System

Abstract

To provide a system.SOLUTION: Provided is a system including: means mounted to a terminal for a user to input text data; means mounted to the terminal for transmitting the inputted text data to a server; means mounted to the server for transmitting a prompt to a generative AI that extracts inadequate words; means for the generative AI to extract inadequate words; means for preserving inadequate words as the result of extraction in a database; means mounted to the server for inputting the extraction result to spreadsheet software by using an automatization tool; and means mounted to the terminal for displaying the extraction result to the user.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] Human proofreading is time-consuming and costly, and there is a possibility of errors due to human error. Furthermore, if there are errors, the results must also be entered by humans, which increases the labor and cost involved. [Means for solving the problem]

[0005] To solve this problem, we provide a system that uses the syntactic analysis function of generative AI to extract erroneous words and automatically enters the results into Excel if any erroneous words are found. Furthermore, by using RPA to extract erroneous words using generative AI and automatically enter the results, this system significantly reduces the man-hours and costs of proofreading work. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] One embodiment of the present invention is a system that undertakes proofreading work that is usually done by humans. This system uses the syntactic analysis function of generative AI to extract incomplete words from the text to be proofread. Specifically, it detects typos and grammatical errors within the text.

[0029] "Example 2"

[0030] Furthermore, if any defects are found, the results are automatically entered into Excel. Specifically, the location, content, and suggested corrections of the defective words are automatically entered into each column of Excel. This saves humans the trouble of manually entering the results.

[0031] "Example 3"

[0032] This system also uses RPA to extract erroneous words using generative AI and automatically input the results. Specifically, it automates the entire process from extracting erroneous words to inputting the results, minimizing human intervention. This makes it possible to significantly reduce the man-hours and costs of proofreading work.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: The system accepts the editing work to be performed by a human. Specifically, the text data to be edited is input into the system.

[0036] Step 2: The system uses the generative AI's syntactic analysis function to extract imperfect words from the input text. Specifically, it detects typos and grammatical errors in the text. Step 3: If imperfect words are detected, the information is saved in a temporary database.

[0037] "Example 2"

[0038] Step 1: Obtain the information of the missing words stored in the temporary database.

[0039] Step 2: Based on the acquired information on defective words, the results are automatically entered into Excel. Specifically, the location, content, and suggested corrections of the defective words are automatically entered into each column of Excel. Step 3: Once the data has been entered into Excel, the results are saved and reported to the user as necessary.

[0040] "Example 3"

[0041] Step 1: The system will use RPA to extract incomplete words using generative AI and automatically input the results. Specifically, a script will be created to automate the entire process from extracting incomplete words to inputting the results.

[0042] Step 2: Run the script you created, which minimizes human intervention and significantly reduces the man-hours and costs of proofreading.

[0043] Step 3: Save the results of the script execution and report them to the user if necessary.

[0044] Example 1

[0045] Next, a description will be given of Example 1 of Form 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."

[0046] Traditional proofreading work is done manually by humans, which takes time and effort, and there is a risk of overlooking typos and grammatical errors. Furthermore, the manual input of proofreading results is cumbersome and inefficient. Furthermore, the lack of automated correction of proofreading results and the adoption of robotic process automation (RPA) has led to a decline in overall work efficiency. To solve these issues, there is a need to automate and streamline proofreading work.

[0047] 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.

[0048] In this invention, the server includes a means for receiving text to be proofread from a user, a means for inputting the received text into the generative AI, and a means for analyzing the analysis results returned from the generative AI and extracting defective words, thereby enabling the automation and efficiency of proofreading work.

[0049] "Proofreading" is the task of checking and correcting typos and grammatical errors in sentences and texts.

[0050] "Generative AI" is an AI technology for natural language processing and syntactic analysis, and has the ability to detect, for example, typos and grammatical errors in text.

[0051] The "syntax analysis function" is a function that analyzes the structure of sentences and text and detects grammatical errors and omissions.

[0052] "Faulty words" are words or phrases that contain typos or grammatical errors present in the text.

[0053] "Spreadsheet software" refers to software for data entry, calculation, analysis, graph creation, etc., and includes, for example, MICROSOFT (registered trademark) EXCEL (registered trademark).

[0054] "User" refers to a person or organization that uses the system to perform proofreading work.

[0055] A "server" is a computer system that receives requests from users, analyzes the text using generative artificial intelligence, and returns the results.

[0056] An "interface" is a means, such as a screen or input form, that allows a user to operate a system.

[0057] "Robotic Process Automation (RPA)" is a technology that automates routine business processes, using software robots to replace human work.

[0058] This invention is a system for improving the efficiency of human proofreading work, extracting defective words from text using the syntactic analysis function of generative artificial intelligence and providing the extracted words to the user. A specific embodiment of this system will be described below.

[0059] Server Processing

[0060] The server receives the text to be reviewed from the user. Specifically, it receives the text data via an HTTP request. The received text is input into a generative AI (e.g., a model with natural language processing technology). An example of this generative AI is GPT-4 (registered trademark) from OpenAI (registered trademark).

[0061] The generative AI analyzes the input text and detects typos and grammatical errors. The server analyzes the analysis results returned by the generative AI and extracts defective words. The extracted defective words are listed and returned to the user as an HTTP response in JSON format.

[0062] Terminal handling

[0063] The terminal provides an interface for the user to enter text to be reviewed, such as a web form or text area, and the text entered by the user is sent to the server by pressing a submit button.

[0064] The list of defective words returned from the server is received by the terminal, which then displays the received defective words to the user. Specifically, the defective words are displayed in a list format on a web page.

[0065] User operations

[0066] The user inputs the text to be proofread into the terminal. The input text is sent to the server by clicking the send button. The user checks the list of defective words returned by the server and corrects the text.

[0067] Specific examples

[0068] For example, consider the case where a user wants to proofread the text "I went to the library yesterday. I borrowed a book there." The user enters this text into a web form on their device and clicks the submit button. The server inputs the received text into a generative artificial intelligence (e.g., GPT-4), and based on the analysis results, detects that there is a grammatical error in the part "I went to the library." The server lists the incorrect words in "I went to the library" and returns them to the device in JSON format. The device displays these incorrect words to the user, who then corrects it to "I went to the library."

[0069] Prompt Sentence Examples

[0070] Please proofread the following text. Identify any spelling or grammatical errors and list any missing words.

[0071] Text: 'I went to the library yesterday. I borrowed a book there.'

[0072] In this way, the system can efficiently proofread the text entered by the user.

[0073] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0074] Step 1:

[0075] The server receives the text to be reviewed from the user.

[0076] Input: Text data entered by the user into the terminal.

[0077] Specific operation: The server receives text data via an HTTP request.

[0078] Output: Received text data

[0079] Step 2:

[0080] The server inputs the received text into a generative artificial intelligence.

[0081] Input: Received text data

[0082] Specific operation: The server sends the text data as a prompt to a generative artificial intelligence (e.g., a model with natural language processing technology).

[0083] Output: Prompt sent to the generative AI

[0084] Step 3:

[0085] Generative AI analyzes input text and detects typos and grammatical errors.

[0086] Input: A prompt sent to the generative AI

[0087] How it works: Generative AI performs syntactic analysis to detect typos and grammatical errors in text.

[0088] Output: Analysis results (data containing typos and grammatical errors)

[0089] Step 4:

[0090] The server analyzes the analysis results returned from the generative artificial intelligence and extracts defective words.

[0091] Input: Analysis results returned by generative artificial intelligence

[0092] Specific operation: The server analyzes the analysis results and lists the defective words.

[0093] Output: List of missing words

[0094] Step 5:

[0095] The server lists the extracted defective words and returns them to the user.

[0096] Input: List of missing words

[0097] Specific operation: The server returns a list of incomplete words in JSON format as an HTTP response.

[0098] Output: A list of invalid words returned to the user

[0099] Step 6:

[0100] The terminal receives the list of defective words returned from the server.

[0101] Input: A list of invalid words returned by the server

[0102] Specific operation: The terminal receives the JSON data returned as an HTTP response.

[0103] Output: List of received invalid words

[0104] Step 7:

[0105] The terminal displays the received defective words to the user.

[0106] Input: List of received invalid words

[0107] Specific operation: The terminal displays defective words in a list format on a web page.

[0108] Output: A list of the missing words shown to the user

[0109] Step 8:

[0110] The user checks the list of defective words returned from the server and corrects the text.

[0111] Input: A list of invalid words shown to the user

[0112] What happens: The user uses the list to correct spelling and grammatical errors in the text.

[0113] Output: Corrected text

[0114] (Application example 1)

[0115] Next, a description will be given of Application Example 1 of Embodiment 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."

[0116] Traditional proofreading work relies on manual human labor, which is time-consuming and labor-intensive, and there is a risk of overlooking typos and grammatical errors. Furthermore, content distribution services require fast and accurate proofreading to maintain the quality of posted articles and comments, but this is difficult to do manually. Furthermore, there is a lack of functionality to automatically input proofreading results into a database, making efficient management difficult.

[0117] 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.

[0118] In this invention, the server includes a means for accepting proofreading work that would otherwise be performed by humans, a means for extracting defective words using the syntactic analysis function of generative AI, a means for automatically inputting the results into a database when defects are found, a means for automatically proofreading articles and comments posted to a content distribution service, a means for detecting typos and grammatical errors and presenting suggested corrections, and a means for the user to confirm and approve the suggested corrections, thereby enabling the efficiency and accuracy of proofreading work to be improved.

[0119] "Proofreading" is the task of detecting and correcting typos and grammatical errors in sentences or text.

[0120] "Generative AI" is a type of artificial intelligence that uses natural language processing to generate and analyze text.

[0121] The "syntax analysis function" is a function that analyzes the structure of a sentence and detects grammatical flaws and errors.

[0122] "Faulty words" are words or phrases that indicate typos or grammatical errors in a sentence.

[0123] A "database" is a system that systematically organizes and stores information so that it can be searched and used as needed.

[0124] A "content distribution service" is a service that provides users with content such as articles and comments via the Internet.

[0125] An "article" is a piece of writing or text on a particular topic.

[0126] A "comment" is an opinion or comment posted by a user on an article or post.

[0127] "Suggested corrections" are suggested corrections for detected typos and grammatical errors.

[0128] A "user" is someone who uses a system or service.

[0129] A system for carrying out this invention is configured as follows: The server includes means for accepting proofreading work that would otherwise be performed by humans, means for extracting defective words using the syntactic analysis function of generative AI, means for automatically inputting the results into a database when defects are found, means for automatically proofreading articles and comments posted to a content distribution service, means for detecting typos and grammatical errors and presenting suggested corrections, and means for the user to confirm and approve the suggested corrections.

[0130] Hardware and software used

[0131] Hardware: Servers, user devices (smartphones, PCs, etc.)

[0132] Software: Python, OpenAI API, database management system (e.g., MySQL (registered trademark))

[0133] Data processing and calculation

[0134] 1. User terminal: The user enters articles and comments and requests them for review.

[0135] 2. Server: Receives the input text and generates a prompt to send to a generative AI model (e.g., OpenAI's GPT-3 (registered trademark)).

[0136] 3. Generative AI model: Detects typographical and grammatical errors in the text based on the prompt and generates suggested corrections.

[0137] 4. Server: Receives the generated revision suggestions and presents them to the user.

[0138] 5. User terminal: The user checks the proposed amendments and approves or amends them.

[0139] 6. Server: Automatically enters the final proofreading results into the database.

[0140] Specific examples

[0141] When a user wants to post a blog post, they enter the text of the post into the application, which automatically reviews it, corrects any typos and grammatical errors, and presents the corrected text to the user. The user can then review the suggested corrections and approve them if necessary.

[0142] Prompt Sentence Examples

[0143] Please identify any typos or grammatical errors in the text below and suggest corrections.

[0144] This is a test text and may contain typos and grammatical errors.

[0145] Proposed amendment:

[0146] In this way, it is possible to improve the efficiency and accuracy of proofreading work.

[0147] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0148] Step 1:

[0149] A user uses a terminal to input text for an article or comment and submit it for review. The input text is sent to the server. The input data is the text entered by the user, and the output data is the text sent to the server.

[0150] Step 2:

[0151] The server generates a prompt sentence to send to the generative AI model from the received text. The input data is the text received from the user, and the output data is the generated prompt sentence. Specifically, the server converts the text into the following format:

[0152] Please identify any typos or grammatical errors in the text below and suggest corrections.

[0153] [user text]

[0154] Proposed amendment:

[0155] Step 3:

[0156] The server sends the generated prompt sentence to a generative AI model (e.g., OpenAI's GPT-3). The input data is the generated prompt sentence, and the output data is the correction suggestions returned by the generative AI model. Specifically, the OpenAI API is used to send the prompt sentence and receive the correction suggestions.

[0157] Step 4:

[0158] The server presents the proposed revisions received from the generative AI model to the user. The input data is the proposed revisions received from the generative AI model, and the output data is the proposed revisions presented to the user. Specifically, the proposed revisions are displayed on the user's device.

[0159] Step 5:

[0160] The user checks the proposed revisions and either approves or corrects them. The input data are the revisions proposed to the user, and the output data is the final text that the user approves or corrects. Specifically, the user checks the revisions and makes corrections if necessary.

[0161] Step 6:

[0162] The server automatically inputs the final text approved or corrected by the user into a database. The input data is the final text approved or corrected by the user, and the output data is the final text stored in the database. Specifically, a database management system (e.g., MySQL) is used to store the final text.

[0163] Example 2

[0164] Next, a description will be given of Example 2 of Form 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."

[0165] Traditional proofreading work is done manually by humans, which takes time and effort, and is prone to errors. The process of detecting and correcting errors is also cumbersome, making it difficult to carry out the work efficiently. Furthermore, the time and effort required to manually input information about detected errors into spreadsheet software is a significant burden.

[0166] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for accepting proofreading work to be performed by humans, a means for extracting deficiencies using the syntactic analysis function of the generative AI model, a means for automatically inputting the results into spreadsheet software when deficiencies are found, a means for sending a prompt to the generative AI model, and a means for receiving the analysis results returned from the generative AI model. This makes it possible to improve the efficiency and accuracy of proofreading work.

[0167] "Proofreading" is the task of detecting and correcting typographical and grammatical errors in a document.

[0168] A "generative AI model" is a model that uses artificial intelligence technology to analyze text data and perform specific tasks.

[0169] The "syntax analysis function" is a function that analyzes the grammatical structure of text data and understands its meaning and relationships.

[0170] "Faulty words" are words or phrases that refer to typographical or grammatical errors present in a document.

[0171] "Spreadsheet software" is software for entering, calculating, analyzing, and visualizing data, and generally manages data in spreadsheet format.

[0172] A "prompt" is an instruction entered into a generative AI model to make it perform a specific task.

[0173] "Business automation technology" is a technology for automating business processes that were previously performed manually.

[0174] The present invention is a system for making proofreading more efficient and improving accuracy. A specific embodiment of this system will be described below.

[0175] First, the user inputs text data through the system interface, which can be a web form or a dedicated application, and the text data entered by the user is sent to the server.

[0176] The server creates a prompt to send to the generative AI model from the received text data. For example, it generates the following prompt:

[0177] Please analyze the text below and find any defects. Please provide the location, content, and suggested fixes for any defects found.

[0178] Text: "This article contains several deficiencies. For example, it contains typos and omissions."

[0179] Next, the server sends the prompt to a generative AI model. The generative AI model is, for example, a model that uses general artificial intelligence technology and analyzes text data. Specifically, the generative AI model performs syntactic analysis of the text data and detects incorrect words.

[0180] Once the generative AI model has completed its analysis, the server receives the analysis results, which include the location, content, and suggested corrections for the missing words. For example, the following results may be returned:

[0181] Incomplete word position: 13

[0182] Deficiency word content: "Several deficiencies"

[0183] Proposed fix: "Some issues"

[0184] The server automatically inputs the missing information into a spreadsheet based on the analysis results it receives. Specifically, it uses Python's openpyxl library to input the data into an Excel file as follows:

[0185] Enter the position of the missing word in column A.

[0186] Enter the contents of the missing words in column B.

[0187] Enter the proposed revisions in column C.

[0188] Finally, the user can download or view the generated Excel file, allowing them to review the information for any deficiencies and make any necessary corrections.

[0189] This system will improve the efficiency and accuracy of proofreading work, eliminating the need for users to manually input results and enabling rapid corrections based on the analysis results of the generative AI model.

[0190] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0191] Step 1:

[0192] The user inputs text data.

[0193] A user inputs text data through the system's interface, for example, using a web form or a dedicated application, and the input text data is sent to the server.

[0194] Input: Text data entered by the user

[0195] Output: Text data sent to the server

[0196] Step 2:

[0197] The server receives the input text data.

[0198] The server receives text data sent by the user, which is sent via a communication protocol such as an HTTP request or WebSocket.

[0199] Input: Text data sent by the user

[0200] Output: Text data stored on the server

[0201] Step 3:

[0202] The server sends a prompt to the generative AI model.

[0203] The server creates a prompt to send to the generative AI model from the received text data. For example, it generates the following prompt:

[0204] Please analyze the text below and find any defects. Please provide the location, content, and suggested fixes for any defects found.

[0205] Text: "This article contains several deficiencies. For example, it contains typos and omissions."

[0206] Input: Text data stored on the server

[0207] Output: The prompt sent to the generative AI model

[0208] Step 4:

[0209] A generative AI model analyzes text data and detects defects.

[0210] The generative AI model receives the prompt sent from the server and analyzes the text data. As a result of the analysis, it identifies the location, content, and suggested corrections of the defects.

[0211] Input: A prompt sent to the generative AI model

[0212] Output: Location, content, and proposed corrections for defects

[0213] Step 5:

[0214] The server obtains the location, content, and proposed corrections of the defect.

[0215] The server receives the analysis results returned by the generative AI model, including the location, content, and suggested fixes for the defects.

[0216] Input: Analysis results returned from the generative AI model

[0217] Output: Location, content, and proposed corrections of defects stored on the server

[0218] Step 6:

[0219] The server enters the incorrect information into the Excel file.

[0220] The server uses Python's openpyxl library to input the missing information into the Excel file. Specifically, the data is input as follows:

[0221] Enter the position of the missing word in column A.

[0222] Enter the contents of the missing words in column B.

[0223] Enter the proposed revisions in column C.

[0224] Input: Location, content, and proposed corrections of defects stored on the server

[0225] Output: Excel file with incomplete information entered

[0226] Step 7:

[0227] The user checks the Excel file.

[0228] The user downloads or views the Excel file generated by the server, allowing the user to check the information about any deficiencies and make any necessary corrections.

[0229] Input: Excel file with incomplete information

[0230] Output: Information on defects confirmed by the user

[0231] (Application example 2)

[0232] Next, a description will be given of Application Example 2 of Form 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."

[0233] In traditional proofreading and inventory management, detecting defects and inputting the results is often done manually, which takes time and effort and increases the risk of human error. Furthermore, in inventory management at logistics centers, scanning for defects and managing the results is not done efficiently, making it difficult to respond quickly.

[0234] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for accepting proofreading work that would be performed by humans, a means for extracting deficiencies using the syntactic analysis function of generative AI, a means for automatically inputting the results into Excel when deficiencies are found, and a means for scanning deficiency data with a smartphone and automatically inputting the results into Excel in an inventory management system at a logistics center. This automates the detection of deficiencies in proofreading work and inventory management and the input of the results, saving time and effort and reducing the risk of human error.

[0235] "Proofreading" is the work of checking the contents of documents and data and correcting errors and omissions.

[0236] "Generative AI" is a type of artificial intelligence that has the ability to generate and analyze data for specific tasks.

[0237] A "syntax analysis function" is a function that analyzes the structure of documents and data and understands their meaning and grammatical relationships.

[0238] "Faulty words" are words or phrases that indicate errors or inappropriate parts in a document or data.

[0239] "Excel" is a type of spreadsheet software and a tool for entering, calculating, analyzing, and visualizing data.

[0240] A "logistics center" is a facility that stores, manages, and ships goods.

[0241] An "inventory management system" is a system for monitoring and managing product inventory.

[0242] A "smartphone" is a type of mobile phone and a multi-function device that can connect to the Internet and use applications.

[0243] "Scanning" is the process of electronically reading data or images.

[0244] "RPA" stands for Robotic Process Automation, a technology for automating business processes.

[0245] To implement the present invention, the following system configuration and program are required.

[0246] System Configuration

[0247] 1. Server: Hosts the generative AI model and provides parsing functionality.

[0248] 2. Terminal: Uses a smartphone to scan defect data as part of the inventory management system at the distribution center.

[0249] 3. Software: Python and the openpyxl library are used for automated data entry and management.

[0250] Program processing

[0251] The server uses a generative AI model to extract deficiency words in proofreading and inventory management. The terminal (smartphone) scans inventory at the logistics center and collects deficiency data. The collected deficiency data is sent to the server and analyzed by the generative AI model. As a result, the location, content, and correction suggestions of the deficiency words are extracted and automatically entered into an Excel file.

[0252] Hardware and software used

[0253] Hardware: Smartphone (for scanning and entering missing data)

[0254] Software: Python, openpyxl library (for working with Excel files)

[0255] Data processing and calculation

[0256] 1. Deficiency data collection: Using smartphones, inventory is scanned at distribution centers to collect defect data.

[0257] 2. Data transmission: The collected deficiency data is transmitted to the server.

[0258] 3. Syntax analysis: A generative AI model on the server analyzes the defective data and extracts the location, content, and suggested corrections for the defective words.

[0259] 4. Automatic data entry: The extracted information of missing words is automatically entered into an Excel file using Python and the openpyxl library.

[0260] Specific examples

[0261] For example, suppose the following defects are detected when inventory is scanned at a logistics center:

[0262] Location: A1, Content: Quantity shortage, Correction: Additional order

[0263] Location: B2, Content: Label error, Proposal: Label correction

[0264] By automatically entering this deficiency data into an Excel file, administrators can quickly take corrective action.

[0265] Prompt Sentence Examples

[0266] "Generate a Python program that automatically inputs deficiency data into Excel for inventory management at a distribution center. The deficiency data will include the location, content, and correction suggestions."

[0267] The above is a specific embodiment for carrying out the present invention.

[0268] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0269] Step 1:

[0270] The terminal (smartphone) scans inventory at the distribution center. The input is identification information such as the inventory barcode or QR code (registered trademark), and the output is incomplete data (e.g., insufficient quantity, incorrect label). Specifically, the smartphone's camera is used to read the inventory barcode and send the information to the application.

[0271] Step 2:

[0272] The terminal (smartphone) sends the collected deficiency data to the server. The input is the deficiency data collected in step 1, and the output is the deficiency data sent to the server. Specifically, the smartphone application sends the deficiency data to the server's API endpoint.

[0273] Step 3:

[0274] The server uses a generative AI model to analyze the deficiency data. The input is the deficiency data sent from the device, and the output is the location, content, and correction suggestions of the deficiency words. Specifically, the server receives the deficiency data, inputs it into the generative AI model, and analyzes it.

[0275] Step 4:

[0276] The server extracts the location, content, and correction suggestions of the defective words based on the analysis results. The input is the analysis results from the generative AI model, and the output is the location, content, and correction suggestions of the extracted defective words. Specifically, the server analyzes the analysis results and extracts the necessary information.

[0277] Step 5:

[0278] The server automatically inputs the extracted deficiency word information into an Excel file. The input is the location, content, and suggested corrections of the extracted deficiency words, and the output is the updated Excel file. Specifically, the server uses Python and the openpyxl library to open the Excel file, input information into the appropriate cells, and save the file.

[0279] Step 6:

[0280] The user checks the updated Excel file and takes the necessary action. The input is the updated Excel file, and the output is the implementation of the countermeasures. Specifically, the user opens the Excel file, checks the location, content, and suggested corrections of the defective words, and takes the necessary action.

