System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving text input by a user; means for selecting a desired sentence for style conversion; means for passing text data received by a server to a generative model and performing style and sentiment analysis; means for the generative model to generate style conversion suggestions based on the analysis results; means for the generative model to recommend an optimal sentence if the user leaves the selection of the sentence to an AI; means for receiving feedback from the user and generating revision suggestions again; and means for providing the user with the final revised text.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Traditional writing corrections require high costs and long waiting times, leaving many writers struggling to objectively evaluate and improve their own writing style. It is also extremely difficult to imitate the writing style of other literary masters, requiring specialized knowledge and skills. Furthermore, properly reflecting the emotion and historical context of a piece of writing is also a challenge. This invention aims to provide fast, affordable, and professional writing corrections to help users improve their writing skills. [Means for solving the problem]

[0005] This invention provides a system that receives text entered by a user and modifies it to match the style of a desired literary figure. Specifically, it includes a means for receiving the text entered by the user and a means for selecting the desired literary figure for style conversion. It also includes a means for the server to pass the received text data to a generative model, which analyzes the writing style and sentiment, and a means for the generative model to generate a style conversion proposal based on the analysis results. If the user chooses to leave the literary figure selection to AI, the generative model provides a means for recommending the most suitable literary figure. The proposed revisions and suggestions are provided in an interactive chat format, and a means for receiving feedback from the user and generating further revisions is also included. The inclusion of a means for providing the final revised text to the user enables rapid and professional text correction. It also includes a means for understanding historical background and cultural nuances to suggest appropriate vocabulary and expressions, and a means for analyzing the flow and structure of the story and providing advice on improving the plot and character development.

[0006] "User" refers to an individual or organization that uses this system to input, correct, or provide feedback on text.

[0007] "Terminal" refers to an electronic device that a user uses to access this system, such as a computer, smartphone, or tablet.

[0008] "Server" refers to the central computer system that receives user input data, analyzes it using the generative model, and generates suggested revisions.

[0009] A "generative model" refers to an AI (artificial intelligence) algorithm that analyzes input text and performs style conversion, sentiment analysis, and generates suggested revisions.

[0010] "Stylistic transformation" refers to changing the vocabulary and structure of a piece of writing to match the style of a particular literary master.

[0011] "Sentiment analysis" refers to analyzing the emotional state of an input sentence and suggesting expressions that accurately reflect that emotion.

[0012] "Proposed revision" refers to the specific content of the revised sentence proposed by the generative model based on the analysis results.

[0013] "Feedback" refers to the opinions and comments that users provide on proposed revisions, which then cause the generative model to generate new revisions.

[0014] "Historical background" refers to the historical period depicted in the text and the cultural and social environment specific to that period.

[0015] "Vocabulary" refers to the set of words and expressions used in a text.

[0016] "Interactive chat style" refers to a style of interaction that takes place interactively between the user and the system, allowing for real-time feedback and corrections. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

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

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

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

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

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

[0032] 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.

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

[0034] 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.

[0035] 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.

[0036] 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.

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

[0038] This invention is a system that uses generative AI to modify a user's writing to match the style of a specific literary master. This system receives the text entered by the user on a server, analyzes and modifies it using an AI engine, and provides the user with a final proposed modification.

[0039] System configuration

[0040] 1. User's Device

[0041] It provides an interface for users to input text and select the desired literary figure.

[0042] It also includes an interface for users to enter feedback.

[0043] 2. Server

[0044] The entered text and information about the literary figure is received and saved.

[0045] Invoke a generative model to analyze style and sentiment.

[0046] Generate suggested fixes and provide them to the user in the form of interactive feedback.

[0047] Receive user feedback and re-analyze and revise.

[0048] Program processing

[0049] 1. The user types text on the device

[0050] Users enter the text they want to edit into the input field, and then select the desired author (e.g., "Japanese Authors") or let the AI ​​select the most suitable author.

[0051] Example: "One day in the forest, I met an old man. He began to tell me a mysterious story."

[0052] 2. The device sends the input data to the server

[0053] The terminal transmits the input text data and the selected author information to the server.

[0054] Send data using an HTTP request.

[0055] 3. The server receives the data and analyzes it using the generative model.

[0056] The server stores the received data in a database.

[0057] The data is passed to an AI engine that performs analysis of style, emotion, and historical context.

[0058] The analysis results are used as features for style conversion.

[0059] 4. Recommending literary figures and generating revision suggestions

[0060] If the user does not specify a literary figure, the AI ​​engine will recommend the most suitable one.

[0061] Generate suggested revisions that match the literary master's writing style.

[0062] Example: The input sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is converted into Akutagawa Ryunosuke's style and corrected to, "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[0063] 5. Interactive Feedback

[0064] The server sends suggested revisions to the user's device in an interactive chat format.

[0065] The user provides feedback on the proposed revisions and the server receives it.

[0066] 6. Generate the final fix

[0067] The server again generates revision suggestions using the user's feedback.

[0068] The final corrected text is sent to the user's device.

[0069] Specific examples

[0070] Example 1: If the user wants to use the style of Ryunosuke Akutagawa

[0071] 1. The user enters the sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." and selects Ryunosuke Akutagawa's writing style.

[0072] 2. The server uses AI to analyze Akutagawa Ryunosuke's writing style and generate suggested revisions that reflect his writing style and the historical context.

[0073] 3. Amendment: "One day in the forest, I met an old man. He began to tell me a profound and mysterious story."

[0074] 4. Return the text to the user and interactively adjust it until the user is satisfied.

[0075] Example 2: When the user entrusts the AI ​​with selecting a literary figure

[0076] 1. The user enters, "Today I really wanted to finish this task," and leaves the selection of a literary figure to the AI.

[0077] 2. The server uses AI to analyze the text and recommends Haruki Murakami as a suitable literary figure.

[0078] 3. Correction: "I really wanted to get this job done today."

[0079] 4. Return the text to the user and make final adjustments based on feedback.

[0080] In this way, the present invention utilizes generative AI to elevate the user's writing to a higher quality.

[0081] The processing flow will be explained below.

[0082] Step 1:

[0083] The user types text into the terminal.

[0084] The user enters the text they want to correct into the system's input field.

[0085] Users can select the author of their choice or choose the "Leave it to the AI" option.

[0086] Example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[0087] Step 2:

[0088] The device sends the user's input data to the server.

[0089] The terminal sends the input text and information about the selected author to the server via an HTTP request.

[0090] Step 3:

[0091] The server receives the document data.

[0092] The server stores the received text data and author information in a database.

[0093] Step 4:

[0094] The server runs a generative model to analyze style and sentiment.

[0095] The server passes the saved data to a generative model, which analyzes the text's style, emotion, and historical context.

[0096] The generative model extracts the characteristics of the received text and analyzes it to convert it into the writing style of a specific literary master.

[0097] Step 5:

[0098] The server will recommend the most suitable author.

[0099] If the user does not specify a literary figure, the generative model will recommend the most appropriate literary figure based on the analysis results.

[0100] The recommendation results are sent to the user's device and displayed.

[0101] Step 6:

[0102] The server generates a revision suggestion.

[0103] Generate suggested revisions to text to match the writing style of literary masters.

[0104] Example: The input sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is converted into Akutagawa Ryunosuke's style and corrected to, "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[0105] Step 7:

[0106] The server provides suggested revisions in the form of interactive feedback.

[0107] The proposed changes are sent to the user's device in an interactive chat format, where the user can review the changes and provide feedback.

[0108] Step 8:

[0109] The user provides feedback on the device.

[0110] Users enter their opinions and comments on the proposed revisions and send them to the server.

[0111] Step 9:

[0112] The server receives the user's feedback and re-analyzes it.

[0113] The server receives the feedback and asks the generative model to reanalyze.

[0114] A re-analyzed correction proposal is generated and sent to the user's device.

[0115] This step is repeated until the user is satisfied.

[0116] Step 10:

[0117] The server generates and sends the final revision proposal.

[0118] Generate a final revision proposal based on user feedback and send it to the device.

[0119] The user can receive the final revised text.

[0120] Example 1

[0121] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0122] Previously, users had to spend a great deal of effort and expertise to adapt their writing to the style of a particular literary master. Furthermore, the feedback and correction process required for style conversion was cumbersome, and it took a long time for users to achieve satisfactory results. This reduced the efficiency with which users could obtain high-quality writing.

[0123] 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.

[0124] In this invention, the server includes means for receiving text entered by a user, means for selecting a desired author for style conversion, means for the terminal to transmit the text data received to the server, means for the server to pass the received text data to a generative model and analyze the writing style and sentiment, means for the generative model to generate a style conversion proposal based on the analysis results, means for the generative model to recommend the most suitable author if the user leaves the author selection to the generative AI, means for providing revision suggestions and proposals in an interactive digital communication format, means for receiving feedback from the user and generating further revision suggestions, and means for providing the final revised text to the user. This enables users to easily obtain high-quality text that matches the writing style of a specific author without specialized knowledge.

[0125] The "means for receiving text entered by the user" is an interface for sending text entered by the user to the server.

[0126] The "means for selecting a desired author for style conversion" is an interface that allows the user to select a desired author for style conversion.

[0127] The "means for transmitting text data received by the terminal to the server" is a communication means for the user's terminal to transmit input text data and author information to the server.

[0128] "Means for passing text data received by the server to a generative model and analyzing the style and emotion" refers to a means for passing text data received by the server to an analysis system and analyzing text characteristics such as style and emotion.

[0129] "Means for a generative model to generate a suggestion for style conversion based on the analysis results" refers to means for a generative artificial intelligence to generate a suggestion for style conversion based on the analysis results.

[0130] "When a user leaves the selection of a literary figure to a generative AI, the means by which the generative model recommends the most suitable literary figure" refers to the means by which the generative AI analyzes the text input by the user and recommends the most suitable literary figure.

[0131] "Means for providing amendments or suggestions in the form of interactive digital communication" means interactive communication means for providing amendments or suggestions to users in real time.

[0132] The "means for receiving feedback from the user and generating a revision proposal again" is a means for receiving feedback from the user and generating a revision proposal again based on the feedback.

[0133] The "means for providing the final corrected text to the user" refers to the means for transmitting and displaying the final corrected text to the user.

[0134] The present invention is a system that corrects text entered by a user to fit a specific writing style. The system receives the text entered by the user, analyzes the writing style, emotion, and historical context using generative artificial intelligence, and generates and provides optimal revision suggestions. The system is implemented using the following hardware and software.

[0135] System configuration

[0136] 1. User's Device

[0137] The user uses software (e.g., a web browser) that opens an interface to the system.

[0138] The user uses an interface, for example an HTML form and JavaScript, to enter text into input fields and select the desired writing style.

[0139] The user presses the send button to send the entered text data and style information from the terminal to the server.

[0140] 2. Server

[0141] The server receives the user's text data sent as an HTTP request, using HTTP server software (e.g., Apache or Nginx) and a backend framework (e.g., Node.js or Django).

[0142] The server stores the received data in a database (e.g., MySQL or PostgreSQL).

[0143] The server then passes the stored data to a generative AI model (such as the GPT-3 model) for analysis, which extracts writing style, sentiment, and historical context.

[0144] The AI ​​engine generates suggested revisions based on the features for style conversion. If the user leaves the selection of a literary figure to the AI, the AI ​​engine will recommend the most suitable author.

[0145] 3. Providing feedback and suggested revisions

[0146] The server provides the generated revision suggestions to the user's device in the form of an interactive digital communication (e.g., real-time chat using WebSocket).

[0147] The user provides feedback on the submitted revisions, which is then sent back to the server.

[0148] The server receives feedback from the user and passes it back to the AI ​​engine to generate suggested revisions.

[0149] The final revised text is sent from the server to the user's device and displayed in the interface.

[0150] Specific examples

[0151] Example 1: When a user desires the writing style of a particular literary figure

[0152] 1. The user enters the sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." and selects the writing style of "Akutagawa Ryunosuke."

[0153] 2. When the user presses the send button, the device sends the text data and information about "Akutagawa Ryunosuke" to the server via an HTTP request.

[0154] 3. The server stores the data in a database and passes it to a generative artificial intelligence for analysis.

[0155] 4. The AI ​​engine generates suggested revisions to fit Akutagawa Ryunosuke's writing style. The suggested revision is, "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[0156] 5. The proposed revision is sent to the user, who then provides feedback, saying, "I'd like this part to sound more archaic."

[0157] 6. The server receives the feedback, asks the AI ​​engine to generate a revision again, and provides the final revised sentence to the user: "One day in the forest, I met an old man. The old man seemed to begin to tell me a deep and mysterious story."

[0158] Prompt Sentence Examples

[0159] "Please revise the following sentence in the style of Akutagawa Ryunosuke:

[0160] One day in the forest, I met an old man who began to tell me a mysterious story.

[0161] In this way, the present invention utilizes generative artificial intelligence to streamline the process by which users obtain high-quality text tailored to a particular writing style.

[0162] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0163] Specific processing flow of the program

[0164] Step 1:

[0165] The user types text into the device.

[0166] How it works: The user opens the device interface and enters the text they want to edit into the input field. They can choose the writing style they want, or let the generative AI choose a literary master. For example, they could enter a sentence like, "One day in the forest, I met an old man. The old man began to tell me a mysterious story."

[0167] Input: The user enters the text and information about the desired author into the input field.

[0168] Output: The text entered in the terminal's input field and information about the literary figure will be obtained.

[0169] Step 2:

[0170] The terminal sends the input data to the server.

[0171] How it works: When the user presses the send button, the device uses an HTTP request to send the entered text data and the selected author information to the server. This request uses the POST method, and the data is encoded in JSON format.

[0172] Input: Text data entered by the user and information about the selected literary figure.

[0173] Output: JSON formatted data sent to the server as an HTTP request.

[0174] Step 3:

[0175] The server receives the data and analyzes it using a generative model.

[0176] How it works: The server stores the received data in a database. It then passes the data to a generative AI (e.g., GPT-3) to begin analysis. The AI ​​engine performs analysis to extract stylistic, emotional, and historical context.

[0177] Input: JSON formatted data sent to the server.

[0178] Output: Data stored in the database and analysis results by the AI ​​engine (features of writing style, emotion, and historical background).

[0179] Step 4:

[0180] A generative model generates style transformation suggestions based on the analysis results.

[0181] How it works: Based on the analysis results, the generative AI generates revision suggestions that match the user's desired writing style. If the user has not specified a literary figure, the AI ​​engine runs an algorithm to recommend the most suitable literary figure.

[0182] Input: Analysis results by the AI ​​engine (features of writing style, emotion, and historical background).

[0183] Output: The generated revision text.

[0184] Step 5:

[0185] The proposed fix is ​​sent to the user's device.

[0186] How it works: The server sends the proposed revisions to the user's device in real time using an interactive digital communication format (e.g., WebSocket). The user's interface displays the generated revisions.

[0187] Input: The generated text of the proposed amendment.

[0188] Output: Suggested fixes sent to the user's device in real time.

[0189] Step 6:

[0190] Users provide feedback on the proposed changes.

[0191] How it works: The user reviews the suggested revisions and enters feedback if necessary (e.g., "I want this part to sound more archaic.") The feedback is then sent to the server again using an HTTP request.

[0192] Input: User feedback.

[0193] Output: User feedback sent to the server.

[0194] Step 7:

[0195] The server re-analyzes the feedback and generates a final revision proposal.

[0196] How it works: Based on the feedback received by the server, the data is passed to the generative AI again to regenerate a revision proposal. The AI ​​engine then generates a new revision proposal that takes the feedback into account and returns it to the server.

[0197] Input: User feedback and original analysis results.

[0198] Output: Final revised draft.

[0199] Step 8:

[0200] Provide the final revision to the user.

[0201] How it works: The server sends the final edited text to the user's device and displays it in the device's interface, allowing the user to review the final result and download or copy it if desired.

[0202] Input: Final amendment.

[0203] Output: The final corrected text that is displayed on the user's terminal.

[0204] This series of processes allows users to easily obtain high-quality text that matches a specific writing style.

[0205] (Application example 1)

[0206] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0207] This invention relates to a system that allows users to generate content in the style of a particular literary master and check the resulting edits in real time. Today, there is a growing demand for tools that allow people to create high-quality content by imitating the writing styles of various authors and literary masters. However, conventional technologies make it difficult to convert or edit writing styles in real time, limiting the user experience. It is necessary to provide a system that solves these problems and allows users to easily generate and receive high-quality literary-style content.

[0208] 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.

[0209] In this invention, the server includes means for receiving text entered by a user, means for selecting a desired author for style conversion, means for passing the text data received by the server to a generative model and analyzing the writing style and sentiment, means for the generative model to generate a style conversion suggestion based on the analysis results, means for the generative model to recommend the most suitable author if the user leaves the author selection to AI, means for providing suggested revisions and suggestions in an interactive chat format, means for receiving feedback from the user and generating further suggested revisions, means for providing the final corrected text to the user, and means for providing the style-converted text to the user through an application running on a mobile device. This enables users to generate content in real time in the writing style of a specific author and instantly confirm and apply high-quality revisions.

[0210] The "means for receiving text entered by the user" is an interface for transmitting text data entered by the user to the system.

[0211] The "means for selecting a desired author for style conversion" is a selection interface that allows a user to select the style of a particular author and convert text into that style.

[0212] "Means for the server to pass the received text data to a generative model and analyze the style and sentiment" refers to the process of using a generative AI model to analyze the style and sentiment of text received from a user.

[0213] The "means by which the generative model generates a proposal for style conversion based on the analysis results" is an algorithm that generates a proposal for converting a sentence into the style of a specified literary figure based on the analysis results.

[0214] "Means for the generative model to recommend the most suitable author when the user leaves the author selection to the AI" refers to the process by which, when the user does not select a writer, the generative AI automatically selects and recommends the author that best suits the input text.

[0215] "Means for providing revisions and suggestions in an interactive chat format" refers to an interface that provides the revised text to the user in a chat format and enables interaction with the user.

[0216] "Means for receiving feedback from users and generating revisions again" refers to the process of revising the text again based on the feedback provided by the users.

[0217] The "means for providing the final revised text to the user" is the interface that provides the user with the final text that incorporates all corrections and feedback.

[0218] "Means for providing stylized text to a user through an application running on a mobile device" is a process for providing corrected text to a user through an application running on a mobile device.

[0219] This invention is a system that uses generative AI to modify text entered by a user to match it with the writing style of a specific literary master. This system is primarily composed of the user's device and a server. Specifically, it is implemented using the following process.

[0220] System configuration

[0221] 1. User's Device

[0222] It provides an interface for users to input text and select the desired literary figure.

[0223] It also includes an interface for receiving feedback from users.

[0224] 2. Server

[0225] The input text data and author selection information are received and saved.

[0226] Launch a generative AI model to analyze style and sentiment.

[0227] Generate suggested fixes and provide them to the user in the form of interactive feedback.

[0228] Receive user feedback and re-analyze and revise.

[0229] Processing Details

[0230] 1. User Input

[0231] Users input text into their devices and select the author they want to read. This information is sent to the server.

[0232] For example, enter the sentence "One day in the forest, I met an old man. The old man began to tell me a mysterious story." and let the selected author be "Author A."

[0233] 2. Server Processing

[0234] The server stores the received data and uses a generative AI model to analyze the text, extracting stylistic and emotional features.

[0235] Based on the analysis results, the text is modified to fit the writing style of the designated author. For example, the input text is converted into the writing style of "Author A."

[0236] The corrected text is presented to the user in an interactive format.

[0237] For example, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." becomes "One day in the forest, I met an old man. The old man began to tell me a deep and mysterious story."

[0238] 3. User feedback and final revisions

[0239] After reviewing the proposed revisions, users can provide feedback and requests for further revisions, and the server will then revise the text based on this feedback.

[0240] The server then sends the final corrected text to the user.

[0241] Hardware and software used

[0242] Hardware: Server (CPU / GPU server), user device (smartphone)

[0243] Software: Flask (server-side implementation), transformers library (generative AI model)

[0244] Specific examples

[0245] When a user inputs "Today, I really wanted to finish this work," and selects the desired author as "Writer B," the server receives this and generates a sentence revised in a style appropriate for "Writer B." For example, "Today, I really wanted to finish this work." The server then receives the user's feedback, makes final revisions, and provides the sentence.

[0246] In this way, users can enjoy high-quality real-time text corrections in the style of a particular literary master.

[0247] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0248] Step 1:

[0249] A means by which users input text into a device

[0250] The user enters a sentence into the input field and selects the desired author. The entered sentence and author information are sent from the terminal to the server.

[0251] Input: Text entered by the user, desired author

[0252] Output: Data sent to the server (text, literary information)

[0253] Specific operation: The user uses a dedicated interface to enter text into the text input field, select the desired author from a drop-down menu or radio buttons, and then press the send button to send the data to the server.

[0254] Step 2:

[0255] A means for the server to store received text data

[0256] The server stores the text data and author information received from the terminal in a database.

[0257] Input: Data sent from the terminal (text, literary information)

[0258] Output: Data stored in the database

[0259] Specific operation: The server deserializes the data received as an HTTP request through the API and saves it in the database.

[0260] Step 3:

[0261] A means for the server to pass data to the generated AI model for analysis

[0262] The server passes the saved text data and information about the author to a generative AI model, which analyzes the writing style and emotions.

[0263] Input: Data stored in the database (text, literary information)

[0264] Output: Style and sentiment features analyzed by the generative AI model

[0265] Specific operation: The server reads the stored data, inputs it into a generative AI model (e.g., GPT-2), and analyzes the stylistic and emotional characteristics.

