System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a document and analyzing the document to generate a summary; means for receiving chat contents and analyzing the chat contents to extract important information; means for automatically generating a base plan of a circular memo based on the summarized document information and the extracted chat contents; and means for transmitting the generated base plan to a user terminal to allow a user to confirm and edit the base plan.SELECTED DRAWING: Figure 1

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] Assistants are responsible for a wide range of tasks, and among them, approval submissions are extremely time-consuming. Even the most skilled assistants spend a great deal of time completing lengthy procedures, but this goes unnoticed. Furthermore, approval submissions require many related documents and chat messages, and the process of organizing and summarizing them to complete a single application is extremely cumbersome. Therefore, there is a need to dramatically reduce the amount of time required for approval submissions and lighten the workload on assistants. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for receiving documents, analyzing them, and generating summaries, a means for receiving chat content, analyzing it, and extracting important information, a means for automatically generating a base draft of the approval application text based on the summarized document information and the extracted chat content, and a means for sending the generated base draft to a user terminal so that the user can check and edit it, thereby streamlining the approval application work of assistants.

[0006] In addition, after the approval application text has been finalized, the system provides comprehensive support for the entire approval application process by including a means to save the confirmed application and generate it in a specified format, as well as a means to generate metadata for various documents uploaded by the user and associate it with the corresponding approval application data. This significantly reduces the workload of assistants and enables efficient and accurate approval applications.

[0007] "Documents" refers to a series of documents such as contracts, estimates, and proposal materials required for approval applications.

[0008] A "summary" is a concise summary of a document, extracted from the contents of the document and containing important information and key points.

[0009] "Chat content" refers to text-based communication between a user and a person in charge, including information related to the approval request.

[0010] "Important information" refers to key points and main points of discussion necessary for approval applications extracted from the chat content.

[0011] "Approval request text" refers to the main text portion of the official application form prepared for the approval request.

[0012] The "base draft" refers to the initial draft of the approval request text automatically generated by AI, and refers to the document in its state before the user makes the final confirmation and editing.

[0013] "User terminal" refers to a hardware device, such as a computer or mobile device, used by an assistant.

[0014] "Metadata" refers to the formal information of a document (e.g., file name, upload date and time, etc.) and auxiliary data for classifying and searching its contents.

[0015] "Specified format" refers to the file format or format (e.g. PDF format, Word format, etc.) in which the final output of the approval application form is required. [Brief explanation of the drawings]

[0016] [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 showing 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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] To specifically implement this invention, a user first prepares a series of documents and chat content required for the approval request using a terminal. This data is then uploaded to a server via the terminal, which analyzes it to extract summaries and important information. Finally, a base draft of the approval request text is generated, which the user can review and edit.

[0038] Program processing

[0039] 1. Upload and summarize documents

[0040] A user uses a terminal to upload a document (such as a contract, estimate, or proposal) to the server.

[0041] The server receives and stores the uploaded documents, which are then analyzed by AI algorithms to extract key information and generate summaries.

[0042] 2. Chat content aggregation and analysis

[0043] The user sends the chat contents with the person in charge from the terminal to the server.

[0044] The server receives and stores the chat content, which is then analyzed by a natural language processing (NLP) module to extract important information needed for approval applications.

[0045] 3. Automatic generation of approval request text

[0046] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content, including the necessary summary information, cost details, and key contract terms.

[0047] The generated base proposal is sent to the terminal for the user to review and edit.

[0048] 4. Final review and editing

[0049] The user checks the base draft of the approval request text generated on the terminal and makes corrections or enters additional information as necessary.

[0050] Once the user finalizes the base proposal and completes the approval application, the server saves the finalized application in a specified format and automatically sends it to superiors or other relevant parties as necessary.

[0051] Specific examples

[0052] 1. Upload and summarize documents

[0053] The user uploads a new contract related to Project X to the server from their device.

[0054] The server receives the contract and generates a summary including the specific contract amount, contract period, and key terms.

[0055] The generated summary can be viewed on the terminal, allowing the user to understand the content.

[0056] 2. Chat content aggregation and analysis

[0057] The user sends the chat contents with the person in charge from the terminal to the server.

[0058] The server analyzes the chat content and extracts the discussion points and agreements necessary for the approval request.

[0059] The organized information can be viewed on the device, allowing users to check the information they need.

[0060] 3. Automatic generation of approval request text

[0061] Based on the summary information and the extracted chat content, the server generates a draft of the approval request text in the form of, "We are requesting approval for a new contract for Project X with a budget of XX yen. The contract period is from month XX / XX to month XX / XX, and the main conditions are as follows..."

[0062] The generated draft can be viewed and edited on your device.

[0063] 4. Final review and editing

[0064] The user checks the generated draft on the terminal and makes corrections as necessary.

[0065] The completed approval application form is finalized, and the server saves it and automatically sends it to the superior.

[0066] In this way, a system can be created that streamlines the approval application process for assistants and significantly reduces the amount of work required.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] The user uses a terminal to upload documents such as contracts, quotations, and proposal materials required for approval applications. The user logs in to the system and clicks the "Upload Documents" button. A file selection dialog box appears, and the user selects the necessary documents and presses the "Upload" button.

[0070] Step 2:

[0071] The device sends the uploaded document to the server, where it is saved in the specified directory.

[0072] Step 3:

[0073] The server receives the stored document and begins analyzing it using AI algorithms, analyzing the document content with natural language processing (NLP) models, extracting key information, and generating a summary.

[0074] Step 4:

[0075] Once the summary is generated, the server sends it in JSON format to the terminal, which displays the summary result for the user to review.

[0076] Step 5:

[0077] The user copies and pastes the chat transcript with the agent using the terminal, and then clicks the "Upload Chat Transcript" button to send it to the server.

[0078] Step 6:

[0079] The server receives the chat content and begins analyzing it with the NLP module. Important information necessary for approval applications is extracted and organized from the chat content.

[0080] Step 7:

[0081] Once the extracted important information has been organized, the server sends it in JSON format to the device, where it is displayed for the user to review.

[0082] Step 8:

[0083] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content. The generated base draft includes summary information, cost details, and key contract terms.

[0084] Step 9:

[0085] Once the generated base plan is complete, the server sends it in JSON format to the device, which displays it in a text editor for the user to review and edit.

[0086] Step 10:

[0087] The user checks the base draft of the approval request text generated on the terminal, and enters corrections or additional information as necessary. The user then clicks the "Finalize" button to finalize the approval request.

[0088] Step 11:

[0089] The server receives the finalized approval request and generates the application in PDF format or other specified format. The generated application is saved on the server and automatically sent to superiors or related parties as needed.

[0090] This series of steps allows the assistant to efficiently complete the approval application process.

[0091] Example 1

[0092] 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."

[0093] Conventional approval application systems required the manual organization of multiple documents and chat messages, and the process of compiling summary information was extremely cumbersome, time-consuming, and labor-intensive. Furthermore, important information was often overlooked or typographical errors occurred, making it difficult to streamline operations. This could result in delays and inaccuracies in the approval application process.

[0094] 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.

[0095] In this invention, the server includes: means for a user to use a terminal to select various data and upload it to the server; means for the server to receive and store the uploaded documents; means for analyzing the documents using an analysis algorithm on the server, extracting important information, and generating summaries; means for the server to receive and store uploaded chat content; means for analyzing the chat content using a natural language processing module and extracting important information; means for automatically generating a base draft of the approval request body using a generative AI model based on the summarized document information and the extracted chat content; and means for sending the generated base draft to the user terminal so that the user can confirm and edit it. This makes it possible to consistently automate the management of approval request-related data, the generation of summaries, and the automatic generation of approval request bodies, thereby improving business efficiency and accuracy.

[0096] A "user" is an entity that operates the system and uploads, checks, and edits data via a terminal.

[0097] "Terminal" refers to a computing device used by a user, and is a device used to upload documents, send chat content, and review and edit generated approval applications.

[0098] A "server" is a computing system that receives data sent from a user terminal, analyzes it, and performs the necessary processing.

[0099] "Document" refers to a document that contains text and information necessary for an approval request, such as a contract, estimate, or proposal document.

[0100] "Chat content" refers to the history of communication between the user and other personnel via email or messaging services.

[0101] An "analysis algorithm" is a computational method or program that analyzes documents and chat content, extracts important information, and summarizes it.

[0102] The "natural language processing module" is a program that converts chat content into structured data and extracts important information.

[0103] A "generative AI model" is an artificial intelligence algorithm that automatically generates a base draft of the approval application text based on input data.

[0104] The "base draft of the approval request text" is an initial draft of the approval request text generated based on the summarized document information and the extracted chat content.

[0105] "Finalization" is the process in which the user checks and edits the basic draft of the approval request text and finalizes it as an official application.

[0106] "Metadata" is attribute information added to various documents and chat content, and is data that makes it easier to manage and search data.

[0107] "Saving" means storing the finalized approval application form and uploaded data in a specified directory on the server.

[0108] To specifically implement this invention, the following steps must be taken: The main hardware consists of a user terminal and a server, and the software used includes an analysis algorithm, a natural language processing module, and a generative AI model.

[0109] First, the user uses the terminal to prepare the documents required for the approval request (e.g., contract, quotation, proposal materials). Then, the user can click the "Select File" button, select the appropriate file, and press the "Upload" button. Similarly, the user prepares the chat content with the person in charge and uploads it to the system. Using the "Select Chat History" button, the user selects the appropriate file and presses the "Send" button to send the chat content to the server.

[0110] Next, the server receives the uploaded documents and chat content and stores them in a specified directory (e.g., " / uploads / documents" or " / uploads / chats"). The server first reads the documents using an analysis algorithm (e.g., DocuAI) and extracts important information. This includes analyzing key items such as the contract amount and contract period using keyword extraction and natural language processing techniques. The server also analyzes the chat content using a natural language processing module (e.g., ChatAnalyzer) and extracts important information (discussion points and agreements).

[0111] The server then creates and sends a prompt to a generative AI model (e.g., GPT-4) based on the summarized document information and the extracted chat content. This generates a base draft for the approval request text. The generated base draft is created in the form of, for example, "For a new contract regarding Project X, we request approval of a budget of XX yen. The contract period is from month XX year to month XX year, and the main conditions are as follows..." The generated draft is first saved as a temporary approval request file on the server and then sent to the user's device.

[0112] The terminal receives the generated draft and displays it for the user to review and edit. For example, a text editor window is opened, allowing the user to edit the draft on the spot. Once the user makes a final confirmation and presses the "Confirm" button, the approval request form is complete.

[0113] The finalized request form is saved on the server in a specified format (for example, HTML or PDF). The save destination is set to a directory such as " / finalized / request_forms". Furthermore, the server automatically sends the saved request form to superiors and related parties as necessary. In this case, it is sent by email to the email addresses of the superiors and related parties.

[0114] Examples of prompt statements

[0115] "I'd like to upload a new contract for Project X. Please extract the contract amount, contract period, and key terms and generate a draft of the approval request text."

[0116] "Please analyze the chat history with the person in charge, extract the agreements and discussion points, and reflect them in the approval request."

[0117] In this way, the specific operations and functions of the terminal, server, and user are linked to realize a system that streamlines approval application work. This embodiment allows users to quickly and accurately create approval application forms, improving the efficiency and accuracy of work.

[0118] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0119] Step 1: The user uses the terminal to prepare and upload the documents required for the approval request.

[0120] Input: Documents such as contracts, quotes, proposals, etc. related to the request selected by the user.

[0121] How it works: The user clicks the "Choose File" button on the device, selects a file, and presses the "Upload" button.

[0122] Output: The document is sent to the server.

[0123] Step 2: The server receives and stores the uploaded document.

[0124] Input: The document uploaded by the user.

[0125] What it does: The server saves the uploaded file in the specified directory (e.g. " / uploads / documents").

[0126] Output: The document is saved on the server.

[0127] Step 3: The server uses an analysis algorithm to analyze the document, extract important information, and generate a summary.

[0128] Input: The saved document.

[0129] How it works: The server uses an analysis algorithm (e.g., DocuAI) to extract key information (e.g., contract amount, contract duration, key terms) and generate a summary.

[0130] Data processing: Analyze the text within a document and summarize key information using keyword extraction and natural language processing techniques.

[0131] Output: A summary of the generated documentation.

[0132] Step 4: The user uses the terminal to prepare the chat content with the agent and sends it to the server.

[0133] Input: The chat transcript between the user and the agent.

[0134] How it works: The user uses the device's "Select Chat History" button to select a file of the chat contents and presses the "Send" button.

[0135] Output: The chat content is sent to the server.

[0136] Step 5: The server receives and stores the chat content.

[0137] Input: The chat content sent by the user.

[0138] What it does: The server saves chat content in a specified directory (e.g. " / uploads / chats").

[0139] Output: Chat content is saved on the server.

[0140] Step 6: The server analyzes the chat content and extracts important information.

[0141] Input: Saved chat transcript.

[0142] How it works: The server uses a natural language processing module (e.g. ChatAnalyzer) to analyze the chat content and extract key discussion points and agreements.

[0143] Data processing: Chat content is analyzed using natural language processing technology to extract important structured information.

[0144] Output: A summary of the parsed chat content.

[0145] Step 7: The server generates a draft of the approval request body based on the summarized document information and the analyzed chat content.

[0146] Input: Summarized document information and parsed chat content.

[0147] How it works: The server sends a prompt to a generative AI model (e.g., GPT-4) to generate a draft of the approval request text. For example, it might generate a draft such as, "I am requesting approval for a new contract for Project X with a budget of XX yen. The contract period is from XX month of XX year to XX month of XX year, and the main conditions are as follows..."

[0148] Data calculation: Based on the document summary and chat information, the generative AI model creates a base draft for the approval request text.

[0149] Output: A draft of the generated decision body.

[0150] Step 8: The server sends the generated draft to the user terminal so that the user can check and edit it.

[0151] Input: A generated draft of the request body.

[0152] What it does: The server sends the draft to the user's terminal, where it opens in a text editor window.

[0153] Output: The draft displayed on the user's terminal.

[0154] Step 9: The user checks the draft on their device and makes edits.

[0155] Input: The draft displayed on the user's terminal.

[0156] How it works: The user edits the draft in a text editor, making any necessary corrections or adding additional information.

[0157] Output: Edited body of the approval request.

[0158] Step 10: The user presses the "Confirm" button to finalize the approval request form.

[0159] Input: Edited request body.

[0160] Operation: The user presses the "Confirm" button to finalize the application form.

[0161] Output: Finalized approval request form.

[0162] Step 11: The server saves the finalized approval application in the specified format and automatically sends it to the specified parties.

[0163] Input: Finalized approval request form.

[0164] Operation: The server saves the request form in a specified directory (e.g., " / finalized / request_forms") and automatically sends it to the email addresses of the relevant parties.

[0165] Output: Saved and submitted decision request.

[0166] This provides a system that allows for efficient and accurate approval application procedures.

[0167] (Application example 1)

[0168] 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."

[0169] Inventory management and ordering are extremely important in running a brick-and-mortar store, but they require a great deal of time and effort. Creating appropriate ordering requests based on chats between staff and product inventory information requires specialized knowledge and attention. Traditional methods are time-consuming and prone to errors. Furthermore, there is a need for an efficient method for responding quickly and accurately based on this information.

[0170] 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.

[0171] In this invention, the server includes means for receiving documents and analyzing them to generate summaries, means for receiving chat content and analyzing it to extract important information, means for automatically generating a base draft of the approval application text based on the summarized document information and the extracted chat content, means for sending the generated base draft to a user terminal so that the user can confirm and edit it, means for analyzing product inventory information and automatically generating an order request, and means for sending the generated order request to a user terminal so that the user can confirm and edit it. This makes product inventory management and ordering operations more efficient, reducing the burden on staff and reducing errors.

[0172] "Document" refers to documents and materials created to provide information, including contracts, estimates, proposals, and the like.

[0173] A "summary" is a short summary of the main information of a document, allowing a user to quickly grasp the content.

[0174] "Chat content" refers to text-based communication between users, including the exchange of electronic messages.

[0175] "Important information" refers to information that is particularly required in subsequent processes, and refers to key data necessary for decision-making and document generation.

[0176] The "application body" is a document that contains detailed information necessary to obtain approval for the application, including the application content, purpose, cost, and period.

[0177] A "base draft" is a draft generated as an early stage of the final document, and serves as a basis for users to review and edit.

[0178] "Product inventory information" is data indicating the quantity and status of products handled at a store, including remaining stock and out-of-stock status.

[0179] An "order request" is a request to place an order to replenish product inventory, and includes a specific quantity and type of product.

[0180] A "user terminal" is a device operated by a user, such as a smartphone, tablet, or PC.

[0181] "Analysis" is the scrutiny of data to extract and understand specific information, often using specific algorithms or models.

[0182] "Automatic generation" means that a system creates documents or information without human intervention, based on specific rules or algorithms.

[0183] To specifically implement the present invention, a system is constructed according to the following procedure.

[0184] First, the user terminal prepares product inventory information and chat messages between staff members and uploads them to the server. At this time, the product inventory information includes stock quantities and out-of-stock status, and the chat messages include messages about inventory management and ordering.

[0185] The server then receives and analyzes the uploaded documents and chat content using text analysis techniques such as natural language processing (NLP) algorithms and TF-IDF, using Python, NLTK, the Transformers library, and scikit-learn.

[0186] The server summarizes the documents and extracts important information from the chat, such as a list of products that are low in stock or items that need to be reordered.

[0187] The server then automatically generates a base order proposal based on the summarized document information and the extracted chat content, including specific product names, order quantities, and reasons.

[0188] The generated order request is sent to the user's terminal. The user can check the request contents on the terminal and edit them as necessary. The finalized order request is saved on the server and automatically sent to the administrator in the specified format.

[0189] (Example)

[0190] For example, suppose a "list of products that are low in stock" is uploaded as a document, and a message such as "Item A is low in stock, so we need to order more. Please also order more item C" is sent as chat content between staff members.

[0191] The server parses this information and generates the following prompt:

[0192] Prompt: Based on product availability information and chat with staff, generate the following order request:

[0193] Document: A list of products that are low in stock. Item A, Item B...

[0194] Chat content: We are running low on item A and need to order more. Please also order item C.

[0195] Based on this prompt, the server generates an order request like this:

[0196] Abridged document: A list of products that are low on stock. Item A, Item B…

[0197] Order Request:

[0198] 1. Item A is running low in stock, so we need to place an additional order.

[0199] 2. Please also add item C.

[0200] In this way, the present invention can improve the efficiency of product inventory management and ordering operations, reduce the burden on staff, and reduce the risk of missing an order.

[0201] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0202] Step 1:

[0203] Users upload product inventory information and chat messages between staff members from their devices to the server. At this time, the product inventory information includes stock quantities and out-of-stock status, while the chat messages include messages about inventory management and ordering.

[0204] Input: Product inventory information (stock quantity, out-of-stock status), chat content (text message)

[0205] Output: Product inventory information uploaded to the server and chat content

[0206] Step 2:

[0207] The server receives the uploaded product inventory information and chat content and stores them for subsequent analysis.

[0208] Input: User uploaded information

[0209] Output: Data stored in the server

[0210] Step 3:

[0211] The server uses natural language processing (NLP) algorithms to analyze product inventory information and generate summaries, using Python, NLTK, and the Transformers library.

[0212] Input: Product inventory information stored on the server

[0213] Output: Summarized product inventory information

[0214] Step 4:

[0215] The server analyzes chat content and extracts important information using text analysis techniques such as TF-IDF, specifically using scikit-learn software.

[0216] Input: Chat contents saved on the server

[0217] Output: Extracted important information

[0218] Step 5:

[0219] The server automatically generates a base order proposal based on the summarized product inventory information and the extracted chat content, including the specific product name, order quantity, and reason.

[0220] Input: Summarized product inventory information, extracted key information

[0221] Output: A basic draft of the purchase order request

[0222] Step 6:

[0223] The server sends the generated base draft of the order request to the user terminal, where the user can check the request contents and edit them as necessary.

[0224] Input: Base proposal for purchase order request

[0225] Output: Request sent to the user's device

[0226] Step 7:

[0227] After the user finalizes, confirms, and edits the order request, the server saves the finalized order request, which is then automatically sent to the administrator or other relevant parties in the specified format.

[0228] Input: User confirmed and edited order request

[0229] Output: Finalized order request saved and automatically sent to administrator

[0230] 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.

[0231] To specifically implement this invention, a user begins by preparing the documents and chat content required for the approval request using a terminal. This data is then uploaded to a server via the terminal, where it is analyzed to extract summaries and important information. Furthermore, an emotion engine is used to recognize the user's emotional state and provide feedback based on that, resulting in a more efficient approval request process.

[0232] Program processing

[0233] 1. Upload and summarize documents

[0234] A user uses a terminal to upload a document (such as a contract, quotation, or proposal) to the server. The user logs in to the system, clicks the "Upload Document" button, selects the required file, and presses the "Upload" button.

