System

Abstract

A system is provided.SOLUTION: The system includes a means for receiving a sentence input by a user, a generation Ali means for analyzing the received sentence, a means for generating a correction proposal on the basis of a result analyzed by the generation Ali means, a means for displaying the correction proposal to the user, and a means for posting the sentence corrected on the basis of the approval of the user to various network services.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] The goal is to solve the problem of users of network services unintentionally posting messages containing inappropriate language, causing misunderstandings and trouble. Specifically, it is necessary to prevent messages containing offensive language, inflammatory language, or legal or ethical issues from being posted, and to provide an environment where network services can be used with peace of mind. [Means for solving the problem]

[0005] The present invention provides a system including a means for receiving text entered by a user, a generating means for analyzing the received text, a means for generating suggested revisions based on the results of the analysis by the generating means, a means for displaying the suggested revisions to the user, and a means for posting the revised text to various network services with the user's approval.The system further solves the above problems by having a means for checking the analysis for the presence or absence of offensive language, inflammatory language, and legal or ethical issues, and by including a means for generating suggested revisions in real time in cooperation with network services before posting.

[0006] "User" refers to an individual or organization that uses a network service.

[0007] "Input" refers to the act of a user creating text and providing it as data to the system.

[0008] "Means for receiving" refers to a method or device that allows the system to obtain the text entered by the user.

[0009] "Generative ant means" refers to a method or apparatus for analyzing input text using artificial intelligence techniques.

[0010] "Analysis" refers to the process of evaluating the content of a text to identify offensive, inflammatory, or legal or ethical issues.

[0011] "Suggested corrections" refer to appropriate expressions or alternative sentences suggested based on the results of the analysis.

[0012] "Means for displaying" refers to a method or device for visually presenting revision suggestions to a user.

[0013] "Approval" refers to the act of the user reviewing and agreeing to the proposed revision.

[0014] "Means for posting" refers to a method or device for publishing the revised text on a network service.

[0015] "Network services" refers to social networking sites and other online platforms.

[0016] "Offensive language" refers to words or phrases that have the intent or effect of hurting others.

[0017] "Inflammatory language" refers to words or phrases that are likely to provoke others and provoke an inappropriate response.

[0018] "Legal and ethical issues" refers to problems with the text that may violate the law or social ethics.

[0019] "Real-time" refers to processing that occurs almost instantaneously. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

[0022] First, the terms used in the following description will be explained.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] The present invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. This system is implemented using a terminal and a server. The specific operation and processing of this system are described below.

[0042] System configuration

[0043] 1. Terminal

[0044] This is the device through which the user enters the text they wish to post. This includes smartphones, tablets, and PCs.

[0045] The terminal is responsible for receiving input from the user and transmitting that data to the server.

[0046] 2. Server

[0047] It is the central system that receives and analyzes text sent from the terminal.

[0048] The server uses a generative ant method to analyze the text and identify problematic sections.

[0049] System program processing

[0050] 1. User Input

[0051] A user uses a device to input text into a social networking site posting screen. For example, the user inputs the text, "My boss is so stupid I can't stand it."

[0052] 2. Sending text

[0053] The terminal sends the text entered by the user to the server. Specifically, it converts the text data into an appropriate data format such as JSON and sends a POST request to the server.

[0054] 3. Receiving and analyzing text

[0055] The server receives requests from devices and extracts the text data contained within. The generation AI analyzes the received text using natural language processing technology and evaluates the content of the text. This analysis identifies offensive language, inflammatory language, and legal and ethical issues.

[0056] For example, the expression "too stupid" is judged to be offensive.

[0057] 4. Generating correction suggestions

[0058] The server generates appropriate correction suggestions based on the analysis results. The generation AI creates a suggestion to correct the expression "too stupid" to "I sometimes feel that I lack understanding."

[0059] 5. Submitting a revision suggestion

[0060] The server compiles the proposed changes and sends them to the device, which sends the changes in JSON format as a POST request.

[0061] 6. User Verification and Authorization

[0062] The terminal receives the proposed revisions and displays them to the user. The user checks the proposed revisions and either approves or re-edits them. If the user agrees with the proposal, he or she clicks the "Approve" button.

[0063] Example: Acknowledge the user's suggestion that "I sometimes feel I lack understanding."

[0064] 7. Final Post

[0065] The device resends the approved revised text to the server, which then posts the revised text via the API of the various SNS services.

[0066] Example: Finally, the sentence "I sometimes feel like I lack understanding" is posted on social media.

[0067] This will provide an environment where users can post on network services with peace of mind. By implementing this system, misunderstandings and trouble caused by inappropriate expressions can be prevented, and smooth communication will be promoted.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] A user uses a device to input text into a social media posting screen. For example, the user types, "My boss is so stupid I can't stand it."

[0071] Step 2:

[0072] The device receives text input from the user, converts this input information into a data format such as JSON, and prepares it for transmission to the server.

[0073] Step 3:

[0074] The device sends the prepared JSON format text data to the server as a POST request.

[0075] Step 4:

[0076] The server receives the request from the device, parses the JSON data, and extracts the text.

[0077] Step 5:

[0078] The server uses generative AI tools to analyze the extracted text using natural language processing (NLP) algorithms to detect offensive language, inflammatory language, and legal and ethical issues.

[0079] Step 6:

[0080] Based on the analysis results, the generative AI generates suggested corrections. For example, it creates a suggestion to correct the expression "too stupid" to "I sometimes feel that I lack understanding."

[0081] Step 7:

[0082] The server generates correction suggestions and compiles them in JSON format, which is then sent to the device as a POST request.

[0083] Step 8:

[0084] The device receives the correction suggestions from the server and displays them visually to the user, such as in the form of a pop-up or notification.

[0085] Step 9:

[0086] The user checks the proposed corrections and selects whether to approve or re-edit. For example, the user agrees with the proposal and clicks the "Approve" button.

[0087] Step 10:

[0088] After receiving the user's approval, the terminal converts the revised text into JSON format and sends it again to the server as a POST request.

[0089] Step 11:

[0090] The server makes a final check of the revised text received and issues a posting request using the API of various SNS services.

[0091] Step 12:

[0092] The various SNS services receive the posting request from the server and post the corrected sentence to the user's account. Finally, the sentence "I sometimes feel that I lack understanding" is displayed on the SNS as the user's post.

[0093] This will prevent users from posting inappropriate content and avoid misunderstandings and problems on network services.

[0094] Example 1

[0095] 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."

[0096] In modern network services, posts by users often contain offensive or misleading language, which can result in trouble or misunderstandings. This can be detrimental to users and other stakeholders. Conventional systems lacked the means to detect such problems in real time and to appropriately correct them, which sometimes hindered smooth communication.

[0097] 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.

[0098] In this invention, the server includes means for receiving text entered by a user, means for converting the received text into JSON format and transmitting it to the server, a generation AI means for analyzing the text received by the server, means for generating revision suggestions based on the results of the analysis by the generation AI means, means for compiling the revision suggestions in JSON format and transmitting them to the terminal, means for displaying the revision suggestions to the user and obtaining the user's approval, and means for resending the approved revised text to the server and posting it to various network services. This allows users to check the revision suggestions before posting text and to correct misleading or offensive language in advance. This facilitates communication on network services and prevents problems caused by inappropriate language.

[0099] A "user" is an entity that uses a network service and inputs, transmits, and checks text through a terminal.

[0100] A "terminal" is a device that allows a user to input text and communicate data with a server. Examples of such devices include smartphones, tablets, and PCs.

[0101] The "server" is a central system that receives text sent by users, analyzes it using a generative AI model, and generates and sends correction suggestions.

[0102] A "generative AI model" is an algorithm or system that uses natural language processing techniques to analyze text and generate appropriate correction suggestions.

[0103] The "JSON format" is a lightweight data exchange format for structuring and representing data, and is an abbreviation for JavaScript Object Notation.

[0104] A "POST request" is a method in the HTTP protocol for a client to send data to a server.

[0105] "Suggested modifications" are suggestions for modifying offensive or misleading language identified through analysis to make it more appropriate.

[0106] "Network services" is a general term for various online platforms and services used via the Internet, including social networking sites, blogs, and forums.

[0107] "Natural language processing technology" refers to technology that enables computers to understand, interpret, and generate human language. Specifically, it includes technologies such as morphological analysis, sentence analysis, and machine translation.

[0108] The "HTTP protocol" is a communication protocol for transferring data over the Internet and is an abbreviation for Hypertext Transfer Protocol.

[0109] "Various network services" refers to multiple different online platforms and services, and means a collection of web services provided for specific fields or purposes.

[0110] These definitions clarify each component of the system and its role.

[0111] This invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. The system is primarily composed of a terminal and a server. This section describes the detailed configuration and operation of the system.

[0112] System configuration

[0113] 1. Terminal

[0114] A terminal is a device that allows a user to input text and send it to a server. Examples include smartphones, tablets, and PCs. The terminal converts the text data input by the user into JSON format and sends it to the server. The terminal uses a library for HTTP communication (e.g., JavaScript's fetch function or Python's requests library).

[0115] 2. Server

[0116] The server is a central system that receives and analyzes text sent from the device. The server analyzes the text using a generative AI model (e.g., OpenAI GPT-3). This analysis uses natural language processing libraries such as the Natural Language Toolkit (NLTK) and spaCy. The server generates appropriate correction suggestions based on the analysis results and sends them to the device in JSON format. Web frameworks such as Flask and Django are used for communication on the server side.

[0117] Example of operation

[0118] A specific example of the operation of this system is given below.

[0119] 1. User Input

[0120] The user uses the device to input text into the SNS posting screen. For example, they input the text "My boss is so stupid I can't stand it." At this point, an input field and a send button are displayed on the device screen.

[0121] 2. Sending text

[0122] The device sends the text entered by the user to the server. Specifically, a program on the device converts this text into JSON format and sends it to the server as an HTTP POST request.

[0123] 3. Text Analysis

[0124] The server receives the POST request sent from the device and extracts the text data from the request body. The generative AI model analyzes the received text and determines that the expression "too stupid" is offensive.

[0125] 4. Generating correction suggestions

[0126] The server generates appropriate correction suggestions based on the analysis results, for example, suggesting that the expression "too stupid" be changed to "I sometimes feel that I lack understanding."

[0127] An example of a prompt sentence is as follows:

[0128] Input sentence: "My boss is so stupid I can't stand it."

[0129] Prompts for generating example revision suggestions:

[0130] "Please revise the following sentence to make it less offensive and less misleading.

[0131] "My boss is so stupid I can't handle it"

[0132] 5. Submit and review your proposed revisions

[0133] The server compiles the proposed revisions in JSON format and sends them to the device. The device receives the proposed revisions and displays them to the user. The user can then review the displayed proposals and approve or re-edit them.

[0134] 6. Final Post

[0135] If the user approves the suggestion, the device sends the revised text back to the server, which passes it to the APIs of various network services and ultimately posts it to the social networking site.

[0136] This system allows users to check and correct offensive or misleading language before posting, thereby facilitating smoother communication on network services and preventing problems caused by inappropriate language.

[0137] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0138] Step 1:

[0139] The user uses the device to input text into the SNS posting screen. For example, they might input the phrase "My boss is so stupid I can't stand it." At this point, the input text is displayed in the input field on the device.

