System

Abstract

A system is provided.SOLUTION: A system comprising: means for providing a user interface for uploading a document; means for inputting a proofing condition; means for receiving and storing the uploaded document and the proofing condition; means for submitting the received document and proofing condition to a generative model for proofing; means for receiving a proofing result from the generative model; means for displaying the proofing result in the user interface; and means for making the finally proofed document downloadable.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] Traditional document proofreading requires a large number of staff and a lot of time, often disrupting normal business operations. Furthermore, manual proofreading is prone to human error and can lack accuracy. This problem is particularly pronounced when proofreading securities reports, quarterly reports, documents required by the Companies Act, and English disclosure documents, which require fast and accurate output. Therefore, there is a need for a method to streamline document proofreading and significantly reduce the number of staff and time required. [Means for solving the problem]

[0005] The present invention provides a system including a means for providing a user interface for uploading documents, a means for inputting proofreading conditions, a means for receiving and saving the uploaded document and the proofreading conditions, a means for sending the received document and the proofreading conditions to a generative model to request proofreading, a means for receiving proofreading results from the generative model, a means for displaying the proofreading results on the user interface, and a means for making the final proofread document available for download. The present invention also includes a means for encrypting confidential information and replacing it with obscured characters or dummy characters before sending it to the generative model. The system also includes a means for allowing the user to make additional corrections to the proofreading results. This improves the efficiency of document proofreading work, significantly reducing the number of people and time required, while improving accuracy.

[0006] "User interface" refers to the screen and operation method that allows a user to interact with a system.

[0007] "Proofreading conditions" refer to the specific standards or rules used when revising a document.

[0008] A "generative model" refers to an algorithm or program that uses artificial intelligence to automatically analyze and modify the content of a document.

[0009] "Confidential information" refers to important data or information that should be kept private or restricted for a company or individual.

[0010] "Encryption" refers to the technology of converting information so that it cannot be understood by third parties, thereby maintaining security.

[0011] "Hidden characters" refers to substitute characters used to conceal certain characters or information.

[0012] "Dummy" refers to false data used to hide real data or information.

[0013] "Proofreading results" refers to information about the document corrected by the generative model and the corrections made.

[0014] "Final confirmation" refers to the act of the user finally checking the content and approving that the corrections have been completed.

[0015] "Downloading" refers to the act of importing data stored on a server into a terminal. [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] The present invention relates to a system for efficiently proofreading a particular document, which has the following main functions:

[0038] 1. The user uploads the document to be proofread.

[0039] A user logs in to the system using a browser and uploads a document to be proofread. The uploaded document is received by the server and temporarily stored. The user interface is designed to make it easy to select and upload a document file.

[0040] 2. The server presents the calibration conditions

[0041] The server provides the user with an interface for setting proofreading conditions, such as using only "desu" and "masu," using only Gregorian calendar years, and using only half-width numbers.

[0042] 3. The server sends a proofreading request to the generative model.

[0043] The server sends the proofreading conditions entered by the user and the uploaded document to a generative model (artificial intelligence model). The generative model analyzes the content of the document and makes corrections based on the proofreading conditions.

[0044] 4. The generative model performs calibration

[0045] The generative model proofreads the content in the document, highlighting any corrections and adding annotations to make the corrections easier to understand, allowing the user to see at a glance what has been changed and how.

[0046] 5. The generative model sends the calibration results back to the server

[0047] The generative model returns the proofread document to the server, which receives and stores the proofread results.

[0048] 6. The server presents the proofreading results to the user

[0049] The server receives the proofreading results and displays them on the user interface, allowing the user to review the proofreading results and check the content of the revised document.

[0050] 7. User performs final confirmation and downloads document

[0051] The user checks the proofreading results and makes additional corrections if necessary. After completing the corrections, the user clicks the final confirmation button to complete the proofreading process. The server generates the final proofread document and makes it available for the user to download.

[0052] Specific examples

[0053] Example 1:

[0054] An employee in the finance and accounting department uploads a draft of a quarterly report to the system. The user enters the following into the proofreading condition input form: "Use honorific expressions consistently using '~desu' and '~masu'," "Write dates and times in the Gregorian calendar," and "Use numbers consistently in half-width characters." The server receives this and sends it to the generative model. The generative model proofreads the draft according to the conditions and returns a document with the corrections highlighted. The user checks the proofreading results and finally downloads the revised quarterly report.

[0055] Example 2:

[0056] A sales manager requests proofreading of an English disclosure document. The user uploads the document and enters proofreading conditions, such as "consistency in American English," "display numbers in half-width characters," and "use periods as punctuation marks." The server sends this to the generative model, which then proofreads it based on the conditions. The proofreading results are highlighted, and the user can review them and download the final revised English disclosure document.

[0057] These examples clearly demonstrate the system's functionality and flow, which can significantly reduce time and errors compared to traditional manual calibration procedures.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] The user accesses the login screen, enters authentication information, and clicks the login button. The device sends the authentication information to the server. The server verifies the authentication information, and if the user has valid access rights, the dashboard screen is displayed.

[0061] Step 2:

[0062] The user clicks the document upload button and selects the document file to be proofread. The terminal sends a request to upload the selected document file to the server. The server receives the document file and saves it in a temporary directory.

[0063] Step 3:

[0064] The server provides the user with an interface for setting proofreading conditions. The user enters conditions into the proofreading condition input form, such as using "~desu" and "~masu" consistently, using the Gregorian calendar, and using half-width numbers, and then clicks the send button. The terminal then sends these proofreading conditions to the server.

[0065] Step 4:

[0066] The server executes an API request to send the received document file and proofreading conditions to the generative model (artificial intelligence model). The server sends the document file and proofreading conditions to the generative model.

[0067] Step 5:

[0068] The generative model analyzes the received document and the proofreading conditions, performs the proofreading, corrects the content in the document, highlights the corrections, and adds annotations, and then sends the proofread document back to the server.

[0069] Step 6:

[0070] The server receives and stores the calibration results returned from the generative model. The server generates an interface to display the calibration results and presents them to the user. The user checks the calibration results and makes additional corrections as necessary.

[0071] Step 7:

[0072] The user clicks the final confirmation button to complete the proofreading. The server generates the final proofread document and provides a link for the user to download. The device allows the user to download the final file.

[0073] Example 1

[0074] 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."

[0075] Conventional document proofreading work is time-consuming and prone to errors, so there was a need for a system that could proofread documents efficiently and accurately.

[0076] 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.

[0077] In this invention, the server includes means for providing a user interface for uploading documents, means for inputting proofreading conditions, means for receiving and saving the uploaded documents and proofreading conditions, means for sending the received documents and proofreading conditions to a generative AI model and requesting proofreading, means for receiving proofreading results from the generative AI model, means for displaying the proofreading results on a user interface, highlighting corrections and adding annotations, and means for making the final proofread document available for download, thereby improving the efficiency and accuracy of document proofreading.

[0078] The "means for providing a user interface for uploading a document" is a function for providing an interface that a user uses to send a document file to be proofread to the system.

[0079] The "means for inputting proofreading conditions" is a function that provides an interface for a user to input specific conditions or rules required for proofreading a document.

[0080] "Means for receiving and storing uploaded documents and proofreading conditions" refers to a function that receives document files uploaded by users and proofreading conditions entered by users on the server side and stores them temporarily or permanently.

[0081] "Means for sending the received document and proofreading conditions to the generative AI model and requesting proofreading" is a function in which the server sends the received document file and proofreading conditions to the generative AI model and requests it to proofread the document.

[0082] "Means for receiving proofreading results from the generative AI model" refers to a function that receives the result data of a document that has been proofread by the generative AI model on the server side.

[0083] "Means for displaying proofreading results on a user interface, highlighting corrections, and adding annotations" refers to a function that presents a document proofread by a generative AI model to the user, highlights corrections with color or underlines to make them visually easier to understand, and adds necessary explanations as annotations.

[0084] The "means for making the final proofread document available for download" is a download function provided to enable the user to obtain the final proofread document file from the system.

[0085] The present invention relates to a system for efficiently proofreading a specific document, which has the following main functions:

[0086] A user logs in to the system using a browser on their device and uploads a document to be proofread. At this time, the browser sends user authentication information to the server, and the server authenticates the user. The user interface is designed to allow users to easily select document files and upload them to the system. The uploaded document is received by the server and temporarily stored.

[0087] Next, the server provides the user with an interface for setting proofreading conditions. Through this interface, the user can input proofreading conditions such as using only "desu" and "masu," using only Gregorian calendar years, and using only half-width numbers. These proofreading conditions are recorded by the server.

[0088] The server sends the user-entered proofreading criteria and the uploaded document to the generative AI model via an HTTP request. The generative AI model analyzes the content of the document and makes corrections based on the proofreading criteria. For example, it uses natural language processing technology to unify the writing style and format.

[0089] The generative AI model can also highlight and annotate corrections to make them easier to understand. The corrected document is then sent back to the server, which receives and stores the proofread results.

[0090] The server then displays the proofreading results on the user interface. Users can see at a glance which parts have been corrected and how, through highlighting and annotation of the corrections. If necessary, users can make additional corrections.

[0091] The document that has been finalized is generated by the server so that it can be downloaded by the user. The user can click the final confirmation button, complete the proofreading work, and then download the final proofread document.

[0092] Specific examples

[0093] Example 1:

[0094] An employee in the finance and accounting department uploads a draft of a quarterly report to the system. The user enters the following into the proofreading condition input form: "Use honorific expressions consistently using 'desu' and 'masu'," "Write dates and times in the Gregorian calendar format," and "Use half-width numbers consistently." The server receives this and sends a prompt to the generative AI model: "Please proofread the document to use consistent writing style using the desu / masu style, Gregorian calendar format, and half-width numbers consistently." The generative AI model proofreads the draft according to the conditions and returns the document with the corrections highlighted. The user checks the proofreading results and finally downloads the revised quarterly report.

[0095] Example 2:

[0096] A sales manager requests proofreading of an English disclosure document. The user uploads the document and enters proofreading conditions such as "consistent use of American English," "display numbers in half-width characters," and "use periods as punctuation marks." The server sends this to the generative AI model, which generates a prompt: "Please proofread the document's English expressions in American English, using half-width characters and periods." The generative model proofreads based on the conditions. The proofreading results are highlighted, and the user can review them and download the final revised English disclosure document.

[0097] These examples clearly demonstrate the system's functionality and flow, which can significantly reduce time and errors compared to traditional manual calibration procedures.

[0098] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0099] Program processing flow

[0100] Step 1: User uploads document to be proofread

[0101] Input: A user logs into the system using a browser on a terminal and selects a document file.

[0102] Processing: The user clicks the "Upload Document" button, and the terminal sends the selected document file to the server.

[0103] Output: The server temporarily stores the received document file in storage.

[0104] Specific behavior: The browser opens a file selection dialog, the user selects a document file, and presses the upload button.

[0105] Step 2: The server submits the calibration conditions

[0106] Input: The request after the user uploads a document.

[0107] Processing: The server displays the proofreading condition input form on the user interface.

[0108] Output: A form is displayed where the user can enter the calibration criteria.

[0109] What happens: The server generates an HTML form and sends it to the device's browser. The user interface displays options such as "uniform writing style," "number format," and "date and time format."

[0110] Step 3: User inputs calibration conditions

[0111] Input: The user inputs the calibration conditions in the specified interface.

[0112] Process: The user clicks the "Submit" button and the entered calibration conditions are sent to the server.

[0113] Output: The calibration conditions are saved on the server.

[0114] Specific actions: This includes the user entering values ​​into the input fields for each condition and pressing the submit button.

[0115] Step 4: The server sends a proofreading request to the generative model

[0116] Input: Document files stored on the server and proofreading requirements.

[0117] Processing: The server generates a prompt based on the document file and proofreading conditions, and sends it to the generative AI model via an HTTP request.

[0118] Output: The generative AI model receives data for calibration.

[0119] Specific behavior: The server constructs a prompt and sends a request to the API endpoint. Example prompt: 'Please proofread the document to ensure it uses consistent writing style, the Western calendar year format, and half-width numbers.'

[0120] Step 5: The generative model performs calibration

[0121] Input: Received document file and prompt text.

[0122] Processing: The generative AI model analyzes the document content and corrects it based on the specified proofreading criteria.

[0123] Output: Document data after proofreading is complete.

[0124] What it does: The generative AI model uses natural language processing techniques to unify style and format, highlight corrections, and annotate them.

[0125] Step 6: The generative model sends the calibration results back to the server

[0126] Input: Document data after proofreading is complete.

[0127] Processing: The generative AI model sends the modified document back to the server.

[0128] Output: The calibration results are saved on the server.

[0129] Specific operation: The generative AI model sends the proofreading results to the server via an HTTP response, which is then received and stored by the server.

[0130] Step 7: The server presents the proof to the user

[0131] Input: Calibration results stored on the server.

[0132] Processing: The server displays the calibration results on the user interface.

[0133] Output: The modified document is displayed to the user.

[0134] What happens: The server generates an HTML page, highlights the proofreading results and corrections, annotates it, and sends it to the user's browser.

[0135] Step 8: User provides final confirmation and downloads document

[0136] Input: User action to review the proof results.

[0137] Processing: The user clicks the final confirmation button, and if any additional corrections are made, these are also sent to the server.

[0138] Output: You will be provided with a download link for the final proofread document file.

[0139] Specific behavior: The user checks the proofreading results, makes any necessary corrections, and clicks the final confirmation button. The server generates the final document and displays a page with a download link.

[0140] (Application example 1)

[0141] 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."

[0142] In the manufacturing industry, inconsistent content and typographical errors in operation guides and maintenance procedure documents can lead to confusion on-site and human error, hindering efficient work. Furthermore, manual proofreading is time-consuming and labor-intensive, increasing the risk of work delays and human error. To solve these problems, a system is needed that can efficiently and accurately proofread documents and easily check them on-site.

[0143] 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.

[0144] In this invention, the server includes means for providing a user interface for uploading documents, means for inputting proofreading conditions, means for receiving and saving the uploaded document and the proofreading conditions, means for sending the document to a generative model to request proofreading, means for receiving proofreading results from the generative model, means for displaying the proofreading results on the user interface, means for making the final proofread document available for download, and, if the document to be proofread is an operation guide or maintenance procedure document for manufacturing equipment, means for highlighting corrections on a display device to support rapid on-site response. This enables standardization and accurate proofreading of operation guide and maintenance procedure documents at manufacturing sites, reduces human error, and enables efficient operation.

[0145] "Document" means a document containing specific information, such as an operating guide for manufacturing equipment or a maintenance procedure document.

[0146] "User Interface" means the interface through which a user interacts with the system to upload documents and enter proofing requirements.

[0147] "Proofreading conditions" are specific conditions set by the user to unify the content of a document and correct typographical errors.

[0148] A "generative model" is an artificial intelligence model that proofreads uploaded documents based on proofreading conditions set by the user.

[0149] "Upload" is the act of a user transferring a document from a local device to a server.

[0150] A "server" is a computer system that stores documents and proofreading conditions received through a user interface and sends proofreading requests to a generative model.

[0151] "Highlight display" is a display method that visually emphasizes the parts that have been corrected by proofreading.

[0152] A "display device" is a display device provided to a user so that the calibration results can be viewed.

[0153] "Downloading" is the act of a user transferring the final proofed document from the server to their device.

[0154] "On-site response" is a response method to support fast and accurate work at the manufacturing site.

[0155] The system for implementing this invention mainly consists of a server, a terminal, and a generative AI model. A user uses the terminal to upload a document and enter proofreading conditions. The server sends the received document and proofreading conditions to the generative AI model, which then performs the proofreading process. After proofreading is complete, the proofreading results from the generative AI model are displayed on the user interface, and the final proofread document is made available for download.

[0156] The system is implemented using the following hardware and software:

[0157] 1. Hardware used:

[0158] Server: Responsible for storing documents and proofreading conditions and communicating with the generative AI model.

[0159] Device: A device such as a PC or tablet that users use to upload documents or review proofread results.

[0160] Display Device: A display device that visually presents the calibration results to the user.

[0161] 2. Software used:

[0162] Flask: A lightweight Python web framework that provides user interfaces and server-side processing.

[0163] OpenAI (registered trademark) API: Communicates with generative AI models and performs calibration processes.

[0164] The server first receives a document upload request from the device and stores the document. Next, it sends the document along with the proofreading conditions entered by the user to the generative AI model. The generative AI model proofreads the document based on these inputs and returns the corrected document to the server. The server receives the proofreading results and displays them on a user interface so that the user can easily check them.

[0165] The user can check the displayed calibration results and make any necessary corrections. After final confirmation, the user can download the calibration results. This process enables quick response on-site, improving work efficiency and reducing errors.

[0166] For example:

[0167] The administrator uploads the "Operation Manual.docx" file and enters the following as the proofreading conditions: "Consistent terminology, correct spelling errors." The document is saved on the server and sent to the generative model as a prompt.

[0168] Example prompt:

[0169] Please proofread your document under the following conditions:

[0170] Conditions: Standardize terminology, correct typos

[0171] Documents: (Contents of the operation manual)

[0172] Calibration results:

[0173] This system enables standardization and accurate proofreading of operating guides and maintenance procedure documents at manufacturing sites, supporting efficient operations.

[0174] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0175] Step 1:

[0176] The user uploads a document on the device.

[0177] Input: Document file uploaded by the user

[0178] How it works: The user selects the target document file through the device's browser and clicks the upload button.

[0179] Output: Sending document files from the terminal to the server

[0180] Step 2:

[0181] The server receives the document file and temporarily stores it.

[0182] Input: Uploaded document file

[0183] Operation: The server saves the received document file in the specified directory.

[0184] Output: Path of the saved document file

[0185] Step 3:

[0186] The user inputs the calibration conditions on the terminal.

[0187] Input: Calibration conditions entered by the user

[0188] How it works: The user enters the necessary conditions (e.g., "standardize terminology," "correct typos," etc.) into the proofreading conditions input form on the device's browser.

[0189] Output: Calibration conditions sent from the terminal to the server

[0190] Step 4:

[0191] The server stores the received calibration conditions.

[0192] Input: Calibration conditions

[0193] Operation: The server stores the received calibration conditions in an internal database or in a temporary file.

[0194] Output: Path of saved calibration conditions

[0195] Step 5:

[0196] The server sends the document file and proofreading conditions to the generative AI model.

[0197] Input: Path of saved document file, saved proofreading conditions

[0198] How it works: The server combines the document content with the proofreading conditions to generate a prompt sentence and sends it to the generative AI model via an API.

[0199] Output: Send prompt sentences to the generative AI model and request proofreading

[0200] Step 6:

[0201] The generative AI model proofreads the document and sends the results back to the server.

[0202] Input: prompt text, document file, proofreading conditions

[0203] How it works: The generative AI model analyzes and proofreads a document based on the prompt sentence, and generates proofread results.

[0204] Output: Proofread document data

[0205] Step 7:

[0206] The server receives the calibration results and temporarily stores them.

[0207] Input: Calibration results from a generative AI model

[0208] Operation: The server stores the received calibration results in the specified directory.

[0209] Output: Path of saved calibration results

[0210] Step 8:

[0211] The server displays the calibration results on a user interface.

[0212] Input: Path to saved calibration results

[0213] Operation: The server reads the proofreading result file and generates HTML and JavaScript code to display in the user interface.

[0214] Output: Proofread results displayed in the user's browser

[0215] Step 9:

[0216] The user checks the proofreading results and makes additional corrections as necessary.

[0217] Input: Calibration results on the user interface

[0218] Operation: The user checks the displayed proofreading results, makes any necessary corrections, and sends the corrections to the server.

[0219] Output: Corrected calibration results

[0220] Step 10:

[0221] The user performs a final check and downloads the proofread document.

[0222] Input: Proofreading results that have been finalized by the user

[0223] How it works: The user clicks the Final Confirm button to confirm the final proof and send it to the server, which prepares the final proof for the user to download.

[0224] Output: Path to the final proofreading results that can be downloaded

[0225] This series of steps allows users to efficiently proofread documents and respond quickly on-site.

[0226] 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.

[0227] This invention relates to a system for improving the efficiency of document proofreading and adjusting an interface based on a user's emotions. This system has the following main functions and emotion engine:

[0228] 1. The user uploads the document to be proofread.

[0229] A user logs in to the system using a browser and uploads a document to be proofread. The uploaded document is received by the server and temporarily stored. The user interface is designed to make it easy to select and upload a document file.

[0230] 2. The server presents the calibration conditions

[0231] The server provides the user with an interface for setting proofreading conditions, such as using only "desu" and "masu," using only Gregorian calendar years, and using only half-width numbers.

[0232] 3. The server sends a proofreading request to the generative model.

[0233] The server sends the proofreading conditions entered by the user and the uploaded document to a generative model (artificial intelligence model). The generative model analyzes the content of the document and makes corrections based on the proofreading conditions.

[0234] 4. The generative model performs calibration

[0235] The generative model proofreads the content in the document, highlighting any corrections and adding annotations to make the corrections easier to understand, allowing the user to see at a glance what has been changed and how.

[0236] 5. The generative model sends the calibration results back to the server

[0237] The generative model returns the proofread document to the server, which receives and stores the proofread results.

[0238] 6. The server presents the proofreading results to the user

[0239] The server receives the proofreading results and displays them on the user interface, allowing the user to review the proofreading results and check the content of the revised document.

[0240] 7. User performs final confirmation and downloads document

[0241] The user checks the proofreading results and makes additional corrections if necessary. After completing the corrections, the user clicks the final confirmation button to complete the proofreading process. The server generates the final proofread document and makes it available for the user to download.

[0242] Emotion Engine Functions

[0243] 1. Recognize user emotions

[0244] The device's built-in camera and microphone are used to analyze the user's facial expressions and voice tone in real time, and the server uses an emotion engine to recognize the user's emotions (e.g., joy, anger, sadness, surprise, fear).

[0245] 2. Dynamic interface adjustment

[0246] The emotion engine dynamically changes the interface color and layout based on the user's emotions. For example, if the user is feeling stressed, the colors will be calmer and the navigation will be simplified.

[0247] 3. Providing guide messages and help

[0248] The emotion engine analyzes the user's emotions, and if it determines that the user is feeling stressed, the server will provide guidance messages and help, such as "You seem tired. Please take a break."