[0235] The device sends the uploaded document to the server, which receives and stores it, and the server analyzes the stored document using an AI algorithm to generate a summary.

[0236] 2. Chat content aggregation and analysis

[0237] The user copies and pastes the chat contents with the agent from their device and clicks the "Upload Chat Content" button to send it to the server.

[0238] The server receives and stores the chat content, which is then analyzed using a natural language processing (NLP) module to extract important information needed for approval applications.

[0239] 3. Automatic generation of approval request text

[0240] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content. The generated base draft includes summary information, cost details, and key contract terms.

[0241] The base plan is sent in JSON format to the terminal, where it is displayed in a text editor for the user to review and edit.

[0242] 4. Use of Emotion Engine

[0243] The emotion engine integrated into the server recognizes the user's emotional state from facial expressions, tone of voice, typing speed, etc., and analyzes the emotional data, thereby monitoring the user's stress level and satisfaction in real time.

[0244] The emotion engine provides feedback based on the user's emotional state and makes suggestions or corrections to the base draft of the approval request text as needed.

[0245] 5. Final review and editing

[0246] The user checks the base draft of the approval request text generated on the terminal, and enters corrections or additional information as necessary. The user then clicks the "Finalize" button to finalize the approval request.

[0247] The server receives the finalized approval request and generates a document in PDF format or other specified format. The generated document is saved on the server and automatically sent to superiors or related parties as needed.

[0248] Specific examples

[0249] 1. Upload and summarize documents

[0250] A user uploads a new contract related to Project X from their device to the server. The server receives the contract and generates a summary including the contract amount, contract period, and key terms. The generated summary can be viewed on the device, allowing the user to understand the contents.

[0251] 2. Chat content aggregation and analysis

[0252] The user sends the chat contents with the person in charge from their device to the server. The server analyzes the chat contents, extracts the discussion points and agreements necessary for the approval application, and the organized information can be viewed on the device.

[0253] 3. Automatic generation of approval request text

[0254] Based on the summary information and the extracted chat content, the server generates a draft of the approval request text in the form of, "We are requesting approval for a new contract for Project X with a budget of XX yen. The contract period is from month XX / XX to month XX / XX, and the main conditions are as follows..." which can then be viewed and edited on the terminal.

[0255] 4. Use of Emotion Engine

[0256] While the user is reviewing the draft, the emotion engine monitors the user's emotional state in real time. For example, if the user is feeling stressed, the emotion engine will detect this and suggest changes to the draft, allowing the user to complete the approval request in a way that satisfies them.

[0257] 5. Final review and editing

[0258] After the user checks the draft and makes any necessary corrections, they press the "Finalize" button to finalize the approval request. The server receives this, generates an application in the specified format, and automatically sends it.

[0259] This process reduces the workload of the assistant and enables efficient and accurate approval applications. The use of an emotion engine also reduces the psychological burden on the user.

[0260] The processing flow will be explained below.

[0261] Step 1:

[0262] The user uses a terminal to upload documents related to the approval request, such as contracts, quotations, and proposal materials. The user logs in to the system and clicks the "Upload Documents" button. A file selection dialog box appears, and the user selects the necessary documents and presses the "Upload" button.

[0263] Step 2:

[0264] The device sends the uploaded document to the server, where it is saved in the specified directory.

[0265] Step 3:

[0266] The server receives the stored document and begins analyzing it using AI algorithms. The document content is analyzed using a natural language processing (NLP) model, and important information is extracted and a summary is generated. Specifically, key information such as the contract details, contract amount, and contract period is summarized.

[0267] Step 4:

[0268] Once the summary is generated, the server sends it in JSON format to the terminal, where it is displayed for the user to review. The user can then review the summary and check for any issues.

[0269] Step 5:

[0270] A user can copy and paste the chat transcript with the person in charge using a terminal and then click the "Upload Chat Transcript" button to send it to the server, for example, to send text containing important discussions and decisions about a project.

[0271] Step 6:

[0272] The server receives the chat content and begins analyzing it with the NLP module. It extracts important information necessary for approval requests from the chat content and classifies it into categories (e.g., agreed matters, unresolved matters, cost-related matters).

[0273] Step 7:

[0274] Once the extracted important information has been organized, the server sends it to the terminal in JSON format. The terminal displays the organized information so that the user can review it. The user can then review the information and check to see if any elements necessary for the approval request have been omitted.

[0275] Step 8:

[0276] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content. The generated base draft includes summary information, cost details, and key contract terms.

[0277] Step 9:

[0278] Once the generated base plan is complete, the server sends it in JSON format to the device, which displays it in a text editor for the user to review and edit.

[0279] Step 10:

[0280] The emotion engine integrated into the server recognizes the user's emotional state through the camera and microphone on the user's device. The emotion engine analyzes facial expressions, tone of voice, typing speed, etc. to assess the user's stress level and satisfaction in real time. For example, if the user is feeling stressed, the engine will detect this and make suggestions to help the user relax.

[0281] Step 11:

[0282] The emotion engine provides feedback based on the user's emotional state and makes suggestions or corrections to the base draft of the approval request text as needed. For example, if the emotion engine determines that the user is dissatisfied, it will suggest, "Please add more details about this part."

[0283] Step 12:

[0284] The user checks the base draft of the approval request text generated on the device, and makes corrections or adds additional information as necessary, taking into consideration feedback from the emotion engine. The user then clicks the "Finalize" button to finalize the approval request.

[0285] Step 13:

[0286] The server receives the finalized approval request and generates the application in PDF format or other specified format. The generated application is saved on the server and automatically sent to superiors or related parties as needed.

[0287] This series of steps allows the assistant to efficiently complete the approval process. The use of the emotion engine also reduces the psychological burden on the user.

[0288] Example 2

[0289] 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."

[0290] In today's business environment, the approval process is often complex and time-consuming. Collecting, summarizing, and analyzing the documents and chat content required for approval, as well as creating the application text, requires a significant amount of time and effort. This process also places a significant psychological burden on users. Conventional systems were unable to adequately resolve these issues, making it difficult to process approval requests efficiently and accurately.

[0291] 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. In this invention, the server includes means for receiving a document, analyzing it, and generating a summary, means for receiving chat content, analyzing it, and extracting important information, means for automatically generating a base draft of the approval request body based on the summarized document information and the extracted chat content, means for sending the generated base draft to a user terminal so that the user can confirm and edit it, and means for recognizing the user's emotional state and providing feedback based on it. This enables efficient and accurate approval requests and reduces the psychological burden on the user.

[0292] "Document" is a general term for documents stored in electronic format, including contracts, estimates, proposal materials, etc.

[0293] A "summary" is a short summary of the main information or key points of a document, based on an analysis of its contents.

[0294] "Chat content" refers to the record of messages and communications between the user and the person in charge.

[0295] "Important information" refers to important data that affects the content of the application, such as discussion points and agreements required for approval applications.

[0296] The "application text" is a formal document that explains the application content and refers to text created based on summary information and important information.

[0297] The "base proposal" refers to the initial draft of the approval request text automatically generated by the AI ​​model.

[0298] "User terminal" refers to an electronic device used by a user, such as a computer or smartphone.

[0299] "Emotional state" refers to the user's psychological and emotional state, and is measured from facial expressions, tone of voice, typing speed, etc.

[0300] "Feedback" refers to advice, suggestions, and improvements provided based on the user's emotional state.

[0301] To specifically implement this invention, a user begins by preparing the documents and chat content required for the approval application using a terminal. These data are then uploaded to a server via the terminal. The server analyzes the data to extract summaries and important information, and further recognizes the user's emotional state using an emotion engine and provides feedback based on that information.

[0302] Hardware and software used

[0303] 1. Terminal

[0304] The computer or smartphone you use (e.g., Windows PC, Mac, iOS device, Android device).

[0305] User interface using a web browser (e.g., Google Chrome, Mozilla Firefox, Safari).

[0306] 2. Server

[0307] Server computers for data storage and processing (e.g., Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure).

[0308] Python-based AI algorithms for data analysis (e.g., BERT, spaCy, NLTK).

[0309] A generative AI model written in JavaScript used to automatically generate the text of approval requests.

[0310] 3. Emotion Engine

[0311] An engine for real-time recognition of the user's emotional state (e.g., OpenFace, Microsoft Azure Cognitive Services).

[0312] A library for facial expression recognition, voice tone analysis, and keystroke speed analysis.

[0313] Specific examples

[0314] 1. Upload and summarize documents

[0315] The user uploads a new contract related to the project to the server from their device. The user logs in to the system on a web browser, clicks the "Document Upload" button, selects the required file, and presses the "Upload" button. The server stores the received contract and analyzes it using a Python-based AI algorithm to generate a summary including the contract amount, contract period, and key terms. The generated summary is stored in a database and can be viewed on the device.

[0316] 2. Chat content aggregation and analysis

[0317] The user copies the chat transcript with the person in charge, pastes it into the "Chat transcript upload" field in the system, and clicks the "Upload" button. The server receives this chat transcript and stores it in a database. A natural language processing (NLP) module is used to analyze the chat transcript and extract the discussion points and agreements necessary for the approval request. This extracted information can be viewed on the device.

[0318] 3. Automatic generation of approval request text

[0319] The server uses a generative AI model to automatically generate a base draft of the approval request text based on the saved summary information and the extracted chat content. The generated base draft includes summary information, cost details, and key contract terms. It is sent in JSON format to the device and displayed in the device's text editor.

[0320] 4. Use of Emotion Engine

[0321] While the user is reviewing the base proposal, the emotion engine monitors the user's emotional state in real time. The emotion engine analyzes emotional data from facial expression recognition, voice tone analysis, and keystroke speed analysis. If the user is feeling stressed, a suggestion such as "Try changing the expression in this part" will pop up on the screen as emotional feedback. This allows the user to complete the approval request text in a way that satisfies them.

[0322] 5. Final review and editing

[0323] The user checks the generated base draft of the approval request text and makes any necessary corrections in a text editor. Once the review is complete, the user clicks the "Finalize" button. The server receives the finalized approval request and generates an application form in the specified format using LaTeX or PDFkit. The generated application form is saved in a database and automatically sent to superiors or related parties as needed.

[0324] Prompt Sentence Examples

[0325] Here is an example of how a user might enter a prompt in a web browser:

[0326] "I'm uploading a new contract for Project X. Please generate a summary including the contract amount, contract duration, and key terms."

[0327] Such prompts allow the server to perform appropriate processing, resulting in efficient and accurate approval requests.

[0328] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0329] Step 1:

[0330] A user logs in to the system on a terminal by opening a web browser, accessing the system's login screen, entering their user ID and password, and clicking the "Login" button. This displays the system's main menu.

[0331] Step 2:

[0332] The user clicks the "Upload Document" button, selects the required file, and presses the "Upload" button. Specifically, the user selects a file from a file selection dialog using the HTML input tag, and then clicks the button. This operation sends the document data from the terminal to the server. The input is the document file selected by the user, and the output is the document data saved on the server.

[0333] Step 3:

[0334] The server receives and stores the uploaded document. Specifically, it stores the received file in a cloud storage service such as AWS S3. The input is the document data sent from the device, and the output is the URL of the destination in the cloud storage.

[0335] Step 4:

[0336] The server analyzes the stored documents using a Python-based AI algorithm and generates summaries. Specifically, it performs text analysis using the BERT model. The input is the document data retrieved from the destination URL, and the output is the summary text.

[0337] Step 5:

[0338] The user copies the chat contents with the agent on their device, pastes them into the "Upload Chat Content" field, and clicks the "Upload" button. Specifically, the chat contents are pasted using the HTML textarea tag. This sends the chat data from the device to the server. The input is the chat text pasted by the user, and the output is the chat data saved on the server.

[0339] Step 6:

[0340] The server receives and stores the chat data sent from the device. Specifically, it stores the chat data in a database. The input is the chat data sent from the device, and the output is the saved record in the database.

[0341] Step 7:

[0342] The server analyzes the saved chat content using an NLP module to extract important information. Specifically, it uses spaCy and NLTK to perform text analysis. The input is the chat content in the database, and the output is the extracted important information text.

[0343] Step 8:

[0344] The server uses a generative AI model based on the summarized document information and extracted chat content to automatically generate a base draft of the approval request text. Specifically, it runs a generative AI model written in JavaScript to generate the text based on the input data. The input is the document summary and important chat information, and the output is the base draft text of the approval request text.

[0345] Step 9:

[0346] The server sends the generated base proposal in JSON format to the terminal. Specifically, it returns JSON data in the HTTP response. The input is the base proposal text for the approval request body, and the output is the JSON data sent to the terminal.

[0347] Step 10:

[0348] The terminal displays the received base plan in a text editor. Specifically, it displays it in an editable format using an editor such as Quill. The input is JSON data received from the server, and the output is a text editor display that the user can view and edit.

[0349] Step 11:

[0350] The emotion engine monitors the user's emotional state in real time. Specifically, it collects and analyzes data using the device's webcam, microphone, and keyboard typing speed. The input is facial expression, tone of voice, and typing speed data, and the output is analyzed emotional state data.

[0351] Step 12:

[0352] The server provides appropriate feedback based on the emotional state data. Specifically, it displays a pop-up message on the screen with advice such as "It would be good to change the expression in this part." The input is the analyzed emotional state data, and the output is a feedback message that is displayed to the user.

[0353] Step 13:

[0354] The user checks the generated base draft of the approval request text and makes any necessary corrections in a text editor. Specifically, the user edits the text using the keyboard and clicks the "Finalize" button. The input is the base draft text and the user's editing operations, and the output is the finalized approval request text.

[0355] Step 14:

[0356] The server receives the finalized approval request and generates the application in the specified format using LaTeX or PDFkit. Specifically, it converts the text data into LaTeX format and outputs it as a PDF. The input is the text of the finalized approval request, and the output is a PDF application.

[0357] Step 15:

[0358] The server saves the generated application form in a database and automatically sends it to superiors and other relevant parties as needed. Specifically, it saves the form and sends it via email using the SMTP protocol. The input is the PDF application form, and the output is the URL saved in the database and the email that was sent.

[0359] (Application example 2)

[0360] 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."

[0361] In the process of managing the operation and maintenance of autonomous vehicles, processing complex documents and analyzing chat messages is time-consuming. Furthermore, the lack of a system that can monitor the driver's emotional state in real time and provide appropriate feedback increases the psychological burden on the driver. This reduces operational efficiency and frequently leads to delays in responding to vehicle problems.

[0362] 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.

[0363] In this invention, the server includes means for receiving documents and analyzing them to generate summaries, means for receiving chat content and analyzing it to extract important information, means for automatically generating a base draft of the approval application text based on the summarized document information and the extracted chat content, means for sending the generated base draft to a user terminal so that the user can confirm and edit it, means for recognizing the user's emotional state in real time and providing feedback based on the analysis results, and operation management and analysis means that allow drivers to upload documents and malfunction reports. This makes it possible to streamline operation management and the maintenance application process for autonomous vehicles and reduce the psychological burden on drivers.

[0364] "Means for receiving documents, analyzing them, and generating summaries" refers to a function that allows the server to receive various documents uploaded by users, analyzes the documents using natural language processing technology, and automatically generates summaries.

[0365] "Means for receiving chat content, analyzing it, and extracting important information" refers to a function that allows the server to receive chat content provided by the user, analyzes the conversation using natural language processing technology, and automatically extracts important information necessary for approval applications.

[0366] "Means for automatically generating a base draft of the approval application text based on summarized document information and extracted chat content" refers to a function that automatically creates an initial draft of the approval application text based on the document summary generated by analysis and the extracted chat content.

[0367] "Means for sending the generated base draft to the user's terminal and allowing the user to check and edit it" refers to a function that sends an automatically generated base draft of the approval request body to the user's terminal, allowing the user to check it and edit it as necessary.

[0368] "Means of recognizing the user's emotional state in real time and providing feedback based on the analysis results" refers to a function that uses an emotion engine to detect the user's emotional state in real time from their facial expressions, tone of voice, etc., and provides appropriate advice and feedback based on the analysis results.

[0369] The "operation management means and analysis means that allow drivers to upload documents and malfunction reports" refer to multiple functions within the system that allow drivers to upload various documents related to operation management and vehicle malfunction reports to a server and analyze them.

[0370] "Means for saving the confirmed application after the body of the request for approval has been finalized and generating it in a specified format" refers to a function that, after the user finalizes the body of the request for approval, saves the confirmed application form on the server and generates the application form in a specified format (e.g., PDF format).

[0371] "Means for generating metadata for various documents uploaded by users and associating it with the corresponding approval application data" refers to a function that generates metadata from documents uploaded by users and associates that metadata with the corresponding approval application data.

[0372] To implement the present invention, it is necessary to build a system according to the following steps.

[0373] First, we set up the hardware and software used by the system. The main hardware used includes smart glasses, head-mounted displays, smartphones, and servers. The software used includes spaCy as a natural language processing (NLP) module, OpenAI GPT-4 API as a sentiment analysis engine, PostgreSQL as a database, and Flask (Python) as a server framework.

[0374] Document upload and summary generation

[0375] Users use their smart devices to upload documents (e.g., operation plans, contracts) to the server, which then analyzes the received documents using natural language processing technology (spaCy) and generates summaries of important information (route details, time, driver list, etc.).

[0376] Chat content aggregation and analysis

[0377] Users send chat content from their smart devices to the server, which then analyzes the received chat content using a natural language processing module and extracts important information necessary for approval applications. This allows for efficient organization of important discussion points and agreements exchanged between drivers and operation managers.

[0378] Automatic generation of approval request text

[0379] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content. The generated base draft includes the route, budget, contract terms, etc. The base draft is sent to the user's device in JSON format so that the user can view and edit it.

[0380] Analysis and feedback by emotion engine

[0381] The emotion engine integrated into the server analyzes data such as facial expressions, tone of voice, and typing speed to recognize the user's emotional state in real time. Based on the emotion data, the emotion engine evaluates stress levels and satisfaction and provides feedback as needed. For example, if the user is feeling stressed, the emotion engine will display suggestions and corrections.

[0382] Driver operation management and breakdown reporting

[0383] Drivers use smart glasses or head-mounted displays to collect data while driving and upload it to a server. When a malfunction report is sent, the server analyzes it using an NLP module and automatically generates a list of necessary repairs and parts, enabling a prompt response.

[0384] Specific examples

[0385] For example, when a dispatcher notifies the system, "Uploading a trip plan," the system responds, "Generating a trip plan summary... Completed." When a driver inputs, "Submitting a malfunction report about an abnormal engine noise," the system responds, "Analyzing the malfunction report... Generated a list of required parts." The emotion engine also provides real-time feedback, such as, "Your fatigue level has reached 85%, we recommend you take a break."

[0386] Prompt Sentence Examples

[0387] Examples of prompts include:

[0388] "Analyze the flight schedule and summarize the key flight information."

[0389] "Analyze driver reports of malfunctions and create a list of necessary repair parts."

[0390] "Analyze the driver's emotional state in real time and provide appropriate feedback."

[0391] This will make it possible to streamline the operation management and maintenance application process for autonomous vehicles and reduce the psychological burden on drivers.

[0392] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0393] Program processing flow

[0394] Step 1: Uploading documents and generating summaries

[0395] Input: The user uploads documents such as operation plans and contracts from the terminal to the server.

[0396] Processing: The server receives and stores the uploaded document, then uses natural language processing technology (spaCy) to analyze the document, extract key information (e.g., route, time, driver list), and generate a summary.

[0397] Output: Generate and save summarized document information.

[0398] Step 2: Chat content aggregation and analysis

[0399] Input: The user copies the chat contents with the agent from the terminal and sends them to the server.

[0400] Processing: The server stores the received chat content and analyzes it using a natural language processing module. It then extracts important information (e.g., discussion points and agreements) necessary for the approval process.

[0401] Output: Generate and store the extracted important information.

[0402] Step 3: Automatic generation of the approval request text

[0403] Input: Summarized document information and extracted chat transcripts.

[0404] Processing: The server integrates this information and automatically generates a base draft for the approval request, which includes route, budget, contract terms, etc.

[0405] Output: Send the automatically generated base draft of the approval request body to the terminal in JSON format.

[0406] Step 4: Analysis and feedback by the emotion engine

[0407] Input: Data such as the user's facial expressions, tone of voice, and typing speed.

[0408] Processing: The emotion engine (OpenAI GPT-4 API) integrated into the server analyzes these data in real time, evaluates the user's emotional state, and generates appropriate feedback based on the user's stress level and satisfaction.

[0409] Output: Provides feedback to the user based on the analysis results.

[0410] Step 5: Driver management and breakdown reporting

[0411] Input: Driving data and breakdown reports collected by drivers using smart devices.

[0412] Processing: The server receives this data and analyzes it using the NLP module, after which it automatically generates the necessary repairs and parts list.

[0413] Output: The repair items and parts list generated as a result of the analysis are provided to the operation manager.

[0414] Step 6: Finalize and generate application

[0415] Input: A base draft of the approval request body that the user has reviewed and edited.

[0416] Processing: When the user makes a final confirmation and presses the "Finalize" button, the server receives the data and generates the application form in the specified format, such as PDF.

