the review content using AI

[0520] Server: Create specific reviews using text generation AI.

[0521] 5. User confirms and modifies the content

[0522] Device: View automatically generated reviews.

[0523] User: Check the contents and make any necessary changes.

[0524] 6. The server stores and shares the final version

[0525] Server: Generates the final version of the review that reflects the corrections, stores it in the document database, and shares it as needed.

[0526] Prompt Sentence Examples

[0527] "Generate a review based on the user's current emotional state and predict their thoughts about this product. Below is a list of keywords provided by the user: quality, design, price. The user is currently excited and satisfied with their purchase."

[0528] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0529] Step 1:

[0530] User logs in and selects to create a new review

[0531] Input: User login credentials, choice to create review

[0532] Processing: The device sends the user's login credentials to the server. If the server authenticates the login, the review creation screen is displayed on the device.

[0533] Output: Display of review creation screen

[0534] Step 2:

[0535] The server collects and analyzes past reviews

[0536] Input: Product information selected by the user

[0537] Processing: The server collects relevant past reviews from a document database and uses a natural language processing engine (e.g., SpaCy or BERT) to extract titles, sections, keywords, etc. It then generates a template based on these elements.

[0538] Output: Title, section, keywords for template generation

[0539] Step 3:

[0540] The server recognizes the user's emotions and provides feedback

[0541] Input: User input and operation status

[0542] Processing: The emotion engine (Microsoft Azure Cognitive Services Emotion API) recognizes the user's emotional state and feeds the results back to the document generation AI.

[0543] Output: Emotion recognition results (e.g., excitement, satisfaction, etc.)

[0544] Step 4:

[0545] The server automatically generates the review content using AI

[0546] Input: Templates and emotion engine recognition results

[0547] Processing: The server uses text generation AI (OpenAI GPT-4) to automatically generate specific review content based on templates and emotion recognition results. A prompt is used, such as "Please predict the user's thoughts about this product based on their emotional state and generate a review. Below is a list of keywords provided by the user: quality, design, price. The user is currently excited and satisfied with their purchase."

[0548] Output: Generated review text

[0549] Step 5:

[0550] User confirms and modifies the content

[0551] Input: Generated review text

[0552] Processing: The device displays the generated review text to the user. The user checks the content and makes corrections as necessary. The corrections are sent from the device to the server.

[0553] Output: revised review text

[0554] Step 6:

[0555] The server stores and shares the final version

[0556] Input: Revised review text

[0557] Processing: The server reflects the user's modifications, generates the final document, stores it in the document database, and shares the document with other systems and stakeholders as needed.

[0558] Output: Final saved review text

[0559] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0560] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0561] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0562] [Second embodiment]

[0563] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0564] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0565] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0566] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0567] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0568] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0569] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0570] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0571] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0572] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0573] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0574] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0575] This invention relates to a system that automates document creation and allows users to create high-quality documents simply by checking and correcting them. This system executes a series of steps, from the user's selection of the document type, to data collection and analysis by the server, automatic content generation using text generation AI, user corrections, and final saving and sharing.

[0576] System configuration

[0577] The system consists of the following main components:

[0578] User terminal: A device operated by a user, such as a PC or tablet.

[0579] Server: A central processing and data management device.

[0580] Document database: A storage system that stores and manages past related documents.

[0581] Natural language processing engine: Software that analyzes past related documents.

[0582] Text generation AI: An artificial intelligence model that generates text based on input data.

[0583] System Operation Overview

[0584] Start preparing the documents

[0585] A user logs into the system using a terminal and begins creating a document by selecting "Create a new document." The user then selects the type of document they want to create (procedure, presentation, report, etc.). The terminal sends the user's selection to the server.

[0586] Data collection and analysis

[0587] The server references a document database to gather past documents related to the document type selected by the user, then inputs the collected data into a natural language processing engine to extract elements such as titles, sections, and keywords, and then generates a template based on these elements.

[0588] Automatic sentence generation

[0589] The server uses text generation AI to automatically generate specific content based on the generated template. For example, in the case of a procedure manual, detailed explanations of each step are generated, allowing users to eliminate most of the manual work.

[0590] User confirmation and correction

[0591] The generated document is displayed on the device, where the user can review the contents. The user can then make any necessary corrections, such as adding more detail to a specific step or adding notes. The corrections are then sent from the device to the server.

[0592] Save and share the final version

[0593] Once the user has completed their modifications, the server generates and stores the final version of the document, which is then stored in a document database and can be shared with other systems or stakeholders as needed.

[0594] Specific examples

[0595] Example 1: Creating a procedure manual

[0596] 1. The user logs in and selects to create a new procedure.

[0597] User: Log in to the system and select "Create a new procedure manual."

[0598] Terminal: Sends user selection to server.

[0599] 2. The server collects and analyzes past procedures

[0600] Server: Collects past procedures from the document database.

[0601] Server: Analyzes using a natural language processing engine and generates templates.

[0602] 3. The server automatically generates the contents of the procedure manual using AI

[0603] Server: Creates specific instructions for each step using text generation AI.

[0604] 4. The user checks and corrects the content

[0605] Terminal: View automatically generated instructions.

[0606] User: Check the content and make corrections such as "Explain step 3 in more detail."

[0607] 5. The server stores and shares the final version

[0608] Server: Generates the final version of the procedure manual that reflects the corrections, stores it in the document database, and shares it as needed.

[0609] By using the above means, users can efficiently create high-quality documents, thereby improving business efficiency.

[0610] The processing flow will be explained below.

[0611] Step 1:

[0612] The user begins creating a document.

[0613] A user logs into the system and selects the option "Create a new document."

[0614] The terminal sends this user selection to the server as a request.

[0615] Step 2:

[0616] The user selects a document type.

[0617] Users select a document type such as a procedure manual, presentation, or report.

[0618] The terminal transmits the user's selection information to the server.

[0619] Step 3:

[0620] The server collects past related documents.

[0621] The server retrieves historical documents related to the selected document type from the document database.

[0622] The server passes the collected data to a natural language processing engine.

[0623] Step 4:

[0624] The server analyzes the collected data.

[0625] The server uses a natural language processing engine to extract titles, sections, keywords, etc. from the collected documents.

[0626] The server generates a document template based on the analysis results.

[0627] Step 5:

[0628] The server generates the content using text generation AI.

[0629] The server applies a text generation AI to the generated template to generate specific content.

[0630] Through this process, the server automatically fills in the appropriate text for each section of the document.

[0631] Step 6:

[0632] The terminal displays the generated document to the user.

[0633] The terminal displays the completed document in a user interface.

[0634] The terminal provides an interface that allows the user to view and edit the content.

[0635] Step 7:

[0636] The user checks and corrects the content.

[0637] The user checks the displayed document in detail.

[0638] The user edits any parts that need to be corrected and inputs the corrections into the terminal.

[0639] Step 8:

[0640] The terminal transmits the user's modifications to the server.

[0641] The terminal transmits the modifications made by the user to the server.

[0642] Step 9:

[0643] The server reflects the changes.

[0644] The server reflects the modifications sent by the user and generates the final version of the document.

[0645] The server sends a preview of the final version to the device.

[0646] Step 10:

[0647] The device displays a preview of the final version to the user.

[0648] The terminal displays the final document to the user and provides an interface for final confirmation.

[0649] Step 11:

[0650] The user saves the document.

[0651] The user performs an operation to save the document after final confirmation.

[0652] The terminal sends a save request to the server.

[0653] Step 12:

[0654] The server stores the final document.

[0655] The server stores the final document in a document database.

[0656] The server shares the final document with other systems and stakeholders as needed.

[0657] Example 1

[0658] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0659] Modern business requires fast and accurate document creation, but this process is inefficient because it takes a lot of time and effort. Human error is especially likely to occur when checking and correcting document content, requiring multiple checks. To resolve this situation, a system is needed that automates the entire document creation process and reduces the burden on users.

[0660] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0661] In this invention, the server includes means for collecting past related documents and analyzing them using a natural language processing engine, means for generating document templates based on the analysis results, and means for generating specific content using a sentence generation software module based on the generated templates, thereby enabling the automatic generation of high-quality documents.

[0662] "Document type" refers to the category or type of document the user wants to create, including procedures, presentations, reports, and the like.

[0663] A "server" refers to a device that performs central processing and data management and controls the entire system.

[0664] "Past related documents" refers to documents of the same type that were created previously and are stored in a database, and are used as references when creating new documents.

[0665] A "natural language processing engine" is software that analyzes past related documents to extract elements and meanings of text. Specifically, it has the ability to automatically identify titles, sections, keywords, etc. from strings of characters.

[0666] A "template" is a framework that defines the structure and format of a document, and serves as the basis for generating specific documents based on this framework.

[0667] "Text generation software module" refers to an artificial intelligence program that automatically generates specific content based on a template.

[0668] "Terminal" refers to the device that a user uses to access and operate the system, such as a PC or tablet.

[0669] "User interface" refers to the function that provides the screen and operation method for the user to operate on the terminal, including the interface for checking and modifying the document content.

[0670] "Data storage system" refers to a storage device for securely storing generated documents for later access.

[0671] "Visual interface" refers to a graphical user interface (GUI) that allows users to intuitively view and modify document contents.

[0672] "Sharing with other systems or parties" refers to the ability to make the generated final document accessible between different systems or involved users.

[0673] The present invention is a system that automates document creation, allowing the user to create high-quality documents simply by checking and correcting the documents. A specific embodiment of this system will be described below.

[0674] System configuration

[0675] The system consists of the following main components:

[0676] User terminal: A device operated by a user, such as a PC or tablet. It provides a user interface and allows users to check and edit document contents.

[0677] Server: A central device that manages processing and data and controls the entire system. The server accesses the data storage system and is responsible for collecting, analyzing, and storing the necessary data.

[0678] Document database: A storage system that stores and manages past related documents. Previously created documents of the same type are accumulated here and used as reference for creating new documents.

[0679] Natural language processing engine: A software engine that analyzes past related documents and extracts elements such as titles, sections, keywords, etc. Specific examples include spaCy and NLTK.

[0680] Text generation software module: An artificial intelligence program that generates specific content based on templates. Specifically, this applies to generative AI models such as GPT-3.

[0681] System Operation Overview

[0682] Start preparing the documents

[0683] A user logs into the system using a terminal and selects "Create a new document." The user then selects the type of document they want to create (e.g., procedure manual, presentation, report, etc.). The selection information is sent from the terminal to the server.

[0684] Data collection and analysis

[0685] The server references a document database to collect past documents related to the document type selected by the user, then inputs the collected data into a natural language processing engine to extract elements such as titles, sections, and keywords.The server then generates a template based on the extracted elements.

[0686] Automatic sentence generation

[0687] The server uses a text generation software module to automatically generate specific content based on the generated template. For example, in the case of a procedure manual, detailed explanations of each step are generated, eliminating most of the manual work required by the user.

[0688] Example prompt:

[0689] Please create a procedure titled "System Setup" with sections "Preparation," "Installation," and "Configuration."

[0690] User confirmation and correction

[0691] The generated document is displayed on the terminal, and the user can review the contents. The user can then make appropriate corrections, such as adding more detail to a specific step or adding notes. The corrections are then sent from the terminal to the server.

[0692] Save and share the final version

[0693] Once the user's modifications are complete, the server generates the final document and stores it in a data storage system, where it can be shared with other systems or stakeholders as needed.

[0694] As a concrete example, the operation procedure for "creating a new procedure manual" is shown below:

[0695] 1. The user logs in and selects to create a new procedure.

[0696] 2. The server collects past procedure manuals, analyzes them using a natural language processing engine, and generates templates.

[0697] 3. The server automatically generates a procedure manual using a text generation software module based on the template.

[0698] 4. The terminal displays the procedure manual, and the user checks and corrects it.

[0699] 5. The server stores the final version and shares it as needed.

[0700] This allows users to efficiently create high-quality documents, improving business efficiency.

[0701] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0702] Step 1:

[0703] The user logs in to the terminal and accesses the system's web application. The user selects "Create a new document" from the main menu. The user selects the type of document they want to create and sends it to the server. Specifically, the user opens a browser, accesses the system URL, and logs in by entering their user ID and password. They click the "Create a new document" button and select "Procedure" from the drop-down menu. The input is the user's selection information (create a new document and document type), and the output is sending this selection information to the server.

[0704] Step 2:

[0705] The server queries the document database to collect past documents related to the document type selected by the user. The collected data is input into a natural language processing engine to extract elements such as title, section, and keywords. Specifically, the server executes an SQL query to retrieve all documents of the "procedure" type. The retrieved documents are passed to the natural language processing engine, which extracts key elements (title, section, keywords). The input is past procedure documents retrieved from the database, and the output is elements such as title, section, and keywords.

[0706] Step 3:

[0707] The server generates a template based on the extracted elements. Specifically, it passes the elements obtained from the natural language processing engine to the template generation module, which automatically generates a procedure template. The template includes main sections. The input is the extracted element data, and the output is the procedure template.

[0708] Step 4:

[0709] The server uses a text generation software module to generate specific content based on the generated template. The server sends the template and necessary keywords to the generative AI model. The server receives the text automatically generated by the generative AI model and sends it to the user's device. As a specific operation, the following prompt sentence is sent to the generative AI model: "Please create a procedure manual. The title is 'System Setup' and the sections are 'Preparation', 'Installation', and 'Configuration'." The input is the template and keywords, and the output is the automatically generated document.

[0710] Step 5:

[0711] The terminal displays the generated document on the user interface. The user checks the contents and makes corrections as necessary. Specifically, the document generated on the terminal is displayed, and the user selects the parts that need correction and enters edit mode. The corrections are then sent to the server. The input is the automatically generated document, and the output is the user's corrections.

[0712] Step 6:

[0713] The server generates a final version of the document that reflects the user's modifications and saves it in the data storage system. The server generates a final version of the document and saves it in the document database. It also prepares it for sharing with other systems and related parties as needed. Specifically, the server saves the final version of the procedure manual in the database. The user clicks the "Share" button and sends the shared link generated by the system via email or chat. The input is the document that reflects the user's modifications, and the output is the final version of the procedure manual and the shared link.

[0714] (Application example 1)

[0715] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0716] Logistics centers are required to create work instructions and procedures efficiently and accurately. Conventional methods require a lot of manual work, which leads to problems such as errors and reduced work efficiency. Furthermore, it is difficult to generate appropriate instructions based on past data, and it is an issue to quickly provide instructions that are easy for workers to understand.

[0717] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0718] In this invention, the server includes a means for a user to select a document type, a means for collecting past related documents and analyzing them using a natural language processing engine, a means for generating a document template based on the analysis results, a means for generating specific content using a sentence generation AI, a means for a terminal to display the generated document to the user so that the user can confirm and modify the content, and a means for saving the final document that reflects the user's modifications and sharing it with related departments or other processing systems as necessary. This enables efficient and accurate creation of work instructions at a logistics center.

[0719] "Users" are the workers and other related parties who operate the system.

[0720] "Documents" refers to work instructions and procedures at logistics centers.

[0721] A "server" is a central control unit that processes and manages data.

[0722] A "template" refers to a format or framework that serves as a framework for generating a document.

[0723] "Text generation AI" is an artificial intelligence model that automatically generates documents using natural language processing technology.

[0724] A "natural language processing engine" is a software engine that analyzes documents and extracts elements.

[0725] A "terminal" is a device that a user uses to operate the system, and includes a smartphone, tablet, etc.

[0726] A "document database" is a storage system that stores and manages past documents.

[0727] A "prompt sentence" is the initial input given to a text generation AI, and determines the direction of the generated document.

[0728] "Related departments" refers to departments or groups that carry out other operations inside or outside the logistics center.

[0729] "Other processing systems" refers to other systems or applications for sharing generated documents.