[0266] Step 4:

[0267] A means for the generative model to transform writing style based on the analysis results

[0268] The generative model converts the user's writing into the style of a specified literary figure.

[0269] Input: Analysis results (features of style and emotion)

[0270] Output: Stylistically transformed text of proposed revision

[0271] Specific operation: Based on the analysis results, the generative AI model combines the stylistic characteristics of the specified author with the prompt sentence to perform the optimal style conversion.

[0272] Step 5:

[0273] A way to provide suggested fixes via chat

[0274] The server provides the generated revision suggestions to the user in an interactive chat format.

[0275] Input: Stylistically converted text of proposed revision

[0276] Output: Suggested fixes displayed on the user's device

[0277] Specific operation: The server encodes the generated correction proposal in JSON format and sends it to the user's device via API. The correction proposal is displayed in chat format on the user's device interface.

[0278] Step 6:

[0279] A way for users to provide feedback and generate revisions again

[0280] The user can input feedback on the proposed corrections and request further corrections. This feedback is sent back to the server, and the generative AI model then makes further corrections.

[0281] Input: User feedback

[0282] Output: Re-corrected sentence

[0283] Specific operation: The user inputs feedback on the interface of the device and presses the resend button to send the data to the server, which then uses the generative AI model to convert the writing style again.

[0284] Step 7:

[0285] A means of providing the final revised text

[0286] The server again provides the revised final text to the user.

[0287] Input: Data for final revision

[0288] Output: Final revised text

[0289] Specific operation: The server checks the final revised text, sends it to the user's terminal, and displays it on the screen.

[0290] Step 8:

[0291] A means for providing stylized text through an application that runs on a mobile device

[0292] The final revised text is provided to the user through an application running on the user's mobile device.

[0293] Input: Last revised text

[0294] Output: The converted text displayed on a mobile device

[0295] Specific behavior: The final revised text is displayed in the application on the mobile device and provided to the user.

[0296] This allows users to receive high-quality text corrections in real time in the style of a specific literary master.

[0297] 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.

[0298] This invention is a system that uses generative AI and an emotion engine to modify a user's writing to match the style of a specific literary master, generating emotive expressions. This system receives the text entered by the user on a server, analyzes and modifies it using AI and the emotion engine, and provides the user with a final proposed modification.

[0299] System configuration

[0300] 1. User's Device

[0301] It provides an interface for users to input text and select the desired literary figure.

[0302] It also includes an interface for users to enter feedback.

[0303] 2. Server

[0304] The entered text and information about the literary figure is received and saved.

[0305] Activate generative models and sentiment engines to analyze style and sentiment.

[0306] Generate suggested fixes and provide them to the user in the form of interactive feedback.

[0307] Receive user feedback and re-analyze and revise.

[0308] Program processing

[0309] 1. The user types text into the device

[0310] The user enters the text they want to correct into the system's input field.

[0311] Users can select the author of their choice or choose the "Leave it to the AI" option.

[0312] Example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[0313] 2. The device sends the user's input data to the server

[0314] The terminal transmits the input text data and the selected author information to the server.

[0315] Send data using an HTTP request.

[0316] 3. The server receives the data and analyzes it using the generative model and emotion engine.

[0317] The server stores the received text data and author information in a database.

[0318] The data is passed to a generative model and emotion engine to analyze the style, emotion, and historical context of the text.

[0319] The emotion engine identifies the emotion contained in a sentence and extracts features to generate expressions based on that emotion.

[0320] 4. Recommending literary figures and generating revision suggestions

[0321] If the user does not specify a literary figure, the AI ​​engine will recommend the most suitable one.

[0322] Generate suggested revisions that match the literary master's writing style.

[0323] Reflecting the results of the emotion engine, revision suggestions containing emotional expressions are created.

[0324] Example: The input sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is converted into Akutagawa Ryunosuke's style and corrected to, "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[0325] 5. Interactive Feedback

[0326] The server sends suggested revisions to the user's device in an interactive chat format.

[0327] Users can review suggested revisions and provide feedback on emotion and style.

[0328] 6. Generate the final fix

[0329] The server again generates revision suggestions using the user's feedback.

[0330] The emotion engine also analyzes again and generates a final revision proposal with the appropriate emotional expression.

[0331] The final corrected text is sent to the user's device.

[0332] Specific examples

[0333] Example 1: If the user wants to use the style of Ryunosuke Akutagawa

[0334] 1. The user enters the sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." and selects Ryunosuke Akutagawa's writing style.

[0335] 2. The server uses a generative model and emotion engine to analyze the writing style and emotional features, and generates revision suggestions that emphasize the emotion while reflecting the writing style and historical context.

[0336] 3. Amendment: "One day in the forest, I met an old man. He began to tell me a profound and mysterious story."

[0337] 4. Return the text to the user and adjust it interactively based on their feedback.

[0338] Example 2: When the user entrusts the AI ​​with selecting a literary figure

[0339] 1. The user enters, "Today I really wanted to finish this task," and leaves the selection of a literary figure to the AI.

[0340] 2. The server analyzes the text using a generative model and an emotion engine and recommends Haruki Murakami as a suitable literary figure.

[0341] 3. Correction: "Today, I really wanted to get this job done."

[0342] 4. Return the text to the user and make final adjustments based on feedback.

[0343] In this way, the present invention utilizes generative AI and an emotion engine to modify the user's writing to match the style of a specific literary master, elevating it into high-quality, emotionally rich writing.

[0344] The processing flow will be explained below.

[0345] Step 1:

[0346] The user types text into the terminal.

[0347] The user enters the text they want to correct into the system's input field.

[0348] Users can select the author of their choice or choose the "Leave it to the AI" option.

[0349] Example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[0350] Step 2:

[0351] The device sends the user's input data to the server.

[0352] The terminal transmits the input text data and the selected author information to the server via an HTTP request.

[0353] Step 3:

[0354] The server receives the document data.

[0355] The server stores the received text data and author information in a database.

[0356] Step 4:

[0357] The server runs a generative model and an emotion engine to analyze the style and emotion.

[0358] The server passes the saved data to a generative model, which analyzes the text's style, emotion, and historical context.

[0359] The generative model extracts the characteristics of the received text and analyzes it to convert it into the writing style of a specific literary master.

[0360] The emotion engine identifies the emotion contained in a sentence and extracts expression features based on that emotion.

[0361] Step 5:

[0362] The server will recommend the most suitable author.

[0363] If the user does not specify a literary figure, the generative model will recommend the most appropriate literary figure based on the analysis results.

[0364] The recommendation results are sent to the user's device and displayed.

[0365] Step 6:

[0366] The server generates a revision suggestion.

[0367] Generate suggested revisions to text to match the writing style of literary masters.

[0368] The analysis results of the emotion engine are reflected to create revision proposals that include emotionally rich expressions.

[0369] Example: The input sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is converted into Akutagawa Ryunosuke's style and corrected to, "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[0370] Step 7:

[0371] The server provides suggested revisions in the form of interactive feedback.

[0372] Proposed fixes are sent to the user's device in an interactive chat format.

[0373] Users can review suggested revisions and provide feedback on emotion and style.

[0374] Step 8:

[0375] The user provides feedback on the device.

[0376] Users enter their opinions and comments on the proposed revisions and send them to the server.

[0377] Step 9:

[0378] The server receives the user's feedback and re-analyzes it.

[0379] The server receives the feedback and asks the generative model and emotion engine to reanalyze.

[0380] A reanalyzed revision proposal is generated, and a revision proposal including an emotionally rich expression is generated.

[0381] This step is repeated until the user is satisfied.

[0382] Step 10:

[0383] The server generates and sends the final revision proposal.

[0384] Generate a final revision proposal based on user feedback and send it to the device.

[0385] The user can receive the final revised text.

[0386] Example 2

[0387] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0388] Conventional writing systems require users to manually edit and change their writing style, which is a time-consuming process that makes it difficult to create emotive, high-quality writing. Furthermore, if users want to edit their writing to match the style of a particular literary figure, they are required to understand the characteristics of that style and the historical context, which requires a lot of time and effort. Furthermore, it is difficult to make revisions or interactive adjustments based on user feedback, making efficient writing difficult.

[0389] The specification process by the specification 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 receiving text entered by a user, a means for selecting a desired text style for typeface conversion, a means for passing the text data received by the server to a natural language processing model and analyzing the typeface and emotion, a means for the natural language processing model to generate a typeface conversion suggestion based on the analysis results, a means for the natural language processing model to recommend an optimal text style when the user leaves the selection of the text style to artificial intelligence, a means for providing revision suggestions and suggestions in an interactive chat format, a means for receiving feedback from the user and generating another revision suggestion, and a means for providing the final revised text to the user. This allows the user to create high-quality text without hassle and to efficiently and quickly correct the text style and emotional expression.

[0390] "User" refers to an individual or group that uses the system to input text and perform style conversion and sentiment analysis.

[0391] "Means of receiving" refers to the functions and processes for sending text entered by the user to the system and receiving that data on the server.

[0392] "Means for selecting a desired writing style for typeface conversion" refers to an interface or option that allows a user to select a particular author or writing style.

[0393] A "natural language processing model" refers to an algorithm or software that analyzes input text and generates suggested revisions based on a specific style or sentiment.

[0394] "Means for sentiment analysis" refers to the process of identifying the sentiment contained in a text and generating expressions based on that sentiment.

[0395] "Means for generating typeface conversion suggestions" refers to a function or algorithm that generates suggestions for converting the typeface of a text to a style desired by the user based on the analysis results.

[0396] "Leaving the selection of literary figures to artificial intelligence" refers to a usage scenario in which the system automatically recommends the most suitable literary figure without the user having to select a specific author.

[0397] "Interactive chat-style means" refers to an interactive interface that provides generated revisions to the user in real time and allows the user to enter feedback.

[0398] "Means of receiving feedback and generating revision suggestions again" refers to the process of collecting opinions and requests from users and revising the text using a generative AI model based on them.

[0399] "Means for providing the final corrected text to the user" refers to the function of displaying or sending the final text to the user after all correction processes have been completed.

[0400] This invention is a system that uses generative AI and an emotion engine to modify a user's text to fit a specific writing style and generate emotive expressions. This system receives text entered by the user on a server, analyzes and modifies it using a natural language processing model and an emotion engine, and provides the user with a final proposed modification.

[0401] System configuration

[0402] 1. User's Device

[0403] It provides an interface for users to enter text and select the desired writing style.

[0404] It also includes an interface for users to enter feedback.

[0405] Usage example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[0406] 2. Server

[0407] Input text and text style information is received and saved.

[0408] It launches a natural language processing model (e.g., GPT-3) and an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze typeface and emotion.

[0409] Generate suggested fixes and provide them to the user in the form of interactive feedback.

[0410] Receive user feedback and re-analyze and revise.

[0411] Hardware used: High-performance servers (data center servers equipped with CPUs and GPUs, etc.)

[0412] Software used: Python, Flask (web framework), MariaDB (database)

[0413] Program processing

[0414] The system works in the following steps:

[0415] 1. The user types text into the device

[0416] The user enters the text they want to correct into the system's input field.

[0417] Users can choose their preferred writing style or select the "Leave it to AI" option.

[0418] Example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[0419] 2. The device sends the user's input data to the server

[0420] The terminal transmits the input text data and the selected text style information to the server using an HTTP request.

[0421] 3. The server receives the data and analyzes it using the generative model and emotion engine.

[0422] The server stores the received text data and text style information in a database.

[0423] The data is passed to a natural language processing model and emotion engine to analyze the style of writing, emotions, and historical context of the text.

[0424] Usage example: A Python script on the server uses the requests library to send data to the natural language processing model API and the emotion engine API and receive responses.

[0425] 4. Writing style recommendation and revision generation

[0426] If the user does not specify a writing style, the server uses a natural language processing model to recommend the most appropriate writing style.

[0427] Generate revision suggestions tailored to your writing style.

[0428] Reflecting the results of the emotion engine, revision suggestions containing emotional expressions are created.

[0429] Example: The input sentence "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is corrected to "One day in the forest, I met an old man. The old man began to tell me a profound story." based on the recommended writing style.

[0430] 5. Interactive Feedback

[0431] The server sends suggested revisions to the user's device in an interactive chat format.

[0432] Users can review the proposed changes and provide feedback on emotions and typefaces.

[0433] 6. Generate the final fix

[0434] The server uses the user's feedback to generate a new revision, and the emotion engine analyzes it again to generate a final revision with appropriate emotional expressions.

[0435] The final corrected text is sent to the user's device.

[0436] For example, if a user types "I really wanted to finish this work today" and leaves the writing style selection to AI, the server will use a natural language processing model and emotion engine to recommend a style similar to Haruki Murakami's, correcting it to "Today, I really wanted to finish this work." This suggested correction is then provided to the user.

[0437] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0438] Step 1:

[0439] The user types text into the terminal.

[0440] Input: The user enters text into the system's input field and selects the desired writing style.

[0441] Example: A user types "Today was a very busy day" and selects "Natsume Soseki" as the desired writing style.

[0442] Output: The input text and the selected writing style are sent to the server in JSON format.

[0443] Step 2:

[0444] The device sends the user's input data to the server.

[0445] Input: User input data (text and selected writing style).

[0446] Specific operation: When you click the send button on the device, the data is sent to the server via an HTTP request using the JavaScript fetch function. The data is sent in JSON format.

[0447] Output: The JSON data received by the server.

[0448] Step 3:

[0449] The server receives the data and analyzes it using a generative model and emotion engine.

[0450] Input: JSON data sent from the terminal.

[0451] What it does: The server stores the received data in a database, passes it to a generative AI model (e.g., GPT-3) and an emotion engine (e.g., IBM Watson Tone Analyzer), and uses a Python script to send the data to the API through the requests library.

[0452] Data processing: The text data is analyzed to determine writing style, historical context, and emotions.

[0453] Output: The analysis results are returned in JSON format and saved on the server.

[0454] Step 4:

[0455] The server generates recommendations and revisions for literary masters.

[0456] Input: Parsed JSON data.

[0457] How it works: If the user does not specify a writing style, the server uses a generative AI model to recommend the best writing style. A Python script generates revision suggestions based on the analysis results.

[0458] Data processing: Features are extracted for style conversion and emotional expressions are extracted.

[0459] Output: Generated revision suggestions are created in JSON format.

[0460] Step 5:

[0461] Arba provides interactive feedback.

[0462] Input: JSON data of generated correction suggestions.

[0463] Specific operation: The server sends suggested revisions to the device in real time using WebSocket or Ajax, and the user checks the suggested revisions and enters feedback.

[0464] Output: User feedback data is sent to the server.

[0465] Step 6:

[0466] The server generates the final revision.

[0467] Input: User feedback data.

[0468] Specific operation: The server calls the generative AI model and emotion engine again based on the feedback, and refines the proposed revisions. The final revisions are generated.

[0469] Data calculations: Reanalysis and revisions reflecting feedback.

[0470] Output: The final corrected text is sent to the user's device.

[0471] The above is the specific processing flow of the program for this system, which allows users to efficiently create high-quality documents without much effort.

[0472] (Application example 2)

[0473] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0474] Conventional text correction systems lack the ability to convert user-entered text into a specific style and add emotive expressions, making it difficult to generate high-quality text for content creation. Furthermore, there is no easy way to save the generated text and use it on other platforms, limiting the user experience. It is necessary to solve these issues and provide a more convenient text correction system.

[0475] 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.

[0476] In this invention, the server includes means for receiving text entered by a user, means for selecting a desired author for style conversion, means for passing the text data received by the server to a generative model and analyzing the writing style and emotion, means for the generative model to generate a style conversion suggestion based on the analysis results, means for the generative model to recommend the most suitable author if the user leaves the author selection to AI, means for providing revision suggestions and suggestions in an interactive chat format, means for receiving feedback from the user and generating further revision suggestions, means for providing the final revised text to the user, and means for saving the generated text and sharing it on other platforms. This makes it possible to easily generate high-quality, emotionally rich text and furthermore to easily use the generated text on other platforms.

[0477] The "means for receiving text entered by the user" refers to a mechanism for transmitting text data entered by the user through the terminal to the server and receiving it.

[0478] The "means for selecting a desired author for style conversion" is a mechanism that provides an interface for a user to select the style of a desired author as the conversion destination.

[0479] "Means for passing the text data received by the server to the generative model and analyzing the writing style and emotion" refers to a mechanism by which the server inputs the text data received from the user into the generative AI model and analyzes the writing style and emotion.

[0480] "Means by which the generative model generates suggestions for style conversion based on the analysis results" refers to a mechanism by which the generative AI model generates appropriate suggestions for style conversion based on the analysis results of style and emotion.

[0481] "A means for the generative model to recommend the most suitable author when the user leaves the author selection to the AI" is a mechanism that, when the user sets the author selection to be left to the AI, recommends the author that the generative AI model determines to be the most suitable.

[0482] The "means for providing revisions and suggestions in an interactive chat format" is a mechanism for displaying generated revisions and suggestions to users in a chat format, thereby realizing two-way interaction.

[0483] The "means of receiving feedback from the user and generating a new revision proposal" is a mechanism for revising the text based on the feedback entered by the user and generating a new revision proposal.

[0484] The "means for providing the final corrected text to the user" is a mechanism for displaying and providing the final corrected text to the user.

[0485] "Means for saving generated text and sharing it on other platforms" refers to a mechanism for saving generated text within the system and sharing it on other platforms such as social media and blogs.

[0486] The system that realizes this application example is configured as follows: The entire system is executed by the user's terminal, a server, and necessary software components.

[0487] User's device

[0488] 1. Text input interface

[0489] It provides an interface for users to input text and select the author they want, and also includes an option for users to leave the author selection to the AI.

[0490] 2. Feedback Interface

[0491] The system provides an interface for users to input feedback on the generated revisions and request further revisions. This feedback is sent to the server in real time.

[0492] server

[0493] 1. Data Receipt and Storage

[0494] The server receives the text data and author information sent from the terminal and stores them in a database.

[0495] 2. Generative AI Models and Emotion Engines

[0496] The received text data is passed to a generative AI model (e.g., GPT-4) for stylistic and emotional analysis. An emotion engine (e.g., Hume AI) identifies the emotion contained in the text and extracts features to generate expressions based on that emotion.

[0497] 3. Proposal generation for style conversion

[0498] The generative AI model generates suggestions for style conversion based on the analysis results. If the user leaves the selection of the author to the AI, the generative model will recommend the most suitable author. These suggestions are presented in an interactive chat format that is easy for users to understand.

[0499] 4. Feedback Processing

[0500] It receives feedback from users and generates new revision suggestions based on that feedback. It also analyzes the emotion engine again and generates a final revision suggestion with appropriate emotional expressions.

[0501] 5. Final submission, saving and sharing

[0502] The final revised text is provided to the user and saved in the system, and the user can share the generated text on other platforms such as social media or blogs.

[0503] Specific examples

[0504] Prompt Sentence Examples

[0505] User input: "I really wanted to finish this work today."

[0506] User's favorite author: Akutagawa Ryunosuke

[0507] What happens on the server side when the user selects a writer:

[0508] The server receives the sentence, "Today, I really wanted to finish this work," and uses a generative AI model to analyze the style and sentiment. It converts this sentence into the style of Ryunosuke Akutagawa and generates a suggested revision, such as, "Today, I felt that I had to complete this work no matter what."

[0509] Feedback example:

[0510] The user inputs feedback such as "Please emphasize the emotion more." The server receives this feedback, analyzes it again using the generative AI model and emotion engine, and generates a final revision such as "Today, I was determined to finish this task no matter what," and provides it to the user.

[0511] In this way, users can easily generate high-quality, emotive texts and then easily use the generated texts on other platforms.

[0512] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0513] Step 1:

[0514] The user inputs text into the terminal. The user inputs the text into the input field and selects the desired author. The input includes the text as text data and the author selection settings.

[0515] Step 2:

[0516] The terminal sends the input data to the server, and transmits data including text data and information about the literary figure in the form of an HTTP request to the server.

[0517] Step 3:

[0518] The server saves the received data. The server stores the received text data and author information in a database. At this time, the database stores the user ID, the entered text, and author selection information.

[0519] Step 4:

[0520] The server passes the text data to a generative AI model, which analyzes the writing style and sentiment. The server passes the input data to a generative AI model (e.g., GPT-4) and performs style conversion and sentiment analysis. The input is text data and information about famous authors, and the output is a style conversion proposal as the analysis result.

[0521] Step 5:

[0522] Based on the analysis results, a style conversion proposal is generated. The generative AI model generates appropriate style conversion proposals based on the style and sentiment analysis results and returns them to the server. The output is a style-converted sentence.

[0523] Step 6:

[0524] If the option to leave the selection of the author to AI is selected, the server will recommend the most suitable author. The generative AI model will recommend authors based on the analysis results and suggest them to the user. This process depends on the internal algorithm of the generative model. The input is text data and the analysis results, and the output is a recommended author.

[0525] Step 7:

[0526] The server provides the user with suggested revisions and suggestions in an interactive chat format. The server sends the generated revisions to the user's terminal and displays them in an interactive chat format. The input is the converted text, and the output is a chat interface on the user's screen.

[0527] Step 8:

[0528] Receive feedback from the user. The user enters feedback on the proposed revision, which is sent to the server via the terminal. The input is the text data of the user feedback, and the output is the server receiving the feedback data.

[0529] Step 9:

[0530] A revision proposal is generated again based on the feedback. The server again uses the generative AI model and emotion engine to make revisions based on the feedback data. The input is the feedback data and the initial text data, and the output is the final revision proposal.