[0417] Output: Save the generated application form and automatically send it to the relevant parties as needed.

[0418] Through these steps, the system can streamline the operation management and maintenance application process for autonomous vehicles and reduce the psychological burden on drivers.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] [Second embodiment]

[0423] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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).

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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."

[0435] To specifically implement this invention, a user first prepares a series of documents and chat content required for the approval request using a terminal. This data is then uploaded to a server via the terminal, which analyzes it to extract summaries and important information. Finally, a base draft of the approval request text is generated, which the user can review and edit.

[0436] Program processing

[0437] 1. Upload and summarize documents

[0438] A user uses a terminal to upload a document (such as a contract, estimate, or proposal) to the server.

[0439] The server receives and stores the uploaded documents, which are then analyzed by AI algorithms to extract key information and generate summaries.

[0440] 2. Chat content aggregation and analysis

[0441] The user sends the chat contents with the person in charge from the terminal to the server.

[0442] The server receives and stores the chat content, which is then analyzed by a natural language processing (NLP) module to extract important information needed for approval applications.

[0443] 3. Automatic generation of approval request text

[0444] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content, including the necessary summary information, cost details, and key contract terms.

[0445] The generated base proposal is sent to the terminal for the user to review and edit.

[0446] 4. Final review and editing

[0447] The user checks the base draft of the approval request text generated on the terminal and makes corrections or enters additional information as necessary.

[0448] Once the user finalizes the base proposal and completes the approval application, the server saves the finalized application in a specified format and automatically sends it to superiors or other relevant parties as necessary.

[0449] Specific examples

[0450] 1. Upload and summarize documents

[0451] The user uploads a new contract related to Project X to the server from their device.

[0452] The server receives the contract and generates a summary including the specific contract amount, contract period, and key terms.

[0453] The generated summary can be viewed on the terminal, allowing the user to understand the content.

[0454] 2. Chat content aggregation and analysis

[0455] The user sends the chat contents with the person in charge from the terminal to the server.

[0456] The server analyzes the chat content and extracts the discussion points and agreements necessary for the approval request.

[0457] The organized information can be viewed on the device, allowing users to check the information they need.

[0458] 3. Automatic generation of approval request text

[0459] Based on the summary information and the extracted chat content, the server generates a draft of the approval request text in the form of, "We are requesting approval for a new contract for Project X with a budget of XX yen. The contract period is from month XX / XX to month XX / XX, and the main conditions are as follows..."

[0460] The generated draft can be viewed and edited on your device.

[0461] 4. Final review and editing

[0462] The user checks the generated draft on the terminal and makes corrections as necessary.

[0463] The completed approval application form is finalized, and the server saves it and automatically sends it to the superior.

[0464] In this way, a system can be created that streamlines the approval application process for assistants and significantly reduces the amount of work required.

[0465] The processing flow will be explained below.

[0466] Step 1:

[0467] The user uses a terminal to upload documents such as contracts, quotations, and proposal materials required for approval applications. The user logs in to the system and clicks the "Upload Documents" button. A file selection dialog box appears, and the user selects the necessary documents and presses the "Upload" button.

[0468] Step 2:

[0469] The device sends the uploaded document to the server, where it is saved in the specified directory.

[0470] Step 3:

[0471] The server receives the stored document and begins analyzing it using AI algorithms, analyzing the document content with natural language processing (NLP) models, extracting key information, and generating a summary.

[0472] Step 4:

[0473] Once the summary is generated, the server sends it in JSON format to the terminal, which displays the summary result for the user to review.

[0474] Step 5:

[0475] The user copies and pastes the chat transcript with the agent using the terminal, and then clicks the "Upload Chat Transcript" button to send it to the server.

[0476] Step 6:

[0477] The server receives the chat content and begins analyzing it with the NLP module. Important information necessary for approval applications is extracted and organized from the chat content.

[0478] Step 7:

[0479] Once the extracted important information has been organized, the server sends it in JSON format to the device, where it is displayed for the user to review.

[0480] Step 8:

[0481] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content. The generated base draft includes summary information, cost details, and key contract terms.

[0482] Step 9:

[0483] Once the generated base plan is complete, the server sends it in JSON format to the device, which displays it in a text editor for the user to review and edit.

[0484] Step 10:

[0485] The user checks the base draft of the approval request text generated on the terminal, and enters corrections or additional information as necessary. The user then clicks the "Finalize" button to finalize the approval request.

[0486] Step 11:

[0487] The server receives the finalized approval request and generates the application in PDF format or other specified format. The generated application is saved on the server and automatically sent to superiors or related parties as needed.

[0488] This series of steps allows the assistant to efficiently complete the approval application process.

[0489] Example 1

[0490] 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."

[0491] Conventional approval application systems required the manual organization of multiple documents and chat messages, and the process of compiling summary information was extremely cumbersome, time-consuming, and labor-intensive. Furthermore, important information was often overlooked or typographical errors occurred, making it difficult to streamline operations. This could result in delays and inaccuracies in the approval application process.

[0492] 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.

[0493] In this invention, the server includes: means for a user to use a terminal to select various data and upload it to the server; means for the server to receive and store the uploaded documents; means for analyzing the documents using an analysis algorithm on the server, extracting important information, and generating summaries; means for the server to receive and store uploaded chat content; means for analyzing the chat content using a natural language processing module and extracting important information; means for automatically generating a base draft of the approval request body using a generative AI model based on the summarized document information and the extracted chat content; and means for sending the generated base draft to the user terminal so that the user can confirm and edit it. This makes it possible to consistently automate the management of approval request-related data, the generation of summaries, and the automatic generation of approval request bodies, thereby improving business efficiency and accuracy.

[0494] A "user" is an entity that operates the system and uploads, checks, and edits data via a terminal.

[0495] "Terminal" refers to a computing device used by a user, and is a device used to upload documents, send chat content, and review and edit generated approval applications.

[0496] A "server" is a computing system that receives data sent from a user terminal, analyzes it, and performs the necessary processing.

[0497] "Document" refers to a document that contains text and information necessary for an approval request, such as a contract, estimate, or proposal document.

[0498] "Chat content" refers to the history of communication between the user and other personnel via email or messaging services.

[0499] An "analysis algorithm" is a computational method or program that analyzes documents and chat content, extracts important information, and summarizes it.

[0500] The "natural language processing module" is a program that converts chat content into structured data and extracts important information.

[0501] A "generative AI model" is an artificial intelligence algorithm that automatically generates a base draft of the approval application text based on input data.

[0502] The "base draft of the approval request text" is an initial draft of the approval request text generated based on the summarized document information and the extracted chat content.

[0503] "Finalization" is the process in which the user checks and edits the basic draft of the approval request text and finalizes it as an official application.

[0504] "Metadata" is attribute information added to various documents and chat content, and is data that makes it easier to manage and search data.

[0505] "Saving" means storing the finalized approval application form and uploaded data in a specified directory on the server.

[0506] To specifically implement this invention, the following steps must be taken: The main hardware consists of a user terminal and a server, and the software used includes an analysis algorithm, a natural language processing module, and a generative AI model.

[0507] First, the user uses the terminal to prepare the documents required for the approval request (e.g., contract, quotation, proposal materials). Then, the user can click the "Select File" button, select the appropriate file, and press the "Upload" button. Similarly, the user prepares the chat content with the person in charge and uploads it to the system. Using the "Select Chat History" button, the user selects the appropriate file and presses the "Send" button to send the chat content to the server.

[0508] Next, the server receives the uploaded documents and chat content and stores them in a specified directory (e.g., " / uploads / documents" or " / uploads / chats"). The server first reads the documents using an analysis algorithm (e.g., DocuAI) and extracts important information. This includes analyzing key items such as the contract amount and contract period using keyword extraction and natural language processing techniques. The server also analyzes the chat content using a natural language processing module (e.g., ChatAnalyzer) and extracts important information (discussion points and agreements).

[0509] The server then creates and sends a prompt to a generative AI model (e.g., GPT-4) based on the summarized document information and the extracted chat content. This generates a base draft for the approval request text. The generated base draft is created in the form of, for example, "For a new contract regarding Project X, we request approval of a budget of XX yen. The contract period is from month XX year to month XX year, and the main conditions are as follows..." The generated draft is first saved as a temporary approval request file on the server and then sent to the user's device.

[0510] The terminal receives the generated draft and displays it for the user to review and edit. For example, a text editor window is opened, allowing the user to edit the draft on the spot. Once the user makes a final confirmation and presses the "Confirm" button, the approval request form is complete.

[0511] The finalized request form is saved on the server in a specified format (for example, HTML or PDF). The save destination is set to a directory such as " / finalized / request_forms". Furthermore, the server automatically sends the saved request form to superiors and related parties as necessary. In this case, it is sent by email to the email addresses of the superiors and related parties.

[0512] Examples of prompt statements

[0513] "I'd like to upload a new contract for Project X. Please extract the contract amount, contract period, and key terms and generate a draft of the approval request text."

[0514] "Please analyze the chat history with the person in charge, extract the agreements and discussion points, and reflect them in the approval request."

[0515] In this way, the specific operations and functions of the terminal, server, and user are linked to realize a system that streamlines approval application work. This embodiment allows users to quickly and accurately create approval application forms, improving the efficiency and accuracy of work.

[0516] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0517] Step 1: The user uses the terminal to prepare and upload the documents required for the approval request.

[0518] Input: Documents such as contracts, quotes, proposals, etc. related to the request selected by the user.

[0519] How it works: The user clicks the "Choose File" button on the device, selects a file, and presses the "Upload" button.

[0520] Output: The document is sent to the server.

[0521] Step 2: The server receives and stores the uploaded document.

[0522] Input: The document uploaded by the user.

[0523] What it does: The server saves the uploaded file in the specified directory (e.g. " / uploads / documents").

[0524] Output: The document is saved on the server.

[0525] Step 3: The server uses an analysis algorithm to analyze the document, extract important information, and generate a summary.

[0526] Input: The saved document.

[0527] How it works: The server uses an analysis algorithm (e.g., DocuAI) to extract key information (e.g., contract amount, contract duration, key terms) and generate a summary.

[0528] Data processing: Analyze the text within a document and summarize key information using keyword extraction and natural language processing techniques.

[0529] Output: A summary of the generated documentation.

[0530] Step 4: The user uses the terminal to prepare the chat content with the agent and sends it to the server.

[0531] Input: The chat transcript between the user and the agent.

[0532] How it works: The user uses the device's "Select Chat History" button to select a file of the chat contents and presses the "Send" button.

[0533] Output: The chat content is sent to the server.

[0534] Step 5: The server receives and stores the chat content.

[0535] Input: The chat content sent by the user.

[0536] What it does: The server saves chat content in a specified directory (e.g. " / uploads / chats").

[0537] Output: Chat content is saved on the server.

[0538] Step 6: The server analyzes the chat content and extracts important information.

[0539] Input: Saved chat transcript.

[0540] How it works: The server uses a natural language processing module (e.g. ChatAnalyzer) to analyze the chat content and extract key discussion points and agreements.

[0541] Data processing: Chat content is analyzed using natural language processing technology to extract important structured information.

[0542] Output: A summary of the parsed chat content.

[0543] Step 7: The server generates a draft of the approval request body based on the summarized document information and the analyzed chat content.

[0544] Input: Summarized document information and parsed chat content.

[0545] How it works: The server sends a prompt to a generative AI model (e.g., GPT-4) to generate a draft of the approval request text. For example, it might generate a draft such as, "I am requesting approval for a new contract for Project X with a budget of XX yen. The contract period is from XX month of XX year to XX month of XX year, and the main conditions are as follows..."

[0546] Data calculation: Based on the document summary and chat information, the generative AI model creates a base draft for the approval request text.

[0547] Output: A draft of the generated decision body.

[0548] Step 8: The server sends the generated draft to the user terminal so that the user can check and edit it.

[0549] Input: A generated draft of the request body.

[0550] What it does: The server sends the draft to the user's terminal, where it opens in a text editor window.

[0551] Output: The draft displayed on the user's terminal.

[0552] Step 9: The user checks the draft on their device and makes edits.

[0553] Input: The draft displayed on the user's terminal.

[0554] How it works: The user edits the draft in a text editor, making any necessary corrections or adding additional information.

[0555] Output: Edited body of the approval request.

[0556] Step 10: The user presses the "Confirm" button to finalize the approval request form.

[0557] Input: Edited request body.

[0558] Operation: The user presses the "Confirm" button to finalize the application form.

[0559] Output: Finalized approval request form.

[0560] Step 11: The server saves the finalized approval application in the specified format and automatically sends it to the specified parties.

[0561] Input: Finalized approval request form.

[0562] Operation: The server saves the request form in a specified directory (e.g., " / finalized / request_forms") and automatically sends it to the email addresses of the relevant parties.

[0563] Output: Saved and submitted decision request.

[0564] This provides a system that allows for efficient and accurate approval application procedures.

[0565] (Application example 1)

[0566] 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."

[0567] Inventory management and ordering are extremely important in running a brick-and-mortar store, but they require a great deal of time and effort. Creating appropriate ordering requests based on chats between staff and product inventory information requires specialized knowledge and attention. Traditional methods are time-consuming and prone to errors. Furthermore, there is a need for an efficient method for responding quickly and accurately based on this information.

[0568] 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.

[0569] In this invention, the server includes means for receiving documents and analyzing them to generate summaries, means for receiving chat content and analyzing it to extract important information, means for automatically generating a base draft of the approval application text based on the summarized document information and the extracted chat content, means for sending the generated base draft to a user terminal so that the user can confirm and edit it, means for analyzing product inventory information and automatically generating an order request, and means for sending the generated order request to a user terminal so that the user can confirm and edit it. This makes product inventory management and ordering operations more efficient, reducing the burden on staff and reducing errors.

[0570] "Document" refers to documents and materials created to provide information, including contracts, estimates, proposals, and the like.

[0571] A "summary" is a short summary of the main information of a document, allowing a user to quickly grasp the content.

[0572] "Chat content" refers to text-based communication between users, including the exchange of electronic messages.

[0573] "Important information" refers to information that is particularly required in subsequent processes, and refers to key data necessary for decision-making and document generation.

[0574] The "application body" is a document that contains detailed information necessary to obtain approval for the application, including the application content, purpose, cost, and period.

[0575] A "base draft" is a draft generated as an early stage of the final document, and serves as a basis for users to review and edit.

[0576] "Product inventory information" is data indicating the quantity and status of products handled at a store, including remaining stock and out-of-stock status.

[0577] An "order request" is a request to place an order to replenish product inventory, and includes a specific quantity and type of product.

[0578] A "user terminal" is a device operated by a user, such as a smartphone, tablet, or PC.

[0579] "Analysis" is the scrutiny of data to extract and understand specific information, often using specific algorithms or models.

[0580] "Automatic generation" means that a system creates documents or information without human intervention, based on specific rules or algorithms.

[0581] To specifically implement the present invention, a system is constructed according to the following procedure.

[0582] First, the user terminal prepares product inventory information and chat messages between staff members and uploads them to the server. At this time, the product inventory information includes stock quantities and out-of-stock status, and the chat messages include messages about inventory management and ordering.

[0583] The server then receives and analyzes the uploaded documents and chat content using text analysis techniques such as natural language processing (NLP) algorithms and TF-IDF, using Python, NLTK, the Transformers library, and scikit-learn.

[0584] The server summarizes the documents and extracts important information from the chat, such as a list of products that are low in stock or items that need to be reordered.

[0585] The server then automatically generates a base order proposal based on the summarized document information and the extracted chat content, including specific product names, order quantities, and reasons.

[0586] The generated order request is sent to the user's terminal. The user can check the request contents on the terminal and edit them as necessary. The finalized order request is saved on the server and automatically sent to the administrator in the specified format.

[0587] (Example)

[0588] For example, suppose a "list of products that are low in stock" is uploaded as a document, and a message such as "Item A is low in stock, so we need to order more. Please also order more item C" is sent as chat content between staff members.

[0589] The server parses this information and generates the following prompt:

[0590] Prompt: Based on product availability information and chat with staff, generate the following order request:

[0591] Document: A list of products that are low in stock. Item A, Item B...

[0592] Chat content: We are running low on item A and need to order more. Please also order item C.

[0593] Based on this prompt, the server generates an order request like this:

[0594] Abridged document: A list of products that are low on stock. Item A, Item B…

[0595] Order Request:

[0596] 1. Item A is running low in stock, so we need to place an additional order.

[0597] 2. Please also add item C.

[0598] In this way, the present invention can improve the efficiency of product inventory management and ordering operations, reduce the burden on staff, and reduce the risk of missing an order.

[0599] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0600] Step 1:

[0601] Users upload product inventory information and chat messages between staff members from their devices to the server. At this time, the product inventory information includes stock quantities and out-of-stock status, while the chat messages include messages about inventory management and ordering.

[0602] Input: Product inventory information (stock quantity, out-of-stock status), chat content (text message)

[0603] Output: Product inventory information uploaded to the server and chat content

[0604] Step 2:

[0605] The server receives the uploaded product inventory information and chat content and stores them for subsequent analysis.

[0606] Input: User uploaded information

[0607] Output: Data stored in the server

[0608] Step 3:

[0609] The server uses natural language processing (NLP) algorithms to analyze product inventory information and generate summaries, using Python, NLTK, and the Transformers library.

[0610] Input: Product inventory information stored on the server

[0611] Output: Summarized product inventory information

[0612] Step 4:

[0613] The server analyzes chat content and extracts important information using text analysis techniques such as TF-IDF, specifically using scikit-learn software.

[0614] Input: Chat contents saved on the server

[0615] Output: Extracted important information

[0616] Step 5:

[0617] The server automatically generates a base order proposal based on the summarized product inventory information and the extracted chat content, including the specific product name, order quantity, and reason.

[0618] Input: Summarized product inventory information, extracted key information

[0619] Output: A basic draft of the purchase order request

[0620] Step 6:

[0621] The server sends the generated base draft of the order request to the user terminal, where the user can check the request contents and edit them as necessary.

[0622] Input: Base proposal for purchase order request

[0623] Output: Request sent to the user's device

[0624] Step 7:

[0625] After the user finalizes, confirms, and edits the order request, the server saves the finalized order request, which is then automatically sent to the administrator or other relevant parties in the specified format.

[0626] Input: User confirmed and edited order request

[0627] Output: Finalized order request saved and automatically sent to administrator

[0628] 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.

[0629] To specifically implement this invention, a user begins by preparing the documents and chat content required for the approval request using a terminal. This data is then uploaded to a server via the terminal, where it is analyzed to extract summaries and important information. Furthermore, an emotion engine is used to recognize the user's emotional state and provide feedback based on that, resulting in a more efficient approval request process.

[0630] Program processing

[0631] 1. Upload and summarize documents

[0632] A user uses a terminal to upload a document (such as a contract, quotation, or proposal) to the server. The user logs in to the system, clicks the "Upload Document" button, selects the required file, and presses the "Upload" button.

[0633] The device sends the uploaded document to the server, which receives and stores it, and the server analyzes the stored document using an AI algorithm to generate a summary.

[0634] 2. Chat content aggregation and analysis

[0635] The user copies and pastes the chat contents with the agent from their device and clicks the "Upload Chat Content" button to send it to the server.

[0636] The server receives and stores the chat content, which is then analyzed using a natural language processing (NLP) module to extract important information needed for approval applications.

[0637] 3. Automatic generation of approval request text

[0638] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content. The generated base draft includes summary information, cost details, and key contract terms.

[0639] The base plan is sent in JSON format to the terminal, where it is displayed in a text editor for the user to review and edit.

[0640] 4. Use of Emotion Engine

[0641] The emotion engine integrated into the server recognizes the user's emotional state from facial expressions, tone of voice, typing speed, etc., and analyzes the emotional data, thereby monitoring the user's stress level and satisfaction in real time.

[0642] The emotion engine provides feedback based on the user's emotional state and makes suggestions or corrections to the base draft of the approval request text as needed.

[0643] 5. Final review and editing

[0644] The user checks the base draft of the approval request text generated on the terminal, and enters corrections or additional information as necessary. The user then clicks the "Finalize" button to finalize the approval request.

[0645] The server receives the finalized approval request and generates a document in PDF format or other specified format. The generated document is saved on the server and automatically sent to superiors or related parties as needed.

[0646] Specific examples

[0647] 1. Upload and summarize documents

[0648] A user uploads a new contract related to Project X from their device to the server. The server receives the contract and generates a summary including the contract amount, contract period, and key terms. The generated summary can be viewed on the device, allowing the user to understand the contents.

[0649] 2. Chat content aggregation and analysis

[0650] The user sends the chat contents with the person in charge from their device to the server. The server analyzes the chat contents, extracts the discussion points and agreements necessary for the approval application, and the organized information can be viewed on the device.

[0651] 3. Automatic generation of approval request text

[0652] Based on the summary information and the extracted chat content, the server generates a draft of the approval request text in the form of, "We are requesting approval for a new contract for Project X with a budget of XX yen. The contract period is from month XX / XX to month XX / XX, and the main conditions are as follows..." which can then be viewed and edited on the terminal.