[0140] Input: User input: "My boss is so stupid I can't do my job"

[0141] Output: Text that appears in the terminal input field

[0142] Step 2:

[0143] The terminal converts the text entered by the user into JSON format, using the JSON.stringify function in JavaScript or the json.dumps method in Python to structure the text data, and then sends it to the server using an HTTP POST request.

[0144] Input: Raw text data entered by the user

[0145] Output: JSON format data ({"text": "My boss is so stupid I can't stand it"})

[0146] Step 3:

[0147] The server receives the POST request from the device, extracts the JSON-formatted text data from the body, and then analyzes the text using a generative AI model (e.g., OpenAI GPT-3). This analysis uses natural language processing techniques to identify offensive or misleading language.

[0148] Input: JSON data sent from the terminal

[0149] Output: Analysis results (identification of offensive or misleading expressions)

[0150] Step 4:

[0151] The server generates appropriate correction suggestions based on the analysis results. For example, using a generative AI model, it suggests correcting the expression "too stupid" to "I sometimes feel that I lack understanding." A specific example of a generated correction suggestion is shown below.

[0152] Input: Analysis result (problematic expression)

[0153] Output: Suggested correction ("I sometimes feel I lack understanding")

[0154] Step 5:

[0155] The server then compiles the proposed changes in JSON format and sends them to the device as a POST request, using a web framework such as Flask or Django to generate an HTTP response.

[0156] Input: Proposed text

[0157] Output: JSON format correction suggestion data ({"suggestion": "Sometimes I feel I don't understand"})

[0158] Step 6:

[0159] The terminal receives the proposed corrections and displays them to the user. The user can then review the displayed proposal and approve or re-edit it. If the user agrees with the proposal, they click the "Approve" button.

[0160] Input: JSON formatted correction suggestions received from the server

[0161] Output: The suggested corrections shown to the user, and the user's approval action.

[0162] Step 7:

[0163] The device then sends the revised text, approved by the user, back to the server. The server then passes the revised text to the APIs of various network services, and ultimately posts it to social networking sites. Specifically, posting is done using the Twitter API, Facebook API, etc.

[0164] Input: User-approved revised sentence ("I sometimes feel I lack understanding.")

[0165] Output: Corrected text posted to the network service

[0166] (Application example 1)

[0167] 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."

[0168] In today's Internet environment, it is important for users to be able to freely express their opinions, but inappropriate or misleading expressions can be included, which can lead to disputes between users and legal issues.In addition, there is a lack of means to provide appropriate correction suggestions in real time, making it difficult to provide a safe and smooth communication environment.

[0169] 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.

[0170] In this invention, the server includes means for receiving text entered by a user, means for analyzing the received text, means for generating revision suggestions based on the results of the analysis by the means for generating revision suggestions, means for displaying the revision suggestions to the user, means for posting the revised text to various network services based on the user's approval, and means for generating revision suggestions in real time and instantly and securely processing the user's input. This makes it possible to provide a safe and smooth communication environment by checking the appropriateness of the text entered by the user in advance and suggesting revisions.

[0171] "User" refers to a user who uses a system or service to input text.

[0172] "Means for receiving input text" refers to a function for receiving text input by a user from a terminal.

[0173] "Generative ant method" is a technology that uses generative AI to analyze input text and identify problem areas.

[0174] The "means for generating correction suggestions" is a function that suggests appropriate corrections based on the problem areas identified by the generation AI means.

[0175] The "means for displaying revision suggestions to the user" is a function for displaying the generated revision suggestions on the user's device.

[0176] "Means for posting revised text based on user approval to various network services" refers to a function for posting revised text to network services after the user's suggested revisions have been confirmed and approved.

[0177] "Means for generating correction suggestions in real time and instantly and securely processing user input" refers to a function that instantly analyzes text entered by a user and safely generates correction suggestions.

[0178] This invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. The specific operation and processing of this system are described below.

[0179] System configuration

[0180] 1. Terminal

[0181] This is the device through which the user enters the text they wish to post. This includes smartphones, tablets, and PCs.

[0182] The terminal is responsible for receiving input from the user and transmitting that data to the server.

[0183] 2. Server

[0184] It is the central system that receives and analyzes text sent from the terminal.

[0185] The server uses a generative AI model to analyze the text and identify problem areas.

[0186] Hardware and software used

[0187] Hardware: Server machine (on-premise or cloud server)

[0188] Software: Flask, OpenAI API

[0189] Program Description

[0190] Program processing

[0191] 1. User Input

[0192] The user uses the device to input text into the SNS posting screen. For example, they can input a sentence such as, "My boss is so stupid I can't stand it."

[0193] 2. Sending text

[0194] The terminal sends the text entered by the user to the server. At this time, the text data is converted into an appropriate data format such as JSON format and sent to the server via a POST request.

[0195] 3. Receiving and analyzing text

[0196] The server receives requests from devices and extracts the text data contained within. The generative AI model then analyzes the received text using natural language processing technology and evaluates its content. This analysis identifies offensive, inflammatory, and legally or ethically questionable expressions. For example, the expression "too stupid" is deemed offensive.

[0197] 4. Generating correction suggestions

[0198] The server generates appropriate correction suggestions based on the analysis results. The generative AI model creates a suggestion to correct the expression "too stupid" to "I sometimes feel that I lack understanding."

[0199] 5. Submitting a revision suggestion

[0200] The server compiles and sends correction suggestions to the device, which returns them in JSON format.

[0201] 6. User Verification and Authorization

[0202] The terminal receives the suggested revisions and displays them to the user. The user checks the suggested revisions and approves or re-edits them. If the user agrees with the proposal, he or she clicks the "Approve" button. For example, the user approves the suggestion that "I sometimes feel that I lack understanding."

[0203] 7. Final Post

[0204] The device resends the approved revised sentence to the server, and the server posts the revised sentence via the API of various SNS. For example, the sentence "Sometimes I feel I lack understanding" is posted to the SNS.

[0205] Examples and prompts

[0206] Examples:

[0207] User input: "What Tanaka said in yesterday's meeting was completely meaningless."

[0208] Analysis request: {"text": "What Tanaka said at yesterday's meeting was completely meaningless."}

[0209] Suggested revision: "What Tanaka said in yesterday's meeting was a little hard to understand."

[0210] Example prompts to input to a generative AI model:

[0211] Prompt: "Please revise the following sentence to remove any inappropriate language: What Tanaka said at the meeting yesterday was completely meaningless."

[0212] This invention provides an environment in which users can post on network services with peace of mind. It is expected that smooth communication will be promoted by preventing misunderstandings and trouble caused by inappropriate expressions.

[0213] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0214] Step 1:

[0215] The device receives text entered by the user. At this time, the user enters text into a social media posting screen or chat app. The device temporarily stores this input, and when the user clicks the send button, the text data is sent to the server. At this time, the input data is converted to JSON format.

[0216] Step 2:

[0217] The server receives JSON-formatted text data sent from the device, analyzes the received data, and extracts only the necessary text. The extracted text then becomes the input data for the generative AI model.

[0218] Step 3:

[0219] The server analyzes the received text using a generative AI model. This analysis uses natural language processing technology to check for offensive language, inflammatory language, or legal or ethical issues. As a result of the analysis, any inappropriate content is identified.

[0220] Step 4:

[0221] The server generates appropriate correction suggestions based on the analysis results. The generative AI model uses the prompt "Please correct the following sentence and remove inappropriate expressions: [original sentence]." For example, in response to the sentence "My boss is so stupid I can't stand it," the server generates the correction suggestion "Sometimes I feel he lacks understanding."

[0222] Step 5:

[0223] The server compiles the generated revision suggestions into JSON format and sends them to the device. The device receives the revision suggestions and displays them to the user. The revision suggestions are typically displayed side by side, with the original text and the revised text.

[0224] Step 6:

[0225] The user can review the proposed edits on their device. They can either approve the suggested edits or make further edits themselves. If the approve button is pressed, the device converts the edited text back into JSON format and sends it to the server.

[0226] Step 7:

[0227] The server receives the approved revised text and posts it through the API of various network services. The server then posts the revised text to SNS or chat apps using the necessary authentication information. As a result, a safe text that the user "feels like they don't understand" is posted to the SNS.

[0228] These steps enable us to check and correct the appropriateness of the text entered by users in real time, providing an environment where users can post to network services with peace of mind.

[0229] 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.

[0230] The present invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. This system is combined with an emotion engine that recognizes the user's emotions to improve the accuracy of analysis and the appropriateness of correction suggestions. The specific operation and processing of this system are described below.

[0231] System configuration

[0232] 1. Terminal

[0233] This is the device through which the user enters the text they wish to post. This includes smartphones, tablets, and PCs.

[0234] The terminal is responsible for receiving input from the user and transmitting that data to the server.

[0235] 2. Server

[0236] It is the central system that receives and analyzes text sent from the terminal.

[0237] The server uses a generative ant method and an emotion engine to analyze the text and identify problematic parts.

[0238] System program processing

[0239] 1. User Input

[0240] A user uses a device to input text into a social media posting screen. For example, the user types, "My boss is so stupid I can't stand it."

[0241] 2. Sending text

[0242] The terminal sends the text entered by the user to the server. Specifically, it converts the text data into an appropriate data format such as JSON and sends a POST request to the server.

[0243] 3. Receiving and analyzing text

[0244] The server receives the request from the terminal and extracts the text data contained therein.

[0245] The server uses the generative ant method to analyze the extracted text using natural language processing (NLP) algorithms to detect offensive language, inflammatory language, and legal and ethical issues.

[0246] The emotion engine then analyzes the user's emotions from the received text. The emotion engine identifies emotions from keywords, context, and specific expressions in the text. For example, emotions such as anger, sadness, and joy can be detected.

[0247] 4. Generating correction suggestions

[0248] The server generates appropriate correction suggestions based on the problematic part identified by the generation ant method and the emotion analysis results of the emotion engine. For example, if the emotion engine judges the aggressive expression "too stupid" to be "anger," it will correct it to "I sometimes feel that you lack understanding."

[0249] 5. Submitting a revision suggestion

[0250] The server compiles the proposed changes and sends them to the device in JSON format.

[0251] 6. User Verification and Authorization

[0252] The terminal receives the proposed revisions from the server and displays them to the user. The user checks the proposed revisions and either approves or re-edits them. If the user agrees with the proposal, he or she clicks the "Approve" button.

[0253] Example: Acknowledge the user's suggestion that "I sometimes feel I lack understanding."

[0254] 7. Final Post

[0255] The device resends the approved revised text to the server, which then performs a final check of the revised text and issues a posting request via the API of the various SNS services.

[0256] Example: Finally, the sentence "I sometimes feel like I lack understanding" is posted on social media.

[0257] This allows users to prevent posts containing inappropriate or misleading language and avoid problems on network services.In addition, the emotion engine provides appropriate revision suggestions that take the user's emotions into consideration, promoting smoother communication.

[0258] The processing flow will be explained below.

[0259] Step 1:

[0260] A user uses a device to input text into a social media posting screen. For example, the user types, "My boss is so stupid I can't stand it."

[0261] Step 2:

[0262] The device receives text input from the user, converts this input information into a data format such as JSON, and prepares it for transmission to the server.

[0263] Step 3:

[0264] The device sends the prepared JSON format text data to the server as a POST request.

[0265] Step 4:

[0266] The server receives the request from the device, parses the JSON data, and extracts the text.