[0281] Example 3

[0282] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0283] Conventional proofreading work is done manually by humans, which takes time and effort, resulting in high costs. Manual proofreading is also prone to errors, which can lead to inconsistent quality. Furthermore, even in systems that use generative AI models to extract defective words and automatically input the results, each process is executed individually, which reduces overall efficiency.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0285] In this invention, the server includes a means for a user to input text data, a means for a terminal to send the input data to the server, a means for the server to send a prompt sentence to the generative AI model, a means for the generative AI model to extract defective words, a means for the server to save the extracted results in a database, a means for the server to automatically input the results using an automation tool, and a means for the terminal to display the results to the user, thereby making it possible to improve the efficiency of proofreading work and reduce costs.

[0286] "Human proofreading" refers to the process of checking the content of sentences or texts and correcting errors or inappropriate expressions.

[0287] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate and analyze text.

[0288] "Syntax analysis function" refers to the function for analyzing the grammar and structure of text and understanding its meaning.

[0289] "Incomplete words" refer to errors or inappropriate expressions contained in a sentence.

[0290] "Spreadsheet software" is software for organizing and calculating data, and generally handles data in spreadsheet format.

[0291] "User" refers to a person who uses the system.

[0292] "Terminal" refers to a device through which a user accesses the system.

[0293] A "server" is a computer that is the central part of a system and processes and stores data.

[0294] A "prompt sentence" refers to an input sentence used to give instructions to a generative AI model.

[0295] A "database" refers to a system for efficiently storing and managing data.

[0296] "Automation tools" refer to software or systems that perform specific tasks automatically.

[0297] "Means for automatically inputting results" refers to a method or system for automatically inputting extracted defective words and correction results.

[0298] "Means for displaying results" refers to a method or system for displaying the processing results to the user.

[0299] This invention is a system that uses a generative AI model to extract defective words and automatically input the results. Specific embodiments of this system will be described below.

[0300] First, a user inputs text data using a terminal. The terminal then sends the text data to a server via a web browser or a dedicated application. For example, if a user inputs the text "This sentence is very good," the terminal sends this data to the server using an HTTP POST request.

[0301] The server generates a prompt to send the received text data to the generative AI model. Specifically, it uses the Python requests library to send the following prompt to the generative AI model:

[0302] "Please extract the missing words from the following text: This sentence is very good."

[0303] The generative AI model analyzes the prompt received from the server and extracts incomplete words. For example, it determines that the expression "very good" is ambiguous and extracts it as an incomplete word.

[0304] The server stores the missing words received from the generative AI model in a database, specifically by running SQL queries to store the extracted missing words using a database management system such as MySQL.

[0305] Next, the server uses an automation tool (e.g., UiPath) to automatically correct the extracted defective words, for example, changing "very good" to "excellent." This correction result is saved back in the database.

[0306] Finally, the terminal displays the correction results received from the server to the user. Specifically, the corrected text "This sentence is very good" is displayed on the web browser.

[0307] This system enables the efficiency of proofreading work and cost reduction. In addition, the use of a generative AI model enables highly accurate extraction and correction of defective words. The flow of the identification process in Example 3 will be explained using Figure 15.

[0308] Step 1:

[0309] The user inputs text data.

[0310] A user inputs text data using a terminal interface. For example, the user inputs "This article is very good" into a form on a web browser. The input data is the text data entered by the user.

[0311] Step 2:

[0312] The terminal sends the input data to the server.

[0313] The terminal sends the text data entered by the user to the server. Specifically, it sends the data using an HTTP POST request. The input is the text data entered by the user, and the output is the text data sent to the server.

[0314] Step 3:

[0315] The server sends a prompt to the generative AI model.

[0316] The server generates a prompt sentence to send to the generative AI model from the received text data. For example, it creates a prompt sentence such as "Please extract the missing words from the following text: This sentence is very good." The input is the text data sent to the server, and the output is the prompt sentence to be sent to the generative AI model.

[0317] Step 4:

[0318] The generative AI model extracts incomplete words.

[0319] The generative AI model analyzes the prompt received from the server and extracts incomplete words. For example, it determines that the expression "very good" is ambiguous and extracts it as an incomplete word. The input is the prompt sent to the generative AI model, and the output is the extracted incomplete word.

[0320] Step 5:

[0321] The server stores the extracted results in a database.

[0322] The server stores the defective words received from the generative AI model in a database. Specifically, it inserts them into the database using an SQL query. The input is the extracted defective words, and the output is the defective words stored in the database.

[0323] Step 6:

[0324] The server uses an automation tool to populate the results.

[0325] The server uses an RPA tool (e.g., UiPath) to automatically correct the extracted defective words. For example, it changes "very good" to "excellent." The input is the defective words stored in the database, and the output is the corrected text data.

[0326] Step 7:

[0327] The terminal displays the results to the user.

[0328] The terminal displays the correction results received from the server to the user. Specifically, the corrected text "This sentence is very good" is displayed on the web browser. The input is the corrected text data, and the output is the correction results displayed to the user.

[0329] (Application example 3)

[0330] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0331] Traditional proofreading work was done manually by humans, which required a great deal of time and effort and was inefficient. Also, while it was possible to extract defective words using generative AI, there was no system that could automatically input the results and suggest corrections. Furthermore, there was a lack of functionality to analyze the text entered by the user in real time and make correction suggestions, which made it inconvenient for users.

[0332] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes a means for accepting proofreading work that would be performed by humans, a means for extracting deficiencies using the syntactic analysis function of generative AI, a means for automatically inputting the results into a database when deficiencies are found, a means for analyzing text entered by a user in real time, extracting deficiencies and suggesting corrections, and a means for presenting the suggested corrections to the user and applying the corrections with one tap. This makes it possible to improve the efficiency of proofreading work, reduce costs, and improve user convenience.

[0333] "Proofreading" is the task of checking the content of sentences and documents and correcting errors and omissions.

[0334] "Generative AI" is a type of artificial intelligence, a model that has the ability to generate appropriate outputs for specific inputs.

[0335] The "syntax analysis function" is a function that analyzes the structure of a sentence and identifies grammatical elements.

[0336] "Incorrect words" are words or phrases that refer to errors or inappropriate expressions contained in a sentence.

[0337] A "database" is a collection of data that is systematically organized so that information can be searched and managed efficiently.

[0338] "Real-time analysis" is a processing method in which analysis is performed immediately as data is entered.

[0339] A "correction suggestion" is a suggestion that presents an appropriate correction method for a detected defective word.

[0340] "One Tap" is a feature that allows users to complete an operation by simply tapping the device screen once.

[0341] "RPA" stands for Robotic Process Automation, a technology that automates routine business processes.

[0342] The system for implementing this invention consists of three main elements: a server, a terminal, and a user.

[0343] server

[0344] The server uses a generative AI model to extract incomplete words and generate correction suggestions. Specifically, the server uses the following software and hardware:

[0345] Software: OpenAI API, Python

[0346] Hardware: A server machine with a powerful processor and sufficient memory

[0347] The server receives the text entered by the user and analyzes it in real time using a generative AI model. As a result of the analysis, it generates incorrect words and suggested corrections, and automatically enters the results into a database.

[0348] Terminal

[0349] A terminal is a device on which a user enters text and reviews and applies suggested corrections. This typically applies to mobile devices such as smartphones and tablets.

[0350] The device receives the correction suggestions sent from the server and presents them to the user, who can apply the correction suggestions with a single tap on the device screen.

[0351] User

[0352] Users are individuals or companies who perform proofreading tasks. They use their terminals to enter text and review and apply correction suggestions from the server.

[0353] Specific examples

[0354] For example, suppose a user is creating a blog post. The user enters "This is a test text. It contains some incorrect words." The terminal sends this text to the server.

[0355] The server analyzes this text using a generative AI model to extract the missing words and generate correction suggestions, such as "misspelled word: misspelled word, suggested correction: misspelled word." The server then automatically enters these suggestions into a database and sends them to the device.

[0356] The device presents the suggested corrections to the user, who can then apply the corrections and view the corrected text with a single tap on the device screen.

[0357] Prompt Sentence Examples

[0358] Please extract the missing words from the text below and suggest corrections.

[0359] Text: This is the text of the test. It contains some missing words.

[0360] Incomplete words and suggested corrections:

[0361] In this way, the present invention can improve the efficiency of proofreading work, reduce costs, and improve user convenience.

[0362] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0363] Step 1:

[0364] The user inputs text into the terminal. For example, the user inputs "This is a test text. It contains some incorrect words." The input text is sent from the terminal to the server.

[0365] Step 2:

[0366] The server receives the text sent from the device. It inputs the received text into a generative AI model and performs syntactic analysis. Specifically, it uses the OpenAI API to analyze the text and extracts erroneous words. The input is the user's text, and the output is the erroneous words and suggested corrections.

[0367] Step 3:

[0368] The server analyzes the defective words and suggested corrections obtained from the generative AI model. The analysis results are automatically entered into a database. Specifically, the extracted defective words and suggested corrections are saved in the database. The input is the defective words and suggested corrections, and the output is the results saved in the database.

[0369] Step 4:

[0370] The server transmits the revision suggestions stored in the database to the terminal. Specifically, it transmits data to the terminal to present the revision suggestions to the user. The input is the revision suggestions from the database, and the output is the data sent to the terminal.

[0371] Step 5:

[0372] The terminal receives the correction suggestions sent from the server. It displays the received correction suggestions to the user. Specifically, it displays the defective words and their correction suggestions on the screen so that the user can confirm them. The input is the correction suggestions from the server, and the output is what is displayed to the user.

[0373] Step 6:

[0374] The user applies the suggested corrections with a single tap on the device screen. Specifically, the user confirms the suggested corrections and taps the Apply button to reflect the corrections in the text. The input is the confirmation and application of the suggested corrections, and the output is the corrected text.

[0375] Step 7:

[0376] The terminal displays the corrected text to the user. Specifically, it displays the text with the corrections applied on the screen for the user to review. The input is the corrected text, and the output is what is displayed to the user.

[0377] In this way, users can efficiently review and correct text.

[0378] 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.

[0379] "Example 1"

[0380] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system recognizes a user's emotions and adjusts the priority of review tasks based on those emotions. Specifically, if the system recognizes that a user is feeling angry or frustrated, it prioritizes the user's review tasks. On the other hand, if the system recognizes that a user is feeling happy or satisfied, it processes the user's review tasks as usual.

[0381] "Example 2"

[0382] Furthermore, the emotion engine changes the way the review results are displayed based on the user's emotions. Specifically, if the engine recognizes that the user is feeling angry or frustrated, it highlights the review results. If the engine recognizes that the user is feeling happy or satisfied, it displays the review results normally.

[0383] "Example 3"

[0384] For example, if the system recognizes that a user is feeling angry, it will prioritize the user's proofreading and highlight the proofreading results in red. On the other hand, if the system recognizes that a user is feeling happy, it will process the user's proofreading as usual and highlight the proofreading results in green. This allows the system to respond appropriately according to the user's emotions.

[0385] The processing flow of each embodiment will be described below.

[0386] "Example 1"

[0387] Step 1: The system recognizes the user's emotions using an emotion engine.

[0388] Step 2: If the emotion engine recognizes that the user's emotion is anger or dissatisfaction, it instructs the system to prioritize the user's review work.

[0389] Step 3: The system adjusts the priority of the review tasks according to the instructions from the emotion engine and processes them as a priority.

[0390] "Example 2"

[0391] Step 1: The system recognizes the user's emotions using an emotion engine.

[0392] Step 2: If the emotion engine recognizes that the user's emotion is anger or frustration, it instructs the system to highlight the revision.

[0393] Step 3: The system follows instructions from the emotion engine to change the way the review results are displayed and highlight them.

[0394] "Example 3"

[0395] Step 1: The system recognizes the user's emotions using an emotion engine.

[0396] Step 2: If the emotion engine recognizes that the user's emotion is anger, it instructs the system to prioritize the user's review and highlight the review results in red.

[0397] Step 3: The system adjusts the priority of the review tasks according to the instructions from the emotion engine, prioritizes the tasks, and highlights the review results in red.

[0398] Step 4: If the emotion engine recognizes that the user's emotion is joy, it instructs the system to process the user's review as usual and display the review results in green.

[0399] Step 5: The system follows the instructions from the emotion engine and processes the review as normal, displaying the review results in green.

[0400] Example 1

[0401] Next, a description will be given of Example 1 of Form 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."

[0402] Traditional proofreading work is done manually by humans, which is time-consuming, labor-intensive, and inefficient. Furthermore, because the process is uniform without considering the user's feelings, there is a risk of user satisfaction decreasing. Furthermore, there is a lack of a system that automatically detects and corrects typos and grammatical errors, which can lead to a decrease in the accuracy of proofreading. To solve these issues, an efficient and user-friendly proofreading system is needed.

[0403] 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.

[0404] In this invention, the server includes a means for accepting proofreading work to be performed by humans, a means for extracting deficiencies using the syntactic analysis function of generative AI, a means for automatically inputting the results into spreadsheet software when deficiencies are found, a means for recognizing user emotions, and a means for adjusting the priority of proofreading work based on the user emotions. This makes it possible to perform proofreading work efficiently and with high accuracy, and to adjust the priority taking into account the user's emotions.

[0405] "Proofreading" is the work of checking and correcting typos, grammatical errors, and consistency of content in sentences and texts.

[0406] "Generative AI" is a type of artificial intelligence that has the ability to generate output in natural language or other formats in response to specific inputs.

[0407] The "syntax analysis function" is a function that analyzes the grammatical structure of text and detects typos and grammatical errors.

[0408] "Imperfect words" are words or phrases that indicate typographical or grammatical errors present in the text.

[0409] "Spreadsheet software" is software for data entry, calculation, analysis, graph creation, etc., and generally handles data in spreadsheet format.

[0410] "Means for recognizing emotions" refers to technology that analyzes user input and behavioral data to identify their emotional state.

[0411] The "means for adjusting priority" is a function for changing the order of processing based on specific conditions and responding according to the importance and urgency of the processing.

[0412] "Business automation technology" is a technology for automating repetitive business processes, with the aim of improving efficiency and accuracy.

[0413] The present invention is a system for providing an efficient and user-friendly proofreading service. A specific embodiment of this system will be described below.

[0414] System configuration

[0415] This system consists of three main components: a server, a terminal, and a user. The server uses a generative AI model to analyze text syntax and extract defective words. The terminal recognizes the user's emotions and adjusts the priority of editing tasks based on those emotions. The user inputs the text to be edited and receives feedback from the system.

[0416] Hardware and software used

[0417] Generative AI model: For example, GPT-4 is used as a generative AI model. This model excels at natural language processing and is suitable for text syntactic analysis.

[0418] Emotion recognition engine: For example, the emotion recognition API from Azure (registered trademark) Cognitive Services is used as the emotion recognition engine. This is a technology for analyzing emotions from user input and behavioral data.

[0419] Spreadsheet software: Use common spreadsheet software (e.g., Microsoft Excel) for automated entry of results.

[0420] System Operation

[0421] 1. Proofreading services

[0422] A user inputs text data to be reviewed into the system, for example by entering text into a web form or by uploading a file.

[0423] 2. Sending text data

[0424] The terminal transmits the text data received from the user to the server. At this time, the data is encrypted before transmission.

[0425] 3. Performing parsing

[0426] The server inputs the received text data into a generative AI model and performs syntactic analysis, detecting typos and grammatical errors.

[0427] 4. Extracting incomplete words

[0428] The server extracts and lists imperfect words from the results of the syntax analysis. For example, imperfect words such as "typographical error" and "grammatical error" are included in the list.

[0429] 5. Sending the results

[0430] The server transmits a list of the extracted defective words to the terminal.

[0431] 6. Displaying the results

[0432] The terminal displays the list of imperfect words received from the server to the user, who then checks the displayed imperfect words and makes corrections.

[0433] 7. Collecting Emotional Data

[0434] The device collects user input and behavioral data, such as the user's keyboard typing speed, mouse movements, and data from a facial recognition camera.

[0435] 8. Performing Sentiment Analysis

[0436] The device inputs the collected data into an emotion recognition engine to analyze the user's emotions.

[0437] 9. Transmission of Emotional Data

[0438] The device sends the analysis results, which include emotional data such as "anger" or "frustration," to the server.

[0439] 10. Adjusting the priorities of review work

[0440] The server adjusts the priority of review tasks based on the received emotion data, for example, giving priority to review tasks for users who are feeling angry.

[0441] 11. Sending the Review Results

[0442] The server transmits the processed proofreading results to the terminal based on the priority.

[0443] 12. Viewing Review Results

[0444] The terminal displays the proofreading results received from the server to the user, who then checks the results and makes corrections as necessary.

[0445] Specific examples

[0446] Input text: "This sentence may contain typos."

[0447] Output: The defective word "typo" is detected.

[0448] Prompt Sentence Examples

[0449] Please find any spelling or grammatical errors in the text below.

[0450] Text: "This sentence may contain typos."

[0451] In this way, the system achieves efficient and accurate proofreading and adjusts priorities in consideration of the user's feelings.

[0452] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0453] Step 1:

[0454] A user inputs text data to be reviewed into the system, for example by entering text into a web form or by uploading a file.

[0455] Input: Text data to be reviewed

[0456] Output: Text data sent to the terminal

[0457] Step 2:

[0458] The terminal transmits the text data received from the user to the server. At this time, the data is encrypted before transmission.

[0459] Input: Text data received from the user

[0460] Output: Text data sent to the server

[0461] Step 3:

[0462] The server inputs the received text data into a generative AI model and performs syntactic analysis, detecting typos and grammatical errors.

[0463] Input: Text data sent from the terminal

[0464] Data processing: Syntactic analysis using generative AI models

[0465] Output: Parsing results (list of typos and grammatical errors)

[0466] Step 4:

[0467] The server extracts and lists imperfect words from the results of the syntax analysis. For example, imperfect words such as "typographical error" and "grammatical error" are included in the list.

[0468] Input: Parsed result

[0469] Data processing: Extraction of incomplete words

[0470] Output: A list of missing words

[0471] Step 5:

[0472] The server transmits a list of the extracted defective words to the terminal.

[0473] Input: List of missing words

[0474] Output: A list of bad words sent to the terminal

[0475] Step 6:

[0476] The terminal displays the list of imperfect words received from the server to the user, who then checks the displayed imperfect words and makes corrections.

[0477] Input: A list of invalid words sent from the server

[0478] Output: A list of flawed words displayed to the user

[0479] Step 7:

[0480] The device collects user input and behavioral data, such as the user's keyboard typing speed, mouse movements, and data from a facial recognition camera.

[0481] Input: User input and behavioral data

[0482] Output: Collected emotion data

[0483] Step 8:

[0484] The device inputs the collected data into an emotion recognition engine to analyze the user's emotions.

[0485] Input: Collected emotion data

[0486] Data processing: Emotion analysis using an emotion recognition engine

[0487] Output: Emotion analysis results

[0488] Step 9:

[0489] The device sends the analysis results, which include emotional data such as "anger" or "frustration," to the server.

[0490] Input: Sentiment analysis results

[0491] Output: Emotion data sent to the server

[0492] Step 10:

[0493] The server adjusts the priority of review tasks based on the received emotion data, for example, giving priority to review tasks for users who are feeling angry.

[0494] Input: Emotion data

[0495] Data processing: Adjusting priorities for review work

[0496] Output: Priority-adjusted review work

[0497] Step 11:

[0498] The server transmits the processed proofreading results to the terminal based on the priority.

[0499] Input: Priority-adjusted review work

[0500] Output: Review results sent to your device

[0501] Step 12:

[0502] The terminal displays the proofreading results received from the server to the user, who then checks the results and makes corrections as necessary.

[0503] Input: Review results sent from the server

[0504] Output: The review results that are displayed to the user

[0505] (Application example 1)

[0506] Next, a description will be given of Application Example 1 of Embodiment 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."

[0507] Traditional proofreading work is done manually by humans, which takes time and effort, and is inefficient. Furthermore, it is difficult to improve user satisfaction because it is not possible to respond flexibly to user emotions. Furthermore, there is also the problem of delays in improving the quality of content due to the delay in feedback of proofreading results.

[0508] 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.

[0509] In this invention, the server includes a means for accepting proofreading work that would otherwise be performed by humans, a means for extracting deficiencies using the syntactic analysis function of generative AI, a means for automatically inputting the results into spreadsheet software when deficiencies are found, a means for recognizing the user's emotions and adjusting the priority of the proofreading work based on those emotions, and a means for providing feedback on the proofreading results to the user and suggesting revisions, thereby making it possible to improve the efficiency of proofreading work and user satisfaction.

[0510] "Proofreading" is the task of detecting and correcting typos and grammatical errors in sentences and text.

[0511] "Generative AI" is a type of artificial intelligence that performs natural language processing such as text generation and syntactic analysis.

[0512] The "syntax analysis function" is a function that analyzes the structure of sentences and text and detects grammatical errors and typos.

[0513] "Faulty words" are words or phrases that indicate typos or grammatical errors in a sentence or text.

[0514] "Spreadsheet software" is software for data entry, calculation, analysis, graph creation, etc., and generally handles data in spreadsheet format.

[0515] "Means for recognizing emotions" refers to technology that analyzes and recognizes emotions from the user's facial expressions, tone of voice, etc.

[0516] The "means for adjusting the priority of review tasks" is a technique for changing the processing order of review tasks based on the recognized user sentiment.

[0517] "Feedback" is the process of returning the review results to the user and suggesting corrections.

[0518] "Proposals for correction" are specific correction methods or suggestions for the detected defective words.

[0519] "Business automation technology" is a technology for automating tasks that were previously performed manually, with the aim of improving efficiency and accuracy.

[0520] A system for implementing this invention is configured as follows: A server uses a generative AI model to perform syntactic analysis of text and extract defective words. Text entered by a user is analyzed in real time to detect typos and grammatical errors. The detected defective words are automatically entered into spreadsheet software.

[0521] Furthermore, the server uses an emotion recognition engine to analyze the user's emotions. The server captures the user's facial expressions and tone of voice using a camera and microphone, and analyzes them with the emotion recognition engine to determine whether the user is feeling angry, frustrated, happy, or satisfied. Based on this emotional information, the server adjusts the priority of the proofreading tasks. For example, if a user is feeling angry or frustrated, the server will prioritize the proofreading tasks for that user.

[0522] The results of the review are fed back to the user, and suggested revisions are presented. The user can then review the suggested revisions and make any necessary changes.

[0523] Specific hardware includes smartphones, smart glasses, and head-mounted displays, while software includes generative AI models (e.g., GPT-4), emotion recognition engines (e.g., Affectiva), and real-time data processing frameworks (e.g., Apache Kafka).

[0524] As a concrete example, consider a user trying to post a blog post. While the user is typing the post wearing smart glasses, the system analyzes the text in real time to detect typos and grammatical errors. At the same time, the system recognizes the user's emotions from their facial expressions and tone of voice, and prioritizes editing if the user is angry.

[0525] An example of a prompt for the generative AI model is as follows:

[0526] Please proofread the text below, spot any spelling or grammatical errors, and suggest corrections.

[0527] Text: "The weather is very nice today. I'm going to the park."

[0528] In this way, the system improves the efficiency of proofreading work and user satisfaction.

[0529] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0530] Step 1:

[0531] A user inputs text using a device (smartphone, smart glasses, head-mounted display).

[0532] Input: Text data entered by the user.

[0533] Output: The input text data is sent to the server in real time.

[0534] How it works: When a user types a blog post or content, the device sends the text data to the server in real time.

[0535] Step 2:

[0536] The server uses a generative AI model (e.g., GPT-4) to parse the received text data.

[0537] Input: Text data sent from the terminal.

[0538] Output: As a result of the parsing, defective words (typos and grammatical errors) are extracted.

[0539] What it does: The server uses a generative AI model to analyze text data and detect typos and grammatical errors.