[0653] 4. Use of Emotion Engine

[0654] While the user is reviewing the draft, the emotion engine monitors the user's emotional state in real time. For example, if the user is feeling stressed, the emotion engine will detect this and suggest changes to the draft, allowing the user to complete the approval request in a way that satisfies them.

[0655] 5. Final review and editing

[0656] After the user checks the draft and makes any necessary corrections, they press the "Finalize" button to finalize the approval request. The server receives this, generates an application in the specified format, and automatically sends it.

[0657] This process reduces the workload of the assistant and enables efficient and accurate approval applications. The use of an emotion engine also reduces the psychological burden on the user.

[0658] The processing flow will be explained below.

[0659] Step 1:

[0660] The user uses a terminal to upload documents related to the approval request, such as contracts, quotations, and proposal materials. The user logs in to the system and clicks the "Upload Documents" button. A file selection dialog box appears, and the user selects the necessary documents and presses the "Upload" button.

[0661] Step 2:

[0662] The device sends the uploaded document to the server, where it is saved in the specified directory.

[0663] Step 3:

[0664] The server receives the stored document and begins analyzing it using AI algorithms. The document content is analyzed using a natural language processing (NLP) model, and important information is extracted and a summary is generated. Specifically, key information such as the contract details, contract amount, and contract period is summarized.

[0665] Step 4:

[0666] Once the summary is generated, the server sends it in JSON format to the terminal, where it is displayed for the user to review. The user can then review the summary and check for any issues.

[0667] Step 5:

[0668] A user can copy and paste the chat transcript with the person in charge using a terminal and then click the "Upload Chat Transcript" button to send it to the server, for example, to send text containing important discussions and decisions about a project.

[0669] Step 6:

[0670] The server receives the chat content and begins analyzing it with the NLP module. It extracts important information necessary for approval requests from the chat content and classifies it into categories (e.g., agreed matters, unresolved matters, cost-related matters).

[0671] Step 7:

[0672] Once the extracted important information has been organized, the server sends it to the terminal in JSON format. The terminal displays the organized information so that the user can review it. The user can then review the information and check to see if any elements necessary for the approval request have been omitted.

[0673] Step 8:

[0674] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content. The generated base draft includes summary information, cost details, and key contract terms.

[0675] Step 9:

[0676] Once the generated base plan is complete, the server sends it in JSON format to the device, which displays it in a text editor for the user to review and edit.

[0677] Step 10:

[0678] The emotion engine integrated into the server recognizes the user's emotional state through the camera and microphone on the user's device. The emotion engine analyzes facial expressions, tone of voice, typing speed, etc. to assess the user's stress level and satisfaction in real time. For example, if the user is feeling stressed, the engine will detect this and make suggestions to help the user relax.

[0679] Step 11:

[0680] The emotion engine provides feedback based on the user's emotional state and makes suggestions or corrections to the base draft of the approval request text as needed. For example, if the emotion engine determines that the user is dissatisfied, it will suggest, "Please add more details about this part."

[0681] Step 12:

[0682] The user checks the base draft of the approval request text generated on the device, and makes corrections or adds additional information as necessary, taking into consideration feedback from the emotion engine. The user then clicks the "Finalize" button to finalize the approval request.

[0683] Step 13:

[0684] The server receives the finalized approval request and generates the application in PDF format or other specified format. The generated application is saved on the server and automatically sent to superiors or related parties as needed.

[0685] This series of steps allows the assistant to efficiently complete the approval process. The use of the emotion engine also reduces the psychological burden on the user.

[0686] Example 2

[0687] 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."

[0688] In today's business environment, the approval process is often complex and time-consuming. Collecting, summarizing, and analyzing the documents and chat content required for approval, as well as creating the application text, requires a significant amount of time and effort. This process also places a significant psychological burden on users. Conventional systems were unable to adequately resolve these issues, making it difficult to process approval requests efficiently and accurately.

[0689] 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. In this invention, the server includes means for receiving a document, analyzing it, and generating a summary, means for receiving chat content, analyzing it, and extracting important information, means for automatically generating a base draft of the approval request body based on the summarized document information and the extracted chat content, means for sending the generated base draft to a user terminal so that the user can confirm and edit it, and means for recognizing the user's emotional state and providing feedback based on it. This enables efficient and accurate approval requests and reduces the psychological burden on the user.

[0690] "Document" is a general term for documents stored in electronic format, including contracts, estimates, proposal materials, etc.

[0691] A "summary" is a short summary of the main information or key points of a document, based on an analysis of its contents.

[0692] "Chat content" refers to the record of messages and communications between the user and the person in charge.

[0693] "Important information" refers to important data that affects the content of the application, such as discussion points and agreements required for approval applications.

[0694] The "application text" is a formal document that explains the application content and refers to text created based on summary information and important information.

[0695] The "base proposal" refers to the initial draft of the approval request text automatically generated by the AI ​​model.

[0696] "User terminal" refers to an electronic device used by a user, such as a computer or smartphone.

[0697] "Emotional state" refers to the user's psychological and emotional state, and is measured from facial expressions, tone of voice, typing speed, etc.

[0698] "Feedback" refers to advice, suggestions, and improvements provided based on the user's emotional state.

[0699] To specifically implement this invention, a user begins by preparing the documents and chat content required for the approval application using a terminal. These data are then uploaded to a server via the terminal. The server analyzes the data to extract summaries and important information, and further recognizes the user's emotional state using an emotion engine and provides feedback based on that information.

[0700] Hardware and software used

[0701] 1. Terminal

[0702] The computer or smartphone you use (e.g., Windows PC, Mac, iOS device, Android device).

[0703] User interface using a web browser (e.g., Google Chrome, Mozilla Firefox, Safari).

[0704] 2. Server

[0705] Server computers for data storage and processing (e.g., Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure).

[0706] Python-based AI algorithms for data analysis (e.g., BERT, spaCy, NLTK).

[0707] A generative AI model written in JavaScript used to automatically generate the text of approval requests.

[0708] 3. Emotion Engine

[0709] An engine for real-time recognition of the user's emotional state (e.g., OpenFace, Microsoft Azure Cognitive Services).

[0710] A library for facial expression recognition, voice tone analysis, and keystroke speed analysis.

[0711] Specific examples

[0712] 1. Upload and summarize documents

[0713] The user uploads a new contract related to the project to the server from their device. The user logs in to the system on a web browser, clicks the "Document Upload" button, selects the required file, and presses the "Upload" button. The server stores the received contract and analyzes it using a Python-based AI algorithm to generate a summary including the contract amount, contract period, and key terms. The generated summary is stored in a database and can be viewed on the device.

[0714] 2. Chat content aggregation and analysis

[0715] The user copies the chat transcript with the person in charge, pastes it into the "Chat transcript upload" field in the system, and clicks the "Upload" button. The server receives this chat transcript and stores it in a database. A natural language processing (NLP) module is used to analyze the chat transcript and extract the discussion points and agreements necessary for the approval request. This extracted information can be viewed on the device.

[0716] 3. Automatic generation of approval request text

[0717] The server uses a generative AI model to automatically generate a base draft of the approval request text based on the saved summary information and the extracted chat content. The generated base draft includes summary information, cost details, and key contract terms. It is sent in JSON format to the device and displayed in the device's text editor.

[0718] 4. Use of Emotion Engine

[0719] While the user is reviewing the base proposal, the emotion engine monitors the user's emotional state in real time. The emotion engine analyzes emotional data from facial expression recognition, voice tone analysis, and keystroke speed analysis. If the user is feeling stressed, a suggestion such as "Try changing the expression in this part" will pop up on the screen as emotional feedback. This allows the user to complete the approval request text in a way that satisfies them.

[0720] 5. Final review and editing

[0721] The user checks the generated base draft of the approval request text and makes any necessary corrections in a text editor. Once the review is complete, the user clicks the "Finalize" button. The server receives the finalized approval request and generates an application form in the specified format using LaTeX or PDFkit. The generated application form is saved in a database and automatically sent to superiors or related parties as needed.

[0722] Prompt Sentence Examples

[0723] Here is an example of how a user might enter a prompt in a web browser:

[0724] "I'm uploading a new contract for Project X. Please generate a summary including the contract amount, contract duration, and key terms."

[0725] Such prompts allow the server to perform appropriate processing, resulting in efficient and accurate approval requests.

[0726] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0727] Step 1:

[0728] A user logs in to the system on a terminal by opening a web browser, accessing the system's login screen, entering their user ID and password, and clicking the "Login" button. This displays the system's main menu.

[0729] Step 2:

[0730] The user clicks the "Upload Document" button, selects the required file, and presses the "Upload" button. Specifically, the user selects a file from a file selection dialog using the HTML input tag, and then clicks the button. This operation sends the document data from the terminal to the server. The input is the document file selected by the user, and the output is the document data saved on the server.

[0731] Step 3:

[0732] The server receives and stores the uploaded document. Specifically, it stores the received file in a cloud storage service such as AWS S3. The input is the document data sent from the device, and the output is the URL of the destination in the cloud storage.

[0733] Step 4:

[0734] The server analyzes the stored documents using a Python-based AI algorithm and generates summaries. Specifically, it performs text analysis using the BERT model. The input is the document data retrieved from the destination URL, and the output is the summary text.

[0735] Step 5:

[0736] The user copies the chat contents with the agent on their device, pastes them into the "Upload Chat Content" field, and clicks the "Upload" button. Specifically, the chat contents are pasted using the HTML textarea tag. This sends the chat data from the device to the server. The input is the chat text pasted by the user, and the output is the chat data saved on the server.

[0737] Step 6:

[0738] The server receives and stores the chat data sent from the device. Specifically, it stores the chat data in a database. The input is the chat data sent from the device, and the output is the saved record in the database.

[0739] Step 7:

[0740] The server analyzes the saved chat content using an NLP module to extract important information. Specifically, it uses spaCy and NLTK to perform text analysis. The input is the chat content in the database, and the output is the extracted important information text.

[0741] Step 8:

[0742] The server uses a generative AI model based on the summarized document information and extracted chat content to automatically generate a base draft of the approval request text. Specifically, it runs a generative AI model written in JavaScript to generate the text based on the input data. The input is the document summary and important chat information, and the output is the base draft text of the approval request text.

[0743] Step 9:

[0744] The server sends the generated base proposal in JSON format to the terminal. Specifically, it returns JSON data in the HTTP response. The input is the base proposal text for the approval request body, and the output is the JSON data sent to the terminal.

[0745] Step 10:

[0746] The terminal displays the received base plan in a text editor. Specifically, it displays it in an editable format using an editor such as Quill. The input is JSON data received from the server, and the output is a text editor display that the user can view and edit.

[0747] Step 11:

[0748] The emotion engine monitors the user's emotional state in real time. Specifically, it collects and analyzes data using the device's webcam, microphone, and keyboard typing speed. The input is facial expression, tone of voice, and typing speed data, and the output is analyzed emotional state data.

[0749] Step 12:

[0750] The server provides appropriate feedback based on the emotional state data. Specifically, it displays a pop-up message on the screen with advice such as "It would be good to change the expression in this part." The input is the analyzed emotional state data, and the output is a feedback message that is displayed to the user.

[0751] Step 13:

[0752] The user checks the generated base draft of the approval request text and makes any necessary corrections in a text editor. Specifically, the user edits the text using the keyboard and clicks the "Finalize" button. The input is the base draft text and the user's editing operations, and the output is the finalized approval request text.

[0753] Step 14:

[0754] The server receives the finalized approval request and generates the application in the specified format using LaTeX or PDFkit. Specifically, it converts the text data into LaTeX format and outputs it as a PDF. The input is the text of the finalized approval request, and the output is a PDF application.

[0755] Step 15:

[0756] The server saves the generated application form in a database and automatically sends it to superiors and other relevant parties as needed. Specifically, it saves the form and sends it via email using the SMTP protocol. The input is the PDF application form, and the output is the URL saved in the database and the email that was sent.

[0757] (Application example 2)

[0758] 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."

[0759] In the process of managing the operation and maintenance of autonomous vehicles, processing complex documents and analyzing chat messages is time-consuming. Furthermore, the lack of a system that can monitor the driver's emotional state in real time and provide appropriate feedback increases the psychological burden on the driver. This reduces operational efficiency and frequently leads to delays in responding to vehicle problems.

[0760] 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.

[0761] In this invention, the server includes means for receiving documents and analyzing them to generate summaries, means for receiving chat content and analyzing it to extract important information, means for automatically generating a base draft of the approval application text based on the summarized document information and the extracted chat content, means for sending the generated base draft to a user terminal so that the user can confirm and edit it, means for recognizing the user's emotional state in real time and providing feedback based on the analysis results, and operation management and analysis means that allow drivers to upload documents and malfunction reports. This makes it possible to streamline operation management and the maintenance application process for autonomous vehicles and reduce the psychological burden on drivers.

[0762] "Means for receiving documents, analyzing them, and generating summaries" refers to a function that allows the server to receive various documents uploaded by users, analyzes the documents using natural language processing technology, and automatically generates summaries.

[0763] "Means for receiving chat content, analyzing it, and extracting important information" refers to a function that allows the server to receive chat content provided by the user, analyzes the conversation using natural language processing technology, and automatically extracts important information necessary for approval applications.

[0764] "Means for automatically generating a base draft of the approval application text based on summarized document information and extracted chat content" refers to a function that automatically creates an initial draft of the approval application text based on the document summary generated by analysis and the extracted chat content.

[0765] "Means for sending the generated base draft to the user's terminal and allowing the user to check and edit it" refers to a function that sends an automatically generated base draft of the approval request body to the user's terminal, allowing the user to check it and edit it as necessary.

[0766] "Means of recognizing the user's emotional state in real time and providing feedback based on the analysis results" refers to a function that uses an emotion engine to detect the user's emotional state in real time from their facial expressions, tone of voice, etc., and provides appropriate advice and feedback based on the analysis results.

[0767] The "operation management means and analysis means that allow drivers to upload documents and malfunction reports" refer to multiple functions within the system that allow drivers to upload various documents related to operation management and vehicle malfunction reports to a server and analyze them.

[0768] "Means for saving the confirmed application after the body of the request for approval has been finalized and generating it in a specified format" refers to a function that, after the user finalizes the body of the request for approval, saves the confirmed application form on the server and generates the application form in a specified format (e.g., PDF format).

[0769] "Means for generating metadata for various documents uploaded by users and associating it with the corresponding approval application data" refers to a function that generates metadata from documents uploaded by users and associates that metadata with the corresponding approval application data.

[0770] To implement the present invention, it is necessary to build a system according to the following steps.

[0771] First, we set up the hardware and software used by the system. The main hardware used includes smart glasses, head-mounted displays, smartphones, and servers. The software used includes spaCy as a natural language processing (NLP) module, OpenAI GPT-4 API as a sentiment analysis engine, PostgreSQL as a database, and Flask (Python) as a server framework.

[0772] Document upload and summary generation

[0773] Users use their smart devices to upload documents (e.g., operation plans, contracts) to the server, which then analyzes the received documents using natural language processing technology (spaCy) and generates summaries of important information (route details, time, driver list, etc.).

[0774] Chat content aggregation and analysis

[0775] Users send chat content from their smart devices to the server, which then analyzes the received chat content using a natural language processing module and extracts important information necessary for approval applications. This allows for efficient organization of important discussion points and agreements exchanged between drivers and operation managers.

[0776] Automatic generation of approval request text

[0777] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content. The generated base draft includes the route, budget, contract terms, etc. The base draft is sent to the user's device in JSON format so that the user can view and edit it.

[0778] Analysis and feedback by emotion engine

[0779] The emotion engine integrated into the server analyzes data such as facial expressions, tone of voice, and typing speed to recognize the user's emotional state in real time. Based on the emotion data, the emotion engine evaluates stress levels and satisfaction and provides feedback as needed. For example, if the user is feeling stressed, the emotion engine will display suggestions and corrections.

[0780] Driver operation management and breakdown reporting

[0781] Drivers use smart glasses or head-mounted displays to collect data while driving and upload it to a server. When a malfunction report is sent, the server analyzes it using an NLP module and automatically generates a list of necessary repairs and parts, enabling a prompt response.

[0782] Specific examples

[0783] For example, when a dispatcher notifies the system, "Uploading a trip plan," the system responds, "Generating a trip plan summary... Completed." When a driver inputs, "Submitting a malfunction report about an abnormal engine noise," the system responds, "Analyzing the malfunction report... Generated a list of required parts." The emotion engine also provides real-time feedback, such as, "Your fatigue level has reached 85%, we recommend you take a break."

[0784] Prompt Sentence Examples

[0785] Examples of prompts include:

[0786] "Analyze the flight schedule and summarize the key flight information."

[0787] "Analyze driver reports of malfunctions and create a list of necessary repair parts."

[0788] "Analyze the driver's emotional state in real time and provide appropriate feedback."

[0789] This will make it possible to streamline the operation management and maintenance application process for autonomous vehicles and reduce the psychological burden on drivers.

[0790] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0791] Program processing flow

[0792] Step 1: Uploading documents and generating summaries

[0793] Input: The user uploads documents such as operation plans and contracts from the terminal to the server.

[0794] Processing: The server receives and stores the uploaded document, then uses natural language processing technology (spaCy) to analyze the document, extract key information (e.g., route, time, driver list), and generate a summary.

[0795] Output: Generate and save summarized document information.

[0796] Step 2: Chat content aggregation and analysis

[0797] Input: The user copies the chat contents with the agent from the terminal and sends them to the server.

[0798] Processing: The server stores the received chat content and analyzes it using a natural language processing module. It then extracts important information (e.g., discussion points and agreements) necessary for the approval process.

[0799] Output: Generate and store the extracted important information.

[0800] Step 3: Automatic generation of the approval request text

[0801] Input: Summarized document information and extracted chat transcripts.

[0802] Processing: The server integrates this information and automatically generates a base draft for the approval request, which includes route, budget, contract terms, etc.

[0803] Output: Send the automatically generated base draft of the approval request body to the terminal in JSON format.

[0804] Step 4: Analysis and feedback by the emotion engine

[0805] Input: Data such as the user's facial expressions, tone of voice, and typing speed.

[0806] Processing: The emotion engine (OpenAI GPT-4 API) integrated into the server analyzes these data in real time, evaluates the user's emotional state, and generates appropriate feedback based on the user's stress level and satisfaction.

[0807] Output: Provides feedback to the user based on the analysis results.

[0808] Step 5: Driver management and breakdown reporting

[0809] Input: Driving data and breakdown reports collected by drivers using smart devices.

[0810] Processing: The server receives this data and analyzes it using the NLP module, after which it automatically generates the necessary repairs and parts list.

[0811] Output: The repair items and parts list generated as a result of the analysis are provided to the operation manager.

[0812] Step 6: Finalize and generate application

[0813] Input: A base draft of the approval request body that the user has reviewed and edited.

[0814] Processing: When the user makes a final confirmation and presses the "Finalize" button, the server receives the data and generates the application form in the specified format, such as PDF.

[0815] Output: Save the generated application form and automatically send it to the relevant parties as needed.

[0816] Through these steps, the system can streamline the operation management and maintenance application process for autonomous vehicles and reduce the psychological burden on drivers.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] [Third embodiment]

[0821] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0822] 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.

[0823] 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).

[0824] 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.

[0825] 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.

[0826] 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).

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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."

[0833] To specifically implement this invention, a user first prepares a series of documents and chat content required for the approval request using a terminal. This data is then uploaded to a server via the terminal, which analyzes it to extract summaries and important information. Finally, a base draft of the approval request text is generated, which the user can review and edit.

[0834] Program processing

[0835] 1. Upload and summarize documents

[0836] A user uses a terminal to upload a document (such as a contract, estimate, or proposal) to the server.

[0837] The server receives and stores the uploaded documents, which are then analyzed by AI algorithms to extract key information and generate summaries.

[0838] 2. Chat content aggregation and analysis

[0839] The user sends the chat contents with the person in charge from the terminal to the server.

[0840] The server receives and stores the chat content, which is then analyzed by a natural language processing (NLP) module to extract important information needed for approval applications.

[0841] 3. Automatic generation of approval request text

[0842] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content, including the necessary summary information, cost details, and key contract terms.

[0843] The generated base proposal is sent to the terminal for the user to review and edit.

[0844] 4. Final review and editing

[0845] The user checks the base draft of the approval request text generated on the terminal and makes corrections or enters additional information as necessary.

[0846] Once the user finalizes the base proposal and completes the approval application, the server saves the finalized application in a specified format and automatically sends it to superiors or other relevant parties as necessary.

[0847] Specific examples

[0848] 1. Upload and summarize documents

[0849] The user uploads a new contract related to Project X to the server from their device.

[0850] The server receives the contract and generates a summary including the specific contract amount, contract period, and key terms.

[0851] The generated summary can be viewed on the terminal, allowing the user to understand the content.