[0267] Step 5:

[0268] The server uses generative AI tools to analyze the extracted text using natural language processing (NLP) algorithms to detect offensive language, inflammatory language, and legal and ethical issues.

[0269] Step 6:

[0270] The server uses an emotion engine to analyze the user's emotions from the text, which identifies emotions from keywords, context, and specific expressions in the text.

[0271] Step 7:

[0272] The server generates correction suggestions based on the analysis results of the generation AI means and the emotion analysis results of the emotion engine.

[0273] Examples:

[0274] The server determines from the analysis results of the generated AI that the expression "too stupid" is offensive.

[0275] The server determines from the analysis results of the emotion engine that the user's emotion is "anger."

[0276] The server generates the correction suggestion "I sometimes feel that I lack understanding."

[0277] Step 8:

[0278] The server generates correction suggestions and compiles them in JSON format, which is then sent to the device as a POST request.

[0279] Step 9:

[0280] The device receives the correction suggestions from the server and displays them visually to the user, such as in the form of a pop-up or notification.

[0281] Step 10:

[0282] The user checks the proposed corrections and selects whether to approve or re-edit. For example, the user agrees with the proposal and clicks the "Approve" button.

[0283] Step 11:

[0284] After receiving the user's approval, the terminal converts the revised text into JSON format and sends it again to the server as a POST request.

[0285] Step 12:

[0286] The server checks the received corrections and issues a posting request using the API of various SNS services.

[0287] Step 13:

[0288] The SNS receives the posting request from the server and posts the corrected sentence to the user's account. Finally, the sentence "I sometimes feel that I lack understanding" is displayed on the SNS.

[0289] As described above, this system analyzes user input and generates appropriate correction suggestions by combining an emotion engine and generative AI methods, preventing inappropriate posts before they occur. This promotes smooth communication on network services.

[0290] Example 2

[0291] 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."

[0292] In today's network services, users may post inappropriate or misleading content, which can cause trouble with other users. Furthermore, emotional posts that users did not intend to post may be published, which can have social repercussions and cause psychological stress. There is a need for a method to address these issues and ensure that posting content is appropriate.

[0293] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving a sentence input by a user using a terminal, means including a generative AI model for analyzing the received sentence, means for generating a revision suggestion using an emotion engine based on the analysis result by the generative AI model, means for displaying the generated revision suggestion to the user, and means for posting the revised sentence to various network services based on the user's approval. This allows the user to optimize the content of the sentence and control emotions before posting, making it possible to prevent inappropriate expressions and trouble.

[0294] "User" refers to an individual or organization that accesses the network service, inputs text, and posts it.

[0295] A "terminal" is a device that a user uses to access network services, and examples include smartphones, tablets, and personal computers.

[0296] A "server" refers to a computer system that receives and processes data sent from a terminal via a network.

[0297] A "generative AI model" refers to an artificial intelligence algorithm that uses natural language processing (NLP) to analyze text.

[0298] An "emotion engine" refers to a system that identifies and analyzes a user's emotions from keywords and context within a sentence.

[0299] "Correction Suggestions" refers to suggestions for correcting inappropriate or misleading expressions in sentences entered by users, based on the results of analysis by the generative AI model and emotion engine.

[0300] "Network services" refers to various online platforms and social networking services provided via the Internet.

[0301] This invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. This system is characterized by improving the accuracy of analysis and the appropriateness of suggested corrections by combining it with an emotion engine that recognizes the user's emotions.

[0302] System configuration

[0303] 1. Terminal

[0304] The terminal is the device into which the user inputs the text they wish to post. Specifically, this includes smartphones, tablets, and PCs. The terminal is responsible for receiving input from the user and sending that data to the server.

[0305] 2. Server

[0306] The server is the center of the system, receiving and analyzing text data sent from the device. The server uses a generative AI model and emotion engine to analyze the text and identify problem areas.

[0307] System Operation

[0308] User input and submission

[0309] When a user uses a device to enter text into the SNS posting screen and clicks the "Post" button, the device converts the entered text into JSON format or similar and sends a POST request to the server.

[0310] Text analysis

[0311] The server extracts the received text data and analyzes it using a natural language processing (NLP) algorithm with a generative AI model. This analysis detects offensive or inflammatory language, as well as legal and ethical issues. The emotion engine also analyzes the user's emotions from keywords and context within the text, identifying emotions such as "anger" or "sadness."

[0312] Generate correction suggestions

[0313] The server generates appropriate correction suggestions based on the analysis results of the generative AI model and emotion engine. For example, it suggests correcting an offensive expression such as "too stupid" to "I sometimes feel that you lack understanding."

[0314] Submitting and Viewing Suggested Revisions

[0315] The server sends the generated revision suggestions in JSON format to the terminal. The terminal receives the revision suggestions and displays them to the user. The user checks the displayed revision suggestions and approves or re-edits them. For example, if the user approves the suggestion that "I feel there are some gaps in understanding," he or she clicks the "Approve" button.

[0316] Last Post

[0317] Once the user approves the revised text, it is sent back to the server. After final confirmation, the server issues a posting request to the appropriate network service via the API of each SNS. For example, the final text, "I sometimes feel that I lack understanding," is posted to the SNS.

[0318] Specific examples

[0319] Prompt Sentence Examples

[0320] "Enter the text you're planning to post on social media below and our system will suggest ways to edit inappropriate or misleading content."

[0321] This system allows users to prevent posts containing inappropriate or misleading language, thereby avoiding trouble on network services. Furthermore, by using an emotion engine, appropriate revision suggestions are provided that take the user's emotions into consideration, promoting smoother communication.

[0322] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0323] Step 1:

[0324] The user uses the device to input text into the SNS posting screen. For example, the user might input "My boss is too stupid to handle." This input text is the input data.

[0325] Step 2:

[0326] When the user clicks the "Post" button, the device converts the entered text data into JSON format. This conversion process results in JSON format text data. The device then sends the obtained text data to the server as an HTTP POST request. The input data is the text entered by the user, and the output data is the data converted into JSON format.

[0327] Step 3:

[0328] The server receives an HTTP POST request from the device. The server extracts the text data from the request and prepares it for analysis. The input data is the JSON format data sent from the device, and the output data is the extracted text.

[0329] Step 4:

[0330] The server analyzes the extracted text using a generative AI model. The generative AI model detects offensive language, inflammatory language, and legal and ethical issues in the text. It also uses an emotion engine to identify the user's emotions from keywords and context within the text. The input data is the extracted text, and the output data is the results of the analysis and emotion analysis.

[0331] Step 5:

[0332] The server generates appropriate correction suggestions based on the analysis results and the emotion engine results. For example, the expression "too stupid" is corrected to "I sometimes feel that it lacks understanding." The input data are the analysis results and emotion analysis results, and the output data are correction suggestions.

[0333] Step 6:

[0334] The server converts the generated revision suggestions into JSON format and sends them to the terminal as an HTTP response. The input data is the revision suggestions, and the output data is the JSON-formatted revision suggestions.

[0335] Step 7:

[0336] The terminal displays the JSON-formatted correction suggestions received from the server on the user interface. The user can then review the correction suggestions and approve or re-edit them. The input data is the correction suggestions received from the server, and the output data is the result of the user's approval or re-editing.

[0337] Step 8:

[0338] If the user approves the proposed revision, the terminal sends the approved revised text to the server again. The input data is the approved revised text, and the output data is a POST request sent to the server.

[0339] Step 9:

[0340] The server then performs a final check of the received revised text data. If there are no problems with the content, it issues a posting request through the API of the various SNS. The input data is the approved revised text, and the output data is the posting request sent to the SNS. For example, the text "Sometimes I feel I lack understanding" is posted to the SNS.

[0341] This series of processes allows users to correct posts containing inappropriate language in advance and engage in appropriate communication.

[0342] (Application example 2)

[0343] 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."

[0344] In current network services, inappropriate or misleading expressions are often posted, which can lead to communication problems and trouble. Furthermore, appropriate suggestions for correction based on the user's feelings are rarely made, further complicating the problem. The objective of this invention is to solve these problems and promote smooth communication online.

[0345] 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.

[0346] In this invention, the server includes means for receiving text entered by a user, means for analyzing the received text, means for generating a revision proposal based on the analysis result by the generation means, means for analyzing the user's emotions using an emotion engine, means for optimizing the revision proposal based on the emotion analysis result, means for displaying the revision proposal to the user, and means for posting the revised text to various network services based on the user's approval. This allows appropriate revision proposals that take the user's emotions into consideration to be provided in real time, making it possible to prevent problems on network services and ensure smooth communication.

[0347] The "means for receiving text input by the user" is a communication means for transmitting text data input by the user from the terminal to the server.

[0348] The "means for generating and analyzing received text" refers to a means for analyzing text data received by the server using natural language processing and identifying inappropriate or misleading expressions.

[0349] The "means for generating correction proposals" is a means for generating specific proposals for correcting identified problem areas based on the analysis results.

[0350] "Means for analyzing user emotions using an emotion engine" refers to a means for analyzing emotions from text input by the user and identifying emotional states such as joy, anger, sadness, etc.

[0351] The "means for optimizing revision suggestions based on emotion analysis results" is a means for making revision suggestions adapted to the emotional state of the user based on the emotion analysis results of the emotion engine.

[0352] The "means for displaying the revision suggestions to the user" refers to a screen display means for displaying the generated revision suggestions on the user's terminal so that the user can confirm the revision suggestions.

[0353] "Means for posting revised text based on the user's approval to various network services" refers to a communication means for posting revised text to network services such as SNS and chat services after the user approves the proposed revision.

[0354] The present invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. Specific operations of this system and an embodiment thereof will be described below.

[0355] System configuration

[0356] 1. Terminal

[0357] This is the device through which the user enters the text they wish to post. This includes smartphones, tablets, and PCs.

[0358] The terminal is responsible for receiving input from the user and transmitting that data to the server.

[0359] 2. Server

[0360] It is the central system that receives and analyzes text sent from the terminal.

[0361] The server uses a generative method and an emotion engine to analyze the text and identify problem areas.

[0362] The system optimizes revision suggestions based on the results of sentiment analysis, generating and providing the most appropriate revision suggestions for users.

[0363] Hardware and software used

[0364] Hardware

[0365] User devices such as smartphones, smart glasses, tablets, and personal computers.

[0366] Server (using a high-performance data processing server).

[0367] software

[0368] Use TextBlob as a natural language processing (NLP) library.

[0369] We use a hypothetical EmotionEngine as an emotion analysis library.

[0370] The JSON format and HTTP protocol are used to format and communicate the data.

[0371] Specific examples of embodiments

[0372] A user enters the message they wish to post on social media into the input field on their device. For example, the user might type, "Today's meeting was a complete waste of time." This sentence is sent from the device to the server, where it is analyzed. A natural language processing algorithm (TextBlob) determines whether the sentence contains positive or negative expressions, and an emotion engine (EmotionEngine) analyzes the user's emotion as "anger." Based on this, the server generates a correction suggestion, suggesting, "Let's change this to a more positive expression." This suggestion is displayed on the user's device, and if the user accepts the suggestion, the corrected sentence is changed to, "I learned a lot from today's meeting." The corrected sentence is finally posted to the social media platform.

[0373] Prompt Sentence Examples

[0374] The prompt text to be input to the generative AI model is set as follows:

[0375] Please revise the following sentences to make them more appropriate and positive:

[0376] "Today's meeting was a complete waste of time."