[0249] 4. Positive Feedback

[0250] The emotion engine recognizes the user's emotions and if it determines that the user is satisfied, the server will provide positive feedback, such as "Good job!" or "Keep it up!"

[0251] Specific examples

[0252] Example 1:

[0253] An employee in the finance and accounting department uploads a draft of a quarterly report to the system. The user enters the following into the proofreading condition input form: "Use honorific expressions consistent with '~desu' and '~masu'," "Write dates and times in the Gregorian calendar," and "Use numbers consistent with half-width characters." The server receives this and sends it to the generative model. The generative model proofreads the draft according to the conditions and returns the document with the corrections highlighted. The user reviews the proofreading results and finally downloads the corrected quarterly report. If the user feels stressed during the proofreading process, the emotion engine recognizes this and displays a relaxing message.

[0254] Example 2:

[0255] A sales manager requests proofreading of an English disclosure document. The user uploads the document and enters proofreading conditions, such as "consistency in American English," "display numbers in half-width characters," and "use periods as punctuation marks." The server sends this to the generative model, which then proofreads it based on the conditions. The proofreading results are highlighted, and the user can review them and download the final revised English disclosure document. If the user is satisfied, the emotion engine provides positive feedback, such as "Good job!"

[0256] This system can streamline document proofreading and provide support according to the user's feelings, thereby improving the quality and efficiency of work and the user's work experience.

[0257] The processing flow will be explained below.

[0258] Step 1:

[0259] The user logs in to the system using a browser. The terminal enters the user's authentication information (user name and password) and clicks the login button. The terminal sends the authentication information to the server. The server verifies the authentication information, and if the user has valid access rights, the dashboard screen is displayed.

[0260] Step 2:

[0261] The user clicks the document upload button on the dashboard screen and selects the document file to be proofread. The terminal sends a request to upload the selected document file to the server. The server receives the document file and saves it in a temporary directory.

[0262] Step 3:

[0263] The server provides the user with an interface for setting proofreading conditions. The user enters conditions into the proofreading condition input form, such as using the same spelling "desu" and "masu," using the Gregorian calendar, and using half-width numbers, and then clicks the send button. The terminal sends these proofreading conditions to the server. The server saves the received proofreading conditions.

[0264] Step 4:

[0265] The server obtains the document file and the proofreading conditions, and executes an API request to send a proofreading request to the generative model (artificial intelligence model). The server sends the document file and the proofreading conditions to the generative model.

[0266] Step 5:

[0267] The generative model analyzes the received document and the proofreading conditions and performs the proofreading. The generative model corrects the content in the document, highlights the corrections, and adds annotations. The generative model returns the proofread document to the server.

[0268] Step 6:

[0269] The server receives and stores the calibration results returned from the generative model. The server generates an interface to display the calibration results and presents them to the user. The user checks the calibration results and makes additional corrections as necessary.

[0270] Step 7:

[0271] The user clicks the final confirmation button to complete the proofreading. The server generates the final proofread document and provides a link for the user to download. The device allows the user to download the final file.

[0272] Step 8:

[0273] The emotion engine analyzes the user's emotions in real time. The device's camera and microphone are used to collect the user's facial expressions and voice tone. The server uses the emotion engine to recognize the user's emotions (e.g., joy, anger, sadness, surprise, fear).

[0274] Step 9:

[0275] The emotion engine dynamically adjusts the interface based on the user's emotions. For example, if the user is feeling stressed, the color tone and layout of the device's interface will change to provide a relaxing environment.

[0276] Step 10:

[0277] The emotion engine analyzes the user's emotions, and if it determines that the user is feeling stressed, the server displays a guide message or help, such as "You look tired. Please take a break."

[0278] Step 11:

[0279] The emotion engine analyzes the user's emotions, and if it determines that the user is satisfied, the server provides positive feedback, such as displaying messages like "Good job!" or "Keep it up!" on the device.

[0280] Example 2

[0281] 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."

[0282] Document proofreading requires time and effort, and there is also the issue of it being difficult to accurately reflect the proofreading conditions set by the user. Furthermore, there is also a need for efficient, high-quality proofreading while reducing the user's psychological stress, but conventional systems have not sufficiently pursued this goal. Furthermore, it is also necessary to deal with the leaking of confidential information and unauthorized access.

[0283] 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.

[0284] In this invention, the server includes means for providing a user interface for uploading documents, means for inputting proofreading conditions, means for receiving and saving the uploaded document and the proofreading conditions, means for transmitting the received document and the proofreading conditions to a generative model to request proofreading, means for receiving proofreading results from the generative model, means for displaying the proofreading results on the user interface, means for recognizing a user's emotions and dynamically adjusting the interface, means for making the final proofread document available for download, means for encrypting confidential information and replacing it with obscured characters or dummy characters before transmitting it to the generative model, means for using a camera or microphone connected to the terminal to recognize the user's emotions, means for changing the interface to a more subdued color scheme and simplifying navigation when the user is feeling stressed, means for providing guide messages or help when it is determined that the user is feeling stressed, and means for providing positive feedback when it is determined that the user is satisfied. This makes it possible to efficiently proofread documents, provide support according to the user's emotions, obtain high-quality proofreading results, and protect confidential information.

[0285] A "document" is a collection of information that includes text, images, tables, etc., and is in a format such as PDF or Word.

[0286] A "user interface" is a graphical or text-based interface through which a user interacts with a system and provides the means to perform operations.

[0287] "Proofreading conditions" are specific rules and standards regarding document editing and formatting, including, for example, standardization of honorific expressions, the format for writing dates and times, and the method of notating numbers.

[0288] A "generative model" is a computer program that uses artificial intelligence to perform a specific task, such as correcting a document based on proofreading criteria.

[0289] "Confidential information" is information that is important to a company or individual and that must be protected from unauthorized access or disclosure.

[0290] "Encryption" is a technology that converts data using a specific algorithm to make it unreadable to third parties.

[0291] "Hidden characters" are symbols or characters used to hide confidential information, and are a means of concealing the original information.

[0292] "Dummy" is pseudo data used in place of real data to protect confidential information.

[0293] A "terminal" is a device that allows a user to access and operate the system, and includes personal computers, smartphones, etc.

[0294] A "camera" is a device that takes still images and videos and outputs them as video data.

[0295] A "microphone" is a device that picks up sound and outputs it as audio data.

[0296] The "emotion engine" is software that analyzes the user's facial expressions and tone of voice to recognize the user's emotional state in real time.

[0297] "Dynamic adjustment" refers to automatically changing system settings and displays based on real-time data and conditions.

[0298] A "guide message" is a text or audio message that provides the user with instructions and advice on how to use the system.

[0299] "Positive feedback" refers to providing a positive evaluation or message of encouragement for a user's behavior or status.

[0300] This invention relates to a system for improving the efficiency of document proofreading and for adjusting the interface based on the user's emotions. This system combines a user interface for uploading documents, an interface for inputting proofreading conditions, a proofreading process using a generative model, an emotion engine that recognizes the user's emotions, and a dynamic interface adjustment function.

[0301] The specific steps for using the system are as follows: First, the user opens a browser on their device and logs in to the system. They then drag and drop the document to be proofread through the document upload user interface, or select it from the file selection dialog. The uploaded document is sent to the server and temporarily saved.

[0302] The server then presents the user with an interface for entering proofreading conditions. The user uses the proofreading condition input form to enter specific proofreading conditions, such as "use honorific expressions consistently with '~desu' and '~masu'," "dates and times written in the Gregorian calendar," and "numbers consistently in half-width characters." This allows the user's unique proofreading standards to be registered in the system.

[0303] The server then sends the user-entered proofreading criteria and the uploaded document to the generative AI model, which analyzes the document's contents and makes corrections based on the specified proofreading criteria. The generative AI model can highlight and annotate the corrections in the document.

[0304] Once the generative AI model has completed the proofreading, it sends the proofreading results back to the server. The server receives the proofreading results and displays them on the user interface for the user to review. The user can then review the proofreading results and make additional corrections as needed. Once the corrections are complete, the user can click the final confirmation button to download the final proofread document from the server.

[0305] One of the features of this system is its emotion engine, which uses the device's built-in camera and microphone to analyze the user's facial expressions and voice tone in real time to recognize the user's emotional state. For example, if the emotion engine recognizes that the user is feeling stressed, the server will change the interface color to a more subdued tone and simplify navigation. Furthermore, if the server determines that the user is feeling stressed, it will display a guide message such as, "You look tired. Please take a break."

[0306] Furthermore, if the server determines that the user is satisfied, it will provide positive feedback such as "Good job!" or "Keep it up." Similarly, to protect confidential information, the system has the ability to encrypt it and replace it with obscured or dummy characters before sending the document to the generative AI model.

[0307] Specific examples

[0308] Example 1:

[0309] An employee in the finance and accounting department uploads a draft of a quarterly report to the system. The user enters the following into the proofreading condition input form: "Use honorific expressions consistently using '~desu' and '~masu'," "Write dates and times in the Gregorian calendar," and "Use single-byte numbers consistently." The server receives this, formats the data, and sends it to the generative AI model. The generative AI model proofreads the draft according to the conditions and returns the document to the server with the corrections highlighted. The server saves the proofreading results and displays them on the user interface. The user checks the proofreading results and finally downloads the corrected quarterly report. If the user feels stressed during the proofreading process, the device's camera and microphone detect this, and the emotion engine responds by displaying a relaxing message.

[0310] Example 2:

[0311] A sales manager requests proofreading of an English disclosure document. The user uploads the document and enters proofreading conditions such as "consistency in American English," "display numbers in half-width characters," and "use periods as punctuation marks." The server sends this to the generative AI model, which then proofreads it based on the conditions. The proofreading results are highlighted, and the user can review them and download the final revised English disclosure document. If the user is satisfied, the emotion engine provides positive feedback such as "Good job!"

[0312] Prompt Sentence Examples

[0313] Proofread a draft of the quarterly report.

[0314] Condition 1: Use honorific expressions consistently, using "~desu" and "~masu"

[0315] Condition 2: Dates and times are written in the Gregorian calendar

[0316] Condition 3: Numbers must be in half-width characters

[0317] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0318] Step 1:

[0319] A user uploads a document to the system

[0320] Users access the system through a browser and log in by entering their user ID and password. After logging in, users select the document to be proofread using the file selection dialog or the drag-and-drop function and click the upload button.

[0321] Input: Document file to be proofread (PDF, Word, etc.)

[0322] Output: Document file uploaded to the server

[0323] Specific operation: A file selection dialog opens in the browser, the user selects a file and clicks the "Upload" button, which sends the document file to the server.

[0324] Step 2:

[0325] The server receives the document and stores it temporarily.

[0326] The server temporarily stores documents received from users in a specific directory (e.g., / uploads), with a unique file name to avoid conflicts.

[0327] Input: Document file sent by the user

[0328] Output: Temporarily saved document file

[0329] Specific operation: The server receives the file and stores it in the server's storage with a file name that is given a unique identifier.

[0330] Step 3:

[0331] The server provides the user with an interface for inputting calibration conditions.

[0332] The server displays an interface for the user to input proofreading conditions. The input form includes fields for standardizing honorific expressions, date and time formats, number notation, etc.

[0333] Input: None (interface provided)

[0334] Output: User interface showing the proofreading condition input form

[0335] Specific operation: The server generates an HTML form for entering proofreading conditions and displays it in the user's browser. The user enters the proofreading conditions into this form.

[0336] Step 4:

[0337] The user inputs the calibration conditions.

[0338] The user enters the necessary conditions into the proofreading conditions input form and clicks the "Submit" button. For example, they can enter things like "Use honorific expressions consistently, such as '~desu' and '~masu'," "Write dates and times in the Gregorian calendar," and "Use half-width numbers consistently."

[0339] Input: Calibration conditions entered by the user

[0340] Output: Calibration conditions sent to the server

[0341] Specific operation: The user enters the desired proofreading conditions into the form on the browser using text boxes, etc., and clicks the "Submit" button. This operation sends the proofreading conditions to the server.

[0342] Step 5:

[0343] The server stores the calibration conditions

[0344] The server receives the calibration conditions entered by the user and stores them in a database, where they are available for subsequent processing.

[0345] Input: Calibration conditions submitted by the user

[0346] Output: Calibration conditions stored in the database

[0347] Specific operation: The calibration condition data received on the server side is parsed and inserted into the database. This makes the calibration conditions permanent.

[0348] Step 6:

[0349] The server sends a proofreading request to the generative AI model

[0350] The server combines the document file and the proofreading conditions into a single payload and sends it to the generative AI model. It then sends a request to a specific API endpoint to ask the model to perform the proofreading process.

[0351] Input: Uploaded document file, user proofreading conditions

[0352] Output: Proofreading requests sent to the generative AI model

[0353] Specific operation: The document file and proofreading conditions are formatted into JSON format on the server side and sent to the generative AI model via the REST API.

[0354] Step 7:

[0355] Generative AI models analyze and proofread documents

[0356] A generative AI model analyzes the document's content and corrects errors based on the specified proofreading criteria, highlighting corrections and adding annotations as needed.

[0357] Input: The document file and proofreading conditions sent to the generative AI model

[0358] Output: Proofread document file

[0359] How it works: Natural language processing algorithms run within the generative AI model to proofread the document, generating a document with highlights and annotations.

[0360] Step 8:

[0361] The generative AI model sends the calibration results back to the server

[0362] The generative AI model sends the proofread document back to the server, which receives and stores it.

[0363] Input: Proofread results sent from the generative AI model

[0364] Output: Saved proofreading result document file

[0365] Specific operation: The server receives response data from the generative AI model and temporarily stores it.

[0366] Step 9:

[0367] The server displays the calibration results on the user interface

[0368] The server receives and stores the proofreading results, and then displays them on the user interface, allowing the user to review them and check the corrections.

[0369] Input: Saved proofreading result document file

[0370] Output: Calibration results displayed in the user interface

[0371] Specific operation: The server generates and sends HTML for displaying the proofreading results to the browser. The proofreading results are displayed in the user's browser.

[0372] Step 10:

[0373] The user checks the proofreading results and makes any necessary corrections.

[0374] The user checks the proofreading results displayed on the user interface, and makes any necessary corrections. After making the corrections, the user clicks the final confirmation button.

[0375] Input: User operation to check the calibration results

[0376] Output: Final proofread document file

[0377] Specific operation: The user checks the proofreading results in the browser, makes additional corrections using a text editor if necessary, and clicks the final confirmation button.

[0378] Step 11:

[0379] The server generates the finalized document and makes it available for download.

[0380] The server generates a finalized document and saves it in file storage for the user to download. The user clicks the download button to retrieve the finalized document.

[0381] Input: Final verified document file

[0382] Output: A document file that users can download

[0383] Specific operation: The server generates the final document file based on the latest proofreading results and generates a downloadable URL. The user clicks the "Download" button in their browser to obtain the file.

[0384] (Application example 2)

[0385] 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."

[0386] While conventional document proofreading systems aim to efficiently proofread document content, they do not provide support that takes into account the editor's emotional state. This can lead to stress and a lack of concentration during proofreading. Furthermore, the lack of emotional interface adjustments and appropriate feedback can negatively impact the efficiency and quality of the editor's work. Therefore, there is a need for a system that provides support that responds to the editor's emotions, further streamlining proofreading and improving the work experience.

[0387] 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.

[0388] In this invention, the server includes means for providing a user interface for uploading documents, means for inputting proofreading conditions, means for receiving and saving the uploaded document and the proofreading conditions, means for sending the received document and the proofreading conditions to a generative AI model to request proofreading, means for receiving proofreading results from the generative AI model, means for displaying the proofreading results on the user interface, means for making the final proofread document available for download, means for analyzing the editor's emotions, means for dynamically adjusting the interface based on the editor's emotions, means for providing guide messages and help based on the editor's emotions, and means for providing positive feedback in accordance with the editor's emotions. This enables efficient document proofreading work while taking into account the editor's emotional state.

[0389] A "document upload user interface" is an interface that a user uses to upload a document to be proofread into the system.

[0390] The "means for inputting proofreading conditions" is a means for a user to input conditions necessary for proofreading a document.

[0391] The "means for receiving and storing uploaded documents and proofreading conditions" refers to the means by which the system receives and stores documents uploaded by users and proofreading conditions entered by users.

[0392] "Means for sending the received document and proofreading conditions to the generative AI model and requesting proofreading" refers to the means by which the system sends the received document and proofreading conditions to the generative AI model and requests proofreading of the document.

[0393] "Means for receiving proofreading results from the generative AI model" refers to means for receiving proofreading results returned from the generative AI model.

[0394] The "means for displaying the proofreading results on a user interface" is an interface for displaying the received proofreading results to the user.

[0395] "Means for making the final proofread document available for download" refers to the means used by a user to download the final proofread document.

[0396] "Means for analyzing the editor's emotions" refers to a means for analyzing the editor's facial expressions, voice, etc., to determine their emotional state.

[0397] The "means for dynamically adjusting the interface based on the editor's emotions" refers to a means for dynamically changing the color tone and layout of the interface according to the editor's emotional state.

[0398] The "means for providing a guide message or help based on the emotions of an editor" refers to a means for providing an appropriate guide message or help based on the emotional state of an editor.

[0399] "Means for providing positive feedback according to the editor's emotions" refers to means for providing positive feedback according to the editor's emotional state.

[0400] The present invention provides a system for improving the efficiency of document proofreading work and providing dynamic support based on the editor's emotions. An embodiment of this system will be described in detail below.

[0401] Overall system configuration

[0402] The system includes a user interface for uploading documents, a means for inputting proofreading conditions, a means for receiving and storing the uploaded documents and proofreading conditions, a means for sending the received documents and proofreading conditions to a generative AI model to request proofreading, a means for receiving proofreading results from the generative AI model, a means for displaying the proofreading results on the user interface, a means for making the final proofread document available for download, a means for analyzing editor emotions, a means for dynamically adjusting the interface based on the editor emotions, a means for providing guide messages and help based on the editor emotions, and a means for providing positive feedback according to the editor emotions.

[0403] Hardware and software used

[0404] Hardware:

[0405] Devices (PC, smartphone, tablet)

[0406] Camera (for analyzing the editor's facial expressions)

[0407] Microphone (for editor's voice tone analysis)

[0408] software:

[0409] Server-side framework (Django)

[0410] Facial expression analysis software (OpenCV)

[0411] Audio Tone Analysis Software (pyttsx3)

[0412] Generative AI model (OpenAI GPT-3 (registered trademark))

[0413] Database (SQLite)

[0414] Specific operation of the system

[0415] 1. Uploading documents and setting proofreading conditions

[0416] Editors log in to the system from their terminals and upload documents. The user interface is designed to make uploading documents easy. Next, an interface is provided for entering proofreading conditions, and editors enter proofreading conditions such as "consistency of writing style" and "half-width numbers."

[0417] 2. Submitting documents and proofreading requirements

[0418] The server receives and stores the uploaded document and the entered proofreading conditions. This data is then sent to the generative AI model (GPT-3) for proofreading.

[0419] 3. Performing calibration and receiving the results

[0420] The generative AI model proofreads the received document based on the proofreading criteria. The proofread document is highlighted and annotated with each correction. The proofreading results are sent back to the server and saved again.

[0421] 4. View and download calibration results

[0422] The server displays the proofreading results on a user interface. The editor can review the corrections and make additional edits as needed. Finally, the edited document can be downloaded.

[0423] 5. Sentiment Analysis and Dynamic Support

[0424] The system uses the device's camera and microphone to analyze the editor's emotions. It uses OpenCV to analyze facial expressions and pyttsx3 to analyze vocal tone. The editor's emotions are determined in real time, and the interface color tone and layout are dynamically changed accordingly. For example, if the editor is feeling stressed, the color tone will be changed to a relaxing one and navigation will be simplified. If the system determines that the editor is feeling stressed, it will display appropriate guide messages and help. For example, it will display a message such as, "You look tired. Please take a break."

[0425] 6. Positive Feedback

[0426] If the emotion engine determines that the editor is satisfied, the system will provide positive feedback, such as a message like "Nice job! Keep it up!"

[0427] Examples of concrete examples and prompts

[0428] As a concrete example, an editor uploads a blog post to the system and enters the following proofreading criteria:

[0429] "Unify all writing styles to "~desu""

[0430] Dates and times are written in the Gregorian calendar.

[0431] "Unify numbers to half-width"

[0432] An example of a prompt that would be generated based on this condition is:

[0433] Correct this content: 'Today's weather is sunny.' with conditions: 'Use honorific expressions consistent with "~desu," use the Gregorian calendar for dates, and use half-width numbers.'

[0434] By implementing this invention, it is possible to efficiently proofread documents while taking into account the emotional state of the editor, and the editor's work experience is improved through dynamic interface adjustment and appropriate feedback.

[0435] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0436] Step 1:

[0437] A user logs in to the system using a terminal. The user inputs login information (user name, password), which the server receives and authenticates. As an output of successful authentication, a dashboard dedicated to the user is displayed.

[0438] Step 2:

[0439] The user uploads the document to be proofread using the document upload user interface. The input is the uploaded document file, which the server receives and temporarily stores. The output is an upload confirmation message.

[0440] Step 3:

[0441] The user inputs the proofreading conditions using the proofreading conditions input interface. The input is the text of the proofreading conditions, which the server receives and stores. The output displays a confirmation message for the input conditions.

[0442] Step 4:

[0443] The server sends the received document and proofreading conditions to the generative AI model. The input is the saved document and proofreading conditions, and data format conversion and prompt generation are performed. A prompt that clearly indicates the conditions for proofreading is generated and this data is sent to the generative AI model (GPT-3). As an output, a status message indicating that it has been sent to the generative AI model is recorded in the server log.

[0444] Step 5:

[0445] The generative AI model performs the proofreading and sends the proofreading results back to the server. The input is data based on the prompt sentence, and the generative AI model analyzes this data, performs the proofreading, and outputs the results. The server receives the proofreading results and saves them again. The proofreading result data is obtained as the output.

[0446] Step 6:

[0447] The server displays the proofreading results on the user interface. The input is the saved proofreading result data, which is then processed to display in a format that is easy for the user to view. Specifically, the proofreading points are highlighted and annotations are added as necessary. The output is the proofreading results displayed on the user interface.

[0448] Step 7:

[0449] The user checks the proofreading results and makes additional corrections as necessary. The input is the user's corrections, which are received and reflected in the proofreading result data. The output is the final corrected document data.