[0852] 2. Chat content aggregation and analysis

[0853] The user sends the chat contents with the person in charge from the terminal to the server.

[0854] The server analyzes the chat content and extracts the discussion points and agreements necessary for the approval request.

[0855] The organized information can be viewed on the device, allowing users to check the information they need.

[0856] 3. Automatic generation of approval request text

[0857] Based on the summary information and the extracted chat content, the server generates a draft of the approval request text in the form of, "We are requesting approval for a new contract for Project X with a budget of XX yen. The contract period is from month XX / XX to month XX / XX, and the main conditions are as follows..."

[0858] The generated draft can be viewed and edited on your device.

[0859] 4. Final review and editing

[0860] The user checks the generated draft on the terminal and makes corrections as necessary.

[0861] The completed approval application form is finalized, and the server saves it and automatically sends it to the superior.

[0862] In this way, a system can be created that streamlines the approval application process for assistants and significantly reduces the amount of work required.

[0863] The processing flow will be explained below.

[0864] Step 1:

[0865] The user uses a terminal to upload documents such as contracts, quotations, and proposal materials required for approval applications. The user logs in to the system and clicks the "Upload Documents" button. A file selection dialog box appears, and the user selects the necessary documents and presses the "Upload" button.

[0866] Step 2:

[0867] The device sends the uploaded document to the server, where it is saved in the specified directory.

[0868] Step 3:

[0869] The server receives the stored document and begins analyzing it using AI algorithms, analyzing the document content with natural language processing (NLP) models, extracting key information, and generating a summary.

[0870] Step 4:

[0871] Once the summary is generated, the server sends it in JSON format to the terminal, which displays the summary result for the user to review.

[0872] Step 5:

[0873] The user copies and pastes the chat transcript with the agent using the terminal, and then clicks the "Upload Chat Transcript" button to send it to the server.

[0874] Step 6:

[0875] The server receives the chat content and begins analyzing it with the NLP module. Important information necessary for approval applications is extracted and organized from the chat content.

[0876] Step 7:

[0877] Once the extracted important information has been organized, the server sends it in JSON format to the device, where it is displayed for the user to review.

[0878] Step 8:

[0879] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content. The generated base draft includes summary information, cost details, and key contract terms.

[0880] Step 9:

[0881] Once the generated base plan is complete, the server sends it in JSON format to the device, which displays it in a text editor for the user to review and edit.

[0882] Step 10:

[0883] The user checks the base draft of the approval request text generated on the terminal, and enters corrections or additional information as necessary. The user then clicks the "Finalize" button to finalize the approval request.

[0884] Step 11:

[0885] The server receives the finalized approval request and generates the application in PDF format or other specified format. The generated application is saved on the server and automatically sent to superiors or related parties as needed.

[0886] This series of steps allows the assistant to efficiently complete the approval application process.

[0887] Example 1

[0888] 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."

[0889] Conventional approval application systems required the manual organization of multiple documents and chat messages, and the process of compiling summary information was extremely cumbersome, time-consuming, and labor-intensive. Furthermore, important information was often overlooked or typographical errors occurred, making it difficult to streamline operations. This could result in delays and inaccuracies in the approval application process.

[0890] 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.

[0891] In this invention, the server includes: means for a user to use a terminal to select various data and upload it to the server; means for the server to receive and store the uploaded documents; means for analyzing the documents using an analysis algorithm on the server, extracting important information, and generating summaries; means for the server to receive and store uploaded chat content; means for analyzing the chat content using a natural language processing module and extracting important information; means for automatically generating a base draft of the approval request body using a generative AI model based on the summarized document information and the extracted chat content; and means for sending the generated base draft to the user terminal so that the user can confirm and edit it. This makes it possible to consistently automate the management of approval request-related data, the generation of summaries, and the automatic generation of approval request bodies, thereby improving business efficiency and accuracy.

[0892] A "user" is an entity that operates the system and uploads, checks, and edits data via a terminal.

[0893] "Terminal" refers to a computing device used by a user, and is a device used to upload documents, send chat content, and review and edit generated approval applications.

[0894] A "server" is a computing system that receives data sent from a user terminal, analyzes it, and performs the necessary processing.

[0895] "Document" refers to a document that contains text and information necessary for an approval request, such as a contract, estimate, or proposal document.

[0896] "Chat content" refers to the history of communication between the user and other personnel via email or messaging services.

[0897] An "analysis algorithm" is a computational method or program that analyzes documents and chat content, extracts important information, and summarizes it.

[0898] The "natural language processing module" is a program that converts chat content into structured data and extracts important information.

[0899] A "generative AI model" is an artificial intelligence algorithm that automatically generates a base draft of the approval application text based on input data.

[0900] The "base draft of the approval request text" is an initial draft of the approval request text generated based on the summarized document information and the extracted chat content.

[0901] "Finalization" is the process in which the user checks and edits the basic draft of the approval request text and finalizes it as an official application.

[0902] "Metadata" is attribute information added to various documents and chat content, and is data that makes it easier to manage and search data.

[0903] "Saving" means storing the finalized approval application form and uploaded data in a specified directory on the server.

[0904] To specifically implement this invention, the following steps must be taken: The main hardware consists of a user terminal and a server, and the software used includes an analysis algorithm, a natural language processing module, and a generative AI model.

[0905] First, the user uses the terminal to prepare the documents required for the approval request (e.g., contract, quotation, proposal materials). Then, the user can click the "Select File" button, select the appropriate file, and press the "Upload" button. Similarly, the user prepares the chat content with the person in charge and uploads it to the system. Using the "Select Chat History" button, the user selects the appropriate file and presses the "Send" button to send the chat content to the server.

[0906] Next, the server receives the uploaded documents and chat content and stores them in a specified directory (e.g., " / uploads / documents" or " / uploads / chats"). The server first reads the documents using an analysis algorithm (e.g., DocuAI) and extracts important information. This includes analyzing key items such as the contract amount and contract period using keyword extraction and natural language processing techniques. The server also analyzes the chat content using a natural language processing module (e.g., ChatAnalyzer) and extracts important information (discussion points and agreements).

[0907] The server then creates and sends a prompt to a generative AI model (e.g., GPT-4) based on the summarized document information and the extracted chat content. This generates a base draft for the approval request text. The generated base draft is created in the form of, for example, "For a new contract regarding Project X, we request approval of a budget of XX yen. The contract period is from month XX year to month XX year, and the main conditions are as follows..." The generated draft is first saved as a temporary approval request file on the server and then sent to the user's device.

[0908] The terminal receives the generated draft and displays it for the user to review and edit. For example, a text editor window is opened, allowing the user to edit the draft on the spot. Once the user makes a final confirmation and presses the "Confirm" button, the approval request form is complete.

[0909] The finalized request form is saved on the server in a specified format (for example, HTML or PDF). The save destination is set to a directory such as " / finalized / request_forms". Furthermore, the server automatically sends the saved request form to superiors and related parties as necessary. In this case, it is sent by email to the email addresses of the superiors and related parties.

[0910] Examples of prompt statements

[0911] "I'd like to upload a new contract for Project X. Please extract the contract amount, contract period, and key terms and generate a draft of the approval request text."

[0912] "Please analyze the chat history with the person in charge, extract the agreements and discussion points, and reflect them in the approval request."

[0913] In this way, the specific operations and functions of the terminal, server, and user are linked to realize a system that streamlines approval application work. This embodiment allows users to quickly and accurately create approval application forms, improving the efficiency and accuracy of work.

[0914] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0915] Step 1: The user uses the terminal to prepare and upload the documents required for the approval request.

[0916] Input: Documents such as contracts, quotes, proposals, etc. related to the request selected by the user.

[0917] How it works: The user clicks the "Choose File" button on the device, selects a file, and presses the "Upload" button.

[0918] Output: The document is sent to the server.

[0919] Step 2: The server receives and stores the uploaded document.

[0920] Input: The document uploaded by the user.

[0921] What it does: The server saves the uploaded file in the specified directory (e.g. " / uploads / documents").

[0922] Output: The document is saved on the server.

[0923] Step 3: The server uses an analysis algorithm to analyze the document, extract important information, and generate a summary.

[0924] Input: The saved document.

[0925] How it works: The server uses an analysis algorithm (e.g., DocuAI) to extract key information (e.g., contract amount, contract duration, key terms) and generate a summary.

[0926] Data processing: Analyze the text within a document and summarize key information using keyword extraction and natural language processing techniques.

[0927] Output: A summary of the generated documentation.

[0928] Step 4: The user uses the terminal to prepare the chat content with the agent and sends it to the server.

[0929] Input: The chat transcript between the user and the agent.

[0930] How it works: The user uses the device's "Select Chat History" button to select a file of the chat contents and presses the "Send" button.

[0931] Output: The chat content is sent to the server.

[0932] Step 5: The server receives and stores the chat content.

[0933] Input: The chat content sent by the user.

[0934] What it does: The server saves chat content in a specified directory (e.g. " / uploads / chats").

[0935] Output: Chat content is saved on the server.

[0936] Step 6: The server analyzes the chat content and extracts important information.

[0937] Input: Saved chat transcript.

[0938] How it works: The server uses a natural language processing module (e.g. ChatAnalyzer) to analyze the chat content and extract key discussion points and agreements.

[0939] Data processing: Chat content is analyzed using natural language processing technology to extract important structured information.

[0940] Output: A summary of the parsed chat content.

[0941] Step 7: The server generates a draft of the approval request body based on the summarized document information and the analyzed chat content.

[0942] Input: Summarized document information and parsed chat content.

[0943] How it works: The server sends a prompt to a generative AI model (e.g., GPT-4) to generate a draft of the approval request text. For example, it might generate a draft such as, "I am requesting approval for a new contract for Project X with a budget of XX yen. The contract period is from XX month of XX year to XX month of XX year, and the main conditions are as follows..."

[0944] Data calculation: Based on the document summary and chat information, the generative AI model creates a base draft for the approval request text.

[0945] Output: A draft of the generated decision body.

[0946] Step 8: The server sends the generated draft to the user terminal so that the user can check and edit it.

[0947] Input: A generated draft of the request body.

[0948] What it does: The server sends the draft to the user's terminal, where it opens in a text editor window.

[0949] Output: The draft displayed on the user's terminal.

[0950] Step 9: The user checks the draft on their device and makes edits.

[0951] Input: The draft displayed on the user's terminal.

[0952] How it works: The user edits the draft in a text editor, making any necessary corrections or adding additional information.

[0953] Output: Edited body of the approval request.

[0954] Step 10: The user presses the "Confirm" button to finalize the approval request form.

[0955] Input: Edited request body.

[0956] Operation: The user presses the "Confirm" button to finalize the application form.

[0957] Output: Finalized approval request form.

[0958] Step 11: The server saves the finalized approval application in the specified format and automatically sends it to the specified parties.

[0959] Input: Finalized approval request form.

[0960] Operation: The server saves the request form in a specified directory (e.g., " / finalized / request_forms") and automatically sends it to the email addresses of the relevant parties.

[0961] Output: Saved and submitted decision request.

[0962] This provides a system that allows for efficient and accurate approval application procedures.

[0963] (Application example 1)

[0964] 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."

[0965] Inventory management and ordering are extremely important in running a brick-and-mortar store, but they require a great deal of time and effort. Creating appropriate ordering requests based on chats between staff and product inventory information requires specialized knowledge and attention. Traditional methods are time-consuming and prone to errors. Furthermore, there is a need for an efficient method for responding quickly and accurately based on this information.

[0966] 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.

[0967] In this invention, the server includes means for receiving documents and analyzing them to generate summaries, means for receiving chat content and analyzing it to extract important information, means for automatically generating a base draft of the approval application text based on the summarized document information and the extracted chat content, means for sending the generated base draft to a user terminal so that the user can confirm and edit it, means for analyzing product inventory information and automatically generating an order request, and means for sending the generated order request to a user terminal so that the user can confirm and edit it. This makes product inventory management and ordering operations more efficient, reducing the burden on staff and reducing errors.

[0968] "Document" refers to documents and materials created to provide information, including contracts, estimates, proposals, and the like.

[0969] A "summary" is a short summary of the main information of a document, allowing a user to quickly grasp the content.

[0970] "Chat content" refers to text-based communication between users, including the exchange of electronic messages.

[0971] "Important information" refers to information that is particularly required in subsequent processes, and refers to key data necessary for decision-making and document generation.

[0972] The "application body" is a document that contains detailed information necessary to obtain approval for the application, including the application content, purpose, cost, and period.

[0973] A "base draft" is a draft generated as an early stage of the final document, and serves as a basis for users to review and edit.

[0974] "Product inventory information" is data indicating the quantity and status of products handled at a store, including remaining stock and out-of-stock status.

[0975] An "order request" is a request to place an order to replenish product inventory, and includes a specific quantity and type of product.

[0976] A "user terminal" is a device operated by a user, such as a smartphone, tablet, or PC.

[0977] "Analysis" is the scrutiny of data to extract and understand specific information, often using specific algorithms or models.

[0978] "Automatic generation" means that a system creates documents or information without human intervention, based on specific rules or algorithms.

[0979] To specifically implement the present invention, a system is constructed according to the following procedure.

[0980] First, the user terminal prepares product inventory information and chat messages between staff members and uploads them to the server. At this time, the product inventory information includes stock quantities and out-of-stock status, and the chat messages include messages about inventory management and ordering.

[0981] The server then receives and analyzes the uploaded documents and chat content using text analysis techniques such as natural language processing (NLP) algorithms and TF-IDF, using Python, NLTK, the Transformers library, and scikit-learn.

[0982] The server summarizes the documents and extracts important information from the chat, such as a list of products that are low in stock or items that need to be reordered.

[0983] The server then automatically generates a base order proposal based on the summarized document information and the extracted chat content, including specific product names, order quantities, and reasons.

[0984] The generated order request is sent to the user's terminal. The user can check the request contents on the terminal and edit them as necessary. The finalized order request is saved on the server and automatically sent to the administrator in the specified format.

[0985] (Example)

[0986] For example, suppose a "list of products that are low in stock" is uploaded as a document, and a message such as "Item A is low in stock, so we need to order more. Please also order more item C" is sent as chat content between staff members.

[0987] The server parses this information and generates the following prompt:

[0988] Prompt: Based on product availability information and chat with staff, generate the following order request:

[0989] Document: A list of products that are low in stock. Item A, Item B...

[0990] Chat content: We are running low on item A and need to order more. Please also order item C.

[0991] Based on this prompt, the server generates an order request like this:

[0992] Abridged document: A list of products that are low on stock. Item A, Item B…

[0993] Order Request:

[0994] 1. Item A is running low in stock, so we need to place an additional order.

[0995] 2. Please also add item C.

[0996] In this way, the present invention can improve the efficiency of product inventory management and ordering operations, reduce the burden on staff, and reduce the risk of missing an order.

[0997] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0998] Step 1:

[0999] Users upload product inventory information and chat messages between staff members from their devices to the server. At this time, the product inventory information includes stock quantities and out-of-stock status, while the chat messages include messages about inventory management and ordering.

[1000] Input: Product inventory information (stock quantity, out-of-stock status), chat content (text message)

[1001] Output: Product inventory information uploaded to the server and chat content

[1002] Step 2:

[1003] The server receives the uploaded product inventory information and chat content and stores them for subsequent analysis.

[1004] Input: User uploaded information

[1005] Output: Data stored in the server

[1006] Step 3:

[1007] The server uses natural language processing (NLP) algorithms to analyze product inventory information and generate summaries, using Python, NLTK, and the Transformers library.

[1008] Input: Product inventory information stored on the server

[1009] Output: Summarized product inventory information

[1010] Step 4:

[1011] The server analyzes chat content and extracts important information using text analysis techniques such as TF-IDF, specifically using scikit-learn software.

[1012] Input: Chat contents saved on the server

[1013] Output: Extracted important information

[1014] Step 5:

[1015] The server automatically generates a base order proposal based on the summarized product inventory information and the extracted chat content, including the specific product name, order quantity, and reason.

[1016] Input: Summarized product inventory information, extracted key information

[1017] Output: A basic draft of the purchase order request

[1018] Step 6:

[1019] The server sends the generated base draft of the order request to the user terminal, where the user can check the request contents and edit them as necessary.

[1020] Input: Base proposal for purchase order request

[1021] Output: Request sent to the user's device

[1022] Step 7:

[1023] After the user finalizes, confirms, and edits the order request, the server saves the finalized order request, which is then automatically sent to the administrator or other relevant parties in the specified format.

[1024] Input: User confirmed and edited order request

[1025] Output: Finalized order request saved and automatically sent to administrator

[1026] 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.

[1027] To specifically implement this invention, a user begins by preparing the documents and chat content required for the approval request using a terminal. This data is then uploaded to a server via the terminal, where it is analyzed to extract summaries and important information. Furthermore, an emotion engine is used to recognize the user's emotional state and provide feedback based on that, resulting in a more efficient approval request process.

[1028] Program processing

[1029] 1. Upload and summarize documents

[1030] A user uses a terminal to upload a document (such as a contract, quotation, or proposal) to the server. The user logs in to the system, clicks the "Upload Document" button, selects the required file, and presses the "Upload" button.

[1031] The device sends the uploaded document to the server, which receives and stores it, and the server analyzes the stored document using an AI algorithm to generate a summary.

[1032] 2. Chat content aggregation and analysis

[1033] The user copies and pastes the chat contents with the agent from their device and clicks the "Upload Chat Content" button to send it to the server.

[1034] The server receives and stores the chat content, which is then analyzed using a natural language processing (NLP) module to extract important information needed for approval applications.

[1035] 3. Automatic generation of approval request text

[1036] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content. The generated base draft includes summary information, cost details, and key contract terms.

[1037] The base plan is sent in JSON format to the terminal, where it is displayed in a text editor for the user to review and edit.

[1038] 4. Use of Emotion Engine

[1039] The emotion engine integrated into the server recognizes the user's emotional state from facial expressions, tone of voice, typing speed, etc., and analyzes the emotional data, thereby monitoring the user's stress level and satisfaction in real time.

[1040] The emotion engine provides feedback based on the user's emotional state and makes suggestions or corrections to the base draft of the approval request text as needed.

[1041] 5. Final review and editing

[1042] The user checks the base draft of the approval request text generated on the terminal, and enters corrections or additional information as necessary. The user then clicks the "Finalize" button to finalize the approval request.

[1043] The server receives the finalized approval request and generates a document in PDF format or other specified format. The generated document is saved on the server and automatically sent to superiors or related parties as needed.

[1044] Specific examples

[1045] 1. Upload and summarize documents

[1046] A user uploads a new contract related to Project X from their device to the server. The server receives the contract and generates a summary including the contract amount, contract period, and key terms. The generated summary can be viewed on the device, allowing the user to understand the contents.

[1047] 2. Chat content aggregation and analysis

[1048] The user sends the chat contents with the person in charge from their device to the server. The server analyzes the chat contents, extracts the discussion points and agreements necessary for the approval application, and the organized information can be viewed on the device.

[1049] 3. Automatic generation of approval request text

[1050] Based on the summary information and the extracted chat content, the server generates a draft of the approval request text in the form of, "We are requesting approval for a new contract for Project X with a budget of XX yen. The contract period is from month XX / XX to month XX / XX, and the main conditions are as follows..." which can then be viewed and edited on the terminal.

[1051] 4. Use of Emotion Engine

[1052] While the user is reviewing the draft, the emotion engine monitors the user's emotional state in real time. For example, if the user is feeling stressed, the emotion engine will detect this and suggest changes to the draft, allowing the user to complete the approval request in a way that satisfies them.

[1053] 5. Final review and editing

[1054] After the user checks the draft and makes any necessary corrections, they press the "Finalize" button to finalize the approval request. The server receives this, generates an application in the specified format, and automatically sends it.

[1055] This process reduces the workload of the assistant and enables efficient and accurate approval applications. The use of an emotion engine also reduces the psychological burden on the user.

[1056] The processing flow will be explained below.

[1057] Step 1:

[1058] The user uses a terminal to upload documents related to the approval request, such as contracts, quotations, and proposal materials. The user logs in to the system and clicks the "Upload Documents" button. A file selection dialog box appears, and the user selects the necessary documents and presses the "Upload" button.

[1059] Step 2:

[1060] The device sends the uploaded document to the server, where it is saved in the specified directory.

[1061] Step 3:

[1062] The server receives the stored document and begins analyzing it using AI algorithms. The document content is analyzed using a natural language processing (NLP) model, and important information is extracted and a summary is generated. Specifically, key information such as the contract details, contract amount, and contract period is summarized.

[1063] Step 4:

[1064] Once the summary is generated, the server sends it in JSON format to the terminal, where it is displayed for the user to review. The user can then review the summary and check for any issues.

[1065] Step 5:

[1066] A user can copy and paste the chat transcript with the person in charge using a terminal and then click the "Upload Chat Transcript" button to send it to the server, for example, to send text containing important discussions and decisions about a project.

[1067] Step 6:

[1068] The server receives the chat content and begins analyzing it with the NLP module. It extracts important information necessary for approval requests from the chat content and classifies it into categories (e.g., agreed matters, unresolved matters, cost-related matters).