[0377] In this way, this system makes appropriate correction suggestions in real time, taking into account the user's feelings, thereby preventing problems on network services and realizing smooth communication.

[0378] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0379] Step 1:

[0380] The user enters text into the SNS posting screen. For example, the user enters, "Today's meeting was a complete waste of time." The input data is saved in text format on the device.

[0381] Step 2:

[0382] The terminal converts the text entered by the user into an appropriate data format, such as JSON, and sends a POST request to the server. At this stage, the input data is the text entered by the user. The output data is the text data converted into JSON format.

[0383] Step 3:

[0384] The server receives a POST request from the terminal and extracts the text data contained therein. The server analyzes the received data format (JSON format) and extracts the input text. The input data at this stage is text data in JSON format. The output data is text in text format.

[0385] Step 4:

[0386] The server analyzes the received text using a natural language processing (NLP) algorithm (TextBlob). This analysis determines whether the text has a positive or negative tendency, or whether it contains offensive or inflammatory language. The input data is text in text format, and the output data is the analysis result, which includes the tendency of the text and whether it contains offensive language.

[0387] Step 5:

[0388] The server uses an emotion engine to analyze the user's emotions from the received text. The emotion engine identifies emotions from keywords, context, and specific expressions in the text. For example, it detects emotions such as "anger," "sadness," and "joy." The input data is text in text format, and the output data is the emotion analysis results.

[0389] Step 6:

[0390] The server generates appropriate correction suggestions based on the problem areas identified by the generation means and the sentiment analysis results. For example, it suggests correcting a negative expression such as "it was a waste" to something like "I learned very little." The input data are the analysis results and sentiment analysis results, and the output data are the correction suggestions.

[0391] Step 7:

[0392] The server compiles the revision suggestions into JSON format and sends them to the device. The device receives the revision suggestions from the server and displays them to the user. The input data is the revision suggestions, and the output data is the revision suggestions displayed to the user.

[0393] Step 8:

[0394] The user checks the proposed revisions and approves or re-edits them. For example, the user approves the suggestion that "there was little learning." At this stage, the input data is the proposed revisions, and the output data is the user's approval result.

[0395] Step 9:

[0396] The device resends the approved revised text to the server. The server performs a final check of the revised text and issues a posting request through the API of the various SNS. The input data is the approved revised text, and the output data is the result of posting to the SNS.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] [Second embodiment]

[0401] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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."

[0413] The present invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. This system is implemented using a terminal and a server. The specific operation and processing of this system are described below.

[0414] System configuration

[0415] 1. Terminal

[0416] This is the device through which the user enters the text they wish to post. This includes smartphones, tablets, and PCs.

[0417] The terminal is responsible for receiving input from the user and transmitting that data to the server.

[0418] 2. Server

[0419] It is the central system that receives and analyzes text sent from the terminal.

[0420] The server uses a generative ant method to analyze the text and identify problematic sections.

[0421] System program processing

[0422] 1. User Input

[0423] A user uses a device to input text into a social networking site posting screen. For example, the user inputs the text, "My boss is so stupid I can't stand it."

[0424] 2. Sending text

[0425] The terminal sends the text entered by the user to the server. Specifically, it converts the text data into an appropriate data format such as JSON and sends a POST request to the server.

[0426] 3. Receiving and analyzing text

[0427] The server receives requests from devices and extracts the text data contained within. The generation AI analyzes the received text using natural language processing technology and evaluates the content of the text. This analysis identifies offensive language, inflammatory language, and legal and ethical issues.

[0428] For example, the expression "too stupid" is judged to be offensive.

[0429] 4. Generating correction suggestions

[0430] The server generates appropriate correction suggestions based on the analysis results. The generation AI creates a suggestion to correct the expression "too stupid" to "I sometimes feel that I lack understanding."

[0431] 5. Submitting a revision suggestion

[0432] The server compiles the proposed changes and sends them to the device, which sends the changes in JSON format as a POST request.

[0433] 6. User Verification and Authorization

[0434] The terminal receives the proposed revisions and displays them to the user. The user checks the proposed revisions and either approves or re-edits them. If the user agrees with the proposal, he or she clicks the "Approve" button.

[0435] Example: Acknowledge the user's suggestion that "I sometimes feel I lack understanding."

[0436] 7. Final Post

[0437] The device resends the approved revised text to the server, which then posts the revised text via the API of the various SNS services.

[0438] Example: Finally, the sentence "I sometimes feel like I lack understanding" is posted on social media.

[0439] This will provide an environment where users can post on network services with peace of mind. By implementing this system, misunderstandings and trouble caused by inappropriate expressions can be prevented, and smooth communication will be promoted.

[0440] The processing flow will be explained below.

[0441] Step 1:

[0442] A user uses a device to input text into a social media posting screen. For example, the user types, "My boss is so stupid I can't stand it."

[0443] Step 2:

[0444] The device receives text input from the user, converts this input information into a data format such as JSON, and prepares it for transmission to the server.

[0445] Step 3:

[0446] The device sends the prepared JSON format text data to the server as a POST request.

[0447] Step 4:

[0448] The server receives the request from the device, parses the JSON data, and extracts the text.

[0449] Step 5:

[0450] The server uses generative AI tools to analyze the extracted text using natural language processing (NLP) algorithms to detect offensive language, inflammatory language, and legal and ethical issues.

[0451] Step 6:

[0452] Based on the analysis results, the generative AI generates suggested corrections. For example, it creates a suggestion to correct the expression "too stupid" to "I sometimes feel that I lack understanding."

[0453] Step 7:

[0454] The server generates correction suggestions and compiles them in JSON format, which is then sent to the device as a POST request.

[0455] Step 8:

[0456] The device receives the correction suggestions from the server and displays them visually to the user, such as in the form of a pop-up or notification.

[0457] Step 9:

[0458] The user checks the proposed corrections and selects whether to approve or re-edit. For example, the user agrees with the proposal and clicks the "Approve" button.

[0459] Step 10:

[0460] After receiving the user's approval, the terminal converts the revised text into JSON format and sends it again to the server as a POST request.

[0461] Step 11:

[0462] The server makes a final check of the revised text received and issues a posting request using the API of various SNS services.

[0463] Step 12:

[0464] The various SNS services receive the posting request from the server and post the corrected sentence to the user's account. Finally, the sentence "I sometimes feel that I lack understanding" is displayed on the SNS as the user's post.

[0465] This will prevent users from posting inappropriate content and avoid misunderstandings and problems on network services.

[0466] Example 1

[0467] 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."

[0468] In modern network services, posts by users often contain offensive or misleading language, which can result in trouble or misunderstandings. This can be detrimental to users and other stakeholders. Conventional systems lacked the means to detect such problems in real time and to appropriately correct them, which sometimes hindered smooth communication.

[0469] 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.

[0470] In this invention, the server includes means for receiving text entered by a user, means for converting the received text into JSON format and transmitting it to the server, a generation AI means for analyzing the text received by the server, means for generating revision suggestions based on the results of the analysis by the generation AI means, means for compiling the revision suggestions in JSON format and transmitting them to the terminal, means for displaying the revision suggestions to the user and obtaining the user's approval, and means for resending the approved revised text to the server and posting it to various network services. This allows users to check the revision suggestions before posting text and to correct misleading or offensive language in advance. This facilitates communication on network services and prevents problems caused by inappropriate language.

[0471] A "user" is an entity that uses a network service and inputs, transmits, and checks text through a terminal.

[0472] A "terminal" is a device that allows a user to input text and communicate data with a server. Examples of such devices include smartphones, tablets, and PCs.

[0473] The "server" is a central system that receives text sent by users, analyzes it using a generative AI model, and generates and sends correction suggestions.

[0474] A "generative AI model" is an algorithm or system that uses natural language processing techniques to analyze text and generate appropriate correction suggestions.

[0475] The "JSON format" is a lightweight data exchange format for structuring and representing data, and is an abbreviation for JavaScript Object Notation.

[0476] A "POST request" is a method in the HTTP protocol for a client to send data to a server.

[0477] "Suggested modifications" are suggestions for modifying offensive or misleading language identified through analysis to make it more appropriate.

[0478] "Network services" is a general term for various online platforms and services used via the Internet, including social networking sites, blogs, and forums.

[0479] "Natural language processing technology" refers to technology that enables computers to understand, interpret, and generate human language. Specifically, it includes technologies such as morphological analysis, sentence analysis, and machine translation.

[0480] The "HTTP protocol" is a communication protocol for transferring data over the Internet and is an abbreviation for Hypertext Transfer Protocol.

[0481] "Various network services" refers to multiple different online platforms and services, and means a collection of web services provided for specific fields or purposes.

[0482] These definitions clarify each component of the system and its role.

[0483] This invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. The system is primarily composed of a terminal and a server. This section describes the detailed configuration and operation of the system.

[0484] System configuration

[0485] 1. Terminal

[0486] A terminal is a device that allows a user to input text and send it to a server. Examples include smartphones, tablets, and PCs. The terminal converts the text data input by the user into JSON format and sends it to the server. The terminal uses a library for HTTP communication (e.g., JavaScript's fetch function or Python's requests library).

[0487] 2. Server

[0488] The server is a central system that receives and analyzes text sent from the device. The server analyzes the text using a generative AI model (e.g., OpenAI GPT-3). This analysis uses natural language processing libraries such as the Natural Language Toolkit (NLTK) and spaCy. The server generates appropriate correction suggestions based on the analysis results and sends them to the device in JSON format. Web frameworks such as Flask and Django are used for communication on the server side.

[0489] Example of operation

[0490] A specific example of the operation of this system is given below.

[0491] 1. User Input

[0492] The user uses the device to input text into the SNS posting screen. For example, they input the text "My boss is so stupid I can't stand it." At this point, an input field and a send button are displayed on the device screen.

[0493] 2. Sending text

[0494] The device sends the text entered by the user to the server. Specifically, a program on the device converts this text into JSON format and sends it to the server as an HTTP POST request.

[0495] 3. Text Analysis

[0496] The server receives the POST request sent from the device and extracts the text data from the request body. The generative AI model analyzes the received text and determines that the expression "too stupid" is offensive.

[0497] 4. Generating correction suggestions

[0498] The server generates appropriate correction suggestions based on the analysis results, for example, suggesting that the expression "too stupid" be changed to "I sometimes feel that I lack understanding."

[0499] An example of a prompt sentence is as follows:

[0500] Input sentence: "My boss is so stupid I can't stand it."

[0501] Prompts for generating example revision suggestions:

[0502] "Please revise the following sentence to make it less offensive and less misleading.

[0503] "My boss is so stupid I can't handle it"

[0504] 5. Submit and review your proposed revisions

[0505] The server compiles the proposed revisions in JSON format and sends them to the device. The device receives the proposed revisions and displays them to the user. The user can then review the displayed proposals and approve or re-edit them.

[0506] 6. Final Post

[0507] If the user approves the suggestion, the device sends the revised text back to the server, which passes it to the APIs of various network services and ultimately posts it to the social networking site.

[0508] This system allows users to check and correct offensive or misleading language before posting, thereby facilitating smoother communication on network services and preventing problems caused by inappropriate language.

[0509] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0510] Step 1:

[0511] The user uses the device to input text into the SNS posting screen. For example, they might input the phrase "My boss is so stupid I can't stand it." At this point, the input text is displayed in the input field on the device.