[0540] Step 3:

[0541] The server automatically inputs the extracted defective words into spreadsheet software.

[0542] Input: Incomplete words extracted as a result of parsing.

[0543] Output: The missing words are automatically entered into a spreadsheet.

[0544] Specific operation: The server automatically inputs the extracted defective words into a spreadsheet software and records them.

[0545] Step 4:

[0546] The device captures the user's facial expressions and tone of voice using a camera and microphone and sends them to the server.

[0547] Input: User's facial expression and voice data.

[0548] Output: The acquired facial expression and voice data is sent to the server.

[0549] Specific operation: The device uses a camera and microphone to capture the user's facial expressions and tone of voice and transmits them to the server.

[0550] Step 5:

[0551] The server uses an emotion recognition engine (e.g., Affectiva) to analyze the received facial expression and voice data and recognize the user's emotions.

[0552] Input: Facial expression data and voice data sent from the device.

[0553] Output: User's emotional information (anger, frustration, joy, satisfaction, etc.).

[0554] Specific operation: The server uses an emotion recognition engine to analyze facial expression data and voice data and recognize the user's emotions.

[0555] Step 6:

[0556] The server adjusts the priority of review tasks based on the recognized emotion information.

[0557] Input: User emotion information.

[0558] Output: Review priorities are adjusted.

[0559] Specific operation: The server changes the processing order of review tasks based on the user's emotional information. For example, it prioritizes review tasks for users who are angry or dissatisfied.

[0560] Step 7:

[0561] The server provides feedback on the review results to the user and suggests corrections.

[0562] Input: Reviewing priorities based on parsing results and sentiment information.

[0563] Output: Review results and suggested revisions fed back to the user.

[0564] Specific operation: The server provides feedback on the review results to the user and suggests specific corrections. The user can review the suggested corrections and make corrections as necessary.

[0565] Example 2

[0566] Next, a description will be given of Example 2 of Form 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."

[0567] Traditional proofreading work is time-consuming and labor-intensive because it is done manually by humans, and is prone to errors. Furthermore, manually entering proofreading results into spreadsheet software is cumbersome and inefficient. Furthermore, there is also the issue that the user experience is not improved because feedback based on the user's emotions is not provided.

[0568] 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.

[0569] In this invention, the server includes a means for accepting proofreading work to be performed by humans, a means for extracting deficiencies using the syntactic analysis function of generative artificial intelligence, a means for automatically inputting the results into spreadsheet software when deficiencies are found, a means for analyzing user emotions, and a means for changing the display method of the proofreading results based on the user emotions. This not only makes it possible to improve the efficiency and accuracy of proofreading work, but also makes it possible to provide appropriate feedback according to the user's emotions, thereby improving the user experience.

[0570] The "means for accepting human proofreading work" is a function for receiving text entered by a user and proofreading that text.

[0571] The "means for extracting defective words using the syntactic analysis function of generative artificial intelligence" is a function that uses generative artificial intelligence to detect grammatical and spelling errors in text and identify defective words and phrases.

[0572] The "means for automatically inputting the results into spreadsheet software when an error is found" is a function that automatically inputs information about detected errors into spreadsheet software.

[0573] The "means for analyzing user emotions" is a function for inferring emotions from the user's input and actions and analyzing the emotional state.

[0574] The "means for changing the display method of the proofreading results based on the user's emotions" is a function for dynamically changing the display method of the proofreading results in accordance with the analyzed user's emotions.

[0575] "Robotic process automation" refers to technology that automates processes that were previously done manually and enables efficient processing.

[0576] MODE FOR CARRYING OUT THE INVENTION

[0577] This invention is a system that proofreads text entered by a user and automatically inputs any errors into spreadsheet software. It also has the function of changing the way the proofreading results are displayed based on the user's emotions.

[0578] Hardware and software used

[0579] server:

[0580] The server provides the computational resources to run generative artificial intelligence (e.g., GPT-4).

[0581] The server uses the API of spreadsheet software (e.g., Microsoft Excel) to automatically enter the missing information.

[0582] Device:

[0583] A user inputs text using a terminal such as a personal computer or a smartphone.

[0584] The terminal communicates with the server to send text and receive proofread results.

[0585] software:

[0586] Generative AI (e.g., GPT-4) reviews text.

[0587] Spreadsheet software (e.g., Microsoft Excel) is used to record deficiency information.

[0588] The emotion engine is used to analyze the emotions of the user.

[0589] Data processing and calculation

[0590] Text Review:

[0591] The server receives the text entered by the user and proofreads it using generative artificial intelligence, which detects grammatical errors, spelling mistakes, and unnatural expressions.

[0592] Record of deficiencies:

[0593] The server uses the output of the generative artificial intelligence to identify the location, content, and proposed corrections of defects, which are then automatically entered into a spreadsheet program.

[0594] Sentiment analysis:

[0595] The server analyzes the user's emotions using an emotion engine, which infers emotions from the user's inputs and actions and analyzes the user's emotional state.

[0596] Change the display method:

[0597] The server then changes the way the review results are displayed based on the analyzed user's emotions. For example, if the server detects that the user is feeling angry or frustrated, it highlights the review results in red. Conversely, if the server detects that the user is feeling happy or satisfied, the review results are displayed normally.

[0598] Specific examples

[0599] User enters text:

[0600] The user uses the terminal to type "This is a simple text."

[0601] The server receives the text:

[0602] The server receives the text entered by the user.

[0603] The server uses a generative AI model to proofread the text:

[0604] The server uses generative artificial intelligence (e.g., GPT-4) to determine that "smple" should be corrected to "simple."

[0605] The server detects the flaw and generates a fix:

[0606] The server identifies the position of "smple" as the second character, the content as "smple," and the proposed revision as "simple."

[0607] The server enters the missing information into Excel:

[0608] The server enters "Position: 2", "Content: simple", and "Proposed fix: simple" into each column of Excel.

[0609] The server analyzes the user's emotions:

[0610] The server uses an emotion engine to analyze that the user is feeling angry.

[0611] The server changes how reviews are displayed based on sentiment:

[0612] The server will highlight the review results in red.

[0613] Example prompt sentence:

[0614] Create a program that reviews text entered by a user and automatically enters any errors into a spreadsheet. Also, add a feature that changes the way the review results are displayed based on the user's emotions. For example, if the user is angry or frustrated, highlight the review results, but if the user is happy or satisfied, display them normally.

[0615] In this way, the system reviews the user's input text, automatically inputs any deficiencies into the spreadsheet software, and further changes the way the review results are displayed depending on the user's emotions.

[0616] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0617] Step 1:

[0618] The user enters text.

[0619] A user uses a terminal to enter text into an input form in the system, for example, "This is a simple text." The entered text is sent to the server.

[0620] Step 2:

[0621] The server receives the text.

[0622] The server receives the text entered by the user. The received text is passed to the generative AI model. The input is the user's text, and the output is the input data for the generative AI model.

[0623] Step 3:

[0624] The server uses a generative AI model to proofread the text.

[0625] The server uses a generative AI model (e.g., GPT-4) to proofread the received text. The generative AI model detects grammatical errors, spelling mistakes, and unnatural expressions. The input is the received text, and the output is the results of the flaw detection. For example, the word "smple" may be corrected to "simple."

[0626] Step 4:

[0627] The server detects the flaws and generates suggested fixes.

[0628] The server identifies the location, content, and proposed correction of the defect based on the output of the generative AI model. The input is the output of the generative AI model, and the output is information on the location, content, and proposed correction of the defect. For example, the location of "smple" is identified as the second character, the content is "smple," and the proposed correction is identified as "simple."

[0629] Step 5:

[0630] The server enters the incomplete information into Excel.

[0631] The server automatically inputs information about the defects into a spreadsheet (e.g., Microsoft Excel). The input is the location, content, and proposed correction information for the defects, and the output is the information recorded in the Excel file. Specifically, "Location: 2," "Content: simple," and "Proposed correction: simple" are entered in each column of the Excel file.

[0632] Step 6:

[0633] The server analyzes the user's emotions.

[0634] The server analyzes the user's emotions using an emotion engine. The input is the user's input and behavioral data, and the output is the user's emotional state. The emotion engine estimates emotions from keyboard typing speed, mouse movement, etc.

[0635] Step 7:

[0636] The server changes how the review results are displayed based on the sentiment.

[0637] The server changes the display of the review results based on the analyzed user's emotions. The input is the user's emotional state, and the output is the changed display. For example, if the user is recognized as feeling angry or frustrated, the review results are highlighted in red. Conversely, if the user is recognized as feeling happy or satisfied, the review results are displayed normally.

[0638] (Application example 2)

[0639] Next, a description will be given of Application Example 2 of Form 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."

[0640] In conventional proofreading systems, inputting results when there are deficiencies is done manually, which is inefficient and prone to human error. Furthermore, the system does not consider how to display feedback according to the user's emotions, which results in a poor user experience.

[0641] 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.

[0642] In this invention, the server includes a means for accepting proofreading work that would otherwise be performed by humans, a means for extracting erroneous words using the syntactic analysis function of generative AI, a means for automatically inputting the results into Excel when erroneous words are found, and a means for changing the display method of the proofreading results based on the user's emotions using an emotion engine, thereby making it possible to improve the efficiency of proofreading work and the user experience.

[0643] A "means for accepting human proofreading" is a system or function that accepts and manages the processes and methods for human proofreading.

[0644] "Means for extracting defective words using the syntactic analysis function of generative AI" refers to a system or function that uses the syntactic analysis function of generative AI to automatically find words in a sentence that contain defects or errors.

[0645] "Means for automatically entering results into Excel when defects are found" refers to a system or function that automatically enters information about detected defects or errors into an Excel file without manual operation.

[0646] "Means for changing the display method of proofreading results based on the user's emotions using an emotion engine" refers to a system or function that uses an emotion engine to recognize the user's emotions and dynamically changes the display method of proofreading results in accordance with those emotions.

[0647] A system for carrying out this invention has the following configuration: A server includes means for accepting proofreading work that would otherwise be performed by humans, means for extracting defective words using the syntactic analysis function of generative AI, means for automatically inputting the results into Excel when defects are found, and means for changing the display method of the proofreading results based on the user's emotions using an emotion engine.

[0648] First, the server accepts the human proofreading task. This is to receive the text or document provided by the user and begin the proofreading process. Next, the generative AI's syntactic analysis function is used to extract incomplete words from the received text. This generative AI uses natural language processing techniques to analyze the text and identify words and phrases that contain errors or incompleteness.

[0649] If any errors are detected, the server automatically enters the results into an Excel file. This process is intended to eliminate manual input work and improve efficiency. Specifically, information such as the location, content, and suggested corrections of the detected errors is automatically entered into each column of the Excel file.

[0650] Furthermore, an emotion engine is used to recognize user emotions and change the way the review results are displayed based on that emotion. For example, if the user is recognized as feeling angry or frustrated, the review results will be highlighted, and if the user is recognized as feeling happy or satisfied, the review results will be displayed normally. This emotion engine is used to analyze user feedback and reviews to identify emotions.

[0651] The following hardware and software are used to implement this system. The hardware requires a PC or server. The software includes "openpyxl" for Excel file manipulation, "emotion_recognition" for emotion recognition, and "feedback_analysis" for feedback analysis.

[0652] For example, if a customer provides feedback such as "The quality of the product is poor," this feedback is entered into Excel and the emotion is recognized as "dissatisfied" and is therefore highlighted.

[0653] Example prompts to input to a generative AI model:

[0654] Analyze customer feedback, identify sentiment, and enter the results into Excel. The feedback is, "The product quality is poor."

[0655] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0656] Step 1:

[0657] The server receives text to be reviewed from the user, which may be feedback or reviews provided by the user. The input is the text provided by the user, and the output is the text data stored on the server.

[0658] Step 2:

[0659] The server uses the generative AI's syntactic analysis function to extract incomplete words from the received text. The input is the text data saved in step 1, and the output is a list of incomplete words. Specifically, the generative AI analyzes the text and identifies words and phrases that contain errors or incompleteness.

[0660] Step 3:

[0661] The server automatically inputs the extracted information about the defective words into an Excel file. The input is the list of defective words obtained in step 2, and the output is an updated Excel file. Specifically, information such as the location, content, and suggested corrections of the defective words is entered into each column of the Excel file.

[0662] Step 4:

[0663] The server uses an emotion engine to recognize the user's emotion. The input is the text data saved in step 1, and the output is the user's emotion information. Specifically, the emotion engine analyzes the text and identifies the user's emotion.

[0664] Step 5:

[0665] The server changes the display method of the review results based on the recognized user emotion. The input is the user emotion information obtained in step 4, and the output is the review results with the display method changed. Specifically, if the user is feeling angry or dissatisfied, the review results are highlighted, and if the user is feeling happy or satisfied, they are displayed normally.

[0666] Step 6:

[0667] The server saves the updated Excel file and notifies the user. The input is the Excel file updated in step 3, and the output is a notification to the user. Specifically, it saves the Excel file and notifies the user that the results have been updated.

[0668] Example 3

[0669] Next, a third embodiment of the third embodiment will be described. 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."

[0670] Conventional proofreading work is done manually by humans, which takes time and effort, and has the problem of being inefficient. In addition, it is difficult to improve user satisfaction because it is not possible to respond to emotions. Furthermore, there was a lack of means to automate the extraction of defective words and the input of the results, so the automation of proofreading work has not progressed.

[0671] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for accepting proofreading work to be performed by humans, a means for extracting erroneous words using the syntactic analysis function of generative AI, a means for automatically inputting the results into spreadsheet software when an error is found, a means for recognizing emotions from the user's input text, and a means for displaying the proofreading results in a color-coded format based on the recognized emotions. This makes it possible to streamline and automate proofreading work and to respond appropriately to the user's emotions.

[0672] "Proofreading" is the task of checking and correcting errors and deficiencies in sentences and documents.

[0673] "Generative AI" is a type of artificial intelligence, a model that has the ability to generate natural language in response to specific input.

[0674] The "syntax analysis function" is a function that analyzes the structure of a sentence and identifies grammatical elements.

[0675] "Incorrect words" are words or phrases that refer to errors or inappropriate expressions contained in a sentence.

[0676] "Spreadsheet software" is software for organizing, calculating, and analyzing data, and generally handles data in spreadsheet format.

[0677] "Emotion recognition" is a technology that analyzes a user's emotions from input text and identifies specific emotions.

[0678] "Robotic process automation" is a technology that uses software robots to automate business processes.

[0679] "Color coding" is a method of displaying text or data in different colors based on certain conditions.

[0680] MODE FOR CARRYING OUT THE INVENTION

[0681] This invention is a system that uses generative AI to automate proofreading tasks and respond to user emotions. A specific embodiment of this system is described below.

[0682] Hardware and software used

[0683] Server: Hosts the generative AI model and processes data. Specifically, a server equipped with a high-performance GPU is used.

[0684] Terminal: Provides an interface for users to access. Specifically, a PC running Windows (registered trademark) or MacOS (registered trademark) is used.

[0685] Software: Using natural language processing models as generative AI models and automation software as RPA tools.

[0686] Program processing

[0687] 1. User input: The user enters the text to be reviewed into the terminal. For example, they may enter "This sentence makes absolutely no sense!"

[0688] 2. Emotion recognition: The server uses the generative AI model to recognize the user's emotion from the input text. The generative AI model is given a prompt sentence, "This sentence makes no sense at all!", and the emotion is recognized as "anger."

[0689] 3. Extraction of incomplete words: The server uses the generative AI model to extract incomplete words from the text. The generative AI model is prompted with the sentence, "Please extract the incomplete words in this sentence," and the model extracts "I don't understand the meaning" as the incomplete word.

[0690] 4. Automatic input of results: The server uses the RPA tool to automatically input the extracted deficiencies as review results. The RPA tool then inputs the results in the specified format.

[0691] 5. Display according to emotion: The server displays the proofreading results in red or green depending on the user's emotion. For example, if the user feels "anger," the proofreading results will be highlighted in red, and "I don't understand" will be displayed in red. On the other hand, if the user feels "joy," the proofreading results will be displayed in green.

[0692] Specific examples

[0693] Example 1: When the user feels angry

[0694] User input: "This sentence makes absolutely no sense!"

[0695] Emotion recognition: The server uses a generative AI model to recognize the emotion as "anger."

[0696] Extraction of incomplete words: The server extracts "I don't understand the meaning" as an incomplete word.

[0697] Automatic input of results: The server uses an RPA tool to highlight "I don't understand the meaning" in red as a proofreading result.

[0698] Example 2: When the user feels delighted

[0699] User input: "This article is very clear!"

[0700] Emotion recognition: The server uses a generative AI model to recognize the emotion as "joy."

[0701] Extracting defective words: The server does not extract defective words.

[0702] Automatic input of results: The server uses an RPA tool to display the proofreading results in green.

[0703] This system makes it possible to improve the efficiency and automate the proofreading work, and to respond appropriately to the user's feelings. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0704] Step 1: User Input

[0705] Specific operation: The user inputs the text to be reviewed from the terminal. For example, the user opens a browser on their PC and enters "This sentence makes no sense at all!" into a dedicated web form.

[0706] Input: The text entered by the user.

[0707] Output: The entered text is sent to the server.

[0708] Step 2: Emotion Recognition

[0709] Specific operation: The server uses a generative AI model to recognize the user's emotion from the input text. The server receives the text and inputs the prompt "This sentence makes no sense at all!" to the generative AI model. The generative AI model analyzes the text and recognizes the emotion as "anger."

[0710] Input: The text entered by the user.

[0711] Output: A perceived emotion (e.g., "anger").

[0712] Step 3: Extracting missing words

[0713] Specific operation: The server uses the generative AI model to extract missing words from the text. The server again inputs the prompt "Please extract the missing words in this sentence" to the generative AI model. The generative AI model extracts "I don't understand the meaning" as the missing word.

[0714] Input: The text entered by the user.

[0715] Output: Extracted deficiency words (e.g. "I don't understand the meaning").

[0716] Step 4: Autofill Results

[0717] Specific operation: The server uses the RPA tool to automatically input the extracted defective word as the proofreading result. The server starts the RPA tool and inputs the extracted defective word "I don't understand the meaning" as the proofreading result. The RPA tool inputs the result according to the specified format.

[0718] Input: The extracted flawed words.

[0719] Output: The proofreading results are automatically entered into a spreadsheet.

[0720] Step 5: Emotional Display

[0721] Specific operation: The server displays the proofreading results in red or green depending on the user's emotion. The server checks the emotion recognition results, and if the user is feeling "anger," the proofreading results are highlighted in red. For example, "I don't understand the meaning" is displayed in red. On the other hand, if the user is feeling "joy," the proofreading results are displayed in green.

[0722] Input: Recognized emotions and review results.

[0723] Output: Review results color-coded according to sentiment.

[0724] (Application example 3)

[0725] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0726] Conventional proofreading work relies on manual human labor, which is time-consuming and costly. It also faces the challenge of being unable to respond to user emotions, making it difficult to improve user satisfaction. Furthermore, there was a lack of means to automate the extraction of defective words and the input of the results, making it difficult to achieve efficient proofreading work.

[0727] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for accepting proofreading work to be performed by humans, means for extracting deficiencies using the syntactic analysis function of generative AI, means for automatically inputting the results into spreadsheet software when deficiencies are found, means for recognizing user emotions, means for changing the priority of the proofreading work according to the user emotions, and means for displaying the proofreading results in a color according to the user emotions. This makes it possible to improve the efficiency of proofreading work and reduce costs, and also enables appropriate responses according to the user emotions.

[0728] "Proofreading" is the task of checking and correcting errors and omissions in texts and content.

[0729] "Generative AI" is a system that generates text and data using artificial intelligence technology.

[0730] The "syntax analysis function" is a function that analyzes the structure of a sentence and detects grammatical errors and deficiencies.

[0731] "Incorrect words" refer to errors or inappropriate words contained in a sentence.

[0732] "Spreadsheet software" is software for entering, calculating, and analyzing data.

[0733] "Means for recognizing user emotions" refers to technology that analyzes and identifies emotions from text entered by the user.

[0734] The "means for changing the priority of review work" is a function for adjusting the priority of review work based on the user's feelings.

[0735] The "means for displaying the proofreading results in a color corresponding to the user's emotion" is a technique for visually displaying the proofreading results in different colors according to the user's emotion.

[0736] "RPA" stands for Robotic Process Automation, a technology that automates business processes.

[0737] The system for implementing this invention has the following configuration: The server uses a generative AI model to extract incomplete words and recognize the user's emotions. Based on this, it changes the priority of the proofreading work and displays the proofreading results in a color that corresponds to the user's emotions.

[0738] Hardware and software used

[0739] 1. Server: A server with high-performance computing power is required. Specifically, a server equipped with a GPU to run generative AI models is desirable.

[0740] 2. Generative AI models: Use generative AI models such as OpenAI's GPT-3.

[0741] 3. Emotion recognition software: Use natural language processing libraries such as TextBlob to analyze user emotions.

[0742] 4. Spreadsheet software: Use spreadsheet software such as Microsoft Excel to auto-enter the results.

[0743] 5. Programming language: Various processes are implemented using Python.

[0744] Data processing and calculation

[0745] The server receives the text data entered by the user and uses a generative AI model to extract incomplete words. It then uses emotion recognition software such as TextBlob to analyze the user's emotions. If the emotion is "anger," the editing task is given high priority and the result is displayed in red. If the emotion is "joy," the task is processed at normal priority and the result is displayed in green.

[0746] Specific examples

[0747] If a user posts a comment saying "This service is terrible! I can't use it at all!", the server would process it as follows:

[0748] 1. Extracting incomplete words: Using a generative AI model, we extract words like "terrible" and "unusable" as incomplete words.

[0749] 2. Emotion Recognition: Using TextBlob, we recognize the emotion of "anger" from comments.

[0750] 3. Change in priority: Because the emotion is "anger," the proofreading work is given a higher priority.

[0751] 4. View Results: Highlight the review results in red.

[0752] Prompt Sentence Examples

[0753] Below is an example of a prompt sentence to input to a generative AI model.

[0754] Please extract the missing words from the following sentence: This service is really terrible! It's completely unusable!

[0755] In this way, the server can respond appropriately to the user's feelings, thereby improving the efficiency of the proofreading work and user satisfaction.

[0756] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0757] Step 1:

[0758] The server receives text data entered by users, including comments and reviews posted by users. To process this text data, the server first sends a prompt to the generative AI model.

[0759] Step 2:

[0760] The server uses a generative AI model to extract incomplete words. Specifically, it inputs a prompt sentence into the generative AI model and obtains incomplete words as output from the model. For example, in response to a comment such as "This service is really terrible! It's completely unusable!", "terrible" and "unusable" are extracted as incomplete words.

[0761] Step 3:

[0762] The server uses emotion recognition software to analyze the user's emotions based on the extracted inappropriate words. The input includes the user's comment and the extracted inappropriate words. Using a natural language processing library such as TextBlob, the server calculates the emotion polarity of the comment and classifies the emotion as "anger," "joy," or "neutral."

[0763] Step 4:

[0764] The server changes the priority of the review task based on the emotion recognition results. The emotion recognition results are included as input. For example, if the emotion is "anger," the review task is set to a high priority, and if the emotion is "joy," the review task is processed at a normal priority.

[0765] Step 5:

[0766] The server displays the proofreading results in a color that corresponds to the user's emotion. The input includes the emotion recognition result and the proofreading result. For example, if the emotion is "anger," the proofreading result is highlighted in red, and if the emotion is "joy," it is displayed in green.

[0767] Step 6:

[0768] The server automatically inputs the final proofreading results into spreadsheet software. The input includes the proofreading results and their display format. This allows appropriate responses based on the user's feelings, improving the efficiency of proofreading work and user satisfaction.

[0769] 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.

[0770] 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> ) 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.