[1069] Step 7:

[1070] Once the extracted important information has been organized, the server sends it to the terminal in JSON format. The terminal displays the organized information so that the user can review it. The user can then review the information and check to see if any elements necessary for the approval request have been omitted.

[1071] Step 8:

[1072] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content. The generated base draft includes summary information, cost details, and key contract terms.

[1073] Step 9:

[1074] Once the generated base plan is complete, the server sends it in JSON format to the device, which displays it in a text editor for the user to review and edit.

[1075] Step 10:

[1076] The emotion engine integrated into the server recognizes the user's emotional state through the camera and microphone on the user's device. The emotion engine analyzes facial expressions, tone of voice, typing speed, etc. to assess the user's stress level and satisfaction in real time. For example, if the user is feeling stressed, the engine will detect this and make suggestions to help the user relax.

[1077] Step 11:

[1078] The emotion engine provides feedback based on the user's emotional state and makes suggestions or corrections to the base draft of the approval request text as needed. For example, if the emotion engine determines that the user is dissatisfied, it will suggest, "Please add more details about this part."

[1079] Step 12:

[1080] The user checks the base draft of the approval request text generated on the device, and makes corrections or adds additional information as necessary, taking into consideration feedback from the emotion engine. The user then clicks the "Finalize" button to finalize the approval request.

[1081] Step 13:

[1082] The server receives the finalized approval request and generates the application in PDF format or other specified format. The generated application is saved on the server and automatically sent to superiors or related parties as needed.

[1083] This series of steps allows the assistant to efficiently complete the approval process. The use of the emotion engine also reduces the psychological burden on the user.

[1084] Example 2

[1085] 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."

[1086] In today's business environment, the approval process is often complex and time-consuming. Collecting, summarizing, and analyzing the documents and chat content required for approval, as well as creating the application text, requires a significant amount of time and effort. This process also places a significant psychological burden on users. Conventional systems were unable to adequately resolve these issues, making it difficult to process approval requests efficiently and accurately.

[1087] 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. In this invention, the server includes means for receiving a document, analyzing it, and generating a summary, means for receiving chat content, analyzing it, and extracting important information, means for automatically generating a base draft of the approval request body based on the summarized document information and the extracted chat content, means for sending the generated base draft to a user terminal so that the user can confirm and edit it, and means for recognizing the user's emotional state and providing feedback based on it. This enables efficient and accurate approval requests and reduces the psychological burden on the user.

[1088] "Document" is a general term for documents stored in electronic format, including contracts, estimates, proposal materials, etc.

[1089] A "summary" is a short summary of the main information or key points of a document, based on an analysis of its contents.

[1090] "Chat content" refers to the record of messages and communications between the user and the person in charge.

[1091] "Important information" refers to important data that affects the content of the application, such as discussion points and agreements required for approval applications.

[1092] The "application text" is a formal document that explains the application content and refers to text created based on summary information and important information.

[1093] The "base proposal" refers to the initial draft of the approval request text automatically generated by the AI ​​model.

[1094] "User terminal" refers to an electronic device used by a user, such as a computer or smartphone.

[1095] "Emotional state" refers to the user's psychological and emotional state, and is measured from facial expressions, tone of voice, typing speed, etc.

[1096] "Feedback" refers to advice, suggestions, and improvements provided based on the user's emotional state.

[1097] To specifically implement this invention, a user begins by preparing the documents and chat content required for the approval application using a terminal. These data are then uploaded to a server via the terminal. The server analyzes the data to extract summaries and important information, and further recognizes the user's emotional state using an emotion engine and provides feedback based on that information.

[1098] Hardware and software used

[1099] 1. Terminal

[1100] The computer or smartphone you use (e.g., Windows PC, Mac, iOS device, Android device).

[1101] User interface using a web browser (e.g., Google Chrome, Mozilla Firefox, Safari).

[1102] 2. Server

[1103] Server computers for data storage and processing (e.g., Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure).

[1104] Python-based AI algorithms for data analysis (e.g., BERT, spaCy, NLTK).

[1105] A generative AI model written in JavaScript used to automatically generate the text of approval requests.

[1106] 3. Emotion Engine

[1107] An engine for real-time recognition of the user's emotional state (e.g., OpenFace, Microsoft Azure Cognitive Services).

[1108] A library for facial expression recognition, voice tone analysis, and keystroke speed analysis.

[1109] Specific examples

[1110] 1. Upload and summarize documents

[1111] The user uploads a new contract related to the project to the server from their device. The user logs in to the system on a web browser, clicks the "Document Upload" button, selects the required file, and presses the "Upload" button. The server stores the received contract and analyzes it using a Python-based AI algorithm to generate a summary including the contract amount, contract period, and key terms. The generated summary is stored in a database and can be viewed on the device.

[1112] 2. Chat content aggregation and analysis

[1113] The user copies the chat transcript with the person in charge, pastes it into the "Chat transcript upload" field in the system, and clicks the "Upload" button. The server receives this chat transcript and stores it in a database. A natural language processing (NLP) module is used to analyze the chat transcript and extract the discussion points and agreements necessary for the approval request. This extracted information can be viewed on the device.

[1114] 3. Automatic generation of approval request text

[1115] The server uses a generative AI model to automatically generate a base draft of the approval request text based on the saved summary information and the extracted chat content. The generated base draft includes summary information, cost details, and key contract terms. It is sent in JSON format to the device and displayed in the device's text editor.

[1116] 4. Use of Emotion Engine

[1117] While the user is reviewing the base proposal, the emotion engine monitors the user's emotional state in real time. The emotion engine analyzes emotional data from facial expression recognition, voice tone analysis, and keystroke speed analysis. If the user is feeling stressed, a suggestion such as "Try changing the expression in this part" will pop up on the screen as emotional feedback. This allows the user to complete the approval request text in a way that satisfies them.

[1118] 5. Final review and editing

[1119] The user checks the generated base draft of the approval request text and makes any necessary corrections in a text editor. Once the review is complete, the user clicks the "Finalize" button. The server receives the finalized approval request and generates an application form in the specified format using LaTeX or PDFkit. The generated application form is saved in a database and automatically sent to superiors or related parties as needed.

[1120] Prompt Sentence Examples

[1121] Here is an example of how a user might enter a prompt in a web browser:

[1122] "I'm uploading a new contract for Project X. Please generate a summary including the contract amount, contract duration, and key terms."

[1123] Such prompts allow the server to perform appropriate processing, resulting in efficient and accurate approval requests.

[1124] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1125] Step 1:

[1126] A user logs in to the system on a terminal by opening a web browser, accessing the system's login screen, entering their user ID and password, and clicking the "Login" button. This displays the system's main menu.

[1127] Step 2:

[1128] The user clicks the "Upload Document" button, selects the required file, and presses the "Upload" button. Specifically, the user selects a file from a file selection dialog using the HTML input tag, and then clicks the button. This operation sends the document data from the terminal to the server. The input is the document file selected by the user, and the output is the document data saved on the server.

[1129] Step 3:

[1130] The server receives and stores the uploaded document. Specifically, it stores the received file in a cloud storage service such as AWS S3. The input is the document data sent from the device, and the output is the URL of the destination in the cloud storage.

[1131] Step 4:

[1132] The server analyzes the stored documents using a Python-based AI algorithm and generates summaries. Specifically, it performs text analysis using the BERT model. The input is the document data retrieved from the destination URL, and the output is the summary text.

[1133] Step 5:

[1134] The user copies the chat contents with the agent on their device, pastes them into the "Upload Chat Content" field, and clicks the "Upload" button. Specifically, the chat contents are pasted using the HTML textarea tag. This sends the chat data from the device to the server. The input is the chat text pasted by the user, and the output is the chat data saved on the server.

[1135] Step 6:

[1136] The server receives and stores the chat data sent from the device. Specifically, it stores the chat data in a database. The input is the chat data sent from the device, and the output is the saved record in the database.

[1137] Step 7:

[1138] The server analyzes the saved chat content using an NLP module to extract important information. Specifically, it uses spaCy and NLTK to perform text analysis. The input is the chat content in the database, and the output is the extracted important information text.

[1139] Step 8:

[1140] The server uses a generative AI model based on the summarized document information and extracted chat content to automatically generate a base draft of the approval request text. Specifically, it runs a generative AI model written in JavaScript to generate the text based on the input data. The input is the document summary and important chat information, and the output is the base draft text of the approval request text.

[1141] Step 9:

[1142] The server sends the generated base proposal in JSON format to the terminal. Specifically, it returns JSON data in the HTTP response. The input is the base proposal text for the approval request body, and the output is the JSON data sent to the terminal.

[1143] Step 10:

[1144] The terminal displays the received base plan in a text editor. Specifically, it displays it in an editable format using an editor such as Quill. The input is JSON data received from the server, and the output is a text editor display that the user can view and edit.

[1145] Step 11:

[1146] The emotion engine monitors the user's emotional state in real time. Specifically, it collects and analyzes data using the device's webcam, microphone, and keyboard typing speed. The input is facial expression, tone of voice, and typing speed data, and the output is analyzed emotional state data.

[1147] Step 12:

[1148] The server provides appropriate feedback based on the emotional state data. Specifically, it displays a pop-up message on the screen with advice such as "It would be good to change the expression in this part." The input is the analyzed emotional state data, and the output is a feedback message that is displayed to the user.

[1149] Step 13:

[1150] The user checks the generated base draft of the approval request text and makes any necessary corrections in a text editor. Specifically, the user edits the text using the keyboard and clicks the "Finalize" button. The input is the base draft text and the user's editing operations, and the output is the finalized approval request text.

[1151] Step 14:

[1152] The server receives the finalized approval request and generates the application in the specified format using LaTeX or PDFkit. Specifically, it converts the text data into LaTeX format and outputs it as a PDF. The input is the text of the finalized approval request, and the output is a PDF application.

[1153] Step 15:

[1154] The server saves the generated application form in a database and automatically sends it to superiors and other relevant parties as needed. Specifically, it saves the form and sends it via email using the SMTP protocol. The input is the PDF application form, and the output is the URL saved in the database and the email that was sent.

[1155] (Application example 2)

[1156] 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."

[1157] In the process of managing the operation and maintenance of autonomous vehicles, processing complex documents and analyzing chat messages is time-consuming. Furthermore, the lack of a system that can monitor the driver's emotional state in real time and provide appropriate feedback increases the psychological burden on the driver. This reduces operational efficiency and frequently leads to delays in responding to vehicle problems.

[1158] 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.

[1159] In this invention, the server includes means for receiving documents and analyzing them to generate summaries, means for receiving chat content and analyzing it to extract important information, means for automatically generating a base draft of the approval application text based on the summarized document information and the extracted chat content, means for sending the generated base draft to a user terminal so that the user can confirm and edit it, means for recognizing the user's emotional state in real time and providing feedback based on the analysis results, and operation management and analysis means that allow drivers to upload documents and malfunction reports. This makes it possible to streamline operation management and the maintenance application process for autonomous vehicles and reduce the psychological burden on drivers.

[1160] "Means for receiving documents, analyzing them, and generating summaries" refers to a function that allows the server to receive various documents uploaded by users, analyzes the documents using natural language processing technology, and automatically generates summaries.

[1161] "Means for receiving chat content, analyzing it, and extracting important information" refers to a function that allows the server to receive chat content provided by the user, analyzes the conversation using natural language processing technology, and automatically extracts important information necessary for approval applications.

[1162] "Means for automatically generating a base draft of the approval application text based on summarized document information and extracted chat content" refers to a function that automatically creates an initial draft of the approval application text based on the document summary generated by analysis and the extracted chat content.

[1163] "Means for sending the generated base draft to the user's terminal and allowing the user to check and edit it" refers to a function that sends an automatically generated base draft of the approval request body to the user's terminal, allowing the user to check it and edit it as necessary.

[1164] "Means of recognizing the user's emotional state in real time and providing feedback based on the analysis results" refers to a function that uses an emotion engine to detect the user's emotional state in real time from their facial expressions, tone of voice, etc., and provides appropriate advice and feedback based on the analysis results.

[1165] The "operation management means and analysis means that allow drivers to upload documents and malfunction reports" refer to multiple functions within the system that allow drivers to upload various documents related to operation management and vehicle malfunction reports to a server and analyze them.

[1166] "Means for saving the confirmed application after the body of the request for approval has been finalized and generating it in a specified format" refers to a function that, after the user finalizes the body of the request for approval, saves the confirmed application form on the server and generates the application form in a specified format (e.g., PDF format).

[1167] "Means for generating metadata for various documents uploaded by users and associating it with the corresponding approval application data" refers to a function that generates metadata from documents uploaded by users and associates that metadata with the corresponding approval application data.

[1168] To implement the present invention, it is necessary to build a system according to the following steps.

[1169] First, we set up the hardware and software used by the system. The main hardware used includes smart glasses, head-mounted displays, smartphones, and servers. The software used includes spaCy as a natural language processing (NLP) module, OpenAI GPT-4 API as a sentiment analysis engine, PostgreSQL as a database, and Flask (Python) as a server framework.

[1170] Document upload and summary generation

[1171] Users use their smart devices to upload documents (e.g., operation plans, contracts) to the server, which then analyzes the received documents using natural language processing technology (spaCy) and generates summaries of important information (route details, time, driver list, etc.).

[1172] Chat content aggregation and analysis

[1173] Users send chat content from their smart devices to the server, which then analyzes the received chat content using a natural language processing module and extracts important information necessary for approval applications. This allows for efficient organization of important discussion points and agreements exchanged between drivers and operation managers.

[1174] Automatic generation of approval request text

[1175] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content. The generated base draft includes the route, budget, contract terms, etc. The base draft is sent to the user's device in JSON format so that the user can view and edit it.

[1176] Analysis and feedback by emotion engine

[1177] The emotion engine integrated into the server analyzes data such as facial expressions, tone of voice, and typing speed to recognize the user's emotional state in real time. Based on the emotion data, the emotion engine evaluates stress levels and satisfaction and provides feedback as needed. For example, if the user is feeling stressed, the emotion engine will display suggestions and corrections.

[1178] Driver operation management and breakdown reporting

[1179] Drivers use smart glasses or head-mounted displays to collect data while driving and upload it to a server. When a malfunction report is sent, the server analyzes it using an NLP module and automatically generates a list of necessary repairs and parts, enabling a prompt response.

[1180] Specific examples

[1181] For example, when a dispatcher notifies the system, "Uploading a trip plan," the system responds, "Generating a trip plan summary... Completed." When a driver inputs, "Submitting a malfunction report about an abnormal engine noise," the system responds, "Analyzing the malfunction report... Generated a list of required parts." The emotion engine also provides real-time feedback, such as, "Your fatigue level has reached 85%, we recommend you take a break."

[1182] Prompt Sentence Examples

[1183] Examples of prompts include:

[1184] "Analyze the flight schedule and summarize the key flight information."

[1185] "Analyze driver reports of malfunctions and create a list of necessary repair parts."

[1186] "Analyze the driver's emotional state in real time and provide appropriate feedback."

[1187] This will make it possible to streamline the operation management and maintenance application process for autonomous vehicles and reduce the psychological burden on drivers.

[1188] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1189] Program processing flow

[1190] Step 1: Uploading documents and generating summaries

[1191] Input: The user uploads documents such as operation plans and contracts from the terminal to the server.

[1192] Processing: The server receives and stores the uploaded document, then uses natural language processing technology (spaCy) to analyze the document, extract key information (e.g., route, time, driver list), and generate a summary.

[1193] Output: Generate and save summarized document information.

[1194] Step 2: Chat content aggregation and analysis

[1195] Input: The user copies the chat contents with the agent from the terminal and sends them to the server.

[1196] Processing: The server stores the received chat content and analyzes it using a natural language processing module. It then extracts important information (e.g., discussion points and agreements) necessary for the approval process.

[1197] Output: Generate and store the extracted important information.

[1198] Step 3: Automatic generation of the approval request text

[1199] Input: Summarized document information and extracted chat transcripts.

[1200] Processing: The server integrates this information and automatically generates a base draft for the approval request, which includes route, budget, contract terms, etc.

[1201] Output: Send the automatically generated base draft of the approval request body to the terminal in JSON format.

[1202] Step 4: Analysis and feedback by the emotion engine

[1203] Input: Data such as the user's facial expressions, tone of voice, and typing speed.

[1204] Processing: The emotion engine (OpenAI GPT-4 API) integrated into the server analyzes these data in real time, evaluates the user's emotional state, and generates appropriate feedback based on the user's stress level and satisfaction.

[1205] Output: Provides feedback to the user based on the analysis results.

[1206] Step 5: Driver management and breakdown reporting

[1207] Input: Driving data and breakdown reports collected by drivers using smart devices.

[1208] Processing: The server receives this data and analyzes it using the NLP module, after which it automatically generates the necessary repairs and parts list.

[1209] Output: The repair items and parts list generated as a result of the analysis are provided to the operation manager.

[1210] Step 6: Finalize and generate application

[1211] Input: A base draft of the approval request body that the user has reviewed and edited.

[1212] Processing: When the user makes a final confirmation and presses the "Finalize" button, the server receives the data and generates the application form in the specified format, such as PDF.

[1213] Output: Save the generated application form and automatically send it to the relevant parties as needed.

[1214] Through these steps, the system can streamline the operation management and maintenance application process for autonomous vehicles and reduce the psychological burden on drivers.

[1215] 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.

[1216] 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.

[1217] 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.

[1218] [Fourth embodiment]

[1219] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1220] 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.

[1221] 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).

[1222] 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.

[1223] 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.

[1224] 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).

[1225] 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.

[1226] 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.

[1227] 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.

[1228] 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.

[1229] 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.

[1230] 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.

[1231] 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."

[1232] To specifically implement this invention, a user first prepares a series of documents and chat content required for the approval request using a terminal. This data is then uploaded to a server via the terminal, which analyzes it to extract summaries and important information. Finally, a base draft of the approval request text is generated, which the user can review and edit.

[1233] Program processing

[1234] 1. Upload and summarize documents

[1235] A user uses a terminal to upload a document (such as a contract, estimate, or proposal) to the server.

[1236] The server receives and stores the uploaded documents, which are then analyzed by AI algorithms to extract key information and generate summaries.

[1237] 2. Chat content aggregation and analysis

[1238] The user sends the chat contents with the person in charge from the terminal to the server.

[1239] The server receives and stores the chat content, which is then analyzed by a natural language processing (NLP) module to extract important information needed for approval applications.

[1240] 3. Automatic generation of approval request text

[1241] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content, including the necessary summary information, cost details, and key contract terms.

[1242] The generated base proposal is sent to the terminal for the user to review and edit.

[1243] 4. Final review and editing

[1244] The user checks the base draft of the approval request text generated on the terminal and makes corrections or enters additional information as necessary.

[1245] Once the user finalizes the base proposal and completes the approval application, the server saves the finalized application in a specified format and automatically sends it to superiors or other relevant parties as necessary.

[1246] Specific examples

[1247] 1. Upload and summarize documents

[1248] The user uploads a new contract related to Project X to the server from their device.

[1249] The server receives the contract and generates a summary including the specific contract amount, contract period, and key terms.

[1250] The generated summary can be viewed on the terminal, allowing the user to understand the content.

[1251] 2. Chat content aggregation and analysis

[1252] The user sends the chat contents with the person in charge from the terminal to the server.

[1253] The server analyzes the chat content and extracts the discussion points and agreements necessary for the approval request.

[1254] The organized information can be viewed on the device, allowing users to check the information they need.

[1255] 3. Automatic generation of approval request text

[1256] Based on the summary information and the extracted chat content, the server generates a draft of the approval request text in the form of, "We are requesting approval for a new contract for Project X with a budget of XX yen. The contract period is from month XX / XX to month XX / XX, and the main conditions are as follows..."

[1257] The generated draft can be viewed and edited on your device.

[1258] 4. Final review and editing

[1259] The user checks the generated draft on the terminal and makes corrections as necessary.

[1260] The completed approval application form is finalized, and the server saves it and automatically sends it to the superior.

[1261] In this way, a system can be created that streamlines the approval application process for assistants and significantly reduces the amount of work required.

[1262] The processing flow will be explained below.

[1263] Step 1:

[1264] The user uses a terminal to upload documents such as contracts, quotations, and proposal materials required for approval applications. The user logs in to the system and clicks the "Upload Documents" button. A file selection dialog box appears, and the user selects the necessary documents and presses the "Upload" button.

[1265] Step 2:

[1266] The device sends the uploaded document to the server, where it is saved in the specified directory.

[1267] Step 3:

[1268] The server receives the stored document and begins analyzing it using AI algorithms, analyzing the document content with natural language processing (NLP) models, extracting key information, and generating a summary.

[1269] Step 4:

[1270] Once the summary is generated, the server sends it in JSON format to the terminal, which displays the summary result for the user to review.

[1271] Step 5:

[1272] The user copies and pastes the chat transcript with the agent using the terminal, and then clicks the "Upload Chat Transcript" button to send it to the server.