[0512] Input: User input: "My boss is so stupid I can't do my job"

[0513] Output: Text that appears in the terminal input field

[0514] Step 2:

[0515] The terminal converts the text entered by the user into JSON format, using the JSON.stringify function in JavaScript or the json.dumps method in Python to structure the text data, and then sends it to the server using an HTTP POST request.

[0516] Input: Raw text data entered by the user

[0517] Output: JSON format data ({"text": "My boss is so stupid I can't stand it"})

[0518] Step 3:

[0519] The server receives the POST request from the device, extracts the JSON-formatted text data from the body, and then analyzes the text using a generative AI model (e.g., OpenAI GPT-3). This analysis uses natural language processing techniques to identify offensive or misleading language.

[0520] Input: JSON data sent from the terminal

[0521] Output: Analysis results (identification of offensive or misleading expressions)

[0522] Step 4:

[0523] The server generates appropriate correction suggestions based on the analysis results. For example, using a generative AI model, it suggests correcting the expression "too stupid" to "I sometimes feel that I lack understanding." A specific example of a generated correction suggestion is shown below.

[0524] Input: Analysis result (problematic expression)

[0525] Output: Suggested correction ("I sometimes feel I lack understanding")

[0526] Step 5:

[0527] The server then compiles the proposed changes in JSON format and sends them to the device as a POST request, using a web framework such as Flask or Django to generate an HTTP response.

[0528] Input: Proposed text

[0529] Output: JSON format correction suggestion data ({"suggestion": "Sometimes I feel I don't understand"})

[0530] Step 6:

[0531] The terminal receives the proposed corrections and displays them to the user. The user can then review the displayed proposal and approve or re-edit it. If the user agrees with the proposal, they click the "Approve" button.

[0532] Input: JSON formatted correction suggestions received from the server

[0533] Output: The suggested corrections shown to the user, and the user's approval action.

[0534] Step 7:

[0535] The device then sends the revised text, approved by the user, back to the server. The server then passes the revised text to the APIs of various network services, and ultimately posts it to social networking sites. Specifically, posting is done using the Twitter API, Facebook API, etc.

[0536] Input: User-approved revised sentence ("I sometimes feel I lack understanding.")

[0537] Output: Corrected text posted to the network service

[0538] (Application example 1)

[0539] 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."

[0540] In today's Internet environment, it is important for users to be able to freely express their opinions, but inappropriate or misleading expressions can be included, which can lead to disputes between users and legal issues.In addition, there is a lack of means to provide appropriate correction suggestions in real time, making it difficult to provide a safe and smooth communication environment.

[0541] 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.

[0542] In this invention, the server includes means for receiving text entered by a user, means for analyzing the received text, means for generating revision suggestions based on the results of the analysis by the means for generating revision suggestions, means for displaying the revision suggestions to the user, means for posting the revised text to various network services based on the user's approval, and means for generating revision suggestions in real time and instantly and securely processing the user's input. This makes it possible to provide a safe and smooth communication environment by checking the appropriateness of the text entered by the user in advance and suggesting revisions.

[0543] "User" refers to a user who uses a system or service to input text.

[0544] "Means for receiving input text" refers to a function for receiving text input by a user from a terminal.

[0545] "Generative ant method" is a technology that uses generative AI to analyze input text and identify problem areas.

[0546] The "means for generating correction suggestions" is a function that suggests appropriate corrections based on the problem areas identified by the generation AI means.

[0547] The "means for displaying revision suggestions to the user" is a function for displaying the generated revision suggestions on the user's device.

[0548] "Means for posting revised text based on user approval to various network services" refers to a function for posting revised text to network services after the user's suggested revisions have been confirmed and approved.

[0549] "Means for generating correction suggestions in real time and instantly and securely processing user input" refers to a function that instantly analyzes text entered by a user and safely generates correction suggestions.

[0550] This invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. The specific operation and processing of this system are described below.

[0551] System configuration

[0552] 1. Terminal

[0553] This is the device through which the user enters the text they wish to post. This includes smartphones, tablets, and PCs.

[0554] The terminal is responsible for receiving input from the user and transmitting that data to the server.

[0555] 2. Server

[0556] It is the central system that receives and analyzes text sent from the terminal.

[0557] The server uses a generative AI model to analyze the text and identify problem areas.

[0558] Hardware and software used

[0559] Hardware: Server machine (on-premise or cloud server)

[0560] Software: Flask, OpenAI API

[0561] Program Description

[0562] Program processing

[0563] 1. User Input

[0564] The user uses the device to input text into the SNS posting screen. For example, they can input a sentence such as, "My boss is so stupid I can't stand it."

[0565] 2. Sending text

[0566] The terminal sends the text entered by the user to the server. At this time, the text data is converted into an appropriate data format such as JSON format and sent to the server via a POST request.

[0567] 3. Receiving and analyzing text

[0568] The server receives requests from devices and extracts the text data contained within. The generative AI model then analyzes the received text using natural language processing technology and evaluates its content. This analysis identifies offensive, inflammatory, and legally or ethically questionable expressions. For example, the expression "too stupid" is deemed offensive.

[0569] 4. Generating correction suggestions

[0570] The server generates appropriate correction suggestions based on the analysis results. The generative AI model creates a suggestion to correct the expression "too stupid" to "I sometimes feel that I lack understanding."

[0571] 5. Submitting a revision suggestion

[0572] The server compiles and sends correction suggestions to the device, which returns them in JSON format.

[0573] 6. User Verification and Authorization

[0574] The terminal receives the suggested revisions and displays them to the user. The user checks the suggested revisions and approves or re-edits them. If the user agrees with the proposal, he or she clicks the "Approve" button. For example, the user approves the suggestion that "I sometimes feel that I lack understanding."

[0575] 7. Final Post

[0576] The device resends the approved revised sentence to the server, and the server posts the revised sentence via the API of various SNS. For example, the sentence "Sometimes I feel I lack understanding" is posted to the SNS.

[0577] Examples and prompts

[0578] Examples:

[0579] User input: "What Tanaka said in yesterday's meeting was completely meaningless."

[0580] Analysis request: {"text": "What Tanaka said at yesterday's meeting was completely meaningless."}

[0581] Suggested revision: "What Tanaka said in yesterday's meeting was a little hard to understand."

[0582] Example prompts to input to a generative AI model:

[0583] Prompt: "Please revise the following sentence to remove any inappropriate language: What Tanaka said at the meeting yesterday was completely meaningless."

[0584] This invention provides an environment in which users can post on network services with peace of mind. It is expected that smooth communication will be promoted by preventing misunderstandings and trouble caused by inappropriate expressions.

[0585] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0586] Step 1:

[0587] The device receives text entered by the user. At this time, the user enters text into a social media posting screen or chat app. The device temporarily stores this input, and when the user clicks the send button, the text data is sent to the server. At this time, the input data is converted to JSON format.

[0588] Step 2:

[0589] The server receives JSON-formatted text data sent from the device, analyzes the received data, and extracts only the necessary text. The extracted text then becomes the input data for the generative AI model.

[0590] Step 3:

[0591] The server analyzes the received text using a generative AI model. This analysis uses natural language processing technology to check for offensive language, inflammatory language, or legal or ethical issues. As a result of the analysis, any inappropriate content is identified.

[0592] Step 4:

[0593] The server generates appropriate correction suggestions based on the analysis results. The generative AI model uses the prompt "Please correct the following sentence and remove inappropriate expressions: [original sentence]." For example, in response to the sentence "My boss is so stupid I can't stand it," the server generates the correction suggestion "Sometimes I feel he lacks understanding."

[0594] Step 5:

[0595] The server compiles the generated revision suggestions into JSON format and sends them to the device. The device receives the revision suggestions and displays them to the user. The revision suggestions are typically displayed side by side, with the original text and the revised text.

[0596] Step 6:

[0597] The user can review the proposed edits on their device. They can either approve the suggested edits or make further edits themselves. If the approve button is pressed, the device converts the edited text back into JSON format and sends it to the server.

[0598] Step 7:

[0599] The server receives the approved revised text and posts it through the API of various network services. The server then posts the revised text to SNS or chat apps using the necessary authentication information. As a result, a safe text that the user "feels like they don't understand" is posted to the SNS.

[0600] These steps enable us to check and correct the appropriateness of the text entered by users in real time, providing an environment where users can post to network services with peace of mind.

[0601] 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.

[0602] The present invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. This system is combined with an emotion engine that recognizes the user's emotions to improve the accuracy of analysis and the appropriateness of correction suggestions. The specific operation and processing of this system are described below.

[0603] System configuration

[0604] 1. Terminal

[0605] This is the device through which the user enters the text they wish to post. This includes smartphones, tablets, and PCs.

[0606] The terminal is responsible for receiving input from the user and transmitting that data to the server.

[0607] 2. Server

[0608] It is the central system that receives and analyzes text sent from the terminal.

[0609] The server uses a generative ant method and an emotion engine to analyze the text and identify problematic parts.

[0610] System program processing

[0611] 1. User Input

[0612] A user uses a device to input text into a social media posting screen. For example, the user types, "My boss is so stupid I can't stand it."

[0613] 2. Sending text

[0614] The terminal sends the text entered by the user to the server. Specifically, it converts the text data into an appropriate data format such as JSON and sends a POST request to the server.

[0615] 3. Receiving and analyzing text

[0616] The server receives the request from the terminal and extracts the text data contained therein.

[0617] The server uses the generative ant method to analyze the extracted text using natural language processing (NLP) algorithms to detect offensive language, inflammatory language, and legal and ethical issues.

[0618] The emotion engine then analyzes the user's emotions from the received text. The emotion engine identifies emotions from keywords, context, and specific expressions in the text. For example, emotions such as anger, sadness, and joy can be detected.

[0619] 4. Generating correction suggestions

[0620] The server generates appropriate correction suggestions based on the problematic part identified by the generation ant method and the emotion analysis results of the emotion engine. For example, if the emotion engine judges the aggressive expression "too stupid" to be "anger," it will correct it to "I sometimes feel that you lack understanding."

[0621] 5. Submitting a revision suggestion

[0622] The server compiles the proposed changes and sends them to the device in JSON format.

[0623] 6. User Verification and Authorization

[0624] The terminal receives the proposed revisions from the server and displays them to the user. The user checks the proposed revisions and either approves or re-edits them. If the user agrees with the proposal, he or she clicks the "Approve" button.

[0625] Example: Acknowledge the user's suggestion that "I sometimes feel I lack understanding."

[0626] 7. Final Post

[0627] The device resends the approved revised text to the server, which then performs a final check of the revised text and issues a posting request via the API of the various SNS services.

[0628] Example: Finally, the sentence "I sometimes feel like I lack understanding" is posted on social media.

[0629] This allows users to prevent posts containing inappropriate or misleading language and avoid problems on network services.In addition, the emotion engine provides appropriate revision suggestions that take the user's emotions into consideration, promoting smoother communication.

[0630] The processing flow will be explained below.

[0631] Step 1:

[0632] A user uses a device to input text into a social media posting screen. For example, the user types, "My boss is so stupid I can't stand it."

[0633] Step 2:

[0634] The device receives text input from the user, converts this input information into a data format such as JSON, and prepares it for transmission to the server.

[0635] Step 3:

[0636] The device sends the prepared JSON format text data to the server as a POST request.