[0730] The system for implementing this invention automatically generates work instructions and procedures within a logistics center, providing efficient and accurate instructions. The system is composed of the following main components:

[0731] Key Components

[0732] User terminal: A device operated by a user, such as a smartphone or tablet.

[0733] Server: A cloud server (e.g., AWS, Google Cloud, Microsoft Azure, etc.) that processes and manages data.

[0734] Document database: A cloud storage (e.g., Amazon S3, Google Cloud Storage, etc.) that stores and manages historical documents.

[0735] Natural language processing engine: A software engine for parsing documents and extracting elements (e.g., AWS Comprehend, Google Cloud Natural Language API, SpaCy, etc.).

[0736] Text generation AI: An artificial intelligence model that automatically generates documents using natural language processing technology (e.g., OpenAI GPT-3, GPT-4, etc.).

[0737] System Operation Overview

[0738] 1. Log in and create a new instruction

[0739] The user logs into the app using their smartphone and selects "Create new instructions." The selection is sent to the server.

[0740] 2. Data collection and template generation

[0741] The server collects relevant past documents from cloud storage and analyzes the data using a natural language processing engine, generating templates based on the analysis results.

[0742] 3. Automatic document generation

[0743] Based on the generated template, the server uses text generation AI to automatically generate specific content.

[0744] 4. User confirmation and correction

[0745] The generated document is displayed on the user's terminal, and the user can check the content and make corrections as necessary. The corrections are then sent to the server and reflected.

[0746] 5. Save and share the final version

[0747] The server generates a final version of the document that reflects the changes, stores it in cloud storage, and shares it with related departments and other processing systems as needed.

[0748] Specific examples

[0749] For example, when a distribution center worker creates a new inventory work order, they might use the following prompt:

[0750] Prompt Text: "Logistics Directive Template: Title: Shelf Inventory\nSections: Preparation, Process, Completion\nKeywords: inventory, checklist, items, count"

[0751] Based on this prompt, the AI ​​generates specific work instructions like this:

[0752] Title: Shelf Inventory

[0753] Sections:

[0754] 1. Preparation

[0755] - Verify the inventory list and necessary tools.

[0756] - Ensure all team members are briefed on the process.

[0757] 2. Process

[0758] - Follow the checklist to count each item on the shelves.

[0759] - Record discrepancies with existing records.

[0760] 3. Completion

[0761] - Review the collected data for accuracy.

[0762] - Report any major issues to the supervisor.

[0763] Keywords: inventory, checklist, items, count

[0764] In this way, the system supports the efficient and accurate creation of work instructions in logistics centers.

[0765] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0766] Step 1:

[0767] The user logs into the app using their smartphone and selects "Create new instruction." The login and selection information is entered and sent to the server, which receives the data and proceeds to the next processing step.

[0768] Step 2:

[0769] The server accesses the document database to collect past related documents. Past instruction data is input, and the collected data is sent to the natural language processing engine. The server uses the natural language processing engine to analyze the collected documents and extract elements such as titles, sections, and keywords. The extracted elements are output and the process proceeds to the next processing step.

[0770] Step 3:

[0771] The server generates a document template based on the analysis results. The analyzed data is used as input to generate the template. The generated template is sent to a text generation AI, which automatically generates a specific document. The generated document is output and proceeds to the next processing step.

[0772] Step 4:

[0773] The generated document is sent to the user's terminal. The terminal displays this automatically generated document to the user. The user checks the document contents and makes corrections as necessary (for example, "Please explain step 3 in detail"). The corrected content is sent from the terminal to the server as input.

[0774] Step 5:

[0775] The server receives the submitted corrections and generates the final document. Using the correction data as input, the server generates the final document and stores it in the document database. The stored final document is shared with related departments and other processing systems as needed. Sharing the output final document with other related systems allows work within the logistics center to be carried out efficiently and accurately.

[0776] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0777] This invention relates to a system that automates document creation, recognizing the user's emotions and creating high-quality documents simply by confirming and correcting them. This system executes a series of processes, from the user selecting the document type, to the server recognizing emotions using an emotion engine, collecting and analyzing data, automatically generating content using a text generation AI, and finally saving and sharing the document after the user has made corrections.

[0778] System configuration

[0779] The system consists of the following main components:

[0780] User terminal: A device operated by a user, such as a PC or tablet.

[0781] Server: A central processing and data management device.

[0782] Document database: A storage system that stores and manages past related documents.

[0783] Natural language processing engine: Software that analyzes past related documents.

[0784] Text generation AI: An artificial intelligence model that generates text based on input data.

[0785] Emotion engine: Software that recognizes the user's emotions and provides feedback to the system.

[0786] System Operation Overview

[0787] Start preparing the documents

[0788] A user logs into the system using a terminal and begins creating a document by selecting "Create a new document." The user then selects the type of document they want to create (procedure, presentation, report, etc.). The terminal sends the user's selection to the server.

[0789] Data collection and analysis

[0790] The server references a document database to gather past documents related to the document type selected by the user, then inputs the collected data into a natural language processing engine to extract elements such as titles, sections, and keywords, and then generates a template based on these elements.

[0791] Emotion recognition

[0792] In addition, the server uses an emotion engine to recognize the user's emotions, which detects the user's current emotions based on their inputs and operating situations, and optimizes the document generation process accordingly.

[0793] Automatic sentence generation

[0794] The server uses text generation AI to automatically generate specific content based on the generated template. For example, in the case of a procedure manual, detailed explanations of each step are generated. The text generation AI also adjusts tone and style as needed, taking into account the recognition results of the emotion engine.

[0795] User confirmation and correction

[0796] The generated document is displayed on the device, where the user can review the contents. The user can then make any necessary corrections, such as adding more detail to a specific step or adding notes. The corrections are then sent from the device to the server.

[0797] Save and share the final version

[0798] Once the user has completed their modifications, the server generates and stores the final version of the document, which is then stored in a document database and can be shared with other systems or stakeholders as needed.

[0799] Specific examples

[0800] Example 1: Creating a procedure manual

[0801] 1. The user logs in and selects to create a new procedure.

[0802] User: Log in to the system and select "Create a new procedure manual."

[0803] Terminal: Sends user selection to server.

[0804] 2. The server collects and analyzes past procedures

[0805] Server: Collects past procedures from the document database.

[0806] Server: Analyzes using a natural language processing engine and generates templates.

[0807] 3. The server recognizes the user's emotions and provides feedback

[0808] Server: Recognizes user emotions using an emotion engine.

[0809] Server: Based on the recognition results, the tone and style of the text generation AI are adjusted.

[0810] 4. The server automatically generates the contents of the procedure manual using AI

[0811] Server: Creates specific instructions for each step using text generation AI.

[0812] 5. The user checks and corrects the content

[0813] Terminal: View automatically generated instructions.

[0814] User: Check the content and make corrections such as "Explain step 3 in more detail."

[0815] 6. The server stores and shares the final version

[0816] Server: Generates the final version of the procedure manual that reflects the corrections, stores it in the document database, and shares it as needed.

[0817] By using the above means, users can efficiently create high-quality documents, which improves business efficiency and also allows for individual responses that reflect the user's feelings.

[0818] The processing flow will be explained below.

[0819] Step 1:

[0820] The user begins creating a document.

[0821] The user logs into the system and selects "Create a new document."

[0822] The terminal sends this user selection to the server as a request.

[0823] Step 2:

[0824] The user selects a document type.

[0825] Users select a document type such as a procedure manual, presentation, or report.

[0826] The terminal transmits the user's selection information to the server.

[0827] Step 3:

[0828] The server collects past related documents.

[0829] The server collects historical documents related to the selected document type from a document database.

[0830] The server passes the collected data to a natural language processing engine.

[0831] Step 4:

[0832] The server analyzes the collected data.

[0833] The server uses a natural language processing engine to extract titles, sections, keywords, etc. from the collected documents.

[0834] The server generates a document template based on the analysis results.

[0835] Step 5:

[0836] The server uses an emotion engine to recognize the user's emotions.

[0837] The server receives the user's operation status and input contents from the terminal and sends them to the emotion engine.

[0838] The emotion engine analyzes the user's emotions and feeds the results back to the server.

[0839] Step 6:

[0840] The server generates the content using text generation AI.

[0841] Based on the generated template, the server applies text generation AI to generate specific content.

[0842] Taking into account the results of the emotion engine, the text generation AI adjusts tone and style as needed.

[0843] Step 7:

[0844] The terminal displays the generated document to the user.

[0845] The terminal displays the completed document in a user interface.

[0846] The terminal provides an interface that allows the user to view and edit the content.

[0847] Step 8:

[0848] The user checks and corrects the content.

[0849] The user checks the displayed document in detail.

[0850] The user edits any parts that need to be corrected and inputs the corrections into the terminal.

[0851] Step 9:

[0852] The terminal transmits the user's modifications to the server.

[0853] The terminal transmits the modifications made by the user to the server.

[0854] Step 10:

[0855] The server reflects the changes.

[0856] The server reflects the modifications sent by the user and generates the final version of the document.

[0857] The server sends a preview of the final version to the device.

[0858] Step 11:

[0859] The device displays a preview of the final version to the user.

[0860] The terminal displays the final document to the user and provides an interface for final confirmation.

[0861] Step 12:

[0862] The user saves the document.

[0863] The user performs an operation to save the document after final confirmation.

[0864] The terminal sends a save request to the server.

[0865] Step 13:

[0866] The server stores the final document.

[0867] The server stores the final document in a document database.

[0868] The server shares the final document with other systems and stakeholders as needed.

[0869] Specific examples

[0870] Example 1: Creating a procedure manual

[0871] 1. The user logs in and selects to create a new procedure.

[0872] User: Log in to the system and select "Create a new procedure manual."

[0873] Terminal: Sends user selection to server.

[0874] 2. The server collects and analyzes past procedures

[0875] Server: Collects past procedures from a document database, analyzes them using a natural language processing engine, and generates templates.

[0876] 3. The server uses the emotion engine to recognize the user's emotions.

[0877] Server: The server receives the user's operation status and input information from the device and sends it to the emotion engine to analyze the user's emotions.

[0878] Server: Receives the results of the emotion engine and reflects them in the sentence generation AI.

[0879] 4. The server automatically generates the contents of the procedure manual using AI

[0880] Server: Using text generation AI, we create detailed instructions for each step and adjust the tone and style.

[0881] 5. The user checks and corrects the content

[0882] Terminal: View automatically generated instructions.

[0883] User: Check the content and make corrections such as "Explain step 3 in more detail."

[0884] 6. The server stores and shares the final version

[0885] Server: Generates the final version of the procedure manual that reflects the corrections, stores it in the document database, and shares it as needed.

[0886] By using the above means, users can efficiently create high-quality documents, which improves business efficiency and also allows for individual responses that reflect the user's feelings.

[0887] Example 2

[0888] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0889] In conventional document creation systems, the process of users manually creating and editing documents is time-consuming and laborious, making it inefficient. Furthermore, it is difficult to create documents that take into account the user's emotions and circumstances, making it difficult to create high-quality documents tailored to individual users. Therefore, there is a demand for a system that reduces user labor and allows for the rapid creation of high-quality documents that can be tailored to individual users.

[0890] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0891] In this invention, the server includes means for collecting past related documents and analyzing them using a natural language processing engine, means for generating document templates based on the analysis results, means for recognizing user emotions using an emotion engine, and means for adjusting tone and style using a sentence generation AI to generate specific content. This reduces the user's workload and enables the rapid creation of high-quality documents that can be tailored to individual needs.

[0892] A "user" is an entity that uses the system to create, check, and modify documents.

[0893] A "terminal" is a device operated by a user, and includes devices such as PCs and tablets.

[0894] A "server" is a device that centrally manages and processes data.

[0895] "Document type" refers to the specific type of document the user wants to create, including procedures, presentation materials, reports, and the like.

[0896] "Related historical documents" are existing documents stored in the document database that are related to the document type selected by the user.

[0897] A "natural language processing engine" is software for analyzing text data and extracting necessary information.

[0898] A "document template" is a format that includes the basic structure and main items of a document, and is generated based on collected document data.

[0899] An "emotion engine" is software that recognizes a user's emotions based on the user's input and operational status, and feeds that emotional information back into the system.

[0900] "Sentence generation AI" is an artificial intelligence model that automatically generates sentences based on specified data and templates.

[0901] "Tone and style" refers to the atmosphere and expression of the generated text, and is adjusted according to the user's emotions and situation.

[0902] The "final document" is the document that is completed by reflecting the user's modifications and is finally saved.

[0903] A "document database" is a storage system for storing and managing past related documents, final versions of documents, and the like.

[0904] The present invention relates to a system that enables users to efficiently create high-quality documents. This system comprises a means for the user to select a document type, a means for a server to recognize emotions using an emotion engine, a means for collecting past related documents and analyzing them with a natural language processing engine, a means for generating templates, a means for generating content using a sentence generation AI, and a means for a terminal to display the generated document to the user and allow them to make corrections. The specific configuration and operation of this system are described below.

[0905] System configuration

[0906] The system consists of the following main components:

[0907] User terminal: A device operated by a user, such as a PC or tablet.

[0908] Server: A central device that manages and processes data.

[0909] Document database: A storage system that stores and manages past related documents.

[0910] Natural language processing engine: Software that analyzes past related documents.

[0911] Text generation AI: An artificial intelligence model that generates text based on input data.

[0912] Emotion engine: Software that recognizes the user's emotions and provides feedback to the system.

[0913] Operation overview

[0914] Start preparing the documents

[0915] A user logs into the system using a terminal and selects "Create a new document." The user then selects the type of document they want to create (procedure, presentation, report, etc.). The terminal sends this selection to the server.

[0916] Data collection and analysis

[0917] The server accesses a document database to gather past documents related to the document type selected by the user, then inputs the collected data into a natural language processing engine to extract elements such as titles, sections, and keywords, and then generates a template based on these elements.

[0918] Emotion recognition

[0919] In addition, the server uses an emotion engine to recognize the user's emotions, which detects the user's current emotions based on their inputs and operation situations, and optimizes the document generation process accordingly.

[0920] Automatic sentence generation

[0921] The server uses text generation AI to automatically generate specific content based on the generated template. For example, in the case of a procedure manual, detailed explanations of each step are generated. The server also adjusts the tone and style of writing based on the recognition results of the emotion engine.

[0922] User confirmation and correction

[0923] The generated document is displayed on the device, where the user can review the contents and make any necessary corrections, such as adding detailed explanations for specific steps or adding notes. The corrections are then sent from the device to the server.

[0924] Save and share the final version

[0925] Once the user has completed their modifications, the server generates and stores the final version of the document, which is stored in a document database and can be shared with other systems or stakeholders as needed.

[0926] Specific examples

[0927] Example 1: Creating a procedure manual

[0928] 1. The user logs in and selects to create a new procedure.

[0929] User: Log in to the system and select "Create a new procedure manual."

[0930] Terminal: Sends the user's selection to the server.

[0931] 2. The server collects and analyzes past procedures

[0932] Server: Collects past procedures from a document database.

[0933] Server: Analyzes using a natural language processing engine and generates templates.

[0934] 3. The server recognizes the user's emotions and provides feedback

[0935] Server: Recognizes user emotions using an emotion engine.

[0936] Server: Based on the recognition results, adjust the tone and style of the text generation AI.

[0937] 4. The server automatically generates the contents of the procedure manual using AI

[0938] Server: Uses text generation AI to create specific instructions for each step in the procedure manual.

[0939] 5. The user checks and corrects the content

[0940] Terminal: View the automatically generated instructions.

[0941] User: Check the content and make corrections such as "Add more detail to the explanation of Step 3."

[0942] 6. The server stores and shares the final version

[0943] Server: Generates the final version of the procedure manual that reflects the modifications and stores it in the document database.