[0450] Step 8:

[0451] The server generates a link to download the final proofread document. The input is the final corrected document data, and it converts this data into a downloadable format (e.g., PDF or Word file). As an output, a download link is displayed in the user interface.

[0452] Step 9:

[0453] The system analyzes the user's emotions using the device's built-in camera and microphone. The input is camera video and audio data, which are analyzed in real time using OpenCV and pyttsx3. The system determines the emotional state and obtains corresponding data. The output is the editor's analyzed emotional state.

[0454] Step 10:

[0455] The server dynamically adjusts the interface based on the editor's emotions. The input is data on the editor's emotional state, and the color tone and layout of the interface are dynamically changed based on this. Specifically, if the editor is feeling stressed, the color tone is changed to a calmer tone and simple navigation is provided. The output is a dynamic change of the user interface.

[0456] Step 11:

[0457] It provides guide messages and help based on the editor's emotions. The input is the result data of the emotion analysis, and an appropriate message is generated based on this. Specifically, it provides a message such as "You look tired. Please take a break." As an output, the guide message or help is displayed in the user interface.

[0458] Step 12:

[0459] It provides positive feedback based on the editor's sentiment. The input is the result data of sentiment analysis, and based on this, it generates a positive message. Specifically, it provides a message such as "Good job! Keep it up!". As an output, the positive feedback is displayed in the user interface.

[0460] 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.

[0461] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0462] 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.

[0463] [Second embodiment]

[0464] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0465] 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.

[0466] 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).

[0467] 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.

[0468] 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.

[0469] 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).

[0470] 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. 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.

[0471] 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.

[0472] 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.

[0473] 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.

[0474] In the smart glasses 214, 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.

[0475] 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."

[0476] The present invention relates to a system for efficiently proofreading a particular document, which has the following main functions:

[0477] 1. The user uploads the document to be proofread.

[0478] A user logs in to the system using a browser and uploads a document to be proofread. The uploaded document is received by the server and temporarily stored. The user interface is designed to make it easy to select and upload a document file.

[0479] 2. The server presents the calibration conditions

[0480] The server provides the user with an interface for setting proofreading conditions, such as using only "desu" and "masu," using only Gregorian calendar years, and using only half-width numbers.

[0481] 3. The server sends a proofreading request to the generative model.

[0482] The server sends the proofreading conditions entered by the user and the uploaded document to a generative model (artificial intelligence model). The generative model analyzes the content of the document and makes corrections based on the proofreading conditions.

[0483] 4. The generative model performs calibration

[0484] The generative model proofreads the content in the document, highlighting any corrections and adding annotations to make the corrections easier to understand, allowing the user to see at a glance what has been changed and how.

[0485] 5. The generative model sends the calibration results back to the server

[0486] The generative model returns the proofread document to the server, which receives and stores the proofread results.

[0487] 6. The server presents the proofreading results to the user

[0488] The server receives the proofreading results and displays them on the user interface, allowing the user to review the proofreading results and check the content of the revised document.

[0489] 7. User performs final confirmation and downloads document

[0490] The user checks the proofreading results and makes additional corrections if necessary. After completing the corrections, the user clicks the final confirmation button to complete the proofreading process. The server generates the final proofread document and makes it available for the user to download.

[0491] Specific examples

[0492] Example 1:

[0493] An employee in the finance and accounting department uploads a draft of a quarterly report to the system. The user enters the following into the proofreading condition input form: "Use honorific expressions consistent with '~desu' and '~masu'," "Write dates and times in the Gregorian calendar," and "Use numbers consistent with half-width characters." The server receives this and sends it to the generative model. The generative model proofreads the draft according to the conditions and returns a document with the corrections highlighted. The user checks the proofreading results and finally downloads the corrected quarterly report.

[0494] Example 2:

[0495] A sales manager requests proofreading of an English disclosure document. The user uploads the document and enters proofreading conditions, such as "consistency in American English," "display numbers in half-width characters," and "use periods as punctuation marks." The server sends this to the generative model, which then proofreads it based on the conditions. The proofreading results are highlighted, and the user can review them and download the final revised English disclosure document.

[0496] These examples clearly demonstrate the system's functionality and flow, which can significantly reduce time and errors compared to traditional manual calibration procedures.

[0497] The processing flow will be explained below.

[0498] Step 1:

[0499] The user accesses the login screen, enters authentication information, and clicks the login button. The device sends the authentication information to the server. The server verifies the authentication information, and if the user has valid access rights, the dashboard screen is displayed.

[0500] Step 2:

[0501] The user clicks the document upload button and selects the document file to be proofread. The terminal sends a request to upload the selected document file to the server. The server receives the document file and saves it in a temporary directory.

[0502] Step 3:

[0503] The server provides the user with an interface for setting proofreading conditions. The user enters conditions into the proofreading condition input form, such as using "~desu" and "~masu" consistently, using the Gregorian calendar, and using half-width numbers, and then clicks the send button. The terminal then sends these proofreading conditions to the server.

[0504] Step 4:

[0505] The server executes an API request to send the received document file and proofreading conditions to the generative model (artificial intelligence model). The server sends the document file and proofreading conditions to the generative model.

[0506] Step 5:

[0507] The generative model analyzes the received document and the proofreading conditions, performs the proofreading, corrects the content in the document, highlights the corrections, and adds annotations, and then sends the proofread document back to the server.

[0508] Step 6:

[0509] The server receives and stores the calibration results returned from the generative model. The server generates an interface to display the calibration results and presents them to the user. The user checks the calibration results and makes additional corrections as necessary.

[0510] Step 7:

[0511] The user clicks the final confirmation button to complete the proofreading. The server generates the final proofread document and provides a link for the user to download. The device allows the user to download the final file.

[0512] Example 1

[0513] 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."

[0514] Conventional document proofreading work is time-consuming and prone to errors, so there was a need for a system that could proofread documents efficiently and accurately.

[0515] 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.

[0516] In this invention, the server includes means for providing a user interface for uploading documents, means for inputting proofreading conditions, means for receiving and saving the uploaded documents and proofreading conditions, means for sending the received documents and proofreading conditions to a generative AI model and requesting proofreading, means for receiving proofreading results from the generative AI model, means for displaying the proofreading results on a user interface, highlighting corrections and adding annotations, and means for making the final proofread document available for download, thereby improving the efficiency and accuracy of document proofreading.

[0517] The "means for providing a user interface for uploading a document" is a function for providing an interface that a user uses to send a document file to be proofread to the system.

[0518] The "means for inputting proofreading conditions" is a function that provides an interface for a user to input specific conditions or rules required for proofreading a document.

[0519] "Means for receiving and storing uploaded documents and proofreading conditions" refers to a function that receives document files uploaded by users and proofreading conditions entered by users on the server side and stores them temporarily or permanently.

[0520] "Means for sending the received document and proofreading conditions to the generative AI model and requesting proofreading" is a function in which the server sends the received document file and proofreading conditions to the generative AI model and requests it to proofread the document.

[0521] "Means for receiving proofreading results from the generative AI model" refers to a function that receives the result data of a document that has been proofread by the generative AI model on the server side.

[0522] "Means for displaying proofreading results on a user interface, highlighting corrections, and adding annotations" refers to a function that presents a document proofread by a generative AI model to the user, highlights corrections with color or underlines to make them visually easier to understand, and adds necessary explanations as annotations.

[0523] The "means for making the final proofread document available for download" is a download function provided to enable the user to obtain the final proofread document file from the system.

[0524] The present invention relates to a system for efficiently proofreading a specific document, which has the following main functions:

[0525] A user logs in to the system using a browser on their device and uploads a document to be proofread. At this time, the browser sends user authentication information to the server, and the server authenticates the user. The user interface is designed to allow users to easily select document files and upload them to the system. The uploaded document is received by the server and temporarily stored.

[0526] Next, the server provides the user with an interface for setting proofreading conditions. Through this interface, the user can input proofreading conditions such as using only "desu" and "masu," using only Gregorian calendar years, and using only half-width numbers. These proofreading conditions are recorded by the server.

[0527] The server sends the user-entered proofreading criteria and the uploaded document to the generative AI model via an HTTP request. The generative AI model analyzes the content of the document and makes corrections based on the proofreading criteria. For example, it uses natural language processing technology to unify the writing style and format.

[0528] The generative AI model can also highlight and annotate corrections to make them easier to understand. The corrected document is then sent back to the server, which receives and stores the proofread results.

[0529] The server then displays the proofreading results on the user interface. Users can see at a glance which parts have been corrected and how, through highlighting and annotation of the corrections. If necessary, users can make additional corrections.

[0530] The document that has been finalized is generated by the server so that it can be downloaded by the user. The user can click the final confirmation button, complete the proofreading work, and then download the final proofread document.

[0531] Specific examples

[0532] Example 1:

[0533] An employee in the finance and accounting department uploads a draft of a quarterly report to the system. The user enters the following into the proofreading condition input form: "Use honorific expressions consistently using 'desu' and 'masu'," "Write dates and times in the Gregorian calendar format," and "Use half-width numbers consistently." The server receives this and sends a prompt to the generative AI model: "Please proofread the document to use consistent writing style using the desu / masu style, Gregorian calendar format, and half-width numbers consistently." The generative AI model proofreads the draft according to the conditions and returns the document with the corrections highlighted. The user checks the proofreading results and finally downloads the revised quarterly report.

[0534] Example 2:

[0535] A sales manager requests proofreading of an English disclosure document. The user uploads the document and enters proofreading conditions such as "consistent use of American English," "display numbers in half-width characters," and "use periods as punctuation marks." The server sends this to the generative AI model, which generates a prompt: "Please proofread the document's English expressions in American English, using half-width characters and periods." The generative model proofreads based on the conditions. The proofreading results are highlighted, and the user can review them and download the final revised English disclosure document.

[0536] These examples clearly demonstrate the system's functionality and flow, which can significantly reduce time and errors compared to traditional manual calibration procedures.

[0537] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0538] Program processing flow

[0539] Step 1: User uploads document to be proofread

[0540] Input: A user logs into the system using a browser on a terminal and selects a document file.

[0541] Processing: The user clicks the "Upload Document" button, and the terminal sends the selected document file to the server.

[0542] Output: The server temporarily stores the received document file in storage.

[0543] Specific behavior: The browser opens a file selection dialog, the user selects a document file, and presses the upload button.

[0544] Step 2: The server submits the calibration conditions

[0545] Input: The request after the user uploads a document.

[0546] Processing: The server displays the proofreading condition input form on the user interface.

[0547] Output: A form is displayed where the user can enter the calibration criteria.

[0548] What happens: The server generates an HTML form and sends it to the device's browser. The user interface displays options such as "uniform writing style," "number format," and "date and time format."

[0549] Step 3: User inputs calibration conditions

[0550] Input: The user inputs the calibration conditions in the specified interface.

[0551] Process: The user clicks the "Submit" button and the entered calibration conditions are sent to the server.

[0552] Output: The calibration conditions are saved on the server.

[0553] Specific actions: This includes the user entering values ​​into the input fields for each condition and pressing the submit button.

[0554] Step 4: The server sends a proofreading request to the generative model

[0555] Input: Document files stored on the server and proofreading requirements.

[0556] Processing: The server generates a prompt based on the document file and proofreading conditions, and sends it to the generative AI model via an HTTP request.

[0557] Output: The generative AI model receives data for calibration.

[0558] Specific behavior: The server constructs a prompt and sends a request to the API endpoint. Example prompt: 'Please proofread the document to ensure it uses consistent writing style, the Western calendar year format, and half-width numbers.'

[0559] Step 5: The generative model performs calibration

[0560] Input: Received document file and prompt text.

[0561] Processing: The generative AI model analyzes the document content and corrects it based on the specified proofreading criteria.

[0562] Output: Document data after proofreading is complete.

[0563] What it does: The generative AI model uses natural language processing techniques to unify style and format, highlight corrections, and annotate them.

[0564] Step 6: The generative model sends the calibration results back to the server

[0565] Input: Document data after proofreading is complete.

[0566] Processing: The generative AI model sends the modified document back to the server.

[0567] Output: The calibration results are saved on the server.

[0568] Specific operation: The generative AI model sends the proofreading results to the server via an HTTP response, which is then received and stored by the server.

[0569] Step 7: The server presents the proof to the user

[0570] Input: Calibration results stored on the server.

[0571] Processing: The server displays the calibration results on the user interface.

[0572] Output: The modified document is displayed to the user.

[0573] What happens: The server generates an HTML page, highlights the proofreading results and corrections, annotates it, and sends it to the user's browser.

[0574] Step 8: User provides final confirmation and downloads document

[0575] Input: User action to review the proof results.

[0576] Processing: The user clicks the final confirmation button, and if any additional corrections are made, these are also sent to the server.

[0577] Output: You will be provided with a download link for the final proofread document file.

[0578] Specific behavior: The user checks the proofreading results, makes any necessary corrections, and clicks the final confirmation button. The server generates the final document and displays a page with a download link.

[0579] (Application example 1)

[0580] 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."

[0581] In the manufacturing industry, inconsistent content and typographical errors in operation guides and maintenance procedure documents can lead to confusion on-site and human error, hindering efficient work. Furthermore, manual proofreading is time-consuming and labor-intensive, increasing the risk of work delays and human error. To solve these problems, a system is needed that can efficiently and accurately proofread documents and easily check them on-site.

[0582] 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.

[0583] In this invention, the server includes means for providing a user interface for uploading documents, means for inputting proofreading conditions, means for receiving and saving the uploaded document and the proofreading conditions, means for sending the document to a generative model to request proofreading, means for receiving proofreading results from the generative model, means for displaying the proofreading results on the user interface, means for making the final proofread document available for download, and, if the document to be proofread is an operation guide or maintenance procedure document for manufacturing equipment, means for highlighting corrections on a display device to support rapid on-site response. This enables standardization and accurate proofreading of operation guide and maintenance procedure documents at manufacturing sites, reduces human error, and enables efficient operation.

[0584] "Document" means a document containing specific information, such as an operating guide for manufacturing equipment or a maintenance procedure document.

[0585] "User Interface" means the interface through which a user interacts with the system to upload documents and enter proofing requirements.

[0586] "Proofreading conditions" are specific conditions set by the user to unify the content of a document and correct typographical errors.

[0587] A "generative model" is an artificial intelligence model that proofreads uploaded documents based on proofreading conditions set by the user.

[0588] "Upload" is the act of a user transferring a document from a local device to a server.

[0589] A "server" is a computer system that stores documents and proofreading conditions received through a user interface and sends proofreading requests to a generative model.

[0590] "Highlight display" is a display method that visually emphasizes the parts that have been corrected by proofreading.

[0591] A "display device" is a display device provided to a user so that the calibration results can be viewed.

[0592] "Downloading" is the act of a user transferring the final proofed document from the server to their device.

[0593] "On-site response" is a response method to support fast and accurate work at the manufacturing site.

[0594] The system for implementing this invention mainly consists of a server, a terminal, and a generative AI model. A user uses the terminal to upload a document and enter proofreading conditions. The server sends the received document and proofreading conditions to the generative AI model, which then performs the proofreading process. After proofreading is complete, the proofreading results from the generative AI model are displayed on the user interface, and the final proofread document is made available for download.

[0595] The system is implemented using the following hardware and software:

[0596] 1. Hardware used:

[0597] Server: Responsible for storing documents and proofreading conditions and communicating with the generative AI model.

[0598] Device: A device such as a PC or tablet that users use to upload documents or review proofread results.

[0599] Display Device: A display device that visually presents the calibration results to the user.

[0600] 2. Software used:

[0601] Flask: A lightweight Python web framework that provides user interfaces and server-side processing.

[0602] OpenAI API: Communicates with generative AI models and performs calibration.

[0603] The server first receives a document upload request from the device and stores the document. Next, it sends the document along with the proofreading conditions entered by the user to the generative AI model. The generative AI model proofreads the document based on these inputs and returns the corrected document to the server. The server receives the proofreading results and displays them on a user interface so that the user can easily check them.

[0604] The user can check the displayed calibration results and make any necessary corrections. After final confirmation, the user can download the calibration results. This process enables quick response on-site, improving work efficiency and reducing errors.

[0605] For example:

[0606] The administrator uploads the "Operation Manual.docx" file and enters the following as the proofreading conditions: "Consistent terminology, correct spelling errors." The document is saved on the server and sent to the generative model as a prompt.

[0607] Example prompt:

[0608] Please proofread your document under the following conditions:

[0609] Conditions: Standardize terminology, correct typos

[0610] Documents: (Contents of the operation manual)

[0611] Calibration results:

[0612] This system enables standardization and accurate proofreading of operating guides and maintenance procedure documents at manufacturing sites, supporting efficient operations.

[0613] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0614] Step 1:

[0615] The user uploads a document on the device.

[0616] Input: Document file uploaded by the user

[0617] How it works: The user selects the target document file through the device's browser and clicks the upload button.

[0618] Output: Sending document files from the terminal to the server

[0619] Step 2:

[0620] The server receives the document file and temporarily stores it.

[0621] Input: Uploaded document file

[0622] Operation: The server saves the received document file in the specified directory.

[0623] Output: Path of the saved document file

[0624] Step 3:

[0625] The user inputs the calibration conditions on the terminal.

[0626] Input: Calibration conditions entered by the user

[0627] How it works: The user enters the necessary conditions (e.g., "standardize terminology," "correct typos," etc.) into the proofreading conditions input form on the device's browser.

[0628] Output: Calibration conditions sent from the terminal to the server

[0629] Step 4:

[0630] The server stores the received calibration conditions.

[0631] Input: Calibration conditions

[0632] Operation: The server stores the received calibration conditions in an internal database or in a temporary file.

[0633] Output: Path of saved calibration conditions

[0634] Step 5:

[0635] The server sends the document file and proofreading conditions to the generative AI model.

[0636] Input: Path of saved document file, saved proofreading conditions

[0637] How it works: The server combines the document content with the proofreading conditions to generate a prompt sentence and sends it to the generative AI model via an API.

[0638] Output: Send prompt sentences to the generative AI model and request proofreading

[0639] Step 6:

[0640] The generative AI model proofreads the document and sends the results back to the server.

[0641] Input: prompt text, document file, proofreading conditions

[0642] How it works: The generative AI model analyzes and proofreads a document based on the prompt sentence, and generates proofread results.

[0643] Output: Proofread document data

[0644] Step 7:

[0645] The server receives the calibration results and temporarily stores them.

[0646] Input: Calibration results from a generative AI model

[0647] Operation: The server stores the received calibration results in the specified directory.

[0648] Output: Path of saved calibration results

[0649] Step 8:

[0650] The server displays the calibration results on a user interface.

[0651] Input: Path to saved calibration results

[0652] How it works: The server reads the proofreading result file and generates HTML and JavaScript code to display in the user interface.

[0653] Output: Proofread results displayed in the user's browser

[0654] Step 9:

[0655] The user checks the proofreading results and makes additional corrections as necessary.

[0656] Input: Calibration results on the user interface

[0657] Operation: The user checks the displayed proofreading results, makes any necessary corrections, and sends the corrections to the server.

[0658] Output: Corrected calibration results

[0659] Step 10:

[0660] The user performs a final check and downloads the proofread document.

[0661] Input: Proofreading results that have been finalized by the user

[0662] How it works: The user clicks the Final Confirm button to confirm the final proof and send it to the server, which prepares the final proof for the user to download.

[0663] Output: Path to the final proofreading results that can be downloaded

[0664] This series of steps allows users to efficiently proofread documents and respond quickly on-site.

[0665] 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.

[0666] This invention relates to a system for improving the efficiency of document proofreading and adjusting an interface based on a user's emotions. This system has the following main functions and emotion engine:

[0667] 1. The user uploads the document to be proofread.

[0668] A user logs in to the system using a browser and uploads a document to be proofread. The uploaded document is received by the server and temporarily stored. The user interface is designed to make it easy to select and upload a document file.

[0669] 2. The server presents the calibration conditions

[0670] The server provides the user with an interface for setting proofreading conditions, such as using only "desu" and "masu," using only Gregorian calendar years, and using only half-width numbers.

[0671] 3. The server sends a proofreading request to the generative model.

[0672] The server sends the proofreading conditions entered by the user and the uploaded document to a generative model (artificial intelligence model). The generative model analyzes the content of the document and makes corrections based on the proofreading conditions.

[0673] 4. The generative model performs calibration

[0674] The generative model proofreads the content in the document, highlighting any corrections and adding annotations to make the corrections easier to understand, allowing the user to see at a glance what has been changed and how.

[0675] 5. The generative model sends the calibration results back to the server

[0676] The generative model returns the proofread document to the server, which receives and stores the proofread results.

[0677] 6. The server presents the proofreading results to the user

[0678] The server receives the proofreading results and displays them on the user interface, allowing the user to review the proofreading results and check the content of the revised document.

[0679] 7. User performs final confirmation and downloads document

[0680] The user checks the proofreading results and makes additional corrections if necessary. After completing the corrections, the user clicks the final confirmation button to complete the proofreading process. The server generates the final proofread document and makes it available for the user to download.

[0681] Emotion Engine Functions

[0682] 1. Recognize user emotions

[0683] The device's built-in camera and microphone are used to analyze the user's facial expressions and voice tone in real time, and the server uses an emotion engine to recognize the user's emotions (e.g., joy, anger, sadness, surprise, fear).

[0684] 2. Dynamic interface adjustment

[0685] The emotion engine dynamically changes the interface color and layout based on the user's emotions. For example, if the user is feeling stressed, the colors will be calmer and the navigation will be simplified.

[0686] 3. Providing guide messages and help

[0687] The emotion engine analyzes the user's emotions, and if it determines that the user is feeling stressed, the server will provide guidance messages and help, such as "You seem tired. Please take a break."

[0688] 4. Positive Feedback

[0689] The emotion engine recognizes the user's emotions and if it determines that the user is satisfied, the server will provide positive feedback, such as "Good job!" or "Keep it up!"

[0690] Specific examples

[0691] Example 1:

[0692] An employee in the finance and accounting department uploads a draft of a quarterly report to the system. The user enters the following into the proofreading condition input form: "Use honorific expressions consistent with '~desu' and '~masu'," "Write dates and times in the Gregorian calendar," and "Use numbers consistent with half-width characters." The server receives this and sends it to the generative model. The generative model proofreads the draft according to the conditions and returns a document with the corrections highlighted. The user reviews the proofreading results and finally downloads the corrected quarterly report. If the user feels stressed during the proofreading process, the emotion engine recognizes this and displays a relaxing message.