[0531] Step 10:

[0532] The final revised text is provided to the user terminal. The server sends the final revision proposal to the user terminal, where the user can view it. The input is the final revision proposal, and the output is the final text on the user terminal.

[0533] Step 11:

[0534] Save the generated text and share it on other platforms. Users have the option to save the final revision proposal within the system and share it on other platforms such as social media or blogs. The input is the final revision proposal, and the output is saving the generated text and generating a sharing link.

[0535] 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.

[0536] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0537] 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.

[0538] [Second embodiment]

[0539] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0540] 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.

[0541] 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).

[0542] 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.

[0543] 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.

[0544] 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).

[0545] 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.

[0546] 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.

[0547] 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.

[0548] 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.

[0549] 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.

[0550] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0551] This invention is a system that uses generative AI to modify a user's writing to match the style of a specific literary master. This system receives the text entered by the user on a server, analyzes and modifies it using an AI engine, and provides the user with a final proposed modification.

[0552] System configuration

[0553] 1. User's Device

[0554] It provides an interface for users to input text and select the desired literary figure.

[0555] It also includes an interface for users to enter feedback.

[0556] 2. Server

[0557] The entered text and information about the literary figure is received and saved.

[0558] Invoke a generative model to analyze style and sentiment.

[0559] Generate suggested fixes and provide them to the user in the form of interactive feedback.

[0560] Receive user feedback and re-analyze and revise.

[0561] Program processing

[0562] 1. The user types text on the device

[0563] Users enter the text they want to edit into the input field, and then select the desired author (e.g., "Japanese Authors") or let the AI ​​select the most suitable author.

[0564] Example: "One day in the forest, I met an old man. He began to tell me a mysterious story."

[0565] 2. The device sends the input data to the server

[0566] The terminal transmits the input text data and the selected author information to the server.

[0567] Send data using an HTTP request.

[0568] 3. The server receives the data and analyzes it using the generative model.

[0569] The server stores the received data in a database.

[0570] The data is passed to an AI engine that performs analysis of style, emotion, and historical context.

[0571] The analysis results are used as features for style conversion.

[0572] 4. Recommending literary figures and generating revision suggestions

[0573] If the user does not specify a literary figure, the AI ​​engine will recommend the most suitable one.

[0574] Generate suggested revisions that match the literary master's writing style.

[0575] Example: The input sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is converted into Ryunosuke Akutagawa's style and corrected to "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[0576] 5. Interactive Feedback

[0577] The server sends suggested revisions to the user's device in an interactive chat format.

[0578] The user provides feedback on the proposed revisions and the server receives it.

[0579] 6. Generate the final fix

[0580] The server again generates revision suggestions using the user's feedback.

[0581] The final corrected text is sent to the user's device.

[0582] Specific examples

[0583] Example 1: If the user wants to use the style of Ryunosuke Akutagawa

[0584] 1. The user enters the sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." and selects Akutagawa Ryunosuke's writing style.

[0585] 2. The server uses AI to analyze Akutagawa Ryunosuke's writing style and generate suggested revisions that reflect his writing style and the historical context.

[0586] 3. Revised version: "One day in the forest, I met an old man. He began to tell me a profound and mysterious story."

[0587] 4. Return the text to the user and interactively adjust it until the user is satisfied.

[0588] Example 2: When the user entrusts the AI ​​with selecting a literary figure

[0589] 1. The user enters, "Today I really wanted to finish this task," and leaves the selection of a literary figure to the AI.

[0590] 2. The server uses AI to analyze the text and recommends Haruki Murakami as a suitable literary figure.

[0591] 3. Correction: "I really wanted to get this job done today."

[0592] 4. Return the text to the user and make final adjustments based on feedback.

[0593] In this way, the present invention utilizes generative AI to elevate the user's writing to a higher quality.

[0594] The processing flow will be explained below.

[0595] Step 1:

[0596] The user types text into the terminal.

[0597] The user enters the text they want to correct into the system's input field.

[0598] Users can select the author of their choice or choose the "Leave it to the AI" option.

[0599] Example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[0600] Step 2:

[0601] The device sends the user's input data to the server.

[0602] The terminal sends the input text and information about the selected author to the server via an HTTP request.

[0603] Step 3:

[0604] The server receives the document data.

[0605] The server stores the received text data and author information in a database.

[0606] Step 4:

[0607] The server runs a generative model to analyze style and sentiment.

[0608] The server passes the saved data to a generative model, which analyzes the text's style, emotion, and historical context.

[0609] The generative model extracts the characteristics of the received text and analyzes it to convert it into the writing style of a specific literary master.

[0610] Step 5:

[0611] The server will recommend the most suitable author.

[0612] If the user does not specify a literary figure, the generative model will recommend the most appropriate literary figure based on the analysis results.

[0613] The recommendation results are sent to the user's device and displayed.

[0614] Step 6:

[0615] The server generates a revision suggestion.

[0616] Generate suggested revisions to text to match the writing style of literary masters.

[0617] Example: The input sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is converted into Ryunosuke Akutagawa's style and corrected to "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[0618] Step 7:

[0619] The server provides suggested revisions in the form of interactive feedback.

[0620] The proposed changes are sent to the user's device in an interactive chat format, where the user can review the changes and provide feedback.

[0621] Step 8:

[0622] The user provides feedback on the device.

[0623] Users enter their opinions and comments on the proposed revisions and send them to the server.

[0624] Step 9:

[0625] The server receives the user's feedback and re-analyzes it.

[0626] The server receives the feedback and asks the generative model to reanalyze.

[0627] A re-analyzed correction proposal is generated and sent to the user's device.

[0628] This step is repeated until the user is satisfied.

[0629] Step 10:

[0630] The server generates and sends the final revision proposal.

[0631] Generate a final revision proposal based on user feedback and send it to the device.

[0632] The user can receive the final revised text.

[0633] Example 1

[0634] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0635] Previously, users had to spend a great deal of effort and expertise to adapt their writing to the style of a particular literary master. Furthermore, the feedback and correction process required for style conversion was cumbersome, and it took a long time for users to achieve satisfactory results. This reduced the efficiency with which users could obtain high-quality writing.

[0636] 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.

[0637] In this invention, the server includes means for receiving text entered by a user, means for selecting a desired author for style conversion, means for transmitting the text data received by the terminal to the server, means for the server to pass the received text data to a generative model and analyze the writing style and sentiment, means for the generative model to generate a style conversion proposal based on the analysis results, means for the generative model to recommend the most suitable author if the user leaves the author selection to the generative AI, means for providing revision suggestions and proposals in an interactive digital communication format, means for receiving feedback from the user and generating further revision suggestions, and means for providing the final revised text to the user. This enables users to easily obtain high-quality text that matches the writing style of a specific author without specialized knowledge.

[0638] The "means for receiving text entered by the user" is an interface for sending text entered by the user to the server.

[0639] The "means for selecting a desired author for style conversion" is an interface that allows the user to select a desired author for style conversion.

[0640] The "means for transmitting text data received by the terminal to the server" is a communication means for the user's terminal to transmit input text data and author information to the server.

[0641] "Means for passing text data received by the server to a generative model and analyzing the style and emotion" refers to a means for passing text data received by the server to an analysis system and analyzing text characteristics such as style and emotion.

[0642] "Means for a generative model to generate a suggestion for style conversion based on the analysis results" refers to means for a generative artificial intelligence to generate a suggestion for style conversion based on the analysis results.

[0643] "When a user leaves the selection of a literary figure to a generative AI, the means by which the generative model recommends the most suitable literary figure" refers to the means by which the generative AI analyzes the text input by the user and recommends the most suitable literary figure.

[0644] "Means for providing amendments or suggestions in the form of interactive digital communication" means interactive communication means for providing amendments or suggestions to users in real time.

[0645] The "means for receiving feedback from the user and generating a revision proposal again" is a means for receiving feedback from the user and generating a revision proposal again based on the feedback.

[0646] The "means for providing the final corrected text to the user" refers to the means for transmitting and displaying the final corrected text to the user.

[0647] The present invention is a system that corrects text entered by a user to fit a specific writing style. The system receives the text entered by the user, analyzes the writing style, emotion, and historical context using generative artificial intelligence, and generates and provides optimal revision suggestions. The system is implemented using the following hardware and software.

[0648] System configuration

[0649] 1. User's Device

[0650] The user uses software (e.g., a web browser) that opens an interface to the system.

[0651] The user uses an interface, for example an HTML form and JavaScript, to enter text into input fields and select the desired writing style.

[0652] The user presses the send button to send the entered text data and style information from the terminal to the server.

[0653] 2. Server

[0654] The server receives the user's text data sent as an HTTP request, using HTTP server software (e.g., Apache or Nginx) and a backend framework (e.g., Node.js or Django).

[0655] The server stores the received data in a database (e.g., MySQL or PostgreSQL).

[0656] The server then passes the stored data to a generative AI model (such as the GPT-3 model) for analysis, which extracts writing style, sentiment, and historical context.

[0657] The AI ​​engine generates suggested revisions based on the features for style conversion. If the user leaves the selection of a literary figure to the AI, the AI ​​engine will recommend the most suitable author.

[0658] 3. Providing feedback and suggested revisions

[0659] The server provides the generated revision suggestions to the user's device in the form of an interactive digital communication (e.g., real-time chat using WebSocket).

[0660] The user provides feedback on the submitted revisions, which is then sent back to the server.

[0661] The server receives feedback from the user and passes it back to the AI ​​engine to generate suggested revisions.

[0662] The final revised text is sent from the server to the user's device and displayed in the interface.

[0663] Specific examples

[0664] Example 1: When a user desires the writing style of a particular literary figure

[0665] 1. The user enters the sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." and selects the writing style of "Akutagawa Ryunosuke."

[0666] 2. When the user presses the send button, the device sends the text data and information about "Akutagawa Ryunosuke" to the server via an HTTP request.

[0667] 3. The server stores the data in a database and passes it to a generative artificial intelligence for analysis.

[0668] 4. The AI ​​engine generates suggested revisions to fit Akutagawa Ryunosuke's writing style. The suggested revision is, "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[0669] 5. The proposed revision is sent to the user, who then provides feedback, saying, "I'd like this part to sound more archaic."

[0670] 6. The server receives the feedback, asks the AI ​​engine to generate a revision again, and provides the final revised sentence to the user: "One day in the forest, I met an old man. The old man seemed to begin to tell me a deep and mysterious story."

[0671] Prompt Sentence Examples

[0672] "Please revise the following sentence in the style of Ryunosuke Akutagawa:

[0673] One day in the forest, I met an old man who began to tell me a mysterious story.

[0674] In this way, the present invention utilizes generative artificial intelligence to streamline the process by which users obtain high-quality text tailored to a particular writing style.

[0675] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0676] Specific processing flow of the program

[0677] Step 1:

[0678] The user types text into the device.

[0679] How it works: The user opens the device interface and enters the text they want to edit into the input field. They can choose the writing style they want, or let the generative AI choose a literary master. For example, they could enter a sentence like, "One day in the forest, I met an old man. The old man began to tell me a mysterious story."

[0680] Input: The user enters the text and information about the desired author into the input field.

[0681] Output: The text entered in the terminal's input field and information about the literary figure will be obtained.

[0682] Step 2:

[0683] The terminal sends the input data to the server.

[0684] How it works: When the user presses the send button, the device uses an HTTP request to send the entered text data and the selected author information to the server. This request uses the POST method, and the data is encoded in JSON format.

[0685] Input: Text data entered by the user and information about the selected literary figure.

[0686] Output: JSON formatted data sent to the server as an HTTP request.

[0687] Step 3:

[0688] The server receives the data and analyzes it using a generative model.

[0689] How it works: The server stores the received data in a database. It then passes the data to a generative AI (e.g., GPT-3) to begin analysis. The AI ​​engine performs analysis to extract stylistic, emotional, and historical context.

[0690] Input: JSON formatted data sent to the server.

[0691] Output: Data stored in the database and analysis results by the AI ​​engine (features of writing style, emotion, and historical background).

[0692] Step 4:

[0693] A generative model generates style transformation suggestions based on the analysis results.

[0694] How it works: Based on the analysis results, the generative AI generates revision suggestions that match the user's desired writing style. If the user has not specified a literary figure, the AI ​​engine runs an algorithm to recommend the most suitable literary figure.

[0695] Input: Analysis results by the AI ​​engine (features of writing style, emotion, and historical background).

[0696] Output: The generated revision text.

[0697] Step 5:

[0698] The proposed fix is ​​sent to the user's device.

[0699] How it works: The server sends the proposed revisions to the user's device in real time using an interactive digital communication format (e.g., WebSocket). The user's interface displays the generated revisions.

[0700] Input: The generated text of the proposed amendment.

[0701] Output: Suggested fixes sent to the user's device in real time.

[0702] Step 6:

[0703] Users provide feedback on proposed changes.

[0704] How it works: The user reviews the suggested revisions and enters feedback if necessary (e.g., "I want this part to sound more archaic.") The feedback is then sent to the server again using an HTTP request.

[0705] Input: User feedback.

[0706] Output: User feedback sent to the server.

[0707] Step 7:

[0708] The server re-analyzes the feedback and generates a final revision proposal.

[0709] How it works: Based on the feedback received by the server, the data is passed to the generative AI again to regenerate a revision proposal. The AI ​​engine then generates a new revision proposal that takes the feedback into account and returns it to the server.

[0710] Input: User feedback and original analysis results.

[0711] Output: Final revised draft.

[0712] Step 8:

[0713] Provide the final revision to the user.

[0714] How it works: The server sends the final edited text to the user's device and displays it in the device's interface, allowing the user to review the final result and download or copy it if desired.

[0715] Input: Final amendment.

[0716] Output: The final corrected text that is displayed on the user's terminal.

[0717] This series of processes allows users to easily obtain high-quality text that matches a specific writing style.

[0718] (Application example 1)

[0719] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0720] This invention relates to a system that allows users to generate content in the style of a particular literary master and check the resulting edits in real time. Today, there is a growing demand for tools that allow people to create high-quality content by imitating the writing styles of various authors and literary masters. However, conventional technologies make it difficult to convert or edit writing styles in real time, limiting the user experience. It is necessary to provide a system that solves these problems and allows users to easily generate and receive high-quality literary-style content.

[0721] 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.

[0722] In this invention, the server includes means for receiving text entered by a user, means for selecting a desired author for style conversion, means for passing the text data received by the server to a generative model and analyzing the writing style and sentiment, means for the generative model to generate a style conversion suggestion based on the analysis results, means for the generative model to recommend the most suitable author if the user leaves the author selection to AI, means for providing suggested revisions and suggestions in an interactive chat format, means for receiving feedback from the user and generating further suggested revisions, means for providing the final corrected text to the user, and means for providing the style-converted text to the user through an application running on a mobile device. This enables users to generate content in real time in the writing style of a specific author and instantly confirm and apply high-quality revisions.

[0723] The "means for receiving text entered by the user" is an interface for transmitting text data entered by the user to the system.

[0724] The "means for selecting a desired author for style conversion" is a selection interface that allows a user to select the style of a particular author and convert text into that style.

[0725] "Means for the server to pass the received text data to a generative model and analyze the style and sentiment" refers to the process of using a generative AI model to analyze the style and sentiment of text received from a user.

[0726] The "means by which the generative model generates a proposal for style conversion based on the analysis results" is an algorithm that generates a proposal for converting a sentence into the style of a specified literary figure based on the analysis results.

[0727] "Means for the generative model to recommend the most suitable author when the user leaves the author selection to the AI" refers to the process by which, when the user does not select a writer, the generative AI automatically selects and recommends the author that best suits the input text.

[0728] "Means for providing revisions and suggestions in an interactive chat format" refers to an interface that provides the revised text to the user in a chat format and enables interaction with the user.

[0729] "Means for receiving feedback from users and generating revisions again" refers to the process of revising the text again based on the feedback provided by the users.

[0730] The "means for providing the final revised text to the user" is the interface that provides the user with the final text that incorporates all corrections and feedback.

[0731] "Means for providing stylized text to a user through an application running on a mobile device" is a process for providing corrected text to a user through an application running on a mobile device.

[0732] This invention is a system that uses generative AI to modify text entered by a user to match it with the writing style of a specific literary master. This system is primarily composed of the user's device and a server. Specifically, it is implemented using the following process.

[0733] System configuration

[0734] 1. User's Device

[0735] It provides an interface for users to input text and select the desired literary figure.

[0736] It also includes an interface for receiving feedback from users.

[0737] 2. Server

[0738] The input text data and author selection information are received and saved.

[0739] Launch a generative AI model to analyze style and sentiment.

[0740] Generate suggested fixes and provide them to the user in the form of interactive feedback.

[0741] Receive user feedback and re-analyze and revise.

[0742] Processing Details

[0743] 1. User Input

[0744] Users input text into their devices and select the author they want to read. This information is sent to the server.

[0745] For example, enter the sentence "One day in the forest, I met an old man. The old man began to tell me a mysterious story." and let the selected author be "Author A."

[0746] 2. Server Processing

[0747] The server stores the received data and uses a generative AI model to analyze the text, extracting stylistic and emotional features.

[0748] Based on the analysis results, the text is modified to fit the writing style of the designated author. For example, the input text is converted into the writing style of "Author A."

[0749] The corrected text is presented to the user in an interactive format.

[0750] For example, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." becomes "One day in the forest, I met an old man. The old man began to tell me a deep and mysterious story."

[0751] 3. User feedback and final revisions

[0752] After reviewing the proposed revisions, users can provide feedback and requests for further revisions, and the server will then revise the text based on this feedback.

[0753] The server then sends the final corrected text to the user.

[0754] Hardware and software used

[0755] Hardware: Server (CPU / GPU server), user device (smartphone)

[0756] Software: Flask (server-side implementation), transformers library (generative AI model)

[0757] Specific examples

[0758] When a user inputs "Today, I really wanted to finish this work," and selects the desired author as "Writer B," the server receives this and generates a sentence revised in a style appropriate for "Writer B." For example, "Today, I really wanted to finish this work." The server then receives the user's feedback, makes final revisions, and provides the sentence.

[0759] In this way, users can enjoy high-quality real-time text corrections in the style of a particular literary master.

[0760] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0761] Step 1:

[0762] A means by which users input text into a device

[0763] The user enters a sentence into the input field and selects the desired author. The entered sentence and author information are sent from the terminal to the server.

[0764] Input: Text entered by the user, desired author

[0765] Output: Data sent to the server (text, literary information)

[0766] Specific operation: The user uses a dedicated interface to enter text into the text input field, select the desired author from a drop-down menu or radio buttons, and then press the send button to send the data to the server.

[0767] Step 2:

[0768] A means for the server to store received text data

[0769] The server stores the text data and author information received from the terminal in a database.

[0770] Input: Data sent from the terminal (text, literary information)

[0771] Output: Data stored in the database

[0772] Specific operation: The server deserializes the data received as an HTTP request through the API and saves it in the database.

[0773] Step 3:

[0774] A means for the server to pass data to the generated AI model for analysis

[0775] The server passes the saved text data and information about the author to a generative AI model, which analyzes the writing style and emotions.

[0776] Input: Data stored in the database (text, literary information)

[0777] Output: Style and sentiment features analyzed by the generative AI model

[0778] Specific operation: The server reads the stored data, inputs it into a generative AI model (e.g., GPT-2), and analyzes the stylistic and emotional characteristics.

[0779] Step 4:

[0780] A means for the generative model to transform writing style based on the analysis results

[0781] The generative model converts the user's writing into the style of a specified literary figure.

[0782] Input: Analysis results (features of style and emotion)

[0783] Output: Stylistically transformed text of proposed revision

[0784] Specific operation: Based on the analysis results, the generative AI model combines the stylistic characteristics of the specified author with the prompt sentence to perform the optimal style conversion.

[0785] Step 5:

[0786] A way to provide suggested fixes via chat

[0787] The server provides the generated revision suggestions to the user in an interactive chat format.

[0788] Input: Stylistically converted text of proposed revision

[0789] Output: Suggested fixes displayed on the user's device

[0790] Specific operation: The server encodes the generated correction proposal in JSON format and sends it to the user's device via API. The correction proposal is displayed in chat format on the user's device interface.

[0791] Step 6:

[0792] A way for users to provide feedback and generate revisions again

[0793] The user can input feedback on the proposed corrections and request further corrections. This feedback is sent back to the server, and the generative AI model then makes further corrections.

[0794] Input: User feedback

[0795] Output: Re-corrected sentence

[0796] Specific operation: The user inputs feedback on the interface of the device and presses the resend button to send the data to the server, which then uses the generative AI model to convert the writing style again.

[0797] Step 7:

[0798] A means of providing the final revised text

[0799] The server again provides the revised final text to the user.

[0800] Input: Data for final revision

[0801] Output: Final revised text

[0802] Specific operation: The server checks the final revised text, sends it to the user's terminal, and displays it on the screen.

[0803] Step 8:

[0804] A means for providing stylized text through an application that runs on a mobile device

[0805] The final revised text is provided to the user through an application running on the user's mobile device.

[0806] Input: Last revised text

[0807] Output: The converted text displayed on a mobile device

[0808] Specific behavior: The final revised text is displayed in the application on the mobile device and provided to the user.

[0809] This allows users to receive high-quality text corrections in real time in the style of a specific literary master.

[0810] 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.

[0811] This invention is a system that uses generative AI and an emotion engine to modify a user's writing to match the style of a specific literary master, generating emotive expressions. This system receives the text entered by the user on a server, analyzes and modifies it using AI and the emotion engine, and provides the user with a final proposed modification.