[0637] Step 4:

[0638] The server receives the request from the device, parses the JSON data, and extracts the text.

[0639] Step 5:

[0640] The server uses generative AI tools to analyze the extracted text using natural language processing (NLP) algorithms to detect offensive language, inflammatory language, and legal and ethical issues.

[0641] Step 6:

[0642] The server uses an emotion engine to analyze the user's emotions from the text, which identifies emotions from keywords, context, and specific expressions in the text.

[0643] Step 7:

[0644] The server generates correction suggestions based on the analysis results of the generation AI means and the emotion analysis results of the emotion engine.

[0645] Examples:

[0646] The server determines from the analysis results of the generated AI that the expression "too stupid" is offensive.

[0647] The server determines from the analysis results of the emotion engine that the user's emotion is "anger."

[0648] The server generates the correction suggestion "I sometimes feel that I lack understanding."

[0649] Step 8:

[0650] The server generates correction suggestions and compiles them in JSON format, which is then sent to the device as a POST request.

[0651] Step 9:

[0652] The device receives the correction suggestions from the server and displays them visually to the user, such as in the form of a pop-up or notification.

[0653] Step 10:

[0654] The user checks the proposed corrections and selects whether to approve or re-edit. For example, the user agrees with the proposal and clicks the "Approve" button.

[0655] Step 11:

[0656] After receiving the user's approval, the terminal converts the revised text into JSON format and sends it again to the server as a POST request.

[0657] Step 12:

[0658] The server checks the received corrections and issues a posting request using the API of various SNS services.

[0659] Step 13:

[0660] The SNS receives the posting request from the server and posts the corrected sentence to the user's account. Finally, the sentence "I sometimes feel that I lack understanding" is displayed on the SNS.

[0661] As described above, this system analyzes user input and generates appropriate correction suggestions by combining an emotion engine and generative AI methods, preventing inappropriate posts before they occur. This promotes smooth communication on network services.

[0662] Example 2

[0663] 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."

[0664] In today's network services, users may post inappropriate or misleading content, which can cause trouble with other users. Furthermore, emotional posts that users did not intend to post may be published, which can have social repercussions and cause psychological stress. There is a need for a method to address these issues and ensure that posting content is appropriate.

[0665] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving a sentence input by a user using a terminal, means including a generative AI model for analyzing the received sentence, means for generating a revision suggestion using an emotion engine based on the analysis result by the generative AI model, means for displaying the generated revision suggestion to the user, and means for posting the revised sentence to various network services based on the user's approval. This allows the user to optimize the content of the sentence and control emotions before posting, making it possible to prevent inappropriate expressions and trouble.

[0666] "User" refers to an individual or organization that accesses the network service, inputs text, and posts it.

[0667] A "terminal" is a device that a user uses to access network services, and examples include smartphones, tablets, and personal computers.

[0668] A "server" refers to a computer system that receives and processes data sent from a terminal via a network.

[0669] A "generative AI model" refers to an artificial intelligence algorithm that uses natural language processing (NLP) to analyze text.

[0670] An "emotion engine" refers to a system that identifies and analyzes a user's emotions from keywords and context within a sentence.

[0671] "Correction Suggestions" refers to suggestions for correcting inappropriate or misleading expressions in sentences entered by users, based on the results of analysis by the generative AI model and emotion engine.

[0672] "Network services" refers to various online platforms and social networking services provided via the Internet.

[0673] This invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. This system is characterized by improving the accuracy of analysis and the appropriateness of suggested corrections by combining it with an emotion engine that recognizes the user's emotions.

[0674] System configuration

[0675] 1. Terminal

[0676] The terminal is the device into which the user inputs the text they wish to post. Specifically, this includes smartphones, tablets, and PCs. The terminal is responsible for receiving input from the user and sending that data to the server.

[0677] 2. Server

[0678] The server is the center of the system, receiving and analyzing text data sent from the device. The server uses a generative AI model and emotion engine to analyze the text and identify problem areas.

[0679] System Operation

[0680] User input and submission

[0681] When a user uses a device to enter text into the SNS posting screen and clicks the "Post" button, the device converts the entered text into JSON format or similar and sends a POST request to the server.

[0682] Text analysis

[0683] The server extracts the received text data and analyzes it using a natural language processing (NLP) algorithm with a generative AI model. This analysis detects offensive or inflammatory language, as well as legal and ethical issues. The emotion engine also analyzes the user's emotions from keywords and context within the text, identifying emotions such as "anger" or "sadness."

[0684] Generate correction suggestions

[0685] The server generates appropriate correction suggestions based on the analysis results of the generative AI model and emotion engine. For example, it suggests correcting an offensive expression such as "too stupid" to "I sometimes feel that you lack understanding."

[0686] Submitting and Viewing Suggested Revisions

[0687] The server sends the generated revision suggestions in JSON format to the terminal. The terminal receives the revision suggestions and displays them to the user. The user checks the displayed revision suggestions and approves or re-edits them. For example, if the user approves the suggestion that "I feel there are some gaps in understanding," he or she clicks the "Approve" button.

[0688] Last Post

[0689] Once the user approves the revised text, it is sent back to the server. After final confirmation, the server issues a posting request to the appropriate network service via the API of each SNS. For example, the final text, "I sometimes feel that I lack understanding," is posted to the SNS.

[0690] Specific examples

[0691] Prompt Sentence Examples

[0692] "Enter the text you're planning to post on social media below and our system will suggest ways to edit inappropriate or misleading content."

[0693] This system allows users to prevent posts containing inappropriate or misleading language, thereby avoiding trouble on network services. Furthermore, by using an emotion engine, appropriate revision suggestions are provided that take the user's emotions into consideration, promoting smoother communication.

[0694] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0695] Step 1:

[0696] The user uses the device to input text into the SNS posting screen. For example, the user might input "My boss is too stupid to handle." This input text is the input data.

[0697] Step 2:

[0698] When the user clicks the "Post" button, the device converts the entered text data into JSON format. This conversion process results in JSON format text data. The device then sends the obtained text data to the server as an HTTP POST request. The input data is the text entered by the user, and the output data is the data converted into JSON format.

[0699] Step 3:

[0700] The server receives an HTTP POST request from the device. The server extracts the text data from the request and prepares it for analysis. The input data is the JSON format data sent from the device, and the output data is the extracted text.

[0701] Step 4:

[0702] The server analyzes the extracted text using a generative AI model. The generative AI model detects offensive language, inflammatory language, and legal and ethical issues in the text. It also uses an emotion engine to identify the user's emotions from keywords and context within the text. The input data is the extracted text, and the output data is the results of the analysis and emotion analysis.

[0703] Step 5:

[0704] The server generates appropriate correction suggestions based on the analysis results and the emotion engine results. For example, the expression "too stupid" is corrected to "I sometimes feel that it lacks understanding." The input data are the analysis results and emotion analysis results, and the output data are correction suggestions.

[0705] Step 6:

[0706] The server converts the generated revision suggestions into JSON format and sends them to the terminal as an HTTP response. The input data is the revision suggestions, and the output data is the JSON-formatted revision suggestions.

[0707] Step 7:

[0708] The terminal displays the JSON-formatted correction suggestions received from the server on the user interface. The user can then review the correction suggestions and approve or re-edit them. The input data is the correction suggestions received from the server, and the output data is the result of the user's approval or re-editing.

[0709] Step 8:

[0710] If the user approves the proposed revision, the terminal sends the approved revised text to the server again. The input data is the approved revised text, and the output data is a POST request sent to the server.

[0711] Step 9:

[0712] The server then performs a final check of the received revised text data. If there are no problems with the content, it issues a posting request through the API of the various SNS. The input data is the approved revised text, and the output data is the posting request sent to the SNS. For example, the text "Sometimes I feel I lack understanding" is posted to the SNS.

[0713] This series of processes allows users to correct posts containing inappropriate language in advance and engage in appropriate communication.

[0714] (Application example 2)

[0715] 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."

[0716] In current network services, inappropriate or misleading expressions are often posted, which can lead to communication problems and trouble. Furthermore, appropriate suggestions for correction based on the user's feelings are rarely made, further complicating the problem. The objective of this invention is to solve these problems and promote smooth communication online.

[0717] 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.

[0718] In this invention, the server includes means for receiving text entered by a user, means for analyzing the received text, means for generating a revision proposal based on the analysis result by the generation means, means for analyzing the user's emotions using an emotion engine, means for optimizing the revision proposal based on the emotion analysis result, means for displaying the revision proposal to the user, and means for posting the revised text to various network services based on the user's approval. This allows appropriate revision proposals that take the user's emotions into consideration to be provided in real time, making it possible to prevent problems on network services and ensure smooth communication.

[0719] The "means for receiving text input by the user" is a communication means for transmitting text data input by the user from the terminal to the server.

[0720] The "means for generating and analyzing received text" refers to a means for analyzing text data received by the server using natural language processing and identifying inappropriate or misleading expressions.

[0721] The "means for generating correction proposals" is a means for generating specific proposals for correcting identified problem areas based on the analysis results.

[0722] "Means for analyzing user emotions using an emotion engine" refers to a means for analyzing emotions from text input by the user and identifying emotional states such as joy, anger, sadness, etc.

[0723] The "means for optimizing revision suggestions based on emotion analysis results" is a means for making revision suggestions adapted to the emotional state of the user based on the emotion analysis results of the emotion engine.

[0724] The "means for displaying the revision suggestions to the user" refers to a screen display means for displaying the generated revision suggestions on the user's terminal so that the user can confirm the revision suggestions.

[0725] "Means for posting revised text based on the user's approval to various network services" refers to a communication means for posting revised text to network services such as SNS and chat services after the user approves the proposed revision.

[0726] The present invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. Specific operations of this system and an embodiment thereof will be described below.

[0727] System configuration

[0728] 1. Terminal

[0729] This is the device through which the user enters the text they wish to post. This includes smartphones, tablets, and PCs.

[0730] The terminal is responsible for receiving input from the user and transmitting that data to the server.

[0731] 2. Server

[0732] It is the central system that receives and analyzes text sent from the terminal.

[0733] The server uses a generative method and an emotion engine to analyze the text and identify problem areas.

[0734] The system optimizes revision suggestions based on the results of sentiment analysis, generating and providing the most appropriate revision suggestions for users.

[0735] Hardware and software used

[0736] Hardware

[0737] User devices such as smartphones, smart glasses, tablets, and personal computers.

[0738] Server (using a high-performance data processing server).

[0739] software

[0740] Use TextBlob as a natural language processing (NLP) library.

[0741] We use a hypothetical EmotionEngine as an emotion analysis library.

[0742] The JSON format and HTTP protocol are used to format and communicate the data.

[0743] Specific examples of embodiments

[0744] A user enters the message they wish to post on social media into the input field on their device. For example, the user might type, "Today's meeting was a complete waste of time." This sentence is sent from the device to the server, where it is analyzed. A natural language processing algorithm (TextBlob) determines whether the sentence contains positive or negative expressions, and an emotion engine (EmotionEngine) analyzes the user's emotion as "anger." Based on this, the server generates a correction suggestion, suggesting, "Let's change this to a more positive expression." This suggestion is displayed on the user's device, and if the user accepts the suggestion, the corrected sentence is changed to, "I learned a lot from today's meeting." The corrected sentence is finally posted to the social media platform.