[0771] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0772] 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.

[0773] [Second embodiment]

[0774] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0775] 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.

[0776] 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).

[0777] 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.

[0778] 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.

[0779] 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).

[0780] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0785] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0786] "Example 1"

[0787] One embodiment of the present invention is a system that undertakes proofreading work that is usually done by humans. This system uses the syntactic analysis function of generative AI to extract incomplete words from the text to be proofread. Specifically, it detects typos and grammatical errors within the text.

[0788] "Example 2"

[0789] Furthermore, if any defects are found, the results are automatically entered into Excel. Specifically, the location, content, and suggested corrections of the defective words are automatically entered into each column of Excel. This saves humans the trouble of manually entering the results.

[0790] "Example 3"

[0791] This system also uses RPA to extract erroneous words using generative AI and automatically input the results. Specifically, it automates the entire process from extracting erroneous words to inputting the results, minimizing human intervention. This makes it possible to significantly reduce the man-hours and costs of proofreading work.

[0792] The processing flow of each embodiment will be described below.

[0793] "Example 1"

[0794] Step 1: The system accepts the editing work to be performed by a human. Specifically, the text data to be edited is input into the system.

[0795] Step 2: The system uses the generative AI's syntactic analysis function to extract imperfect words from the input text. Specifically, it detects typos and grammatical errors in the text. Step 3: If imperfect words are detected, the information is saved in a temporary database.

[0796] "Example 2"

[0797] Step 1: Obtain the information of the missing words stored in the temporary database.

[0798] Step 2: Based on the acquired information on defective words, the results are automatically entered into Excel. Specifically, the location, content, and suggested corrections of the defective words are automatically entered into each column of Excel. Step 3: Once the data has been entered into Excel, the results are saved and reported to the user as necessary.

[0799] "Example 3"

[0800] Step 1: The system will use RPA to extract incomplete words using generative AI and automatically input the results. Specifically, a script will be created to automate the entire process from extracting incomplete words to inputting the results.

[0801] Step 2: Run the script you created, which minimizes human intervention and significantly reduces the man-hours and costs of proofreading.

[0802] Step 3: Save the results of the script execution and report them to the user if necessary.

[0803] Example 1

[0804] Next, a description will be given of Example 1 of Form 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."

[0805] Traditional proofreading work is done manually by humans, which takes time and effort, and there is a risk of overlooking typos and grammatical errors. Furthermore, the manual input of proofreading results is cumbersome and inefficient. Furthermore, the lack of automated correction of proofreading results and the adoption of robotic process automation (RPA) has led to a decline in overall work efficiency. To solve these issues, there is a need to automate and streamline proofreading work.

[0806] 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.

[0807] In this invention, the server includes a means for receiving text to be proofread from a user, a means for inputting the received text into the generative AI, and a means for analyzing the analysis results returned from the generative AI and extracting defective words, thereby enabling the automation and efficiency of proofreading work.

[0808] "Proofreading" is the task of checking and correcting typos and grammatical errors in sentences and texts.

[0809] "Generative AI" is an AI technology for natural language processing and syntactic analysis, and has the ability to detect, for example, typos and grammatical errors in text.

[0810] The "syntax analysis function" is a function that analyzes the structure of sentences and text and detects grammatical errors and omissions.

[0811] "Faulty words" are words or phrases that contain typos or grammatical errors present in the text.

[0812] "Spreadsheet software" refers to software for data entry, calculation, analysis, graph creation, etc., such as Microsoft Excel.

[0813] "User" refers to a person or organization that uses the system to perform proofreading work.

[0814] A "server" is a computer system that receives requests from users, analyzes the text using generative artificial intelligence, and returns the results.

[0815] An "interface" is a means, such as a screen or input form, that allows a user to operate a system.

[0816] "Robotic Process Automation (RPA)" is a technology that automates routine business processes, using software robots to replace human work.

[0817] This invention is a system for improving the efficiency of human proofreading work, extracting defective words from text using the syntactic analysis function of generative artificial intelligence and providing the extracted words to the user. A specific embodiment of this system will be described below.

[0818] Server Processing

[0819] The server receives the text to be reviewed from the user. Specifically, it receives the text data via an HTTP request. The received text is then input into a generative AI (e.g., a model with natural language processing technology). An example of this generative AI is OpenAI's GPT-4.

[0820] The generative AI analyzes the input text and detects typos and grammatical errors. The server analyzes the analysis results returned by the generative AI and extracts defective words. The extracted defective words are listed and returned to the user as an HTTP response in JSON format.

[0821] Terminal handling

[0822] The terminal provides an interface for the user to enter text to be reviewed, such as a web form or text area, and the text entered by the user is sent to the server by pressing a submit button.

[0823] The list of defective words returned from the server is received by the terminal, which then displays the received defective words to the user. Specifically, the defective words are displayed in a list format on a web page.

[0824] User operations

[0825] The user inputs the text to be proofread into the terminal. The input text is sent to the server by clicking the send button. The user checks the list of defective words returned by the server and corrects the text.

[0826] Specific examples

[0827] For example, consider the case where a user wants to proofread the text "I went to the library yesterday. I borrowed a book there." The user enters this text into a web form on their device and clicks the submit button. The server inputs the received text into a generative artificial intelligence (e.g., GPT-4), and based on the analysis results, detects that there is a grammatical error in the part "I went to the library." The server lists the incorrect words in "I went to the library" and returns them to the device in JSON format. The device displays these incorrect words to the user, who then corrects it to "I went to the library."

[0828] Prompt Sentence Examples

[0829] Please proofread the following text. Identify any spelling or grammatical errors and list any missing words.

[0830] Text: 'I went to the library yesterday. I borrowed a book there.'

[0831] In this way, the system can efficiently proofread the text entered by the user.

[0832] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0833] Step 1:

[0834] The server receives the text to be reviewed from the user.

[0835] Input: Text data entered by the user into the terminal.

[0836] Specific operation: The server receives text data via an HTTP request.

[0837] Output: Received text data

[0838] Step 2:

[0839] The server inputs the received text into a generative artificial intelligence.

[0840] Input: Received text data

[0841] Specific operation: The server sends the text data as a prompt to a generative artificial intelligence (e.g., a model with natural language processing technology).

[0842] Output: Prompt sent to the generative AI

[0843] Step 3:

[0844] Generative AI analyzes input text and detects typos and grammatical errors.

[0845] Input: A prompt sent to the generative AI

[0846] How it works: Generative AI performs syntactic analysis to detect typos and grammatical errors in text.

[0847] Output: Analysis results (data containing typos and grammatical errors)

[0848] Step 4:

[0849] The server analyzes the analysis results returned from the generative artificial intelligence and extracts defective words.

[0850] Input: Analysis results returned by generative artificial intelligence

[0851] Specific operation: The server analyzes the analysis results and lists the defective words.

[0852] Output: List of missing words

[0853] Step 5:

[0854] The server lists the extracted defective words and returns them to the user.

[0855] Input: List of missing words

[0856] Specific operation: The server returns a list of incomplete words in JSON format as an HTTP response.

[0857] Output: A list of invalid words returned to the user

[0858] Step 6:

[0859] The terminal receives the list of defective words returned from the server.

[0860] Input: A list of invalid words returned by the server

[0861] Specific operation: The terminal receives the JSON data returned as an HTTP response.

[0862] Output: List of received invalid words

[0863] Step 7:

[0864] The terminal displays the received defective words to the user.

[0865] Input: List of received invalid words

[0866] Specific operation: The terminal displays defective words in a list format on a web page.

[0867] Output: A list of the missing words shown to the user

[0868] Step 8:

[0869] The user checks the list of defective words returned from the server and corrects the text.

[0870] Input: A list of invalid words shown to the user

[0871] What happens: The user uses the list to correct spelling and grammatical errors in the text.

[0872] Output: Corrected text

[0873] (Application example 1)

[0874] Next, a description will be given of Application Example 1 of Form 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."

[0875] Traditional proofreading work relies on manual human labor, which is time-consuming and labor-intensive, and there is a risk of overlooking typos and grammatical errors. Furthermore, content distribution services require fast and accurate proofreading to maintain the quality of posted articles and comments, but this is difficult to do manually. Furthermore, there is a lack of functionality to automatically input proofreading results into a database, making efficient management difficult.

[0876] 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.

[0877] In this invention, the server includes a means for accepting proofreading work that would otherwise be performed by humans, a means for extracting defective words using the syntactic analysis function of generative AI, a means for automatically inputting the results into a database when defects are found, a means for automatically proofreading articles and comments posted to a content distribution service, a means for detecting typos and grammatical errors and presenting suggested corrections, and a means for the user to confirm and approve the suggested corrections, thereby enabling the efficiency and accuracy of proofreading work to be improved.

[0878] "Proofreading" is the task of detecting and correcting typos and grammatical errors in sentences or text.

[0879] "Generative AI" is a type of artificial intelligence that uses natural language processing to generate and analyze text.

[0880] The "syntax analysis function" is a function that analyzes the structure of a sentence and detects grammatical flaws and errors.

[0881] "Faulty words" are words or phrases that indicate typos or grammatical errors in a sentence.

[0882] A "database" is a system that systematically organizes and stores information so that it can be searched and used as needed.

[0883] A "content distribution service" is a service that provides users with content such as articles and comments via the Internet.

[0884] An "article" is a piece of writing or text on a particular topic.

[0885] A "comment" is an opinion or comment posted by a user on an article or post.

[0886] "Suggested corrections" are suggested corrections for detected typos and grammatical errors.

[0887] A "user" is someone who uses a system or service.

[0888] A system for carrying out this invention is configured as follows: The server includes means for accepting proofreading work that would otherwise be performed by humans, means for extracting defective words using the syntactic analysis function of generative AI, means for automatically inputting the results into a database when defects are found, means for automatically proofreading articles and comments posted to a content distribution service, means for detecting typos and grammatical errors and presenting suggested corrections, and means for the user to confirm and approve the suggested corrections.

[0889] Hardware and software used

[0890] Hardware: Servers, user devices (smartphones, PCs, etc.)

[0891] Software: Python, OpenAI API, database management system (e.g., MySQL)

[0892] Data processing and calculation

[0893] 1. User terminal: The user enters articles and comments and requests them for review.

[0894] 2. Server: Receives the input text and generates a prompt to send to a generative AI model (e.g., OpenAI's GPT-3).

[0895] 3. Generative AI model: Detects typographical and grammatical errors in the text based on the prompt and generates suggested corrections.

[0896] 4. Server: Receives the generated revision suggestions and presents them to the user.

[0897] 5. User terminal: The user checks the proposed amendments and approves or amends them.

[0898] 6. Server: Automatically enters the final proofreading results into the database.

[0899] Specific examples

[0900] When a user wants to post a blog post, they enter the text of the post into the application, which automatically reviews it, corrects any typos and grammatical errors, and presents the corrected text to the user. The user can then review the suggested corrections and approve them if necessary.

[0901] Prompt Sentence Examples

[0902] Please identify any typos or grammatical errors in the text below and suggest corrections.

[0903] This is a test text and may contain typos and grammatical errors.

[0904] Proposed amendment:

[0905] In this way, it is possible to improve the efficiency and accuracy of proofreading work.

[0906] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0907] Step 1:

[0908] A user uses a terminal to input text for an article or comment and submit it for review. The input text is sent to the server. The input data is the text entered by the user, and the output data is the text sent to the server.

[0909] Step 2:

[0910] The server generates a prompt sentence to send to the generative AI model from the received text. The input data is the text received from the user, and the output data is the generated prompt sentence. Specifically, the server converts the text into the following format:

[0911] Please identify any typos or grammatical errors in the text below and suggest corrections.

[0912] [user text]

[0913] Proposed amendment:

[0914] Step 3:

[0915] The server sends the generated prompt sentence to a generative AI model (e.g., OpenAI's GPT-3). The input data is the generated prompt sentence, and the output data is the correction suggestions returned by the generative AI model. Specifically, the OpenAI API is used to send the prompt sentence and receive the correction suggestions.

[0916] Step 4:

[0917] The server presents the proposed revisions received from the generative AI model to the user. The input data is the proposed revisions received from the generative AI model, and the output data is the proposed revisions presented to the user. Specifically, the proposed revisions are displayed on the user's device.

[0918] Step 5:

[0919] The user checks the proposed revisions and either approves or corrects them. The input data are the revisions proposed to the user, and the output data is the final text that the user approves or corrects. Specifically, the user checks the revisions and makes corrections if necessary.

[0920] Step 6:

[0921] The server automatically inputs the final text approved or corrected by the user into a database. The input data is the final text approved or corrected by the user, and the output data is the final text stored in the database. Specifically, a database management system (e.g., MySQL) is used to store the final text.

[0922] Example 2

[0923] Next, a description will be given of Example 2 of Form 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."

[0924] Traditional proofreading work is done manually by humans, which takes time and effort, and is prone to errors. The process of detecting and correcting errors is also cumbersome, making it difficult to carry out the work efficiently. Furthermore, the time and effort required to manually input information about detected errors into spreadsheet software is a significant burden.

[0925] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for accepting proofreading work to be performed by humans, a means for extracting deficiencies using the syntactic analysis function of the generative AI model, a means for automatically inputting the results into spreadsheet software when deficiencies are found, a means for sending a prompt to the generative AI model, and a means for receiving the analysis results returned from the generative AI model. This makes it possible to improve the efficiency and accuracy of proofreading work.

[0926] "Proofreading" is the task of detecting and correcting typographical and grammatical errors in a document.

[0927] A "generative AI model" is a model that uses artificial intelligence technology to analyze text data and perform specific tasks.

[0928] The "syntax analysis function" is a function that analyzes the grammatical structure of text data and understands its meaning and relationships.

[0929] "Faulty words" are words or phrases that refer to typographical or grammatical errors present in a document.

[0930] "Spreadsheet software" is software for entering, calculating, analyzing, and visualizing data, and generally manages data in spreadsheet format.

[0931] A "prompt" is an instruction entered into a generative AI model to make it perform a specific task.

[0932] "Business automation technology" is a technology for automating business processes that were previously performed manually.

[0933] The present invention is a system for making proofreading more efficient and improving accuracy. A specific embodiment of this system will be described below.

[0934] First, the user inputs text data through the system interface, which can be a web form or a dedicated application, and the text data entered by the user is sent to the server.

[0935] The server creates a prompt to send to the generative AI model from the received text data. For example, it generates the following prompt:

[0936] Please analyze the text below and find any defects. Please provide the location, content, and suggested fixes for any defects found.

[0937] Text: "This article contains several deficiencies. For example, it contains typos and omissions."

[0938] Next, the server sends the prompt to a generative AI model. The generative AI model is, for example, a model that uses general artificial intelligence technology and analyzes text data. Specifically, the generative AI model performs syntactic analysis of the text data and detects incorrect words.

[0939] Once the generative AI model has completed its analysis, the server receives the analysis results, which include the location, content, and suggested corrections for the missing words. For example, the following results may be returned:

[0940] Incomplete word position: 13

[0941] Deficiency word content: "Several deficiencies"

[0942] Proposed fix: "Some issues"

[0943] The server automatically inputs the missing information into a spreadsheet based on the analysis results it receives. Specifically, it uses Python's openpyxl library to input the data into an Excel file as follows:

[0944] Enter the position of the missing word in column A.

[0945] Enter the contents of the missing words in column B.

[0946] Enter the proposed revisions in column C.

[0947] Finally, the user can download or view the generated Excel file, allowing them to review the information for any deficiencies and make any necessary corrections.

[0948] This system will improve the efficiency and accuracy of proofreading work, eliminating the need for users to manually input results and enabling rapid corrections based on the analysis results of the generative AI model.

[0949] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0950] Step 1:

[0951] The user inputs text data.

[0952] A user inputs text data through the system's interface, for example, using a web form or a dedicated application, and the input text data is sent to the server.

[0953] Input: Text data entered by the user

[0954] Output: Text data sent to the server

[0955] Step 2:

[0956] The server receives the input text data.

[0957] The server receives text data sent by the user, which is sent via a communication protocol such as an HTTP request or WebSocket.

[0958] Input: Text data sent by the user

[0959] Output: Text data stored on the server

[0960] Step 3:

[0961] The server sends a prompt to the generative AI model.

[0962] The server creates a prompt to send to the generative AI model from the received text data. For example, it generates the following prompt:

[0963] Please analyze the following text and find any defects. Please provide the location, content, and suggested fixes for any defects found.

[0964] Text: "This article contains some deficiencies. For example, it contains typos and omissions."

[0965] Input: Text data stored on the server

[0966] Output: The prompt sent to the generative AI model

[0967] Step 4:

[0968] A generative AI model analyzes text data and detects defects.

[0969] The generative AI model receives the prompt sent from the server and analyzes the text data. As a result of the analysis, it identifies the location, content, and suggested corrections of the defects.

[0970] Input: A prompt sent to the generative AI model

[0971] Output: Location, content, and proposed corrections for defects

[0972] Step 5:

[0973] The server obtains the location, content, and proposed corrections of the defect.

[0974] The server receives the analysis results returned by the generative AI model, including the location, content, and suggested fixes for the defects.

[0975] Input: Analysis results returned from the generative AI model

[0976] Output: Location, content, and proposed corrections of defects stored on the server

[0977] Step 6:

[0978] The server enters the incorrect information into the Excel file.

[0979] The server uses Python's openpyxl library to input the missing information into the Excel file. Specifically, the data is input as follows:

[0980] Enter the position of the missing word in column A.

[0981] Enter the contents of the missing words in column B.

[0982] Enter the proposed revisions in column C.

[0983] Input: Location, content, and proposed corrections of defects stored on the server

[0984] Output: Excel file with incomplete information entered

[0985] Step 7:

[0986] The user checks the Excel file.

[0987] The user downloads or views the Excel file generated by the server, allowing the user to check the information for any deficiencies and make any necessary corrections.

[0988] Input: Excel file with incomplete information

[0989] Output: Information on defects confirmed by the user

[0990] (Application example 2)

[0991] Next, a description will be given of Application Example 2 of Form 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."

[0992] In traditional proofreading and inventory management, detecting defects and inputting the results is often done manually, which takes time and effort and increases the risk of human error. Furthermore, in inventory management at logistics centers, scanning for defects and managing the results is not done efficiently, making it difficult to respond quickly.

[0993] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for accepting proofreading work that would be performed by humans, a means for extracting deficiencies using the syntactic analysis function of generative AI, a means for automatically inputting the results into Excel when deficiencies are found, and a means for scanning deficiency data with a smartphone and automatically inputting the results into Excel in an inventory management system at a logistics center. This automates the detection of deficiencies in proofreading work and inventory management and the input of the results, saving time and effort and reducing the risk of human error.

[0994] "Proofreading" is the work of checking the contents of documents and data and correcting errors and omissions.

[0995] "Generative AI" is a type of artificial intelligence that has the ability to generate and analyze data for specific tasks.

[0996] A "syntax analysis function" is a function that analyzes the structure of documents and data and understands their meaning and grammatical relationships.

[0997] "Faulty words" are words or phrases that indicate errors or inappropriate parts in a document or data.

[0998] "Excel" is a type of spreadsheet software and a tool for entering, calculating, analyzing, and visualizing data.

[0999] A "logistics center" is a facility that stores, manages, and ships goods.

[1000] An "inventory management system" is a system for monitoring and managing product inventory.

[1001] A "smartphone" is a type of mobile phone and a multi-function device that can connect to the Internet and use applications.

[1002] "Scanning" is the process of electronically reading data or images.

[1003] "RPA" stands for Robotic Process Automation, a technology for automating business processes.

[1004] To implement the present invention, the following system configuration and program are required.

[1005] System Configuration

[1006] 1. Server: Hosts the generative AI model and provides parsing functionality.

[1007] 2. Terminal: Using a smartphone, it scans out defective data as part of the inventory management system at the distribution center.

[1008] 3. Software: Python and the openpyxl library are used for automated data entry and management.

[1009] Program processing

[1010] The server uses a generative AI model to extract deficiency words in proofreading and inventory management. The terminal (smartphone) scans inventory at the logistics center and collects deficiency data. The collected deficiency data is sent to the server and analyzed by the generative AI model. As a result, the location, content, and correction suggestions of the deficiency words are extracted and automatically entered into an Excel file.

[1011] Hardware and software used

[1012] Hardware: Smartphone (for scanning and entering missing data)

[1013] Software: Python, openpyxl library (for working with Excel files)

[1014] Data processing and calculation

[1015] 1. Deficiency data collection: Using smartphones, inventory is scanned at distribution centers to collect defect data.

[1016] 2. Data transmission: The collected deficiency data is transmitted to the server.

[1017] 3. Syntax analysis: A generative AI model on the server analyzes the defective data and extracts the location, content, and suggested corrections for the defective words.

[1018] 4. Automatic data entry: The extracted information of missing words is automatically entered into an Excel file using Python and the openpyxl library.

[1019] Specific examples

[1020] For example, suppose the following defects are detected when inventory is scanned at a logistics center:

[1021] Location: A1, Content: Quantity shortage, Correction: Additional order

[1022] Location: B2, Content: Label error, Proposal: Label correction

[1023] By automatically entering this deficiency data into an Excel file, administrators can quickly take corrective action.

[1024] Prompt Sentence Examples

[1025] "Generate a Python program that automatically inputs deficiency data into Excel for inventory management at a distribution center. The deficiency data will include the location, content, and correction suggestions."

[1026] The above is a specific embodiment for carrying out the present invention.

[1027] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1028] Step 1:

[1029] The terminal (smartphone) scans inventory at the distribution center. The input is identification information such as the inventory barcode or QR code, and the output is incomplete data (e.g., insufficient quantity, incorrect label). Specifically, the smartphone camera is used to read the inventory barcode and send that information to the application.

[1030] Step 2:

[1031] The terminal (smartphone) sends the collected deficiency data to the server. The input is the deficiency data collected in step 1, and the output is the deficiency data sent to the server. Specifically, the smartphone application sends the deficiency data to the server's API endpoint.

[1032] Step 3:

[1033] The server uses a generative AI model to analyze the deficiency data. The input is the deficiency data sent from the device, and the output is the location, content, and correction suggestions of the deficiency words. Specifically, the server receives the deficiency data, inputs it into the generative AI model, and analyzes it.

[1034] Step 4:

[1035] The server extracts the location, content, and correction suggestions of the defective words based on the analysis results. The input is the analysis results from the generative AI model, and the output is the location, content, and correction suggestions of the extracted defective words. Specifically, the server analyzes the analysis results and extracts the necessary information.

[1036] Step 5:

[1037] The server automatically inputs the extracted deficiency word information into an Excel file. The input is the location, content, and suggested corrections of the extracted deficiency words, and the output is the updated Excel file. Specifically, the server uses Python and the openpyxl library to open the Excel file, input information into the appropriate cells, and save the file.

[1038] Step 6:

[1039] The user checks the updated Excel file and takes the necessary action. The input is the updated Excel file, and the output is the implementation of the countermeasures. Specifically, the user opens the Excel file, checks the location, content, and suggested corrections of the defective words, and takes the necessary action.

[1040] Example 3

[1041] Next, a description will be given of Example 3 of Form Example 3. 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."

[1042] Conventional proofreading work is done manually by humans, which takes time and effort, resulting in high costs. Manual proofreading is also prone to errors, which can lead to inconsistent quality. Furthermore, even in systems that use generative AI models to extract defective words and automatically input the results, each process is executed individually, which reduces overall efficiency.

[1043] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1044] In this invention, the server includes a means for a user to input text data, a means for a terminal to send the input data to the server, a means for the server to send a prompt sentence to the generative AI model, a means for the generative AI model to extract defective words, a means for the server to save the extracted results in a database, a means for the server to automatically input the results using an automation tool, and a means for the terminal to display the results to the user, thereby making it possible to improve the efficiency of proofreading work and reduce costs.