[0812] System configuration

[0813] 1. User's Device

[0814] It provides an interface for users to input text and select the desired literary figure.

[0815] It also includes an interface for users to enter feedback.

[0816] 2. Server

[0817] The entered text and information about the literary figure is received and saved.

[0818] Activate generative models and sentiment engines to analyze style and sentiment.

[0819] Generate suggested fixes and provide them to the user in the form of interactive feedback.

[0820] Receive user feedback and re-analyze and revise.

[0821] Program processing

[0822] 1. The user types text into the device

[0823] The user enters the text they want to correct into the system's input field.

[0824] Users can select the author of their choice or choose the "Leave it to the AI" option.

[0825] Example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[0826] 2. The device sends the user's input data to the server

[0827] The terminal transmits the input text data and the selected author information to the server.

[0828] Send data using an HTTP request.

[0829] 3. The server receives the data and analyzes it using the generative model and emotion engine.

[0830] The server stores the received text data and author information in a database.

[0831] The data is passed to a generative model and emotion engine to analyze the style, emotion, and historical context of the text.

[0832] The emotion engine identifies the emotion contained in a sentence and extracts features to generate expressions based on that emotion.

[0833] 4. Recommending literary figures and generating revision suggestions

[0834] If the user does not specify a literary figure, the AI ​​engine will recommend the most suitable one.

[0835] Generate suggested revisions that match the literary master's writing style.

[0836] Reflecting the results of the emotion engine, revision suggestions containing emotional expressions are created.

[0837] Example: The input sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is converted into Akutagawa Ryunosuke's style and corrected to, "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[0838] 5. Interactive Feedback

[0839] The server sends suggested revisions to the user's device in an interactive chat format.

[0840] Users can review suggested revisions and provide feedback on emotion and style.

[0841] 6. Generate the final fix

[0842] The server again generates revision suggestions using the user's feedback.

[0843] The emotion engine also analyzes again and generates a final revision proposal with the appropriate emotional expression.

[0844] The final corrected text is sent to the user's device.

[0845] Specific examples

[0846] Example 1: If the user wants to use the style of Ryunosuke Akutagawa

[0847] 1. The user enters the sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." and selects Ryunosuke Akutagawa's writing style.

[0848] 2. The server uses a generative model and emotion engine to analyze the writing style and emotional features, and generates revision suggestions that emphasize the emotion while reflecting the writing style and historical context.

[0849] 3. Amendment: "One day in the forest, I met an old man. He began to tell me a profound and mysterious story."

[0850] 4. Return the text to the user and adjust it interactively based on their feedback.

[0851] Example 2: When the user entrusts the AI ​​with selecting a literary figure

[0852] 1. The user enters, "Today I really wanted to finish this task," and leaves the selection of a literary figure to the AI.

[0853] 2. The server analyzes the text using a generative model and an emotion engine and recommends Haruki Murakami as a suitable literary figure.

[0854] 3. Correction: "Today, I really wanted to get this job done."

[0855] 4. Return the text to the user and make final adjustments based on feedback.

[0856] In this way, the present invention utilizes generative AI and an emotion engine to modify the user's writing to match the style of a specific literary master, elevating it into high-quality, emotionally rich writing.

[0857] The processing flow will be explained below.

[0858] Step 1:

[0859] The user types text into the terminal.

[0860] The user enters the text they want to correct into the system's input field.

[0861] Users can select the author of their choice or choose the "Leave it to the AI" option.

[0862] Example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[0863] Step 2:

[0864] The device sends the user's input data to the server.

[0865] The terminal transmits the input text data and the selected author information to the server via an HTTP request.

[0866] Step 3:

[0867] The server receives the document data.

[0868] The server stores the received text data and author information in a database.

[0869] Step 4:

[0870] The server runs a generative model and an emotion engine to analyze the style and emotion.

[0871] The server passes the saved data to a generative model, which analyzes the text's style, emotion, and historical context.

[0872] The generative model extracts the characteristics of the received text and analyzes it to convert it into the writing style of a specific literary master.

[0873] The emotion engine identifies the emotion contained in a sentence and extracts expression features based on that emotion.

[0874] Step 5:

[0875] The server will recommend the most suitable author.

[0876] If the user does not specify a literary figure, the generative model will recommend the most appropriate literary figure based on the analysis results.

[0877] The recommendation results are sent to the user's device and displayed.

[0878] Step 6:

[0879] The server generates a revision suggestion.

[0880] Generate suggested revisions to text to match the writing style of literary masters.

[0881] The analysis results of the emotion engine are reflected to create revision proposals that include emotionally rich expressions.

[0882] Example: The input sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is converted into Akutagawa Ryunosuke's style and corrected to, "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[0883] Step 7:

[0884] The server provides suggested revisions in the form of interactive feedback.

[0885] Proposed fixes are sent to the user's device in an interactive chat format.

[0886] Users can review suggested revisions and provide feedback on emotion and style.

[0887] Step 8:

[0888] The user provides feedback on the device.

[0889] Users enter their opinions and comments on the proposed revisions and send them to the server.

[0890] Step 9:

[0891] The server receives the user's feedback and re-analyzes it.

[0892] The server receives the feedback and asks the generative model and emotion engine to reanalyze.

[0893] A reanalyzed revision proposal is generated, and a revision proposal including an emotionally rich expression is generated.

[0894] This step is repeated until the user is satisfied.

[0895] Step 10:

[0896] The server generates and sends the final revision proposal.

[0897] Generate a final revision proposal based on user feedback and send it to the device.

[0898] The user can receive the final revised text.

[0899] Example 2

[0900] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0901] Conventional writing systems require users to manually edit and change their writing style, which is a time-consuming process that makes it difficult to create emotive, high-quality writing. Furthermore, if users want to edit their writing to match the style of a particular literary figure, they are required to understand the characteristics of that style and the historical context, which requires a lot of time and effort. Furthermore, it is difficult to make revisions or interactive adjustments based on user feedback, making efficient writing difficult.

[0902] The specification process by the specification 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 receiving text entered by a user, a means for selecting a desired text style for typeface conversion, a means for passing the text data received by the server to a natural language processing model and analyzing the typeface and emotion, a means for the natural language processing model to generate a typeface conversion suggestion based on the analysis results, a means for the natural language processing model to recommend an optimal text style when the user leaves the selection of the text style to artificial intelligence, a means for providing revision suggestions and suggestions in an interactive chat format, a means for receiving feedback from the user and generating another revision suggestion, and a means for providing the final revised text to the user. This allows the user to create high-quality text without hassle and to efficiently and quickly correct the text style and emotional expression.

[0903] "User" refers to an individual or group that uses the system to input text and perform style conversion and sentiment analysis.

[0904] "Means of receiving" refers to the functions and processes for sending text entered by the user to the system and receiving that data on the server.

[0905] "Means for selecting a desired writing style for typeface conversion" refers to an interface or option that allows a user to select a particular author or writing style.

[0906] A "natural language processing model" refers to an algorithm or software that analyzes input text and generates suggested revisions based on a specific style or sentiment.

[0907] "Means for sentiment analysis" refers to the process of identifying the sentiment contained in a text and generating expressions based on that sentiment.

[0908] "Means for generating typeface conversion suggestions" refers to a function or algorithm that generates suggestions for converting the typeface of a text to a style desired by the user based on the analysis results.

[0909] "Leaving the selection of literary figures to artificial intelligence" refers to a usage scenario in which the system automatically recommends the most suitable literary figure without the user having to select a specific author.

[0910] "Interactive chat-style means" refers to an interactive interface that provides generated revisions to the user in real time and allows the user to enter feedback.

[0911] "Means of receiving feedback and generating revision suggestions again" refers to the process of collecting opinions and requests from users and revising the text using a generative AI model based on them.

[0912] "Means for providing the final corrected text to the user" refers to the function of displaying or sending the final text to the user after all correction processes have been completed.

[0913] This invention is a system that uses generative AI and an emotion engine to modify a user's text to fit a specific writing style and generate emotive expressions. This system receives text entered by the user on a server, analyzes and modifies it using a natural language processing model and an emotion engine, and provides the user with a final proposed modification.

[0914] System configuration

[0915] 1. User's Device

[0916] It provides an interface for users to enter text and select the desired writing style.

[0917] It also includes an interface for users to enter feedback.

[0918] Usage example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[0919] 2. Server

[0920] Input text and text style information is received and saved.

[0921] It launches a natural language processing model (e.g., GPT-3) and an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze typeface and emotion.

[0922] Generate suggested fixes and provide them to the user in the form of interactive feedback.

[0923] Receive user feedback and re-analyze and revise.

[0924] Hardware used: High-performance servers (data center servers equipped with CPUs and GPUs, etc.)

[0925] Software used: Python, Flask (web framework), MariaDB (database)

[0926] Program processing

[0927] The system works in the following steps:

[0928] 1. The user types text into the device

[0929] The user enters the text they want to correct into the system's input field.

[0930] Users can choose their preferred writing style or select the "Leave it to AI" option.

[0931] Example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[0932] 2. The device sends the user's input data to the server

[0933] The terminal transmits the input text data and the selected text style information to the server using an HTTP request.

[0934] 3. The server receives the data and analyzes it using the generative model and emotion engine.

[0935] The server stores the received text data and text style information in a database.

[0936] The data is passed to a natural language processing model and emotion engine to analyze the style of writing, emotions, and historical context of the text.

[0937] Usage example: A Python script on the server uses the requests library to send data to the natural language processing model API and the emotion engine API and receive responses.

[0938] 4. Writing style recommendation and revision generation

[0939] If the user does not specify a writing style, the server uses a natural language processing model to recommend the most appropriate writing style.

[0940] Generate revision suggestions tailored to your writing style.

[0941] Reflecting the results of the emotion engine, revision suggestions containing emotional expressions are created.

[0942] Example: The input sentence "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is corrected to "One day in the forest, I met an old man. The old man began to tell me a profound story." based on the recommended writing style.

[0943] 5. Interactive Feedback

[0944] The server sends suggested revisions to the user's device in an interactive chat format.

[0945] Users can review the proposed changes and provide feedback on emotions and typefaces.

[0946] 6. Generate the final fix

[0947] The server uses the user's feedback to generate a new revision, and the emotion engine analyzes it again to generate a final revision with appropriate emotional expressions.

[0948] The final corrected text is sent to the user's device.

[0949] For example, if a user types "I really wanted to finish this work today" and leaves the writing style selection to AI, the server will use a natural language processing model and emotion engine to recommend a style similar to Haruki Murakami's, correcting it to "Today, I really wanted to finish this work." This suggested correction is then provided to the user.

[0950] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0951] Step 1:

[0952] The user types text into the terminal.

[0953] Input: The user enters text into the system's input field and selects the desired writing style.

[0954] Example: A user types "Today was a very busy day" and selects "Natsume Soseki" as the desired writing style.

[0955] Output: The input text and the selected writing style are sent to the server in JSON format.

[0956] Step 2:

[0957] The device sends the user's input data to the server.

[0958] Input: User input data (text and selected writing style).

[0959] Specific operation: When you click the send button on the device, the data is sent to the server via an HTTP request using the JavaScript fetch function. The data is sent in JSON format.

[0960] Output: The JSON data received by the server.

[0961] Step 3:

[0962] The server receives the data and analyzes it using a generative model and emotion engine.

[0963] Input: JSON data sent from the terminal.

[0964] What it does: The server stores the received data in a database, passes it to a generative AI model (e.g., GPT-3) and an emotion engine (e.g., IBM Watson Tone Analyzer), and uses a Python script to send the data to the API through the requests library.

[0965] Data processing: The text data is analyzed to determine writing style, historical context, and emotions.

[0966] Output: The analysis results are returned in JSON format and saved on the server.

[0967] Step 4:

[0968] The server generates recommendations and revisions for literary masters.

[0969] Input: Parsed JSON data.

[0970] How it works: If the user does not specify a writing style, the server uses a generative AI model to recommend the best writing style. A Python script generates revision suggestions based on the analysis results.

[0971] Data processing: Features are extracted for style conversion and emotional expressions are extracted.

[0972] Output: Generated revision suggestions are created in JSON format.

[0973] Step 5:

[0974] Arba provides interactive feedback.

[0975] Input: JSON data of generated correction suggestions.

[0976] Specific operation: The server sends suggested revisions to the device in real time using WebSocket or Ajax, and the user checks the suggested revisions and enters feedback.

[0977] Output: User feedback data is sent to the server.

[0978] Step 6:

[0979] The server generates the final revision.

[0980] Input: User feedback data.

[0981] Specific operation: The server calls the generative AI model and emotion engine again based on the feedback, and refines the proposed revisions. The final revisions are generated.

[0982] Data calculations: Reanalysis and revisions reflecting feedback.

[0983] Output: The final corrected text is sent to the user's device.

[0984] The above is the specific processing flow of the program for this system, which allows users to efficiently create high-quality documents without much effort.

[0985] (Application example 2)

[0986] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0987] Conventional text correction systems lack the ability to convert user-entered text into a specific style and add emotive expressions, making it difficult to generate high-quality text for content creation. Furthermore, there is no easy way to save the generated text and use it on other platforms, limiting the user experience. It is necessary to solve these issues and provide a more convenient text correction system.

[0988] 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.

[0989] In this invention, the server includes means for receiving text entered by a user, means for selecting a desired author for style conversion, means for passing the text data received by the server to a generative model and analyzing the writing style and emotion, means for the generative model to generate a style conversion suggestion based on the analysis results, means for the generative model to recommend the most suitable author if the user leaves the author selection to AI, means for providing revision suggestions and suggestions in an interactive chat format, means for receiving feedback from the user and generating further revision suggestions, means for providing the final revised text to the user, and means for saving the generated text and sharing it on other platforms. This makes it possible to easily generate high-quality, emotionally rich text and furthermore to easily use the generated text on other platforms.

[0990] The "means for receiving text entered by the user" refers to a mechanism for transmitting text data entered by the user through the terminal to the server and receiving it.

[0991] The "means for selecting a desired author for style conversion" is a mechanism that provides an interface for a user to select the style of a desired author as the conversion destination.

[0992] "Means for passing the text data received by the server to the generative model and analyzing the writing style and emotion" refers to a mechanism by which the server inputs the text data received from the user into the generative AI model and analyzes the writing style and emotion.

[0993] "Means by which the generative model generates suggestions for style conversion based on the analysis results" refers to a mechanism by which the generative AI model generates appropriate suggestions for style conversion based on the analysis results of style and emotion.

[0994] "A means for the generative model to recommend the most suitable author when the user leaves the author selection to the AI" is a mechanism that, when the user sets the author selection to be left to the AI, recommends the author that the generative AI model determines to be the most suitable.

[0995] The "means for providing revisions and suggestions in an interactive chat format" is a mechanism for displaying generated revisions and suggestions to users in a chat format, thereby realizing two-way interaction.

[0996] The "means of receiving feedback from the user and generating a new revision proposal" is a mechanism for revising the text based on the feedback entered by the user and generating a new revision proposal.

[0997] The "means for providing the final corrected text to the user" is a mechanism for displaying and providing the final corrected text to the user.

[0998] "Means for saving generated text and sharing it on other platforms" refers to a mechanism for saving generated text within the system and sharing it on other platforms such as social media and blogs.

[0999] The system that realizes this application example is configured as follows: The entire system is executed by the user's terminal, a server, and necessary software components.

[1000] User's device

[1001] 1. Text input interface

[1002] It provides an interface for users to input text and select the author they want, and also includes an option for users to leave the author selection to the AI.

[1003] 2. Feedback Interface

[1004] The system provides an interface for users to input feedback on the generated revisions and request further revisions. This feedback is sent to the server in real time.

[1005] server

[1006] 1. Data Receipt and Storage

[1007] The server receives the text data and author information sent from the terminal and stores them in a database.

[1008] 2. Generative AI Models and Emotion Engines

[1009] The received text data is passed to a generative AI model (e.g., GPT-4) for stylistic and emotional analysis. An emotion engine (e.g., Hume AI) identifies the emotion contained in the text and extracts features to generate expressions based on that emotion.

[1010] 3. Proposal generation for style conversion

[1011] The generative AI model generates suggestions for style conversion based on the analysis results. If the user leaves the selection of the author to the AI, the generative model will recommend the most suitable author. These suggestions are presented in an interactive chat format that is easy for users to understand.

[1012] 4. Feedback Processing

[1013] It receives feedback from users and generates new revision suggestions based on that feedback. It also analyzes the emotion engine again and generates a final revision suggestion with appropriate emotional expressions.

[1014] 5. Final submission, saving and sharing

[1015] The final revised text is provided to the user and saved in the system, and the user can share the generated text on other platforms such as social media or blogs.

[1016] Specific examples

[1017] Prompt Sentence Examples

[1018] User input: "I really wanted to finish this work today."

[1019] User's favorite author: Akutagawa Ryunosuke

[1020] What happens on the server side when the user selects a writer:

[1021] The server receives the sentence, "Today, I really wanted to finish this work," and uses a generative AI model to analyze the style and sentiment. It converts this sentence into the style of Ryunosuke Akutagawa and generates a suggested revision, such as, "Today, I felt that I had to complete this work no matter what."

[1022] Feedback example:

[1023] The user inputs feedback such as "Please emphasize the emotion more." The server receives this feedback, analyzes it again using the generative AI model and emotion engine, and generates a final revision such as "Today, I was determined to finish this task no matter what," and provides it to the user.

[1024] In this way, users can easily generate high-quality, emotive texts and then easily use the generated texts on other platforms.

[1025] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1026] Step 1:

[1027] The user inputs text into the terminal. The user inputs the text into the input field and selects the desired author. The input includes the text as text data and the author selection settings.

[1028] Step 2:

[1029] The terminal sends the input data to the server, and transmits data including text data and information about the literary figure in the form of an HTTP request to the server.

[1030] Step 3:

[1031] The server saves the received data. The server stores the received text data and author information in a database. At this time, the database stores the user ID, the entered text, and author selection information.

[1032] Step 4:

[1033] The server passes the text data to a generative AI model, which analyzes the writing style and sentiment. The server passes the input data to a generative AI model (e.g., GPT-4) and performs style conversion and sentiment analysis. The input is text data and information about famous authors, and the output is a style conversion proposal as the analysis result.

[1034] Step 5:

[1035] Based on the analysis results, a style conversion proposal is generated. The generative AI model generates appropriate style conversion proposals based on the style and sentiment analysis results and returns them to the server. The output is a style-converted sentence.

[1036] Step 6:

[1037] If the option to leave the selection of the author to AI is selected, the server will recommend the most suitable author. The generative AI model will recommend authors based on the analysis results and suggest them to the user. This process depends on the internal algorithm of the generative model. The input is text data and the analysis results, and the output is a recommended author.

[1038] Step 7:

[1039] The server provides the user with suggested revisions and suggestions in an interactive chat format. The server sends the generated revisions to the user's terminal and displays them in an interactive chat format. The input is the converted text, and the output is a chat interface on the user's screen.

[1040] Step 8:

[1041] Receive feedback from the user. The user enters feedback on the proposed revision, which is sent to the server via the terminal. The input is the text data of the user feedback, and the output is the server receiving the feedback data.

[1042] Step 9:

[1043] A revision proposal is generated again based on the feedback. The server again uses the generative AI model and emotion engine to make revisions based on the feedback data. The input is the feedback data and the initial text data, and the output is the final revision proposal.

[1044] Step 10:

[1045] The final revised text is provided to the user terminal. The server sends the final revision proposal to the user terminal, where the user can view it. The input is the final revision proposal, and the output is the final text on the user terminal.

[1046] Step 11:

[1047] Save the generated text and share it on other platforms. Users have the option to save the final revision proposal within the system and share it on other platforms such as social media or blogs. The input is the final revision proposal, and the output is saving the generated text and generating a sharing link.

[1048] 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.

[1049] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1050] 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.

[1051] [Third embodiment]

[1052] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1053] 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.

[1054] 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).

[1055] 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.

[1056] 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.

[1057] 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).

[1058] 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.

[1059] 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.

[1060] 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.

[1061] 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.

[1062] 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.

[1063] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1064] This invention is a system that uses generative AI to modify a user's writing to match the style of a specific literary master. This system receives the text entered by the user on a server, analyzes and modifies it using an AI engine, and provides the user with a final proposed modification.

[1065] System configuration

[1066] 1. User's Device

[1067] It provides an interface for users to input text and select the desired literary figure.

[1068] It also includes an interface for users to enter feedback.

[1069] 2. Server

[1070] The entered text and information about the literary figure is received and saved.

[1071] Invoke a generative model to analyze style and sentiment.

[1072] Generate suggested fixes and provide them to the user in the form of interactive feedback.

[1073] Receive user feedback and re-analyze and revise.

[1074] Program processing

[1075] 1. The user types text on the device

[1076] Users enter the text they want to edit into the input field, and then select the desired author (e.g., "Japanese Authors") or let the AI ​​select the most suitable author.

[1077] Example: "One day in the forest, I met an old man. He began to tell me a mysterious story."

[1078] 2. The device sends the input data to the server

[1079] The terminal transmits the input text data and the selected author information to the server.

[1080] Send data using an HTTP request.

[1081] 3. The server receives the data and analyzes it using the generative model.

[1082] The server stores the received data in a database.

[1083] The data is passed to an AI engine that performs analysis of style, emotion, and historical context.

[1084] The analysis results are used as features for style conversion.

[1085] 4. Recommending literary figures and generating revision suggestions

[1086] If the user does not specify a literary figure, the AI ​​engine will recommend the most suitable one.

[1087] Generate suggested revisions that match the literary master's writing style.