[0745] Prompt Sentence Examples

[0746] The prompt text to be input to the generative AI model is set as follows:

[0747] Please revise the following sentences to make them more appropriate and positive:

[0748] "Today's meeting was a complete waste of time."

[0749] In this way, this system makes appropriate correction suggestions in real time, taking into account the user's feelings, thereby preventing problems on network services and realizing smooth communication.

[0750] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0751] Step 1:

[0752] The user enters text into the SNS posting screen. For example, the user enters, "Today's meeting was a complete waste of time." The input data is saved in text format on the device.

[0753] Step 2:

[0754] The terminal converts the text entered by the user into an appropriate data format, such as JSON, and sends a POST request to the server. At this stage, the input data is the text entered by the user. The output data is the text data converted into JSON format.

[0755] Step 3:

[0756] The server receives a POST request from the terminal and extracts the text data contained therein. The server analyzes the received data format (JSON format) and extracts the input text. The input data at this stage is text data in JSON format. The output data is text in text format.

[0757] Step 4:

[0758] The server analyzes the received text using a natural language processing (NLP) algorithm (TextBlob). This analysis determines whether the text has a positive or negative tendency, or whether it contains offensive or inflammatory language. The input data is text in text format, and the output data is the analysis result, which includes the tendency of the text and whether it contains offensive language.

[0759] Step 5:

[0760] The server uses an emotion engine to analyze the user's emotions from the received text. The emotion engine identifies emotions from keywords, context, and specific expressions in the text. For example, it detects emotions such as "anger," "sadness," and "joy." The input data is text in text format, and the output data is the emotion analysis results.

[0761] Step 6:

[0762] The server generates appropriate correction suggestions based on the problem areas identified by the generation means and the sentiment analysis results. For example, it suggests correcting a negative expression such as "it was a waste" to something like "I learned very little." The input data are the analysis results and sentiment analysis results, and the output data are the correction suggestions.

[0763] Step 7:

[0764] The server compiles the revision suggestions into JSON format and sends them to the device. The device receives the revision suggestions from the server and displays them to the user. The input data is the revision suggestions, and the output data is the revision suggestions displayed to the user.

[0765] Step 8:

[0766] The user checks the proposed revisions and approves or re-edits them. For example, the user approves the suggestion that "there was little learning." At this stage, the input data is the proposed revisions, and the output data is the user's approval result.

[0767] Step 9:

[0768] The device resends the approved revised text to the server. The server performs a final check of the revised text and issues a posting request through the API of the various SNS. The input data is the approved revised text, and the output data is the result of posting to the SNS.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] [Third embodiment]

[0773] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0774] 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.

[0775] 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).

[0776] 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.

[0777] 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.

[0778] 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).

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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."

[0785] The present invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. This system is implemented using a terminal and a server. The specific operation and processing of this system are described below.

[0786] System configuration

[0787] 1. Terminal

[0788] This is the device through which the user enters the text they wish to post. This includes smartphones, tablets, and PCs.

[0789] The terminal is responsible for receiving input from the user and transmitting that data to the server.

[0790] 2. Server

[0791] It is the central system that receives and analyzes text sent from the terminal.

[0792] The server uses a generative ant method to analyze the text and identify problematic sections.

[0793] System program processing

[0794] 1. User Input

[0795] A user uses a device to input text into a social networking site posting screen. For example, the user inputs the text, "My boss is so stupid I can't stand it."

[0796] 2. Sending text

[0797] The terminal sends the text entered by the user to the server. Specifically, it converts the text data into an appropriate data format such as JSON and sends a POST request to the server.

[0798] 3. Receiving and analyzing text

[0799] The server receives requests from devices and extracts the text data contained within. The generation AI analyzes the received text using natural language processing technology and evaluates the content of the text. This analysis identifies offensive language, inflammatory language, and legal and ethical issues.

[0800] For example, the expression "too stupid" is judged to be offensive.

[0801] 4. Generating correction suggestions

[0802] The server generates appropriate correction suggestions based on the analysis results. The generation AI creates a suggestion to correct the expression "too stupid" to "I sometimes feel that I lack understanding."

[0803] 5. Submitting a revision suggestion

[0804] The server compiles the proposed changes and sends them to the device, which sends the changes in JSON format as a POST request.

[0805] 6. User Verification and Authorization

[0806] The terminal receives the proposed revisions and displays them to the user. The user checks the proposed revisions and either approves or re-edits them. If the user agrees with the proposal, he or she clicks the "Approve" button.

[0807] Example: Acknowledge the user's suggestion that "I sometimes feel I lack understanding."

[0808] 7. Final Post

[0809] The device resends the approved revised text to the server, which then posts the revised text via the API of the various SNS services.

[0810] Example: Finally, the sentence "I sometimes feel like I lack understanding" is posted on social media.

[0811] This will provide an environment where users can post on network services with peace of mind. By implementing this system, misunderstandings and trouble caused by inappropriate expressions can be prevented, and smooth communication will be promoted.

[0812] The processing flow will be explained below.

[0813] Step 1:

[0814] A user uses a device to input text into a social media posting screen. For example, the user types, "My boss is so stupid I can't stand it."

[0815] Step 2:

[0816] The device receives text input from the user, converts this input information into a data format such as JSON, and prepares it for transmission to the server.

[0817] Step 3:

[0818] The device sends the prepared JSON format text data to the server as a POST request.

[0819] Step 4:

[0820] The server receives the request from the device, parses the JSON data, and extracts the text.

[0821] Step 5:

[0822] The server uses generative AI tools to analyze the extracted text using natural language processing (NLP) algorithms to detect offensive language, inflammatory language, and legal and ethical issues.

[0823] Step 6:

[0824] Based on the analysis results, the generative AI generates suggested corrections. For example, it creates a suggestion to correct the expression "too stupid" to "I sometimes feel that I lack understanding."

[0825] Step 7:

[0826] The server generates correction suggestions and compiles them in JSON format, which is then sent to the device as a POST request.

[0827] Step 8:

[0828] The device receives the correction suggestions from the server and displays them visually to the user, such as in the form of a pop-up or notification.

[0829] Step 9:

[0830] The user checks the proposed corrections and selects whether to approve or re-edit. For example, the user agrees with the proposal and clicks the "Approve" button.

[0831] Step 10:

[0832] After receiving the user's approval, the terminal converts the revised text into JSON format and sends it again to the server as a POST request.

[0833] Step 11:

[0834] The server makes a final check of the revised text received and issues a posting request using the API of various SNS services.

[0835] Step 12:

[0836] The various SNS services receive the posting request from the server and post the corrected sentence to the user's account. Finally, the sentence "I sometimes feel that I lack understanding" is displayed on the SNS as the user's post.

[0837] This will prevent users from posting inappropriate content and avoid misunderstandings and problems on network services.

[0838] Example 1

[0839] 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."

[0840] In modern network services, posts by users often contain offensive or misleading language, which can result in trouble or misunderstandings. This can be detrimental to users and other stakeholders. Conventional systems lacked the means to detect such problems in real time and to appropriately correct them, which sometimes hindered smooth communication.

[0841] 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.

[0842] In this invention, the server includes means for receiving text entered by a user, means for converting the received text into JSON format and transmitting it to the server, a generation AI means for analyzing the text received by the server, means for generating revision suggestions based on the results of the analysis by the generation AI means, means for compiling the revision suggestions in JSON format and transmitting them to the terminal, means for displaying the revision suggestions to the user and obtaining the user's approval, and means for resending the approved revised text to the server and posting it to various network services. This allows users to check the revision suggestions before posting text and to correct misleading or offensive language in advance. This facilitates communication on network services and prevents problems caused by inappropriate language.

[0843] A "user" is an entity that uses a network service and inputs, transmits, and checks text through a terminal.

[0844] A "terminal" is a device that allows a user to input text and communicate data with a server. Examples of such devices include smartphones, tablets, and PCs.

[0845] The "server" is a central system that receives text sent by users, analyzes it using a generative AI model, and generates and sends correction suggestions.

[0846] A "generative AI model" is an algorithm or system that uses natural language processing techniques to analyze text and generate appropriate correction suggestions.

[0847] The "JSON format" is a lightweight data exchange format for structuring and representing data, and is an abbreviation for JavaScript Object Notation.

[0848] A "POST request" is a method in the HTTP protocol for a client to send data to a server.

[0849] "Suggested modifications" are suggestions for modifying offensive or misleading language identified through analysis to make it more appropriate.

[0850] "Network services" is a general term for various online platforms and services used via the Internet, including social networking sites, blogs, and forums.

[0851] "Natural language processing technology" refers to technology that enables computers to understand, interpret, and generate human language. Specifically, it includes technologies such as morphological analysis, sentence analysis, and machine translation.

[0852] The "HTTP protocol" is a communication protocol for transferring data over the Internet and is an abbreviation for Hypertext Transfer Protocol.

[0853] "Various network services" refers to multiple different online platforms and services, and means a collection of web services provided for specific fields or purposes.

[0854] These definitions clarify each component of the system and its role.

[0855] This invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. The system is primarily composed of a terminal and a server. This section describes the detailed configuration and operation of the system.

[0856] System configuration

[0857] 1. Terminal

[0858] A terminal is a device that allows a user to input text and send it to a server. Examples include smartphones, tablets, and PCs. The terminal converts the text data input by the user into JSON format and sends it to the server. The terminal uses a library for HTTP communication (e.g., JavaScript's fetch function or Python's requests library).

[0859] 2. Server

[0860] The server is a central system that receives and analyzes text sent from the device. The server analyzes the text using a generative AI model (e.g., OpenAI GPT-3). This analysis uses natural language processing libraries such as the Natural Language Toolkit (NLTK) and spaCy. The server generates appropriate correction suggestions based on the analysis results and sends them to the device in JSON format. Web frameworks such as Flask and Django are used for communication on the server side.

[0861] Example of operation

[0862] A specific example of the operation of this system is given below.

[0863] 1. User Input

[0864] The user uses the device to input text into the SNS posting screen. For example, they input the text "My boss is so stupid I can't stand it." At this point, an input field and a send button are displayed on the device screen.

[0865] 2. Sending text

[0866] The device sends the text entered by the user to the server. Specifically, a program on the device converts this text into JSON format and sends it to the server as an HTTP POST request.

[0867] 3. Text Analysis

[0868] The server receives the POST request sent from the device and extracts the text data from the request body. The generative AI model analyzes the received text and determines that the expression "too stupid" is offensive.

[0869] 4. Generating correction suggestions

[0870] The server generates appropriate correction suggestions based on the analysis results, for example, suggesting that the expression "too stupid" be changed to "I sometimes feel that I lack understanding."

[0871] An example of a prompt sentence is as follows:

[0872] Input sentence: "My boss is so stupid I can't stand it."

[0873] Prompts for generating example revision suggestions:

[0874] "Please revise the following sentence to make it less offensive and less misleading.

[0875] "My boss is so stupid I can't handle it"

[0876] 5. Submit and review your proposed revisions

[0877] The server compiles the proposed revisions in JSON format and sends them to the device. The device receives the proposed revisions and displays them to the user. The user can then review the displayed proposals and approve or re-edit them.

[0878] 6. Final Post

[0879] If the user approves the suggestion, the device sends the revised text back to the server, which passes it to the APIs of various network services and ultimately posts it to the social networking site.

[0880] This system allows users to check and correct offensive or misleading language before posting, thereby facilitating smoother communication on network services and preventing problems caused by inappropriate language.