[1045] "Human proofreading" refers to the process of checking the content of sentences or texts and correcting errors or inappropriate expressions.

[1046] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate and analyze text.

[1047] "Syntax analysis function" refers to the function for analyzing the grammar and structure of text and understanding its meaning.

[1048] "Incomplete words" refer to errors or inappropriate expressions contained in a sentence.

[1049] "Spreadsheet software" is software for organizing and calculating data, and generally handles data in spreadsheet format.

[1050] "User" refers to a person who uses the system.

[1051] "Terminal" refers to a device through which a user accesses the system.

[1052] A "server" is a computer that is the central part of a system and processes and stores data.

[1053] A "prompt sentence" refers to an input sentence used to give instructions to a generative AI model.

[1054] A "database" refers to a system for efficiently storing and managing data.

[1055] "Automation tools" refer to software or systems that perform specific tasks automatically.

[1056] "Means for automatically inputting results" refers to a method or system for automatically inputting extracted defective words and correction results.

[1057] "Means for displaying results" refers to a method or system for displaying the processing results to the user.

[1058] This invention is a system that uses a generative AI model to extract defective words and automatically input the results. Specific embodiments of this system will be described below.

[1059] First, a user inputs text data using a terminal. The terminal then sends the text data to a server via a web browser or a dedicated application. For example, if a user inputs the text "This sentence is very good," the terminal sends this data to the server using an HTTP POST request.

[1060] The server generates a prompt to send the received text data to the generative AI model. Specifically, it uses the Python requests library to send the following prompt to the generative AI model:

[1061] "Please extract the missing words from the following text: This sentence is very good."

[1062] The generative AI model analyzes the prompt received from the server and extracts incomplete words. For example, it determines that the expression "very good" is ambiguous and extracts it as an incomplete word.

[1063] The server stores the missing words received from the generative AI model in a database, specifically by running SQL queries to store the extracted missing words using a database management system such as MySQL.

[1064] Next, the server uses an automation tool (e.g., UiPath) to automatically correct the extracted defective words, for example, changing "very good" to "excellent." This correction result is saved back in the database.

[1065] Finally, the terminal displays the correction results received from the server to the user. Specifically, the corrected text "This sentence is very good" is displayed on the web browser.

[1066] This system enables the efficiency of proofreading work and cost reduction. In addition, the use of a generative AI model enables highly accurate extraction and correction of defective words. The flow of the identification process in Example 3 will be explained using Figure 15.

[1067] Step 1:

[1068] The user inputs text data.

[1069] A user inputs text data using a terminal interface. For example, the user inputs "This article is very good" into a form on a web browser. The input data is the text data entered by the user.

[1070] Step 2:

[1071] The terminal sends the input data to the server.

[1072] The terminal sends the text data entered by the user to the server. Specifically, it sends the data using an HTTP POST request. The input is the text data entered by the user, and the output is the text data sent to the server.

[1073] Step 3:

[1074] The server sends a prompt to the generative AI model.

[1075] The server generates a prompt sentence to send to the generative AI model from the received text data. For example, it creates a prompt sentence such as "Please extract the missing words from the following text: This sentence is very good." The input is the text data sent to the server, and the output is the prompt sentence to be sent to the generative AI model.

[1076] Step 4:

[1077] The generative AI model extracts incomplete words.

[1078] The generative AI model analyzes the prompt received from the server and extracts incomplete words. For example, it determines that the expression "very good" is ambiguous and extracts it as an incomplete word. The input is the prompt sent to the generative AI model, and the output is the extracted incomplete word.

[1079] Step 5:

[1080] The server stores the extracted results in a database.

[1081] The server stores the defective words received from the generative AI model in a database. Specifically, it inserts them into the database using an SQL query. The input is the extracted defective words, and the output is the defective words stored in the database.

[1082] Step 6:

[1083] The server uses an automation tool to populate the results.

[1084] The server uses an RPA tool (e.g., UiPath) to automatically correct the extracted defective words. For example, it changes "very good" to "excellent." The input is the defective words stored in the database, and the output is the corrected text data.

[1085] Step 7:

[1086] The terminal displays the results to the user.

[1087] The terminal displays the correction results received from the server to the user. Specifically, the corrected text "This sentence is very good" is displayed on the web browser. The input is the corrected text data, and the output is the correction results displayed to the user.

[1088] (Application example 3)

[1089] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1090] Traditional proofreading work was done manually by humans, which required a great deal of time and effort and was inefficient. Also, while it was possible to extract defective words using generative AI, there was no system that could automatically input the results and suggest corrections. Furthermore, there was a lack of functionality to analyze the text entered by the user in real time and make correction suggestions, which made it inconvenient for users.

[1091] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes a means for accepting proofreading work that would be performed by humans, a means for extracting deficiencies using the syntactic analysis function of generative AI, a means for automatically inputting the results into a database when deficiencies are found, a means for analyzing text entered by a user in real time, extracting deficiencies and suggesting corrections, and a means for presenting the suggested corrections to the user and applying the corrections with one tap. This makes it possible to improve the efficiency of proofreading work, reduce costs, and improve user convenience.

[1092] "Proofreading" is the task of checking the content of sentences and documents and correcting errors and omissions.

[1093] "Generative AI" is a type of artificial intelligence, a model that has the ability to generate appropriate outputs for specific inputs.

[1094] The "syntax analysis function" is a function that analyzes the structure of a sentence and identifies grammatical elements.

[1095] "Incorrect words" are words or phrases that refer to errors or inappropriate expressions contained in a sentence.

[1096] A "database" is a collection of data that is systematically organized so that information can be searched and managed efficiently.

[1097] "Real-time analysis" is a processing method in which analysis is performed immediately as data is entered.

[1098] A "correction suggestion" is a suggestion that presents an appropriate correction method for a detected defective word.

[1099] "One Tap" is a feature that allows users to complete an operation by simply tapping the device screen once.

[1100] "RPA" stands for Robotic Process Automation, a technology that automates routine business processes.

[1101] The system for implementing this invention consists of three main elements: a server, a terminal, and a user.

[1102] server

[1103] The server uses a generative AI model to extract incomplete words and generate correction suggestions. Specifically, the server uses the following software and hardware:

[1104] Software: OpenAI API, Python

[1105] Hardware: A server machine with a powerful processor and sufficient memory

[1106] The server receives the text entered by the user and analyzes it in real time using a generative AI model. As a result of the analysis, it generates incorrect words and suggested corrections, and automatically enters the results into a database.

[1107] Terminal

[1108] A terminal is a device on which a user enters text and reviews and applies suggested corrections. This typically applies to mobile devices such as smartphones and tablets.

[1109] The device receives the correction suggestions sent from the server and presents them to the user, who can apply the correction suggestions with a single tap on the device screen.

[1110] User

[1111] Users are individuals or companies who perform proofreading tasks. They use their terminals to enter text and review and apply correction suggestions from the server.

[1112] Specific examples

[1113] For example, suppose a user is creating a blog post. The user enters "This is a test text. It contains some incorrect words." The terminal sends this text to the server.

[1114] The server analyzes this text using a generative AI model to extract the missing words and generate correction suggestions, such as "misspelled word: misspelled word, suggested correction: misspelled word." The server then automatically enters these suggestions into a database and sends them to the device.

[1115] The device presents the suggested corrections to the user, who can then apply the corrections and view the corrected text with a single tap on the device screen.

[1116] Prompt Sentence Examples

[1117] Please extract the missing words from the text below and suggest corrections.

[1118] Text: This is the text of the test. It contains some missing words.

[1119] Incomplete words and suggested corrections:

[1120] In this way, the present invention can improve the efficiency of proofreading work, reduce costs, and improve user convenience.

[1121] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1122] Step 1:

[1123] The user inputs text into the terminal. For example, the user inputs "This is a test text. It contains some incorrect words." The input text is sent from the terminal to the server.

[1124] Step 2:

[1125] The server receives the text sent from the device. It inputs the received text into a generative AI model and performs syntactic analysis. Specifically, it uses the OpenAI API to analyze the text and extracts erroneous words. The input is the user's text, and the output is the erroneous words and suggested corrections.

[1126] Step 3:

[1127] The server analyzes the defective words and suggested corrections obtained from the generative AI model. The analysis results are automatically entered into a database. Specifically, the extracted defective words and suggested corrections are saved in the database. The input is the defective words and suggested corrections, and the output is the results saved in the database.

[1128] Step 4:

[1129] The server transmits the revision suggestions stored in the database to the terminal. Specifically, it transmits data to the terminal to present the revision suggestions to the user. The input is the revision suggestions from the database, and the output is the data sent to the terminal.

[1130] Step 5:

[1131] The terminal receives the correction suggestions sent from the server. It displays the received correction suggestions to the user. Specifically, it displays the defective words and their correction suggestions on the screen so that the user can confirm them. The input is the correction suggestions from the server, and the output is what is displayed to the user.

[1132] Step 6:

[1133] The user applies the suggested corrections with a single tap on the device screen. Specifically, the user confirms the suggested corrections and taps the Apply button to reflect the corrections in the text. The input is the confirmation and application of the suggested corrections, and the output is the corrected text.

[1134] Step 7:

[1135] The terminal displays the corrected text to the user. Specifically, it displays the text with the corrections applied on the screen for the user to review. The input is the corrected text, and the output is what is displayed to the user.

[1136] In this way, users can efficiently review and correct text.

[1137] 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.

[1138] "Example 1"

[1139] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system recognizes a user's emotions and adjusts the priority of review tasks based on those emotions. Specifically, if the system recognizes that a user is feeling angry or frustrated, it prioritizes the user's review tasks. On the other hand, if the system recognizes that a user is feeling happy or satisfied, it processes the user's review tasks as usual.

[1140] "Example 2"

[1141] Furthermore, the emotion engine changes the way the review results are displayed based on the user's emotions. Specifically, if the engine recognizes that the user is feeling angry or frustrated, it highlights the review results. If the engine recognizes that the user is feeling happy or satisfied, it displays the review results normally.

[1142] "Example 3"

[1143] For example, if the system recognizes that a user is feeling angry, it will prioritize the user's proofreading and highlight the proofreading results in red. On the other hand, if the system recognizes that a user is feeling happy, it will process the user's proofreading as usual and highlight the proofreading results in green. This allows the system to respond appropriately according to the user's emotions.

[1144] The processing flow of each embodiment will be described below.

[1145] "Example 1"

[1146] Step 1: The system recognizes the user's emotions using an emotion engine.

[1147] Step 2: If the emotion engine recognizes that the user's emotion is anger or dissatisfaction, it instructs the system to prioritize the user's review work.

[1148] Step 3: The system adjusts the priority of the review tasks according to the instructions from the emotion engine and processes them as a priority.

[1149] "Example 2"

[1150] Step 1: The system recognizes the user's emotions using an emotion engine.

[1151] Step 2: If the emotion engine recognizes that the user's emotion is anger or frustration, it instructs the system to highlight the revision.

[1152] Step 3: The system follows instructions from the emotion engine to change the way the review results are displayed and highlight them.

[1153] "Example 3"

[1154] Step 1: The system recognizes the user's emotions using an emotion engine.

[1155] Step 2: If the emotion engine recognizes that the user's emotion is anger, it instructs the system to prioritize the user's review and highlight the review results in red.

[1156] Step 3: The system adjusts the priority of the review tasks according to the instructions from the emotion engine, prioritizes the tasks, and highlights the review results in red.

[1157] Step 4: If the emotion engine recognizes that the user's emotion is joy, it instructs the system to process the user's review as usual and display the review results in green.

[1158] Step 5: The system follows the instructions from the emotion engine and processes the review as normal, displaying the review results in green.

[1159] Example 1

[1160] Next, a description will be given of Example 1 of Form 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."

[1161] Traditional proofreading work is done manually by humans, which is time-consuming, labor-intensive, and inefficient. Furthermore, because the process is uniform without considering the user's feelings, there is a risk of user satisfaction decreasing. Furthermore, there is a lack of a system that automatically detects and corrects typos and grammatical errors, which can lead to a decrease in the accuracy of proofreading. To solve these issues, an efficient and user-friendly proofreading system is needed.

[1162] 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.

[1163] In this invention, the server includes a means for accepting proofreading work to be performed by humans, a means for extracting deficiencies using the syntactic analysis function of generative AI, a means for automatically inputting the results into spreadsheet software when deficiencies are found, a means for recognizing user emotions, and a means for adjusting the priority of proofreading work based on the user emotions. This makes it possible to perform proofreading work efficiently and with high accuracy, and to adjust the priority taking into account the user's emotions.

[1164] "Proofreading" is the work of checking and correcting typos, grammatical errors, and consistency of content in sentences and texts.

[1165] "Generative AI" is a type of artificial intelligence that has the ability to generate output in natural language or other formats in response to specific inputs.

[1166] The "syntax analysis function" is a function that analyzes the grammatical structure of text and detects typos and grammatical errors.

[1167] "Imperfect words" are words or phrases that indicate typographical or grammatical errors present in the text.

[1168] "Spreadsheet software" is software for data entry, calculation, analysis, graph creation, etc., and generally handles data in spreadsheet format.

[1169] "Means for recognizing emotions" refers to technology that analyzes user input and behavioral data to identify their emotional state.

[1170] The "means for adjusting priority" is a function for changing the order of processing based on specific conditions and responding according to the importance and urgency of the processing.

[1171] "Business automation technology" is a technology for automating repetitive business processes, with the aim of improving efficiency and accuracy.

[1172] The present invention is a system for providing an efficient and user-friendly proofreading service. A specific embodiment of this system will be described below.

[1173] System configuration

[1174] This system consists of three main components: a server, a terminal, and a user. The server uses a generative AI model to analyze text syntax and extract defective words. The terminal recognizes the user's emotions and adjusts the priority of editing tasks based on those emotions. The user inputs the text to be edited and receives feedback from the system.

[1175] Hardware and software used

[1176] Generative AI model: For example, GPT-4 is used as a generative AI model. This model excels at natural language processing and is suitable for text syntactic analysis.

[1177] Emotion recognition engine: For example, the emotion recognition API from Azure Cognitive Services is used as the emotion recognition engine. This is a technology for analyzing emotions from user input and behavioral data.

[1178] Spreadsheet software: Use common spreadsheet software (e.g., Microsoft Excel) for automated entry of results.

[1179] System Operation

[1180] 1. Proofreading services

[1181] A user inputs text data to be reviewed into the system, for example by entering text into a web form or by uploading a file.

[1182] 2. Sending text data

[1183] The terminal transmits the text data received from the user to the server. At this time, the data is encrypted before transmission.

[1184] 3. Performing parsing

[1185] The server inputs the received text data into a generative AI model and performs syntactic analysis, detecting typos and grammatical errors.

[1186] 4. Extracting incomplete words

[1187] The server extracts and lists imperfect words from the results of the syntax analysis. For example, imperfect words such as "typographical error" and "grammatical error" are included in the list.

[1188] 5. Sending the results

[1189] The server transmits a list of the extracted defective words to the terminal.

[1190] 6. Displaying the results

[1191] The terminal displays the list of imperfect words received from the server to the user, who then checks the displayed imperfect words and makes corrections.

[1192] 7. Collecting Emotional Data

[1193] The device collects user input and behavioral data, such as the user's keyboard typing speed, mouse movements, and data from a facial recognition camera.

[1194] 8. Performing Sentiment Analysis

[1195] The device inputs the collected data into an emotion recognition engine to analyze the user's emotions.

[1196] 9. Transmission of Emotional Data

[1197] The device sends the analysis results, which include emotional data such as "anger" or "frustration," to the server.

[1198] 10. Adjusting the priorities of review work

[1199] The server adjusts the priority of review tasks based on the received emotion data, for example, giving priority to review tasks for users who are feeling angry.

[1200] 11. Sending the Review Results

[1201] The server transmits the processed proofreading results to the terminal based on the priority.

[1202] 12. Viewing Review Results

[1203] The terminal displays the proofreading results received from the server to the user, who then checks the results and makes corrections as necessary.

[1204] Specific examples

[1205] Input text: "This sentence may contain typos."

[1206] Output: The missing word "typo" is detected.

[1207] Prompt Sentence Examples

[1208] Please find any spelling or grammatical errors in the text below.

[1209] Text: "This sentence may contain typos."

[1210] In this way, the system achieves efficient and accurate proofreading and adjusts priorities in consideration of the user's feelings.

[1211] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1212] Step 1:

[1213] A user inputs text data to be reviewed into the system, for example by entering text into a web form or by uploading a file.

[1214] Input: Text data to be reviewed

[1215] Output: Text data sent to the terminal

[1216] Step 2:

[1217] The terminal transmits the text data received from the user to the server. At this time, the data is encrypted before transmission.

[1218] Input: Text data received from the user

[1219] Output: Text data sent to the server

[1220] Step 3:

[1221] The server inputs the received text data into a generative AI model and performs syntactic analysis, detecting typos and grammatical errors.

[1222] Input: Text data sent from the terminal

[1223] Data processing: Syntactic analysis using generative AI models

[1224] Output: Parsing results (list of typos and grammatical errors)

[1225] Step 4:

[1226] The server extracts and lists imperfect words from the results of the syntax analysis. For example, imperfect words such as "typographical error" and "grammatical error" are included in the list.

[1227] Input: Parsed result

[1228] Data processing: Extraction of incomplete words

[1229] Output: A list of missing words

[1230] Step 5:

[1231] The server transmits a list of the extracted defective words to the terminal.

[1232] Input: List of missing words

[1233] Output: A list of bad words sent to the terminal

[1234] Step 6:

[1235] The terminal displays the list of imperfect words received from the server to the user, who then checks the displayed imperfect words and makes corrections.

[1236] Input: A list of invalid words sent from the server

[1237] Output: A list of flawed words displayed to the user

[1238] Step 7:

[1239] The device collects user input and behavioral data, such as the user's keyboard typing speed, mouse movements, and data from a facial recognition camera.

[1240] Input: User input and behavioral data

[1241] Output: Collected emotion data

[1242] Step 8:

[1243] The device inputs the collected data into an emotion recognition engine to analyze the user's emotions.

[1244] Input: Collected emotion data

[1245] Data processing: Emotion analysis using an emotion recognition engine

[1246] Output: Emotion analysis results

[1247] Step 9:

[1248] The device sends the analysis results, which include emotional data such as "anger" or "frustration," to the server.

[1249] Input: Sentiment analysis results

[1250] Output: Emotion data sent to the server

[1251] Step 10:

[1252] The server adjusts the priority of review tasks based on the received emotion data, for example, giving priority to review tasks for users who are feeling angry.

[1253] Input: Emotion data

[1254] Data processing: Adjusting priorities for review work

[1255] Output: Priority-adjusted review work

[1256] Step 11:

[1257] The server transmits the processed proofreading results to the terminal based on the priority.

[1258] Input: Priority-adjusted review work

[1259] Output: Review results sent to your device

[1260] Step 12:

[1261] The terminal displays the proofreading results received from the server to the user, who then checks the results and makes corrections as necessary.

[1262] Input: Review results sent from the server

[1263] Output: The review results that are displayed to the user

[1264] (Application example 1)

[1265] Next, a description will be given of Application Example 1 of Form 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."

[1266] Traditional proofreading work is done manually by humans, which takes time and effort, and is inefficient. Furthermore, it is difficult to improve user satisfaction because it is not possible to respond flexibly to user emotions. Furthermore, there is also the problem of delays in improving the quality of content due to the delay in feedback of proofreading results.

[1267] 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.

[1268] In this invention, the server includes a means for accepting proofreading work that would otherwise be performed by humans, a means for extracting deficiencies using the syntactic analysis function of generative AI, a means for automatically inputting the results into spreadsheet software when deficiencies are found, a means for recognizing the user's emotions and adjusting the priority of the proofreading work based on those emotions, and a means for providing feedback on the proofreading results to the user and suggesting revisions, thereby making it possible to improve the efficiency of proofreading work and user satisfaction.

[1269] "Proofreading" is the task of detecting and correcting typos and grammatical errors in sentences and text.

[1270] "Generative AI" is a type of artificial intelligence that performs natural language processing such as text generation and syntactic analysis.

[1271] The "syntax analysis function" is a function that analyzes the structure of sentences and text and detects grammatical errors and typos.

[1272] "Faulty words" are words or phrases that indicate typos or grammatical errors in a sentence or text.

[1273] "Spreadsheet software" is software for data entry, calculation, analysis, graph creation, etc., and generally handles data in spreadsheet format.

[1274] "Means for recognizing emotions" refers to technology that analyzes and recognizes emotions from the user's facial expressions, tone of voice, etc.

[1275] The "means for adjusting the priority of review tasks" is a technique for changing the processing order of review tasks based on the recognized user sentiment.

[1276] "Feedback" is the process of returning the review results to the user and suggesting corrections.

[1277] "Proposals for correction" are specific correction methods or suggestions for the detected defective words.

[1278] "Business automation technology" is a technology for automating tasks that were previously performed manually, with the aim of improving efficiency and accuracy.

[1279] A system for implementing this invention is configured as follows: A server uses a generative AI model to perform syntactic analysis of text and extract defective words. Text entered by a user is analyzed in real time to detect typos and grammatical errors. The detected defective words are automatically entered into spreadsheet software.

[1280] Furthermore, the server uses an emotion recognition engine to analyze the user's emotions. The server captures the user's facial expressions and tone of voice using a camera and microphone, and analyzes them with the emotion recognition engine to determine whether the user is feeling angry, frustrated, happy, or satisfied. Based on this emotional information, the server adjusts the priority of the proofreading tasks. For example, if a user is feeling angry or frustrated, the server will prioritize the proofreading tasks for that user.

[1281] The results of the review are fed back to the user, and suggested revisions are presented. The user can then review the suggested revisions and make any necessary changes.

[1282] Specific hardware includes smartphones, smart glasses, and head-mounted displays, while software includes generative AI models (e.g., GPT-4), emotion recognition engines (e.g., Affectiva), and real-time data processing frameworks (e.g., Apache Kafka).

[1283] As a concrete example, consider a user trying to post a blog post. While the user is typing the post wearing smart glasses, the system analyzes the text in real time to detect typos and grammatical errors. At the same time, the system recognizes the user's emotions from their facial expressions and tone of voice, and prioritizes editing if the user is angry.

[1284] An example of a prompt for the generative AI model is as follows:

[1285] Please proofread the text below, spot any spelling or grammatical errors, and suggest corrections.

[1286] Text: "The weather is very nice today. I'm going to the park."

[1287] In this way, the system improves the efficiency of proofreading work and user satisfaction.

[1288] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1289] Step 1:

[1290] A user inputs text using a device (smartphone, smart glasses, head-mounted display).

[1291] Input: Text data entered by the user.

[1292] Output: The input text data is sent to the server in real time.

[1293] How it works: When a user types a blog post or content, the device sends the text data to the server in real time.

[1294] Step 2:

[1295] The server uses a generative AI model (e.g., GPT-4) to parse the received text data.

[1296] Input: Text data sent from the terminal.

[1297] Output: As a result of the parsing, defective words (typos and grammatical errors) are extracted.

[1298] What it does: The server uses a generative AI model to analyze text data and detect typos and grammatical errors.

[1299] Step 3:

[1300] The server automatically inputs the extracted defective words into spreadsheet software.

[1301] Input: Incomplete words extracted as a result of parsing.

[1302] Output: The missing words are automatically entered into a spreadsheet.

[1303] Specific operation: The server automatically inputs the extracted defective words into a spreadsheet software and records them.

[1304] Step 4:

[1305] The device captures the user's facial expressions and tone of voice using a camera and microphone and sends them to the server.

[1306] Input: User's facial expression and voice data.

[1307] Output: The acquired facial expression and voice data is sent to the server.

[1308] Specific operation: The device uses a camera and microphone to capture the user's facial expressions and tone of voice and transmits them to the server.