[0944] Server: Generate a link to share with other departments or stakeholders as needed.

[0945] Examples of prompt statements

[0946] 1. Specify the document type: "Start creating a new procedure. First, enter the procedure title."

[0947] 2. Key Element Analysis: "Based on the procedure title entered, extract key sections and keywords from related historical documents."

[0948] 3. Emotion detection: "Recognize the user's current emotion and adjust the tone and style of the document appropriately. If the user is relaxed, use a friendly tone."

[0949] 4. Auto-generated prompt: "Draw each step of the procedure in detail using the following format. For example, Step 1: Prepare the necessary tools and materials. Step 2: Explain the procedure. Step 3: Notes."

[0950] The above is a detailed description of an embodiment of the present invention. This system allows users to quickly and efficiently create high-quality documents, and also provides personalized responses that take into account the user's emotions.

[0951] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0952] System program processing flow

[0953] Step 1: User logs in and selects Create New Document

[0954] A user logs in to the system using a terminal.

[0955] Input: User ID and password

[0956] Output: Authentication status of the user

[0957] The server validates the credentials and displays the dashboard if authentication is successful.

[0958] Input: User ID and password

[0959] Output: Dashboard screen

[0960] The user selects "Create a new document" from the dashboard.

[0961] Input: User's document type selection

[0962] Output: Document type data

[0963] The terminal transmits the user's selection to the server.

[0964] Step 2: The server collects and analyzes past document data

[0965] The server accesses a document database to collect past documents related to the document type selected by the user.

[0966] Input: Document type data

[0967] Output: Dataset of collected historical documents

[0968] The server uses a natural language processing engine to extract elements such as titles, sections, and keywords from the collected documents.

[0969] Input: Dataset of historical documents

[0970] Output: Extracted elements (title, sections, keywords, etc.)

[0971] The server generates a template based on these extracted elements.

[0972] Input: Extracted elements

[0973] Output: Generated template

[0974] Step 3: The server recognizes the user's emotion

[0975] The server starts the emotion engine and analyzes the user's input and operation status.

[0976] Input: User operation log, input text

[0977] Output: Emotion recognition result

[0978] The server determines the user's current emotion (e.g., stress, relaxation, or impatience) based on the results of the emotion engine.

[0979] Input: Emotion recognition results

[0980] Output: User's emotional state data

[0981] The server feeds this emotional information back into the sentence generation process.

[0982] Step 4: The server automatically generates the content using text generation AI

[0983] The server uses a sentence generation AI to automatically generate sentences based on the generated template and the results of the emotion engine.

[0984] Input: Template, emotional state data

[0985] Output: The specific document content generated

[0986] The server generates the specific document content, for example in the case of a procedure manual, creating a detailed description of each step.

[0987] Input: Template, emotional state data

[0988] Output: Specific procedure manual content

[0989] The server takes emotional information into account and adjusts tone and style.

[0990] Step 5: User confirms and modifies the content

[0991] The terminal displays the generated document to the user.

[0992] Input: Generated document content

[0993] Output: Displayed document

[0994] The user reviews the document and identifies any necessary revisions (e.g., "More details on step 3").

[0995] Input: Displayed document, user modifications

[0996] Output: Correction instruction data

[0997] The terminal transmits correction instruction data to the server.

[0998] Step 6: The server saves and shares the final version

[0999] The server generates the final document reflecting the user's modifications.

[1000] Input: Correction instruction data, initial generated document content

[1001] Output: Final document

[1002] The server stores the final version of the document in a document database.

[1003] Input: Final document

[1004] Output: Save status

[1005] The server generates links to share the document with other systems or stakeholders as needed.

[1006] This allows users to quickly and efficiently create high-quality documents, and the system also provides personalized responses that reflect the user's feelings.

[1007] (Application example 2)

[1008] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1009] Currently, creating product reviews and customer support emails on online shopping sites faces the challenge of generating high-quality text that reflects user emotions. This can lead to inconsistencies in the content of reviews and emails, resulting in a poor user experience. Another problem is the effort required for users to manually edit the content of reviews and emails. To solve these issues, a system is needed that automatically generates appropriate text that takes user emotions into account.

[1010] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1011] In this invention, the server includes a means for allowing a user to select a document type, a means for collecting past related documents and analyzing them using a natural language processing engine, and a means for generating a document template based on the analysis results, which allows the tone and style of writing to be adjusted based on the user's emotions, making it possible to automatically generate high-quality product reviews and customer support emails.

[1012] A "user terminal" is a device that a user uses to operate the system, and includes general computer devices such as smartphones and tablets.

[1013] A "server" is a central device for processing and managing large amounts of data, and oversees database operations and AI processing.

[1014] A "document database" is a storage system that stores and manages historical related documents and provides quick data access.

[1015] A "natural language processing engine" is a general term for software that analyzes input text and understands its meaning.

[1016] "Text generation AI" is an artificial intelligence model that automatically generates text based on input data, producing high-quality text according to specific rules and patterns.

[1017] An "emotion engine" is software that recognizes the user's emotions and provides feedback to the system based on those emotions.

[1018] A "template" is a format that serves as a basic framework for efficiently generating a variety of documents, and defines the components of each document.

[1019] "Tone of the text" refers to the emotional tone or style of the generated text, which is adjusted based on the user's emotions.

[1020] "Style" refers to the manner or style of writing, and can range from formal to casual.

[1021] A "visual interface" is a screen display mechanism that allows users to visually view and manipulate documents and data.

[1022] MODE FOR CARRYING OUT THE INVENTION

[1023] System Configuration

[1024] In practicing this invention, the system includes as major components:

[1025] 1. User terminal: A device operated by a user, such as a smartphone or tablet.

[1026] 2. Server: A central device for processing and managing large amounts of data, using AWS (Amazon Web Services) or Google Cloud Platform.

[1027] 3. Document database: A storage system that stores and manages historical related documents.

[1028] 4. Natural language processing engine: A general term for software that analyzes input text and understands its meaning. Examples include SpaCy and BERT.

[1029] 5. Text generation AI: An artificial intelligence model that automatically generates text based on input data. We will use OpenAI GPT-4 as a concrete example.

[1030] 6. Emotion Engine: Software that recognizes the user's emotions and provides feedback to the system based on those emotions. It uses the Microsoft Azure Cognitive Services Emotion API.

[1031] Processing flow

[1032] 1. User operation: The user logs in to the system using a terminal and selects the document type (product review or customer support email).

[1033] 2. Data collection and analysis: The server collects relevant documents from the document database and analyzes them using a natural language processing engine (e.g., SpaCy or BERT), extracting elements such as titles, sections, and keywords, and generating templates.

[1034] 3. Emotion Recognition: The emotion engine (Microsoft Azure Cognitive Services Emotion API) detects the current emotion based on the user's input and operation context. Based on the emotional state, the text generation AI adjusts the tone and style of writing.

[1035] 4. Automatic sentence generation: Based on the template generated in the previous step, the server uses sentence generation AI (OpenAI GPT-4) to automatically generate specific content, resulting in high-quality sentences that reflect emotions.

[1036] 5. Confirmation and correction: The terminal displays the generated text to the user. The user can confirm the content and make corrections as necessary. The corrected data is sent back to the server.

[1037] 6. Final saving and sharing: The server reflects the user's modifications, generates the final version of the document, and saves it in the document database. If necessary, the document can be shared with other systems or stakeholders.

[1038] Specific examples

[1039] Example 1: Creating a product review

[1040] 1. User logs in and selects to create a new review

[1041] User: Log in to the system and select "Create a new review."

[1042] Terminal: Sends user selection to server.

[1043] 2. The server collects and analyzes past reviews

[1044] Server: Collects past reviews from a document database.

[1045] Server: Analyzes using a natural language processing engine and generates templates.

[1046] 3. The server recognizes the user's emotions and provides feedback

[1047] Server: Recognizes user emotions using an emotion engine.

[1048] Server: Based on the recognition results, the tone and style of the sentence generation AI are adjusted.

[1049] 4. The server automatically generates the review content using AI

[1050] Server: Create specific reviews using text generation AI.

[1051] 5. User confirms and modifies the content

[1052] Device: View automatically generated reviews.

[1053] User: Check the contents and make any necessary changes.

[1054] 6. The server stores and shares the final version

[1055] Server: Generates the final version of the review that reflects the corrections, stores it in the document database, and shares it as needed.

[1056] Prompt Sentence Examples

[1057] "Generate a review based on the user's current emotional state and predict their thoughts about this product. Below is a list of keywords provided by the user: quality, design, price. The user is currently excited and satisfied with their purchase."

[1058] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1059] Step 1:

[1060] User logs in and selects to create a new review

[1061] Input: User login credentials, choice to create review

[1062] Processing: The device sends the user's login credentials to the server. If the server authenticates the login, the review creation screen is displayed on the device.

[1063] Output: Display of review creation screen

[1064] Step 2:

[1065] The server collects and analyzes past reviews

[1066] Input: Product information selected by the user

[1067] Processing: The server collects relevant past reviews from a document database and uses a natural language processing engine (e.g., SpaCy or BERT) to extract titles, sections, keywords, etc. It then generates a template based on these elements.

[1068] Output: Title, section, keywords for template generation

[1069] Step 3:

[1070] The server recognizes the user's emotions and provides feedback

[1071] Input: User input and operation status

[1072] Processing: The emotion engine (Microsoft Azure Cognitive Services Emotion API) recognizes the user's emotional state and feeds the results back to the document generation AI.

[1073] Output: Emotion recognition results (e.g., excitement, satisfaction, etc.)

[1074] Step 4:

[1075] The server automatically generates the review content using AI

[1076] Input: Templates and emotion engine recognition results

[1077] Processing: The server uses text generation AI (OpenAI GPT-4) to automatically generate specific review content based on templates and emotion recognition results. A prompt is used, such as "Please predict the user's thoughts about this product based on their emotional state and generate a review. Below is a list of keywords provided by the user: quality, design, price. The user is currently excited and satisfied with their purchase."

[1078] Output: Generated review text

[1079] Step 5:

[1080] User confirms and modifies the content

[1081] Input: Generated review text

[1082] Processing: The device displays the generated review text to the user. The user checks the content and makes corrections as necessary. The corrections are sent from the device to the server.

[1083] Output: revised review text

[1084] Step 6:

[1085] The server stores and shares the final version

[1086] Input: Revised review text

[1087] Processing: The server reflects the user's modifications, generates the final document, stores it in the document database, and shares the document with other systems and stakeholders as needed.

[1088] Output: Final saved review text

[1089] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1091] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1092] [Third embodiment]

[1093] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1094] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1095] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1096] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1097] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1099] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1100] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1101] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1103] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1104] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[1105] This invention relates to a system that automates document creation and allows users to create high-quality documents simply by checking and correcting them. This system executes a series of steps, from the user's selection of the document type, to data collection and analysis by the server, automatic content generation using text generation AI, user corrections, and final saving and sharing.

[1106] System configuration

[1107] The system consists of the following main components:

[1108] User terminal: A device operated by a user, such as a PC or tablet.

[1109] Server: A central processing and data management device.

[1110] Document database: A storage system that stores and manages past related documents.

[1111] Natural language processing engine: Software that analyzes past related documents.

[1112] Text generation AI: An artificial intelligence model that generates text based on input data.

[1113] System Operation Overview

[1114] Start preparing the documents

[1115] A user logs into the system using a terminal and begins creating a document by selecting "Create a new document." The user then selects the type of document they want to create (procedure, presentation, report, etc.). The terminal sends the user's selection to the server.

[1116] Data collection and analysis

[1117] The server references a document database to gather past documents related to the document type selected by the user, then inputs the collected data into a natural language processing engine to extract elements such as titles, sections, and keywords, and then generates a template based on these elements.

[1118] Automatic sentence generation

[1119] The server uses text generation AI to automatically generate specific content based on the generated template. For example, in the case of a procedure manual, detailed explanations of each step are generated, allowing users to eliminate most of the manual work.

[1120] User confirmation and correction

[1121] The generated document is displayed on the device, where the user can review the contents. The user can then make any necessary corrections, such as adding more detail to a specific step or adding notes. The corrections are then sent from the device to the server.

[1122] Save and share the final version

[1123] Once the user has completed their modifications, the server generates and stores the final version of the document, which is then stored in a document database and can be shared with other systems or stakeholders as needed.

[1124] Specific examples

[1125] Example 1: Creating a procedure manual

[1126] 1. The user logs in and selects to create a new procedure.

[1127] User: Log in to the system and select "Create a new procedure manual."

[1128] Terminal: Sends user selection to server.

[1129] 2. The server collects and analyzes past procedures

[1130] Server: Collects past procedures from the document database.

[1131] Server: Analyzes using a natural language processing engine and generates templates.

[1132] 3. The server automatically generates the contents of the procedure manual using AI

[1133] Server: Creates specific instructions for each step using text generation AI.

[1134] 4. The user checks and corrects the content

[1135] Terminal: View automatically generated instructions.

[1136] User: Check the content and make corrections such as "Explain step 3 in more detail."

[1137] 5. The server stores and shares the final version

[1138] Server: Generates the final version of the procedure manual that reflects the corrections, stores it in the document database, and shares it as needed.

[1139] By using the above means, users can efficiently create high-quality documents, thereby improving business efficiency.

[1140] The processing flow will be explained below.

[1141] Step 1:

[1142] The user begins creating a document.

[1143] A user logs into the system and selects the option "Create a new document."

[1144] The terminal sends this user selection to the server as a request.

[1145] Step 2:

[1146] The user selects a document type.

[1147] Users select a document type such as a procedure manual, presentation, or report.

[1148] The terminal transmits the user's selection information to the server.

[1149] Step 3:

[1150] The server collects past related documents.

[1151] The server retrieves historical documents related to the selected document type from the document database.

[1152] The server passes the collected data to a natural language processing engine.

[1153] Step 4:

[1154] The server analyzes the collected data.

[1155] The server uses a natural language processing engine to extract titles, sections, keywords, etc. from the collected documents.

[1156] The server generates a document template based on the analysis results.

[1157] Step 5:

[1158] The server generates the content using text generation AI.

[1159] The server applies a text generation AI to the generated template to generate specific content.

[1160] Through this process, the server automatically fills in the appropriate text for each section of the document.

[1161] Step 6:

[1162] The terminal displays the generated document to the user.

[1163] The terminal displays the completed document in a user interface.

[1164] The terminal provides an interface that allows the user to view and edit the content.

[1165] Step 7:

[1166] The user checks and corrects the content.

[1167] The user checks the displayed document in detail.

[1168] The user edits any parts that need to be corrected and inputs the corrections into the terminal.

[1169] Step 8:

[1170] The terminal transmits the user's modifications to the server.

[1171] The terminal transmits the modifications made by the user to the server.

[1172] Step 9:

[1173] The server reflects the changes.

[1174] The server reflects the modifications sent by the user and generates the final version of the document.

[1175] The server sends a preview of the final version to the device.

[1176] Step 10:

[1177] The device displays a preview of the final version to the user.

[1178] The terminal displays the final document to the user and provides an interface for final confirmation.

[1179] Step 11:

[1180] The user saves the document.

[1181] The user performs an operation to save the document after final confirmation.

[1182] The terminal sends a save request to the server.

[1183] Step 12:

[1184] The server stores the final document.

[1185] The server stores the final document in a document database.

[1186] The server shares the final document with other systems and stakeholders as needed.

[1187] Example 1

[1188] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1189] Modern business requires fast and accurate document creation, but this process is inefficient because it takes a lot of time and effort. Human error is especially likely to occur when checking and correcting document content, requiring multiple checks. To resolve this situation, a system is needed that automates the entire document creation process and reduces the burden on users.