[1088] Example: The input sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is converted into Ryunosuke Akutagawa's style and corrected to "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[1089] 5. Interactive Feedback

[1090] The server sends suggested revisions to the user's device in an interactive chat format.

[1091] The user provides feedback on the proposed revisions and the server receives it.

[1092] 6. Generate the final fix

[1093] The server again generates revision suggestions using the user's feedback.

[1094] The final corrected text is sent to the user's device.

[1095] Specific examples

[1096] Example 1: If the user wants to use the style of Ryunosuke Akutagawa

[1097] 1. The user enters the sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." and selects Akutagawa Ryunosuke's writing style.

[1098] 2. The server uses AI to analyze Akutagawa Ryunosuke's writing style and generate suggested revisions that reflect his writing style and the historical context.

[1099] 3. Revised version: "One day in the forest, I met an old man. He began to tell me a profound and mysterious story."

[1100] 4. Return the text to the user and interactively adjust it until the user is satisfied.

[1101] Example 2: When the user entrusts the AI ​​with selecting a literary figure

[1102] 1. The user enters, "Today I really wanted to finish this task," and leaves the selection of a literary figure to the AI.

[1103] 2. The server uses AI to analyze the text and recommends Haruki Murakami as a suitable literary figure.

[1104] 3. Correction: "I really wanted to get this job done today."

[1105] 4. Return the text to the user and make final adjustments based on feedback.

[1106] In this way, the present invention utilizes generative AI to elevate the user's writing to a higher quality.

[1107] The processing flow will be explained below.

[1108] Step 1:

[1109] The user types text into the terminal.

[1110] The user enters the text they want to correct into the system's input field.

[1111] Users can select the author of their choice or choose the "Leave it to the AI" option.

[1112] Example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[1113] Step 2:

[1114] The device sends the user's input data to the server.

[1115] The terminal sends the input text and information about the selected author to the server via an HTTP request.

[1116] Step 3:

[1117] The server receives the document data.

[1118] The server stores the received text data and author information in a database.

[1119] Step 4:

[1120] The server runs a generative model to analyze style and sentiment.

[1121] The server passes the saved data to a generative model, which analyzes the text's style, emotion, and historical context.

[1122] The generative model extracts the characteristics of the received text and analyzes it to convert it into the writing style of a specific literary master.

[1123] Step 5:

[1124] The server will recommend the most suitable author.

[1125] If the user does not specify a literary figure, the generative model will recommend the most appropriate literary figure based on the analysis results.

[1126] The recommendation results are sent to the user's device and displayed.

[1127] Step 6:

[1128] The server generates a revision suggestion.

[1129] Generate suggested revisions to text to match the writing style of literary masters.

[1130] Example: The input sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is converted into Ryunosuke Akutagawa's style and corrected to "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[1131] Step 7:

[1132] The server provides suggested revisions in the form of interactive feedback.

[1133] The proposed changes are sent to the user's device in an interactive chat format, where the user can review the changes and provide feedback.

[1134] Step 8:

[1135] The user provides feedback on the device.

[1136] Users enter their opinions and comments on the proposed revisions and send them to the server.

[1137] Step 9:

[1138] The server receives the user's feedback and re-analyzes it.

[1139] The server receives the feedback and asks the generative model to reanalyze.

[1140] A re-analyzed correction proposal is generated and sent to the user's device.

[1141] This step is repeated until the user is satisfied.

[1142] Step 10:

[1143] The server generates and sends the final revision proposal.

[1144] Generate a final revision proposal based on user feedback and send it to the device.

[1145] The user can receive the final revised text.

[1146] Example 1

[1147] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1148] Previously, users had to spend a great deal of effort and expertise to adapt their writing to the style of a particular literary master. Furthermore, the feedback and correction process required for style conversion was cumbersome, and it took a long time for users to achieve satisfactory results. This reduced the efficiency with which users could obtain high-quality writing.

[1149] 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.

[1150] In this invention, the server includes means for receiving text entered by a user, means for selecting a desired author for style conversion, means for transmitting the text data received by the terminal to the server, means for the server to pass the received text data to a generative model and analyze the writing style and sentiment, means for the generative model to generate a style conversion proposal based on the analysis results, means for the generative model to recommend the most suitable author if the user leaves the author selection to the generative AI, means for providing revision suggestions and proposals in an interactive digital communication format, means for receiving feedback from the user and generating further revision suggestions, and means for providing the final revised text to the user. This enables users to easily obtain high-quality text that matches the writing style of a specific author without specialized knowledge.

[1151] The "means for receiving text entered by the user" is an interface for sending text entered by the user to the server.

[1152] The "means for selecting a desired author for style conversion" is an interface that allows the user to select a desired author for style conversion.

[1153] The "means for transmitting text data received by the terminal to the server" is a communication means for the user's terminal to transmit input text data and author information to the server.

[1154] "Means for passing text data received by the server to a generative model and analyzing the style and emotion" refers to a means for passing text data received by the server to an analysis system and analyzing text characteristics such as style and emotion.

[1155] "Means for a generative model to generate a suggestion for style conversion based on the analysis results" refers to means for a generative artificial intelligence to generate a suggestion for style conversion based on the analysis results.

[1156] "When a user leaves the selection of a literary figure to a generative AI, the means by which the generative model recommends the most suitable literary figure" refers to the means by which the generative AI analyzes the text input by the user and recommends the most suitable literary figure.

[1157] "Means for providing amendments or suggestions in the form of interactive digital communication" means interactive communication means for providing amendments or suggestions to users in real time.

[1158] The "means for receiving feedback from the user and generating a revision proposal again" is a means for receiving feedback from the user and generating a revision proposal again based on the feedback.

[1159] The "means for providing the final corrected text to the user" refers to the means for transmitting and displaying the final corrected text to the user.

[1160] The present invention is a system that corrects text entered by a user to fit a specific writing style. The system receives the text entered by the user, analyzes the writing style, emotion, and historical context using generative artificial intelligence, and generates and provides optimal revision suggestions. The system is implemented using the following hardware and software.

[1161] System configuration

[1162] 1. User's Device

[1163] The user uses software (e.g., a web browser) that opens an interface to the system.

[1164] The user uses an interface, for example an HTML form and JavaScript, to enter text into input fields and select the desired writing style.

[1165] The user presses the send button to send the entered text data and style information from the terminal to the server.

[1166] 2. Server

[1167] The server receives the user's text data sent as an HTTP request, using HTTP server software (e.g., Apache or Nginx) and a backend framework (e.g., Node.js or Django).

[1168] The server stores the received data in a database (e.g., MySQL or PostgreSQL).

[1169] The server then passes the stored data to a generative AI model (such as the GPT-3 model) for analysis, which extracts writing style, sentiment, and historical context.

[1170] The AI ​​engine generates suggested revisions based on the features for style conversion. If the user leaves the selection of a literary figure to the AI, the AI ​​engine will recommend the most suitable author.

[1171] 3. Providing feedback and suggested revisions

[1172] The server provides the generated revision suggestions to the user's device in the form of an interactive digital communication (e.g., real-time chat using WebSocket).

[1173] The user provides feedback on the submitted revisions, which is then sent back to the server.

[1174] The server receives feedback from the user and passes it back to the AI ​​engine to generate suggested revisions.

[1175] The final revised text is sent from the server to the user's device and displayed in the interface.

[1176] Specific examples

[1177] Example 1: When a user desires the writing style of a particular literary figure

[1178] 1. The user enters the sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." and selects the writing style of "Akutagawa Ryunosuke."

[1179] 2. When the user presses the send button, the device sends the text data and information about "Akutagawa Ryunosuke" to the server via an HTTP request.

[1180] 3. The server stores the data in a database and passes it to a generative artificial intelligence for analysis.

[1181] 4. The AI ​​engine generates suggested revisions to fit Akutagawa Ryunosuke's writing style. The suggested revision is, "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[1182] 5. The proposed revision is sent to the user, who then provides feedback, saying, "I'd like this part to sound more archaic."

[1183] 6. The server receives the feedback, asks the AI ​​engine to generate a revision again, and provides the final revised sentence to the user: "One day in the forest, I met an old man. The old man seemed to begin to tell me a deep and mysterious story."

[1184] Prompt Sentence Examples

[1185] "Please revise the following sentence in the style of Ryunosuke Akutagawa:

[1186] One day in the forest, I met an old man who began to tell me a mysterious story.

[1187] In this way, the present invention utilizes generative artificial intelligence to streamline the process by which users obtain high-quality text tailored to a particular writing style.

[1188] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1189] Specific processing flow of the program

[1190] Step 1:

[1191] The user types text into the device.

[1192] How it works: The user opens the device interface and enters the text they want to edit into the input field. They can choose the writing style they want, or let the generative AI choose a literary master. For example, they could enter a sentence like, "One day in the forest, I met an old man. The old man began to tell me a mysterious story."

[1193] Input: The user enters the text and information about the desired author into the input field.

[1194] Output: The text entered in the terminal's input field and information about the literary figure will be obtained.

[1195] Step 2:

[1196] The terminal sends the input data to the server.

[1197] How it works: When the user presses the send button, the device uses an HTTP request to send the entered text data and the selected author information to the server. This request uses the POST method, and the data is encoded in JSON format.

[1198] Input: Text data entered by the user and information about the selected literary figure.

[1199] Output: JSON formatted data sent to the server as an HTTP request.

[1200] Step 3:

[1201] The server receives the data and analyzes it using a generative model.

[1202] How it works: The server stores the received data in a database. It then passes the data to a generative AI (e.g., GPT-3) to begin analysis. The AI ​​engine performs analysis to extract stylistic, emotional, and historical context.

[1203] Input: JSON formatted data sent to the server.

[1204] Output: Data stored in the database and analysis results by the AI ​​engine (features of writing style, emotion, and historical background).

[1205] Step 4:

[1206] A generative model generates style transformation suggestions based on the analysis results.

[1207] How it works: Based on the analysis results, the generative AI generates revision suggestions that match the user's desired writing style. If the user has not specified a literary figure, the AI ​​engine runs an algorithm to recommend the most suitable literary figure.

[1208] Input: Analysis results by the AI ​​engine (features of writing style, emotion, and historical background).

[1209] Output: The generated revision text.

[1210] Step 5:

[1211] The proposed fix is ​​sent to the user's device.

[1212] How it works: The server sends the proposed revisions to the user's device in real time using an interactive digital communication format (e.g., WebSocket). The user's interface displays the generated revisions.

[1213] Input: The generated text of the proposed amendment.

[1214] Output: Suggested fixes sent to the user's device in real time.

[1215] Step 6:

[1216] Users provide feedback on proposed changes.

[1217] How it works: The user reviews the suggested revisions and enters feedback if necessary (e.g., "I want this part to sound more archaic.") The feedback is then sent to the server again using an HTTP request.

[1218] Input: User feedback.

[1219] Output: User feedback sent to the server.

[1220] Step 7:

[1221] The server re-analyzes the feedback and generates a final revision proposal.

[1222] How it works: Based on the feedback received by the server, the data is passed to the generative AI again to regenerate a revision proposal. The AI ​​engine then generates a new revision proposal that takes the feedback into account and returns it to the server.

[1223] Input: User feedback and original analysis results.

[1224] Output: Final revised draft.

[1225] Step 8:

[1226] Provide the final revision to the user.

[1227] How it works: The server sends the final edited text to the user's device and displays it in the device's interface, allowing the user to review the final result and download or copy it if desired.

[1228] Input: Final amendment.

[1229] Output: The final corrected text that is displayed on the user's terminal.

[1230] This series of processes allows users to easily obtain high-quality text that matches a specific writing style.

[1231] (Application example 1)

[1232] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1233] This invention relates to a system that allows users to generate content in the style of a particular literary master and check the resulting edits in real time. Today, there is a growing demand for tools that allow people to create high-quality content by imitating the writing styles of various authors and literary masters. However, conventional technologies make it difficult to convert or edit writing styles in real time, limiting the user experience. It is necessary to provide a system that solves these problems and allows users to easily generate and receive high-quality literary-style content.

[1234] 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.

[1235] In this invention, the server includes means for receiving text entered by a user, means for selecting a desired author for style conversion, means for passing the text data received by the server to a generative model and analyzing the writing style and sentiment, means for the generative model to generate a style conversion suggestion based on the analysis results, means for the generative model to recommend the most suitable author if the user leaves the author selection to AI, means for providing suggested revisions and suggestions in an interactive chat format, means for receiving feedback from the user and generating further suggested revisions, means for providing the final corrected text to the user, and means for providing the style-converted text to the user through an application running on a mobile device. This enables users to generate content in real time in the writing style of a specific author and instantly confirm and apply high-quality revisions.

[1236] The "means for receiving text entered by the user" is an interface for transmitting text data entered by the user to the system.

[1237] The "means for selecting a desired author for style conversion" is a selection interface that allows a user to select the style of a particular author and convert text into that style.

[1238] "Means for the server to pass the received text data to a generative model and analyze the style and sentiment" refers to the process of using a generative AI model to analyze the style and sentiment of text received from a user.

[1239] The "means by which the generative model generates a proposal for style conversion based on the analysis results" is an algorithm that generates a proposal for converting a sentence into the style of a specified literary figure based on the analysis results.

[1240] "Means for the generative model to recommend the most suitable author when the user leaves the author selection to the AI" refers to the process by which the generative AI automatically selects and recommends the author best suited to the input text when the user does not select a writer.

[1241] "Means for providing revisions and suggestions in an interactive chat format" refers to an interface that provides the revised text to the user in a chat format and enables interaction with the user.

[1242] "Means for receiving feedback from users and generating revisions again" refers to the process of revising the text again based on the feedback provided by the users.

[1243] The "means for providing the final revised text to the user" is the interface that provides the user with the final text that incorporates all corrections and feedback.

[1244] "Means for providing stylized text to a user through an application running on a mobile device" is a process for providing corrected text to a user through an application running on a mobile device.

[1245] This invention is a system that uses generative AI to modify text entered by a user to match it with the writing style of a specific literary master. This system is primarily composed of the user's device and a server. Specifically, it is implemented using the following process.

[1246] System configuration

[1247] 1. User's Device

[1248] It provides an interface for users to input text and select the desired literary figure.

[1249] It also includes an interface for receiving feedback from users.

[1250] 2. Server

[1251] The input text data and author selection information are received and saved.

[1252] Launch a generative AI model to analyze style and sentiment.

[1253] Generate suggested fixes and provide them to the user in the form of interactive feedback.

[1254] Receive user feedback and re-analyze and revise.

[1255] Processing Details

[1256] 1. User Input

[1257] Users input text into their devices and select the author they want to read. This information is sent to the server.

[1258] For example, enter the sentence "One day in the forest, I met an old man. The old man began to tell me a mysterious story." and let the selected author be "Author A."

[1259] 2. Server Processing

[1260] The server stores the received data and uses a generative AI model to analyze the text, extracting stylistic and emotional features.

[1261] Based on the analysis results, the text is modified to fit the writing style of the designated author. For example, the input text is converted into the writing style of "Author A."

[1262] The corrected text is presented to the user in an interactive format.

[1263] For example, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." becomes "One day in the forest, I met an old man. The old man began to tell me a deep and mysterious story."

[1264] 3. User feedback and final revisions

[1265] After reviewing the proposed revisions, users can provide feedback and requests for further revisions, and the server will then revise the text based on this feedback.

[1266] The server then sends the final corrected text to the user.

[1267] Hardware and software used

[1268] Hardware: Server (CPU / GPU server), user device (smartphone)

[1269] Software: Flask (server-side implementation), transformers library (generative AI model)

[1270] Specific examples

[1271] When a user inputs "Today, I really wanted to finish this work," and selects the desired author as "Writer B," the server receives this and generates a sentence revised in a style appropriate for "Writer B." For example, "Today, I really wanted to finish this work." The server then receives the user's feedback, makes final revisions, and provides the sentence.

[1272] In this way, users can enjoy high-quality real-time text corrections in the style of a particular literary master.

[1273] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1274] Step 1:

[1275] A means by which users input text into a device

[1276] The user enters a sentence into the input field and selects the desired author. The entered sentence and author information are sent from the terminal to the server.

[1277] Input: Text entered by the user, desired author

[1278] Output: Data sent to the server (text, literary information)

[1279] Specific operation: The user uses a dedicated interface to enter text into the text input field, select the desired author from a drop-down menu or radio buttons, and then press the send button to send the data to the server.

[1280] Step 2:

[1281] A means for the server to store received text data

[1282] The server stores the text data and author information received from the terminal in a database.

[1283] Input: Data sent from the terminal (text, literary information)

[1284] Output: Data stored in the database

[1285] Specific operation: The server deserializes the data received as an HTTP request through the API and saves it in the database.

[1286] Step 3:

[1287] A means for the server to pass data to the generated AI model for analysis

[1288] The server passes the saved text data and information about the author to a generative AI model, which analyzes the writing style and emotions.

[1289] Input: Data stored in the database (text, literary information)

[1290] Output: Style and sentiment features analyzed by the generative AI model

[1291] Specific operation: The server reads the stored data, inputs it into a generative AI model (e.g., GPT-2), and analyzes the stylistic and emotional characteristics.

[1292] Step 4:

[1293] A means for the generative model to transform writing style based on the analysis results

[1294] The generative model converts the user's writing into the style of a specified literary figure.

[1295] Input: Analysis results (features of style and emotion)

[1296] Output: Stylistically transformed text of proposed revision

[1297] Specific operation: Based on the analysis results, the generative AI model combines the stylistic characteristics of the specified author with the prompt sentence to perform the optimal style conversion.

[1298] Step 5:

[1299] A way to provide suggested fixes via chat

[1300] The server provides the generated revision suggestions to the user in an interactive chat format.

[1301] Input: Stylistically converted text of proposed revision

[1302] Output: Suggested fixes displayed on the user's device

[1303] Specific operation: The server encodes the generated correction proposal in JSON format and sends it to the user's device via API. The correction proposal is displayed in chat format on the user's device interface.

[1304] Step 6:

[1305] A way for users to provide feedback and generate revisions again

[1306] The user can input feedback on the proposed corrections and request further corrections. This feedback is sent back to the server, and the generative AI model then makes further corrections.

[1307] Input: User feedback

[1308] Output: Re-corrected sentence

[1309] Specific operation: The user inputs feedback on the interface of the device and presses the resend button to send the data to the server, which then uses the generative AI model to convert the writing style again.

[1310] Step 7:

[1311] A means of providing the final revised text

[1312] The server again provides the revised final text to the user.

[1313] Input: Data for final revision

[1314] Output: Final revised text

[1315] Specific operation: The server checks the final revised text, sends it to the user's terminal, and displays it on the screen.

[1316] Step 8:

[1317] A means for providing stylized text through an application that runs on a mobile device

[1318] The final revised text is provided to the user through an application running on the user's mobile device.

[1319] Input: Last revised text

[1320] Output: The converted text displayed on a mobile device

[1321] Specific behavior: The final revised text is displayed in the application on the mobile device and provided to the user.

[1322] This allows users to receive high-quality text corrections in real time in the style of a specific literary master.

[1323] 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.

[1324] This invention is a system that uses generative AI and an emotion engine to modify a user's writing to match the style of a specific literary master, generating emotive expressions. This system receives the text entered by the user on a server, analyzes and modifies it using AI and the emotion engine, and provides the user with a final proposed modification.

[1325] System configuration

[1326] 1. User's Device

[1327] It provides an interface for users to input text and select the desired literary figure.

[1328] It also includes an interface for users to enter feedback.

[1329] 2. Server

[1330] The entered text and information about the literary figure is received and saved.

[1331] Activate generative models and sentiment engines to analyze style and sentiment.

[1332] Generate suggested fixes and provide them to the user in the form of interactive feedback.

[1333] Receive user feedback and re-analyze and revise.

[1334] Program processing

[1335] 1. The user types text into the device

[1336] The user enters the text they want to correct into the system's input field.

[1337] Users can select the author of their choice or choose the "Leave it to the AI" option.

[1338] Example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[1339] 2. The device sends the user's input data to the server

[1340] The terminal transmits the input text data and the selected author information to the server.

[1341] Send data using an HTTP request.

[1342] 3. The server receives the data and analyzes it using the generative model and emotion engine.

[1343] The server stores the received text data and author information in a database.

[1344] The data is passed to a generative model and emotion engine to analyze the style, emotion, and historical context of the text.

[1345] The emotion engine identifies the emotion contained in a sentence and extracts features to generate expressions based on that emotion.

[1346] 4. Recommending literary figures and generating revision suggestions

[1347] If the user does not specify a literary figure, the AI ​​engine will recommend the most suitable one.

[1348] Generate suggested revisions that match the literary master's writing style.

[1349] Reflecting the results of the emotion engine, revision suggestions containing emotional expressions are created.

[1350] Example: The input sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is converted into Ryunosuke Akutagawa's style and corrected to "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[1351] 5. Interactive Feedback

[1352] The server sends suggested revisions to the user's device in an interactive chat format.

[1353] Users can review suggested revisions and provide feedback on emotion and style.

[1354] 6. Generate the final fix

[1355] The server again generates revision suggestions using the user's feedback.

[1356] The emotion engine also analyzes again and generates a final revision proposal with the appropriate emotional expression.

[1357] The final corrected text is sent to the user's device.

[1358] Specific examples

[1359] Example 1: If the user wants to use the style of Ryunosuke Akutagawa

[1360] 1. The user enters the sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." and selects Akutagawa Ryunosuke's writing style.

[1361] 2. The server uses a generative model and emotion engine to analyze the writing style and emotional features, and generates revision suggestions that emphasize the emotion while reflecting the writing style and historical context.