[1309] Step 5:

[1310] The server uses an emotion recognition engine (e.g., Affectiva) to analyze the received facial expression and voice data and recognize the user's emotions.

[1311] Input: Facial expression data and voice data sent from the device.

[1312] Output: User's emotional information (anger, frustration, joy, satisfaction, etc.).

[1313] Specific operation: The server uses an emotion recognition engine to analyze facial expression data and voice data and recognize the user's emotions.

[1314] Step 6:

[1315] The server adjusts the priority of review tasks based on the recognized emotion information.

[1316] Input: User emotion information.

[1317] Output: Review priorities are adjusted.

[1318] Specific operation: The server changes the processing order of review tasks based on the user's emotional information. For example, it prioritizes review tasks for users who are angry or dissatisfied.

[1319] Step 7:

[1320] The server provides feedback on the review results to the user and suggests corrections.

[1321] Input: Reviewing priorities based on parsing results and sentiment information.

[1322] Output: Review results and suggested revisions fed back to the user.

[1323] Specific operation: The server provides feedback on the review results to the user and suggests specific corrections. The user can review the suggested corrections and make corrections as necessary.

[1324] Example 2

[1325] Next, a description will be given of Example 2 of Form 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."

[1326] Traditional proofreading work is time-consuming and labor-intensive because it is done manually by humans, and is prone to errors. Furthermore, manually entering proofreading results into spreadsheet software is cumbersome and inefficient. Furthermore, there is also the issue that the user experience is not improved because feedback based on the user's emotions is not provided.

[1327] 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.

[1328] In this invention, the server includes a means for accepting proofreading work to be performed by humans, a means for extracting deficiencies using the syntactic analysis function of generative artificial intelligence, a means for automatically inputting the results into spreadsheet software when deficiencies are found, a means for analyzing user emotions, and a means for changing the display method of the proofreading results based on the user emotions. This not only makes it possible to improve the efficiency and accuracy of proofreading work, but also makes it possible to provide appropriate feedback according to the user's emotions, thereby improving the user experience.

[1329] The "means for accepting human proofreading work" is a function for receiving text entered by a user and proofreading that text.

[1330] The "means for extracting defective words using the syntactic analysis function of generative artificial intelligence" is a function that uses generative artificial intelligence to detect grammatical and spelling errors in text and identify defective words and phrases.

[1331] The "means for automatically inputting the results into spreadsheet software when a defect is found" is a function that automatically inputs information about detected defects into spreadsheet software.

[1332] The "means for analyzing user emotions" is a function for inferring emotions from the user's input and actions and analyzing the emotional state.

[1333] The "means for changing the display method of the proofreading results based on the user's emotions" is a function for dynamically changing the display method of the proofreading results in accordance with the analyzed user's emotions.

[1334] "Robotic process automation" refers to technology that automates processes that were previously done manually and enables efficient processing.

[1335] MODE FOR CARRYING OUT THE INVENTION

[1336] This invention is a system that proofreads text entered by a user and automatically inputs any errors into spreadsheet software. It also has the function of changing the way the proofreading results are displayed based on the user's emotions.

[1337] Hardware and software used

[1338] server:

[1339] The server provides the computational resources to run generative artificial intelligence (e.g., GPT-4).

[1340] The server uses the API of spreadsheet software (e.g., Microsoft Excel) to automatically enter the missing information.

[1341] Device:

[1342] A user inputs text using a terminal such as a personal computer or a smartphone.

[1343] The terminal communicates with the server to send text and receive proofread results.

[1344] software:

[1345] Generative AI (e.g., GPT-4) reviews text.

[1346] Spreadsheet software (e.g., Microsoft Excel) is used to record deficiency information.

[1347] The emotion engine is used to analyze the emotions of the user.

[1348] Data processing and calculation

[1349] Text Review:

[1350] The server receives the text entered by the user and proofreads it using generative artificial intelligence, which detects grammatical errors, spelling mistakes, and unnatural expressions.

[1351] Record of deficiencies:

[1352] The server uses the output of the generative artificial intelligence to identify the location, content, and proposed corrections of defects, which are then automatically entered into a spreadsheet program.

[1353] Sentiment analysis:

[1354] The server analyzes the user's emotions using an emotion engine, which infers emotions from the user's inputs and actions and analyzes the user's emotional state.

[1355] Change the display method:

[1356] The server then changes the way the review results are displayed based on the analyzed user's emotions. For example, if the server detects that the user is feeling angry or frustrated, it highlights the review results in red. Conversely, if the server detects that the user is feeling happy or satisfied, the review results are displayed normally.

[1357] Specific examples

[1358] User enters text:

[1359] The user uses the terminal to type "This is a simple text."

[1360] The server receives the text:

[1361] The server receives the text entered by the user.

[1362] The server uses a generative AI model to proofread the text:

[1363] The server uses generative artificial intelligence (e.g., GPT-4) to determine that "smple" should be corrected to "simple."

[1364] The server detects the flaw and generates a fix:

[1365] The server identifies the position of "smple" as the second character, the content as "smple," and the proposed revision as "simple."

[1366] The server enters the missing information into Excel:

[1367] The server enters "Position: 2", "Content: simple", and "Proposed fix: simple" into each column of Excel.

[1368] The server analyzes the user's emotions:

[1369] The server uses an emotion engine to analyze that the user is feeling angry.

[1370] The server changes how reviews are displayed based on sentiment:

[1371] The server will highlight the review results in red.

[1372] Example prompt sentence:

[1373] Create a program that reviews text entered by a user and automatically enters any errors into a spreadsheet. Also, add a feature that changes the way the review results are displayed based on the user's emotions. For example, if the user is angry or frustrated, highlight the review results, but if the user is happy or satisfied, display them normally.

[1374] In this way, the system reviews the user's input text, automatically inputs any deficiencies into the spreadsheet software, and further changes the way the review results are displayed depending on the user's emotions.

[1375] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1376] Step 1:

[1377] The user enters text.

[1378] A user uses a terminal to enter text into an input form in the system, for example, "This is a simple text." The entered text is sent to the server.

[1379] Step 2:

[1380] The server receives the text.

[1381] The server receives the text entered by the user. The received text is passed to the generative AI model. The input is the user's text, and the output is the input data for the generative AI model.

[1382] Step 3:

[1383] The server uses a generative AI model to proofread the text.

[1384] The server uses a generative AI model (e.g., GPT-4) to proofread the received text. The generative AI model detects grammatical errors, spelling mistakes, and unnatural expressions. The input is the received text, and the output is the results of the flaw detection. For example, the word "smple" may be corrected to "simple."

[1385] Step 4:

[1386] The server detects the flaws and generates suggested fixes.

[1387] The server identifies the location, content, and proposed correction of the defect based on the output of the generative AI model. The input is the output of the generative AI model, and the output is information on the location, content, and proposed correction of the defect. For example, the location of "smple" is identified as the second character, the content is "smple," and the proposed correction is identified as "simple."

[1388] Step 5:

[1389] The server enters the incomplete information into Excel.

[1390] The server automatically inputs information about the defects into a spreadsheet (e.g., Microsoft Excel). The input is the location, content, and proposed correction information for the defects, and the output is the information recorded in the Excel file. Specifically, "Location: 2," "Content: simple," and "Proposed correction: simple" are entered in each column of the Excel file.

[1391] Step 6:

[1392] The server analyzes the user's emotions.

[1393] The server analyzes the user's emotions using an emotion engine. The input is the user's input and behavioral data, and the output is the user's emotional state. The emotion engine estimates emotions from keyboard typing speed, mouse movement, etc.

[1394] Step 7:

[1395] The server changes how the review results are displayed based on the sentiment.

[1396] The server changes the display of the review results based on the analyzed user's emotions. The input is the user's emotional state, and the output is the changed display. For example, if the user is recognized as feeling angry or frustrated, the review results are highlighted in red. Conversely, if the user is recognized as feeling happy or satisfied, the review results are displayed normally.

[1397] (Application example 2)

[1398] Next, a description will be given of Application Example 2 of Form 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."

[1399] In conventional proofreading systems, inputting results when there are deficiencies is done manually, which is inefficient and prone to human error. Furthermore, the system does not consider how to display feedback according to the user's emotions, which results in a poor user experience.

[1400] 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.

[1401] In this invention, the server includes a means for accepting proofreading work that would otherwise be performed by humans, a means for extracting erroneous words using the syntactic analysis function of generative AI, a means for automatically inputting the results into Excel when erroneous words are found, and a means for changing the display method of the proofreading results based on the user's emotions using an emotion engine, thereby making it possible to improve the efficiency of proofreading work and the user experience.

[1402] A "means for accepting human proofreading" is a system or function that accepts and manages the processes and methods for human proofreading.

[1403] "Means for extracting defective words using the syntactic analysis function of generative AI" refers to a system or function that uses the syntactic analysis function of generative AI to automatically find words in a sentence that contain defects or errors.

[1404] "Means for automatically entering results into Excel when defects are found" refers to a system or function that automatically enters information about detected defects or errors into an Excel file without manual operation.

[1405] "Means for changing the display method of proofreading results based on the user's emotions using an emotion engine" refers to a system or function that uses an emotion engine to recognize the user's emotions and dynamically changes the display method of proofreading results in accordance with those emotions.

[1406] A system for carrying out this invention has the following configuration: A server includes means for accepting proofreading work that would otherwise be performed by humans, means for extracting defective words using the syntactic analysis function of generative AI, means for automatically inputting the results into Excel when defects are found, and means for changing the display method of the proofreading results based on the user's emotions using an emotion engine.

[1407] First, the server accepts the human proofreading task. This is to receive the text or document provided by the user and begin the proofreading process. Next, the generative AI's syntactic analysis function is used to extract incomplete words from the received text. This generative AI uses natural language processing techniques to analyze the text and identify words and phrases that contain errors or incompleteness.

[1408] If any errors are detected, the server automatically enters the results into an Excel file. This process is intended to eliminate manual input work and improve efficiency. Specifically, information such as the location, content, and suggested corrections of the detected errors is automatically entered into each column of the Excel file.

[1409] Furthermore, an emotion engine is used to recognize user emotions and change the way the review results are displayed based on that emotion. For example, if the user is recognized as feeling angry or frustrated, the review results will be highlighted, and if the user is recognized as feeling happy or satisfied, the review results will be displayed normally. This emotion engine is used to analyze user feedback and reviews to identify emotions.

[1410] The following hardware and software are used to implement this system. The hardware requires a PC or server. The software includes "openpyxl" for Excel file manipulation, "emotion_recognition" for emotion recognition, and "feedback_analysis" for feedback analysis.

[1411] For example, if a customer provides feedback such as "The quality of the product is poor," this feedback is entered into Excel and the emotion is recognized as "dissatisfied" and is therefore highlighted.

[1412] Example prompts to input to a generative AI model:

[1413] Analyze customer feedback, identify sentiment, and enter the results into Excel. The feedback is, "The product quality is poor."

[1414] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1415] Step 1:

[1416] The server receives text to be reviewed from the user, which may be feedback or reviews provided by the user. The input is the text provided by the user, and the output is the text data stored on the server.

[1417] Step 2:

[1418] The server uses the generative AI's syntactic analysis function to extract incomplete words from the received text. The input is the text data saved in step 1, and the output is a list of incomplete words. Specifically, the generative AI analyzes the text and identifies words and phrases that contain errors or incompleteness.

[1419] Step 3:

[1420] The server automatically inputs the extracted information about the defective words into an Excel file. The input is the list of defective words obtained in step 2, and the output is an updated Excel file. Specifically, information such as the location, content, and suggested corrections of the defective words is entered into each column of the Excel file.

[1421] Step 4:

[1422] The server uses an emotion engine to recognize the user's emotion. The input is the text data saved in step 1, and the output is the user's emotion information. Specifically, the emotion engine analyzes the text and identifies the user's emotion.

[1423] Step 5:

[1424] The server changes the display method of the review results based on the recognized user emotion. The input is the user emotion information obtained in step 4, and the output is the review results with the display method changed. Specifically, if the user is feeling angry or dissatisfied, the review results are highlighted, and if the user is feeling happy or satisfied, they are displayed normally.

[1425] Step 6:

[1426] The server saves the updated Excel file and notifies the user. The input is the Excel file updated in step 3, and the output is a notification to the user. Specifically, it saves the Excel file and notifies the user that the results have been updated.

[1427] Example 3

[1428] Next, a description will be given of Example 3 of Form Example 3. 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."

[1429] Conventional proofreading work is done manually by humans, which takes time and effort, and has the problem of being inefficient. In addition, it is difficult to improve user satisfaction because it is not possible to respond to emotions. Furthermore, there was a lack of means to automate the extraction of defective words and the input of the results, so the automation of proofreading work has not progressed.

[1430] The identification process by the identification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a means for accepting proofreading work to be performed by humans, a means for extracting erroneous words using the syntactic analysis function of generative AI, a means for automatically inputting the results into spreadsheet software when an error is found, a means for recognizing emotions from the user's input text, and a means for displaying the proofreading results in a color-coded format based on the recognized emotions. This makes it possible to streamline and automate proofreading work and to respond appropriately to the user's emotions.

[1431] "Proofreading" is the task of checking and correcting errors and deficiencies in sentences and documents.

[1432] "Generative AI" is a type of artificial intelligence, a model that has the ability to generate natural language in response to specific input.

[1433] The "syntax analysis function" is a function that analyzes the structure of a sentence and identifies grammatical elements.

[1434] "Incorrect words" are words or phrases that refer to errors or inappropriate expressions contained in a sentence.

[1435] "Spreadsheet software" is software for organizing, calculating, and analyzing data, and generally handles data in spreadsheet format.

[1436] "Emotion recognition" is a technology that analyzes a user's emotions from input text and identifies specific emotions.

[1437] "Robotic process automation" is a technology that uses software robots to automate business processes.

[1438] "Color coding" is a method of displaying text or data in different colors based on certain conditions.

[1439] MODE FOR CARRYING OUT THE INVENTION

[1440] This invention is a system that uses generative AI to automate proofreading tasks and respond to user emotions. A specific embodiment of this system is described below.

[1441] Hardware and software used

[1442] Server: Hosts the generative AI model and processes data. Specifically, a server equipped with a high-performance GPU is used.

[1443] Terminal: Provides an interface for users to access the system. Specifically, a PC running Windows or MacOS is used.

[1444] Software: Using natural language processing models as generative AI models and automation software as RPA tools.

[1445] Program processing

[1446] 1. User input: The user enters the text to be reviewed into the terminal. For example, they may enter "This sentence makes absolutely no sense!"

[1447] 2. Emotion recognition: The server uses the generative AI model to recognize the user's emotion from the input text. The generative AI model is given a prompt sentence, "This sentence makes no sense at all!", and the emotion is recognized as "anger."

[1448] 3. Extraction of incomplete words: The server uses the generative AI model to extract incomplete words from the text. The generative AI model is prompted with the sentence, "Please extract the incomplete words in this sentence," and the model extracts "I don't understand the meaning" as the incomplete word.

[1449] 4. Automatic input of results: The server uses the RPA tool to automatically input the extracted deficiencies as review results. The RPA tool then inputs the results in the specified format.

[1450] 5. Display according to emotion: The server displays the proofreading results in red or green depending on the user's emotion. For example, if the user feels "anger," the proofreading results will be highlighted in red, and "I don't understand" will be displayed in red. On the other hand, if the user feels "joy," the proofreading results will be displayed in green.

[1451] Specific examples

[1452] Example 1: When the user feels angry

[1453] User input: "This sentence makes absolutely no sense!"

[1454] Emotion recognition: The server uses a generative AI model to recognize the emotion as "anger."

[1455] Extraction of incomplete words: The server extracts "I don't understand the meaning" as an incomplete word.

[1456] Automatic input of results: The server uses an RPA tool to highlight "I don't understand the meaning" in red as a proofreading result.

[1457] Example 2: When the user is happy

[1458] User input: "This article is very clear!"

[1459] Emotion recognition: The server uses a generative AI model to recognize the emotion as "joy."

[1460] Extracting defective words: The server does not extract defective words.

[1461] Automatic input of results: The server uses an RPA tool to display the proofreading results in green.

[1462] This system makes it possible to improve the efficiency and automate the proofreading work, and to respond appropriately to the user's feelings. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1463] Step 1: User Input

[1464] Specific operation: The user inputs the text to be reviewed from the terminal. For example, the user opens a browser on their PC and enters "This sentence makes no sense at all!" into a dedicated web form.

[1465] Input: The text entered by the user.

[1466] Output: The entered text is sent to the server.

[1467] Step 2: Emotion Recognition

[1468] Specific operation: The server uses a generative AI model to recognize the user's emotion from the input text. The server receives the text and inputs the prompt "This sentence makes no sense at all!" to the generative AI model. The generative AI model analyzes the text and recognizes the emotion as "anger."

[1469] Input: The text entered by the user.

[1470] Output: A perceived emotion (e.g., "anger").

[1471] Step 3: Extracting missing words

[1472] Specific operation: The server uses the generative AI model to extract missing words from the text. The server again inputs the prompt "Please extract the missing words in this sentence" to the generative AI model. The generative AI model extracts "I don't understand the meaning" as the missing word.

[1473] Input: The text entered by the user.

[1474] Output: Extracted deficiency words (e.g. "I don't understand the meaning").

[1475] Step 4: Autofill Results

[1476] Specific operation: The server uses the RPA tool to automatically input the extracted defective word as the proofreading result. The server starts the RPA tool and inputs the extracted defective word "I don't understand the meaning" as the proofreading result. The RPA tool inputs the result according to the specified format.

[1477] Input: The extracted flawed words.

[1478] Output: The proofreading results are automatically entered into a spreadsheet.

[1479] Step 5: Emotional Display

[1480] Specific operation: The server displays the proofreading results in red or green depending on the user's emotion. The server checks the emotion recognition results, and if the user is feeling "anger," the proofreading results are highlighted in red. For example, "I don't understand the meaning" is displayed in red. On the other hand, if the user is feeling "joy," the proofreading results are displayed in green.

[1481] Input: Recognized emotions and review results.

[1482] Output: Review results color-coded according to sentiment.

[1483] (Application example 3)

[1484] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1485] Conventional proofreading work relies on manual human labor, which is time-consuming and costly. It also faces the challenge of being unable to respond to user emotions, making it difficult to improve user satisfaction. Furthermore, there was a lack of means to automate the extraction of defective words and the input of the results, making it difficult to achieve efficient proofreading work.

[1486] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for accepting proofreading work to be performed by humans, means for extracting deficiencies using the syntactic analysis function of generative AI, means for automatically inputting the results into spreadsheet software when deficiencies are found, means for recognizing user emotions, means for changing the priority of the proofreading work according to the user emotions, and means for displaying the proofreading results in a color according to the user emotions. This makes it possible to improve the efficiency of proofreading work and reduce costs, and also enables appropriate responses according to the user emotions.

[1487] "Proofreading" is the task of checking and correcting errors and omissions in texts and content.

[1488] "Generative AI" is a system that generates text and data using artificial intelligence technology.

[1489] The "syntax analysis function" is a function that analyzes the structure of a sentence and detects grammatical errors and deficiencies.

[1490] "Incorrect words" refer to errors or inappropriate words contained in a sentence.

[1491] "Spreadsheet software" is software for entering, calculating, and analyzing data.

[1492] "Means for recognizing user emotions" refers to technology that analyzes and identifies emotions from text entered by the user.

[1493] The "means for changing the priority of review work" is a function for adjusting the priority of review work based on the user's feelings.

[1494] The "means for displaying the proofreading results in a color corresponding to the user's emotion" is a technique for visually displaying the proofreading results in different colors according to the user's emotion.

[1495] "RPA" stands for Robotic Process Automation, a technology that automates business processes.

[1496] The system for implementing this invention has the following configuration: The server uses a generative AI model to extract incomplete words and recognize the user's emotions. Based on this, it changes the priority of the proofreading work and displays the proofreading results in a color that corresponds to the user's emotions.

[1497] Hardware and software used

[1498] 1. Server: A server with high-performance computing power is required. Specifically, a server equipped with a GPU to run generative AI models is desirable.

[1499] 2. Generative AI models: Use generative AI models such as OpenAI's GPT-3.

[1500] 3. Emotion recognition software: Use natural language processing libraries such as TextBlob to analyze user emotions.

[1501] 4. Spreadsheet software: Use spreadsheet software such as Microsoft Excel to auto-enter the results.

[1502] 5. Programming language: Various processes are implemented using Python.

[1503] Data processing and calculation

[1504] The server receives the text data entered by the user and uses a generative AI model to extract incomplete words. It then uses emotion recognition software such as TextBlob to analyze the user's emotions. If the emotion is "anger," the editing task is given high priority and the result is displayed in red. If the emotion is "joy," the task is processed at normal priority and the result is displayed in green.

[1505] Specific examples

[1506] If a user posts a comment saying "This service is terrible! I can't use it at all!", the server would process it as follows:

[1507] 1. Extracting incomplete words: Using a generative AI model, we extract words like "terrible" and "unusable" as incomplete words.

[1508] 2. Emotion Recognition: Using TextBlob, we recognize the emotion of "anger" from comments.

[1509] 3. Change in priority: Because the emotion is "anger," the proofreading work is given a higher priority.

[1510] 4. View Results: Highlight the review results in red.

[1511] Prompt Sentence Examples

[1512] Below is an example of a prompt sentence to input to a generative AI model.

[1513] Please extract the missing words from the following sentence: This service is really terrible! It's completely unusable!

[1514] In this way, the server can respond appropriately to the user's feelings, thereby improving the efficiency of the proofreading work and user satisfaction.

[1515] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1516] Step 1:

[1517] The server receives text data entered by users, including comments and reviews posted by users. To process this text data, the server first sends a prompt to the generative AI model.

[1518] Step 2:

[1519] The server uses a generative AI model to extract incomplete words. Specifically, it inputs a prompt sentence into the generative AI model and obtains incomplete words as output from the model. For example, in response to a comment such as "This service is really terrible! It's completely unusable!", "terrible" and "unusable" are extracted as incomplete words.

[1520] Step 3:

[1521] The server uses emotion recognition software to analyze the user's emotions based on the extracted inappropriate words. The input includes the user's comment and the extracted inappropriate words. Using a natural language processing library such as TextBlob, the server calculates the emotion polarity of the comment and classifies the emotion as "anger," "joy," or "neutral."

[1522] Step 4:

[1523] The server changes the priority of the review task based on the emotion recognition results. The emotion recognition results are included as input. For example, if the emotion is "anger," the review task is set to a high priority, and if the emotion is "joy," the review task is processed at a normal priority.

[1524] Step 5:

[1525] The server displays the review results in a color that corresponds to the user's emotion. The input includes the emotion recognition result and the review result. For example, if the emotion is "anger," the review result is highlighted in red, and if the emotion is "joy," it is displayed in green.

[1526] Step 6:

[1527] The server automatically inputs the final proofreading results into spreadsheet software. The input includes the proofreading results and their display format. This allows appropriate responses based on the user's feelings, improving the efficiency of proofreading work and user satisfaction.

[1528] 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.

[1529] 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> ) 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.

[1530] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1531] 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.

[1532] [Third embodiment]

[1533] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1534] 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.

[1535] 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).

[1536] 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.

[1537] 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.

[1538] 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).

[1539] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1540] 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.

[1541] 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.

[1542] 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.

[1543] 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.

[1544] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1545] "Example 1"

[1546] One embodiment of the present invention is a system that undertakes proofreading work that is usually done by humans. This system uses the syntactic analysis function of generative AI to extract incomplete words from the text to be proofread. Specifically, it detects typos and grammatical errors within the text.

[1547] "Example 2"

[1548] Furthermore, if any defects are found, the results are automatically entered into Excel. Specifically, the location, content, and suggested corrections of the defective words are automatically entered into each column of Excel. This saves humans the trouble of manually entering the results.