[0693] Example 2:

[0694] A sales manager requests proofreading of an English disclosure document. The user uploads the document and enters proofreading conditions, such as "consistency in American English," "display numbers in half-width characters," and "use periods as punctuation marks." The server sends this to the generative model, which then proofreads it based on the conditions. The proofreading results are highlighted, and the user can review them and download the final revised English disclosure document. If the user is satisfied, the emotion engine provides positive feedback, such as "Good job!"

[0695] This system can streamline document proofreading and provide support according to the user's feelings, thereby improving the quality and efficiency of work and the user's work experience.

[0696] The processing flow will be explained below.

[0697] Step 1:

[0698] The user logs in to the system using a browser. The terminal enters the user's authentication information (user name and password) and clicks the login button. The terminal sends the authentication information to the server. The server verifies the authentication information, and if the user has valid access rights, the dashboard screen is displayed.

[0699] Step 2:

[0700] The user clicks the document upload button on the dashboard screen and selects the document file to be proofread. The terminal sends a request to upload the selected document file to the server. The server receives the document file and saves it in a temporary directory.

[0701] Step 3:

[0702] The server provides the user with an interface for setting proofreading conditions. The user enters conditions into the proofreading condition input form, such as using the same spelling "desu" and "masu," using the Gregorian calendar, and using half-width numbers, and then clicks the send button. The terminal sends these proofreading conditions to the server. The server saves the received proofreading conditions.

[0703] Step 4:

[0704] The server obtains the document file and the proofreading conditions, and executes an API request to send a proofreading request to the generative model (artificial intelligence model). The server sends the document file and the proofreading conditions to the generative model.

[0705] Step 5:

[0706] The generative model analyzes the received document and the proofreading conditions and performs the proofreading. The generative model corrects the content in the document, highlights the corrections, and adds annotations. The generative model returns the proofread document to the server.

[0707] Step 6:

[0708] The server receives and stores the calibration results returned from the generative model. The server generates an interface to display the calibration results and presents them to the user. The user checks the calibration results and makes additional corrections as necessary.

[0709] Step 7:

[0710] The user clicks the final confirmation button to complete the proofreading. The server generates the final proofread document and provides a link for the user to download. The device allows the user to download the final file.

[0711] Step 8:

[0712] The emotion engine analyzes the user's emotions in real time. The device's camera and microphone are used to collect the user's facial expressions and voice tone. The server uses the emotion engine to recognize the user's emotions (e.g., joy, anger, sadness, surprise, fear).

[0713] Step 9:

[0714] The emotion engine dynamically adjusts the interface based on the user's emotions. For example, if the user is feeling stressed, the color tone and layout of the device's interface will change to provide a relaxing environment.

[0715] Step 10:

[0716] The emotion engine analyzes the user's emotions, and if it determines that the user is feeling stressed, the server displays a guide message or help, such as "You look tired. Please take a break."

[0717] Step 11:

[0718] The emotion engine analyzes the user's emotions, and if it determines that the user is satisfied, the server provides positive feedback, such as displaying messages like "Good job!" or "Keep it up!" on the device.

[0719] Example 2

[0720] 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."

[0721] Document proofreading requires time and effort, and there is also the issue of it being difficult to accurately reflect the proofreading conditions set by the user. Furthermore, there is also a need for efficient, high-quality proofreading while reducing the user's psychological stress, but conventional systems have not sufficiently pursued this goal. Furthermore, it is also necessary to deal with the leaking of confidential information and unauthorized access.

[0722] 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.

[0723] In this invention, the server includes means for providing a user interface for uploading documents, means for inputting proofreading conditions, means for receiving and saving the uploaded document and the proofreading conditions, means for transmitting the received document and the proofreading conditions to a generative model to request proofreading, means for receiving proofreading results from the generative model, means for displaying the proofreading results on the user interface, means for recognizing a user's emotions and dynamically adjusting the interface, means for making the final proofread document available for download, means for encrypting confidential information and replacing it with obscured characters or dummy characters before transmitting it to the generative model, means for using a camera or microphone connected to the terminal to recognize the user's emotions, means for changing the interface to a more subdued color scheme and simplifying navigation when the user is feeling stressed, means for providing guide messages or help when it is determined that the user is feeling stressed, and means for providing positive feedback when it is determined that the user is satisfied. This makes it possible to efficiently proofread documents, provide support according to the user's emotions, obtain high-quality proofreading results, and protect confidential information.

[0724] A "document" is a collection of information that includes text, images, tables, etc., and is in a format such as PDF or Word.

[0725] A "user interface" is a graphical or text-based interface through which a user interacts with a system and provides the means to perform operations.

[0726] "Proofreading conditions" are specific rules and standards regarding document editing and formatting, including, for example, standardization of honorific expressions, the format for writing dates and times, and the method of notating numbers.

[0727] A "generative model" is a computer program that uses artificial intelligence to perform a specific task, such as correcting a document based on proofreading criteria.

[0728] "Confidential information" is information that is important to a company or individual and that must be protected from unauthorized access or disclosure.

[0729] "Encryption" is a technology that converts data using a specific algorithm to make it unreadable to third parties.

[0730] "Hidden characters" are symbols or characters used to hide confidential information, and are a means of concealing the original information.

[0731] "Dummy" is pseudo data used in place of real data to protect confidential information.

[0732] A "terminal" is a device that allows a user to access and operate the system, and includes personal computers, smartphones, etc.

[0733] A "camera" is a device that takes still images and videos and outputs them as video data.

[0734] A "microphone" is a device that picks up sound and outputs it as audio data.

[0735] The "emotion engine" is software that analyzes the user's facial expressions and tone of voice to recognize the user's emotional state in real time.

[0736] "Dynamic adjustment" refers to automatically changing system settings and displays based on real-time data and conditions.

[0737] A "guide message" is a text or audio message that provides the user with instructions and advice on how to use the system.

[0738] "Positive feedback" refers to providing a positive evaluation or message of encouragement for a user's behavior or status.

[0739] This invention relates to a system for improving the efficiency of document proofreading and for adjusting the interface based on the user's emotions. This system combines a user interface for uploading documents, an interface for inputting proofreading conditions, a proofreading process using a generative model, an emotion engine that recognizes the user's emotions, and a dynamic interface adjustment function.

[0740] The specific steps for using the system are as follows: First, the user opens a browser on their device and logs in to the system. They then drag and drop the document to be proofread through the document upload user interface, or select it from the file selection dialog. The uploaded document is sent to the server and temporarily saved.

[0741] The server then presents the user with an interface for entering proofreading conditions. The user uses the proofreading condition input form to enter specific proofreading conditions, such as "use honorific expressions consistently with '~desu' and '~masu'," "dates and times written in the Gregorian calendar," and "numbers consistently in half-width characters." This allows the user's unique proofreading standards to be registered in the system.

[0742] The server then sends the user-entered proofreading criteria and the uploaded document to the generative AI model, which analyzes the document's contents and makes corrections based on the specified proofreading criteria. The generative AI model can highlight and annotate the corrections in the document.

[0743] Once the generative AI model has completed the proofreading, it sends the proofreading results back to the server. The server receives the proofreading results and displays them on the user interface for the user to review. The user can then review the proofreading results and make additional corrections as needed. Once the corrections are complete, the user can click the final confirmation button to download the final proofread document from the server.

[0744] One of the features of this system is its emotion engine, which uses the device's built-in camera and microphone to analyze the user's facial expressions and voice tone in real time to recognize the user's emotional state. For example, if the emotion engine recognizes that the user is feeling stressed, the server will change the interface color to a more subdued tone and simplify navigation. Furthermore, if the server determines that the user is feeling stressed, it will display a guide message such as, "You look tired. Please take a break."

[0745] Furthermore, if the server determines that the user is satisfied, it will provide positive feedback such as "Good job!" or "Keep it up." Similarly, to protect confidential information, the system has the ability to encrypt it and replace it with obscured or dummy characters before sending the document to the generative AI model.

[0746] Specific examples

[0747] Example 1:

[0748] An employee in the finance and accounting department uploads a draft of a quarterly report to the system. The user enters the following into the proofreading condition input form: "Use honorific expressions consistently using '~desu' and '~masu'," "Write dates and times in the Gregorian calendar," and "Use numbers consistently in half-width characters." The server receives this, formats the data, and sends it to the generative AI model. The generative AI model proofreads the draft according to the conditions and returns the document to the server with the corrections highlighted. The server saves the proofreading results and displays them on the user interface. The user checks the proofreading results and finally downloads the corrected quarterly report. If the user feels stressed during the proofreading process, the device's camera and microphone detect this, and the emotion engine responds by displaying a relaxing message.

[0749] Example 2:

[0750] A sales manager requests proofreading of an English disclosure document. The user uploads the document and enters proofreading conditions such as "consistency in American English," "display numbers in half-width characters," and "use periods as punctuation marks." The server sends this to the generative AI model, which then proofreads it based on the conditions. The proofreading results are highlighted, and the user can review them and download the final revised English disclosure document. If the user is satisfied, the emotion engine provides positive feedback such as "Good job!"

[0751] Prompt Sentence Examples

[0752] Proofread a draft of the quarterly report.

[0753] Condition 1: Use honorific expressions consistently, using "~desu" and "~masu"

[0754] Condition 2: Dates and times are written in the Gregorian calendar

[0755] Condition 3: Numbers must be in half-width characters

[0756] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0757] Step 1:

[0758] A user uploads a document to the system

[0759] Users access the system through a browser and log in by entering their user ID and password. After logging in, users select the document to be proofread using the file selection dialog or the drag-and-drop function and click the upload button.

[0760] Input: Document file to be proofread (PDF, Word, etc.)

[0761] Output: Document file uploaded to the server

[0762] Specific operation: A file selection dialog opens in the browser, the user selects a file and clicks the "Upload" button, which sends the document file to the server.

[0763] Step 2:

[0764] The server receives the document and stores it temporarily.

[0765] The server temporarily stores documents received from users in a specific directory (e.g., / uploads), with a unique file name to avoid conflicts.

[0766] Input: Document file sent by the user

[0767] Output: Temporarily saved document file

[0768] Specific operation: The server receives the file and stores it in the server's storage with a file name that is given a unique identifier.

[0769] Step 3:

[0770] The server provides the user with an interface for inputting calibration conditions.

[0771] The server displays an interface for the user to input proofreading conditions. The input form includes fields for standardizing honorific expressions, date and time formats, number notation, etc.

[0772] Input: None (interface provided)

[0773] Output: User interface showing the proofreading condition input form

[0774] Specific operation: The server generates an HTML form for entering proofreading conditions and displays it in the user's browser. The user enters the proofreading conditions into this form.

[0775] Step 4:

[0776] The user inputs the calibration conditions.

[0777] The user enters the necessary conditions into the proofreading conditions input form and clicks the "Submit" button. For example, they can enter things like "Use honorific expressions consistent with '~desu' and '~masu'," "Write dates and times in the Gregorian calendar," and "Use half-width numbers consistent."

[0778] Input: Calibration conditions entered by the user

[0779] Output: Calibration conditions sent to the server

[0780] Specific operation: The user enters the desired proofreading conditions into the form on the browser using text boxes, etc., and clicks the "Submit" button. This operation sends the proofreading conditions to the server.

[0781] Step 5:

[0782] The server stores the calibration conditions

[0783] The server receives the calibration conditions entered by the user and stores them in a database, where they are available for subsequent processing.

[0784] Input: Calibration conditions submitted by the user

[0785] Output: Calibration conditions stored in the database

[0786] Specific operation: The calibration condition data received on the server side is parsed and inserted into the database. This makes the calibration conditions permanent.

[0787] Step 6:

[0788] The server sends a proofreading request to the generative AI model

[0789] The server combines the document file and the proofreading conditions into a single payload and sends it to the generative AI model. It then sends a request to a specific API endpoint to ask the model to perform the proofreading process.

[0790] Input: Uploaded document file, user proofreading conditions

[0791] Output: Proofreading requests sent to the generative AI model

[0792] Specific operation: The document file and proofreading conditions are formatted into JSON format on the server side and sent to the generative AI model via the REST API.

[0793] Step 7:

[0794] Generative AI models analyze and proofread documents

[0795] A generative AI model analyzes the document's content and corrects errors based on the specified proofreading criteria, highlighting corrections and adding annotations as needed.

[0796] Input: The document file and proofreading conditions sent to the generative AI model

[0797] Output: Proofread document file

[0798] How it works: Natural language processing algorithms run within the generative AI model to proofread the document, generating a document with highlights and annotations.

[0799] Step 8:

[0800] The generative AI model sends the calibration results back to the server

[0801] The generative AI model sends the proofread document back to the server, which receives and stores it.

[0802] Input: Proofread results sent from the generative AI model

[0803] Output: Saved proofreading result document file

[0804] Specific operation: The server receives response data from the generative AI model and temporarily stores it.

[0805] Step 9:

[0806] The server displays the calibration results on the user interface

[0807] The server receives and stores the proofreading results, and then displays them on the user interface, allowing the user to review them and check the corrections.

[0808] Input: Saved proofreading result document file

[0809] Output: Calibration results displayed in the user interface

[0810] Specific operation: The server generates and sends HTML for displaying the proofreading results to the browser. The proofreading results are displayed in the user's browser.

[0811] Step 10:

[0812] The user checks the proofreading results and makes any necessary corrections.

[0813] The user checks the proofreading results displayed on the user interface, and makes any necessary corrections. After making the corrections, the user clicks the final confirmation button.

[0814] Input: User operation to check the calibration results

[0815] Output: Final proofread document file

[0816] Specific operation: The user checks the proofreading results in the browser, makes additional corrections using a text editor if necessary, and clicks the final confirmation button.

[0817] Step 11:

[0818] The server generates the finalized document and makes it available for download.

[0819] The server generates a finalized document and saves it in file storage for the user to download. The user clicks the download button to retrieve the finalized document.

[0820] Input: Final verified document file

[0821] Output: A document file that users can download

[0822] Specific operation: The server generates the final document file based on the latest proofreading results and generates a downloadable URL. The user clicks the "Download" button in their browser to obtain the file.

[0823] (Application example 2)

[0824] 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."

[0825] While conventional document proofreading systems aim to efficiently proofread document content, they do not provide support that takes into account the editor's emotional state. This can lead to stress and a lack of concentration during proofreading. Furthermore, the lack of emotional interface adjustments and appropriate feedback can negatively impact the efficiency and quality of the editor's work. Therefore, there is a need for a system that provides support that responds to the editor's emotions, further streamlining proofreading and improving the work experience.

[0826] 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.

[0827] In this invention, the server includes means for providing a user interface for uploading documents, means for inputting proofreading conditions, means for receiving and saving the uploaded document and the proofreading conditions, means for sending the received document and the proofreading conditions to a generative AI model to request proofreading, means for receiving proofreading results from the generative AI model, means for displaying the proofreading results on the user interface, means for making the final proofread document available for download, means for analyzing the editor's emotions, means for dynamically adjusting the interface based on the editor's emotions, means for providing guide messages and help based on the editor's emotions, and means for providing positive feedback in accordance with the editor's emotions. This enables efficient document proofreading work while taking into account the editor's emotional state.

[0828] A "document upload user interface" is an interface that a user uses to upload a document to be proofread into the system.

[0829] The "means for inputting proofreading conditions" is a means for a user to input conditions necessary for proofreading a document.

[0830] The "means for receiving and storing uploaded documents and proofreading conditions" refers to the means by which the system receives and stores documents uploaded by users and proofreading conditions entered by users.

[0831] "Means for sending the received document and proofreading conditions to the generative AI model and requesting proofreading" refers to the means by which the system sends the received document and proofreading conditions to the generative AI model and requests proofreading of the document.

[0832] "Means for receiving proofreading results from the generative AI model" refers to means for receiving proofreading results returned from the generative AI model.

[0833] The "means for displaying the proofreading results on a user interface" is an interface for displaying the received proofreading results to the user.

[0834] "Means for making the final proofread document available for download" refers to the means used by a user to download the final proofread document.

[0835] "Means for analyzing the editor's emotions" refers to a means for analyzing the editor's facial expressions, voice, etc., to determine their emotional state.

[0836] The "means for dynamically adjusting the interface based on the editor's emotions" refers to a means for dynamically changing the color tone and layout of the interface according to the editor's emotional state.

[0837] The "means for providing a guide message or help based on the emotions of an editor" refers to a means for providing an appropriate guide message or help based on the emotional state of an editor.

[0838] "Means for providing positive feedback according to the editor's emotions" refers to means for providing positive feedback according to the editor's emotional state.

[0839] The present invention provides a system for improving the efficiency of document proofreading work and providing dynamic support based on the editor's emotions. An embodiment of this system will be described in detail below.

[0840] Overall system configuration

[0841] The system includes a user interface for uploading documents, a means for inputting proofreading conditions, a means for receiving and storing the uploaded documents and proofreading conditions, a means for sending the received documents and proofreading conditions to a generative AI model to request proofreading, a means for receiving proofreading results from the generative AI model, a means for displaying the proofreading results on the user interface, a means for making the final proofread document available for download, a means for analyzing editor emotions, a means for dynamically adjusting the interface based on the editor emotions, a means for providing guide messages and help based on the editor emotions, and a means for providing positive feedback according to the editor emotions.

[0842] Hardware and software used

[0843] Hardware:

[0844] Devices (PC, smartphone, tablet)

[0845] Camera (for analyzing the editor's facial expressions)

[0846] Microphone (for editor's voice tone analysis)

[0847] software:

[0848] Server-side framework (Django)

[0849] Facial expression analysis software (OpenCV)

[0850] Audio Tone Analysis Software (pyttsx3)

[0851] Generative AI model (OpenAI GPT-3)

[0852] Database (SQLite)

[0853] Specific operation of the system

[0854] 1. Uploading documents and setting proofreading conditions

[0855] Editors log in to the system from their terminals and upload documents. The user interface is designed to make uploading documents easy. Next, an interface is provided for entering proofreading conditions, and editors enter proofreading conditions such as "consistency of writing style" and "half-width numbers."

[0856] 2. Submitting documents and proofreading requirements

[0857] The server receives and stores the uploaded document and the entered proofreading conditions. This data is then sent to the generative AI model (GPT-3) for proofreading.

[0858] 3. Performing calibration and receiving the results

[0859] The generative AI model proofreads the received document based on the proofreading criteria. The proofread document is highlighted and annotated with each correction. The proofreading results are sent back to the server and saved again.

[0860] 4. View and download calibration results

[0861] The server displays the proofreading results on a user interface. The editor can review the corrections and make additional edits as needed. Finally, the edited document can be downloaded.

[0862] 5. Sentiment Analysis and Dynamic Support

[0863] The system uses the device's camera and microphone to analyze the editor's emotions. It uses OpenCV to analyze facial expressions and pyttsx3 to analyze vocal tone. The editor's emotions are determined in real time, and the interface color tone and layout are dynamically changed accordingly. For example, if the editor is feeling stressed, the color tone will be changed to a relaxing one and navigation will be simplified. If the system determines that the editor is feeling stressed, it will display appropriate guide messages and help. For example, it will display a message such as, "You look tired. Please take a break."

[0864] 6. Positive Feedback

[0865] If the emotion engine determines that the editor is satisfied, the system will provide positive feedback, such as a message like "Nice job! Keep it up!"

[0866] Examples of concrete examples and prompts

[0867] As a concrete example, an editor uploads a blog post to the system and enters the following proofreading criteria:

[0868] "Unify all writing styles with "~desu""

[0869] Dates and times are written in the Gregorian calendar.

[0870] "Unify numbers to half-width"

[0871] An example of a prompt that would be generated based on this condition is:

[0872] Correct this content: 'Today's weather is sunny.' with conditions: 'Use honorific expressions consistent with "~desu", use the Gregorian calendar for dates, and use half-width numbers.'

[0873] By implementing this invention, it is possible to efficiently proofread documents while taking into account the emotional state of the editor, and the editor's work experience is improved through dynamic interface adjustment and appropriate feedback.

[0874] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0875] Step 1:

[0876] A user logs in to the system using a terminal. The user inputs login information (user name, password), which the server receives and authenticates. As an output of successful authentication, a dashboard dedicated to the user is displayed.

[0877] Step 2:

[0878] The user uploads the document to be proofread using the document upload user interface. The input is the uploaded document file, which the server receives and temporarily stores. The output is an upload confirmation message.

[0879] Step 3:

[0880] The user inputs the proofreading conditions using the proofreading conditions input interface. The input is the text of the proofreading conditions, which the server receives and stores. The output displays a confirmation message for the input conditions.

[0881] Step 4:

[0882] The server sends the received document and proofreading conditions to the generative AI model. The input is the saved document and proofreading conditions, and data format conversion and prompt generation are performed. A prompt that clearly indicates the conditions for proofreading is generated and this data is sent to the generative AI model (GPT-3). As an output, a status message indicating that it has been sent to the generative AI model is recorded in the server log.

[0883] Step 5:

[0884] The generative AI model performs the proofreading and sends the proofreading results back to the server. The input is data based on the prompt sentence, and the generative AI model analyzes this data, performs the proofreading, and outputs the results. The server receives the proofreading results and saves them again. The proofreading result data is obtained as the output.

[0885] Step 6:

[0886] The server displays the proofreading results on the user interface. The input is the saved proofreading result data, which is then processed to display in a format that is easy for the user to view. Specifically, the proofreading points are highlighted and annotations are added as necessary. The output is the proofreading results displayed on the user interface.

[0887] Step 7:

[0888] The user checks the proofreading results and makes additional corrections as necessary. The input is the user's corrections, which are received and reflected in the proofreading result data. The output is the final corrected document data.

[0889] Step 8:

[0890] The server generates a link to download the final proofread document. The input is the final corrected document data, and it converts this data into a downloadable format (e.g., PDF or Word file). As an output, a download link is displayed in the user interface.

[0891] Step 9:

[0892] The system analyzes the user's emotions using the device's built-in camera and microphone. The input is camera video and audio data, which are analyzed in real time using OpenCV and pyttsx3. The system determines the emotional state and obtains corresponding data. The output is the editor's analyzed emotional state.

[0893] Step 10:

[0894] The server dynamically adjusts the interface based on the editor's emotions. The input is data on the editor's emotional state, and the color tone and layout of the interface are dynamically changed based on this. Specifically, if the editor is feeling stressed, the color tone is changed to a calmer tone and simple navigation is provided. The output is a dynamic change of the user interface.