[1273] Step 6:

[1274] The server receives the chat content and begins analyzing it with the NLP module. Important information necessary for approval applications is extracted and organized from the chat content.

[1275] Step 7:

[1276] Once the extracted important information has been organized, the server sends it in JSON format to the device, where it is displayed for the user to review.

[1277] Step 8:

[1278] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content. The generated base draft includes summary information, cost details, and key contract terms.

[1279] Step 9:

[1280] Once the generated base plan is complete, the server sends it in JSON format to the device, which displays it in a text editor for the user to review and edit.

[1281] Step 10:

[1282] The user checks the base draft of the approval request text generated on the terminal, and enters corrections or additional information as necessary. The user then clicks the "Finalize" button to finalize the approval request.

[1283] Step 11:

[1284] The server receives the finalized approval request and generates the application in PDF format or other specified format. The generated application is saved on the server and automatically sent to superiors or related parties as needed.

[1285] This series of steps allows the assistant to efficiently complete the approval application process.

[1286] Example 1

[1287] 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."

[1288] Conventional approval application systems required the manual organization of multiple documents and chat messages, and the process of compiling summary information was extremely cumbersome, time-consuming, and labor-intensive. Furthermore, important information was often overlooked or typographical errors occurred, making it difficult to streamline operations. This could result in delays and inaccuracies in the approval application process.

[1289] 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.

[1290] In this invention, the server includes: means for a user to use a terminal to select various data and upload it to the server; means for the server to receive and store the uploaded documents; means for analyzing the documents using an analysis algorithm on the server, extracting important information, and generating summaries; means for the server to receive and store uploaded chat content; means for analyzing the chat content using a natural language processing module and extracting important information; means for automatically generating a base draft of the approval request body using a generative AI model based on the summarized document information and the extracted chat content; and means for sending the generated base draft to the user terminal so that the user can confirm and edit it. This makes it possible to consistently automate the management of approval request-related data, the generation of summaries, and the automatic generation of approval request bodies, thereby improving business efficiency and accuracy.

[1291] A "user" is an entity that operates the system and uploads, checks, and edits data via a terminal.

[1292] "Terminal" refers to a computing device used by a user, and is a device used to upload documents, send chat content, and review and edit generated approval applications.

[1293] A "server" is a computing system that receives data sent from a user terminal, analyzes it, and performs the necessary processing.

[1294] "Document" refers to a document that contains text and information necessary for an approval request, such as a contract, estimate, or proposal document.

[1295] "Chat content" refers to the history of communication between the user and other personnel via email or messaging services.

[1296] An "analysis algorithm" is a computational method or program that analyzes documents and chat content, extracts important information, and summarizes it.

[1297] The "natural language processing module" is a program that converts chat content into structured data and extracts important information.

[1298] A "generative AI model" is an artificial intelligence algorithm that automatically generates a base draft of the approval application text based on input data.

[1299] The "base draft of the approval request text" is an initial draft of the approval request text generated based on the summarized document information and the extracted chat content.

[1300] "Finalization" is the process in which the user checks and edits the basic draft of the approval request text and finalizes it as an official application.

[1301] "Metadata" is attribute information added to various documents and chat content, and is data that makes it easier to manage and search data.

[1302] "Saving" means storing the finalized approval application form and uploaded data in a specified directory on the server.

[1303] To specifically implement this invention, the following steps must be taken: The main hardware consists of a user terminal and a server, and the software used includes an analysis algorithm, a natural language processing module, and a generative AI model.

[1304] First, the user uses the terminal to prepare the documents required for the approval request (e.g., contract, quotation, proposal materials). Then, the user can click the "Select File" button, select the appropriate file, and press the "Upload" button. Similarly, the user prepares the chat content with the person in charge and uploads it to the system. Using the "Select Chat History" button, the user selects the appropriate file and presses the "Send" button to send the chat content to the server.

[1305] Next, the server receives the uploaded documents and chat content and stores them in a specified directory (e.g., " / uploads / documents" or " / uploads / chats"). The server first reads the documents using an analysis algorithm (e.g., DocuAI) and extracts important information. This includes analyzing key items such as the contract amount and contract period using keyword extraction and natural language processing techniques. The server also analyzes the chat content using a natural language processing module (e.g., ChatAnalyzer) and extracts important information (discussion points and agreements).

[1306] The server then creates and sends a prompt to a generative AI model (e.g., GPT-4) based on the summarized document information and the extracted chat content. This generates a base draft for the approval request text. The generated base draft is created in the form of, for example, "For a new contract regarding Project X, we request approval of a budget of XX yen. The contract period is from month XX year to month XX year, and the main conditions are as follows..." The generated draft is first saved as a temporary approval request file on the server and then sent to the user's device.

[1307] The terminal receives the generated draft and displays it for the user to review and edit. For example, a text editor window is opened, allowing the user to edit the draft on the spot. Once the user makes a final confirmation and presses the "Confirm" button, the approval request form is complete.

[1308] The finalized request form is saved on the server in a specified format (for example, HTML or PDF). The save destination is set to a directory such as " / finalized / request_forms". Furthermore, the server automatically sends the saved request form to superiors and related parties as necessary. In this case, it is sent by email to the email addresses of the superiors and related parties.

[1309] Examples of prompt statements

[1310] "I'd like to upload a new contract for Project X. Please extract the contract amount, contract period, and key terms and generate a draft of the approval request text."

[1311] "Please analyze the chat history with the person in charge, extract the agreements and discussion points, and reflect them in the approval request."

[1312] In this way, the specific operations and functions of the terminal, server, and user are linked to realize a system that streamlines approval application work. This embodiment allows users to quickly and accurately create approval application forms, improving the efficiency and accuracy of work.

[1313] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1314] Step 1: The user uses the terminal to prepare and upload the documents required for the approval request.

[1315] Input: Documents such as contracts, quotes, proposals, etc. related to the request selected by the user.

[1316] How it works: The user clicks the "Choose File" button on the device, selects a file, and presses the "Upload" button.

[1317] Output: The document is sent to the server.

[1318] Step 2: The server receives and stores the uploaded document.

[1319] Input: The document uploaded by the user.

[1320] What it does: The server saves the uploaded file in the specified directory (e.g. " / uploads / documents").

[1321] Output: The document is saved on the server.

[1322] Step 3: The server uses an analysis algorithm to analyze the document, extract important information, and generate a summary.

[1323] Input: The saved document.

[1324] How it works: The server uses an analysis algorithm (e.g., DocuAI) to extract key information (e.g., contract amount, contract duration, key terms) and generate a summary.

[1325] Data processing: Analyze the text within a document and summarize key information using keyword extraction and natural language processing techniques.

[1326] Output: A summary of the generated documentation.

[1327] Step 4: The user uses the terminal to prepare the chat content with the agent and sends it to the server.

[1328] Input: The chat transcript between the user and the agent.

[1329] How it works: The user uses the device's "Select Chat History" button to select a file of the chat contents and presses the "Send" button.

[1330] Output: The chat content is sent to the server.

[1331] Step 5: The server receives and stores the chat content.

[1332] Input: The chat content sent by the user.

[1333] What it does: The server saves chat content in a specified directory (e.g. " / uploads / chats").

[1334] Output: Chat content is saved on the server.

[1335] Step 6: The server analyzes the chat content and extracts important information.

[1336] Input: Saved chat transcript.

[1337] How it works: The server uses a natural language processing module (e.g. ChatAnalyzer) to analyze the chat content and extract key discussion points and agreements.

[1338] Data processing: Chat content is analyzed using natural language processing technology to extract important structured information.

[1339] Output: A summary of the parsed chat content.

[1340] Step 7: The server generates a draft of the approval request body based on the summarized document information and the analyzed chat content.

[1341] Input: Summarized document information and parsed chat content.

[1342] How it works: The server sends a prompt to a generative AI model (e.g., GPT-4) to generate a draft of the approval request text. For example, it might generate a draft such as, "I am requesting approval for a new contract for Project X with a budget of XX yen. The contract period is from XX month of XX year to XX month of XX year, and the main conditions are as follows..."

[1343] Data calculation: Based on the document summary and chat information, the generative AI model creates a base draft for the approval request text.

[1344] Output: A draft of the generated decision body.

[1345] Step 8: The server sends the generated draft to the user terminal so that the user can check and edit it.

[1346] Input: A generated draft of the request body.

[1347] What it does: The server sends the draft to the user's terminal, where it opens in a text editor window.

[1348] Output: The draft displayed on the user's terminal.

[1349] Step 9: The user checks the draft on their device and makes edits.

[1350] Input: The draft displayed on the user's terminal.

[1351] How it works: The user edits the draft in a text editor, making any necessary corrections or adding additional information.

[1352] Output: Edited body of the approval request.

[1353] Step 10: The user presses the "Confirm" button to finalize the approval request form.

[1354] Input: Edited request body.

[1355] Operation: The user presses the "Confirm" button to finalize the application form.

[1356] Output: Finalized approval request form.

[1357] Step 11: The server saves the finalized approval application in the specified format and automatically sends it to the specified parties.

[1358] Input: Finalized approval request form.

[1359] Operation: The server saves the request form in a specified directory (e.g., " / finalized / request_forms") and automatically sends it to the email addresses of the relevant parties.

[1360] Output: Saved and submitted decision request.

[1361] This provides a system that allows for efficient and accurate approval application procedures.

[1362] (Application example 1)

[1363] 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."

[1364] Inventory management and ordering are extremely important in running a brick-and-mortar store, but they require a great deal of time and effort. Creating appropriate ordering requests based on chats between staff and product inventory information requires specialized knowledge and attention. Traditional methods are time-consuming and prone to errors. Furthermore, there is a need for an efficient method for responding quickly and accurately based on this information.

[1365] 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.

[1366] In this invention, the server includes means for receiving documents and analyzing them to generate summaries, means for receiving chat content and analyzing it to extract important information, means for automatically generating a base draft of the approval application text based on the summarized document information and the extracted chat content, means for sending the generated base draft to a user terminal so that the user can confirm and edit it, means for analyzing product inventory information and automatically generating an order request, and means for sending the generated order request to a user terminal so that the user can confirm and edit it. This makes product inventory management and ordering operations more efficient, reducing the burden on staff and reducing errors.

[1367] "Document" refers to documents and materials created to provide information, including contracts, estimates, proposals, and the like.

[1368] A "summary" is a short summary of the main information of a document, allowing a user to quickly grasp the content.

[1369] "Chat content" refers to text-based communication between users, including the exchange of electronic messages.

[1370] "Important information" refers to information that is particularly required in subsequent processes, and refers to key data necessary for decision-making and document generation.

[1371] The "application body" is a document that contains detailed information necessary to obtain approval for the application, including the application content, purpose, cost, and period.

[1372] A "base draft" is a draft generated as an early stage of the final document, and serves as a basis for users to review and edit.

[1373] "Product inventory information" is data indicating the quantity and status of products handled at a store, including remaining stock and out-of-stock status.

[1374] An "order request" is a request to place an order to replenish product inventory, and includes a specific quantity and type of product.

[1375] A "user terminal" is a device operated by a user, such as a smartphone, tablet, or PC.

[1376] "Analysis" is the scrutiny of data to extract and understand specific information, often using specific algorithms or models.

[1377] "Automatic generation" means that a system creates documents or information without human intervention, based on specific rules or algorithms.

[1378] To specifically implement the present invention, a system is constructed according to the following procedure.

[1379] First, the user terminal prepares product inventory information and chat messages between staff members and uploads them to the server. At this time, the product inventory information includes stock quantities and out-of-stock status, and the chat messages include messages about inventory management and ordering.

[1380] The server then receives and analyzes the uploaded documents and chat content using text analysis techniques such as natural language processing (NLP) algorithms and TF-IDF, using Python, NLTK, the Transformers library, and scikit-learn.

[1381] The server summarizes the documents and extracts important information from the chat, such as a list of products that are low in stock or items that need to be reordered.

[1382] The server then automatically generates a base order proposal based on the summarized document information and the extracted chat content, including specific product names, order quantities, and reasons.

[1383] The generated order request is sent to the user's terminal. The user can check the request contents on the terminal and edit them as necessary. The finalized order request is saved on the server and automatically sent to the administrator in the specified format.

[1384] (Example)

[1385] For example, suppose a "list of products that are low in stock" is uploaded as a document, and a message such as "Item A is low in stock, so we need to order more. Please also order more item C" is sent as chat content between staff members.

[1386] The server parses this information and generates the following prompt:

[1387] Prompt: Based on product availability information and chat with staff, generate the following order request:

[1388] Document: A list of products that are low in stock. Item A, Item B...

[1389] Chat content: We are running low on item A and need to order more. Please also order item C.

[1390] Based on this prompt, the server generates an order request like this:

[1391] Abridged document: A list of products that are low on stock. Item A, Item B…

[1392] Order Request:

[1393] 1. Item A is running low in stock, so we need to place an additional order.

[1394] 2. Please also add item C.

[1395] In this way, the present invention can improve the efficiency of product inventory management and ordering operations, reduce the burden on staff, and reduce the risk of missing an order.

[1396] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1397] Step 1:

[1398] Users upload product inventory information and chat messages between staff members from their devices to the server. At this time, the product inventory information includes stock quantities and out-of-stock status, while the chat messages include messages about inventory management and ordering.

[1399] Input: Product inventory information (stock quantity, out-of-stock status), chat content (text message)

[1400] Output: Product inventory information uploaded to the server and chat content

[1401] Step 2:

[1402] The server receives the uploaded product inventory information and chat content and stores them for subsequent analysis.

[1403] Input: User uploaded information

[1404] Output: Data stored in the server

[1405] Step 3:

[1406] The server uses natural language processing (NLP) algorithms to analyze product inventory information and generate summaries, using Python, NLTK, and the Transformers library.

[1407] Input: Product inventory information stored on the server

[1408] Output: Summarized product inventory information

[1409] Step 4:

[1410] The server analyzes chat content and extracts important information using text analysis techniques such as TF-IDF, specifically using scikit-learn software.

[1411] Input: Chat contents saved on the server

[1412] Output: Extracted important information

[1413] Step 5:

[1414] The server automatically generates a base order proposal based on the summarized product inventory information and the extracted chat content, including the specific product name, order quantity, and reason.

[1415] Input: Summarized product inventory information, extracted key information

[1416] Output: A basic draft of the purchase order request

[1417] Step 6:

[1418] The server sends the generated base draft of the order request to the user terminal, where the user can check the request contents and edit them as necessary.

[1419] Input: Base proposal for purchase order request

[1420] Output: Request sent to the user's device

[1421] Step 7:

[1422] After the user finalizes, confirms, and edits the order request, the server saves the finalized order request, which is then automatically sent to the administrator or other relevant parties in the specified format.

[1423] Input: User confirmed and edited order request

[1424] Output: Finalized order request saved and automatically sent to administrator

[1425] 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.

[1426] To specifically implement this invention, a user begins by preparing the documents and chat content required for the approval request using a terminal. This data is then uploaded to a server via the terminal, where it is analyzed to extract summaries and important information. Furthermore, an emotion engine is used to recognize the user's emotional state and provide feedback based on that, resulting in a more efficient approval request process.

[1427] Program processing

[1428] 1. Upload and summarize documents

[1429] A user uses a terminal to upload a document (such as a contract, quotation, or proposal) to the server. The user logs in to the system, clicks the "Upload Document" button, selects the required file, and presses the "Upload" button.

[1430] The device sends the uploaded document to the server, which receives and stores it, and the server analyzes the stored document using an AI algorithm to generate a summary.

[1431] 2. Chat content aggregation and analysis

[1432] The user copies and pastes the chat contents with the agent from their device and clicks the "Upload Chat Content" button to send it to the server.

[1433] The server receives and stores the chat content, which is then analyzed using a natural language processing (NLP) module to extract important information needed for approval applications.

[1434] 3. Automatic generation of approval request text

[1435] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content. The generated base draft includes summary information, cost details, and key contract terms.

[1436] The base plan is sent in JSON format to the terminal, where it is displayed in a text editor for the user to review and edit.

[1437] 4. Use of Emotion Engine

[1438] The emotion engine integrated into the server recognizes the user's emotional state from facial expressions, tone of voice, typing speed, etc., and analyzes the emotional data, thereby monitoring the user's stress level and satisfaction in real time.

[1439] The emotion engine provides feedback based on the user's emotional state and makes suggestions or corrections to the base draft of the approval request text as needed.

[1440] 5. Final review and editing

[1441] The user checks the base draft of the approval request text generated on the terminal, and enters corrections or additional information as necessary. The user then clicks the "Finalize" button to finalize the approval request.

[1442] The server receives the finalized approval request and generates a document in PDF format or other specified format. The generated document is saved on the server and automatically sent to superiors or related parties as needed.

[1443] Specific examples

[1444] 1. Upload and summarize documents

[1445] A user uploads a new contract related to Project X from their device to the server. The server receives the contract and generates a summary including the contract amount, contract period, and key terms. The generated summary can be viewed on the device, allowing the user to understand the contents.

[1446] 2. Chat content aggregation and analysis

[1447] The user sends the chat contents with the person in charge from their device to the server. The server analyzes the chat contents, extracts the discussion points and agreements necessary for the approval application, and the organized information can be viewed on the device.

[1448] 3. Automatic generation of approval request text

[1449] Based on the summary information and the extracted chat content, the server generates a draft of the approval request text in the form of, "We are requesting approval for a new contract for Project X with a budget of XX yen. The contract period is from month XX / XX to month XX / XX, and the main conditions are as follows..." which can then be viewed and edited on the terminal.

[1450] 4. Use of Emotion Engine

[1451] While the user is reviewing the draft, the emotion engine monitors the user's emotional state in real time. For example, if the user is feeling stressed, the emotion engine will detect this and suggest changes to the draft, allowing the user to complete the approval request in a way that satisfies them.

[1452] 5. Final review and editing

[1453] After the user checks the draft and makes any necessary corrections, they press the "Finalize" button to finalize the approval request. The server receives this, generates an application in the specified format, and automatically sends it.

[1454] This process reduces the workload of the assistant and enables efficient and accurate approval applications. The use of an emotion engine also reduces the psychological burden on the user.

[1455] The processing flow will be explained below.

[1456] Step 1:

[1457] The user uses a terminal to upload documents related to the approval request, such as contracts, quotations, and proposal materials. The user logs in to the system and clicks the "Upload Documents" button. A file selection dialog box appears, and the user selects the necessary documents and presses the "Upload" button.

[1458] Step 2:

[1459] The device sends the uploaded document to the server, where it is saved in the specified directory.

[1460] Step 3:

[1461] The server receives the stored document and begins analyzing it using AI algorithms. The document content is analyzed using a natural language processing (NLP) model, and important information is extracted and a summary is generated. Specifically, key information such as the contract details, contract amount, and contract period is summarized.

[1462] Step 4:

[1463] Once the summary is generated, the server sends it in JSON format to the terminal, where it is displayed for the user to review. The user can then review the summary and check for any issues.

[1464] Step 5:

[1465] A user can copy and paste the chat transcript with the person in charge using a terminal and then click the "Upload Chat Transcript" button to send it to the server, for example, to send text containing important discussions and decisions about a project.

[1466] Step 6:

[1467] The server receives the chat content and begins analyzing it with the NLP module. It extracts important information necessary for approval requests from the chat content and classifies it into categories (e.g., agreed matters, unresolved matters, cost-related matters).

[1468] Step 7:

[1469] Once the extracted important information has been organized, the server sends it to the terminal in JSON format. The terminal displays the organized information so that the user can review it. The user can then review the information and check to see if any elements necessary for the approval request have been omitted.

[1470] Step 8:

[1471] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content. The generated base draft includes summary information, cost details, and key contract terms.

[1472] Step 9:

[1473] Once the generated base plan is complete, the server sends it in JSON format to the device, which displays it in a text editor for the user to review and edit.

[1474] Step 10:

[1475] The emotion engine integrated into the server recognizes the user's emotional state through the camera and microphone on the user's device. The emotion engine analyzes facial expressions, tone of voice, typing speed, etc. to assess the user's stress level and satisfaction in real time. For example, if the user is feeling stressed, the engine will detect this and make suggestions to help the user relax.

[1476] Step 11:

[1477] The emotion engine provides feedback based on the user's emotional state and makes suggestions or corrections to the base draft of the approval request text as needed. For example, if the emotion engine determines that the user is dissatisfied, it will suggest, "Please add more details about this part."

[1478] Step 12:

[1479] The user checks the base draft of the approval request text generated on the device, and makes corrections or adds additional information as necessary, taking into consideration feedback from the emotion engine. The user then clicks the "Finalize" button to finalize the approval request.

[1480] Step 13:

[1481] The server receives the finalized approval request and generates the application in PDF format or other specified format. The generated application is saved on the server and automatically sent to superiors or related parties as needed.

[1482] This series of steps allows the assistant to efficiently complete the approval process. The use of the emotion engine also reduces the psychological burden on the user.

[1483] Example 2

[1484] 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."