[1190] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1191] In this invention, the server includes means for collecting past related documents and analyzing them using a natural language processing engine, means for generating document templates based on the analysis results, and means for generating specific content using a sentence generation software module based on the generated templates, thereby enabling the automatic generation of high-quality documents.

[1192] "Document type" refers to the category or type of document the user wants to create, including procedures, presentations, reports, and the like.

[1193] A "server" refers to a device that performs central processing and data management and controls the entire system.

[1194] "Past related documents" refers to documents of the same type that were created previously and are stored in a database, and are used as references when creating new documents.

[1195] A "natural language processing engine" is software that analyzes past related documents to extract elements and meanings of text. Specifically, it has the ability to automatically identify titles, sections, keywords, etc. from strings of characters.

[1196] A "template" is a framework that defines the structure and format of a document, and serves as the basis for generating specific documents based on this framework.

[1197] "Text generation software module" refers to an artificial intelligence program that automatically generates specific content based on a template.

[1198] "Terminal" refers to the device that a user uses to access and operate the system, such as a PC or tablet.

[1199] "User interface" refers to the function that provides the screen and operation method for the user to operate on the terminal, including the interface for checking and modifying the document content.

[1200] "Data storage system" refers to a storage device for securely storing generated documents for later access.

[1201] "Visual interface" refers to a graphical user interface (GUI) that allows users to intuitively view and modify document contents.

[1202] "Sharing with other systems or parties" refers to the ability to make the generated final document accessible between different systems or involved users.

[1203] The present invention is a system that automates document creation, allowing the user to create high-quality documents simply by checking and correcting the documents. A specific embodiment of this system will be described below.

[1204] System configuration

[1205] The system consists of the following main components:

[1206] User terminal: A device operated by a user, such as a PC or tablet. It provides a user interface and allows users to check and edit document contents.

[1207] Server: A central device that manages processing and data and controls the entire system. The server accesses the data storage system and is responsible for collecting, analyzing, and storing the necessary data.

[1208] Document database: A storage system that stores and manages past related documents. Previously created documents of the same type are accumulated here and used as reference for creating new documents.

[1209] Natural language processing engine: A software engine that analyzes past related documents and extracts elements such as titles, sections, keywords, etc. Specific examples include spaCy and NLTK.

[1210] Text generation software module: An artificial intelligence program that generates specific content based on templates. Specifically, this applies to generative AI models such as GPT-3.

[1211] System Operation Overview

[1212] Start preparing the documents

[1213] A user logs into the system using a terminal and selects "Create a new document." The user then selects the type of document they want to create (e.g., procedure manual, presentation, report, etc.). The selection information is sent from the terminal to the server.

[1214] Data collection and analysis

[1215] The server references a document database to collect past documents related to the document type selected by the user, then inputs the collected data into a natural language processing engine to extract elements such as titles, sections, and keywords.The server then generates a template based on the extracted elements.

[1216] Automatic sentence generation

[1217] The server uses a text generation software module to automatically generate specific content based on the generated template. For example, in the case of a procedure manual, detailed explanations of each step are generated, eliminating most of the manual work required by the user.

[1218] Example prompt:

[1219] Please create a procedure titled "System Setup" with sections "Preparation," "Installation," and "Configuration."

[1220] User confirmation and correction

[1221] The generated document is displayed on the terminal, and the user can review the contents. The user can then make appropriate corrections, such as adding more detail to a specific step or adding notes. The corrections are then sent from the terminal to the server.

[1222] Save and share the final version

[1223] Once the user's modifications are complete, the server generates the final document and stores it in a data storage system, where it can be shared with other systems or stakeholders as needed.

[1224] As a concrete example, the operation procedure for "creating a new procedure manual" is shown below:

[1225] 1. The user logs in and selects to create a new procedure.

[1226] 2. The server collects past procedure manuals, analyzes them using a natural language processing engine, and generates templates.

[1227] 3. The server automatically generates a procedure manual using a text generation software module based on the template.

[1228] 4. The terminal displays the procedure manual, and the user checks and corrects it.

[1229] 5. The server stores the final version and shares it as needed.

[1230] This allows users to efficiently create high-quality documents, improving business efficiency.

[1231] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1232] Step 1:

[1233] The user logs in to the terminal and accesses the system's web application. The user selects "Create a new document" from the main menu. The user selects the type of document they want to create and sends it to the server. Specifically, the user opens a browser, accesses the system URL, and logs in by entering their user ID and password. They click the "Create a new document" button and select "Procedure" from the drop-down menu. The input is the user's selection information (create a new document and document type), and the output is sending this selection information to the server.

[1234] Step 2:

[1235] The server queries the document database to collect past documents related to the document type selected by the user. The collected data is input into a natural language processing engine to extract elements such as title, section, and keywords. Specifically, the server executes an SQL query to retrieve all documents of the "procedure" type. The retrieved documents are passed to the natural language processing engine, which extracts key elements (title, section, keywords). The input is past procedure documents retrieved from the database, and the output is elements such as title, section, and keywords.

[1236] Step 3:

[1237] The server generates a template based on the extracted elements. Specifically, it passes the elements obtained from the natural language processing engine to the template generation module, which automatically generates a procedure template. The template includes main sections. The input is the extracted element data, and the output is the procedure template.

[1238] Step 4:

[1239] The server uses a text generation software module to generate specific content based on the generated template. The server sends the template and necessary keywords to the generative AI model. The server receives the text automatically generated by the generative AI model and sends it to the user's device. As a specific operation, the following prompt sentence is sent to the generative AI model: "Please create a procedure manual. The title is 'System Setup' and the sections are 'Preparation', 'Installation', and 'Configuration'." The input is the template and keywords, and the output is the automatically generated document.

[1240] Step 5:

[1241] The terminal displays the generated document on the user interface. The user checks the contents and makes corrections as necessary. Specifically, the document generated on the terminal is displayed, and the user selects the parts that need correction and enters edit mode. The corrections are then sent to the server. The input is the automatically generated document, and the output is the user's corrections.

[1242] Step 6:

[1243] The server generates a final version of the document that reflects the user's modifications and saves it in the data storage system. The server generates a final version of the document and saves it in the document database. It also prepares it for sharing with other systems and related parties as needed. Specifically, the server saves the final version of the procedure manual in the database. The user clicks the "Share" button and sends the shared link generated by the system via email or chat. The input is the document that reflects the user's modifications, and the output is the final version of the procedure manual and the shared link.

[1244] (Application example 1)

[1245] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1246] Logistics centers are required to create work instructions and procedures efficiently and accurately. Conventional methods require a lot of manual work, which leads to problems such as errors and reduced work efficiency. Furthermore, it is difficult to generate appropriate instructions based on past data, and it is an issue to quickly provide instructions that are easy for workers to understand.

[1247] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1248] In this invention, the server includes a means for a user to select a document type, a means for collecting past related documents and analyzing them using a natural language processing engine, a means for generating a document template based on the analysis results, a means for generating specific content using a sentence generation AI, a means for a terminal to display the generated document to the user so that the user can confirm and modify the content, and a means for saving the final document that reflects the user's modifications and sharing it with related departments or other processing systems as necessary. This enables efficient and accurate creation of work instructions at a logistics center.

[1249] "Users" are the workers and other related parties who operate the system.

[1250] "Documents" refers to work instructions and procedures at logistics centers.

[1251] A "server" is a central control unit that processes and manages data.

[1252] A "template" refers to a format or framework that serves as a framework for generating a document.

[1253] "Text generation AI" is an artificial intelligence model that automatically generates documents using natural language processing technology.

[1254] A "natural language processing engine" is a software engine that analyzes documents and extracts elements.

[1255] A "terminal" is a device that a user uses to operate the system, and includes a smartphone, tablet, etc.

[1256] A "document database" is a storage system that stores and manages past documents.

[1257] A "prompt sentence" is the initial input given to a text generation AI, and determines the direction of the generated document.

[1258] "Related departments" refers to departments or groups that carry out other operations inside or outside the logistics center.

[1259] "Other processing systems" refers to other systems or applications for sharing generated documents.

[1260] The system for implementing this invention automatically generates work instructions and procedures within a logistics center, providing efficient and accurate instructions. The system is composed of the following main components:

[1261] Key Components

[1262] User terminal: A device operated by a user, such as a smartphone or tablet.

[1263] Server: A cloud server (e.g., AWS, Google Cloud, Microsoft Azure, etc.) that processes and manages data.

[1264] Document database: A cloud storage (e.g., Amazon S3, Google Cloud Storage, etc.) that stores and manages historical documents.

[1265] Natural language processing engine: A software engine for parsing documents and extracting elements (e.g., AWS Comprehend, Google Cloud Natural Language API, SpaCy, etc.).

[1266] Text generation AI: An artificial intelligence model that automatically generates documents using natural language processing technology (e.g., OpenAI GPT-3, GPT-4, etc.).

[1267] System Operation Overview

[1268] 1. Log in and create a new instruction

[1269] The user logs into the app using their smartphone and selects "Create new instructions." The selection is sent to the server.

[1270] 2. Data collection and template generation

[1271] The server collects relevant past documents from cloud storage and analyzes the data using a natural language processing engine, generating templates based on the analysis results.

[1272] 3. Automatic document generation

[1273] Based on the generated template, the server uses text generation AI to automatically generate specific content.

[1274] 4. User confirmation and correction

[1275] The generated document is displayed on the user's terminal, and the user can check the content and make corrections as necessary. The corrections are then sent to the server and reflected.

[1276] 5. Save and share the final version

[1277] The server generates a final version of the document that reflects the changes, stores it in cloud storage, and shares it with related departments and other processing systems as needed.

[1278] Specific examples

[1279] For example, when a distribution center worker creates a new inventory work order, they might use the following prompt:

[1280] Prompt Text: "Logistics Directive Template: Title: Shelf Inventory\nSections: Preparation, Process, Completion\nKeywords: inventory, checklist, items, count"

[1281] Based on this prompt, the AI ​​generates specific work instructions like this:

[1282] Title: Shelf Inventory

[1283] Sections:

[1284] 1. Preparation

[1285] - Verify the inventory list and necessary tools.

[1286] - Ensure all team members are briefed on the process.

[1287] 2. Process

[1288] - Follow the checklist to count each item on the shelves.

[1289] - Record discrepancies with existing records.

[1290] 3. Completion

[1291] - Review the collected data for accuracy.

[1292] - Report any major issues to the supervisor.

[1293] Keywords: inventory, checklist, items, count

[1294] In this way, the system supports the efficient and accurate creation of work instructions in logistics centers.

[1295] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1296] Step 1:

[1297] The user logs into the app using their smartphone and selects "Create new instruction." The login and selection information is entered and sent to the server, which receives the data and proceeds to the next processing step.

[1298] Step 2:

[1299] The server accesses the document database to collect past related documents. Past instruction data is input, and the collected data is sent to the natural language processing engine. The server uses the natural language processing engine to analyze the collected documents and extract elements such as titles, sections, and keywords. The extracted elements are output and the process proceeds to the next processing step.

[1300] Step 3:

[1301] The server generates a document template based on the analysis results. The analyzed data is used as input to generate the template. The generated template is sent to a text generation AI, which automatically generates a specific document. The generated document is output and proceeds to the next processing step.

[1302] Step 4:

[1303] The generated document is sent to the user's terminal. The terminal displays this automatically generated document to the user. The user checks the document contents and makes corrections as necessary (for example, "Please explain step 3 in detail"). The corrected content is sent from the terminal to the server as input.

[1304] Step 5:

[1305] The server receives the submitted corrections and generates the final document. Using the correction data as input, the server generates the final document and stores it in the document database. The stored final document is shared with related departments and other processing systems as needed. Sharing the output final document with other related systems allows work within the logistics center to be carried out efficiently and accurately.

[1306] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1307] This invention relates to a system that automates document creation, recognizing the user's emotions and creating high-quality documents simply by confirming and correcting them. This system executes a series of processes, from the user selecting the document type, to the server recognizing emotions using an emotion engine, collecting and analyzing data, automatically generating content using a text generation AI, and finally saving and sharing the document after the user has made corrections.

[1308] System configuration

[1309] The system consists of the following main components:

[1310] User terminal: A device operated by a user, such as a PC or tablet.

[1311] Server: A central processing and data management device.

[1312] Document database: A storage system that stores and manages past related documents.

[1313] Natural language processing engine: Software that analyzes past related documents.

[1314] Text generation AI: An artificial intelligence model that generates text based on input data.

[1315] Emotion engine: Software that recognizes the user's emotions and provides feedback to the system.

[1316] System Operation Overview

[1317] Start preparing the documents

[1318] A user logs into the system using a terminal and begins creating a document by selecting "Create a new document." The user then selects the type of document they want to create (procedure, presentation, report, etc.). The terminal sends the user's selection to the server.

[1319] Data collection and analysis

[1320] The server references a document database to gather past documents related to the document type selected by the user, then inputs the collected data into a natural language processing engine to extract elements such as titles, sections, and keywords, and then generates a template based on these elements.

[1321] Emotion recognition

[1322] In addition, the server uses an emotion engine to recognize the user's emotions, which detects the user's current emotions based on their inputs and operating situations, and optimizes the document generation process accordingly.

[1323] Automatic sentence generation

[1324] The server uses text generation AI to automatically generate specific content based on the generated template. For example, in the case of a procedure manual, detailed explanations of each step are generated. The text generation AI also adjusts tone and style as needed, taking into account the recognition results of the emotion engine.

[1325] User confirmation and correction

[1326] The generated document is displayed on the device, where the user can review the contents. The user can then make any necessary corrections, such as adding more detail to a specific step or adding notes. The corrections are then sent from the device to the server.

[1327] Save and share the final version

[1328] Once the user has completed their modifications, the server generates and stores the final version of the document, which is then stored in a document database and can be shared with other systems or stakeholders as needed.

[1329] Specific examples

[1330] Example 1: Creating a procedure manual

[1331] 1. The user logs in and selects to create a new procedure.

[1332] User: Log in to the system and select "Create a new procedure manual."

[1333] Terminal: Sends user selection to server.

[1334] 2. The server collects and analyzes past procedures

[1335] Server: Collects past procedures from the document database.

[1336] Server: Analyzes using a natural language processing engine and generates templates.

[1337] 3. The server recognizes the user's emotions and provides feedback

[1338] Server: Recognizes user emotions using an emotion engine.

[1339] Server: Based on the recognition results, the tone and style of the text generation AI are adjusted.

[1340] 4. The server automatically generates the contents of the procedure manual using AI

[1341] Server: Creates specific instructions for each step using text generation AI.

[1342] 5. The user checks and corrects the content

[1343] Terminal: View automatically generated instructions.

[1344] User: Check the content and make corrections such as "Explain step 3 in more detail."

[1345] 6. The server stores and shares the final version

[1346] Server: Generates the final version of the procedure manual that reflects the corrections, stores it in the document database, and shares it as needed.

[1347] By using the above means, users can efficiently create high-quality documents, which improves business efficiency and also allows for individual responses that reflect the user's feelings.

[1348] The processing flow will be explained below.

[1349] Step 1:

[1350] The user begins creating a document.

[1351] The user logs into the system and selects "Create a new document."

[1352] The terminal sends this user selection to the server as a request.

[1353] Step 2:

[1354] The user selects a document type.

[1355] Users select a document type such as a procedure manual, presentation, or report.

[1356] The terminal transmits the user's selection information to the server.

[1357] Step 3:

[1358] The server collects past related documents.

[1359] The server collects historical documents related to the selected document type from a document database.

[1360] The server passes the collected data to a natural language processing engine.

[1361] Step 4:

[1362] The server analyzes the collected data.

[1363] The server uses a natural language processing engine to extract titles, sections, keywords, etc. from the collected documents.

[1364] The server generates a document template based on the analysis results.