[0895] Step 11:

[0896] It provides guide messages and help based on the editor's emotions. The input is the result data of the emotion analysis, and an appropriate message is generated based on this. Specifically, it provides a message such as "You look tired. Please take a break." As an output, the guide message or help is displayed in the user interface.

[0897] Step 12:

[0898] It provides positive feedback based on the editor's sentiment. The input is the result data of sentiment analysis, and based on this, it generates a positive message. Specifically, it provides a message such as "Good job! Keep it up!". As an output, the positive feedback is displayed in the user interface.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] [Third embodiment]

[0903] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0904] 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.

[0905] 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).

[0906] 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.

[0907] 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.

[0908] 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).

[0909] 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. 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.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] 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.

[0914] 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."

[0915] The present invention relates to a system for efficiently proofreading a particular document, which has the following main functions:

[0916] 1. The user uploads the document to be proofread.

[0917] A user logs in to the system using a browser and uploads a document to be proofread. The uploaded document is received by the server and temporarily stored. The user interface is designed to make it easy to select and upload a document file.

[0918] 2. The server presents the calibration conditions

[0919] The server provides the user with an interface for setting proofreading conditions, such as using only "desu" and "masu," using only Gregorian calendar years, and using only half-width numbers.

[0920] 3. The server sends a proofreading request to the generative model.

[0921] The server sends the proofreading conditions entered by the user and the uploaded document to a generative model (artificial intelligence model). The generative model analyzes the content of the document and makes corrections based on the proofreading conditions.

[0922] 4. The generative model performs calibration

[0923] The generative model proofreads the content in the document, highlighting any corrections and adding annotations to make the corrections easier to understand, allowing the user to see at a glance what has been changed and how.

[0924] 5. The generative model sends the calibration results back to the server

[0925] The generative model returns the proofread document to the server, which receives and stores the proofread results.

[0926] 6. The server presents the proofreading results to the user

[0927] The server receives the proofreading results and displays them on the user interface, allowing the user to review the proofreading results and check the content of the revised document.

[0928] 7. User performs final confirmation and downloads document

[0929] The user checks the proofreading results and makes additional corrections if necessary. After completing the corrections, the user clicks the final confirmation button to complete the proofreading process. The server generates the final proofread document and makes it available for the user to download.

[0930] Specific examples

[0931] Example 1:

[0932] An employee in the finance and accounting department uploads a draft of a quarterly report to the system. The user enters the following into the proofreading condition input form: "Use honorific expressions consistent with '~desu' and '~masu'," "Write dates and times in the Gregorian calendar," and "Use numbers consistent with half-width characters." The server receives this and sends it to the generative model. The generative model proofreads the draft according to the conditions and returns a document with the corrections highlighted. The user checks the proofreading results and finally downloads the corrected quarterly report.

[0933] Example 2:

[0934] A sales manager requests proofreading of an English disclosure document. The user uploads the document and enters proofreading conditions, such as "consistency in American English," "display numbers in half-width characters," and "use periods as punctuation marks." The server sends this to the generative model, which then proofreads it based on the conditions. The proofreading results are highlighted, and the user can review them and download the final revised English disclosure document.

[0935] These examples clearly demonstrate the system's functionality and flow, which can significantly reduce time and errors compared to traditional manual calibration procedures.

[0936] The processing flow will be explained below.

[0937] Step 1:

[0938] The user accesses the login screen, enters authentication information, and clicks the login button. The device sends the authentication information to the server. The server verifies the authentication information, and if the user has valid access rights, the dashboard screen is displayed.

[0939] Step 2:

[0940] The user clicks the document upload button and selects the document file to be proofread. The terminal sends a request to upload the selected document file to the server. The server receives the document file and saves it in a temporary directory.

[0941] Step 3:

[0942] The server provides the user with an interface for setting proofreading conditions. The user enters conditions into the proofreading condition input form, such as using "~desu" and "~masu" consistently, using the Gregorian calendar, and using half-width numbers, and then clicks the send button. The terminal then sends these proofreading conditions to the server.

[0943] Step 4:

[0944] The server executes an API request to send the received document file and proofreading conditions to the generative model (artificial intelligence model). The server sends the document file and proofreading conditions to the generative model.

[0945] Step 5:

[0946] The generative model analyzes the received document and the proofreading conditions, performs the proofreading, corrects the content in the document, highlights the corrections, and adds annotations, and then sends the proofread document back to the server.

[0947] Step 6:

[0948] The server receives and stores the calibration results returned from the generative model. The server generates an interface to display the calibration results and presents them to the user. The user checks the calibration results and makes additional corrections as necessary.

[0949] Step 7:

[0950] The user clicks the final confirmation button to complete the proofreading. The server generates the final proofread document and provides a link for the user to download. The device allows the user to download the final file.

[0951] Example 1

[0952] 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."

[0953] Conventional document proofreading work is time-consuming and prone to errors, so there was a need for a system that could proofread documents efficiently and accurately.

[0954] 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.

[0955] In this invention, the server includes means for providing a user interface for uploading documents, means for inputting proofreading conditions, means for receiving and saving the uploaded documents and proofreading conditions, means for sending the received documents and proofreading conditions to a generative AI model and requesting proofreading, means for receiving proofreading results from the generative AI model, means for displaying the proofreading results on a user interface, highlighting corrections and adding annotations, and means for making the final proofread document available for download, thereby improving the efficiency and accuracy of document proofreading.

[0956] The "means for providing a user interface for uploading a document" is a function for providing an interface that a user uses to send a document file to be proofread to the system.

[0957] The "means for inputting proofreading conditions" is a function that provides an interface for a user to input specific conditions or rules required for proofreading a document.

[0958] "Means for receiving and storing uploaded documents and proofreading conditions" refers to a function that receives document files uploaded by users and proofreading conditions entered by users on the server side and stores them temporarily or permanently.

[0959] "Means for sending the received document and proofreading conditions to the generative AI model and requesting proofreading" is a function in which the server sends the received document file and proofreading conditions to the generative AI model and requests it to proofread the document.

[0960] "Means for receiving proofreading results from the generative AI model" refers to a function that receives the result data of a document that has been proofread by the generative AI model on the server side.

[0961] "Means for displaying proofreading results on a user interface, highlighting corrections, and adding annotations" refers to a function that presents a document proofread by a generative AI model to the user, highlights corrections with color or underlines to make them visually easier to understand, and adds necessary explanations as annotations.

[0962] The "means for making the final proofread document available for download" is a download function provided to enable the user to obtain the final proofread document file from the system.

[0963] The present invention relates to a system for efficiently proofreading a specific document, which has the following main functions:

[0964] A user logs in to the system using a browser on their device and uploads a document to be proofread. At this time, the browser sends user authentication information to the server, and the server authenticates the user. The user interface is designed to allow users to easily select document files and upload them to the system. The uploaded document is received by the server and temporarily stored.

[0965] Next, the server provides the user with an interface for setting proofreading conditions. Through this interface, the user can input proofreading conditions such as using only "desu" and "masu," using only Gregorian calendar years, and using only half-width numbers. These proofreading conditions are recorded by the server.

[0966] The server sends the user-entered proofreading criteria and the uploaded document to the generative AI model via an HTTP request. The generative AI model analyzes the content of the document and makes corrections based on the proofreading criteria. For example, it uses natural language processing technology to unify the writing style and format.

[0967] The generative AI model can also highlight and annotate corrections to make them easier to understand. The corrected document is then sent back to the server, which receives and stores the proofread results.

[0968] The server then displays the proofreading results on the user interface. Users can see at a glance which parts have been corrected and how, through highlighting and annotation of the corrections. If necessary, users can make additional corrections.

[0969] The document that has been finalized is generated by the server so that it can be downloaded by the user. The user can click the final confirmation button, complete the proofreading work, and then download the final proofread document.

[0970] Specific examples

[0971] Example 1:

[0972] An employee in the finance and accounting department uploads a draft of a quarterly report to the system. The user enters the following into the proofreading condition input form: "Use honorific expressions consistently using 'desu' and 'masu'," "Write dates and times in the Gregorian calendar format," and "Use half-width numbers consistently." The server receives this and sends a prompt to the generative AI model: "Please proofread the document to use consistent writing style using the desu / masu style, Gregorian calendar format, and half-width numbers consistently." The generative AI model proofreads the draft according to the conditions and returns the document with the corrections highlighted. The user checks the proofreading results and finally downloads the revised quarterly report.

[0973] Example 2:

[0974] A sales manager requests proofreading of an English disclosure document. The user uploads the document and enters proofreading conditions such as "consistent use of American English," "display numbers in half-width characters," and "use periods as punctuation marks." The server sends this to the generative AI model, which generates a prompt: "Please proofread the document's English expressions in American English, using half-width characters and periods." The generative model proofreads based on the conditions. The proofreading results are highlighted, and the user can review them and download the final revised English disclosure document.

[0975] These examples clearly demonstrate the system's functionality and flow, which can significantly reduce time and errors compared to traditional manual calibration procedures.

[0976] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0977] Program processing flow

[0978] Step 1: User uploads document to be proofread

[0979] Input: A user logs into the system using a browser on a terminal and selects a document file.

[0980] Processing: The user clicks the "Upload Document" button, and the terminal sends the selected document file to the server.

[0981] Output: The server temporarily stores the received document file in storage.

[0982] Specific behavior: The browser opens a file selection dialog, the user selects a document file, and presses the upload button.

[0983] Step 2: The server submits the calibration conditions

[0984] Input: The request after the user uploads a document.

[0985] Processing: The server displays the proofreading condition input form on the user interface.

[0986] Output: A form is displayed where the user can enter the calibration criteria.

[0987] What happens: The server generates an HTML form and sends it to the device's browser. The user interface displays options such as "uniform writing style," "number format," and "date and time format."

[0988] Step 3: User inputs calibration conditions

[0989] Input: The user inputs the calibration conditions in the specified interface.

[0990] Process: The user clicks the "Submit" button and the entered calibration conditions are sent to the server.

[0991] Output: The calibration conditions are saved on the server.

[0992] Specific actions: This includes the user entering values ​​into the input fields for each condition and pressing the submit button.

[0993] Step 4: The server sends a proofreading request to the generative model

[0994] Input: Document files stored on the server and proofreading requirements.

[0995] Processing: The server generates a prompt based on the document file and proofreading conditions, and sends it to the generative AI model via an HTTP request.

[0996] Output: The generative AI model receives data for calibration.

[0997] Specific behavior: The server constructs a prompt and sends a request to the API endpoint. Example prompt: 'Please proofread the document to ensure it uses consistent writing style, the Western calendar year format, and half-width numbers.'

[0998] Step 5: The generative model performs calibration

[0999] Input: Received document file and prompt text.

[1000] Processing: The generative AI model analyzes the document content and corrects it based on the specified proofreading criteria.

[1001] Output: Document data after proofreading is complete.

[1002] What it does: The generative AI model uses natural language processing techniques to unify style and format, highlight corrections, and annotate them.

[1003] Step 6: The generative model sends the calibration results back to the server

[1004] Input: Document data after proofreading is complete.

[1005] Processing: The generative AI model sends the modified document back to the server.

[1006] Output: The calibration results are saved on the server.

[1007] Specific operation: The generative AI model sends the proofreading results to the server via an HTTP response, which is then received and stored by the server.

[1008] Step 7: The server presents the proof to the user

[1009] Input: Calibration results stored on the server.

[1010] Processing: The server displays the calibration results on the user interface.

[1011] Output: The modified document is displayed to the user.

[1012] What happens: The server generates an HTML page, highlights the proofreading results and corrections, annotates it, and sends it to the user's browser.

[1013] Step 8: User provides final confirmation and downloads document

[1014] Input: User action to review the proof results.

[1015] Processing: The user clicks the final confirmation button, and if any additional corrections are made, these are also sent to the server.

[1016] Output: You will be provided with a download link for the final proofread document file.

[1017] Specific behavior: The user checks the proofreading results, makes any necessary corrections, and clicks the final confirmation button. The server generates the final document and displays a page with a download link.

[1018] (Application example 1)

[1019] 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."

[1020] In the manufacturing industry, inconsistent content and typographical errors in operation guides and maintenance procedure documents can lead to confusion on-site and human error, hindering efficient work. Furthermore, manual proofreading is time-consuming and labor-intensive, increasing the risk of work delays and human error. To solve these problems, a system is needed that can efficiently and accurately proofread documents and easily check them on-site.

[1021] 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.

[1022] In this invention, the server includes means for providing a user interface for uploading documents, means for inputting proofreading conditions, means for receiving and saving the uploaded document and the proofreading conditions, means for sending the document to a generative model to request proofreading, means for receiving proofreading results from the generative model, means for displaying the proofreading results on the user interface, means for making the final proofread document available for download, and, if the document to be proofread is an operation guide or maintenance procedure document for manufacturing equipment, means for highlighting corrections on a display device to support rapid on-site response. This enables standardization and accurate proofreading of operation guide and maintenance procedure documents at manufacturing sites, reduces human error, and enables efficient operation.

[1023] "Document" means a document containing specific information, such as an operating guide for manufacturing equipment or a maintenance procedure document.

[1024] "User Interface" means the interface through which a user interacts with the system to upload documents and enter proofing requirements.

[1025] "Proofreading conditions" are specific conditions set by the user to unify the content of a document and correct typographical errors.

[1026] A "generative model" is an artificial intelligence model that proofreads uploaded documents based on proofreading conditions set by the user.

[1027] "Upload" is the act of a user transferring a document from a local device to a server.

[1028] A "server" is a computer system that stores documents and proofreading conditions received through a user interface and sends proofreading requests to a generative model.

[1029] "Highlight display" is a display method that visually emphasizes the parts that have been corrected by proofreading.

[1030] A "display device" is a display device provided to a user so that the calibration results can be viewed.

[1031] "Downloading" is the act of a user transferring the final proofed document from the server to their device.

[1032] "On-site response" is a response method to support fast and accurate work at the manufacturing site.

[1033] The system for implementing this invention mainly consists of a server, a terminal, and a generative AI model. A user uses the terminal to upload a document and enter proofreading conditions. The server sends the received document and proofreading conditions to the generative AI model, which then performs the proofreading process. After proofreading is complete, the proofreading results from the generative AI model are displayed on the user interface, and the final proofread document is made available for download.

[1034] The system is implemented using the following hardware and software:

[1035] 1. Hardware used:

[1036] Server: Responsible for storing documents and proofreading conditions and communicating with the generative AI model.

[1037] Device: A device such as a PC or tablet that users use to upload documents or review proofread results.

[1038] Display Device: A display device that visually presents the calibration results to the user.

[1039] 2. Software used:

[1040] Flask: A lightweight Python web framework that provides user interfaces and server-side processing.

[1041] OpenAI API: Communicates with generative AI models and performs calibration.

[1042] The server first receives a document upload request from the device and stores the document. Next, it sends the document along with the proofreading conditions entered by the user to the generative AI model. The generative AI model proofreads the document based on these inputs and returns the corrected document to the server. The server receives the proofreading results and displays them on a user interface so that the user can easily check them.

[1043] The user can check the displayed calibration results and make any necessary corrections. After final confirmation, the user can download the calibration results. This process enables quick response on-site, improving work efficiency and reducing errors.

[1044] For example:

[1045] The administrator uploads the "Operation Manual.docx" file and enters the following as the proofreading conditions: "Consistent terminology, correct spelling errors." The document is saved on the server and sent to the generative model as a prompt.

[1046] Example prompt:

[1047] Please proofread your document under the following conditions:

[1048] Conditions: Standardize terminology, correct typos

[1049] Documents: (Contents of the operation manual)

[1050] Calibration results:

[1051] This system enables standardization and accurate proofreading of operating guides and maintenance procedure documents at manufacturing sites, supporting efficient operations.

[1052] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1053] Step 1:

[1054] The user uploads a document on the device.

[1055] Input: Document file uploaded by the user

[1056] How it works: The user selects the target document file through the device's browser and clicks the upload button.

[1057] Output: Sending document files from the terminal to the server

[1058] Step 2:

[1059] The server receives the document file and temporarily stores it.

[1060] Input: Uploaded document file

[1061] Operation: The server saves the received document file in the specified directory.

[1062] Output: Path of the saved document file

[1063] Step 3:

[1064] The user inputs the calibration conditions on the terminal.

[1065] Input: Calibration conditions entered by the user

[1066] How it works: The user enters the necessary conditions (e.g., "standardize terminology," "correct typos," etc.) into the proofreading conditions input form on the device's browser.

[1067] Output: Calibration conditions sent from the terminal to the server

[1068] Step 4:

[1069] The server stores the received calibration conditions.

[1070] Input: Calibration conditions

[1071] Operation: The server stores the received calibration conditions in an internal database or in a temporary file.

[1072] Output: Path of saved calibration conditions

[1073] Step 5:

[1074] The server sends the document file and proofreading conditions to the generative AI model.

[1075] Input: Path of saved document file, saved proofreading conditions

[1076] How it works: The server combines the document content with the proofreading conditions to generate a prompt sentence and sends it to the generative AI model via an API.

[1077] Output: Send prompt sentences to the generative AI model and request proofreading

[1078] Step 6:

[1079] The generative AI model proofreads the document and sends the results back to the server.

[1080] Input: prompt text, document file, proofreading conditions

[1081] How it works: The generative AI model analyzes and proofreads a document based on the prompt sentence, and generates proofread results.

[1082] Output: Proofread document data

[1083] Step 7:

[1084] The server receives the calibration results and temporarily stores them.

[1085] Input: Calibration results from a generative AI model

[1086] Operation: The server stores the received calibration results in the specified directory.

[1087] Output: Path of saved calibration results

[1088] Step 8:

[1089] The server displays the calibration results on a user interface.

[1090] Input: Path to saved calibration results

[1091] How it works: The server reads the proofreading result file and generates HTML and JavaScript code to display in the user interface.

[1092] Output: Proofread results displayed in the user's browser

[1093] Step 9:

[1094] The user checks the proofreading results and makes additional corrections as necessary.

[1095] Input: Calibration results on the user interface

[1096] Operation: The user checks the displayed proofreading results, makes any necessary corrections, and sends the corrections to the server.

[1097] Output: Corrected calibration results

[1098] Step 10:

[1099] The user performs a final check and downloads the proofread document.

[1100] Input: Proofreading results that have been finalized by the user

[1101] How it works: The user clicks the Final Confirm button to confirm the final proof and send it to the server, which prepares the final proof for the user to download.

[1102] Output: Path to the final proofreading results that can be downloaded

[1103] This series of steps allows users to efficiently proofread documents and respond quickly on-site.

[1104] 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.

[1105] This invention relates to a system for improving the efficiency of document proofreading and adjusting an interface based on a user's emotions. This system has the following main functions and emotion engine:

[1106] 1. The user uploads the document to be proofread.

[1107] A user logs in to the system using a browser and uploads a document to be proofread. The uploaded document is received by the server and temporarily stored. The user interface is designed to make it easy to select and upload a document file.

[1108] 2. The server presents the calibration conditions

[1109] The server provides the user with an interface for setting proofreading conditions, such as using only "desu" and "masu," using only Gregorian calendar years, and using only half-width numbers.

[1110] 3. The server sends a proofreading request to the generative model.

[1111] The server sends the proofreading conditions entered by the user and the uploaded document to a generative model (artificial intelligence model). The generative model analyzes the content of the document and makes corrections based on the proofreading conditions.

[1112] 4. The generative model performs calibration

[1113] The generative model proofreads the content in the document, highlighting any corrections and adding annotations to make the corrections easier to understand, allowing the user to see at a glance what has been changed and how.

[1114] 5. The generative model sends the calibration results back to the server

[1115] The generative model returns the proofread document to the server, which receives and stores the proofread results.

[1116] 6. The server presents the proofreading results to the user

[1117] The server receives the proofreading results and displays them on the user interface, allowing the user to review the proofreading results and check the content of the revised document.

[1118] 7. User performs final confirmation and downloads document

[1119] The user checks the proofreading results and makes additional corrections if necessary. After completing the corrections, the user clicks the final confirmation button to complete the proofreading process. The server generates the final proofread document and makes it available for the user to download.

[1120] Emotion Engine Functions

[1121] 1. Recognize user emotions

[1122] The device's built-in camera and microphone are used to analyze the user's facial expressions and voice tone in real time, and the server uses an emotion engine to recognize the user's emotions (e.g., joy, anger, sadness, surprise, fear).

[1123] 2. Dynamic interface adjustment

[1124] The emotion engine dynamically changes the interface color and layout based on the user's emotions. For example, if the user is feeling stressed, the colors will be calmer and the navigation will be simplified.

[1125] 3. Providing guide messages and help

[1126] The emotion engine analyzes the user's emotions, and if it determines that the user is feeling stressed, the server will provide guidance messages and help, such as "You seem tired. Please take a break."

[1127] 4. Positive Feedback

[1128] The emotion engine recognizes the user's emotions and if it determines that the user is satisfied, the server will provide positive feedback, such as "Good job!" or "Keep it up!"

[1129] Specific examples

[1130] Example 1:

[1131] An employee in the finance and accounting department uploads a draft of a quarterly report to the system. The user enters the following into the proofreading condition input form: "Use honorific expressions consistent with '~desu' and '~masu'," "Write dates and times in the Gregorian calendar," and "Use numbers consistent with half-width characters." The server receives this and sends it to the generative model. The generative model proofreads the draft according to the conditions and returns a document with the corrections highlighted. The user reviews the proofreading results and finally downloads the corrected quarterly report. If the user feels stressed during the proofreading process, the emotion engine recognizes this and displays a relaxing message.

[1132] Example 2:

[1133] A sales manager requests proofreading of an English disclosure document. The user uploads the document and enters proofreading conditions, such as "consistency in American English," "display numbers in half-width characters," and "use periods as punctuation marks." The server sends this to the generative model, which then proofreads it based on the conditions. The proofreading results are highlighted, and the user can review them and download the final revised English disclosure document. If the user is satisfied, the emotion engine provides positive feedback, such as "Good job!"

[1134] This system can streamline document proofreading and provide support according to the user's feelings, thereby improving the quality and efficiency of work and the user's work experience.

[1135] The processing flow will be explained below.