[1485] In today's business environment, the approval process is often complex and time-consuming. Collecting, summarizing, and analyzing the documents and chat content required for approval, as well as creating the application text, requires a significant amount of time and effort. This process also places a significant psychological burden on users. Conventional systems were unable to adequately resolve these issues, making it difficult to process approval requests efficiently and accurately.

[1486] 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. In this invention, the server includes means for receiving a document, analyzing it, and generating a summary, means for receiving chat content, analyzing it, and extracting important information, means for automatically generating a base draft of the approval request body based on the summarized document information and the extracted chat content, means for sending the generated base draft to a user terminal so that the user can confirm and edit it, and means for recognizing the user's emotional state and providing feedback based on it. This enables efficient and accurate approval requests and reduces the psychological burden on the user.

[1487] "Document" is a general term for documents stored in electronic format, including contracts, estimates, proposal materials, etc.

[1488] A "summary" is a short summary of the main information or key points of a document, based on an analysis of its contents.

[1489] "Chat content" refers to the record of messages and communications between the user and the person in charge.

[1490] "Important information" refers to important data that affects the content of the application, such as discussion points and agreements required for approval applications.

[1491] The "application text" is a formal document that explains the application content and refers to text created based on summary information and important information.

[1492] The "base proposal" refers to the initial draft of the approval request text automatically generated by the AI ​​model.

[1493] "User terminal" refers to an electronic device used by a user, such as a computer or smartphone.

[1494] "Emotional state" refers to the user's psychological and emotional state, and is measured from facial expressions, tone of voice, typing speed, etc.

[1495] "Feedback" refers to advice, suggestions, and improvements provided based on the user's emotional state.

[1496] To specifically implement this invention, a user begins by preparing the documents and chat content required for the approval application using a terminal. These data are then uploaded to a server via the terminal. The server analyzes the data to extract summaries and important information, and further recognizes the user's emotional state using an emotion engine and provides feedback based on that information.

[1497] Hardware and software used

[1498] 1. Terminal

[1499] The computer or smartphone you use (e.g., Windows PC, Mac, iOS device, Android device).

[1500] User interface using a web browser (e.g., Google Chrome, Mozilla Firefox, Safari).

[1501] 2. Server

[1502] Server computers for data storage and processing (e.g., Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure).

[1503] Python-based AI algorithms for data analysis (e.g., BERT, spaCy, NLTK).

[1504] A generative AI model written in JavaScript used to automatically generate the text of approval requests.

[1505] 3. Emotion Engine

[1506] An engine for real-time recognition of the user's emotional state (e.g., OpenFace, Microsoft Azure Cognitive Services).

[1507] A library for facial expression recognition, voice tone analysis, and keystroke speed analysis.

[1508] Specific examples

[1509] 1. Upload and summarize documents

[1510] The user uploads a new contract related to the project to the server from their device. The user logs in to the system on a web browser, clicks the "Document Upload" button, selects the required file, and presses the "Upload" button. The server stores the received contract and analyzes it using a Python-based AI algorithm to generate a summary including the contract amount, contract period, and key terms. The generated summary is stored in a database and can be viewed on the device.

[1511] 2. Chat content aggregation and analysis

[1512] The user copies the chat transcript with the person in charge, pastes it into the "Chat transcript upload" field in the system, and clicks the "Upload" button. The server receives this chat transcript and stores it in a database. A natural language processing (NLP) module is used to analyze the chat transcript and extract the discussion points and agreements necessary for the approval request. This extracted information can be viewed on the device.

[1513] 3. Automatic generation of approval request text

[1514] The server uses a generative AI model to automatically generate a base draft of the approval request text based on the saved summary information and the extracted chat content. The generated base draft includes summary information, cost details, and key contract terms. It is sent in JSON format to the device and displayed in the device's text editor.

[1515] 4. Use of Emotion Engine

[1516] While the user is reviewing the base proposal, the emotion engine monitors the user's emotional state in real time. The emotion engine analyzes emotional data from facial expression recognition, voice tone analysis, and keystroke speed analysis. If the user is feeling stressed, a suggestion such as "Try changing the expression in this part" will pop up on the screen as emotional feedback. This allows the user to complete the approval request text in a way that satisfies them.

[1517] 5. Final review and editing

[1518] The user checks the generated base draft of the approval request text and makes any necessary corrections in a text editor. Once the review is complete, the user clicks the "Finalize" button. The server receives the finalized approval request and generates an application form in the specified format using LaTeX or PDFkit. The generated application form is saved in a database and automatically sent to superiors or related parties as needed.

[1519] Prompt Sentence Examples

[1520] Here is an example of how a user might enter a prompt in a web browser:

[1521] "I'm uploading a new contract for Project X. Please generate a summary including the contract amount, contract duration, and key terms."

[1522] Such prompts allow the server to perform appropriate processing, resulting in efficient and accurate approval requests.

[1523] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1524] Step 1:

[1525] A user logs in to the system on a terminal by opening a web browser, accessing the system's login screen, entering their user ID and password, and clicking the "Login" button. This displays the system's main menu.

[1526] Step 2:

[1527] The user clicks the "Upload Document" button, selects the required file, and presses the "Upload" button. Specifically, the user selects a file from a file selection dialog using the HTML input tag, and then clicks the button. This operation sends the document data from the terminal to the server. The input is the document file selected by the user, and the output is the document data saved on the server.

[1528] Step 3:

[1529] The server receives and stores the uploaded document. Specifically, it stores the received file in a cloud storage service such as AWS S3. The input is the document data sent from the device, and the output is the URL of the destination in the cloud storage.

[1530] Step 4:

[1531] The server analyzes the stored documents using a Python-based AI algorithm and generates summaries. Specifically, it performs text analysis using the BERT model. The input is the document data retrieved from the destination URL, and the output is the summary text.

[1532] Step 5:

[1533] The user copies the chat contents with the agent on their device, pastes them into the "Upload Chat Content" field, and clicks the "Upload" button. Specifically, the chat contents are pasted using the HTML textarea tag. This sends the chat data from the device to the server. The input is the chat text pasted by the user, and the output is the chat data saved on the server.

[1534] Step 6:

[1535] The server receives and stores the chat data sent from the device. Specifically, it stores the chat data in a database. The input is the chat data sent from the device, and the output is the saved record in the database.

[1536] Step 7:

[1537] The server analyzes the saved chat content using an NLP module to extract important information. Specifically, it uses spaCy and NLTK to perform text analysis. The input is the chat content in the database, and the output is the extracted important information text.

[1538] Step 8:

[1539] The server uses a generative AI model based on the summarized document information and extracted chat content to automatically generate a base draft of the approval request text. Specifically, it runs a generative AI model written in JavaScript to generate the text based on the input data. The input is the document summary and important chat information, and the output is the base draft text of the approval request text.

[1540] Step 9:

[1541] The server sends the generated base proposal in JSON format to the terminal. Specifically, it returns JSON data in the HTTP response. The input is the base proposal text for the approval request body, and the output is the JSON data sent to the terminal.

[1542] Step 10:

[1543] The terminal displays the received base plan in a text editor. Specifically, it displays it in an editable format using an editor such as Quill. The input is JSON data received from the server, and the output is a text editor display that the user can view and edit.

[1544] Step 11:

[1545] The emotion engine monitors the user's emotional state in real time. Specifically, it collects and analyzes data using the device's webcam, microphone, and keyboard typing speed. The input is facial expression, tone of voice, and typing speed data, and the output is analyzed emotional state data.

[1546] Step 12:

[1547] The server provides appropriate feedback based on the emotional state data. Specifically, it displays a pop-up message on the screen with advice such as "It would be good to change the expression in this part." The input is the analyzed emotional state data, and the output is a feedback message that is displayed to the user.

[1548] Step 13:

[1549] The user checks the generated base draft of the approval request text and makes any necessary corrections in a text editor. Specifically, the user edits the text using the keyboard and clicks the "Finalize" button. The input is the base draft text and the user's editing operations, and the output is the finalized approval request text.

[1550] Step 14:

[1551] The server receives the finalized approval request and generates the application in the specified format using LaTeX or PDFkit. Specifically, it converts the text data into LaTeX format and outputs it as a PDF. The input is the text of the finalized approval request, and the output is a PDF application.

[1552] Step 15:

[1553] The server saves the generated application form in a database and automatically sends it to superiors and other relevant parties as needed. Specifically, it saves the form and sends it via email using the SMTP protocol. The input is the PDF application form, and the output is the URL saved in the database and the email that was sent.

[1554] (Application example 2)

[1555] 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."

[1556] In the process of managing the operation and maintenance of autonomous vehicles, processing complex documents and analyzing chat messages is time-consuming. Furthermore, the lack of a system that can monitor the driver's emotional state in real time and provide appropriate feedback increases the psychological burden on the driver. This reduces operational efficiency and frequently leads to delays in responding to vehicle problems.

[1557] 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.

[1558] In this invention, the server includes means for receiving documents and analyzing them to generate summaries, means for receiving chat content and analyzing it to extract important information, means for automatically generating a base draft of the approval application text based on the summarized document information and the extracted chat content, means for sending the generated base draft to a user terminal so that the user can confirm and edit it, means for recognizing the user's emotional state in real time and providing feedback based on the analysis results, and operation management and analysis means that allow drivers to upload documents and malfunction reports. This makes it possible to streamline operation management and the maintenance application process for autonomous vehicles and reduce the psychological burden on drivers.

[1559] "Means for receiving documents, analyzing them, and generating summaries" refers to a function that allows the server to receive various documents uploaded by users, analyzes the documents using natural language processing technology, and automatically generates summaries.

[1560] "Means for receiving chat content, analyzing it, and extracting important information" refers to a function that allows the server to receive chat content provided by the user, analyzes the conversation using natural language processing technology, and automatically extracts important information necessary for approval applications.

[1561] "Means for automatically generating a base draft of the approval application text based on summarized document information and extracted chat content" refers to a function that automatically creates an initial draft of the approval application text based on the document summary generated by analysis and the extracted chat content.

[1562] "Means for sending the generated base draft to the user's terminal and allowing the user to check and edit it" refers to a function that sends an automatically generated base draft of the approval request body to the user's terminal, allowing the user to check it and edit it as necessary.

[1563] "Means of recognizing the user's emotional state in real time and providing feedback based on the analysis results" refers to a function that uses an emotion engine to detect the user's emotional state in real time from their facial expressions, tone of voice, etc., and provides appropriate advice and feedback based on the analysis results.

[1564] The "operation management means and analysis means that allow drivers to upload documents and malfunction reports" refer to multiple functions within the system that allow drivers to upload various documents related to operation management and vehicle malfunction reports to a server and analyze them.

[1565] "Means for saving the confirmed application after the body of the request for approval has been finalized and generating it in a specified format" refers to a function that, after the user finalizes the body of the request for approval, saves the confirmed application form on the server and generates the application form in a specified format (e.g., PDF format).

[1566] "Means for generating metadata for various documents uploaded by users and associating it with the corresponding approval application data" refers to a function that generates metadata from documents uploaded by users and associates that metadata with the corresponding approval application data.

[1567] To implement the present invention, it is necessary to build a system according to the following steps.

[1568] First, we set up the hardware and software used by the system. The main hardware used includes smart glasses, head-mounted displays, smartphones, and servers. The software used includes spaCy as a natural language processing (NLP) module, OpenAI GPT-4 API as a sentiment analysis engine, PostgreSQL as a database, and Flask (Python) as a server framework.

[1569] Document upload and summary generation

[1570] Users use their smart devices to upload documents (e.g., operation plans, contracts) to the server, which then analyzes the received documents using natural language processing technology (spaCy) and generates summaries of important information (route details, time, driver list, etc.).

[1571] Chat content aggregation and analysis

[1572] Users send chat content from their smart devices to the server, which then analyzes the received chat content using a natural language processing module and extracts important information necessary for approval applications. This allows for efficient organization of important discussion points and agreements exchanged between drivers and operation managers.

[1573] Automatic generation of approval request text

[1574] The server automatically generates a base draft of the approval request text based on the summarized document information and the extracted chat content. The generated base draft includes the route, budget, contract terms, etc. The base draft is sent to the user's device in JSON format so that the user can view and edit it.

[1575] Analysis and feedback by emotion engine

[1576] The emotion engine integrated into the server analyzes data such as facial expressions, tone of voice, and typing speed to recognize the user's emotional state in real time. Based on the emotion data, the emotion engine evaluates stress levels and satisfaction and provides feedback as needed. For example, if the user is feeling stressed, the emotion engine will display suggestions and corrections.

[1577] Driver operation management and breakdown reporting

[1578] Drivers use smart glasses or head-mounted displays to collect data while driving and upload it to a server. When a malfunction report is sent, the server analyzes it using an NLP module and automatically generates a list of necessary repairs and parts, enabling a prompt response.

[1579] Specific examples

[1580] For example, when a dispatcher notifies the system, "Uploading a trip plan," the system responds, "Generating a trip plan summary... Completed." When a driver inputs, "Submitting a malfunction report about an abnormal engine noise," the system responds, "Analyzing the malfunction report... Generated a list of required parts." The emotion engine also provides real-time feedback, such as, "Your fatigue level has reached 85%, we recommend you take a break."

[1581] Prompt Sentence Examples

[1582] Examples of prompts include:

[1583] "Analyze the flight schedule and summarize the key flight information."

[1584] "Analyze driver reports of malfunctions and create a list of necessary repair parts."

[1585] "Analyze the driver's emotional state in real time and provide appropriate feedback."

[1586] This will make it possible to streamline the operation management and maintenance application process for autonomous vehicles and reduce the psychological burden on drivers.

[1587] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1588] Program processing flow

[1589] Step 1: Uploading documents and generating summaries

[1590] Input: The user uploads documents such as operation plans and contracts from the terminal to the server.

[1591] Processing: The server receives and stores the uploaded document, then uses natural language processing technology (spaCy) to analyze the document, extract key information (e.g., route, time, driver list), and generate a summary.

[1592] Output: Generate and save summarized document information.

[1593] Step 2: Chat content aggregation and analysis

[1594] Input: The user copies the chat contents with the agent from the terminal and sends them to the server.

[1595] Processing: The server stores the received chat content and analyzes it using a natural language processing module. It then extracts important information (e.g., discussion points and agreements) necessary for the approval process.

[1596] Output: Generate and store the extracted important information.

[1597] Step 3: Automatic generation of the approval request text

[1598] Input: Summarized document information and extracted chat transcripts.

[1599] Processing: The server integrates this information and automatically generates a base draft for the approval request, which includes route, budget, contract terms, etc.

[1600] Output: Send the automatically generated base draft of the approval request body to the terminal in JSON format.

[1601] Step 4: Analysis and feedback by the emotion engine

[1602] Input: Data such as the user's facial expressions, tone of voice, and typing speed.

[1603] Processing: The emotion engine (OpenAI GPT-4 API) integrated into the server analyzes these data in real time, evaluates the user's emotional state, and generates appropriate feedback based on the user's stress level and satisfaction.

[1604] Output: Provides feedback to the user based on the analysis results.

[1605] Step 5: Driver management and breakdown reporting

[1606] Input: Driving data and breakdown reports collected by drivers using smart devices.

[1607] Processing: The server receives this data and analyzes it using the NLP module, after which it automatically generates the necessary repairs and parts list.

[1608] Output: The repair items and parts list generated as a result of the analysis are provided to the operation manager.

[1609] Step 6: Finalize and generate application

[1610] Input: A base draft of the approval request body that the user has reviewed and edited.

[1611] Processing: When the user makes a final confirmation and presses the "Finalize" button, the server receives the data and generates the application form in the specified format, such as PDF.

[1612] Output: Save the generated application form and automatically send it to the relevant parties as needed.

[1613] Through these steps, the system can streamline the operation management and maintenance application process for autonomous vehicles and reduce the psychological burden on drivers.

[1614] 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.

[1615] 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.

[1616] 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.

[1617] 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.

[1618] 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.

[1619] 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.

[1620] 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).

[1621] 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.

[1622] 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."

[1623] 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.

[1624] 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).

[1625] 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.

[1626] 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.

[1627] 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.

[1628] 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.

[1629] 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.

[1630] 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.

[1631] 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.

[1632] 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.

[1633] 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.

[1634] 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.

[1635] The following is further disclosed regarding the above embodiment.

[1636] (Claim 1)

[1637] means for receiving the document and parsing it to generate a summary;

[1638] A means for receiving and analyzing chat content to extract important information;

[1639] A means for automatically generating a base draft of the approval request text based on the summarized document information and the extracted chat content;

[1640] A means for transmitting the generated base proposal to a user terminal so that the user can check and edit it;

[1641] A system including:

[1642] (Claim 2)

[1643] The system of claim 1, further comprising means for saving the finalized application and generating it in a specified format after finalization of the request body is completed.

[1644] (Claim 3)

[1645] 10. The system of claim 1, further comprising means for generating metadata for various documents uploaded by a user and associating the metadata with corresponding approval request data.

[1646] "Example 1"

[1647] (Claim 1)

[1648] A means for a user to select various data using a terminal and upload it to a server;

[1649] a means for the server to receive and store the uploaded documents;

[1650] a means for analyzing the document using an analysis algorithm on the server, extracting important information, and generating a summary;

[1651] a means for the server to receive and store the uploaded chat content;

[1652] A means for analyzing chat content using a natural language processing module and extracting important information;

[1653] A means for automatically generating a base draft of the approval request text using a generative AI model based on the summarized document information and the extracted chat content;

[1654] A means for transmitting the generated base proposal to a user terminal so that the user can check and edit it;

[1655] A system including:

[1656] (Claim 2)

[1657] The system of claim 1, further comprising means for saving the finalized request for approval in a specified format and automatically sending it to necessary parties after the request for approval body has been finalized.

[1658] (Claim 3)

[1659] The system of claim 1, further comprising means for generating metadata for various data uploaded by a user and associating the metadata with the corresponding approval request data.

[1660] "Application Example 1"

[1661] Rewritten claims

[1662] (Claim 1)

[1663] means for receiving the document and parsing it to generate a summary;

[1664] A means for receiving and analyzing chat content to extract important information;

[1665] A means for automatically generating a base draft of the approval request text based on the summarized document information and the extracted chat content;

[1666] A means for transmitting the generated base proposal to a user terminal so that the user can check and edit it;

[1667] A means of analyzing product inventory information and automatically generating order requests;

[1668] means for transmitting the generated order request to a user terminal so that the user can check and edit the order request;

[1669] A system including:

[1670] (Claim 2)

[1671] The system of claim 1, further comprising means for saving the finalized application and generating it in a specified format after finalization of the request body is completed.

[1672] (Claim 3)

[1673] 10. The system of claim 1, further comprising means for generating metadata for various documents uploaded by a user and associating the metadata with corresponding approval request data.

[1674] "Example 2: Combining Emotion Engines"

[1675] (Claim 1)

[1676] means for receiving the document and parsing it to generate a summary;

[1677] A means for receiving and analyzing chat content to extract important information;

[1678] A means for automatically generating a base draft of the approval request text based on the summarized document information and the extracted chat content;

[1679] A means for transmitting the generated base proposal to a user terminal so that the user can check and edit it;

[1680] means for recognizing a user's emotional state and providing feedback based thereon;

[1681] A system including:

[1682] (Claim 2)

[1683] The system of claim 1, further comprising means for saving the finalized application and generating it in a specified format after finalization of the request body is completed.

[1684] (Claim 3)

[1685] 10. The system of claim 1, further comprising means for generating metadata for various documents uploaded by a user and associating the metadata with corresponding approval request data.

[1686] "Application example 2 when combining emotion engines"

[1687] (Claim 1)

[1688] means for receiving the document and parsing it to generate a summary;

[1689] A means for receiving and analyzing chat content to extract important information;

[1690] A means for automatically generating a base draft of the approval request text based on the summarized document information and the extracted chat content;

[1691] A means for transmitting the generated base proposal to a user terminal so that the user can check and edit it;

[1692] means for recognizing the user's emotional state in real time and providing feedback based on the analysis results;

[1693] a means for managing and analyzing the operation of the vehicle, which allows the driver to upload documents and fault reports;

[1694] A system including:

[1695] (Claim 2)

[1696] The system of claim 1, further comprising means for saving the finalized application and generating it in a specified format after finalization of the request body is completed.

[1697] (Claim 3)

[1698] 10. The system of claim 1, further comprising means for generating metadata for various documents uploaded by a user and associating the metadata with corresponding approval request data. [Explanation of symbols]

[1699] 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. means for receiving the document and parsing it to generate a summary; A means for receiving and analyzing chat content to extract important information; A means for automatically generating a base draft of the approval request text based on the summarized document information and the extracted chat content; A means for transmitting the generated base proposal to a user terminal so that the user can check and edit it; A system including:

2. The system according to claim 1 , further comprising means for storing the finalized application and generating it in a specified format after the body of the approval application has been finalized.

3. The system of claim 1 further comprising means for generating metadata for various documents uploaded by a user and associating the metadata with corresponding approval request data.

Images