[1365] Step 5:

[1366] The server uses an emotion engine to recognize the user's emotions.

[1367] The server receives the user's operation status and input contents from the terminal and sends them to the emotion engine.

[1368] The emotion engine analyzes the user's emotions and feeds the results back to the server.

[1369] Step 6:

[1370] The server generates the content using text generation AI.

[1371] Based on the generated template, the server applies text generation AI to generate specific content.

[1372] Taking into account the results of the emotion engine, the text generation AI adjusts tone and style as needed.

[1373] Step 7:

[1374] The terminal displays the generated document to the user.

[1375] The terminal displays the completed document in a user interface.

[1376] The terminal provides an interface that allows the user to view and edit the content.

[1377] Step 8:

[1378] The user checks and corrects the content.

[1379] The user checks the displayed document in detail.

[1380] The user edits any parts that need to be corrected and inputs the corrections into the terminal.

[1381] Step 9:

[1382] The terminal transmits the user's modifications to the server.

[1383] The terminal transmits the modifications made by the user to the server.

[1384] Step 10:

[1385] The server reflects the changes.

[1386] The server reflects the modifications sent by the user and generates the final version of the document.

[1387] The server sends a preview of the final version to the device.

[1388] Step 11:

[1389] The device displays a preview of the final version to the user.

[1390] The terminal displays the final document to the user and provides an interface for final confirmation.

[1391] Step 12:

[1392] The user saves the document.

[1393] The user performs an operation to save the document after final confirmation.

[1394] The terminal sends a save request to the server.

[1395] Step 13:

[1396] The server stores the final document.

[1397] The server stores the final document in a document database.

[1398] The server shares the final document with other systems and stakeholders as needed.

[1399] Specific examples

[1400] Example 1: Creating a procedure manual

[1401] 1. The user logs in and selects to create a new procedure.

[1402] User: Log in to the system and select "Create a new procedure manual."

[1403] Terminal: Sends user selection to server.

[1404] 2. The server collects and analyzes past procedures

[1405] Server: Collects past procedures from a document database, analyzes them using a natural language processing engine, and generates templates.

[1406] 3. The server uses the emotion engine to recognize the user's emotions.

[1407] Server: The server receives the user's operation status and input information from the device and sends it to the emotion engine to analyze the user's emotions.

[1408] Server: Receives the results of the emotion engine and reflects them in the sentence generation AI.

[1409] 4. The server automatically generates the contents of the procedure manual using AI

[1410] Server: Using text generation AI, we create detailed instructions for each step and adjust the tone and style.

[1411] 5. The user checks and corrects the content

[1412] Terminal: View automatically generated instructions.

[1413] User: Check the content and make corrections such as "Explain step 3 in more detail."

[1414] 6. The server stores and shares the final version

[1415] Server: Generates the final version of the procedure manual that reflects the corrections, stores it in the document database, and shares it as needed.

[1416] By using the above means, users can efficiently create high-quality documents, which improves business efficiency and also allows for individual responses that reflect the user's feelings.

[1417] Example 2

[1418] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1419] In conventional document creation systems, the process of users manually creating and editing documents is time-consuming and laborious, making it inefficient. Furthermore, it is difficult to create documents that take into account the user's emotions and circumstances, making it difficult to create high-quality documents tailored to individual users. Therefore, there is a demand for a system that reduces user labor and allows for the rapid creation of high-quality documents that can be tailored to individual users.

[1420] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1421] In this invention, the server includes means for collecting past related documents and analyzing them using a natural language processing engine, means for generating document templates based on the analysis results, means for recognizing user emotions using an emotion engine, and means for adjusting tone and style using a sentence generation AI to generate specific content. This reduces the user's workload and enables the rapid creation of high-quality documents that can be tailored to individual needs.

[1422] A "user" is an entity that uses the system to create, check, and modify documents.

[1423] A "terminal" is a device operated by a user, and includes devices such as PCs and tablets.

[1424] A "server" is a device that centrally manages and processes data.

[1425] "Document type" refers to the specific type of document the user wants to create, including procedures, presentation materials, reports, and the like.

[1426] "Related historical documents" are existing documents stored in the document database that are related to the document type selected by the user.

[1427] A "natural language processing engine" is software for analyzing text data and extracting necessary information.

[1428] A "document template" is a format that includes the basic structure and main items of a document, and is generated based on collected document data.

[1429] An "emotion engine" is software that recognizes a user's emotions based on the user's input and operational status, and feeds that emotional information back into the system.

[1430] "Sentence generation AI" is an artificial intelligence model that automatically generates sentences based on specified data and templates.

[1431] "Tone and style" refers to the atmosphere and expression of the generated text, and is adjusted according to the user's emotions and situation.

[1432] The "final document" is the document that is completed by reflecting the user's modifications and is finally saved.

[1433] A "document database" is a storage system for storing and managing past related documents, final versions of documents, and the like.

[1434] The present invention relates to a system that enables users to efficiently create high-quality documents. This system comprises a means for the user to select a document type, a means for a server to recognize emotions using an emotion engine, a means for collecting past related documents and analyzing them with a natural language processing engine, a means for generating templates, a means for generating content using a sentence generation AI, and a means for a terminal to display the generated document to the user and allow them to make corrections. The specific configuration and operation of this system are described below.

[1435] System configuration

[1436] The system consists of the following main components:

[1437] User terminal: A device operated by a user, such as a PC or tablet.

[1438] Server: A central device that manages and processes data.

[1439] Document database: A storage system that stores and manages past related documents.

[1440] Natural language processing engine: Software that analyzes past related documents.

[1441] Text generation AI: An artificial intelligence model that generates text based on input data.

[1442] Emotion engine: Software that recognizes the user's emotions and provides feedback to the system.

[1443] Operation overview

[1444] Start preparing the documents

[1445] A user logs into the system using a terminal and selects "Create a new document." The user then selects the type of document they want to create (procedure, presentation, report, etc.). The terminal sends this selection to the server.

[1446] Data collection and analysis

[1447] The server accesses a document database to gather past documents related to the document type selected by the user, then inputs the collected data into a natural language processing engine to extract elements such as titles, sections, and keywords, and then generates a template based on these elements.

[1448] Emotion recognition

[1449] In addition, the server uses an emotion engine to recognize the user's emotions, which detects the user's current emotions based on their inputs and operation situations, and optimizes the document generation process accordingly.

[1450] Automatic sentence generation

[1451] The server uses text generation AI to automatically generate specific content based on the generated template. For example, in the case of a procedure manual, detailed explanations of each step are generated. The server also adjusts the tone and style of writing based on the recognition results of the emotion engine.

[1452] User confirmation and correction

[1453] The generated document is displayed on the device, where the user can review the contents and make any necessary corrections, such as adding detailed explanations for specific steps or adding notes. The corrections are then sent from the device to the server.

[1454] Save and share the final version

[1455] Once the user has completed their modifications, the server generates and stores the final version of the document, which is stored in a document database and can be shared with other systems or stakeholders as needed.

[1456] Specific examples

[1457] Example 1: Creating a procedure manual

[1458] 1. The user logs in and selects to create a new procedure.

[1459] User: Log in to the system and select "Create a new procedure manual."

[1460] Terminal: Sends the user's selection to the server.

[1461] 2. The server collects and analyzes past procedures

[1462] Server: Collects past procedures from a document database.

[1463] Server: Analyzes using a natural language processing engine and generates templates.

[1464] 3. The server recognizes the user's emotions and provides feedback

[1465] Server: Recognizes user emotions using an emotion engine.

[1466] Server: Based on the recognition results, adjust the tone and style of the text generation AI.

[1467] 4. The server automatically generates the contents of the procedure manual using AI

[1468] Server: Uses text generation AI to create specific instructions for each step in the procedure manual.

[1469] 5. The user checks and corrects the content

[1470] Terminal: View the automatically generated instructions.

[1471] User: Check the content and make corrections such as "Add more detail to the explanation of Step 3."

[1472] 6. The server stores and shares the final version

[1473] Server: Generates the final version of the procedure manual that reflects the modifications and stores it in the document database.

[1474] Server: Generate a link to share with other departments or stakeholders as needed.

[1475] Examples of prompt statements

[1476] 1. Specify the document type: "Start creating a new procedure. First, enter the procedure title."

[1477] 2. Key Element Analysis: "Based on the procedure title entered, extract key sections and keywords from related historical documents."

[1478] 3. Emotion detection: "Recognize the user's current emotion and adjust the tone and style of the document appropriately. If the user is relaxed, use a friendly tone."

[1479] 4. Auto-generated prompt: "Draw each step of the procedure in detail using the following format. For example, Step 1: Prepare the necessary tools and materials. Step 2: Explain the procedure. Step 3: Notes."

[1480] The above is a detailed description of an embodiment of the present invention. This system allows users to quickly and efficiently create high-quality documents, and also provides personalized responses that take into account the user's emotions.

[1481] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1482] System program processing flow

[1483] Step 1: User logs in and selects Create New Document

[1484] A user logs in to the system using a terminal.

[1485] Input: User ID and password

[1486] Output: Authentication status of the user

[1487] The server validates the credentials and displays the dashboard if authentication is successful.

[1488] Input: User ID and password

[1489] Output: Dashboard screen

[1490] The user selects "Create a new document" from the dashboard.

[1491] Input: User's document type selection

[1492] Output: Document type data

[1493] The terminal transmits the user's selection to the server.

[1494] Step 2: The server collects and analyzes past document data

[1495] The server accesses a document database to collect past documents related to the document type selected by the user.

[1496] Input: Document type data

[1497] Output: Dataset of collected historical documents

[1498] The server uses a natural language processing engine to extract elements such as titles, sections, and keywords from the collected documents.

[1499] Input: Dataset of historical documents

[1500] Output: Extracted elements (title, sections, keywords, etc.)

[1501] The server generates a template based on these extracted elements.

[1502] Input: Extracted elements

[1503] Output: Generated template

[1504] Step 3: The server recognizes the user's emotion

[1505] The server starts the emotion engine and analyzes the user's input and operation status.

[1506] Input: User operation log, input text

[1507] Output: Emotion recognition result

[1508] The server determines the user's current emotion (e.g., stress, relaxation, or impatience) based on the results of the emotion engine.

[1509] Input: Emotion recognition results

[1510] Output: User's emotional state data

[1511] The server feeds this emotional information back into the sentence generation process.

[1512] Step 4: The server automatically generates the content using text generation AI

[1513] The server uses a sentence generation AI to automatically generate sentences based on the generated template and the results of the emotion engine.

[1514] Input: Template, emotional state data

[1515] Output: The specific document content generated

[1516] The server generates the specific document content, for example in the case of a procedure manual, creating a detailed description of each step.

[1517] Input: Template, emotional state data

[1518] Output: Specific procedure manual content

[1519] The server takes emotional information into account and adjusts tone and style.

[1520] Step 5: User confirms and modifies the content

[1521] The terminal displays the generated document to the user.

[1522] Input: Generated document content

[1523] Output: Displayed document

[1524] The user reviews the document and identifies any necessary revisions (e.g., "More details on step 3").

[1525] Input: Displayed document, user modifications

[1526] Output: Correction instruction data

[1527] The terminal transmits correction instruction data to the server.

[1528] Step 6: The server saves and shares the final version

[1529] The server generates the final document reflecting the user's modifications.

[1530] Input: Correction instruction data, initial generated document content

[1531] Output: Final document

[1532] The server stores the final version of the document in a document database.

[1533] Input: Final document

[1534] Output: Save status

[1535] The server generates links to share the document with other systems or stakeholders as needed.

[1536] This allows users to quickly and efficiently create high-quality documents, and the system also provides personalized responses that reflect the user's feelings.

[1537] (Application example 2)

[1538] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1539] Currently, creating product reviews and customer support emails on online shopping sites faces the challenge of generating high-quality text that reflects user emotions. This can lead to inconsistencies in the content of reviews and emails, resulting in a poor user experience. Another problem is the effort required for users to manually edit the content of reviews and emails. To solve these issues, a system is needed that automatically generates appropriate text that takes user emotions into account.

[1540] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1541] In this invention, the server includes a means for allowing a user to select a document type, a means for collecting past related documents and analyzing them using a natural language processing engine, and a means for generating a document template based on the analysis results, which allows the tone and style of writing to be adjusted based on the user's emotions, making it possible to automatically generate high-quality product reviews and customer support emails.

[1542] A "user terminal" is a device that a user uses to operate the system, and includes general computer devices such as smartphones and tablets.

[1543] A "server" is a central device for processing and managing large amounts of data, and oversees database operations and AI processing.

[1544] A "document database" is a storage system that stores and manages historical related documents and provides quick data access.

[1545] A "natural language processing engine" is a general term for software that analyzes input text and understands its meaning.

[1546] "Text generation AI" is an artificial intelligence model that automatically generates text based on input data, producing high-quality text according to specific rules and patterns.

[1547] An "emotion engine" is software that recognizes the user's emotions and provides feedback to the system based on those emotions.

[1548] A "template" is a format that serves as a basic framework for efficiently generating a variety of documents, and defines the components of each document.

[1549] "Tone of the text" refers to the emotional tone or style of the generated text, which is adjusted based on the user's emotions.

[1550] "Style" refers to the manner or style of writing, and can range from formal to casual.

[1551] A "visual interface" is a screen display mechanism that allows users to visually view and manipulate documents and data.

[1552] MODE FOR CARRYING OUT THE INVENTION

[1553] System Configuration

[1554] In practicing this invention, the system includes as major components:

[1555] 1. User terminal: A device operated by a user, such as a smartphone or tablet.

[1556] 2. Server: A central device for processing and managing large amounts of data, using AWS (Amazon Web Services) or Google Cloud Platform.

[1557] 3. Document database: A storage system that stores and manages historical related documents.

[1558] 4. Natural language processing engine: A general term for software that analyzes input text and understands its meaning. Examples include SpaCy and BERT.

[1559] 5. Text generation AI: An artificial intelligence model that automatically generates text based on input data. We will use OpenAI GPT-4 as a concrete example.

[1560] 6. Emotion Engine: Software that recognizes the user's emotions and provides feedback to the system based on those emotions. It uses the Microsoft Azure Cognitive Services Emotion API.

[1561] Processing flow

[1562] 1. User operation: The user logs in to the system using a terminal and selects the document type (product review or customer support email).

[1563] 2. Data collection and analysis: The server collects relevant documents from the document database and analyzes them using a natural language processing engine (e.g., SpaCy or BERT), extracting elements such as titles, sections, and keywords, and generating templates.

[1564] 3. Emotion Recognition: The emotion engine (Microsoft Azure Cognitive Services Emotion API) detects the current emotion based on the user's input and operation context. Based on the emotional state, the text generation AI adjusts the tone and style of writing.

[1565] 4. Automatic sentence generation: Based on the template generated in the previous step, the server uses sentence generation AI (OpenAI GPT-4) to automatically generate specific content, resulting in high-quality sentences that reflect emotions.

[1566] 5. Confirmation and correction: The terminal displays the generated text to the user. The user can confirm the content and make corrections as necessary. The corrected data is sent back to the server.

[1567] 6. Final saving and sharing: The server reflects the user's modifications, generates the final version of the document, and saves it in the document database. If necessary, the document can be shared with other systems or stakeholders.

[1568] Specific examples

[1569] Example 1: Creating a product review

[1570] 1. User logs in and selects to create a new review

[1571] User: Log in to the system and select "Create a new review."

[1572] Terminal: Sends user selection to server.

[1573] 2. The server collects and analyzes past reviews

[1574] Server: Collects past reviews from a document database.

[1575] Server: Analyzes using a natural language processing engine and generates templates.

[1576] 3. The server recognizes the user's emotions and provides feedback

[1577] Server: Recognizes user emotions using an emotion engine.