[1362] 3. Revised version: "One day in the forest, I met an old man. He began to tell me a profound and mysterious story."

[1363] 4. Return the text to the user and adjust it interactively based on their feedback.

[1364] Example 2: When the user entrusts the AI ​​with selecting a literary figure

[1365] 1. The user enters, "Today I really wanted to finish this task," and leaves the selection of a literary figure to the AI.

[1366] 2. The server analyzes the text using a generative model and an emotion engine and recommends Haruki Murakami as a suitable literary figure.

[1367] 3. Correction: "Today, I really wanted to get this job done."

[1368] 4. Return the text to the user and make final adjustments based on feedback.

[1369] In this way, the present invention utilizes generative AI and an emotion engine to modify the user's writing to match the style of a specific literary master, elevating it into high-quality, emotionally rich writing.

[1370] The processing flow will be explained below.

[1371] Step 1:

[1372] The user types text into the terminal.

[1373] The user enters the text they want to correct into the system's input field.

[1374] Users can select the author of their choice or choose the "Leave it to the AI" option.

[1375] Example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[1376] Step 2:

[1377] The device sends the user's input data to the server.

[1378] The terminal transmits the input text data and the selected author information to the server via an HTTP request.

[1379] Step 3:

[1380] The server receives the document data.

[1381] The server stores the received text data and author information in a database.

[1382] Step 4:

[1383] The server runs a generative model and an emotion engine to analyze the style and emotion.

[1384] The server passes the saved data to a generative model, which analyzes the text's style, emotion, and historical context.

[1385] The generative model extracts the characteristics of the received text and analyzes it to convert it into the writing style of a specific literary master.

[1386] The emotion engine identifies the emotion contained in a sentence and extracts expression features based on that emotion.

[1387] Step 5:

[1388] The server will recommend the most suitable author.

[1389] If the user does not specify a literary figure, the generative model will recommend the most appropriate literary figure based on the analysis results.

[1390] The recommendation results are sent to the user's device and displayed.

[1391] Step 6:

[1392] The server generates a revision suggestion.

[1393] Generate suggested revisions to text to match the writing style of literary masters.

[1394] The analysis results of the emotion engine are reflected to create revision proposals that include emotionally rich expressions.

[1395] Example: The input sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is converted into Ryunosuke Akutagawa's style and corrected to "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[1396] Step 7:

[1397] The server provides suggested revisions in the form of interactive feedback.

[1398] Proposed fixes are sent to the user's device in an interactive chat format.

[1399] Users can review suggested revisions and provide feedback on emotion and style.

[1400] Step 8:

[1401] The user provides feedback on the device.

[1402] Users enter their opinions and comments on the proposed revisions and send them to the server.

[1403] Step 9:

[1404] The server receives the user's feedback and re-analyzes it.

[1405] The server receives the feedback and asks the generative model and emotion engine to reanalyze.

[1406] A reanalyzed revision proposal is generated, and a revision proposal including an emotionally rich expression is generated.

[1407] This step is repeated until the user is satisfied.

[1408] Step 10:

[1409] The server generates and sends the final revision proposal.

[1410] Generate a final revision proposal based on user feedback and send it to the device.

[1411] The user can receive the final revised text.

[1412] Example 2

[1413] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1414] Conventional writing systems require users to manually edit and change their writing style, which is a time-consuming process that makes it difficult to create emotive, high-quality writing. Furthermore, if users want to edit their writing to match the style of a particular literary figure, they are required to understand the characteristics of that style and the historical context, which requires a lot of time and effort. Furthermore, it is difficult to make revisions or interactive adjustments based on user feedback, making efficient writing difficult.

[1415] The specification process by the specification 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 receiving text entered by a user, a means for selecting a desired text style for typeface conversion, a means for passing the text data received by the server to a natural language processing model and analyzing the typeface and emotion, a means for the natural language processing model to generate a typeface conversion suggestion based on the analysis results, a means for the natural language processing model to recommend an optimal text style when the user leaves the selection of the text style to artificial intelligence, a means for providing revision suggestions and suggestions in an interactive chat format, a means for receiving feedback from the user and generating another revision suggestion, and a means for providing the final revised text to the user. This allows the user to create high-quality text without hassle and to efficiently and quickly correct the text style and emotional expression.

[1416] "User" refers to an individual or group that uses the system to input text and perform style conversion and sentiment analysis.

[1417] "Means of receiving" refers to the functions and processes for sending text entered by the user to the system and receiving that data on the server.

[1418] "Means for selecting a desired writing style for typeface conversion" refers to an interface or option that allows a user to select a particular author or writing style.

[1419] A "natural language processing model" refers to an algorithm or software that analyzes input text and generates suggested revisions based on a specific style or sentiment.

[1420] "Means for sentiment analysis" refers to the process of identifying the sentiment contained in a text and generating expressions based on that sentiment.

[1421] "Means for generating typeface conversion suggestions" refers to a function or algorithm that generates suggestions for converting the typeface of a text to a style desired by the user based on the analysis results.

[1422] "Leaving the selection of literary figures to artificial intelligence" refers to a usage scenario in which the system automatically recommends the most suitable literary figure without the user having to select a specific author.

[1423] "Interactive chat-style means" refers to an interactive interface that provides generated revisions to the user in real time and allows the user to enter feedback.

[1424] "Means of receiving feedback and generating revisions again" refers to the process of collecting opinions and requests from users and revising the text using a generative AI model based on them.

[1425] "Means for providing the final corrected text to the user" refers to the function of displaying or sending the final text to the user after all correction processes have been completed.

[1426] This invention is a system that uses generative AI and an emotion engine to modify a user's text to fit a specific writing style and generate emotive expressions. This system receives text entered by the user on a server, analyzes and modifies it using a natural language processing model and an emotion engine, and provides the user with a final proposed modification.

[1427] System configuration

[1428] 1. User's Device

[1429] It provides an interface for users to enter text and select the desired writing style.

[1430] It also includes an interface for users to enter feedback.

[1431] Usage example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[1432] 2. Server

[1433] Input text and text style information is received and saved.

[1434] It launches a natural language processing model (e.g., GPT-3) and an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze typeface and emotion.

[1435] Generate suggested fixes and provide them to the user in the form of interactive feedback.

[1436] Receive user feedback and re-analyze and revise.

[1437] Hardware used: High-performance servers (data center servers equipped with CPUs and GPUs, etc.)

[1438] Software used: Python, Flask (web framework), MariaDB (database)

[1439] Program processing

[1440] The system works in the following steps:

[1441] 1. The user types text into the device

[1442] The user enters the text they want to correct into the system's input field.

[1443] Users can choose their preferred writing style or select the "Leave it to AI" option.

[1444] Example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[1445] 2. The device sends the user's input data to the server

[1446] The terminal transmits the input text data and the selected text style information to the server using an HTTP request.

[1447] 3. The server receives the data and analyzes it using the generative model and emotion engine.

[1448] The server stores the received text data and text style information in a database.

[1449] The data is passed to a natural language processing model and emotion engine to analyze the style of writing, emotions, and historical context of the text.

[1450] Usage example: A Python script on the server uses the requests library to send data to the natural language processing model API and the emotion engine API and receive responses.

[1451] 4. Writing style recommendation and revision generation

[1452] If the user does not specify a writing style, the server uses a natural language processing model to recommend the most appropriate writing style.

[1453] Generate revision suggestions tailored to your writing style.

[1454] Reflecting the results of the emotion engine, revision suggestions containing emotional expressions are created.

[1455] Example: The input sentence "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is corrected to "One day in the forest, I met an old man. The old man began to tell me a profound story." based on the recommended writing style.

[1456] 5. Interactive Feedback

[1457] The server sends suggested revisions to the user's device in an interactive chat format.

[1458] Users can review the proposed changes and provide feedback on emotions and typefaces.

[1459] 6. Generate the final fix

[1460] The server uses the user's feedback to generate a new revision, and the emotion engine analyzes it again to generate a final revision with appropriate emotional expressions.

[1461] The final corrected text is sent to the user's device.

[1462] For example, if a user types "I really wanted to finish this work today" and leaves the writing style selection to AI, the server will use a natural language processing model and emotion engine to recommend a style similar to Haruki Murakami's, correcting it to "Today, I really wanted to finish this work." This suggested correction is then provided to the user.

[1463] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1464] Step 1:

[1465] The user types text into the terminal.

[1466] Input: The user enters text into the system's input field and selects the desired writing style.

[1467] Example: A user types "Today was a very busy day" and selects "Natsume Soseki" as the desired writing style.

[1468] Output: The input text and the selected writing style are sent to the server in JSON format.

[1469] Step 2:

[1470] The device sends the user's input data to the server.

[1471] Input: User input data (text and selected writing style).

[1472] Specific operation: When you click the send button on the device, the data is sent to the server via an HTTP request using the JavaScript fetch function. The data is sent in JSON format.

[1473] Output: The JSON data received by the server.

[1474] Step 3:

[1475] The server receives the data and analyzes it using a generative model and emotion engine.

[1476] Input: JSON data sent from the terminal.

[1477] What it does: The server stores the received data in a database, passes it to a generative AI model (e.g., GPT-3) and an emotion engine (e.g., IBM Watson Tone Analyzer), and uses a Python script to send the data to the API through the requests library.

[1478] Data processing: The text data is analyzed to determine writing style, historical context, and emotions.

[1479] Output: The analysis results are returned in JSON format and saved on the server.

[1480] Step 4:

[1481] The server generates recommendations and revisions for literary masters.

[1482] Input: Parsed JSON data.

[1483] How it works: If the user does not specify a writing style, the server uses a generative AI model to recommend the best writing style. A Python script generates revision suggestions based on the analysis results.

[1484] Data processing: Features are extracted for style conversion and emotional expressions are extracted.

[1485] Output: Generated revision suggestions are created in JSON format.

[1486] Step 5:

[1487] Arba provides interactive feedback.

[1488] Input: JSON data of generated correction suggestions.

[1489] Specific operation: The server sends suggested revisions to the device in real time using WebSocket or Ajax, and the user checks the suggested revisions and enters feedback.

[1490] Output: User feedback data is sent to the server.

[1491] Step 6:

[1492] The server generates the final revision.

[1493] Input: User feedback data.

[1494] Specific operation: The server calls the generative AI model and emotion engine again based on the feedback, and refines the proposed revisions. The final revisions are generated.

[1495] Data calculations: Reanalysis and revisions reflecting feedback.

[1496] Output: The final corrected text is sent to the user's device.

[1497] The above is the specific processing flow of the program for this system, which allows users to efficiently create high-quality documents without much effort.

[1498] (Application example 2)

[1499] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1500] Conventional text correction systems lack the ability to convert user-entered text into a specific style and add emotive expressions, making it difficult to generate high-quality text for content creation. Furthermore, there is no easy way to save the generated text and use it on other platforms, limiting the user experience. It is necessary to solve these issues and provide a more convenient text correction system.

[1501] 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.

[1502] In this invention, the server includes means for receiving text entered by a user, means for selecting a desired author for style conversion, means for passing the text data received by the server to a generative model and analyzing the writing style and emotion, means for the generative model to generate a style conversion suggestion based on the analysis results, means for the generative model to recommend the most suitable author if the user leaves the author selection to AI, means for providing revision suggestions and suggestions in an interactive chat format, means for receiving feedback from the user and generating further revision suggestions, means for providing the final revised text to the user, and means for saving the generated text and sharing it on other platforms. This makes it possible to easily generate high-quality, emotionally rich text and furthermore to easily use the generated text on other platforms.

[1503] The "means for receiving text entered by the user" refers to a mechanism for transmitting text data entered by the user through the terminal to the server and receiving it.

[1504] The "means for selecting a desired author for style conversion" is a mechanism that provides an interface for a user to select the style of a desired author as the conversion destination.

[1505] "Means for passing the text data received by the server to the generative model and analyzing the writing style and emotion" refers to a mechanism by which the server inputs the text data received from the user into the generative AI model and analyzes the writing style and emotion.

[1506] "Means by which the generative model generates suggestions for style conversion based on the analysis results" refers to a mechanism by which the generative AI model generates appropriate suggestions for style conversion based on the analysis results of style and emotion.

[1507] "A means for the generative model to recommend the most suitable author when the user leaves the author selection to the AI" is a mechanism that, when the user sets the author selection to be left to the AI, recommends the author that the generative AI model determines to be the most suitable.

[1508] The "means for providing revisions and suggestions in an interactive chat format" is a mechanism for displaying generated revisions and suggestions to users in a chat format, thereby realizing two-way interaction.

[1509] The "means of receiving feedback from the user and generating a new revision proposal" is a mechanism for revising the text based on the feedback entered by the user and generating a new revision proposal.

[1510] The "means for providing the final corrected text to the user" is a mechanism for displaying and providing the final corrected text to the user.

[1511] "Means for saving generated text and sharing it on other platforms" refers to a mechanism for saving generated text within the system and sharing it on other platforms such as social media and blogs.

[1512] The system that realizes this application example is configured as follows: The entire system is executed by the user's terminal, a server, and necessary software components.

[1513] User's device

[1514] 1. Text input interface

[1515] It provides an interface for users to input text and select the author they want, and also includes an option for users to leave the author selection to the AI.

[1516] 2. Feedback Interface

[1517] The system provides an interface for users to input feedback on the generated revisions and request further revisions. This feedback is sent to the server in real time.

[1518] server

[1519] 1. Data Receipt and Storage

[1520] The server receives the text data and author information sent from the terminal and stores them in a database.

[1521] 2. Generative AI Models and Emotion Engines

[1522] The received text data is passed to a generative AI model (e.g., GPT-4) for stylistic and emotional analysis. An emotion engine (e.g., Hume AI) identifies the emotion contained in the text and extracts features to generate expressions based on that emotion.

[1523] 3. Proposal generation of style conversion

[1524] The generative AI model generates suggestions for style conversion based on the analysis results. If the user leaves the selection of the author to the AI, the generative model will recommend the most suitable author. These suggestions are presented in an interactive chat format that is easy for users to understand.

[1525] 4. Feedback Processing

[1526] It receives feedback from users and generates new revision suggestions based on that feedback. It also analyzes the emotion engine again and generates a final revision suggestion with appropriate emotional expressions.

[1527] 5. Final submission, saving and sharing

[1528] The final revised text is provided to the user and saved in the system, and the user can share the generated text on other platforms such as social media or blogs.

[1529] Specific examples

[1530] Prompt Sentence Examples

[1531] User input: "I really wanted to finish this work today."

[1532] User's favorite author: Akutagawa Ryunosuke

[1533] What happens on the server side when the user selects a writer:

[1534] The server receives the sentence, "Today, I really wanted to finish this work," and uses a generative AI model to analyze the style and sentiment. It converts this sentence into the style of Ryunosuke Akutagawa and generates a suggested revision, such as, "Today, I felt that I had to complete this work no matter what."

[1535] Feedback example:

[1536] The user inputs feedback such as "Please emphasize the emotion more." The server receives this feedback, analyzes it again using the generative AI model and emotion engine, and generates a final revision such as "Today, I was determined to finish this task no matter what," and provides it to the user.

[1537] In this way, users can easily generate high-quality, emotive texts and then easily use the generated texts on other platforms.

[1538] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1539] Step 1:

[1540] The user inputs text into the terminal. The user inputs the text into the input field and selects the desired author. The input includes the text as text data and the author selection settings.

[1541] Step 2:

[1542] The terminal sends the input data to the server, and transmits data including text data and information about the literary figure in the form of an HTTP request to the server.

[1543] Step 3:

[1544] The server saves the received data. The server stores the received text data and author information in a database. At this time, the database stores the user ID, the entered text, and author selection information.

[1545] Step 4:

[1546] The server passes the text data to a generative AI model, which analyzes the writing style and sentiment. The server passes the input data to a generative AI model (e.g., GPT-4) and performs style conversion and sentiment analysis. The input is text data and information about famous authors, and the output is a style conversion proposal as the analysis result.

[1547] Step 5:

[1548] Based on the analysis results, a style conversion proposal is generated. The generative AI model generates appropriate style conversion proposals based on the style and sentiment analysis results and returns them to the server. The output is a style-converted sentence.

[1549] Step 6:

[1550] If the option to leave the selection of the author to AI is selected, the server will recommend the most suitable author. The generative AI model will recommend authors based on the analysis results and suggest them to the user. This process depends on the internal algorithm of the generative model. The input is text data and the analysis results, and the output is a recommended author.

[1551] Step 7:

[1552] The server provides the user with suggested revisions and suggestions in an interactive chat format. The server sends the generated revisions to the user's terminal and displays them in an interactive chat format. The input is the converted text, and the output is a chat interface on the user's screen.

[1553] Step 8:

[1554] Receive feedback from the user. The user enters feedback on the proposed revision, which is sent to the server via the terminal. The input is the text data of the user feedback, and the output is the server receiving the feedback data.

[1555] Step 9:

[1556] A revision proposal is generated again based on the feedback. The server again uses the generative AI model and emotion engine to make revisions based on the feedback data. The input is the feedback data and the initial text data, and the output is the final revision proposal.

[1557] Step 10:

[1558] The final revised text is provided to the user terminal. The server sends the final revision proposal to the user terminal, where the user can view it. The input is the final revision proposal, and the output is the final text on the user terminal.

[1559] Step 11:

[1560] Save the generated text and share it on other platforms. Users have the option to save the final revision proposal within the system and share it on other platforms such as social media or blogs. The input is the final revision proposal, and the output is saving the generated text and generating a sharing link.

[1561] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1562] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1563] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1564] [Fourth embodiment]

[1565] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1566] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1567] 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).

[1568] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1569] 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.

[1570] 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).

[1571] 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.

[1572] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1573] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1574] 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.

[1575] 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.

[1576] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1577] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1578] This invention is a system that uses generative AI to modify a user's writing to match the style of a specific literary master. This system receives the text entered by the user on a server, analyzes and modifies it using an AI engine, and provides the user with a final proposed modification.

[1579] System configuration

[1580] 1. User's Device

[1581] It provides an interface for users to input text and select the desired literary figure.

[1582] It also includes an interface for users to enter feedback.

[1583] 2. Server

[1584] The entered text and information about the literary figure is received and saved.

[1585] Invoke a generative model to analyze style and sentiment.

[1586] Generate suggested fixes and provide them to the user in the form of interactive feedback.

[1587] Receive user feedback and re-analyze and revise.

[1588] Program processing

[1589] 1. The user types text on the device

[1590] Users enter the text they want to edit into the input field, and then select the desired author (e.g., "Japanese Authors") or let the AI ​​select the most suitable author.

[1591] Example: "One day in the forest, I met an old man. He began to tell me a mysterious story."

[1592] 2. The device sends the input data to the server

[1593] The terminal transmits the input text data and the selected author information to the server.

[1594] Send data using an HTTP request.

[1595] 3. The server receives the data and analyzes it using the generative model.

[1596] The server stores the received data in a database.

[1597] The data is passed to an AI engine that performs analysis of style, emotion, and historical context.

[1598] The analysis results are used as features for style conversion.

[1599] 4. Recommending literary figures and generating revision suggestions

[1600] If the user does not specify a literary figure, the AI ​​engine will recommend the most suitable one.

[1601] Generate suggested revisions that match the literary master's writing style.

[1602] Example: The input sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is converted into Ryunosuke Akutagawa's style and corrected to "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[1603] 5. Interactive Feedback

[1604] The server sends suggested revisions to the user's device in an interactive chat format.

[1605] The user provides feedback on the proposed revisions and the server receives it.

[1606] 6. Generate the final fix

[1607] The server again generates revision suggestions using the user's feedback.

[1608] The final corrected text is sent to the user's device.

[1609] Specific examples

[1610] Example 1: If the user wants to use the style of Ryunosuke Akutagawa

[1611] 1. The user enters the sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." and selects Akutagawa Ryunosuke's writing style.

[1612] 2. The server uses AI to analyze Akutagawa Ryunosuke's writing style and generate suggested revisions that reflect his writing style and the historical context.

[1613] 3. Revised version: "One day in the forest, I met an old man. He began to tell me a profound and mysterious story."

[1614] 4. Return the text to the user and interactively adjust it until the user is satisfied.

[1615] Example 2: When the user entrusts the AI ​​with selecting a literary figure

[1616] 1. The user enters, "Today I really wanted to finish this task," and leaves the selection of a literary figure to the AI.

[1617] 2. The server uses AI to analyze the text and recommends Haruki Murakami as a suitable literary figure.

[1618] 3. Correction: "I really wanted to get this job done today."

[1619] 4. Return the text to the user and make final adjustments based on feedback.

[1620] In this way, the present invention utilizes generative AI to elevate the user's writing to a higher quality.

[1621] The processing flow will be explained below.

[1622] Step 1:

[1623] The user types text into the terminal.

[1624] The user enters the text they want to correct into the system's input field.

[1625] Users can select the author of their choice or choose the "Leave it to the AI" option.

[1626] Example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[1627] Step 2:

[1628] The device sends the user's input data to the server.

[1629] The terminal sends the input text and information about the selected author to the server via an HTTP request.

[1630] Step 3:

[1631] The server receives the document data.

[1632] The server stores the received text data and author information in a database.

[1633] Step 4:

[1634] The server runs a generative model to analyze style and sentiment.

[1635] The server passes the saved data to a generative model, which analyzes the text's style, emotion, and historical context.

[1636] The generative model extracts the characteristics of the received text and analyzes it to convert it into the writing style of a specific literary master.

[1637] Step 5:

[1638] The server will recommend the most suitable author.