[1136] Step 1:

[1137] The user logs in to the system using a browser. The terminal enters the user's authentication information (user name and password) and clicks the login button. The terminal sends the authentication information to the server. The server verifies the authentication information, and if the user has valid access rights, the dashboard screen is displayed.

[1138] Step 2:

[1139] The user clicks the document upload button on the dashboard screen and selects the document file to be proofread. The terminal sends a request to upload the selected document file to the server. The server receives the document file and saves it in a temporary directory.

[1140] Step 3:

[1141] The server provides the user with an interface for setting proofreading conditions. The user enters conditions into the proofreading condition input form, such as using the same spelling "desu" and "masu," using the Gregorian calendar, and using half-width numbers, and then clicks the send button. The terminal sends these proofreading conditions to the server. The server saves the received proofreading conditions.

[1142] Step 4:

[1143] The server obtains the document file and the proofreading conditions, and executes an API request to send a proofreading request to the generative model (artificial intelligence model). The server sends the document file and the proofreading conditions to the generative model.

[1144] Step 5:

[1145] The generative model analyzes the received document and the proofreading conditions and performs the proofreading. The generative model corrects the content in the document, highlights the corrections, and adds annotations. The generative model returns the proofread document to the server.

[1146] Step 6:

[1147] The server receives and stores the calibration results returned from the generative model. The server generates an interface to display the calibration results and presents them to the user. The user checks the calibration results and makes additional corrections as necessary.

[1148] Step 7:

[1149] The user clicks the final confirmation button to complete the proofreading. The server generates the final proofread document and provides a link for the user to download. The device allows the user to download the final file.

[1150] Step 8:

[1151] The emotion engine analyzes the user's emotions in real time. The device's camera and microphone are used to collect the user's facial expressions and voice tone. The server uses the emotion engine to recognize the user's emotions (e.g., joy, anger, sadness, surprise, fear).

[1152] Step 9:

[1153] The emotion engine dynamically adjusts the interface based on the user's emotions. For example, if the user is feeling stressed, the color tone and layout of the device's interface will change to provide a relaxing environment.

[1154] Step 10:

[1155] The emotion engine analyzes the user's emotions, and if it determines that the user is feeling stressed, the server displays a guide message or help, such as "You look tired. Please take a break."

[1156] Step 11:

[1157] The emotion engine analyzes the user's emotions, and if it determines that the user is satisfied, the server provides positive feedback, such as displaying messages like "Good job!" or "Keep it up!" on the device.

[1158] Example 2

[1159] 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."

[1160] Document proofreading requires time and effort, and there is also the issue of it being difficult to accurately reflect the proofreading conditions set by the user. Furthermore, there is also a need for efficient, high-quality proofreading while reducing the user's psychological stress, but conventional systems have not sufficiently pursued this goal. Furthermore, it is also necessary to deal with the leaking of confidential information and unauthorized access.

[1161] 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.

[1162] In this invention, the server includes means for providing a user interface for uploading documents, means for inputting proofreading conditions, means for receiving and saving the uploaded document and the proofreading conditions, means for transmitting the received document and the proofreading conditions to a generative model to request proofreading, means for receiving proofreading results from the generative model, means for displaying the proofreading results on the user interface, means for recognizing a user's emotions and dynamically adjusting the interface, means for making the final proofread document available for download, means for encrypting confidential information and replacing it with obscured characters or dummy characters before transmitting it to the generative model, means for using a camera or microphone connected to the terminal to recognize the user's emotions, means for changing the interface to a more subdued color scheme and simplifying navigation when the user is feeling stressed, means for providing guide messages or help when it is determined that the user is feeling stressed, and means for providing positive feedback when it is determined that the user is satisfied. This makes it possible to efficiently proofread documents, provide support according to the user's emotions, obtain high-quality proofreading results, and protect confidential information.

[1163] A "document" is a collection of information that includes text, images, tables, etc., and is in a format such as PDF or Word.

[1164] A "user interface" is a graphical or text-based interface through which a user interacts with a system and provides the means to perform operations.

[1165] "Proofreading conditions" are specific rules and standards regarding document editing and formatting, including, for example, standardization of honorific expressions, the format for writing dates and times, and the method of notating numbers.

[1166] A "generative model" is a computer program that uses artificial intelligence to perform a specific task, such as correcting a document based on proofreading criteria.

[1167] "Confidential information" is information that is important to a company or individual and that must be protected from unauthorized access or disclosure.

[1168] "Encryption" is a technology that converts data using a specific algorithm to make it unreadable to third parties.

[1169] "Hidden characters" are symbols or characters used to hide confidential information, and are a means of concealing the original information.

[1170] "Dummy" is pseudo data used in place of real data to protect confidential information.

[1171] A "terminal" is a device that allows a user to access and operate the system, and includes personal computers, smartphones, etc.

[1172] A "camera" is a device that takes still images and videos and outputs them as video data.

[1173] A "microphone" is a device that picks up sound and outputs it as audio data.

[1174] The "emotion engine" is software that analyzes the user's facial expressions and tone of voice to recognize the user's emotional state in real time.

[1175] "Dynamic adjustment" refers to automatically changing system settings and displays based on real-time data and conditions.

[1176] A "guide message" is a text or audio message that provides the user with instructions and advice on how to use the system.

[1177] "Positive feedback" refers to providing a positive evaluation or message of encouragement for a user's behavior or status.

[1178] This invention relates to a system for improving the efficiency of document proofreading and for adjusting the interface based on the user's emotions. This system combines a user interface for uploading documents, an interface for inputting proofreading conditions, a proofreading process using a generative model, an emotion engine that recognizes the user's emotions, and a dynamic interface adjustment function.

[1179] The specific steps for using the system are as follows: First, the user opens a browser on their device and logs in to the system. They then drag and drop the document to be proofread through the document upload user interface, or select it from the file selection dialog. The uploaded document is sent to the server and temporarily saved.

[1180] The server then presents the user with an interface for entering proofreading conditions. The user uses the proofreading condition input form to enter specific proofreading conditions, such as "use honorific expressions consistently with '~desu' and '~masu'," "dates and times written in the Gregorian calendar," and "numbers consistently in half-width characters." This allows the user's unique proofreading standards to be registered in the system.

[1181] The server then sends the user-entered proofreading criteria and the uploaded document to the generative AI model, which analyzes the document's contents and makes corrections based on the specified proofreading criteria. The generative AI model can highlight and annotate the corrections in the document.

[1182] Once the generative AI model has completed the proofreading, it sends the proofreading results back to the server. The server receives the proofreading results and displays them on the user interface for the user to review. The user can then review the proofreading results and make additional corrections as needed. Once the corrections are complete, the user can click the final confirmation button to download the final proofread document from the server.

[1183] One of the features of this system is its emotion engine, which uses the device's built-in camera and microphone to analyze the user's facial expressions and voice tone in real time to recognize the user's emotional state. For example, if the emotion engine recognizes that the user is feeling stressed, the server will change the interface color to a more subdued tone and simplify navigation. Furthermore, if the server determines that the user is feeling stressed, it will display a guide message such as, "You look tired. Please take a break."

[1184] Furthermore, if the server determines that the user is satisfied, it will provide positive feedback such as "Good job!" or "Keep it up." Similarly, to protect confidential information, the system has the ability to encrypt it and replace it with obscured or dummy characters before sending the document to the generative AI model.

[1185] Specific examples

[1186] Example 1:

[1187] An employee in the finance and accounting department uploads a draft of a quarterly report to the system. The user enters the following into the proofreading condition input form: "Use honorific expressions consistently using '~desu' and '~masu'," "Write dates and times in the Gregorian calendar," and "Use numbers consistently in half-width characters." The server receives this, formats the data, and sends it to the generative AI model. The generative AI model proofreads the draft according to the conditions and returns the document to the server with the corrections highlighted. The server saves the proofreading results and displays them on the user interface. The user checks the proofreading results and finally downloads the corrected quarterly report. If the user feels stressed during the proofreading process, the device's camera and microphone detect this, and the emotion engine responds by displaying a relaxing message.

[1188] Example 2:

[1189] A sales manager requests proofreading of an English disclosure document. The user uploads the document and enters proofreading conditions such as "consistency in American English," "display numbers in half-width characters," and "use periods as punctuation marks." The server sends this to the generative AI model, which then proofreads it based on the conditions. The proofreading results are highlighted, and the user can review them and download the final revised English disclosure document. If the user is satisfied, the emotion engine provides positive feedback such as "Good job!"

[1190] Prompt Sentence Examples

[1191] Proofread a draft of the quarterly report.

[1192] Condition 1: Use honorific expressions consistently, using "~desu" and "~masu"

[1193] Condition 2: Dates and times are written in the Gregorian calendar

[1194] Condition 3: Numbers must be in half-width characters

[1195] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1196] Step 1:

[1197] A user uploads a document to the system

[1198] Users access the system through a browser and log in by entering their user ID and password. After logging in, users select the document to be proofread using the file selection dialog or the drag-and-drop function and click the upload button.

[1199] Input: Document file to be proofread (PDF, Word, etc.)

[1200] Output: Document file uploaded to the server

[1201] Specific operation: A file selection dialog opens in the browser, the user selects a file and clicks the "Upload" button, which sends the document file to the server.

[1202] Step 2:

[1203] The server receives the document and stores it temporarily.

[1204] The server temporarily stores documents received from users in a specific directory (e.g., / uploads), with a unique file name to avoid conflicts.

[1205] Input: Document file sent by the user

[1206] Output: Temporarily saved document file

[1207] Specific operation: The server receives the file and stores it in the server's storage with a file name that is given a unique identifier.

[1208] Step 3:

[1209] The server provides the user with an interface for inputting calibration conditions.

[1210] The server displays an interface for the user to input proofreading conditions. The input form includes fields for standardizing honorific expressions, date and time formats, number notation, etc.

[1211] Input: None (interface provided)

[1212] Output: User interface showing the proofreading condition input form

[1213] Specific operation: The server generates an HTML form for entering proofreading conditions and displays it in the user's browser. The user enters the proofreading conditions into this form.

[1214] Step 4:

[1215] The user inputs the calibration conditions.

[1216] The user enters the necessary conditions into the proofreading conditions input form and clicks the "Submit" button. For example, they can enter things like "Use honorific expressions consistent with '~desu' and '~masu'," "Write dates and times in the Gregorian calendar," and "Use half-width numbers consistent."

[1217] Input: Calibration conditions entered by the user

[1218] Output: Calibration conditions sent to the server

[1219] Specific operation: The user enters the desired proofreading conditions into the form on the browser using text boxes, etc., and clicks the "Submit" button. This operation sends the proofreading conditions to the server.

[1220] Step 5:

[1221] The server stores the calibration conditions

[1222] The server receives the calibration conditions entered by the user and stores them in a database, where they are available for subsequent processing.

[1223] Input: Calibration conditions submitted by the user

[1224] Output: Calibration conditions stored in the database

[1225] Specific operation: The calibration condition data received on the server side is parsed and inserted into the database. This makes the calibration conditions permanent.

[1226] Step 6:

[1227] The server sends a proofreading request to the generative AI model

[1228] The server combines the document file and the proofreading conditions into a single payload and sends it to the generative AI model. It then sends a request to a specific API endpoint to ask the model to perform the proofreading process.

[1229] Input: Uploaded document file, user proofreading conditions

[1230] Output: Proofreading requests sent to the generative AI model

[1231] Specific operation: The document file and proofreading conditions are formatted into JSON format on the server side and sent to the generative AI model via the REST API.

[1232] Step 7:

[1233] Generative AI models analyze and proofread documents

[1234] A generative AI model analyzes the document's content and corrects errors based on the specified proofreading criteria, highlighting corrections and adding annotations as needed.

[1235] Input: The document file and proofreading conditions sent to the generative AI model

[1236] Output: Proofread document file

[1237] How it works: Natural language processing algorithms run within the generative AI model to proofread the document, generating a document with highlights and annotations.

[1238] Step 8:

[1239] The generative AI model sends the calibration results back to the server

[1240] The generative AI model sends the proofread document back to the server, which receives and stores it.

[1241] Input: Proofread results sent from the generative AI model

[1242] Output: Saved proofreading result document file

[1243] Specific operation: The server receives response data from the generative AI model and temporarily stores it.

[1244] Step 9:

[1245] The server displays the calibration results on the user interface

[1246] The server receives and stores the proofreading results, and then displays them on the user interface, allowing the user to review them and check the corrections.

[1247] Input: Saved proofreading result document file

[1248] Output: Calibration results displayed in the user interface

[1249] Specific operation: The server generates and sends HTML for displaying the proofreading results to the browser. The proofreading results are displayed in the user's browser.

[1250] Step 10:

[1251] The user checks the proofreading results and makes any necessary corrections.

[1252] The user checks the proofreading results displayed on the user interface, and makes any necessary corrections. After making the corrections, the user clicks the final confirmation button.

[1253] Input: User operation to check the calibration results

[1254] Output: Final proofread document file

[1255] Specific operation: The user checks the proofreading results in the browser, makes additional corrections using a text editor if necessary, and clicks the final confirmation button.

[1256] Step 11:

[1257] The server generates the finalized document and makes it available for download.

[1258] The server generates a finalized document and saves it in file storage for the user to download. The user clicks the download button to retrieve the finalized document.

[1259] Input: Final verified document file

[1260] Output: A document file that users can download

[1261] Specific operation: The server generates the final document file based on the latest proofreading results and generates a downloadable URL. The user clicks the "Download" button in their browser to obtain the file.

[1262] (Application example 2)

[1263] 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."

[1264] While conventional document proofreading systems aim to efficiently proofread document content, they do not provide support that takes into account the editor's emotional state. This can lead to stress and a lack of concentration during proofreading. Furthermore, the lack of emotional interface adjustments and appropriate feedback can negatively impact the efficiency and quality of the editor's work. Therefore, there is a need for a system that provides support that responds to the editor's emotions, further streamlining proofreading and improving the work experience.

[1265] 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.

[1266] In this invention, the server includes means for providing a user interface for uploading documents, means for inputting proofreading conditions, means for receiving and saving the uploaded document and the proofreading conditions, means for sending the received document and the proofreading conditions to a generative AI model to request proofreading, means for receiving proofreading results from the generative AI model, means for displaying the proofreading results on the user interface, means for making the final proofread document available for download, means for analyzing the editor's emotions, means for dynamically adjusting the interface based on the editor's emotions, means for providing guide messages and help based on the editor's emotions, and means for providing positive feedback in accordance with the editor's emotions. This enables efficient document proofreading work while taking into account the editor's emotional state.

[1267] A "document upload user interface" is an interface that a user uses to upload a document to be proofread into the system.

[1268] The "means for inputting proofreading conditions" is a means for a user to input conditions necessary for proofreading a document.

[1269] The "means for receiving and storing uploaded documents and proofreading conditions" refers to the means by which the system receives and stores documents uploaded by users and proofreading conditions entered by users.

[1270] "Means for sending the received document and proofreading conditions to the generative AI model and requesting proofreading" refers to the means by which the system sends the received document and proofreading conditions to the generative AI model and requests proofreading of the document.

[1271] "Means for receiving proofreading results from the generative AI model" refers to means for receiving proofreading results returned from the generative AI model.

[1272] The "means for displaying the proofreading results on a user interface" is an interface for displaying the received proofreading results to the user.

[1273] "Means for making the final proofread document available for download" refers to the means used by a user to download the final proofread document.

[1274] "Means for analyzing the editor's emotions" refers to a means for analyzing the editor's facial expressions, voice, etc., to determine their emotional state.

[1275] The "means for dynamically adjusting the interface based on the editor's emotions" refers to a means for dynamically changing the color tone and layout of the interface according to the editor's emotional state.

[1276] The "means for providing a guide message or help based on the emotions of an editor" refers to a means for providing an appropriate guide message or help based on the emotional state of an editor.

[1277] "Means for providing positive feedback according to the editor's emotions" refers to means for providing positive feedback according to the editor's emotional state.

[1278] The present invention provides a system for improving the efficiency of document proofreading work and providing dynamic support based on the editor's emotions. An embodiment of this system will be described in detail below.

[1279] Overall system configuration

[1280] The system includes a user interface for uploading documents, a means for inputting proofreading conditions, a means for receiving and storing the uploaded documents and proofreading conditions, a means for sending the received documents and proofreading conditions to a generative AI model to request proofreading, a means for receiving proofreading results from the generative AI model, a means for displaying the proofreading results on the user interface, a means for making the final proofread document available for download, a means for analyzing editor emotions, a means for dynamically adjusting the interface based on the editor emotions, a means for providing guide messages and help based on the editor emotions, and a means for providing positive feedback according to the editor emotions.

[1281] Hardware and software used

[1282] Hardware:

[1283] Devices (PC, smartphone, tablet)

[1284] Camera (for analyzing the editor's facial expressions)

[1285] Microphone (for editor's voice tone analysis)

[1286] software:

[1287] Server-side framework (Django)

[1288] Facial expression analysis software (OpenCV)

[1289] Audio Tone Analysis Software (pyttsx3)

[1290] Generative AI model (OpenAI GPT-3)

[1291] Database (SQLite)

[1292] Specific operation of the system

[1293] 1. Uploading documents and setting proofreading conditions

[1294] Editors log in to the system from their terminals and upload documents. The user interface is designed to make uploading documents easy. Next, an interface is provided for entering proofreading conditions, and editors enter proofreading conditions such as "consistency of writing style" and "half-width numbers."

[1295] 2. Submitting documents and proofreading requirements

[1296] The server receives and stores the uploaded document and the entered proofreading conditions. This data is then sent to the generative AI model (GPT-3) for proofreading.

[1297] 3. Performing calibration and receiving the results

[1298] The generative AI model proofreads the received document based on the proofreading criteria. The proofread document is highlighted and annotated with each correction. The proofreading results are sent back to the server and saved again.

[1299] 4. View and download calibration results

[1300] The server displays the proofreading results on a user interface. The editor can review the corrections and make additional edits as needed. Finally, the edited document can be downloaded.

[1301] 5. Sentiment Analysis and Dynamic Support

[1302] The system uses the device's camera and microphone to analyze the editor's emotions. It uses OpenCV to analyze facial expressions and pyttsx3 to analyze vocal tone. The editor's emotions are determined in real time, and the interface color tone and layout are dynamically changed accordingly. For example, if the editor is feeling stressed, the color tone will be changed to a relaxing one and navigation will be simplified. If the system determines that the editor is feeling stressed, it will display appropriate guide messages and help. For example, it will display a message such as, "You look tired. Please take a break."

[1303] 6. Positive Feedback

[1304] If the emotion engine determines that the editor is satisfied, the system will provide positive feedback, such as a message like "Nice job! Keep it up!"

[1305] Examples of concrete examples and prompts

[1306] As a concrete example, an editor uploads a blog post to the system and enters the following proofreading criteria:

[1307] "Unify all writing styles with "~desu""

[1308] Dates and times are written in the Gregorian calendar.

[1309] "Unify numbers to half-width"

[1310] An example of a prompt that would be generated based on this condition is:

[1311] Correct this content: 'Today's weather is sunny.' with conditions: 'Use honorific expressions consistent with "~desu", use the Gregorian calendar for dates, and use half-width numbers.'

[1312] By implementing this invention, it is possible to efficiently proofread documents while taking into account the emotional state of the editor, and the editor's work experience is improved through dynamic interface adjustment and appropriate feedback.

[1313] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1314] Step 1:

[1315] A user logs in to the system using a terminal. The user inputs login information (user name, password), which the server receives and authenticates. As an output of successful authentication, a dashboard dedicated to the user is displayed.

[1316] Step 2:

[1317] The user uploads the document to be proofread using the document upload user interface. The input is the uploaded document file, which the server receives and temporarily stores. The output is an upload confirmation message.

[1318] Step 3:

[1319] The user inputs the proofreading conditions using the proofreading conditions input interface. The input is the text of the proofreading conditions, which the server receives and stores. The output displays a confirmation message for the input conditions.

[1320] Step 4:

[1321] The server sends the received document and proofreading conditions to the generative AI model. The input is the saved document and proofreading conditions, and data format conversion and prompt generation are performed. A prompt that clearly indicates the conditions for proofreading is generated and this data is sent to the generative AI model (GPT-3). As an output, a status message indicating that it has been sent to the generative AI model is recorded in the server log.

[1322] Step 5:

[1323] The generative AI model performs the proofreading and sends the proofreading results back to the server. The input is data based on the prompt sentence, and the generative AI model analyzes this data, performs the proofreading, and outputs the results. The server receives the proofreading results and saves them again. The proofreading result data is obtained as the output.

[1324] Step 6:

[1325] The server displays the proofreading results on the user interface. The input is the saved proofreading result data, which is then processed to display in a format that is easy for the user to view. Specifically, the proofreading points are highlighted and annotations are added as necessary. The output is the proofreading results displayed on the user interface.

[1326] Step 7:

[1327] The user checks the proofreading results and makes additional corrections as necessary. The input is the user's corrections, which are received and reflected in the proofreading result data. The output is the final corrected document data.

[1328] Step 8:

[1329] The server generates a link to download the final proofread document. The input is the final corrected document data, and it converts this data into a downloadable format (e.g., PDF or Word file). As an output, a download link is displayed in the user interface.

[1330] Step 9:

[1331] The system analyzes the user's emotions using the device's built-in camera and microphone. The input is camera video and audio data, which are analyzed in real time using OpenCV and pyttsx3. The system determines the emotional state and obtains corresponding data. The output is the editor's analyzed emotional state.

[1332] Step 10:

[1333] The server dynamically adjusts the interface based on the editor's emotions. The input is data on the editor's emotional state, and the color tone and layout of the interface are dynamically changed based on this. Specifically, if the editor is feeling stressed, the color tone is changed to a calmer tone and simple navigation is provided. The output is a dynamic change of the user interface.

[1334] Step 11:

[1335] It provides guide messages and help based on the editor's emotions. The input is the result data of the emotion analysis, and an appropriate message is generated based on this. Specifically, it provides a message such as "You look tired. Please take a break." As an output, the guide message or help is displayed in the user interface.

[1336] Step 12:

[1337] It provides positive feedback based on the editor's sentiment. The input is the result data of sentiment analysis, and based on this, it generates a positive message. Specifically, it provides a message such as "Good job! Keep it up!". As an output, the positive feedback is displayed in the user interface.

[1338] 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.

[1339] 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.

[1340] 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.

[1341] [Fourth embodiment]

[1342] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1343] 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.

[1344] 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).