[1549] "Example 3"

[1550] This system also uses RPA to extract erroneous words using generative AI and automatically input the results. Specifically, it automates the entire process from extracting erroneous words to inputting the results, minimizing human intervention. This makes it possible to significantly reduce the man-hours and costs of proofreading work.

[1551] The processing flow of each embodiment will be described below.

[1552] "Example 1"

[1553] Step 1: The system accepts the editing work to be performed by a human. Specifically, the text data to be edited is input into the system.

[1554] Step 2: The system uses the generative AI's syntactic analysis function to extract imperfect words from the input text. Specifically, it detects typos and grammatical errors in the text. Step 3: If imperfect words are detected, the information is saved in a temporary database.

[1555] "Example 2"

[1556] Step 1: Obtain the information of the missing words stored in the temporary database.

[1557] Step 2: Based on the acquired information on defective words, the results are automatically entered into Excel. Specifically, the location, content, and suggested corrections of the defective words are automatically entered into each column of Excel. Step 3: Once the data has been entered into Excel, the results are saved and reported to the user as necessary.

[1558] "Example 3"

[1559] Step 1: The system will use RPA to extract incomplete words using generative AI and automatically input the results. Specifically, a script will be created to automate the entire process from extracting incomplete words to inputting the results.

[1560] Step 2: Run the script you created, which minimizes human intervention and significantly reduces the man-hours and costs of proofreading.

[1561] Step 3: Save the results of the script execution and report them to the user if necessary.

[1562] Example 1

[1563] Next, a description will be given of Example 1 of Form 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."

[1564] Traditional proofreading work is done manually by humans, which takes time and effort, and there is a risk of overlooking typos and grammatical errors. Furthermore, the manual input of proofreading results is cumbersome and inefficient. Furthermore, the lack of automated correction of proofreading results and the adoption of robotic process automation (RPA) has led to a decline in overall work efficiency. To solve these issues, there is a need to automate and streamline proofreading work.

[1565] 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.

[1566] In this invention, the server includes a means for receiving text to be proofread from a user, a means for inputting the received text into the generative AI, and a means for analyzing the analysis results returned from the generative AI and extracting defective words, thereby enabling the automation and efficiency of proofreading work.

[1567] "Proofreading" is the task of checking and correcting typos and grammatical errors in sentences and texts.

[1568] "Generative AI" is an AI technology for natural language processing and syntactic analysis, and has the ability to detect, for example, typos and grammatical errors in text.

[1569] The "syntax analysis function" is a function that analyzes the structure of sentences and text and detects grammatical errors and omissions.

[1570] "Faulty words" are words or phrases that contain typos or grammatical errors present in the text.

[1571] "Spreadsheet software" refers to software for data entry, calculation, analysis, graph creation, etc., such as Microsoft Excel.

[1572] "User" refers to a person or organization that uses the system to perform proofreading work.

[1573] A "server" is a computer system that receives requests from users, analyzes the text using generative artificial intelligence, and returns the results.

[1574] An "interface" is a means, such as a screen or input form, that allows a user to operate a system.

[1575] "Robotic Process Automation (RPA)" is a technology that automates routine business processes, using software robots to replace human work.

[1576] This invention is a system for improving the efficiency of human proofreading work, extracting defective words from text using the syntactic analysis function of generative artificial intelligence and providing the extracted words to the user. A specific embodiment of this system will be described below.

[1577] Server Processing

[1578] The server receives the text to be reviewed from the user. Specifically, it receives the text data via an HTTP request. The received text is then input into a generative AI (e.g., a model with natural language processing technology). An example of this generative AI is OpenAI's GPT-4.

[1579] The generative AI analyzes the input text and detects typos and grammatical errors. The server analyzes the analysis results returned by the generative AI and extracts defective words. The extracted defective words are listed and returned to the user as an HTTP response in JSON format.

[1580] Terminal handling

[1581] The terminal provides an interface for the user to enter text to be reviewed, such as a web form or text area, and the text entered by the user is sent to the server by pressing a submit button.

[1582] The list of defective words returned from the server is received by the terminal, which then displays the received defective words to the user. Specifically, the defective words are displayed in a list format on a web page.

[1583] User operations

[1584] The user inputs the text to be proofread into the terminal. The input text is sent to the server by clicking the send button. The user checks the list of defective words returned by the server and corrects the text.

[1585] Specific examples

[1586] For example, consider the case where a user wants to proofread the text "I went to the library yesterday. I borrowed a book there." The user enters this text into a web form on their device and clicks the submit button. The server inputs the received text into a generative artificial intelligence (e.g., GPT-4), and based on the analysis results, detects that there is a grammatical error in the part "I went to the library." The server lists the incorrect words in "I went to the library" and returns them to the device in JSON format. The device displays these incorrect words to the user, who then corrects it to "I went to the library."

[1587] Prompt Sentence Examples

[1588] Please proofread the following text. Identify any spelling or grammatical errors and list any missing words.

[1589] Text: 'I went to the library yesterday. I borrowed a book there.'

[1590] In this way, the system can efficiently proofread the text entered by the user.

[1591] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1592] Step 1:

[1593] The server receives the text to be reviewed from the user.

[1594] Input: Text data entered by the user into the terminal.

[1595] Specific operation: The server receives text data via an HTTP request.

[1596] Output: Received text data

[1597] Step 2:

[1598] The server inputs the received text into a generative artificial intelligence.

[1599] Input: Received text data

[1600] Specific operation: The server sends the text data as a prompt to a generative artificial intelligence (e.g., a model with natural language processing technology).

[1601] Output: Prompt sent to the generative AI

[1602] Step 3:

[1603] Generative AI analyzes input text and detects typos and grammatical errors.

[1604] Input: A prompt sent to the generative AI

[1605] How it works: Generative AI performs syntactic analysis to detect typos and grammatical errors in text.

[1606] Output: Analysis results (data containing typos and grammatical errors)

[1607] Step 4:

[1608] The server analyzes the analysis results returned from the generative artificial intelligence and extracts defective words.

[1609] Input: Analysis results returned by generative artificial intelligence

[1610] Specific operation: The server analyzes the analysis results and lists the defective words.

[1611] Output: List of missing words

[1612] Step 5:

[1613] The server lists the extracted defective words and returns them to the user.

[1614] Input: List of missing words

[1615] Specific operation: The server returns a list of incomplete words in JSON format as an HTTP response.

[1616] Output: A list of invalid words returned to the user

[1617] Step 6:

[1618] The terminal receives the list of defective words returned from the server.

[1619] Input: A list of invalid words returned by the server

[1620] Specific operation: The terminal receives the JSON data returned as an HTTP response.

[1621] Output: List of received invalid words

[1622] Step 7:

[1623] The terminal displays the received defective words to the user.

[1624] Input: List of received invalid words

[1625] Specific operation: The terminal displays defective words in a list format on a web page.

[1626] Output: A list of the missing words shown to the user

[1627] Step 8:

[1628] The user checks the list of defective words returned from the server and corrects the text.

[1629] Input: A list of invalid words shown to the user

[1630] What happens: The user uses the list to correct spelling and grammatical errors in the text.

[1631] Output: Corrected text

[1632] (Application example 1)

[1633] Next, a description will be given of Application Example 1 of Form 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."

[1634] Traditional proofreading work relies on manual human labor, which is time-consuming and labor-intensive, and there is a risk of overlooking typos and grammatical errors. Furthermore, content distribution services require fast and accurate proofreading to maintain the quality of posted articles and comments, but this is difficult to do manually. Furthermore, there is a lack of functionality to automatically input proofreading results into a database, making efficient management difficult.

[1635] 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.

[1636] In this invention, the server includes a means for accepting proofreading work that would otherwise be performed by humans, a means for extracting defective words using the syntactic analysis function of generative AI, a means for automatically inputting the results into a database when defects are found, a means for automatically proofreading articles and comments posted to a content distribution service, a means for detecting typos and grammatical errors and presenting suggested corrections, and a means for the user to confirm and approve the suggested corrections, thereby enabling the efficiency and accuracy of proofreading work to be improved.

[1637] "Proofreading" is the task of detecting and correcting typos and grammatical errors in sentences or text.

[1638] "Generative AI" is a type of artificial intelligence that uses natural language processing to generate and analyze text.

[1639] The "syntax analysis function" is a function that analyzes the structure of a sentence and detects grammatical flaws and errors.

[1640] "Faulty words" are words or phrases that indicate typos or grammatical errors in a sentence.

[1641] A "database" is a system that systematically organizes and stores information so that it can be searched and used as needed.

[1642] A "content distribution service" is a service that provides users with content such as articles and comments via the Internet.

[1643] An "article" is a piece of writing or text on a particular topic.

[1644] A "comment" is an opinion or comment posted by a user on an article or post.

[1645] "Suggested corrections" are suggested corrections for detected typos and grammatical errors.

[1646] A "user" is someone who uses a system or service.

[1647] A system for carrying out this invention is configured as follows: The server includes means for accepting proofreading work that would otherwise be performed by humans, means for extracting defective words using the syntactic analysis function of generative AI, means for automatically inputting the results into a database when defects are found, means for automatically proofreading articles and comments posted to a content distribution service, means for detecting typos and grammatical errors and presenting suggested corrections, and means for the user to confirm and approve the suggested corrections.

[1648] Hardware and software used

[1649] Hardware: Servers, user devices (smartphones, PCs, etc.)

[1650] Software: Python, OpenAI API, database management system (e.g., MySQL)

[1651] Data processing and calculation

[1652] 1. User terminal: The user enters articles and comments and requests them for review.

[1653] 2. Server: Receives the input text and generates a prompt to send to a generative AI model (e.g., OpenAI's GPT-3).

[1654] 3. Generative AI model: Detects typographical and grammatical errors in the text based on the prompt and generates suggested corrections.

[1655] 4. Server: Receives the generated revision suggestions and presents them to the user.

[1656] 5. User terminal: The user checks the proposed amendments and approves or amends them.

[1657] 6. Server: Automatically enters the final proofreading results into the database.

[1658] Specific examples

[1659] When a user wants to post a blog post, they enter the text of the post into the application, which automatically reviews it, corrects any typos and grammatical errors, and presents the corrected text to the user. The user can then review the suggested corrections and approve them if necessary.

[1660] Prompt Sentence Examples

[1661] Please identify any typos or grammatical errors in the text below and suggest corrections.

[1662] This is a test text and may contain typos and grammatical errors.

[1663] Proposed amendment:

[1664] In this way, it is possible to improve the efficiency and accuracy of proofreading work.

[1665] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1666] Step 1:

[1667] A user uses a terminal to input text for an article or comment and submit it for review. The input text is sent to the server. The input data is the text entered by the user, and the output data is the text sent to the server.

[1668] Step 2:

[1669] The server generates a prompt sentence to send to the generative AI model from the received text. The input data is the text received from the user, and the output data is the generated prompt sentence. Specifically, the server converts the text into the following format:

[1670] Please identify any typos or grammatical errors in the text below and suggest corrections.

[1671] [user text]

[1672] Proposed amendment:

[1673] Step 3:

[1674] The server sends the generated prompt sentence to a generative AI model (e.g., OpenAI's GPT-3). The input data is the generated prompt sentence, and the output data is the correction suggestions returned by the generative AI model. Specifically, the OpenAI API is used to send the prompt sentence and receive the correction suggestions.

[1675] Step 4:

[1676] The server presents the proposed revisions received from the generative AI model to the user. The input data is the proposed revisions received from the generative AI model, and the output data is the proposed revisions presented to the user. Specifically, the proposed revisions are displayed on the user's device.

[1677] Step 5:

[1678] The user checks the proposed revisions and either approves or corrects them. The input data are the revisions proposed to the user, and the output data is the final text that the user approves or corrects. Specifically, the user checks the revisions and makes corrections if necessary.

[1679] Step 6:

[1680] The server automatically inputs the final text approved or corrected by the user into a database. The input data is the final text approved or corrected by the user, and the output data is the final text stored in the database. Specifically, a database management system (e.g., MySQL) is used to store the final text.

[1681] Example 2

[1682] Next, a description will be given of Example 2 of Form 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."

[1683] Traditional proofreading work is done manually by humans, which takes time and effort, and is prone to errors. The process of detecting and correcting errors is also cumbersome, making it difficult to carry out the work efficiently. Furthermore, the time and effort required to manually input information about detected errors into spreadsheet software is a significant burden.

[1684] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for accepting proofreading work to be performed by humans, a means for extracting deficiencies using the syntactic analysis function of the generative AI model, a means for automatically inputting the results into spreadsheet software when deficiencies are found, a means for sending a prompt to the generative AI model, and a means for receiving the analysis results returned from the generative AI model. This makes it possible to improve the efficiency and accuracy of proofreading work.

[1685] "Proofreading" is the task of detecting and correcting typographical and grammatical errors in a document.

[1686] A "generative AI model" is a model that uses artificial intelligence technology to analyze text data and perform specific tasks.

[1687] The "syntax analysis function" is a function that analyzes the grammatical structure of text data and understands its meaning and relationships.

[1688] "Faulty words" are words or phrases that refer to typographical or grammatical errors present in a document.

[1689] "Spreadsheet software" is software for entering, calculating, analyzing, and visualizing data, and generally manages data in spreadsheet format.

[1690] A "prompt" is an instruction entered into a generative AI model to make it perform a specific task.

[1691] "Business automation technology" is a technology for automating business processes that were previously performed manually.

[1692] The present invention is a system for making proofreading more efficient and improving accuracy. A specific embodiment of this system will be described below.

[1693] First, the user inputs text data through the system interface, which can be a web form or a dedicated application, and the text data entered by the user is sent to the server.

[1694] The server creates a prompt to send to the generative AI model from the received text data. For example, it generates the following prompt:

[1695] Please analyze the following text and find any defects. Please provide the location, content, and suggested fixes for any defects found.

[1696] Text: "This article contains some deficiencies. For example, it contains typos and omissions."

[1697] Next, the server sends the prompt to a generative AI model. The generative AI model is, for example, a model that uses general artificial intelligence technology and analyzes text data. Specifically, the generative AI model performs syntactic analysis of the text data and detects incorrect words.

[1698] Once the generative AI model has completed its analysis, the server receives the analysis results, which include the location, content, and suggested corrections for the missing words. For example, the following results may be returned:

[1699] Incomplete word position: 13

[1700] Deficiency word content: "Several deficiencies"

[1701] Proposed fix: "Some issues"

[1702] The server automatically inputs the missing information into a spreadsheet based on the analysis results it receives. Specifically, it uses Python's openpyxl library to input the data into an Excel file as follows:

[1703] Enter the position of the missing word in column A.

[1704] Enter the contents of the missing words in column B.

[1705] Enter the proposed revisions in column C.

[1706] Finally, the user can download or view the generated Excel file, allowing them to review the information for any deficiencies and make any necessary corrections.

[1707] This system will improve the efficiency and accuracy of proofreading work, eliminating the need for users to manually input results and enabling rapid corrections based on the analysis results of the generative AI model.

[1708] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1709] Step 1:

[1710] The user inputs text data.

[1711] A user inputs text data through the system's interface, for example, using a web form or a dedicated application, and the input text data is sent to the server.

[1712] Input: Text data entered by the user

[1713] Output: Text data sent to the server

[1714] Step 2:

[1715] The server receives the input text data.

[1716] The server receives text data sent by the user, which is sent via a communication protocol such as an HTTP request or WebSocket.

[1717] Input: Text data sent by the user

[1718] Output: Text data stored on the server

[1719] Step 3:

[1720] The server sends a prompt to the generative AI model.

[1721] The server creates a prompt to send to the generative AI model from the received text data. For example, it generates the following prompt:

[1722] Please analyze the text below and find any defects. Please provide the location, content, and suggested fixes for any defects found.

[1723] Text: "This article contains several deficiencies. For example, it contains typos and omissions."

[1724] Input: Text data stored on the server

[1725] Output: The prompt sent to the generative AI model

[1726] Step 4:

[1727] A generative AI model analyzes text data and detects defects.

[1728] The generative AI model receives the prompt sent from the server and analyzes the text data. As a result of the analysis, it identifies the location, content, and suggested corrections of the defects.

[1729] Input: A prompt sent to the generative AI model

[1730] Output: Location, content, and proposed corrections for defects

[1731] Step 5:

[1732] The server obtains the location, content, and proposed corrections of the defect.

[1733] The server receives the analysis results returned by the generative AI model, including the location, content, and suggested fixes for the defects.

[1734] Input: Analysis results returned from the generative AI model

[1735] Output: Location, content, and proposed corrections of defects stored on the server

[1736] Step 6:

[1737] The server enters the incorrect information into the Excel file.

[1738] The server uses Python's openpyxl library to input the missing information into the Excel file. Specifically, the data is input as follows:

[1739] Enter the position of the missing word in column A.

[1740] Enter the contents of the missing words in column B.

[1741] Enter the proposed revisions in column C.

[1742] Input: Location, content, and proposed corrections of defects stored on the server

[1743] Output: Excel file with incomplete information entered

[1744] Step 7:

[1745] The user checks the Excel file.

[1746] The user downloads or views the Excel file generated by the server, allowing the user to check the information about any deficiencies and make any necessary corrections.

[1747] Input: Excel file with incomplete information

[1748] Output: Information on defects confirmed by the user

[1749] (Application example 2)

[1750] Next, a description will be given of Application Example 2 of Form 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."

[1751] In traditional proofreading and inventory management, detecting defects and inputting the results is often done manually, which takes time and effort and increases the risk of human error. Furthermore, in inventory management at logistics centers, scanning for defects and managing the results is not done efficiently, making it difficult to respond quickly.

[1752] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for accepting proofreading work that would be performed by humans, a means for extracting deficiencies using the syntactic analysis function of generative AI, a means for automatically inputting the results into Excel when deficiencies are found, and a means for scanning deficiency data with a smartphone and automatically inputting the results into Excel in an inventory management system at a logistics center. This automates the detection of deficiencies in proofreading work and inventory management and the input of the results, saving time and effort and reducing the risk of human error.

[1753] "Proofreading" is the work of checking the contents of documents and data and correcting errors and omissions.

[1754] "Generative AI" is a type of artificial intelligence that has the ability to generate and analyze data for specific tasks.

[1755] A "syntax analysis function" is a function that analyzes the structure of documents and data and understands their meaning and grammatical relationships.

[1756] "Faulty words" are words or phrases that indicate errors or inappropriate parts in a document or data.

[1757] "Excel" is a type of spreadsheet software and a tool for entering, calculating, analyzing, and visualizing data.

[1758] A "logistics center" is a facility that stores, manages, and ships goods.

[1759] An "inventory management system" is a system for monitoring and managing product inventory.

[1760] A "smartphone" is a type of mobile phone and a multi-function device that can connect to the Internet and use applications.

[1761] "Scanning" is the process of electronically reading data or images.

[1762] "RPA" stands for Robotic Process Automation, a technology for automating business processes.

[1763] To implement the present invention, the following system configuration and program are required.

[1764] System Configuration

[1765] 1. Server: Hosts the generative AI model and provides parsing functionality.

[1766] 2. Terminal: Using a smartphone, it scans out defective data as part of the inventory management system at the distribution center.

[1767] 3. Software: Python and the openpyxl library are used for automated data entry and management.

[1768] Program processing

[1769] The server uses a generative AI model to extract deficiency words in proofreading and inventory management. The terminal (smartphone) scans inventory at the logistics center and collects deficiency data. The collected deficiency data is sent to the server and analyzed by the generative AI model. As a result, the location, content, and correction suggestions of the deficiency words are extracted and automatically entered into an Excel file.

[1770] Hardware and software used

[1771] Hardware: Smartphone (for scanning and entering missing data)

[1772] Software: Python, openpyxl library (for working with Excel files)

[1773] Data processing and calculation

[1774] 1. Deficiency data collection: Using smartphones, inventory is scanned at distribution centers to collect defect data.

[1775] 2. Data transmission: The collected deficiency data is transmitted to the server.

[1776] 3. Syntax analysis: A generative AI model on the server analyzes the defective data and extracts the location, content, and suggested corrections for the defective words.

[1777] 4. Automatic data entry: The extracted information of missing words is automatically entered into an Excel file using Python and the openpyxl library.

[1778] Specific examples

[1779] For example, suppose the following defects are detected when inventory is scanned at a logistics center:

[1780] Location: A1, Content: Quantity shortage, Correction: Additional order

[1781] Location: B2, Content: Label error, Proposal: Label correction

[1782] By automatically entering this deficiency data into an Excel file, administrators can quickly take corrective action.

[1783] Prompt Sentence Examples

[1784] "Generate a Python program that automatically inputs deficiency data into Excel for inventory management at a distribution center. The deficiency data will include the location, content, and correction suggestions."

[1785] The above is a specific embodiment for carrying out the present invention.

[1786] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1787] Step 1:

[1788] The terminal (smartphone) scans inventory at the distribution center. The input is identification information such as the inventory barcode or QR code, and the output is incomplete data (e.g., insufficient quantity, incorrect label). Specifically, the smartphone camera is used to read the inventory barcode and send that information to the application.

[1789] Step 2:

[1790] The terminal (smartphone) sends the collected deficiency data to the server. The input is the deficiency data collected in step 1, and the output is the deficiency data sent to the server. Specifically, the smartphone application sends the deficiency data to the server's API endpoint.

[1791] Step 3:

[1792] The server uses a generative AI model to analyze the deficiency data. The input is the deficiency data sent from the device, and the output is the location, content, and correction suggestions of the deficiency words. Specifically, the server receives the deficiency data, inputs it into the generative AI model, and analyzes it.

[1793] Step 4:

[1794] The server extracts the location, content, and correction suggestions of the defective words based on the analysis results. The input is the analysis results from the generative AI model, and the output is the location, content, and correction suggestions of the extracted defective words. Specifically, the server analyzes the analysis results and extracts the necessary information.

[1795] Step 5:

[1796] The server automatically inputs the extracted deficiency word information into an Excel file. The input is the location, content, and suggested corrections of the extracted deficiency words, and the output is the updated Excel file. Specifically, the server uses Python and the openpyxl library to open the Excel file, input information into the appropriate cells, and save the file.

[1797] Step 6:

[1798] The user checks the updated Excel file and takes the necessary action. The input is the updated Excel file, and the output is the implementation of the countermeasures. Specifically, the user opens the Excel file, checks the location, content, and suggested corrections of the defective words, and takes the necessary action.

[1799] Example 3

[1800] Next, a third embodiment of the third embodiment will be described. 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."

[1801] Conventional proofreading work is done manually by humans, which takes time and effort, resulting in high costs. Manual proofreading is also prone to errors, which can lead to inconsistent quality. Furthermore, even in systems that use generative AI models to extract defective words and automatically input the results, each process is executed individually, which reduces overall efficiency.

[1802] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1803] In this invention, the server includes a means for a user to input text data, a means for a terminal to send the input data to the server, a means for the server to send a prompt sentence to the generative AI model, a means for the generative AI model to extract defective words, a means for the server to save the extracted results in a database, a means for the server to automatically input the results using an automation tool, and a means for the terminal to display the results to the user, thereby making it possible to improve the efficiency of proofreading work and reduce costs.

[1804] "Human proofreading" refers to the process of checking the content of sentences or texts and correcting errors or inappropriate expressions.

[1805] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to generate and analyze text.

[1806] "Syntax analysis function" refers to the function for analyzing the grammar and structure of text and understanding its meaning.

[1807] "Incomplete words" refer to errors or inappropriate expressions contained in a sentence.

[1808] "Spreadsheet software" is software for organizing and calculating data, and generally handles data in spreadsheet format.

[1809] "User" refers to a person who uses the system.

[1810] "Terminal" refers to a device through which a user accesses the system.

[1811] A "server" is a computer that is the central part of a system and processes and stores data.