[1639] If the user does not specify a literary figure, the generative model will recommend the most appropriate literary figure based on the analysis results.

[1640] The recommendation results are sent to the user's device and displayed.

[1641] Step 6:

[1642] The server generates a revision suggestion.

[1643] Generate suggested revisions to text to match the writing style of literary masters.

[1644] Example: The input sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is converted into Ryunosuke Akutagawa's style and corrected to "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[1645] Step 7:

[1646] The server provides suggested revisions in the form of interactive feedback.

[1647] The proposed changes are sent to the user's device in an interactive chat format, where the user can review the changes and provide feedback.

[1648] Step 8:

[1649] The user provides feedback on the device.

[1650] Users enter their opinions and comments on the proposed revisions and send them to the server.

[1651] Step 9:

[1652] The server receives the user's feedback and re-analyzes it.

[1653] The server receives the feedback and asks the generative model to reanalyze.

[1654] A re-analyzed correction proposal is generated and sent to the user's device.

[1655] This step is repeated until the user is satisfied.

[1656] Step 10:

[1657] The server generates and sends the final revision proposal.

[1658] Generate a final revision proposal based on user feedback and send it to the device.

[1659] The user can receive the final revised text.

[1660] Example 1

[1661] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1662] Previously, users had to spend a great deal of effort and expertise to adapt their writing to the style of a particular literary master. Furthermore, the feedback and correction process required for style conversion was cumbersome, and it took a long time for users to achieve satisfactory results. This reduced the efficiency with which users could obtain high-quality writing.

[1663] 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.

[1664] In this invention, the server includes means for receiving text entered by a user, means for selecting a desired author for style conversion, means for transmitting the text data received by the terminal to the server, means for the server to pass the received text data to a generative model and analyze the writing style and sentiment, means for the generative model to generate a style conversion proposal based on the analysis results, means for the generative model to recommend the most suitable author if the user leaves the author selection to the generative AI, means for providing revision suggestions and proposals in an interactive digital communication format, means for receiving feedback from the user and generating further revision suggestions, and means for providing the final revised text to the user. This enables users to easily obtain high-quality text that matches the writing style of a specific author without specialized knowledge.

[1665] The "means for receiving text entered by the user" is an interface for sending text entered by the user to the server.

[1666] The "means for selecting a desired author for style conversion" is an interface that allows the user to select a desired author for style conversion.

[1667] The "means for transmitting text data received by the terminal to the server" is a communication means for the user's terminal to transmit input text data and author information to the server.

[1668] "Means for passing text data received by the server to a generative model and analyzing the style and emotion" refers to a means for passing text data received by the server to an analysis system and analyzing text characteristics such as style and emotion.

[1669] "Means for a generative model to generate a suggestion for style conversion based on the analysis results" refers to means for a generative artificial intelligence to generate a suggestion for style conversion based on the analysis results.

[1670] "When a user leaves the selection of a literary figure to a generative AI, the means by which the generative model recommends the most suitable literary figure" refers to the means by which the generative AI analyzes the text input by the user and recommends the most suitable literary figure.

[1671] "Means for providing amendments or suggestions in the form of interactive digital communication" means interactive communication means for providing amendments or suggestions to users in real time.

[1672] The "means for receiving feedback from the user and generating a revision proposal again" is a means for receiving feedback from the user and generating a revision proposal again based on the feedback.

[1673] The "means for providing the final corrected text to the user" refers to the means for transmitting and displaying the final corrected text to the user.

[1674] The present invention is a system that corrects text entered by a user to fit a specific writing style. The system receives the text entered by the user, analyzes the writing style, emotion, and historical context using generative artificial intelligence, and generates and provides optimal revision suggestions. The system is implemented using the following hardware and software.

[1675] System configuration

[1676] 1. User's Device

[1677] The user uses software (e.g., a web browser) that opens an interface to the system.

[1678] The user uses an interface, for example an HTML form and JavaScript, to enter text into input fields and select the desired writing style.

[1679] The user presses the send button to send the entered text data and style information from the terminal to the server.

[1680] 2. Server

[1681] The server receives the user's text data sent as an HTTP request, using HTTP server software (e.g., Apache or Nginx) and a backend framework (e.g., Node.js or Django).

[1682] The server stores the received data in a database (e.g., MySQL or PostgreSQL).

[1683] The server then passes the stored data to a generative AI model (such as the GPT-3 model) for analysis, which extracts writing style, sentiment, and historical context.

[1684] The AI ​​engine generates suggested revisions based on the features for style conversion. If the user leaves the selection of a literary figure to the AI, the AI ​​engine will recommend the most suitable author.

[1685] 3. Providing feedback and suggested revisions

[1686] The server provides the generated revision suggestions to the user's device in the form of an interactive digital communication (e.g., real-time chat using WebSocket).

[1687] The user provides feedback on the submitted revisions, which is then sent back to the server.

[1688] The server receives feedback from the user and passes it back to the AI ​​engine to generate suggested revisions.

[1689] The final revised text is sent from the server to the user's device and displayed in the interface.

[1690] Specific examples

[1691] Example 1: When a user desires the writing style of a particular literary figure

[1692] 1. The user enters the sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." and selects the writing style of "Akutagawa Ryunosuke."

[1693] 2. When the user presses the send button, the device sends the text data and information about "Akutagawa Ryunosuke" to the server via an HTTP request.

[1694] 3. The server stores the data in a database and passes it to a generative artificial intelligence for analysis.

[1695] 4. The AI ​​engine generates suggested revisions to fit Akutagawa Ryunosuke's writing style. The suggested revision is, "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[1696] 5. The proposed revision is sent to the user, who then provides feedback, saying, "I'd like this part to sound more archaic."

[1697] 6. The server receives the feedback, asks the AI ​​engine to generate a revision again, and provides the final revised sentence to the user: "One day in the forest, I met an old man. The old man seemed to begin to tell me a deep and mysterious story."

[1698] Prompt Sentence Examples

[1699] "Please revise the following sentence in the style of Ryunosuke Akutagawa:

[1700] One day in the forest, I met an old man who began to tell me a mysterious story.

[1701] In this way, the present invention utilizes generative artificial intelligence to streamline the process by which users obtain high-quality text tailored to a particular writing style.

[1702] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1703] Specific processing flow of the program

[1704] Step 1:

[1705] The user types text into the device.

[1706] How it works: The user opens the device interface and enters the text they want to edit into the input field. They can choose the writing style they want, or let the generative AI choose a literary master. For example, they could enter a sentence like, "One day in the forest, I met an old man. The old man began to tell me a mysterious story."

[1707] Input: The user enters the text and information about the desired author into the input field.

[1708] Output: The text entered in the terminal's input field and information about the literary figure will be obtained.

[1709] Step 2:

[1710] The terminal sends the input data to the server.

[1711] How it works: When the user presses the send button, the device uses an HTTP request to send the entered text data and the selected author information to the server. This request uses the POST method, and the data is encoded in JSON format.

[1712] Input: Text data entered by the user and information about the selected literary figure.

[1713] Output: JSON formatted data sent to the server as an HTTP request.

[1714] Step 3:

[1715] The server receives the data and analyzes it using a generative model.

[1716] How it works: The server stores the received data in a database. It then passes the data to a generative AI (e.g., GPT-3) to begin analysis. The AI ​​engine performs analysis to extract stylistic, emotional, and historical context.

[1717] Input: JSON formatted data sent to the server.

[1718] Output: Data stored in the database and analysis results by the AI ​​engine (features of writing style, emotion, and historical background).

[1719] Step 4:

[1720] A generative model generates style transformation suggestions based on the analysis results.

[1721] How it works: Based on the analysis results, the generative AI generates revision suggestions that match the user's desired writing style. If the user has not specified a literary figure, the AI ​​engine runs an algorithm to recommend the most suitable literary figure.

[1722] Input: Analysis results by the AI ​​engine (features of writing style, emotion, and historical background).

[1723] Output: The generated revision text.

[1724] Step 5:

[1725] The proposed fix is ​​sent to the user's device.

[1726] How it works: The server sends the proposed revisions to the user's device in real time using an interactive digital communication format (e.g., WebSocket). The user's interface displays the generated revisions.

[1727] Input: The generated text of the proposed amendment.

[1728] Output: Suggested fixes sent to the user's device in real time.

[1729] Step 6:

[1730] Users provide feedback on proposed changes.

[1731] How it works: The user reviews the suggested revisions and enters feedback if necessary (e.g., "I want this part to sound more archaic.") The feedback is then sent to the server again using an HTTP request.

[1732] Input: User feedback.

[1733] Output: User feedback sent to the server.

[1734] Step 7:

[1735] The server re-analyzes the feedback and generates a final revision proposal.

[1736] How it works: Based on the feedback received by the server, the data is passed to the generative AI again to regenerate a revision proposal. The AI ​​engine then generates a new revision proposal that takes the feedback into account and returns it to the server.

[1737] Input: User feedback and original analysis results.

[1738] Output: Final revised draft.

[1739] Step 8:

[1740] Provide the final revision to the user.

[1741] How it works: The server sends the final edited text to the user's device and displays it in the device's interface, allowing the user to review the final result and download or copy it if desired.

[1742] Input: Final amendment.

[1743] Output: The final corrected text that is displayed on the user's terminal.

[1744] This series of processes allows users to easily obtain high-quality text that matches a specific writing style.

[1745] (Application example 1)

[1746] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1747] This invention relates to a system that allows users to generate content in the style of a particular literary master and check the resulting edits in real time. Today, there is a growing demand for tools that allow people to create high-quality content by imitating the writing styles of various authors and literary masters. However, conventional technologies make it difficult to convert or edit writing styles in real time, limiting the user experience. It is necessary to provide a system that solves these problems and allows users to easily generate and receive high-quality literary-style content.

[1748] 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.

[1749] In this invention, the server includes means for receiving text entered by a user, means for selecting a desired author for style conversion, means for passing the text data received by the server to a generative model and analyzing the writing style and sentiment, means for the generative model to generate a style conversion suggestion based on the analysis results, means for the generative model to recommend the most suitable author if the user leaves the author selection to AI, means for providing suggested revisions and suggestions in an interactive chat format, means for receiving feedback from the user and generating further suggested revisions, means for providing the final corrected text to the user, and means for providing the style-converted text to the user through an application running on a mobile device. This enables users to generate content in real time in the writing style of a specific author and instantly confirm and apply high-quality revisions.

[1750] The "means for receiving text entered by the user" is an interface for transmitting text data entered by the user to the system.

[1751] The "means for selecting a desired author for style conversion" is a selection interface that allows a user to select the style of a particular author and convert text into that style.

[1752] "Means for the server to pass the received text data to a generative model and analyze the style and sentiment" refers to the process of using a generative AI model to analyze the style and sentiment of text received from a user.

[1753] The "means by which the generative model generates a proposal for style conversion based on the analysis results" is an algorithm that generates a proposal for converting a sentence into the style of a specified literary figure based on the analysis results.

[1754] "Means for the generative model to recommend the most suitable author when the user leaves the author selection to the AI" refers to the process by which the generative AI automatically selects and recommends the author best suited to the input text when the user does not select a writer.

[1755] "Means for providing revisions and suggestions in an interactive chat format" refers to an interface that provides the revised text to the user in a chat format and enables interaction with the user.

[1756] "Means for receiving feedback from users and generating revisions again" refers to the process of revising the text again based on the feedback provided by the users.

[1757] The "means for providing the final revised text to the user" is the interface that provides the user with the final text that incorporates all corrections and feedback.

[1758] "Means for providing stylized text to a user through an application running on a mobile device" is a process for providing corrected text to a user through an application running on a mobile device.

[1759] This invention is a system that uses generative AI to modify text entered by a user to match it with the writing style of a specific literary master. This system is primarily composed of the user's device and a server. Specifically, it is implemented using the following process.

[1760] System configuration

[1761] 1. User's Device

[1762] It provides an interface for users to input text and select the desired literary figure.

[1763] It also includes an interface for receiving feedback from users.

[1764] 2. Server

[1765] The input text data and author selection information are received and saved.

[1766] Launch a generative AI model to analyze style and sentiment.

[1767] Generate suggested fixes and provide them to the user in the form of interactive feedback.

[1768] Receive user feedback and re-analyze and revise.

[1769] Processing Details

[1770] 1. User Input

[1771] Users input text into their devices and select the author they want to read. This information is sent to the server.

[1772] For example, enter the sentence "One day in the forest, I met an old man. The old man began to tell me a mysterious story." and let the selected author be "Author A."

[1773] 2. Server Processing

[1774] The server stores the received data and uses a generative AI model to analyze the text, extracting stylistic and emotional features.

[1775] Based on the analysis results, the text is modified to fit the writing style of the designated author. For example, the input text is converted into the writing style of "Author A."

[1776] The corrected text is presented to the user in an interactive format.

[1777] For example, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." becomes "One day in the forest, I met an old man. The old man began to tell me a deep and mysterious story."

[1778] 3. User feedback and final revisions

[1779] After reviewing the proposed revisions, users can provide feedback and requests for further revisions, and the server will then revise the text based on this feedback.

[1780] The server then sends the final corrected text to the user.

[1781] Hardware and software used

[1782] Hardware: Server (CPU / GPU server), user device (smartphone)

[1783] Software: Flask (server-side implementation), transformers library (generative AI model)

[1784] Specific examples

[1785] When a user inputs "Today, I really wanted to finish this work," and selects the desired author as "Writer B," the server receives this and generates a sentence revised in a style appropriate for "Writer B." For example, "Today, I really wanted to finish this work." The server then receives the user's feedback, makes final revisions, and provides the sentence.

[1786] In this way, users can enjoy high-quality real-time text corrections in the style of a particular literary master.

[1787] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1788] Step 1:

[1789] A means by which users input text into a device

[1790] The user enters a sentence into the input field and selects the desired author. The entered sentence and author information are sent from the terminal to the server.

[1791] Input: Text entered by the user, desired author

[1792] Output: Data sent to the server (text, literary information)

[1793] Specific operation: The user uses a dedicated interface to enter text into the text input field, select the desired author from a drop-down menu or radio buttons, and then press the send button to send the data to the server.

[1794] Step 2:

[1795] A means for the server to store received text data

[1796] The server stores the text data and author information received from the terminal in a database.

[1797] Input: Data sent from the terminal (text, literary information)

[1798] Output: Data stored in the database

[1799] Specific operation: The server deserializes the data received as an HTTP request through the API and saves it in the database.

[1800] Step 3:

[1801] A means for the server to pass data to the generated AI model for analysis

[1802] The server passes the saved text data and information about the author to a generative AI model, which analyzes the writing style and emotions.

[1803] Input: Data stored in the database (text, literary information)

[1804] Output: Style and sentiment features analyzed by the generative AI model

[1805] Specific operation: The server reads the stored data, inputs it into a generative AI model (e.g., GPT-2), and analyzes the stylistic and emotional characteristics.

[1806] Step 4:

[1807] A means for the generative model to transform writing style based on the analysis results

[1808] The generative model converts the user's writing into the style of a specified literary figure.

[1809] Input: Analysis results (features of style and emotion)

[1810] Output: Stylistically transformed text of proposed revision

[1811] Specific operation: Based on the analysis results, the generative AI model combines the stylistic characteristics of the specified author with the prompt sentence to perform the optimal style conversion.

[1812] Step 5:

[1813] A way to provide suggested fixes via chat

[1814] The server provides the generated revision suggestions to the user in an interactive chat format.

[1815] Input: Stylistically converted text of proposed revision

[1816] Output: Suggested fixes displayed on the user's device

[1817] Specific operation: The server encodes the generated correction proposal in JSON format and sends it to the user's device via API. The correction proposal is displayed in chat format on the user's device interface.

[1818] Step 6:

[1819] A way for users to provide feedback and generate revisions again

[1820] The user can input feedback on the proposed corrections and request further corrections. This feedback is sent back to the server, and the generative AI model then makes further corrections.

[1821] Input: User feedback

[1822] Output: Re-corrected sentence

[1823] Specific operation: The user inputs feedback on the interface of the device and presses the resend button to send the data to the server, which then uses the generative AI model to convert the writing style again.

[1824] Step 7:

[1825] A means of providing the final revised text

[1826] The server again provides the revised final text to the user.

[1827] Input: Data for final revision

[1828] Output: Final revised text

[1829] Specific operation: The server checks the final revised text, sends it to the user's terminal, and displays it on the screen.

[1830] Step 8:

[1831] A means for providing stylized text through an application that runs on a mobile device

[1832] The final revised text is provided to the user through an application running on the user's mobile device.

[1833] Input: Last revised text

[1834] Output: The converted text displayed on a mobile device

[1835] Specific behavior: The final revised text is displayed in the application on the mobile device and provided to the user.

[1836] This allows users to receive high-quality text corrections in real time in the style of a specific literary master.

[1837] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1838] This invention is a system that uses generative AI and an emotion engine to modify a user's writing to match the style of a specific literary master, generating emotive expressions. This system receives the text entered by the user on a server, analyzes and modifies it using AI and the emotion engine, and provides the user with a final proposed modification.

[1839] System configuration

[1840] 1. User's Device

[1841] It provides an interface for users to input text and select the desired literary figure.

[1842] It also includes an interface for users to enter feedback.

[1843] 2. Server

[1844] The entered text and information about the literary figure is received and saved.

[1845] Activate generative models and sentiment engines to analyze style and sentiment.

[1846] Generate suggested fixes and provide them to the user in the form of interactive feedback.

[1847] Receive user feedback and re-analyze and revise.

[1848] Program processing

[1849] 1. The user types text into the device

[1850] The user enters the text they want to correct into the system's input field.

[1851] Users can select the author of their choice or choose the "Leave it to the AI" option.

[1852] Example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[1853] 2. The device sends the user's input data to the server

[1854] The terminal transmits the input text data and the selected author information to the server.

[1855] Send data using an HTTP request.

[1856] 3. The server receives the data and analyzes it using the generative model and emotion engine.

[1857] The server stores the received text data and author information in a database.

[1858] The data is passed to a generative model and emotion engine to analyze the style, emotion, and historical context of the text.

[1859] The emotion engine identifies the emotion contained in a sentence and extracts features to generate expressions based on that emotion.

[1860] 4. Recommending literary figures and generating revision suggestions

[1861] If the user does not specify a literary figure, the AI ​​engine will recommend the most suitable one.

[1862] Generate suggested revisions that match the literary master's writing style.

[1863] Reflecting the results of the emotion engine, revision suggestions containing emotional expressions are created.

[1864] Example: The input sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is converted into Ryunosuke Akutagawa's style and corrected to "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[1865] 5. Interactive Feedback

[1866] The server sends suggested revisions to the user's device in an interactive chat format.

[1867] Users can review suggested revisions and provide feedback on emotion and style.

[1868] 6. Generate the final fix

[1869] The server again generates revision suggestions using the user's feedback.

[1870] The emotion engine also analyzes again and generates a final revision proposal with the appropriate emotional expression.

[1871] The final corrected text is sent to the user's device.

[1872] Specific examples

[1873] Example 1: If the user wants to use the style of Ryunosuke Akutagawa

[1874] 1. The user enters the sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." and selects Akutagawa Ryunosuke's writing style.

[1875] 2. The server uses a generative model and emotion engine to analyze the writing style and emotional features, and generates revision suggestions that emphasize the emotion while reflecting the writing style and historical context.

[1876] 3. Revised version: "One day in the forest, I met an old man. He began to tell me a profound and mysterious story."

[1877] 4. Return the text to the user and adjust it interactively based on their feedback.

[1878] Example 2: When the user entrusts the AI ​​with selecting a literary figure

[1879] 1. The user enters, "Today I really wanted to finish this task," and leaves the selection of a literary figure to the AI.

[1880] 2. The server analyzes the text using a generative model and an emotion engine and recommends Haruki Murakami as a suitable literary figure.

[1881] 3. Correction: "Today, I really wanted to get this job done."

[1882] 4. Return the text to the user and make final adjustments based on feedback.

[1883] In this way, the present invention utilizes generative AI and an emotion engine to modify the user's writing to match the style of a specific literary master, elevating it into high-quality, emotionally rich writing.

[1884] The processing flow will be explained below.

[1885] Step 1:

[1886] The user types text into the terminal.

[1887] The user enters the text they want to correct into the system's input field.

[1888] Users can select the author of their choice or choose the "Leave it to the AI" option.

[1889] Example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[1890] Step 2:

[1891] The device sends the user's input data to the server.

[1892] The terminal transmits the input text data and the selected author information to the server via an HTTP request.

[1893] Step 3:

[1894] The server receives the document data.

[1895] The server stores the received text data and author information in a database.

[1896] Step 4:

[1897] The server runs a generative model and an emotion engine to analyze the style and emotion.

[1898] The server passes the saved data to a generative model, which analyzes the text's style, emotion, and historical context.

[1899] The generative model extracts the characteristics of the received text and analyzes it to convert it into the writing style of a specific literary master.

[1900] The emotion engine identifies the emotion contained in a sentence and extracts expression features based on that emotion.

[1901] Step 5:

[1902] The server will recommend the most suitable author.

[1903] If the user does not specify a literary figure, the generative model will recommend the most appropriate literary figure based on the analysis results.

[1904] The recommendation results are sent to the user's device and displayed.

[1905] Step 6:

[1906] The server generates a revision suggestion.

[1907] Generate suggested revisions to text to match the writing style of literary masters.

[1908] The analysis results of the emotion engine are reflected to create revision proposals that include emotionally rich expressions.