[1345] 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.

[1346] 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.

[1347] 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).

[1348] 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. 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.

[1349] 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.

[1350] 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.

[1351] 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.

[1352] 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.

[1353] 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.

[1354] 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."

[1355] The present invention relates to a system for efficiently proofreading a particular document, which has the following main functions:

[1356] 1. The user uploads the document to be proofread.

[1357] A user logs in to the system using a browser and uploads a document to be proofread. The uploaded document is received by the server and temporarily stored. The user interface is designed to make it easy to select and upload a document file.

[1358] 2. The server presents the calibration conditions

[1359] The server provides the user with an interface for setting proofreading conditions, such as using only "desu" and "masu," using only Gregorian calendar years, and using only half-width numbers.

[1360] 3. The server sends a proofreading request to the generative model.

[1361] The server sends the proofreading conditions entered by the user and the uploaded document to a generative model (artificial intelligence model). The generative model analyzes the content of the document and makes corrections based on the proofreading conditions.

[1362] 4. The generative model performs calibration

[1363] The generative model proofreads the content in the document, highlighting any corrections and adding annotations to make the corrections easier to understand, allowing the user to see at a glance what has been changed and how.

[1364] 5. The generative model sends the calibration results back to the server

[1365] The generative model returns the proofread document to the server, which receives and stores the proofread results.

[1366] 6. The server presents the proofreading results to the user

[1367] The server receives the proofreading results and displays them on the user interface, allowing the user to review the proofreading results and check the content of the revised document.

[1368] 7. User performs final confirmation and downloads document

[1369] The user checks the proofreading results and makes additional corrections if necessary. After completing the corrections, the user clicks the final confirmation button to complete the proofreading process. The server generates the final proofread document and makes it available for the user to download.

[1370] Specific examples

[1371] Example 1:

[1372] An employee in the finance and accounting department uploads a draft of a quarterly report to the system. The user enters the following into the proofreading condition input form: "Use honorific expressions consistent with '~desu' and '~masu'," "Write dates and times in the Gregorian calendar," and "Use numbers consistent with half-width characters." The server receives this and sends it to the generative model. The generative model proofreads the draft according to the conditions and returns a document with the corrections highlighted. The user checks the proofreading results and finally downloads the corrected quarterly report.

[1373] Example 2:

[1374] A sales manager requests proofreading of an English disclosure document. The user uploads the document and enters proofreading conditions, such as "consistency in American English," "display numbers in half-width characters," and "use periods as punctuation marks." The server sends this to the generative model, which then proofreads it based on the conditions. The proofreading results are highlighted, and the user can review them and download the final revised English disclosure document.

[1375] These examples clearly demonstrate the system's functionality and flow, which can significantly reduce time and errors compared to traditional manual calibration procedures.

[1376] The processing flow will be explained below.

[1377] Step 1:

[1378] The user accesses the login screen, enters authentication information, and clicks the login button. The device sends the authentication information to the server. The server verifies the authentication information, and if the user has valid access rights, the dashboard screen is displayed.

[1379] Step 2:

[1380] The user clicks the document upload button and selects the document file to be proofread. The terminal sends a request to upload the selected document file to the server. The server receives the document file and saves it in a temporary directory.

[1381] Step 3:

[1382] The server provides the user with an interface for setting proofreading conditions. The user enters conditions into the proofreading condition input form, such as using "~desu" and "~masu" consistently, using the Gregorian calendar, and using half-width numbers, and then clicks the send button. The terminal then sends these proofreading conditions to the server.

[1383] Step 4:

[1384] The server executes an API request to send the received document file and proofreading conditions to the generative model (artificial intelligence model). The server sends the document file and proofreading conditions to the generative model.

[1385] Step 5:

[1386] The generative model analyzes the received document and the proofreading conditions, performs the proofreading, corrects the content in the document, highlights the corrections, and adds annotations, and then sends the proofread document back to the server.

[1387] Step 6:

[1388] The server receives and stores the calibration results returned from the generative model. The server generates an interface to display the calibration results and presents them to the user. The user checks the calibration results and makes additional corrections as necessary.

[1389] Step 7:

[1390] The user clicks the final confirmation button to complete the proofreading. The server generates the final proofread document and provides a link for the user to download. The device allows the user to download the final file.

[1391] Example 1

[1392] 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."

[1393] Conventional document proofreading work is time-consuming and prone to errors, so there was a need for a system that could proofread documents efficiently and accurately.

[1394] 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.

[1395] In this invention, the server includes means for providing a user interface for uploading documents, means for inputting proofreading conditions, means for receiving and saving the uploaded documents and proofreading conditions, means for sending the received documents and proofreading conditions to a generative AI model and requesting proofreading, means for receiving proofreading results from the generative AI model, means for displaying the proofreading results on a user interface, highlighting corrections and adding annotations, and means for making the final proofread document available for download, thereby improving the efficiency and accuracy of document proofreading.

[1396] The "means for providing a user interface for uploading a document" is a function for providing an interface that a user uses to send a document file to be proofread to the system.

[1397] The "means for inputting proofreading conditions" is a function that provides an interface for a user to input specific conditions or rules required for proofreading a document.

[1398] "Means for receiving and storing uploaded documents and proofreading conditions" refers to a function that receives document files uploaded by users and proofreading conditions entered by users on the server side and stores them temporarily or permanently.

[1399] "Means for sending the received document and proofreading conditions to the generative AI model and requesting proofreading" is a function in which the server sends the received document file and proofreading conditions to the generative AI model and requests it to proofread the document.

[1400] "Means for receiving proofreading results from the generative AI model" refers to a function that receives the result data of a document that has been proofread by the generative AI model on the server side.

[1401] "Means for displaying proofreading results on a user interface, highlighting corrections, and adding annotations" refers to a function that presents a document proofread by a generative AI model to the user, highlights corrections with color or underlines to make them visually easier to understand, and adds necessary explanations as annotations.

[1402] The "means for making the final proofread document available for download" is a download function provided to enable the user to obtain the final proofread document file from the system.

[1403] The present invention relates to a system for efficiently proofreading a specific document, which has the following main functions:

[1404] A user logs in to the system using a browser on their device and uploads a document to be proofread. At this time, the browser sends user authentication information to the server, and the server authenticates the user. The user interface is designed to allow users to easily select document files and upload them to the system. The uploaded document is received by the server and temporarily stored.

[1405] Next, the server provides the user with an interface for setting proofreading conditions. Through this interface, the user can input proofreading conditions such as using only "desu" and "masu," using only Gregorian calendar years, and using only half-width numbers. These proofreading conditions are recorded by the server.

[1406] The server sends the user-entered proofreading criteria and the uploaded document to the generative AI model via an HTTP request. The generative AI model analyzes the content of the document and makes corrections based on the proofreading criteria. For example, it uses natural language processing technology to unify the writing style and format.

[1407] The generative AI model can also highlight and annotate corrections to make them easier to understand. The corrected document is then sent back to the server, which receives and stores the proofread results.

[1408] The server then displays the proofreading results on the user interface. Users can see at a glance which parts have been corrected and how, through highlighting and annotation of the corrections. If necessary, users can make additional corrections.

[1409] The document that has been finalized is generated by the server so that it can be downloaded by the user. The user can click the final confirmation button, complete the proofreading work, and then download the final proofread document.

[1410] Specific examples

[1411] Example 1:

[1412] An employee in the finance and accounting department uploads a draft of a quarterly report to the system. The user enters the following into the proofreading condition input form: "Use honorific expressions consistently using 'desu' and 'masu'," "Write dates and times in the Gregorian calendar format," and "Use half-width numbers consistently." The server receives this and sends a prompt to the generative AI model: "Please proofread the document to use consistent writing style using the desu / masu style, Gregorian calendar format, and half-width numbers consistently." The generative AI model proofreads the draft according to the conditions and returns the document with the corrections highlighted. The user checks the proofreading results and finally downloads the revised quarterly report.

[1413] Example 2:

[1414] A sales manager requests proofreading of an English disclosure document. The user uploads the document and enters proofreading conditions such as "consistent use of American English," "display numbers in half-width characters," and "use periods as punctuation marks." The server sends this to the generative AI model, which generates a prompt: "Please proofread the document's English expressions in American English, using half-width characters and periods." The generative model proofreads based on the conditions. The proofreading results are highlighted, and the user can review them and download the final revised English disclosure document.

[1415] These examples clearly demonstrate the system's functionality and flow, which can significantly reduce time and errors compared to traditional manual calibration procedures.

[1416] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1417] Program processing flow

[1418] Step 1: User uploads document to be proofread

[1419] Input: A user logs into the system using a browser on a terminal and selects a document file.

[1420] Processing: The user clicks the "Upload Document" button, and the terminal sends the selected document file to the server.

[1421] Output: The server temporarily stores the received document file in storage.

[1422] Specific behavior: The browser opens a file selection dialog, the user selects a document file, and presses the upload button.

[1423] Step 2: The server submits the calibration conditions

[1424] Input: The request after the user uploads a document.

[1425] Processing: The server displays the proofreading condition input form on the user interface.

[1426] Output: A form is displayed where the user can enter the calibration criteria.

[1427] What happens: The server generates an HTML form and sends it to the device's browser. The user interface displays options such as "uniform writing style," "number format," and "date and time format."

[1428] Step 3: User inputs calibration conditions

[1429] Input: The user inputs the calibration conditions in the specified interface.

[1430] Process: The user clicks the "Submit" button and the entered calibration conditions are sent to the server.

[1431] Output: The calibration conditions are saved on the server.

[1432] Specific actions: This includes the user entering values ​​into the input fields for each condition and pressing the submit button.

[1433] Step 4: The server sends a proofreading request to the generative model

[1434] Input: Document files stored on the server and proofreading requirements.

[1435] Processing: The server generates a prompt based on the document file and proofreading conditions, and sends it to the generative AI model via an HTTP request.

[1436] Output: The generative AI model receives data for calibration.

[1437] Specific behavior: The server constructs a prompt and sends a request to the API endpoint. Example prompt: 'Please proofread the document to ensure it uses consistent writing style, the Western calendar year format, and half-width numbers.'

[1438] Step 5: The generative model performs calibration

[1439] Input: Received document file and prompt text.

[1440] Processing: The generative AI model analyzes the document content and corrects it based on the specified proofreading criteria.

[1441] Output: Document data after proofreading is complete.

[1442] What it does: The generative AI model uses natural language processing techniques to unify style and format, highlight corrections, and annotate them.

[1443] Step 6: The generative model sends the calibration results back to the server

[1444] Input: Document data after proofreading is complete.

[1445] Processing: The generative AI model sends the modified document back to the server.

[1446] Output: The calibration results are saved on the server.

[1447] Specific operation: The generative AI model sends the proofreading results to the server via an HTTP response, which is then received and stored by the server.

[1448] Step 7: The server presents the proof to the user

[1449] Input: Calibration results stored on the server.

[1450] Processing: The server displays the calibration results on the user interface.

[1451] Output: The modified document is displayed to the user.

[1452] What happens: The server generates an HTML page, highlights the proofreading results and corrections, annotates it, and sends it to the user's browser.

[1453] Step 8: User provides final confirmation and downloads document

[1454] Input: User action to review the proof results.

[1455] Processing: The user clicks the final confirmation button, and if any additional corrections are made, these are also sent to the server.

[1456] Output: You will be provided with a download link for the final proofread document file.

[1457] Specific behavior: The user checks the proofreading results, makes any necessary corrections, and clicks the final confirmation button. The server generates the final document and displays a page with a download link.

[1458] (Application example 1)

[1459] 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."

[1460] In the manufacturing industry, inconsistent content and typographical errors in operation guides and maintenance procedure documents can lead to confusion on-site and human error, hindering efficient work. Furthermore, manual proofreading is time-consuming and labor-intensive, increasing the risk of work delays and human error. To solve these problems, a system is needed that can efficiently and accurately proofread documents and easily check them on-site.

[1461] 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.

[1462] In this invention, the server includes means for providing a user interface for uploading documents, means for inputting proofreading conditions, means for receiving and saving the uploaded document and the proofreading conditions, means for sending the document to a generative model to request proofreading, means for receiving proofreading results from the generative model, means for displaying the proofreading results on the user interface, means for making the final proofread document available for download, and, if the document to be proofread is an operation guide or maintenance procedure document for manufacturing equipment, means for highlighting corrections on a display device to support rapid on-site response. This enables standardization and accurate proofreading of operation guide and maintenance procedure documents at manufacturing sites, reduces human error, and enables efficient operation.

[1463] "Document" means a document containing specific information, such as an operating guide for manufacturing equipment or a maintenance procedure document.

[1464] "User Interface" means the interface through which a user interacts with the system to upload documents and enter proofing requirements.

[1465] "Proofreading conditions" are specific conditions set by the user to unify the content of a document and correct typographical errors.

[1466] A "generative model" is an artificial intelligence model that proofreads uploaded documents based on proofreading conditions set by the user.

[1467] "Upload" is the act of a user transferring a document from a local device to a server.

[1468] A "server" is a computer system that stores documents and proofreading conditions received through a user interface and sends proofreading requests to a generative model.

[1469] "Highlight display" is a display method that visually emphasizes the parts that have been corrected by proofreading.

[1470] A "display device" is a display device provided to a user so that the calibration results can be viewed.

[1471] "Downloading" is the act of a user transferring the final proofed document from the server to their device.

[1472] "On-site response" is a response method to support fast and accurate work at the manufacturing site.

[1473] The system for implementing this invention mainly consists of a server, a terminal, and a generative AI model. A user uses the terminal to upload a document and enter proofreading conditions. The server sends the received document and proofreading conditions to the generative AI model, which then performs the proofreading process. After proofreading is complete, the proofreading results from the generative AI model are displayed on the user interface, and the final proofread document is made available for download.

[1474] The system is implemented using the following hardware and software:

[1475] 1. Hardware used:

[1476] Server: Responsible for storing documents and proofreading conditions and communicating with the generative AI model.

[1477] Device: A device such as a PC or tablet that users use to upload documents or review proofread results.

[1478] Display Device: A display device that visually presents the calibration results to the user.

[1479] 2. Software used:

[1480] Flask: A lightweight Python web framework that provides user interfaces and server-side processing.

[1481] OpenAI API: Communicates with generative AI models and performs calibration.

[1482] The server first receives a document upload request from the device and stores the document. Next, it sends the document along with the proofreading conditions entered by the user to the generative AI model. The generative AI model proofreads the document based on these inputs and returns the corrected document to the server. The server receives the proofreading results and displays them on a user interface so that the user can easily check them.

[1483] The user can check the displayed calibration results and make any necessary corrections. After final confirmation, the user can download the calibration results. This process enables quick response on-site, improving work efficiency and reducing errors.

[1484] For example:

[1485] The administrator uploads the "Operation Manual.docx" file and enters the following as the proofreading conditions: "Consistent terminology, correct spelling errors." The document is saved on the server and sent to the generative model as a prompt.

[1486] Example prompt:

[1487] Please proofread your document under the following conditions:

[1488] Conditions: Standardize terminology, correct typos

[1489] Documents: (Contents of the operation manual)

[1490] Calibration results:

[1491] This system enables standardization and accurate proofreading of operating guides and maintenance procedure documents at manufacturing sites, supporting efficient operations.

[1492] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1493] Step 1:

[1494] The user uploads a document on the device.

[1495] Input: Document file uploaded by the user

[1496] How it works: The user selects the target document file through the device's browser and clicks the upload button.

[1497] Output: Sending document files from the terminal to the server

[1498] Step 2:

[1499] The server receives the document file and temporarily stores it.

[1500] Input: Uploaded document file

[1501] Operation: The server saves the received document file in the specified directory.

[1502] Output: Path of the saved document file

[1503] Step 3:

[1504] The user inputs the calibration conditions on the terminal.

[1505] Input: Calibration conditions entered by the user

[1506] How it works: The user enters the necessary conditions (e.g., "standardize terminology," "correct typos," etc.) into the proofreading conditions input form on the device's browser.

[1507] Output: Calibration conditions sent from the terminal to the server

[1508] Step 4:

[1509] The server stores the received calibration conditions.

[1510] Input: Calibration conditions

[1511] Operation: The server stores the received calibration conditions in an internal database or in a temporary file.

[1512] Output: Path of saved calibration conditions

[1513] Step 5:

[1514] The server sends the document file and proofreading conditions to the generative AI model.

[1515] Input: Path of saved document file, saved proofreading conditions

[1516] How it works: The server combines the document content with the proofreading conditions to generate a prompt sentence and sends it to the generative AI model via an API.

[1517] Output: Send prompt sentences to the generative AI model and request proofreading

[1518] Step 6:

[1519] The generative AI model proofreads the document and sends the results back to the server.

[1520] Input: prompt text, document file, proofreading conditions

[1521] How it works: The generative AI model analyzes and proofreads a document based on the prompt sentence, and generates proofread results.

[1522] Output: Proofread document data

[1523] Step 7:

[1524] The server receives the calibration results and temporarily stores them.

[1525] Input: Calibration results from a generative AI model

[1526] Operation: The server stores the received calibration results in the specified directory.

[1527] Output: Path of saved calibration results

[1528] Step 8:

[1529] The server displays the calibration results on a user interface.

[1530] Input: Path to saved calibration results

[1531] How it works: The server reads the proofreading result file and generates HTML and JavaScript code to display in the user interface.

[1532] Output: Proofread results displayed in the user's browser

[1533] Step 9:

[1534] The user checks the proofreading results and makes additional corrections as necessary.

[1535] Input: Calibration results on the user interface

[1536] Operation: The user checks the displayed proofreading results, makes any necessary corrections, and sends the corrections to the server.

[1537] Output: Corrected calibration results

[1538] Step 10:

[1539] The user performs a final check and downloads the proofread document.

[1540] Input: Proofreading results that have been finalized by the user

[1541] How it works: The user clicks the Final Confirm button to confirm the final proof and send it to the server, which prepares the final proof for the user to download.

[1542] Output: Path to the final proofreading results that can be downloaded

[1543] This series of steps allows users to efficiently proofread documents and respond quickly on-site.

[1544] 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.

[1545] This invention relates to a system for improving the efficiency of document proofreading and adjusting an interface based on a user's emotions. This system has the following main functions and emotion engine:

[1546] 1. The user uploads the document to be proofread.

[1547] A user logs in to the system using a browser and uploads a document to be proofread. The uploaded document is received by the server and temporarily stored. The user interface is designed to make it easy to select and upload a document file.

[1548] 2. The server presents the calibration conditions

[1549] The server provides the user with an interface for setting proofreading conditions, such as using only "desu" and "masu," using only Gregorian calendar years, and using only half-width numbers.

[1550] 3. The server sends a proofreading request to the generative model.

[1551] The server sends the proofreading conditions entered by the user and the uploaded document to a generative model (artificial intelligence model). The generative model analyzes the content of the document and makes corrections based on the proofreading conditions.

[1552] 4. The generative model performs calibration

[1553] The generative model proofreads the content in the document, highlighting any corrections and adding annotations to make the corrections easier to understand, allowing the user to see at a glance what has been changed and how.

[1554] 5. The generative model sends the calibration results back to the server

[1555] The generative model returns the proofread document to the server, which receives and stores the proofread results.

[1556] 6. The server presents the proofreading results to the user

[1557] The server receives the proofreading results and displays them on the user interface, allowing the user to review the proofreading results and check the content of the revised document.

[1558] 7. User performs final confirmation and downloads document

[1559] The user checks the proofreading results and makes additional corrections if necessary. After completing the corrections, the user clicks the final confirmation button to complete the proofreading process. The server generates the final proofread document and makes it available for the user to download.

[1560] Emotion Engine Functions

[1561] 1. Recognize user emotions

[1562] The device's built-in camera and microphone are used to analyze the user's facial expressions and voice tone in real time, and the server uses an emotion engine to recognize the user's emotions (e.g., joy, anger, sadness, surprise, fear).

[1563] 2. Dynamic interface adjustment

[1564] The emotion engine dynamically changes the interface color and layout based on the user's emotions. For example, if the user is feeling stressed, the colors will be calmer and the navigation will be simplified.

[1565] 3. Providing guide messages and help

[1566] The emotion engine analyzes the user's emotions, and if it determines that the user is feeling stressed, the server will provide guidance messages and help, such as "You seem tired. Please take a break."

[1567] 4. Positive Feedback

[1568] The emotion engine recognizes the user's emotions and if it determines that the user is satisfied, the server will provide positive feedback, such as "Good job!" or "Keep it up!"

[1569] Specific examples

[1570] Example 1:

[1571] An employee in the finance and accounting department uploads a draft of a quarterly report to the system. The user enters the following into the proofreading condition input form: "Use honorific expressions consistent with '~desu' and '~masu'," "Write dates and times in the Gregorian calendar," and "Use numbers consistent with half-width characters." The server receives this and sends it to the generative model. The generative model proofreads the draft according to the conditions and returns a document with the corrections highlighted. The user reviews the proofreading results and finally downloads the corrected quarterly report. If the user feels stressed during the proofreading process, the emotion engine recognizes this and displays a relaxing message.

[1572] Example 2:

[1573] A sales manager requests proofreading of an English disclosure document. The user uploads the document and enters proofreading conditions, such as "consistency in American English," "display numbers in half-width characters," and "use periods as punctuation marks." The server sends this to the generative model, which then proofreads it based on the conditions. The proofreading results are highlighted, and the user can review them and download the final revised English disclosure document. If the user is satisfied, the emotion engine provides positive feedback, such as "Good job!"

[1574] This system can streamline document proofreading and provide support according to the user's feelings, thereby improving the quality and efficiency of work and the user's work experience.

[1575] The processing flow will be explained below.

[1576] Step 1:

[1577] The user logs in to the system using a browser. The terminal enters the user's authentication information (user name and password) and clicks the login button. The terminal sends the authentication information to the server. The server verifies the authentication information, and if the user has valid access rights, the dashboard screen is displayed.

[1578] Step 2:

[1579] The user clicks the document upload button on the dashboard screen and selects the document file to be proofread. The terminal sends a request to upload the selected document file to the server. The server receives the document file and saves it in a temporary directory.

[1580] Step 3:

[1581] The server provides the user with an interface for setting proofreading conditions. The user enters conditions into the proofreading condition input form, such as using the same spelling "desu" and "masu," using the Gregorian calendar, and using half-width numbers, and then clicks the send button. The terminal sends these proofreading conditions to the server. The server saves the received proofreading conditions.