[1812] A "prompt sentence" refers to an input sentence used to give instructions to a generative AI model.

[1813] A "database" refers to a system for efficiently storing and managing data.

[1814] "Automation tools" refer to software or systems that perform specific tasks automatically.

[1815] "Means for automatically inputting results" refers to a method or system for automatically inputting extracted defective words and correction results.

[1816] "Means for displaying results" refers to a method or system for displaying the processing results to the user.

[1817] This invention is a system that uses a generative AI model to extract defective words and automatically input the results. Specific embodiments of this system will be described below.

[1818] First, a user inputs text data using a terminal. The terminal then sends the text data to a server via a web browser or a dedicated application. For example, if a user inputs the text "This sentence is very good," the terminal sends this data to the server using an HTTP POST request.

[1819] The server generates a prompt to send the received text data to the generative AI model. Specifically, it uses the Python requests library to send the following prompt to the generative AI model:

[1820] "Please extract the missing words from the following text: This sentence is very good."

[1821] The generative AI model analyzes the prompt received from the server and extracts incomplete words. For example, it determines that the expression "very good" is ambiguous and extracts it as an incomplete word.

[1822] The server stores the missing words received from the generative AI model in a database, specifically by running SQL queries to store the extracted missing words using a database management system such as MySQL.

[1823] Next, the server uses an automation tool (e.g., UiPath) to automatically correct the extracted defective words, for example, changing "very good" to "excellent." This correction result is saved back in the database.

[1824] Finally, the terminal displays the correction results received from the server to the user. Specifically, the corrected text "This sentence is very good" is displayed on the web browser.

[1825] This system enables the efficiency of proofreading work and cost reduction. In addition, the use of a generative AI model enables highly accurate extraction and correction of defective words. The flow of the identification process in Example 3 will be explained using Figure 15.

[1826] Step 1:

[1827] The user inputs text data.

[1828] A user inputs text data using a terminal interface. For example, the user inputs "This article is very good" into a form on a web browser. The input data is the text data entered by the user.

[1829] Step 2:

[1830] The terminal sends the input data to the server.

[1831] The terminal sends the text data entered by the user to the server. Specifically, it sends the data using an HTTP POST request. The input is the text data entered by the user, and the output is the text data sent to the server.

[1832] Step 3:

[1833] The server sends a prompt to the generative AI model.

[1834] The server generates a prompt sentence to send to the generative AI model from the received text data. For example, it creates a prompt sentence such as "Please extract the missing words from the following text: This sentence is very good." The input is the text data sent to the server, and the output is the prompt sentence to be sent to the generative AI model.

[1835] Step 4:

[1836] The generative AI model extracts incomplete words.

[1837] The generative AI model analyzes the prompt received from the server and extracts incomplete words. For example, it determines that the expression "very good" is ambiguous and extracts it as an incomplete word. The input is the prompt sent to the generative AI model, and the output is the extracted incomplete word.

[1838] Step 5:

[1839] The server stores the extracted results in a database.

[1840] The server stores the defective words received from the generative AI model in a database. Specifically, it inserts them into the database using an SQL query. The input is the extracted defective words, and the output is the defective words stored in the database.

[1841] Step 6:

[1842] The server uses an automation tool to populate the results.

[1843] The server uses an RPA tool (e.g., UiPath) to automatically correct the extracted defective words. For example, it changes "very good" to "excellent." The input is the defective words stored in the database, and the output is the corrected text data.

[1844] Step 7:

[1845] The terminal displays the results to the user.

[1846] The terminal displays the correction results received from the server to the user. Specifically, the corrected text "This sentence is very good" is displayed on the web browser. The input is the corrected text data, and the output is the correction results displayed to the user.

[1847] (Application example 3)

[1848] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1849] Traditional proofreading work was done manually by humans, which required a great deal of time and effort and was inefficient. Also, while it was possible to extract defective words using generative AI, there was no system that could automatically input the results and suggest corrections. Furthermore, there was a lack of functionality to analyze the text entered by the user in real time and make correction suggestions, which made it inconvenient for users.

[1850] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes a means for accepting proofreading work that would be performed by humans, a means for extracting deficiencies using the syntactic analysis function of generative AI, a means for automatically inputting the results into a database when deficiencies are found, a means for analyzing text entered by a user in real time, extracting deficiencies and suggesting corrections, and a means for presenting the suggested corrections to the user and applying the corrections with one tap. This makes it possible to improve the efficiency of proofreading work, reduce costs, and improve user convenience.

[1851] "Proofreading" is the task of checking the content of sentences and documents and correcting errors and omissions.

[1852] "Generative AI" is a type of artificial intelligence, a model that has the ability to generate appropriate outputs for specific inputs.

[1853] The "syntax analysis function" is a function that analyzes the structure of a sentence and identifies grammatical elements.

[1854] "Incorrect words" are words or phrases that refer to errors or inappropriate expressions contained in a sentence.

[1855] A "database" is a collection of data that is systematically organized so that information can be searched and managed efficiently.

[1856] "Real-time analysis" is a processing method in which analysis is performed immediately as data is entered.

[1857] A "correction suggestion" is a suggestion that presents an appropriate correction method for a detected defective word.

[1858] "One Tap" is a feature that allows users to complete an operation by simply tapping the device screen once.

[1859] "RPA" stands for Robotic Process Automation, a technology that automates routine business processes.

[1860] The system for implementing this invention consists of three main elements: a server, a terminal, and a user.

[1861] server

[1862] The server uses a generative AI model to extract incomplete words and generate correction suggestions. Specifically, the server uses the following software and hardware:

[1863] Software: OpenAI API, Python

[1864] Hardware: A server machine with a powerful processor and sufficient memory

[1865] The server receives the text entered by the user and analyzes it in real time using a generative AI model. As a result of the analysis, it generates incorrect words and suggested corrections, and automatically enters the results into a database.

[1866] Terminal

[1867] A terminal is a device on which a user enters text and reviews and applies suggested corrections. This typically applies to mobile devices such as smartphones and tablets.

[1868] The device receives the correction suggestions sent from the server and presents them to the user, who can apply the correction suggestions with a single tap on the device screen.

[1869] User

[1870] Users are individuals or companies who perform proofreading tasks. They use their terminals to enter text and review and apply correction suggestions from the server.

[1871] Specific examples

[1872] For example, suppose a user is creating a blog post. The user enters "This is a test text. It contains some incorrect words." The terminal sends this text to the server.

[1873] The server analyzes this text using a generative AI model to extract the missing words and generate correction suggestions, such as "misspelled word: misspelled word, suggested correction: misspelled word." The server then automatically enters these suggestions into a database and sends them to the device.

[1874] The device presents the suggested corrections to the user, who can then apply the corrections and view the corrected text with a single tap on the device screen.

[1875] Prompt Sentence Examples

[1876] Please extract the missing words from the text below and suggest corrections.

[1877] Text: This is the text of the test. It contains some missing words.

[1878] Incomplete words and suggested corrections:

[1879] In this way, the present invention can improve the efficiency of proofreading work, reduce costs, and improve user convenience.

[1880] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1881] Step 1:

[1882] The user inputs text into the terminal. For example, the user inputs "This is a test text. It contains some incorrect words." The input text is sent from the terminal to the server.

[1883] Step 2:

[1884] The server receives the text sent from the device. It inputs the received text into a generative AI model and performs syntactic analysis. Specifically, it uses the OpenAI API to analyze the text and extracts erroneous words. The input is the user's text, and the output is the erroneous words and suggested corrections.

[1885] Step 3:

[1886] The server analyzes the defective words and suggested corrections obtained from the generative AI model. The analysis results are automatically entered into a database. Specifically, the extracted defective words and suggested corrections are saved in the database. The input is the defective words and suggested corrections, and the output is the results saved in the database.

[1887] Step 4:

[1888] The server transmits the revision suggestions stored in the database to the terminal. Specifically, it transmits data to the terminal to present the revision suggestions to the user. The input is the revision suggestions from the database, and the output is the data sent to the terminal.

[1889] Step 5:

[1890] The terminal receives the correction suggestions sent from the server. It displays the received correction suggestions to the user. Specifically, it displays the defective words and their correction suggestions on the screen so that the user can confirm them. The input is the correction suggestions from the server, and the output is what is displayed to the user.

[1891] Step 6:

[1892] The user applies the suggested corrections with a single tap on the device screen. Specifically, the user confirms the suggested corrections and taps the Apply button to reflect the corrections in the text. The input is the confirmation and application of the suggested corrections, and the output is the corrected text.

[1893] Step 7:

[1894] The terminal displays the corrected text to the user. Specifically, it displays the text with the corrections applied on the screen for the user to review. The input is the corrected text, and the output is what is displayed to the user.

[1895] In this way, users can efficiently review and correct text.

[1896] 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.

[1897] "Example 1"

[1898] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system recognizes a user's emotions and adjusts the priority of review tasks based on those emotions. Specifically, if the system recognizes that a user is feeling angry or frustrated, it prioritizes the user's review tasks. On the other hand, if the system recognizes that a user is feeling happy or satisfied, it processes the user's review tasks as usual.

[1899] "Example 2"

[1900] Furthermore, the emotion engine changes the way the review results are displayed based on the user's emotions. Specifically, if the engine recognizes that the user is feeling angry or frustrated, it highlights the review results. If the engine recognizes that the user is feeling happy or satisfied, it displays the review results normally.

[1901] "Example 3"

[1902] For example, if the system recognizes that a user is feeling angry, it will prioritize the user's proofreading and highlight the proofreading results in red. On the other hand, if the system recognizes that a user is feeling happy, it will process the user's proofreading as usual and highlight the proofreading results in green. This allows the system to respond appropriately according to the user's emotions.

[1903] The processing flow of each embodiment will be described below.

[1904] "Example 1"

[1905] Step 1: The system recognizes the user's emotions using an emotion engine.

[1906] Step 2: If the emotion engine recognizes that the user's emotion is anger or dissatisfaction, it instructs the system to prioritize the user's review work.

[1907] Step 3: The system adjusts the priority of the review tasks according to the instructions from the emotion engine and processes them as a priority.

[1908] "Example 2"

[1909] Step 1: The system recognizes the user's emotions using an emotion engine.

[1910] Step 2: If the emotion engine recognizes that the user's emotion is anger or frustration, it instructs the system to highlight the revision.

[1911] Step 3: The system follows instructions from the emotion engine to change the way the review results are displayed and highlight them.

[1912] "Example 3"

[1913] Step 1: The system recognizes the user's emotions using an emotion engine.

[1914] Step 2: If the emotion engine recognizes that the user's emotion is anger, it instructs the system to prioritize the user's review and highlight the review results in red.

[1915] Step 3: The system adjusts the priority of the review tasks according to the instructions from the emotion engine, prioritizes the tasks, and highlights the review results in red.

[1916] Step 4: If the emotion engine recognizes that the user's emotion is joy, it instructs the system to process the user's review as usual and display the review results in green.

[1917] Step 5: The system follows the instructions from the emotion engine and processes the review as normal, displaying the review results in green.

[1918] Example 1

[1919] Next, a description will be given of Example 1 of Form 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."

[1920] Traditional proofreading work is done manually by humans, which is time-consuming, labor-intensive, and inefficient. Furthermore, because the process is uniform without considering the user's feelings, there is a risk of user satisfaction decreasing. Furthermore, there is a lack of a system that automatically detects and corrects typos and grammatical errors, which can lead to a decrease in the accuracy of proofreading. To solve these issues, an efficient and user-friendly proofreading system is needed.

[1921] 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.

[1922] In this invention, the server includes a means for accepting proofreading work to be performed by humans, a means for extracting deficiencies using the syntactic analysis function of generative AI, a means for automatically inputting the results into spreadsheet software when deficiencies are found, a means for recognizing user emotions, and a means for adjusting the priority of proofreading work based on the user emotions. This makes it possible to perform proofreading work efficiently and with high accuracy, and to adjust the priority taking into account the user's emotions.

[1923] "Proofreading" is the work of checking and correcting typos, grammatical errors, and consistency of content in sentences and texts.

[1924] "Generative AI" is a type of artificial intelligence that has the ability to generate output in natural language or other formats in response to specific inputs.

[1925] The "syntax analysis function" is a function that analyzes the grammatical structure of text and detects typos and grammatical errors.

[1926] "Imperfect words" are words or phrases that indicate typographical or grammatical errors present in the text.

[1927] "Spreadsheet software" is software for data entry, calculation, analysis, graph creation, etc., and generally handles data in spreadsheet format.

[1928] "Means for recognizing emotions" refers to technology that analyzes user input and behavioral data to identify their emotional state.

[1929] The "means for adjusting priority" is a function for changing the order of processing based on specific conditions and responding according to the importance and urgency of the processing.

[1930] "Business automation technology" is a technology for automating repetitive business processes, with the aim of improving efficiency and accuracy.

[1931] The present invention is a system for providing an efficient and user-friendly proofreading service. A specific embodiment of this system will be described below.

[1932] System configuration

[1933] This system consists of three main components: a server, a terminal, and a user. The server uses a generative AI model to analyze text syntax and extract defective words. The terminal recognizes the user's emotions and adjusts the priority of editing tasks based on those emotions. The user inputs the text to be edited and receives feedback from the system.

[1934] Hardware and software used

[1935] Generative AI model: For example, GPT-4 is used as a generative AI model. This model excels at natural language processing and is suitable for text syntactic analysis.

[1936] Emotion recognition engine: For example, the emotion recognition API from Azure Cognitive Services is used as the emotion recognition engine. This is a technology for analyzing emotions from user input and behavioral data.

[1937] Spreadsheet software: Use common spreadsheet software (e.g., Microsoft Excel) for automated entry of results.

[1938] System Operation

[1939] 1. Proofreading services

[1940] A user inputs text data to be reviewed into the system, for example by entering text into a web form or by uploading a file.

[1941] 2. Sending text data

[1942] The terminal transmits the text data received from the user to the server. At this time, the data is encrypted before transmission.

[1943] 3. Performing parsing

[1944] The server inputs the received text data into a generative AI model and performs syntactic analysis, detecting typos and grammatical errors.

[1945] 4. Extracting incomplete words

[1946] The server extracts and lists imperfect words from the results of the syntax analysis. For example, imperfect words such as "typographical error" and "grammatical error" are included in the list.

[1947] 5. Sending the results

[1948] The server transmits a list of the extracted defective words to the terminal.

[1949] 6. Displaying the results

[1950] The terminal displays the list of imperfect words received from the server to the user, who then checks the displayed imperfect words and makes corrections.

[1951] 7. Collecting Emotional Data

[1952] The device collects user input and behavioral data, such as the user's keyboard typing speed, mouse movements, and data from a facial recognition camera.

[1953] 8. Performing Sentiment Analysis

[1954] The device inputs the collected data into an emotion recognition engine to analyze the user's emotions.

[1955] 9. Transmission of Emotional Data

[1956] The device sends the analysis results, which include emotional data such as "anger" or "frustration," to the server.

[1957] 10. Adjusting the priorities of review work

[1958] The server adjusts the priority of review tasks based on the received emotion data, for example, giving priority to review tasks for users who are feeling angry.

[1959] 11. Sending the Review Results

[1960] The server transmits the processed proofreading results to the terminal based on the priority.

[1961] 12. Viewing Review Results

[1962] The terminal displays the proofreading results received from the server to the user, who then checks the results and makes corrections as necessary.

[1963] Specific examples

[1964] Input text: "This sentence may contain typos."

[1965] Output: The defective word "typo" is detected.

[1966] Prompt Sentence Examples

[1967] Please find any spelling or grammatical errors in the text below.

[1968] Text: "This sentence may contain typos."

[1969] In this way, the system achieves efficient and accurate proofreading and adjusts priorities in consideration of the user's feelings.

[1970] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1971] Step 1:

[1972] A user inputs text data to be reviewed into the system, for example by entering text into a web form or by uploading a file.

[1973] Input: Text data to be reviewed

[1974] Output: Text data sent to the terminal

[1975] Step 2:

[1976] The terminal transmits the text data received from the user to the server. At this time, the data is encrypted before transmission.

[1977] Input: Text data received from the user

[1978] Output: Text data sent to the server

[1979] Step 3:

[1980] The server inputs the received text data into a generative AI model and performs syntactic analysis, detecting typos and grammatical errors.

[1981] Input: Text data sent from the terminal

[1982] Data processing: Syntactic analysis using generative AI models

[1983] Output: Parsing results (list of typos and grammatical errors)

[1984] Step 4:

[1985] The server extracts and lists imperfect words from the results of the syntax analysis. For example, imperfect words such as "typographical error" and "grammatical error" are included in the list.

[1986] Input: Parsed result

[1987] Data processing: Extraction of incomplete words

[1988] Output: A list of missing words

[1989] Step 5:

[1990] The server transmits a list of the extracted defective words to the terminal.

[1991] Input: List of missing words

[1992] Output: A list of bad words sent to the terminal

[1993] Step 6:

[1994] The terminal displays the list of imperfect words received from the server to the user, who then checks the displayed imperfect words and makes corrections.

[1995] Input: A list of invalid words sent from the server

[1996] Output: A list of flawed words displayed to the user

[1997] Step 7:

[1998] The device collects user input and behavioral data, such as the user's keyboard typing speed, mouse movements, and data from a facial recognition camera.

[1999] Input: User input and behavioral data

[2000] Output: Collected emotion data

[2001] Step 8:

[2002] The device inputs the collected data into an emotion recognition engine to analyze the user's emotions.

[2003] Input: Collected emotion data

[2004] Data processing: Emotion analysis using an emotion recognition engine

[2005] Output: Emotion analysis results

[2006] Step 9:

[2007] The device sends the analysis results, which include emotional data such as "anger" or "frustration," to the server.

[2008] Input: Sentiment analysis results

[2009] Output: Emotion data sent to the server

[2010] Step 10:

[2011] The server adjusts the priority of review tasks based on the received emotion data, for example, giving priority to review tasks for users who are feeling angry.

[2012] Input: Emotion data

[2013] Data processing: Adjusting priorities for review work

[2014] Output: Priority-adjusted review work

[2015] Step 11:

[2016] The server transmits the processed proofreading results to the terminal based on the priority.

[2017] Input: Priority-adjusted review work

[2018] Output: Review results sent to your device

[2019] Step 12:

[2020] The terminal displays the proofreading results received from the server to the user, who then checks the results and makes corrections as necessary.

[2021] Input: Review results sent from the server

[2022] Output: The review results that are displayed to the user

[2023] (Application example 1)

[2024] Next, a description will be given of Application Example 1 of Form 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."

[2025] Traditional proofreading work is done manually by humans, which takes time and effort, and is inefficient. Furthermore, it is difficult to improve user satisfaction because it is not possible to respond flexibly to user emotions. Furthermore, there is also the problem of delays in improving the quality of content due to the delay in feedback of proofreading results.

[2026] 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.

[2027] In this invention, the server includes a means for accepting proofreading work that would otherwise be performed by humans, a means for extracting deficiencies using the syntactic analysis function of generative AI, a means for automatically inputting the results into spreadsheet software when deficiencies are found, a means for recognizing the user's emotions and adjusting the priority of the proofreading work based on those emotions, and a means for providing feedback on the proofreading results to the user and suggesting revisions, thereby making it possible to improve the efficiency of proofreading work and user satisfaction.

[2028] "Proofreading" is the task of detecting and correcting typos and grammatical errors in sentences and text.

[2029] "Generative AI" is a type of artificial intelligence that performs natural language processing such as text generation and syntactic analysis.

[2030] The "syntax analysis function" is a function that analyzes the structure of sentences and text and detects grammatical errors and typos.

[2031] "Faulty words" are words or phrases that indicate typos or grammatical errors in a sentence or text.

[2032] "Spreadsheet software" is software for data entry, calculation, analysis, graph creation, etc., and generally handles data in spreadsheet format.

[2033] "Means for recognizing emotions" refers to technology that analyzes and recognizes emotions from the user's facial expressions, tone of voice, etc.

[2034] The "means for adjusting the priority of review tasks" is a technique for changing the processing order of review tasks based on the recognized user sentiment.

[2035] "Feedback" is the process of returning the review results to the user and suggesting corrections.

[2036] "Proposals for correction" are specific correction methods or suggestions for the detected defective words.

[2037] "Business automation technology" is a technology for automating tasks that were previously performed manually, with the aim of improving efficiency and accuracy.

[2038] A system for implementing this invention is configured as follows: A server uses a generative AI model to perform syntactic analysis of text and extract defective words. Text entered by a user is analyzed in real time to detect typos and grammatical errors. The detected defective words are automatically entered into spreadsheet software.

[2039] Furthermore, the server uses an emotion recognition engine to analyze the user's emotions. The server captures the user's facial expressions and tone of voice using a camera and microphone, and analyzes them with the emotion recognition engine to determine whether the user is feeling angry, frustrated, happy, or satisfied. Based on this emotional information, the server adjusts the priority of the proofreading tasks. For example, if a user is feeling angry or frustrated, the server will prioritize the proofreading tasks for that user.

[2040] The results of the review are fed back to the user, and suggested revisions are presented. The user can then review the suggested revisions and make any necessary changes.

[2041] Specific hardware includes smartphones, smart glasses, and head-mounted displays, while software includes generative AI models (e.g., GPT-4), emotion recognition engines (e.g., Affectiva), and real-time data processing frameworks (e.g., Apache Kafka).

[2042] As a concrete example, consider a user trying to post a blog post. While the user is typing the post wearing smart glasses, the system analyzes the text in real time to detect typos and grammatical errors. At the same time, the system recognizes the user's emotions from their facial expressions and tone of voice, and prioritizes editing if the user is angry.

[2043] An example of a prompt for the generative AI model is as follows:

[2044] Please proofread the text below, spot any spelling or grammatical errors, and suggest corrections.

[2045] Text: "The weather is very nice today. I'm going to the park."

[2046] In this way, the system improves the efficiency of proofreading work and user satisfaction.

[2047] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2048] Step 1:

[2049] A user inputs text using a device (smartphone, smart glasses, head-mounted display).

[2050] Input: Text data entered by the user.

[2051] Output: The input text data is sent to the server in real time.

[2052] How it works: When a user types a blog post or content, the device sends the text data to the server in real time.

[2053] Step 2:

[2054] The server uses a generative AI model (e.g., GPT-4) to parse the received text data.

[2055] Input: Text data sent from the terminal.

[2056] Output: As a result of the parsing, defective words (typos and grammatical errors) are extracted.

[2057] What it does: The server uses a generative AI model to analyze text data and detect typos and grammatical errors.

[2058] Step 3:

[2059] The server automatically inputs the extracted defective words into spreadsheet software.

[2060] Input: Incomplete words extracted as a result of parsing.

[2061] Output: The missing words are automatically entered into a spreadsheet.

[2062] Specific operation: The server automatically inputs the extracted defective words into a spreadsheet software and records them.

[2063] Step 4:

[2064] The device captures the user's facial expressions and tone of voice using a camera and microphone and sends them to the server.

[2065] Input: User's facial expression and voice data.

[2066] Output: The acquired facial expression and voice data is sent to the server.

[2067] Specific op...

Claims

1. a means for inputting text data by a user, the means being provided in the terminal; a means provided in the terminal for transmitting the input text data to a server; A means for transmitting a prompt sentence to a generative AI model that extracts an imperfect word, the means being provided in the server; A means for the generative AI model to extract defective words; A means for storing the result of the extraction of the defective words in a database; a means for automatically inputting the extracted results into a spreadsheet software using an automation tool, the means being provided in the server; a means for displaying the extraction result to the user, the means being provided in the terminal; A system including:

2. The method further includes a means for automatically correcting the results of the proofreading work based on the defective words extracted by the syntax analysis function of the generation AI model. The system of claim 1 .

3. The method further includes a means for robotically automating the extraction of defective words by the generative AI model and the automatic input of the extraction results. The system of claim 1 .

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