[1578] Server: Based on the recognition results, the tone and style of the sentence generation AI are adjusted.

[1579] 4. The server automatically generates the review content using AI

[1580] Server: Create specific reviews using text generation AI.

[1581] 5. User confirms and modifies the content

[1582] Device: View automatically generated reviews.

[1583] User: Check the contents and make any necessary changes.

[1584] 6. The server stores and shares the final version

[1585] Server: Generates the final version of the review that reflects the corrections, stores it in the document database, and shares it as needed.

[1586] Prompt Sentence Examples

[1587] "Generate a review based on the user's current emotional state and predict their thoughts about this product. Below is a list of keywords provided by the user: quality, design, price. The user is currently excited and satisfied with their purchase."

[1588] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1589] Step 1:

[1590] User logs in and selects to create a new review

[1591] Input: User login credentials, choice to create review

[1592] Processing: The device sends the user's login credentials to the server. If the server authenticates the login, the review creation screen is displayed on the device.

[1593] Output: Display of review creation screen

[1594] Step 2:

[1595] The server collects and analyzes past reviews

[1596] Input: Product information selected by the user

[1597] Processing: The server collects relevant past reviews from a document database and uses a natural language processing engine (e.g., SpaCy or BERT) to extract titles, sections, keywords, etc. It then generates a template based on these elements.

[1598] Output: Title, section, keywords for template generation

[1599] Step 3:

[1600] The server recognizes the user's emotions and provides feedback

[1601] Input: User input and operation status

[1602] Processing: The emotion engine (Microsoft Azure Cognitive Services Emotion API) recognizes the user's emotional state and feeds the results back to the document generation AI.

[1603] Output: Emotion recognition results (e.g., excitement, satisfaction, etc.)

[1604] Step 4:

[1605] The server automatically generates the review content using AI

[1606] Input: Templates and emotion engine recognition results

[1607] Processing: The server uses text generation AI (OpenAI GPT-4) to automatically generate specific review content based on templates and emotion recognition results. A prompt is used, such as "Please predict the user's thoughts about this product based on their emotional state and generate a review. Below is a list of keywords provided by the user: quality, design, price. The user is currently excited and satisfied with their purchase."

[1608] Output: Generated review text

[1609] Step 5:

[1610] User confirms and modifies the content

[1611] Input: Generated review text

[1612] Processing: The device displays the generated review text to the user. The user checks the content and makes corrections as necessary. The corrections are sent from the device to the server.

[1613] Output: revised review text

[1614] Step 6:

[1615] The server stores and shares the final version

[1616] Input: Revised review text

[1617] Processing: The server reflects the user's modifications, generates the final document, stores it in the document database, and shares the document with other systems and stakeholders as needed.

[1618] Output: Final saved review text

[1619] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1620] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1621] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1622] [Fourth embodiment]

[1623] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1624] 7, a 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.

[1625] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1626] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1627] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1628] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1629] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1630] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1631] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1632] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1633] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1634] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1635] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1636] This invention relates to a system that automates document creation and allows users to create high-quality documents simply by checking and correcting them. This system executes a series of steps, from the user's selection of the document type, to data collection and analysis by the server, automatic content generation using text generation AI, user corrections, and final saving and sharing.

[1637] System configuration

[1638] The system consists of the following main components:

[1639] User terminal: A device operated by a user, such as a PC or tablet.

[1640] Server: A central processing and data management device.

[1641] Document database: A storage system that stores and manages past related documents.

[1642] Natural language processing engine: Software that analyzes past related documents.

[1643] Text generation AI: An artificial intelligence model that generates text based on input data.

[1644] System Operation Overview

[1645] Start preparing the documents

[1646] A user logs into the system using a terminal and begins creating a document by selecting "Create a new document." The user then selects the type of document they want to create (procedure, presentation, report, etc.). The terminal sends the user's selection to the server.

[1647] Data collection and analysis

[1648] The server references a document database to gather past documents related to the document type selected by the user, then inputs the collected data into a natural language processing engine to extract elements such as titles, sections, and keywords, and then generates a template based on these elements.

[1649] Automatic sentence generation

[1650] The server uses text generation AI to automatically generate specific content based on the generated template. For example, in the case of a procedure manual, detailed explanations of each step are generated, allowing users to eliminate most of the manual work.

[1651] User confirmation and correction

[1652] The generated document is displayed on the device, where the user can review the contents. The user can then make any necessary corrections, such as adding more detail to a specific step or adding notes. The corrections are then sent from the device to the server.

[1653] Save and share the final version

[1654] Once the user has completed their modifications, the server generates and stores the final version of the document, which is then stored in a document database and can be shared with other systems or stakeholders as needed.

[1655] Specific examples

[1656] Example 1: Creating a procedure manual

[1657] 1. The user logs in and selects to create a new procedure.

[1658] User: Log in to the system and select "Create a new procedure manual."

[1659] Terminal: Sends user selection to server.

[1660] 2. The server collects and analyzes past procedures

[1661] Server: Collects past procedures from the document database.

[1662] Server: Analyzes using a natural language processing engine and generates templates.

[1663] 3. The server automatically generates the contents of the procedure manual using AI

[1664] Server: Creates specific instructions for each step using text generation AI.

[1665] 4. The user checks and corrects the content

[1666] Terminal: View automatically generated instructions.

[1667] User: Check the content and make corrections such as "Explain step 3 in more detail."

[1668] 5. The server stores and shares the final version

[1669] Server: Generates the final version of the procedure manual that reflects the corrections, stores it in the document database, and shares it as needed.

[1670] By using the above means, users can efficiently create high-quality documents, thereby improving business efficiency.

[1671] The processing flow will be explained below.

[1672] Step 1:

[1673] The user begins creating a document.

[1674] A user logs into the system and selects the option "Create a new document."

[1675] The terminal sends this user selection to the server as a request.

[1676] Step 2:

[1677] The user selects a document type.

[1678] Users select a document type such as a procedure manual, presentation, or report.

[1679] The terminal transmits the user's selection information to the server.

[1680] Step 3:

[1681] The server collects past related documents.

[1682] The server retrieves historical documents related to the selected document type from the document database.

[1683] The server passes the collected data to a natural language processing engine.

[1684] Step 4:

[1685] The server analyzes the collected data.

[1686] The server uses a natural language processing engine to extract titles, sections, keywords, etc. from the collected documents.

[1687] The server generates a document template based on the analysis results.

[1688] Step 5:

[1689] The server generates the content using text generation AI.

[1690] The server applies a text generation AI to the generated template to generate specific content.

[1691] Through this process, the server automatically fills in the appropriate text for each section of the document.

[1692] Step 6:

[1693] The terminal displays the generated document to the user.

[1694] The terminal displays the completed document in a user interface.

[1695] The terminal provides an interface that allows the user to view and edit the content.

[1696] Step 7:

[1697] The user checks and corrects the content.

[1698] The user checks the displayed document in detail.

[1699] The user edits any parts that need to be corrected and inputs the corrections into the terminal.

[1700] Step 8:

[1701] The terminal transmits the user's modifications to the server.

[1702] The terminal transmits the modifications made by the user to the server.

[1703] Step 9:

[1704] The server reflects the changes.

[1705] The server reflects the modifications sent by the user and generates the final version of the document.

[1706] The server sends a preview of the final version to the device.

[1707] Step 10:

[1708] The device displays a preview of the final version to the user.

[1709] The terminal displays the final document to the user and provides an interface for final confirmation.

[1710] Step 11:

[1711] The user saves the document.

[1712] The user performs an operation to save the document after final confirmation.

[1713] The terminal sends a save request to the server.

[1714] Step 12:

[1715] The server stores the final document.

[1716] The server stores the final document in a document database.

[1717] The server shares the final document with other systems and stakeholders as needed.

[1718] Example 1

[1719] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1720] Modern business requires fast and accurate document creation, but this process is inefficient because it takes a lot of time and effort. Human error is especially likely to occur when checking and correcting document content, requiring multiple checks. To resolve this situation, a system is needed that automates the entire document creation process and reduces the burden on users.

[1721] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1722] In this invention, the server includes means for collecting past related documents and analyzing them using a natural language processing engine, means for generating document templates based on the analysis results, and means for generating specific content using a sentence generation software module based on the generated templates, thereby enabling the automatic generation of high-quality documents.

[1723] "Document type" refers to the category or type of document the user wants to create, including procedures, presentations, reports, and the like.

[1724] A "server" refers to a device that performs central processing and data management and controls the entire system.

[1725] "Past related documents" refers to documents of the same type that were created previously and are stored in a database, and are used as references when creating new documents.

[1726] A "natural language processing engine" is software that analyzes past related documents to extract elements and meanings of text. Specifically, it has the ability to automatically identify titles, sections, keywords, etc. from strings of characters.

[1727] A "template" is a framework that defines the structure and format of a document, and serves as the basis for generating specific documents based on this framework.

[1728] "Text generation software module" refers to an artificial intelligence program that automatically generates specific content based on a template.

[1729] "Terminal" refers to the device that a user uses to access and operate the system, such as a PC or tablet.

[1730] "User interface" refers to the function that provides the screen and operation method for the user to operate on the terminal, including the interface for checking and modifying the document content.

[1731] "Data storage system" refers to a storage device for securely storing generated documents for later access.

[1732] "Visual interface" refers to a graphical user interface (GUI) that allows users to intuitively view and modify document contents.

[1733] "Sharing with other systems or parties" refers to the ability to make the generated final document accessible between different systems or involved users.

[1734] The present invention is a system that automates document creation, allowing the user to create high-quality documents simply by checking and correcting the documents. A specific embodiment of this system will be described below.

[1735] System configuration

[1736] The system consists of the following main components:

[1737] User terminal: A device operated by a user, such as a PC or tablet. It provides a user interface and allows users to check and edit document contents.

[1738] Server: A central device that manages processing and data and controls the entire system. The server accesses the data storage system and is responsible for collecting, analyzing, and storing the necessary data.

[1739] Document database: A storage system that stores and manages past related documents. Previously created documents of the same type are accumulated here and used as reference for creating new documents.

[1740] Natural language processing engine: A software engine that analyzes past related documents and extracts elements such as titles, sections, keywords, etc. Specific examples include spaCy and NLTK.

[1741] Text generation software module: An artificial intelligence program that generates specific content based on templates. Specifically, this applies to generative AI models such as GPT-3.

[1742] System Operation Overview

[1743] Start preparing the documents

[1744] A user logs into the system using a terminal and selects "Create a new document." The user then selects the type of document they want to create (e.g., procedure manual, presentation, report, etc.). The selection information is sent from the terminal to the server.

[1745] Data collection and analysis

[1746] The server references a document database to collect past documents related to the document type selected by the user, then inputs the collected data into a natural language processing engine to extract elements such as titles, sections, and keywords.The server then generates a template based on the extracted elements.

[1747] Automatic sentence generation

[1748] The server uses a text generation software module to automatically generate specific content based on the generated template. For example, in the case of a procedure manual, detailed explanations of each step are generated, eliminating most of the manual work required by the user.

[1749] Example prompt:

[1750] Please create a procedure titled "System Setup" with sections "Preparation," "Installation," and "Configuration."

[1751] User confirmation and correction

[1752] The generated document is displayed on the terminal, and the user can review the contents. The user can then make appropriate corrections, such as adding more detail to a specific step or adding notes. The corrections are then sent from the terminal to the server.

[1753] Save and share the final version

[1754] Once the user's modifications are complete, the server generates the final document and stores it in a data storage system, where it can be shared with other systems or stakeholders as needed.

[1755] As a concrete example, the operation procedure for "creating a new procedure manual" is shown below:

[1756] 1. The user logs in and selects to create a new procedure.

[1757] 2. The server collects past procedure manuals, analyzes them using a natural language processing engine, and generates templates.

[1758] 3. The server automatically generates a procedure manual using a text generation software module based on the template.

[1759] 4. The terminal displays the procedure manual, and the user checks and corrects it.

[1760] 5. The server stores the final version and shares it as needed.

[1761] This allows users to efficiently create high-quality documents, improving business efficiency.

[1762] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1763] Step 1:

[1764] The user logs in to the terminal and accesses the system's web application. The user selects "Create a new document" from the main menu. The user selects the type of document they want to create and sends it to the server. Specifically, the user opens a browser, accesses the system URL, and logs in by entering their user ID and password. They click the "Create a new document" button and select "Procedure" from the drop-down menu. The input is the user's selection information (create a new document and document type), and the output is sending this selection information to the server.

[1765] Step 2:

[1766] The server queries the document database to collect past documents related to the document type selected by the user. The collected data is input into a natural language processing engine to extract elements such as title, section, and keywords. Specifically, the server executes an SQL query to retrieve all documents of the "procedure" type. The retrieved documents are passed to the natural language processing engine, which extracts key elements (title, section, keywords). The input is past procedure documents retrieved from the database, and the output is elements such as title, section, and keywords.

[1767] Step 3:

[1768] The server generates a template based on the extracted elements. Specifically, it passes the elements obtained from the natural language processing engine to the template generation module, which automatically generates a procedure template. The template includes main sections. The input is the extracted element data, and the output is the procedure template.

[1769] Step 4:

[1770] The server uses a text generation software module to generate specific content based on the generated template. The server sends the template and necessary keywords to the generative AI model. The server receives the text automatically generated by the generative AI model and sends it to the user's device. As a specific operation, the following prompt sentence is sent to the generative AI model: "Please create a procedure manual. The title is 'System Setup' and the sections are 'Preparation', 'Installation', and 'Configuration'." The input is the template and keywords, and the output is the automatically generated document.

[1771] Step 5:

[1772] The terminal displays the generated document on the user interface. The user checks the contents and makes corrections as necessary. Specifically, the document generated on the terminal is displayed, and the user selects the parts that need correction and enters edit mode. The corrections are then sent to the server. The input is the automatically generated document, and the output is the user's corrections.

[1773] Step 6:

[1774] The server generates a final version of the document that reflects the user's modifications and saves it in the data storage system. The server generates a final version of the document and saves it in the document database. It also prepares it for sharing with other systems and related parties as needed. Specifically, the server saves the final version of the procedure manual in the database. The user clicks the "Share" button and sends the shared link generated by the system via email or chat. The input is the document that reflects the user's modifications, and the output is the final version of the procedure manual and the shared link.

[1775] (Application example 1)

[1776] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1777] Logistics centers are required to create work instructions and procedures efficiently and accurately. Conventional methods require a lot of manual work, which leads to problems such as errors and reduced work efficiency. Furthermore, it is difficult to generate appropriate instructions based on past data, and it is an issue to quickly provide instructions that are easy for workers to understand.

[1778] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1779] In this invention, the server includes a means for a user to select a document type, a means for collecting past related documents and analyzing them using a natural language processing engine, a means for generating a document template based on the analysis results, a means for generating specific content using a sentence generation AI, a means for a terminal to display the generated document to the user so that the user can confirm and modify the content, and a means for saving the final document that reflects the user's modifications and sharing it with related departments or other processing systems as necessary. This enables efficient and accurate creation of work instructions at a logistics center.

[1780] "Users" are the workers and other related parties who operate the system.

[1781] "Documents" refers to work instructions and procedures at logistics centers.

[1782] A "server" is a central control unit that processes and manages data.

[1783] A "template" refers to a format or framework that serves as a framework for generating a document.

[1784] "Text generation AI" is an artificial intelligence model that automatically generates documents using natural language processing technology.

[1785] A "natural language processing engine" is a software engine that analyzes documents and extracts elements.

[1786] A "terminal" is a device that a user uses to operate the system, and includes a smartphone, tablet, etc.

[1787] A "document database" is a storage system that stores and manages past documents.

[1788] A "prompt sentence" is the initial input given to a text generation AI, and determines the direction of the generated document.