[1582] Step 4:

[1583] The server obtains the document file and the proofreading conditions, and executes an API request to send a proofreading request to the generative model (artificial intelligence model). The server sends the document file and the proofreading conditions to the generative model.

[1584] Step 5:

[1585] The generative model analyzes the received document and the proofreading conditions and performs the proofreading. The generative model corrects the content in the document, highlights the corrections, and adds annotations. The generative model returns the proofread document to the server.

[1586] Step 6:

[1587] The server receives and stores the calibration results returned from the generative model. The server generates an interface to display the calibration results and presents them to the user. The user checks the calibration results and makes additional corrections as necessary.

[1588] Step 7:

[1589] The user clicks the final confirmation button to complete the proofreading. The server generates the final proofread document and provides a link for the user to download. The device allows the user to download the final file.

[1590] Step 8:

[1591] The emotion engine analyzes the user's emotions in real time. The device's camera and microphone are used to collect the user's facial expressions and voice tone. The server uses the emotion engine to recognize the user's emotions (e.g., joy, anger, sadness, surprise, fear).

[1592] Step 9:

[1593] The emotion engine dynamically adjusts the interface based on the user's emotions. For example, if the user is feeling stressed, the color tone and layout of the device's interface will change to provide a relaxing environment.

[1594] Step 10:

[1595] The emotion engine analyzes the user's emotions, and if it determines that the user is feeling stressed, the server displays a guide message or help, such as "You look tired. Please take a break."

[1596] Step 11:

[1597] The emotion engine analyzes the user's emotions, and if it determines that the user is satisfied, the server provides positive feedback, such as displaying messages like "Good job!" or "Keep it up!" on the device.

[1598] Example 2

[1599] 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."

[1600] Document proofreading requires time and effort, and there is also the issue of it being difficult to accurately reflect the proofreading conditions set by the user. Furthermore, there is also a need for efficient, high-quality proofreading while reducing the user's psychological stress, but conventional systems have not sufficiently pursued this goal. Furthermore, it is also necessary to deal with the leaking of confidential information and unauthorized access.

[1601] 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.

[1602] In this invention, the server includes means for providing a user interface for uploading documents, means for inputting proofreading conditions, means for receiving and saving the uploaded document and the proofreading conditions, means for transmitting the received document and the proofreading conditions to a generative model to request proofreading, means for receiving proofreading results from the generative model, means for displaying the proofreading results on the user interface, means for recognizing a user's emotions and dynamically adjusting the interface, means for making the final proofread document available for download, means for encrypting confidential information and replacing it with obscured characters or dummy characters before transmitting it to the generative model, means for using a camera or microphone connected to the terminal to recognize the user's emotions, means for changing the interface to a more subdued color scheme and simplifying navigation when the user is feeling stressed, means for providing guide messages or help when it is determined that the user is feeling stressed, and means for providing positive feedback when it is determined that the user is satisfied. This makes it possible to efficiently proofread documents, provide support according to the user's emotions, obtain high-quality proofreading results, and protect confidential information.

[1603] A "document" is a collection of information that includes text, images, tables, etc., and is in a format such as PDF or Word.

[1604] A "user interface" is a graphical or text-based interface through which a user interacts with a system and provides the means to perform operations.

[1605] "Proofreading conditions" are specific rules and standards regarding document editing and formatting, including, for example, standardization of honorific expressions, the format for writing dates and times, and the method of notating numbers.

[1606] A "generative model" is a computer program that uses artificial intelligence to perform a specific task, such as correcting a document based on proofreading criteria.

[1607] "Confidential information" is information that is important to a company or individual and that must be protected from unauthorized access or disclosure.

[1608] "Encryption" is a technology that converts data using a specific algorithm to make it unreadable to third parties.

[1609] "Hidden characters" are symbols or characters used to hide confidential information, and are a means of concealing the original information.

[1610] "Dummy" is pseudo data used in place of real data to protect confidential information.

[1611] A "terminal" is a device that allows a user to access and operate the system, and includes personal computers, smartphones, etc.

[1612] A "camera" is a device that takes still images and videos and outputs them as video data.

[1613] A "microphone" is a device that picks up sound and outputs it as audio data.

[1614] The "emotion engine" is software that analyzes the user's facial expressions and tone of voice to recognize the user's emotional state in real time.

[1615] "Dynamic adjustment" refers to automatically changing system settings and displays based on real-time data and conditions.

[1616] A "guide message" is a text or audio message that provides the user with instructions and advice on how to use the system.

[1617] "Positive feedback" refers to providing a positive evaluation or message of encouragement for a user's behavior or status.

[1618] This invention relates to a system for improving the efficiency of document proofreading and for adjusting the interface based on the user's emotions. This system combines a user interface for uploading documents, an interface for inputting proofreading conditions, a proofreading process using a generative model, an emotion engine that recognizes the user's emotions, and a dynamic interface adjustment function.

[1619] The specific steps for using the system are as follows: First, the user opens a browser on their device and logs in to the system. They then drag and drop the document to be proofread through the document upload user interface, or select it from the file selection dialog. The uploaded document is sent to the server and temporarily saved.

[1620] The server then presents the user with an interface for entering proofreading conditions. The user uses the proofreading condition input form to enter specific proofreading conditions, such as "use honorific expressions consistently with '~desu' and '~masu'," "dates and times written in the Gregorian calendar," and "numbers consistently in half-width characters." This allows the user's unique proofreading standards to be registered in the system.

[1621] The server then sends the user-entered proofreading criteria and the uploaded document to the generative AI model, which analyzes the document's contents and makes corrections based on the specified proofreading criteria. The generative AI model can highlight and annotate the corrections in the document.

[1622] Once the generative AI model has completed the proofreading, it sends the proofreading results back to the server. The server receives the proofreading results and displays them on the user interface for the user to review. The user can then review the proofreading results and make additional corrections as needed. Once the corrections are complete, the user can click the final confirmation button to download the final proofread document from the server.

[1623] One of the features of this system is its emotion engine, which uses the device's built-in camera and microphone to analyze the user's facial expressions and voice tone in real time to recognize the user's emotional state. For example, if the emotion engine recognizes that the user is feeling stressed, the server will change the interface color to a more subdued tone and simplify navigation. Furthermore, if the server determines that the user is feeling stressed, it will display a guide message such as, "You look tired. Please take a break."

[1624] Furthermore, if the server determines that the user is satisfied, it will provide positive feedback such as "Good job!" or "Keep it up." Similarly, to protect confidential information, the system has the ability to encrypt it and replace it with obscured or dummy characters before sending the document to the generative AI model.

[1625] Specific examples

[1626] Example 1:

[1627] An employee in the finance and accounting department uploads a draft of a quarterly report to the system. The user enters the following into the proofreading condition input form: "Use honorific expressions consistently using '~desu' and '~masu'," "Write dates and times in the Gregorian calendar," and "Use numbers consistently in half-width characters." The server receives this, formats the data, and sends it to the generative AI model. The generative AI model proofreads the draft according to the conditions and returns the document to the server with the corrections highlighted. The server saves the proofreading results and displays them on the user interface. The user checks the proofreading results and finally downloads the corrected quarterly report. If the user feels stressed during the proofreading process, the device's camera and microphone detect this, and the emotion engine responds by displaying a relaxing message.

[1628] Example 2:

[1629] A sales manager requests proofreading of an English disclosure document. The user uploads the document and enters proofreading conditions such as "consistency in American English," "display numbers in half-width characters," and "use periods as punctuation marks." The server sends this to the generative AI model, which then proofreads it based on the conditions. The proofreading results are highlighted, and the user can review them and download the final revised English disclosure document. If the user is satisfied, the emotion engine provides positive feedback such as "Good job!"

[1630] Prompt Sentence Examples

[1631] Proofread a draft of the quarterly report.

[1632] Condition 1: Use honorific expressions consistently, using "~desu" and "~masu"

[1633] Condition 2: Dates and times are written in the Gregorian calendar

[1634] Condition 3: Numbers must be in half-width characters

[1635] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1636] Step 1:

[1637] A user uploads a document to the system

[1638] Users access the system through a browser and log in by entering their user ID and password. After logging in, users select the document to be proofread using the file selection dialog or the drag-and-drop function and click the upload button.

[1639] Input: Document file to be proofread (PDF, Word, etc.)

[1640] Output: Document file uploaded to the server

[1641] Specific operation: A file selection dialog opens in the browser, the user selects a file and clicks the "Upload" button, which sends the document file to the server.

[1642] Step 2:

[1643] The server receives the document and stores it temporarily.

[1644] The server temporarily stores documents received from users in a specific directory (e.g., / uploads), with a unique file name to avoid conflicts.

[1645] Input: Document file sent by the user

[1646] Output: Temporarily saved document file

[1647] Specific operation: The server receives the file and stores it in the server's storage with a file name that is given a unique identifier.

[1648] Step 3:

[1649] The server provides the user with an interface for inputting calibration conditions.

[1650] The server displays an interface for the user to input proofreading conditions. The input form includes fields for standardizing honorific expressions, date and time formats, number notation, etc.

[1651] Input: None (interface provided)

[1652] Output: User interface showing the proofreading condition input form

[1653] Specific operation: The server generates an HTML form for entering proofreading conditions and displays it in the user's browser. The user enters the proofreading conditions into this form.

[1654] Step 4:

[1655] The user inputs the calibration conditions.

[1656] The user enters the necessary conditions into the proofreading conditions input form and clicks the "Submit" button. For example, they can enter things like "Use honorific expressions consistent with '~desu' and '~masu'," "Write dates and times in the Gregorian calendar," and "Use half-width numbers consistent."

[1657] Input: Calibration conditions entered by the user

[1658] Output: Calibration conditions sent to the server

[1659] Specific operation: The user enters the desired proofreading conditions into the form on the browser using text boxes, etc., and clicks the "Submit" button. This operation sends the proofreading conditions to the server.

[1660] Step 5:

[1661] The server stores the calibration conditions

[1662] The server receives the calibration conditions entered by the user and stores them in a database, where they are available for subsequent processing.

[1663] Input: Calibration conditions submitted by the user

[1664] Output: Calibration conditions stored in the database

[1665] Specific operation: The calibration condition data received on the server side is parsed and inserted into the database. This makes the calibration conditions permanent.

[1666] Step 6:

[1667] The server sends a proofreading request to the generative AI model

[1668] The server combines the document file and the proofreading conditions into a single payload and sends it to the generative AI model. It then sends a request to a specific API endpoint to ask the model to perform the proofreading process.

[1669] Input: Uploaded document file, user proofreading conditions

[1670] Output: Proofreading requests sent to the generative AI model

[1671] Specific operation: The document file and proofreading conditions are formatted into JSON format on the server side and sent to the generative AI model via the REST API.

[1672] Step 7:

[1673] Generative AI models analyze and proofread documents

[1674] A generative AI model analyzes the document's content and corrects errors based on the specified proofreading criteria, highlighting corrections and adding annotations as needed.

[1675] Input: The document file and proofreading conditions sent to the generative AI model

[1676] Output: Proofread document file

[1677] How it works: Natural language processing algorithms run within the generative AI model to proofread the document, generating a document with highlights and annotations.

[1678] Step 8:

[1679] The generative AI model sends the calibration results back to the server

[1680] The generative AI model sends the proofread document back to the server, which receives and stores it.

[1681] Input: Proofread results sent from the generative AI model

[1682] Output: Saved proofreading result document file

[1683] Specific operation: The server receives response data from the generative AI model and temporarily stores it.

[1684] Step 9:

[1685] The server displays the calibration results on the user interface

[1686] The server receives and stores the proofreading results, and then displays them on the user interface, allowing the user to review them and check the corrections.

[1687] Input: Saved proofreading result document file

[1688] Output: Calibration results displayed in the user interface

[1689] Specific operation: The server generates and sends HTML for displaying the proofreading results to the browser. The proofreading results are displayed in the user's browser.

[1690] Step 10:

[1691] The user checks the proofreading results and makes any necessary corrections.

[1692] The user checks the proofreading results displayed on the user interface, and makes any necessary corrections. After making the corrections, the user clicks the final confirmation button.

[1693] Input: User operation to check the calibration results

[1694] Output: Final proofread document file

[1695] Specific operation: The user checks the proofreading results in the browser, makes additional corrections using a text editor if necessary, and clicks the final confirmation button.

[1696] Step 11:

[1697] The server generates the finalized document and makes it available for download.

[1698] The server generates a finalized document and saves it in file storage for the user to download. The user clicks the download button to retrieve the finalized document.

[1699] Input: Final verified document file

[1700] Output: A document file that users can download

[1701] Specific operation: The server generates the final document file based on the latest proofreading results and generates a downloadable URL. The user clicks the "Download" button in their browser to obtain the file.

[1702] (Application example 2)

[1703] 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."

[1704] While conventional document proofreading systems aim to efficiently proofread document content, they do not provide support that takes into account the editor's emotional state. This can lead to stress and a lack of concentration during proofreading. Furthermore, the lack of emotional interface adjustments and appropriate feedback can negatively impact the efficiency and quality of the editor's work. Therefore, there is a need for a system that provides support that responds to the editor's emotions, further streamlining proofreading and improving the work experience.

[1705] 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.

[1706] In this invention, the server includes means for providing a user interface for uploading documents, means for inputting proofreading conditions, means for receiving and saving the uploaded document and the proofreading conditions, means for sending the received document and the proofreading conditions to a generative AI model to request proofreading, means for receiving proofreading results from the generative AI model, means for displaying the proofreading results on the user interface, means for making the final proofread document available for download, means for analyzing the editor's emotions, means for dynamically adjusting the interface based on the editor's emotions, means for providing guide messages and help based on the editor's emotions, and means for providing positive feedback in accordance with the editor's emotions. This enables efficient document proofreading work while taking into account the editor's emotional state.

[1707] A "document upload user interface" is an interface that a user uses to upload a document to be proofread into the system.

[1708] The "means for inputting proofreading conditions" is a means for a user to input conditions necessary for proofreading a document.

[1709] The "means for receiving and storing uploaded documents and proofreading conditions" refers to the means by which the system receives and stores documents uploaded by users and proofreading conditions entered by users.

[1710] "Means for sending the received document and proofreading conditions to the generative AI model and requesting proofreading" refers to the means by which the system sends the received document and proofreading conditions to the generative AI model and requests proofreading of the document.

[1711] "Means for receiving proofreading results from the generative AI model" refers to means for receiving proofreading results returned from the generative AI model.

[1712] The "means for displaying the proofreading results on a user interface" is an interface for displaying the received proofreading results to the user.

[1713] "Means for making the final proofread document available for download" refers to the means used by a user to download the final proofread document.

[1714] "Means for analyzing the editor's emotions" refers to a means for analyzing the editor's facial expressions, voice, etc., to determine their emotional state.

[1715] The "means for dynamically adjusting the interface based on the editor's emotions" refers to a means for dynamically changing the color tone and layout of the interface according to the editor's emotional state.

[1716] The "means for providing a guide message or help based on the emotions of an editor" refers to a means for providing an appropriate guide message or help based on the emotional state of an editor.

[1717] "Means for providing positive feedback according to the editor's emotions" refers to means for providing positive feedback according to the editor's emotional state.

[1718] The present invention provides a system for improving the efficiency of document proofreading work and providing dynamic support based on the editor's emotions. An embodiment of this system will be described in detail below.

[1719] Overall system configuration

[1720] The system includes a user interface for uploading documents, a means for inputting proofreading conditions, a means for receiving and storing the uploaded documents and proofreading conditions, a means for sending the received documents and proofreading conditions to a generative AI model to request proofreading, a means for receiving proofreading results from the generative AI model, a means for displaying the proofreading results on the user interface, a means for making the final proofread document available for download, a means for analyzing editor emotions, a means for dynamically adjusting the interface based on the editor emotions, a means for providing guide messages and help based on the editor emotions, and a means for providing positive feedback according to the editor emotions.

[1721] Hardware and software used

[1722] Hardware:

[1723] Devices (PC, smartphone, tablet)

[1724] Camera (for analyzing the editor's facial expressions)

[1725] Microphone (for editor's voice tone analysis)

[1726] software:

[1727] Server-side framework (Django)

[1728] Facial expression analysis software (OpenCV)

[1729] Audio Tone Analysis Software (pyttsx3)

[1730] Generative AI model (OpenAI GPT-3)

[1731] Database (SQLite)

[1732] Specific operation of the system

[1733] 1. Uploading documents and setting proofreading conditions

[1734] Editors log in to the system from their terminals and upload documents. The user interface is designed to make uploading documents easy. Next, an interface is provided for entering proofreading conditions, and editors enter proofreading conditions such as "consistency of writing style" and "half-width numbers."

[1735] 2. Submitting documents and proofreading requirements

[1736] The server receives and stores the uploaded document and the entered proofreading conditions. This data is then sent to the generative AI model (GPT-3) for proofreading.

[1737] 3. Performing calibration and receiving the results

[1738] The generative AI model proofreads the received document based on the proofreading criteria. The proofread document is highlighted and annotated with each correction. The proofreading results are sent back to the server and saved again.

[1739] 4. View and download calibration results

[1740] The server displays the proofreading results on a user interface. The editor can review the corrections and make additional edits as needed. Finally, the edited document can be downloaded.

[1741] 5. Sentiment Analysis and Dynamic Support

[1742] The system uses the device's camera and microphone to analyze the editor's emotions. It uses OpenCV to analyze facial expressions and pyttsx3 to analyze vocal tone. The editor's emotions are determined in real time, and the interface color tone and layout are dynamically changed accordingly. For example, if the editor is feeling stressed, the color tone will be changed to a relaxing one and navigation will be simplified. If the system determines that the editor is feeling stressed, it will display appropriate guide messages and help. For example, it will display a message such as, "You look tired. Please take a break."

[1743] 6. Positive Feedback

[1744] If the emotion engine determines that the editor is satisfied, the system will provide positive feedback, such as a message like "Nice job! Keep it up!"

[1745] Examples of concrete examples and prompts

[1746] As a concrete example, an editor uploads a blog post to the system and enters the following proofreading criteria:

[1747] "Unify all writing styles with "~desu""

[1748] Dates and times are written in the Gregorian calendar.

[1749] "Unify numbers to half-width"

[1750] An example of a prompt that would be generated based on this condition is:

[1751] Correct this content: 'Today's weather is sunny.' with conditions: 'Use honorific expressions consistent with "~desu", use the Gregorian calendar for dates, and use half-width numbers.'

[1752] By implementing this invention, it is possible to efficiently proofread documents while taking into account the emotional state of the editor, and the editor's work experience is improved through dynamic interface adjustment and appropriate feedback.

[1753] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1754] Step 1:

[1755] A user logs in to the system using a terminal. The user inputs login information (user name, password), which the server receives and authenticates. As an output of successful authentication, a dashboard dedicated to the user is displayed.

[1756] Step 2:

[1757] The user uploads the document to be proofread using the document upload user interface. The input is the uploaded document file, which the server receives and temporarily stores. The output is an upload confirmation message.

[1758] Step 3:

[1759] The user inputs the proofreading conditions using the proofreading conditions input interface. The input is the text of the proofreading conditions, which the server receives and stores. The output displays a confirmation message for the input conditions.

[1760] Step 4:

[1761] The server sends the received document and proofreading conditions to the generative AI model. The input is the saved document and proofreading conditions, and data format conversion and prompt generation are performed. A prompt that clearly indicates the conditions for proofreading is generated and this data is sent to the generative AI model (GPT-3). As an output, a status message indicating that it has been sent to the generative AI model is recorded in the server log.

[1762] Step 5:

[1763] The generative AI model performs the proofreading and sends the proofreading results back to the server. The input is data based on the prompt sentence, and the generative AI model analyzes this data, performs the proofreading, and outputs the results. The server receives the proofreading results and saves them again. The proofreading result data is obtained as the output.

[1764] Step 6:

[1765] The server displays the proofreading results on the user interface. The input is the saved proofreading result data, which is then processed to display in a format that is easy for the user to view. Specifically, the proofreading points are highlighted and annotations are added as necessary. The output is the proofreading results displayed on the user interface.

[1766] Step 7:

[1767] The user checks the proofreading results and makes additional corrections as necessary. The input is the user's corrections, which are received and reflected in the proofreading result data. The output is the final corrected document data.

[1768] Step 8:

[1769] The server generates a link to download the final proofread document. The input is the final corrected document data, and it converts this data into a downloadable format (e.g., PDF or Word file). As an output, a download link is displayed in the user interface.

[1770] Step 9:

[1771] The system analyzes the user's emotions using the device's built-in camera and microphone. The input is camera video and audio data, which are analyzed in real time using OpenCV and pyttsx3. The system determines the emotional state and obtains corresponding data. The output is the editor's analyzed emotional state.

[1772] Step 10:

[1773] The server dynamically adjusts the interface based on the editor's emotions. The input is data on the editor's emotional state, and the color tone and layout of the interface are dynamically changed based on this. Specifically, if the editor is feeling stressed, the color tone is changed to a calmer tone and simple navigation is provided. The output is a dynamic change of the user interface.

[1774] Step 11:

[1775] It provides guide messages and help based on the editor's emotions. The input is the result data of the emotion analysis, and an appropriate message is generated based on this. Specifically, it provides a message such as "You look tired. Please take a break." As an output, the guide message or help is displayed in the user interface.

[1776] Step 12:

[1777] It provides positive feedback based on the editor's sentiment. The input is the result data of sentiment analysis, and based on this, it generates a positive message. Specifically, it provides a message such as "Good job! Keep it up!". As an output, the positive feedback is displayed in the user interface.

[1778] 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.

[1779] 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.

[1780] 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.

[1781] 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.

[1782] 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.

[1783] 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.

[1784] 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).

[1785] 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.

[1786] 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."

[1787] 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 trainin...

Claims

1. means for providing a user interface for uploading a document; means for inputting calibration conditions; means for receiving and storing uploaded documents and proofreading requirements; A means for sending the received document and proofreading conditions to a generative model to request proofreading; means for receiving a calibration result from the generative model; means for displaying the calibration results on a user interface; The system includes a means for making the final proofread document available for download.

2. The system of claim 1 , further comprising means for encrypting and replacing confidential information with blank characters or dummy characters before transmitting the confidential information to the generative model.

3. 10. The system of claim 1, further comprising means for allowing a user to make additional corrections to the proofread results.

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