[1909] Example: The input sentence, "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is converted into Ryunosuke Akutagawa's style and corrected to "One day in the forest, I met an old man. The old man began to tell me a profound and mysterious story."

[1910] Step 7:

[1911] The server provides suggested revisions in the form of interactive feedback.

[1912] Proposed fixes are sent to the user's device in an interactive chat format.

[1913] Users can review suggested revisions and provide feedback on emotion and style.

[1914] Step 8:

[1915] The user provides feedback on the device.

[1916] Users enter their opinions and comments on the proposed revisions and send them to the server.

[1917] Step 9:

[1918] The server receives the user's feedback and re-analyzes it.

[1919] The server receives the feedback and asks the generative model and emotion engine to reanalyze.

[1920] A reanalyzed revision proposal is generated, and a revision proposal including an emotionally rich expression is generated.

[1921] This step is repeated until the user is satisfied.

[1922] Step 10:

[1923] The server generates and sends the final revision proposal.

[1924] Generate a final revision proposal based on user feedback and send it to the device.

[1925] The user can receive the final revised text.

[1926] Example 2

[1927] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1928] Conventional writing systems require users to manually edit and change their writing style, which is a time-consuming process that makes it difficult to create emotive, high-quality writing. Furthermore, if users want to edit their writing to match the style of a particular literary figure, they are required to understand the characteristics of that style and the historical context, which requires a lot of time and effort. Furthermore, it is difficult to make revisions or interactive adjustments based on user feedback, making efficient writing difficult.

[1929] The specification process by the specification 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 receiving text entered by a user, a means for selecting a desired text style for typeface conversion, a means for passing the text data received by the server to a natural language processing model and analyzing the typeface and emotion, a means for the natural language processing model to generate a typeface conversion suggestion based on the analysis results, a means for the natural language processing model to recommend an optimal text style when the user leaves the selection of the text style to artificial intelligence, a means for providing revision suggestions and suggestions in an interactive chat format, a means for receiving feedback from the user and generating another revision suggestion, and a means for providing the final revised text to the user. This allows the user to create high-quality text without hassle and to efficiently and quickly correct the text style and emotional expression.

[1930] "User" refers to an individual or group that uses the system to input text and perform style conversion and sentiment analysis.

[1931] "Means of receiving" refers to the functions and processes for sending text entered by the user to the system and receiving that data on the server.

[1932] "Means for selecting a desired writing style for typeface conversion" refers to an interface or option that allows a user to select a particular author or writing style.

[1933] A "natural language processing model" refers to an algorithm or software that analyzes input text and generates suggested revisions based on a specific style or sentiment.

[1934] "Means for sentiment analysis" refers to the process of identifying the sentiment contained in a text and generating expressions based on that sentiment.

[1935] "Means for generating typeface conversion suggestions" refers to a function or algorithm that generates suggestions for converting the typeface of a text to a style desired by the user based on the analysis results.

[1936] "Leaving the selection of literary figures to artificial intelligence" refers to a usage scenario in which the system automatically recommends the most suitable literary figure without the user having to select a specific author.

[1937] "Interactive chat-style means" refers to an interactive interface that provides generated revisions to the user in real time and allows the user to enter feedback.

[1938] "Means of receiving feedback and generating revisions again" refers to the process of collecting opinions and requests from users and revising the text using a generative AI model based on them.

[1939] "Means for providing the final corrected text to the user" refers to the function of displaying or sending the final text to the user after all correction processes have been completed.

[1940] This invention is a system that uses generative AI and an emotion engine to modify a user's text to fit a specific writing style and generate emotive expressions. This system receives text entered by the user on a server, analyzes and modifies it using a natural language processing model and an emotion engine, and provides the user with a final proposed modification.

[1941] System configuration

[1942] 1. User's Device

[1943] It provides an interface for users to enter text and select the desired writing style.

[1944] It also includes an interface for users to enter feedback.

[1945] Usage example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[1946] 2. Server

[1947] Input text and text style information is received and saved.

[1948] It launches a natural language processing model (e.g., GPT-3) and an emotion engine (e.g., IBM Watson Tone Analyzer) to analyze typeface and emotion.

[1949] Generate suggested fixes and provide them to the user in the form of interactive feedback.

[1950] Receive user feedback and re-analyze and revise.

[1951] Hardware used: High-performance servers (data center servers equipped with CPUs and GPUs, etc.)

[1952] Software used: Python, Flask (web framework), MariaDB (database)

[1953] Program processing

[1954] The system works in the following steps:

[1955] 1. The user types text into the device

[1956] The user enters the text they want to correct into the system's input field.

[1957] Users can choose their preferred writing style or select the "Leave it to AI" option.

[1958] Example: A user types, "One day in the forest, I met an old man. He began to tell me a mysterious story."

[1959] 2. The device sends the user's input data to the server

[1960] The terminal transmits the input text data and the selected text style information to the server using an HTTP request.

[1961] 3. The server receives the data and analyzes it using the generative model and emotion engine.

[1962] The server stores the received text data and text style information in a database.

[1963] The data is passed to a natural language processing model and emotion engine to analyze the style of writing, emotions, and historical context of the text.

[1964] Usage example: A Python script on the server uses the requests library to send data to the natural language processing model API and the emotion engine API and receive responses.

[1965] 4. Writing style recommendation and revision generation

[1966] If the user does not specify a writing style, the server uses a natural language processing model to recommend the most appropriate writing style.

[1967] Generate revision suggestions tailored to your writing style.

[1968] Reflecting the results of the emotion engine, revision suggestions containing emotional expressions are created.

[1969] Example: The input sentence "One day in the forest, I met an old man. The old man began to tell me a mysterious story." is corrected to "One day in the forest, I met an old man. The old man began to tell me a profound story." based on the recommended writing style.

[1970] 5. Interactive Feedback

[1971] The server sends suggested revisions to the user's device in an interactive chat format.

[1972] Users can review the proposed changes and provide feedback on emotions and typefaces.

[1973] 6. Generate the final fix

[1974] The server uses the user's feedback to generate a new revision, and the emotion engine analyzes it again to generate a final revision with appropriate emotional expressions.

[1975] The final corrected text is sent to the user's device.

[1976] For example, if a user types "I really wanted to finish this work today" and leaves the writing style selection to AI, the server will use a natural language processing model and emotion engine to recommend a style similar to Haruki Murakami's, correcting it to "Today, I really wanted to finish this work." This suggested correction is then provided to the user.

[1977] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1978] Step 1:

[1979] The user types text into the terminal.

[1980] Input: The user enters text into the system's input field and selects the desired writing style.

[1981] Example: A user types "Today was a very busy day" and selects "Natsume Soseki" as the desired writing style.

[1982] Output: The input text and the selected writing style are sent to the server in JSON format.

[1983] Step 2:

[1984] The device sends the user's input data to the server.

[1985] Input: User input data (text and selected writing style).

[1986] Specific operation: When you click the send button on the device, the data is sent to the server via an HTTP request using the JavaScript fetch function. The data is sent in JSON format.

[1987] Output: The JSON data received by the server.

[1988] Step 3:

[1989] The server receives the data and analyzes it using a generative model and emotion engine.

[1990] Input: JSON data sent from the terminal.

[1991] What it does: The server stores the received data in a database, passes it to a generative AI model (e.g., GPT-3) and an emotion engine (e.g., IBM Watson Tone Analyzer), and uses a Python script to send the data to the API through the requests library.

[1992] Data processing: The text data is analyzed to determine writing style, historical context, and emotions.

[1993] Output: The analysis results are returned in JSON format and saved on the server.

[1994] Step 4:

[1995] The server generates recommendations and revisions for literary masters.

[1996] Input: Parsed JSON data.

[1997] How it works: If the user does not specify a writing style, the server uses a generative AI model to recommend the best writing style. A Python script generates revision suggestions based on the analysis results.

[1998] Data processing: Features are extracted for style conversion and emotional expressions are extracted.

[1999] Output: Generated revision suggestions are created in JSON format.

[2000] Step 5:

[2001] Arba provides interactive feedback.

[2002] Input: JSON data of generated correction suggestions.

[2003] Specific operation: The server sends suggested revisions to the device in real time using WebSocket or Ajax, and the user checks the suggested revisions and enters feedback.

[2004] Output: User feedback data is sent to the server.

[2005] Step 6:

[2006] The server generates the final revision.

[2007] Input: User feedback data.

[2008] Specific operation: The server calls the generative AI model and emotion engine again based on the feedback, and refines the proposed revisions. The final revisions are generated.

[2009] Data calculations: Reanalysis and revisions reflecting feedback.

[2010] Output: The final corrected text is sent to the user's device.

[2011] The above is the specific processing flow of the program for this system, which allows users to efficiently create high-quality documents without much effort.

[2012] (Application example 2)

[2013] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2014] Conventional text correction systems lack the ability to convert user-entered text into a specific style and add emotive expressions, making it difficult to generate high-quality text for content creation. Furthermore, there is no easy way to save the generated text and use it on other platforms, limiting the user experience. It is necessary to solve these issues and provide a more convenient text correction system.

[2015] 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.

[2016] In this invention, the server includes means for receiving text entered by a user, means for selecting a desired author for style conversion, means for passing the text data received by the server to a generative model and analyzing the writing style and emotion, means for the generative model to generate a style conversion suggestion based on the analysis results, means for the generative model to recommend the most suitable author if the user leaves the author selection to AI, means for providing revision suggestions and suggestions in an interactive chat format, means for receiving feedback from the user and generating further revision suggestions, means for providing the final revised text to the user, and means for saving the generated text and sharing it on other platforms. This makes it possible to easily generate high-quality, emotionally rich text and furthermore to easily use the generated text on other platforms.

[2017] The "means for receiving text entered by the user" refers to a mechanism for transmitting text data entered by the user through the terminal to the server and receiving it.

[2018] The "means for selecting a desired author for style conversion" is a mechanism that provides an interface for a user to select the style of a desired author as the conversion destination.

[2019] "Means for passing the text data received by the server to the generative model and analyzing the writing style and emotion" refers to a mechanism by which the server inputs the text data received from the user into the generative AI model and analyzes the writing style and emotion.

[2020] "Means by which the generative model generates suggestions for style conversion based on the analysis results" refers to a mechanism by which the generative AI model generates appropriate suggestions for style conversion based on the analysis results of style and emotion.

[2021] "A means for the generative model to recommend the most suitable author when the user leaves the author selection to the AI" is a mechanism that, when the user sets the author selection to be left to the AI, recommends the author that the generative AI model determines to be the most suitable.

[2022] The "means for providing revisions and suggestions in an interactive chat format" is a mechanism for displaying generated revisions and suggestions to users in a chat format, thereby realizing two-way interaction.

[2023] The "means of receiving feedback from the user and generating a new revision proposal" is a mechanism for revising the text based on the feedback entered by the user and generating a new revision proposal.

[2024] The "means for providing the final corrected text to the user" is a mechanism for displaying and providing the final corrected text to the user.

[2025] "Means for saving generated text and sharing it on other platforms" refers to a mechanism for saving generated text within the system and sharing it on other platforms such as social media and blogs.

[2026] The system that realizes this application example is configured as follows: The entire system is executed by the user's terminal, a server, and necessary software components.

[2027] User's device

[2028] 1. Text input interface

[2029] It provides an interface for users to input text and select the author they want, and also includes an option for users to leave the author selection to the AI.

[2030] 2. Feedback Interface

[2031] The system provides an interface for users to input feedback on the generated revisions and request further revisions. This feedback is sent to the server in real time.

[2032] server

[2033] 1. Data Receipt and Storage

[2034] The server receives the text data and author information sent from the terminal and stores them in a database.

[2035] 2. Generative AI Models and Emotion Engines

[2036] The received text data is passed to a generative AI model (e.g., GPT-4) for stylistic and emotional analysis. An emotion engine (e.g., Hume AI) identifies the emotion contained in the text and extracts features to generate expressions based on that emotion.

[2037] 3. Proposal generation of style conversion

[2038] The generative AI model generates suggestions for style conversion based on the analysis results. If the user leaves the selection of the author to the AI, the generative model will recommend the most suitable author. These suggestions are presented in an interactive chat format that is easy for users to understand.

[2039] 4. Feedback Processing

[2040] It receives feedback from users and generates new revision suggestions based on that feedback. It also analyzes the emotion engine again and generates a final revision suggestion with appropriate emotional expressions.

[2041] 5. Final submission, saving and sharing

[2042] The final revised text is provided to the user and saved in the system, and the user can share the generated text on other platforms such as social media or blogs.

[2043] Specific examples

[2044] Prompt Sentence Examples

[2045] User input: "I really wanted to finish this work today."

[2046] User's favorite author: Akutagawa Ryunosuke

[2047] What happens on the server side when the user selects a writer:

[2048] The server receives the sentence, "Today, I really wanted to finish this work," and uses a generative AI model to analyze the style and sentiment. It converts this sentence into the style of Ryunosuke Akutagawa and generates a suggested revision, such as, "Today, I felt that I had to complete this work no matter what."

[2049] Feedback example:

[2050] The user inputs feedback such as "Please emphasize the emotion more." The server receives this feedback, analyzes it again using the generative AI model and emotion engine, and generates a final revision such as "Today, I was determined to finish this task no matter what," and provides it to the user.

[2051] In this way, users can easily generate high-quality, emotive texts and then easily use the generated texts on other platforms.

[2052] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2053] Step 1:

[2054] The user inputs text into the terminal. The user inputs the text into the input field and selects the desired author. The input includes the text as text data and the author selection settings.

[2055] Step 2:

[2056] The terminal sends the input data to the server, and transmits data including text data and information about the literary figure in the form of an HTTP request to the server.

[2057] Step 3:

[2058] The server saves the received data. The server stores the received text data and author information in a database. At this time, the database stores the user ID, the entered text, and author selection information.

[2059] Step 4:

[2060] The server passes the text data to a generative AI model, which analyzes the writing style and sentiment. The server passes the input data to a generative AI model (e.g., GPT-4) and performs style conversion and sentiment analysis. The input is text data and information about famous authors, and the output is a style conversion proposal as the analysis result.

[2061] Step 5:

[2062] Based on the analysis results, a style conversion proposal is generated. The generative AI model generates appropriate style conversion proposals based on the style and sentiment analysis results and returns them to the server. The output is a style-converted sentence.

[2063] Step 6:

[2064] If the option to leave the selection of the author to AI is selected, the server will recommend the most suitable author. The generative AI model will recommend authors based on the analysis results and suggest them to the user. This process depends on the internal algorithm of the generative model. The input is text data and the analysis results, and the output is a recommended author.

[2065] Step 7:

[2066] The server provides the user with suggested revisions and suggestions in an interactive chat format. The server sends the generated revisions to the user's terminal and displays them in an interactive chat format. The input is the converted text, and the output is a chat interface on the user's screen.

[2067] Step 8:

[2068] Receive feedback from the user. The user enters feedback on the proposed revision, which is sent to the server via the terminal. The input is the text data of the user feedback, and the output is the server receiving the feedback data.

[2069] Step 9:

[2070] A revision proposal is generated again based on the feedback. The server again uses the generative AI model and emotion engine to make revisions based on the feedback data. The input is the feedback data and the initial text data, and the output is the final revision proposal.

[2071] Step 10:

[2072] The final revised text is provided to the user terminal. The server sends the final revision proposal to the user terminal, where the user can view it. The input is the final revision proposal, and the output is the final text on the user terminal.

[2073] Step 11:

[2074] Save the generated text and share it on other platforms. Users have the option to save the final revision proposal within the system and share it on other platforms such as social media or blogs. The input is the final revision proposal, and the output is saving the generated text and generating a sharing link.

[2075] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2076] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2077] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2078] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2079] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2080] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2081] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2082] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2083] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2084] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2085] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2086] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2087] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2088] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2089] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2090] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2091] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2092] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2093] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2094] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2095] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2096] The following is further disclosed regarding the above embodiment.

[2097] (Claim 1)

[2098] means for receiving text entered by a user;

[2099] a means for selecting a desired literary figure for style conversion;

[2100] The server passes the received text data to a generative model for style and sentiment analysis.

[2101] a means for the generative model to generate suggestions for style transformations based on the analysis results;

[2102] When a user leaves the selection of a literary figure to AI, the generative model has a means to recommend the most suitable literary figure,

[2103] A means to provide corrections and suggestions in an interactive chat format;

[2104] A means to receive user feedback and generate revision proposals again;

[2105] A means of providing the final revised text to the user; and

[2106] A system including:

[2107] (Claim 2)

[2108] 10. The system of claim 1, further comprising means for understanding historical context and cultural nuances and suggesting appropriate vocabulary and expressions.

[2109] (Claim 3)

[2110] 10. The system of claim 1, further comprising means for analyzing the flow and structure of a story based on the writing style of a particular literary master and providing advice regarding plot improvement and character development.

[2111] "Example 1"

[2112] (Claim 1)

[2113] means for receiving text entered by a user;

[2114] a means for selecting a desired literary figure for style conversion;

[2115] means for transmitting the text data received by the terminal to a server;

[2116] The server passes the received text data to a generative model for style and sentiment analysis.

[2117] a means for the generative model to generate suggestions for style transformations based on the analysis results;

[2118] When a user entrusts the selection of a literary figure to a generative AI, the generative model must have a way to recommend the most suitable literary figure.

[2119] A means of providing amendments and suggestions in an interactive digital communication format;

[2120] A means to receive user feedback and generate revision proposals again;

[2121] A means of providing the final revised text to the user; and

[2122] A system including:

[2123] (Claim 2)

[2124] 10. The system of claim 1, further comprising means for understanding historical context and cultural nuances and suggesting appropriate vocabulary and expressions.

[2125] (Claim 3)

[2126] 10. The system of claim 1, further comprising means for analyzing the flow and structure of a story based on the writing style of a particular literary master and providing advice regarding plot improvement and character development.

[2127] "Application Example 1"

[2128] (Claim 1)

[2129] means for receiving text entered by a user;

[2130] a means for selecting a desired literary figure for style conversion;

[2131] The server passes the received text data to a generative model for style and sentiment analysis.

[2132] a means for the generative model to generate suggestions for style transformations based on the analysis results;

[2133] When a user leaves the selection of a literary figure to AI, the generative model has a means to recommend the most suitable literary figure,

[2134] A means to provide corrections and suggestions in an interactive chat format;

[2135] A means to receive user feedback and generate revision proposals again;

[2136] A means of providing the final revised text to the user; and

[2137] a means for providing the stylized text to a user through an application running on a mobile device;

[2138] A system including:

[2139] (Claim 2)

[2140] 10. The system of claim 1, further comprising means for understanding historical context and cultural nuances and suggesting appropriate vocabulary and expressions.

[2141] (Claim 3)

[2142] 10. The system of claim 1, further comprising means for analyzing the flow and structure of a story based on the writing style of a particular literary master and providing advice regarding plot improvement and character development.

[2143] "Example 2: Combining Emotion Engines"

[2144] (Claim 1)

[2145] means for receiving text entered by a user;

[2146] means for selecting a desired writing style for typeface conversion;

[2147] The server passes the received text data to a natural language processing model for analyzing the style and sentiment of the text.

[2148] a means for the natural language processing model to generate typeface conversion suggestions based on the analysis results;

[2149] When users leave the choice of writing style to AI, a natural language processing model can recommend the optimal writing style.

[2150] A means to provide corrections and suggestions in an interactive chat format;

[2151] A means to receive user feedback and generate revision proposals again;

[2152] A means of providing the final revised text to the user; and

[2153] A system including:

[2154] (Claim 2)

[2155] 10. The system of claim 1, further comprising means for understanding historical context and cultural nuances and suggesting appropriate vocabulary and expressions.

[2156] (Claim 3)

[2157] 10. The system of claim 1, further comprising means for analyzing narrative flow and structure based on a particular writing style and providing advice regarding plot improvement and character development.

[2158] "Application example 2 when combining emotion engines"

[2159] (Claim 1)

[2160] means for receiving text entered by a user;

[2161] a means for selecting a desired literary figure for style conversion;

[2162] The server passes the received text data to a generative model for style and sentiment analysis.

[2163] a means for the generative model to generate suggestions for style transformations based on the analysis results;

[2164] When a user leaves the selection of a literary figure to AI, the generative model has a means to recommend the most suitable literary figure,

[2165] A means to provide corrections and suggestions in an interactive chat format;

[2166] A means to receive user feedback and generate revision proposals again;

[2167] A means of providing the final revised text to the user; and

[2168] A way to save and share the generated text on other platforms,

[2169] A system including:

[2170] (Claim 2)

[2171] 10. The system of claim 1, further comprising means for understanding historical context and cultural nuances and suggesting appropriate vocabulary and expressions.

[2172] (Claim 3)

[2173] 10. The system of claim 1, further comprising means for analyzing the flow and structure of a story based on the writing style of a particular literary master and providing advice regarding plot improvement and character development. [Explanation of symbols]

[2174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving text entered by a user; a means for selecting a desired literary figure for style conversion; The server passes the received text data to a generative model for style and sentiment analysis. a means for the generative model to generate suggestions for style transformations based on the analysis results; When a user leaves the selection of a literary figure to AI, the generative model has a means to recommend the most suitable literary figure, A means to provide corrections and suggestions in an interactive chat format; A means to receive user feedback and generate revision proposals again; A means of providing the final revised text to the user; and A system including:

2. The system of claim 1 further comprising means for understanding historical context and cultural nuances and suggesting appropriate vocabulary and expressions.

3. 10. The system of claim 1, further comprising means for analyzing the flow and structure of a story based on the writing style of a particular literary master, and providing advice on improving the plot and developing characters.

Images