[1789] "Related departments" refers to departments or groups that carry out other operations inside or outside the logistics center.

[1790] "Other processing systems" refers to other systems or applications for sharing generated documents.

[1791] The system for implementing this invention automatically generates work instructions and procedures within a logistics center, providing efficient and accurate instructions. The system is composed of the following main components:

[1792] Key Components

[1793] User terminal: A device operated by a user, such as a smartphone or tablet.

[1794] Server: A cloud server (e.g., AWS, Google Cloud, Microsoft Azure, etc.) that processes and manages data.

[1795] Document database: A cloud storage (e.g., Amazon S3, Google Cloud Storage, etc.) that stores and manages historical documents.

[1796] Natural language processing engine: A software engine for parsing documents and extracting elements (e.g., AWS Comprehend, Google Cloud Natural Language API, SpaCy, etc.).

[1797] Text generation AI: An artificial intelligence model that automatically generates documents using natural language processing technology (e.g., OpenAI GPT-3, GPT-4, etc.).

[1798] System Operation Overview

[1799] 1. Log in and create a new instruction

[1800] The user logs into the app using their smartphone and selects "Create new instructions." The selection is sent to the server.

[1801] 2. Data collection and template generation

[1802] The server collects relevant past documents from cloud storage and analyzes the data using a natural language processing engine, generating templates based on the analysis results.

[1803] 3. Automatic document generation

[1804] Based on the generated template, the server uses text generation AI to automatically generate specific content.

[1805] 4. User confirmation and correction

[1806] The generated document is displayed on the user's terminal, and the user can check the content and make corrections as necessary. The corrections are then sent to the server and reflected.

[1807] 5. Save and share the final version

[1808] The server generates a final version of the document that reflects the changes, stores it in cloud storage, and shares it with related departments and other processing systems as needed.

[1809] Specific examples

[1810] For example, when a distribution center worker creates a new inventory work order, they might use the following prompt:

[1811] Prompt Text: "Logistics Directive Template: Title: Shelf Inventory\nSections: Preparation, Process, Completion\nKeywords: inventory, checklist, items, count"

[1812] Based on this prompt, the AI ​​generates specific work instructions like this:

[1813] Title: Shelf Inventory

[1814] Sections:

[1815] 1. Preparation

[1816] - Verify the inventory list and necessary tools.

[1817] - Ensure all team members are briefed on the process.

[1818] 2. Process

[1819] - Follow the checklist to count each item on the shelves.

[1820] - Record discrepancies with existing records.

[1821] 3. Completion

[1822] - Review the collected data for accuracy.

[1823] - Report any major issues to the supervisor.

[1824] Keywords: inventory, checklist, items, count

[1825] In this way, the system supports the efficient and accurate creation of work instructions in logistics centers.

[1826] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1827] Step 1:

[1828] The user logs into the app using their smartphone and selects "Create new instruction." The login and selection information is entered and sent to the server, which receives the data and proceeds to the next processing step.

[1829] Step 2:

[1830] The server accesses the document database to collect past related documents. Past instruction data is input, and the collected data is sent to the natural language processing engine. The server uses the natural language processing engine to analyze the collected documents and extract elements such as titles, sections, and keywords. The extracted elements are output and the process proceeds to the next processing step.

[1831] Step 3:

[1832] The server generates a document template based on the analysis results. The analyzed data is used as input to generate the template. The generated template is sent to a text generation AI, which automatically generates a specific document. The generated document is output and proceeds to the next processing step.

[1833] Step 4:

[1834] The generated document is sent to the user's terminal. The terminal displays this automatically generated document to the user. The user checks the document contents and makes corrections as necessary (for example, "Please explain step 3 in detail"). The corrected content is sent from the terminal to the server as input.

[1835] Step 5:

[1836] The server receives the submitted corrections and generates the final document. Using the correction data as input, the server generates the final document and stores it in the document database. The stored final document is shared with related departments and other processing systems as needed. Sharing the output final document with other related systems allows work within the logistics center to be carried out efficiently and accurately.

[1837] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1838] This invention relates to a system that automates document creation, recognizing the user's emotions and creating high-quality documents simply by confirming and correcting them. This system executes a series of processes, from the user selecting the document type, to the server recognizing emotions using an emotion engine, collecting and analyzing data, automatically generating content using a text generation AI, and finally saving and sharing the document after the user has made corrections.

[1839] System configuration

[1840] The system consists of the following main components:

[1841] User terminal: A device operated by a user, such as a PC or tablet.

[1842] Server: A central processing and data management device.

[1843] Document database: A storage system that stores and manages past related documents.

[1844] Natural language processing engine: Software that analyzes past related documents.

[1845] Text generation AI: An artificial intelligence model that generates text based on input data.

[1846] Emotion engine: Software that recognizes the user's emotions and provides feedback to the system.

[1847] System Operation Overview

[1848] Start preparing the documents

[1849] A user logs into the system using a terminal and begins creating a document by selecting "Create a new document." The user then selects the type of document they want to create (procedure, presentation, report, etc.). The terminal sends the user's selection to the server.

[1850] Data collection and analysis

[1851] The server references a document database to gather past documents related to the document type selected by the user, then inputs the collected data into a natural language processing engine to extract elements such as titles, sections, and keywords, and then generates a template based on these elements.

[1852] Emotion recognition

[1853] In addition, the server uses an emotion engine to recognize the user's emotions, which detects the user's current emotions based on their inputs and operating situations, and optimizes the document generation process accordingly.

[1854] Automatic sentence generation

[1855] The server uses text generation AI to automatically generate specific content based on the generated template. For example, in the case of a procedure manual, detailed explanations of each step are generated. The text generation AI also adjusts tone and style as needed, taking into account the recognition results of the emotion engine.

[1856] User confirmation and correction

[1857] The generated document is displayed on the device, where the user can review the contents. The user can then make any necessary corrections, such as adding more detail to a specific step or adding notes. The corrections are then sent from the device to the server.

[1858] Save and share the final version

[1859] Once the user has completed their modifications, the server generates and stores the final version of the document, which is then stored in a document database and can be shared with other systems or stakeholders as needed.

[1860] Specific examples

[1861] Example 1: Creating a procedure manual

[1862] 1. The user logs in and selects to create a new procedure.

[1863] User: Log in to the system and select "Create a new procedure manual."

[1864] Terminal: Sends user selection to server.

[1865] 2. The server collects and analyzes past procedures

[1866] Server: Collects past procedures from the document database.

[1867] Server: Analyzes using a natural language processing engine and generates templates.

[1868] 3. The server recognizes the user's emotions and provides feedback

[1869] Server: Recognizes user emotions using an emotion engine.

[1870] Server: Based on the recognition results, the tone and style of the text generation AI are adjusted.

[1871] 4. The server automatically generates the contents of the procedure manual using AI

[1872] Server: Creates specific instructions for each step using text generation AI.

[1873] 5. The user checks and corrects the content

[1874] Terminal: View automatically generated instructions.

[1875] User: Check the content and make corrections such as "Explain step 3 in more detail."

[1876] 6. The server stores and shares the final version

[1877] Server: Generates the final version of the procedure manual that reflects the corrections, stores it in the document database, and shares it as needed.

[1878] By using the above means, users can efficiently create high-quality documents, which improves business efficiency and also allows for individual responses that reflect the user's feelings.

[1879] The processing flow will be explained below.

[1880] Step 1:

[1881] The user begins creating a document.

[1882] The user logs into the system and selects "Create a new document."

[1883] The terminal sends this user selection to the server as a request.

[1884] Step 2:

[1885] The user selects a document type.

[1886] Users select a document type such as a procedure manual, presentation, or report.

[1887] The terminal transmits the user's selection information to the server.

[1888] Step 3:

[1889] The server collects past related documents.

[1890] The server collects historical documents related to the selected document type from a document database.

[1891] The server passes the collected data to a natural language processing engine.

[1892] Step 4:

[1893] The server analyzes the collected data.

[1894] The server uses a natural language processing engine to extract titles, sections, keywords, etc. from the collected documents.

[1895] The server generates a document template based on the analysis results.

[1896] Step 5:

[1897] The server uses an emotion engine to recognize the user's emotions.

[1898] The server receives the user's operation status and input contents from the terminal and sends them to the emotion engine.

[1899] The emotion engine analyzes the user's emotions and feeds the results back to the server.

[1900] Step 6:

[1901] The server generates the content using text generation AI.

[1902] Based on the generated template, the server applies text generation AI to generate specific content.

[1903] Taking into account the results of the emotion engine, the text generation AI adjusts tone and style as needed.

[1904] Step 7:

[1905] The terminal displays the generated document to the user.

[1906] The terminal displays the completed document in a user interface.

[1907] The terminal provides an interface that allows the user to view and edit the content.

[1908] Step 8:

[1909] The user checks and corrects the content.

[1910] The user checks the displayed document in detail.

[1911] The user edits any parts that need to be corrected and inputs the corrections into the terminal.

[1912] Step 9:

[1913] The terminal transmits the user's modifications to the server.

[1914] The terminal transmits the modifications made by the user to the server.

[1915] Step 10:

[1916] The server reflects the changes.

[1917] The server reflects the modifications sent by the user and generates the final version of the document.

[1918] The server sends a preview of the final version to the device.

[1919] Step 11:

[1920] The device displays a preview of the final version to the user.

[1921] The terminal displays the final document to the user and provides an interface for final confirmation.

[1922] Step 12:

[1923] The user saves the document.

[1924] The user performs an operation to save the document after final confirmation.

[1925] The terminal sends a save request to the server.

[1926] Step 13:

[1927] The server stores the final document.

[1928] The server stores the final document in a document database.

[1929] The server shares the final document with other systems and stakeholders as needed.

[1930] Specific examples

[1931] Example 1: Creating a procedure manual

[1932] 1. The user logs in and selects to create a new procedure.

[1933] User: Log in to the system and select "Create a new procedure manual."

[1934] Terminal: Sends user selection to server.

[1935] 2. The server collects and analyzes past procedures

[1936] Server: Collects past procedures from a document database, analyzes them using a natural language processing engine, and generates templates.

[1937] 3. The server uses the emotion engine to recognize the user's emotions.

[1938] Server: The server receives the user's operation status and input information from the device and sends it to the emotion engine to analyze the user's emotions.

[1939] Server: Receives the results of the emotion engine and reflects them in the sentence generation AI.

[1940] 4. The server automatically generates the contents of the procedure manual using AI

[1941] Server: Using text generation AI, we create detailed instructions for each step and adjust the tone and style.

[1942] 5. The user checks and corrects the content

[1943] Terminal: View automatically generated instructions.

[1944] User: Check the content and make corrections such as "Explain step 3 in more detail."

[1945] 6. The server stores and shares the final version

[1946] Server: Generates the final version of the procedure manual that reflects the corrections, stores it in the document database, and shares it as needed.

[1947] By using the above means, users can efficiently create high-quality documents, which improves business efficiency and also allows for individual responses that reflect the user's feelings.

[1948] Example 2

[1949] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1950] In conventional document creation systems, the process of users manually creating and editing documents is time-consuming and laborious, making it inefficient. Furthermore, it is difficult to create documents that take into account the user's emotions and circumstances, making it difficult to create high-quality documents tailored to individual users. Therefore, there is a demand for a system that reduces user labor and allows for the rapid creation of high-quality documents that can be tailored to individual users.

[1951] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1952] In this invention, the server includes means for collecting past related documents and analyzing them using a natural language processing engine, means for generating document templates based on the analysis results, means for recognizing user emotions using an emotion engine, and means for adjusting tone and style using a sentence generation AI to generate specific content. This reduces the user's workload and enables the rapid creation of high-quality documents that can be tailored to individual needs.

[1953] A "user" is an entity that uses the system to create, check, and modify documents.

[1954] A "terminal" is a device operated by a user, and includes devices such as PCs and tablets.

[1955] A "server" is a device that centrally manages and processes data.

[1956] "Document type" refers to the specific type of document the user wants to create, including procedures, presentation materials, reports, and the like.

[1957] "Related historical documents" are existing documents stored in the document database that are related to the document type selected by the user.

[1958] A "natural language processing engine" is software for analyzing text data and extracting necessary information.

[1959] A "document template" is a format that includes the basic structure and main items of a document, and is generated based on collected document data.

[1960] An "emotion engine" is software that recognizes a user's emotions based on the user's input and operational status, and feeds that emotional information back into the system.

[1961] "Sentence generation AI" is an artificial intelligence model that automatically generates sentences based on specified data and templates.

[1962] "Tone and style" refers to the atmosphere and expression of the generated text, and is adjusted according to the user's emotions and situation.

[1963] The "final document" is the document that is completed by reflecting the user's modifications and is finally saved.

[1964] A "document database" is a storage system for storing and managing past related documents, final versions of documents, and the like.

[1965] The present invention relates to a system that enables users to efficiently create high-quality documents. This system comprises a means for the user to select a document type, a means for a server to recognize emotions using an emotion engine, a means for collecting past related documents and analyzing them with a natural language processing engine, a means for generating templates, a means for generating content using a sentence generation AI, and a means for a terminal to display the generated document to the user and allow them to make corrections. The specific configuration and operation of this system are described below.

[1966] System configuration

[1967] The system consists of the following main components:

[1968] User terminal: A device operated by a user, such as a PC or tablet.

[1969] Server: A central device that manages and processes data.

[1970] Document database: A storage system that stores and manages past related documents.

[1971] Natural language processing engine: Software that analyzes past related documents.

[1972] Text generation AI: An artificial intelligence model that generates text based on input data.

[1973] Emotion engine: Software that recognizes the user's emotions and provides feedback to the system.

[1974] Operation overview

[1975] Start preparing the documents

[1976] A user logs into the system using a terminal and selects "Create a new document." The user then selects the type of document they want to create (procedure, presentation, report, etc.). The terminal sends this selection to the server.

[1977] Data collection and analysis

[1978] The server accesses a document database to gather past documents related to the document type selected by the user, then inputs the collected data into a natural language processing engine to extract elements such as titles, sections, and keywords, and then generates a template based on these elements.

[1979] Emotion recognition

[1980] In addition, the server uses an emotion engine to recognize the user's emotions, which detects the user's current emotions based on their inputs and operation situations, and optimizes the document generation process accordingly.

[1981] Automatic sentence generation

[1982] The server uses text generation AI to automatically generate specific content based on the generated template. For example, in the case of a procedure manual, detailed explanations of each step are generated. The server also adjusts the tone and style of writing based on the recognition results of the emotion engine.

[1983] User confirmation and correction

[1984] The generated document is displayed on the device, where the user can review the contents and make any necessary corrections, such as adding detailed explanations for specific steps or adding notes. The corrections are then sent from the device to the server.

[1985] Save and share the final version

[1986] Once the user has completed their modifications, the server generates and stores the final version of the document, which is stored in a document database and can be shared with other systems or stakeholders as needed.

[1987] Specific examples

[1988] Example 1: Creating a procedure manual

[1989] 1. The user logs in and selects to create a new procedure.

[1990] User: Log in to the system and select "Create a new procedure manual."

[1991] Terminal: Sends the user's selection to the server.

[1992] 2. The server collects and analyzes past procedures

[1993] Server: Collects past procedures from a document database.

[1994] Server: Analyzes using a natural language processing engine and generates templates.

[1995] 3. The server recognizes the user's emotions and provides feedback

[1996] Server: Recognizes user emotions using an emotion engine.

[1997] Server: Based on the recognition results, adjust the tone and style of the text generation AI.

[1998] 4. The server automatically generates the contents of the procedure manual using AI

[1999] Server: Uses text generation AI to create specific instructions for each step in the procedure manual.

[2000] 5. The user checks and corrects the content

[2001] Terminal: View the automatically generated instructions.

[2002] User: Check the content and make corrections such as "Add more detail to the explanation of Step 3."

[2003] 6. The server stores and shares the final version

[2004] Server: Generates the final version of the procedure manual that reflects the modifications and stores it in the document database.

[2005] Server: Generate a link to share with other departments or stakeholders as needed.

[2006] Examples of prompt statements

[2007] 1. Specify the document type: "Start creating a new procedure. First, enter the procedure title."

[2008] 2. Key Element Analysis: "Based on the procedure title entered, extract key sections and keywords from related historical documents."

[2009] 3. Emotion detection: "Recognize the user's current emotion and adjust the tone and style of the document appropriately. If the user is relaxed, use a friendly tone."

[2010] 4. Auto-generated prompt: "Draw each step of the procedure in detail using the following format. For example, Step 1: Prepare the necessary tools and materials. Step 2: Explain the procedure. Step 3: Notes."

[2011] The above is a detailed description of an embodiment of the present invention. This system allows users to quickly and efficiently create high-quality documents, and also provides personalized responses that take into account the user's emotions.

[2012] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2013] System program processing flow

[2014] Step 1: User logs in and selects Create New Document

[2015] A user logs in to the system using a terminal.

[2016] Input: User ID and password

[2017] Output: Authentication status of the user

[2018] The server validates the credentials and displays the dashboard if authentication is successful.

[2019] Input: User ID and password

[2020] Output: Dashboard screen

[2021] The user selects "Create a new document" from the dashboard.

[2022] Input: User's document type selection

[2023] Output: Document type data

[2024] The terminal transmits the user's selection to the server.

[2025] Step 2: The server collects and analyzes past document data

[2026] The server accesses a document database to collect past documents related to the document type selected by the user.

[2027] Input: Document type data

[2028] Output: Dataset of collected historical documents

[2029] The server uses a natural language processing engine to extract elements such as titles, sections, and keywords from the collected documents.

[2030] Input: Dataset of historical documents

[2031] Output: Extracted elements (title, sections, keywords, etc.)

[2032] The server generates a template based on these extracted elements.

[2033] Input: Extracted elements

[2034] Output: Generated template

[2035] Step 3: The server recognizes the user's emotion

[2036] The server starts the emotion engine and analyzes the user's input and operation status.

[2037] Input: User operation log, input text

[2038] Output: Emotion recognition result

[2039] The server determines the user's current emotion (e.g., stress, relaxation, or impatience) based on the results of the emotion engine.

[2040] Input: Emotion recognition results

[2041] Output: User's emotional state data

[2042] The server feeds this emotional information back into the sentence generation process.

[2043] Step 4: The server automatically generates the content using text generation AI

[2044] The server uses a sentence generation AI to automatically generate sentences based on the generated template and the results of the emotion engine.

[2045] Input: Template, emotional state data

[2046] Output: The specific document content generated

[2047] The server generates the specific document content, for example in the case of a procedure manual, creating a detailed description of each step.

[2048] Input: Template, emotional state data

[2049] Output: Specific procedure manual content

[2050] The server takes emotional information into account and adjusts tone and style.

[2051] Step 5: User confirms and modifies the content

[2052] The terminal displays the generated document to the user.

[2053] Input: Generated document content

[2054] Output: Displayed document

[2055] The user reviews the document and identifies any necessary revisions (e.g., "More details on step 3").

[2056] Input: Displayed document, user modifications

[2057] Output: Correction instruction data

[2058] The terminal transmits correction instruction data to the server.

[2059] Step 6: The server saves and shares the final version

[2060] The server generates the final document reflecting the user's modifications.

[2061] Input: Correction instruction data, initial generated document content

[2062] Output: Final document

[2063] The server stores the final version of the document in a document database.

[2064] Input: Final document

[2065] Output: Save status

[2066] The server generates links to share the document with other systems or stakeholders as needed.

[2067] This allows users to quickly and efficiently create high-quality documents, and the system also provides personalized responses that reflect the user's feelings.

[2068] (Application example 2)

[2069] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2070] Currently, creating product reviews and customer support emails on online shopping sites faces the challenge of generating high-quality text that reflects user emotions. This can lead to inconsistencies in the content of reviews and emails, resulting in a poor user experience. Another problem is the effort required for users to manually edit the content of reviews and emails. To solve these issues, a system is needed that automatically generates appropriate text that takes user emotions into account.

[2071] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2072] In this invention, the server includes a means for allowing a user to select a document type, a means for collecting past related documents and analyzing them using a natural language processing engine, and a means for generating a document template based on the analysis results, which allows the tone and style of writing to be adjusted based on the user's emotions, making it possible to automatically generate high-quality product reviews and customer support emails.

[2073] A "user terminal" is a device that a user uses to operate the system, and includes general computer devices such as smartphones and tablets.

[2074] A "server" is a central device for processing and managing large amounts of data, and oversees database operations and AI processing.

[2075] A "document database" is a storage system that stores and manages historical related documents and provides quick data access.

[2076] A "natural language processing engine" is a general term for software that analyzes input text and understands its meaning.

[2077] "Text generation AI" is an artificial intelligence model that automatically generates text based on input data, producing high-quality text according to specific rules and patterns.

[2078] An "emotion engine" is software that recognizes the user's emotions and provides feedback to the system based on those emotions.

[2079] A "template" is a format that serves as a basic framework for efficiently generating a variety of documents, and defines the components of each document.

[2080] "Tone of the text" refers to the emotional tone or style of the generated text, which is adjusted based on the user's emotions.

[2081] "Style" refers to the manner or style of writing, and can range from formal to casual.

[2082] A "visual interface" is a screen display mechanism that allows users to visually view and manipulate documents and data.

[2083] MODE FOR CARRYING OUT THE INVENTION

[2084] System Configuration

[2085] In practicing this invention, the system includes as major components:

[2086] 1. User terminal: A device operated by a user, such as a smartphone or tablet.

[2087] 2. Server: A central device for processing and managing large amounts of data, using AWS (Amazon Web Services) or Google Cloud Platform.

[2088] 3. Document database: A storage system that stores and manages historical related documents.

[2089] 4. Natural language processing engine: A general term for software that analyzes input text and understands its meaning. Examples include SpaCy and BERT.

[2090] 5. Text generation AI: An artificial intelligence model that automatically generates text based on input data. We will use OpenAI GPT-4 as a concrete example.

[2091] 6. Emotion Engine: Software that recognizes the user's emotions and provides feedback to the system based on those emotions. It uses the Microsoft Azure Cognitive Services Emotion API.

[2092] Processing flow

[2093] 1. User operation: The user logs in to the system using a terminal and selects the document type (product review or customer support email).

[2094] 2. Data collection and analysis: The server collects relevant documents from the document database and analyzes them using a natural language processing engine (e.g., SpaCy or BERT), extracting elements such as titles, sections, and keywords, and generating templates.

[2095] 3. Emotion Recognition: The emotion engine (Microsoft Azure Cognitive Services Emotion API) detects the current emotion based on the user's input and operation context. Based on the emotional state, the text generation AI adjusts the tone and style of writing.

[2096] 4. Automatic sentence generation: Based on the template generated in the previous step, the server uses sentence generation AI (OpenAI GPT-4) to automatically generate specific content, resulting in high-quality sentences that reflect emotions.

[2097] 5. Confirmation and correction: The terminal displays the generated text to the user. The user can confirm the content and make corrections as necessary. The corrected data is sent back to the server.

[2098] 6. Final saving and sharing: The server reflects the user's modifications, generates the final version of the document, and saves it in the document database. If necessary, the document can be shared with other systems or stakeholders.

[2099] Specific examples

[2100] Example 1: Creating a product review

[2101] 1. User logs in and selects to create a new review

[2102] User: Log in to the system and select "Create a new review."

[2103] Terminal: Sends user selection to server.

[2104] 2. The server collects and analyzes past reviews

[2105] Server: Collects past reviews from a document database.

[2106] Server: Analyzes using a natural language processing engine and generates templates.

[2107] 3. The server recognizes the user's emotions and provides feedback

[2108] Server: Recognizes user emotions using an emotion engine.

[2109] Server: Based on the recognition results, the tone and style of the sentence generation AI are adjusted.

[2110] 4. The server automatically generates the review content using AI

[2111] Server: Create specific reviews using text generation AI.

[2112] 5. User confirms and modifies the content

[2113] Device: View automatically generated reviews.

[2114] User: Check the contents and make any necessary changes.

[2115] 6. The server stores and shares the final version

[2116] Server: Generates the final version of the review that reflects the corrections, stores it in the document database, and shares it as needed.

[2117] Prompt Sentence Examples

[2118] "Generate a review based on the user's current emotional state and predict their thoughts about this product. Below is a list of keywords provided by the user: quality, design, price. The user is currently excited and satisfied with their purchase."

[2119] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2120] Step 1:

[2121] User logs in and selects to create a new review

[2122] Input: User login credentials, choice to create review

[2123] Processing: The device sends the user's login credentials to the server. If the server authenticates the login, the review creation screen is displayed on the device.

[2124] Output: Display of review creation screen

[2125] Step 2:

[2126] The server collects and analyzes past reviews

[2127] Input: Product information selected by the user

[2128] Processing: The server collects relevant past reviews from a document database and uses a natural language processing engine (e.g., SpaCy or BERT) to extract titles, sections, keywords, etc. It then generates a template based on these elements.

[2129] Output: Title, section, keywords for template generation

[2130] Step 3:

[2131] The server recognizes the user's emotions and provides feedback

[2132] Input: User input and operation status

[2133] Processing: The emotion engine (Microsoft Azure Cognitive Services Emotion API) recognizes the user's emotional state and feeds the results back to the document generation AI.

[2134] Output: Emotion recognition results (e.g., excitement, satisfaction, etc.)

[2135] Step 4:

[2136] The server automatically generates the review content using AI

[2137] Input: Templates and emotion engine recognition results

[2138] Processing: The server uses text generation AI (OpenAI GPT-4) to automatically generate specific review content based on templates and emotion recognition results. A prompt is used, such as "Please predict the user's thoughts about this product based on their emotional state and generate a review. Below is a list of keywords provided by the user: quality, design, price. The user is currently excited and satisfied with their purchase."

[2139] Output: Generated review text

[2140] Step 5:

[2141] User confirms and modifies the content

[2142] Input: Generated review text

[2143] Processing: The device displays the generated review text to the user. The user checks the content and makes corrections as necessary. The corrections are sent from the device to the server.

[2144] Output: revised review text

[2145] Step 6:

[2146] The server stores and shares the final version

[2147] Input: Revised review text

[2148] Processing: The server reflects the user's modifications, generates the final document, stores it in the document database, and shares the document with other systems and stakeholders as needed.

[2149] Output: Final saved review text

[2150] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2151] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2152] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2153] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2154] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2160] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2161] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2163] 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.

[2164] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2165] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2166] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[2167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2171] The following is further disclosed regarding the above embodiment.

[2172] (Claim 1)

[2173] a means for a user to select a document type;

[2174] A means for the server to collect relevant historical documents and analyze them using a natural language processing engine;

[2175] a means for the server to generate a document template based on the analysis results;

[2176] A means for the server to generate specific content using a text generation AI based on the generated template;

[2177] a means for displaying the generated document to the user by the terminal, and for the user to confirm and modify the contents;

[2178] a means for the server to store a final version of the document incorporating the user's modifications;

[2179] A system including:

[2180] (Claim 2)

[2181] 10. The system of claim 1, wherein the terminal means for accepting user modifications provides a visual interface of the document contents.

[2182] (Claim 3)

[2183] 2. The system of claim 1, wherein the means by which the server stores the final document is to store it in a document database and share it with other systems or parties as needed.

[2184] "Example 1"

[2185] (Claim 1)

[2186] a means for a user to select a document type;

[2187] A means for the server to collect relevant historical documents and analyze them using a natural language processing engine;

[2188] a means for the server to generate a document template based on the analysis results;

[2189] A means for the server to generate specific content based on the generated template using a sentence generation software module;

[2190] a means for displaying the generated document to the user by the terminal, and for the user to confirm and modify the contents;

[2191] a means for the server to store a final version of the document incorporating the user's modifications;

[2192] A system including:

[2193] (Claim 2)

[2194] 10. The system of claim 1, wherein the user interface of the terminal provides a visual interface that accepts user modifications.

[2195] (Claim 3)

[2196] 2. The system of claim 1, wherein the means by which the server stores the final document is to store it in a data storage system and share it with other systems or parties as needed.

[2197] "Application Example 1"

[2198] (Claim 1)

[2199] a means for a user to select a document type;

[2200] A means for the server to collect relevant past documents and analyze them using a natural language processing engine;

[2201] means for the server to generate a document template based on the analysis results;

[2202] A means for the server to generate specific content using a text generation AI based on the generated template;

[2203] a means for displaying the generated document to a user on the terminal, and for the user to confirm and modify the contents;

[2204] The server stores the final version of the document that reflects the user's modifications and shares it with related departments and other processing systems as necessary.

[2205] A system including:

[2206] (Claim 2)

[2207] 10. The system of claim 1, wherein the terminal means for accepting user modifications provides a visual interface of the document content.

[2208] (Claim 3)

[2209] 2. The system of claim 1, wherein the means by which the server stores the final document is to store it in a document database and share it with other processing systems or parties as needed.

[2210] "Example 2: Combining Emotion Engines"

[2211] (Claim 1)

[2212] a means for a user to select a document type;

[2213] A means for the server to collect relevant historical documents and analyze them using a natural language processing engine;

[2214] a means for the server to generate a document template based on the analysis results;

[2215] a means for the server to recognize the user's emotion using an emotion engine;

[2216] Based on the emotions recognized by the server, a means of adjusting the tone and style using text generation AI to generate specific content;

[2217] a means for displaying the generated document to the user by the terminal, and for the user to confirm and modify the contents;

[2218] a means for the server to store a final version of the document incorporating the user's modifications;

[2219] A system including:

[2220] (Claim 2)

[2221] 10. The system of claim 1, wherein the terminal means for accepting user modifications provides a visual interface of the document contents.

[2222] (Claim 3)

[2223] 2. The system of claim 1, wherein the means by which the server stores the final document is to store it in a document database and share it with other systems or parties as needed.

[2224] "Application example 2 when combining emotion engines"

[2225] (Claim 1)

[2226] a means for a user to select a document type;

[2227] A means for the server to collect relevant historical documents and analyze them using a natural language processing engine;

[2228] a means for the server to generate a document template based on the analysis results;

[2229] A means for the server to generate specific content using a text generation AI based on the generated template;

[2230] A means to use an emotion engine to recognize user emotions and adjust the tone and style of the text generation AI; and

[2231] a means for displaying the generated document to the user by the terminal, and for the user to confirm and modify the contents;

[2232] The server stores the final document that reflects the user's modifications and provides a means to share it with other systems and stakeholders as needed.

[2233] A system including:

[2234] (Claim 2)

[2235] 10. The system of claim 1, wherein the terminal means for accepting user modifications provides a visual interface of the document content and is adjusted based on emotion recognition results.

[2236] (Claim 3)

[2237] 2. The system of claim 1, wherein the means by which the server stores the final document is to store it in a document database and share it with other systems or parties as needed. [Explanation of symbols]

[2238] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to select a document type; A means for the server to collect relevant historical documents and analyze them using a natural language processing engine; a means for the server to generate a document template based on the analysis results; A means for the server to generate specific content using a text generation AI based on the generated template; a means for displaying the generated document to the user by the terminal, and for the user to confirm and modify the contents; a means for the server to store a final version of the document incorporating the user's modifications; A system including:

2. 2. The system of claim 1, wherein the terminal means for accepting user modifications provides a visual interface of the document contents.

3. 2. The system according to claim 1, wherein the means by which the server stores the final document is to store it in a document database and share it with other systems or parties as necessary.

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