[0881] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0882] Step 1:

[0883] The user uses the device to input text into the SNS posting screen. For example, they might input the phrase "My boss is so stupid I can't stand it." At this point, the input text is displayed in the input field on the device.

[0884] Input: User input: "My boss is so stupid I can't do my job"

[0885] Output: Text that appears in the terminal input field

[0886] Step 2:

[0887] The terminal converts the text entered by the user into JSON format, using the JSON.stringify function in JavaScript or the json.dumps method in Python to structure the text data, and then sends it to the server using an HTTP POST request.

[0888] Input: Raw text data entered by the user

[0889] Output: JSON format data ({"text": "My boss is so stupid I can't stand it"})

[0890] Step 3:

[0891] The server receives the POST request from the device, extracts the JSON-formatted text data from the body, and then analyzes the text using a generative AI model (e.g., OpenAI GPT-3). This analysis uses natural language processing techniques to identify offensive or misleading language.

[0892] Input: JSON data sent from the terminal

[0893] Output: Analysis results (identification of offensive or misleading expressions)

[0894] Step 4:

[0895] The server generates appropriate correction suggestions based on the analysis results. For example, using a generative AI model, it suggests correcting the expression "too stupid" to "I sometimes feel that I lack understanding." A specific example of a generated correction suggestion is shown below.

[0896] Input: Analysis result (problematic expression)

[0897] Output: Suggested correction ("I sometimes feel I lack understanding")

[0898] Step 5:

[0899] The server then compiles the proposed changes in JSON format and sends them to the device as a POST request, using a web framework such as Flask or Django to generate an HTTP response.

[0900] Input: Proposed text

[0901] Output: JSON format correction suggestion data ({"suggestion": "Sometimes I feel I don't understand"})

[0902] Step 6:

[0903] The terminal receives the proposed corrections and displays them to the user. The user can then review the displayed proposal and approve or re-edit it. If the user agrees with the proposal, they click the "Approve" button.

[0904] Input: JSON formatted correction suggestions received from the server

[0905] Output: The suggested corrections shown to the user, and the user's approval action.

[0906] Step 7:

[0907] The device then sends the revised text, approved by the user, back to the server. The server then passes the revised text to the APIs of various network services, and ultimately posts it to social networking sites. Specifically, posting is done using the Twitter API, Facebook API, etc.

[0908] Input: User-approved revised sentence ("I sometimes feel I lack understanding.")

[0909] Output: Corrected text posted to the network service

[0910] (Application example 1)

[0911] 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."

[0912] In today's Internet environment, it is important for users to be able to freely express their opinions, but inappropriate or misleading expressions can be included, which can lead to disputes between users and legal issues.In addition, there is a lack of means to provide appropriate correction suggestions in real time, making it difficult to provide a safe and smooth communication environment.

[0913] 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.

[0914] In this invention, the server includes means for receiving text entered by a user, means for analyzing the received text, means for generating revision suggestions based on the results of the analysis by the means for generating revision suggestions, means for displaying the revision suggestions to the user, means for posting the revised text to various network services based on the user's approval, and means for generating revision suggestions in real time and instantly and securely processing the user's input. This makes it possible to provide a safe and smooth communication environment by checking the appropriateness of the text entered by the user in advance and suggesting revisions.

[0915] "User" refers to a user who uses a system or service to input text.

[0916] "Means for receiving input text" refers to a function for receiving text input by a user from a terminal.

[0917] "Generative ant method" is a technology that uses generative AI to analyze input text and identify problem areas.

[0918] The "means for generating correction suggestions" is a function that suggests appropriate corrections based on the problem areas identified by the generation AI means.

[0919] The "means for displaying revision suggestions to the user" is a function for displaying the generated revision suggestions on the user's device.

[0920] "Means for posting revised text based on user approval to various network services" refers to a function for posting revised text to network services after the user's suggested revisions have been confirmed and approved.

[0921] "Means for generating correction suggestions in real time and instantly and securely processing user input" refers to a function that instantly analyzes text entered by a user and safely generates correction suggestions.

[0922] This invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. The specific operation and processing of this system are described below.

[0923] System configuration

[0924] 1. Terminal

[0925] This is the device through which the user enters the text they wish to post. This includes smartphones, tablets, and PCs.

[0926] The terminal is responsible for receiving input from the user and transmitting that data to the server.

[0927] 2. Server

[0928] It is the central system that receives and analyzes text sent from the terminal.

[0929] The server uses a generative AI model to analyze the text and identify problem areas.

[0930] Hardware and software used

[0931] Hardware: Server machine (on-premise or cloud server)

[0932] Software: Flask, OpenAI API

[0933] Program Description

[0934] Program processing

[0935] 1. User Input

[0936] The user uses the device to input text into the SNS posting screen. For example, they can input a sentence such as, "My boss is so stupid I can't stand it."

[0937] 2. Sending text

[0938] The terminal sends the text entered by the user to the server. At this time, the text data is converted into an appropriate data format such as JSON format and sent to the server via a POST request.

[0939] 3. Receiving and analyzing text

[0940] The server receives requests from devices and extracts the text data contained within. The generative AI model then analyzes the received text using natural language processing technology and evaluates its content. This analysis identifies offensive, inflammatory, and legally or ethically questionable expressions. For example, the expression "too stupid" is deemed offensive.

[0941] 4. Generating correction suggestions

[0942] The server generates appropriate correction suggestions based on the analysis results. The generative AI model creates a suggestion to correct the expression "too stupid" to "I sometimes feel that I lack understanding."

[0943] 5. Submitting a revision suggestion

[0944] The server compiles and sends correction suggestions to the device, which returns them in JSON format.

[0945] 6. User Verification and Authorization

[0946] The terminal receives the suggested revisions and displays them to the user. The user checks the suggested revisions and approves or re-edits them. If the user agrees with the proposal, he or she clicks the "Approve" button. For example, the user approves the suggestion that "I sometimes feel that I lack understanding."

[0947] 7. Final Post

[0948] The device resends the approved revised sentence to the server, and the server posts the revised sentence via the API of various SNS. For example, the sentence "Sometimes I feel I lack understanding" is posted to the SNS.

[0949] Examples and prompts

[0950] Examples:

[0951] User input: "What Tanaka said in yesterday's meeting was completely meaningless."

[0952] Analysis request: {"text": "What Tanaka said at yesterday's meeting was completely meaningless."}

[0953] Suggested revision: "What Tanaka said in yesterday's meeting was a little hard to understand."

[0954] Example prompts to input to a generative AI model:

[0955] Prompt: "Please revise the following sentence to remove any inappropriate language: What Tanaka said at the meeting yesterday was completely meaningless."

[0956] This invention provides an environment in which users can post on network services with peace of mind. It is expected that smooth communication will be promoted by preventing misunderstandings and trouble caused by inappropriate expressions.

[0957] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0958] Step 1:

[0959] The device receives text entered by the user. At this time, the user enters text into a social media posting screen or chat app. The device temporarily stores this input, and when the user clicks the send button, the text data is sent to the server. At this time, the input data is converted to JSON format.

[0960] Step 2:

[0961] The server receives JSON-formatted text data sent from the device, analyzes the received data, and extracts only the necessary text. The extracted text then becomes the input data for the generative AI model.

[0962] Step 3:

[0963] The server analyzes the received text using a generative AI model. This analysis uses natural language processing technology to check for offensive language, inflammatory language, or legal or ethical issues. As a result of the analysis, any inappropriate content is identified.

[0964] Step 4:

[0965] The server generates appropriate correction suggestions based on the analysis results. The generative AI model uses the prompt "Please correct the following sentence and remove inappropriate expressions: [original sentence]." For example, in response to the sentence "My boss is so stupid I can't stand it," the server generates the correction suggestion "Sometimes I feel he lacks understanding."

[0966] Step 5:

[0967] The server compiles the generated revision suggestions into JSON format and sends them to the device. The device receives the revision suggestions and displays them to the user. The revision suggestions are typically displayed side by side, with the original text and the revised text.

[0968] Step 6:

[0969] The user can review the proposed edits on their device. They can either approve the suggested edits or make further edits themselves. If the approve button is pressed, the device converts the edited text back into JSON format and sends it to the server.

[0970] Step 7:

[0971] The server receives the approved revised text and posts it through the API of various network services. The server then posts the revised text to SNS or chat apps using the necessary authentication information. As a result, a safe text that the user "feels like they don't understand" is posted to the SNS.

[0972] These steps enable us to check and correct the appropriateness of the text entered by users in real time, providing an environment where users can post to network services with peace of mind.

[0973] 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.

[0974] The present invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. This system is combined with an emotion engine that recognizes the user's emotions to improve the accuracy of analysis and the appropriateness of correction suggestions. The specific operation and processing of this system are described below.

[0975] System configuration

[0976] 1. Terminal

[0977] This is the device through which the user enters the text they wish to post. This includes smartphones, tablets, and PCs.

[0978] The terminal is responsible for receiving input from the user and transmitting that data to the server.

[0979] 2. Server

[0980] It is the central system that receives and analyzes text sent from the terminal.

[0981] The server uses a generative ant method and an emotion engine to analyze the text and identify problematic parts.

[0982] System program processing

[0983] 1. User Input

[0984] A user uses a device to input text into a social media posting screen. For example, the user types, "My boss is so stupid I can't stand it."

[0985] 2. Sending text

[0986] The terminal sends the text entered by the user to the server. Specifically, it converts the text data into an appropriate data format such as JSON and sends a POST request to the server.

[0987] 3. Receiving and analyzing text

[0988] The server receives the request from the terminal and extracts the text data contained therein.

[0989] The server uses the generative ant method to analyze the extracted text using natural language processing (NLP) algorithms to detect offensive language, inflammatory language, and legal and ethical issues.

[0990] The emotion engine then analyzes the user's emotions from the received text. The emotion engine identifies emotions from keywords, context, and specific expressions in the text. For example, emotions such as anger, sadness, and joy can be detected.

[0991] 4. Generating correction suggestions

[0992] The server generates appropriate correction suggestions based on the problematic part identified by the generation ant method and the emotion analysis results of the emotion engine. For example, if the emotion engine judges the aggressive expression "too stupid" to be "anger," it will correct it to "I sometimes feel that you lack understanding."

[0993] 5. Submitting a revision suggestion

[0994] The server compiles the proposed changes and sends them to the device in JSON format.

[0995] 6. User Verification and Authorization

[0996] The terminal receives the proposed revisions from the server and displays them to the user. The user checks the proposed revisions and either approves or re-edits them. If the user agrees with the proposal, he or she clicks the "Approve" button.

[0997] Example: Acknowledge the user's suggestion that "I sometimes feel I lack understanding."

[0998] 7. Final Post

[0999] The device resends the approved revised text to the server, which then performs a final check of the revised text and issues a posting request via the API of the various SNS services.

[1000] Example: Finally, the sentence "I sometimes feel like I lack understanding" is posted on social media.

[1001] This allows users to prevent posts containing inappropriate or misleading language and avoid problems on network services.In addition, the emotion engine provides appropriate revision suggestions that take the user's emotions into consideration, promoting smoother communication.

[1002] The processing flow will be explained below.

[1003] Step 1:

[1004] A user uses a device to input text into a social media posting screen. For example, the user types, "My boss is so stupid I can't stand it."

[1005] Step 2:

[1006] The device receives text input from the user, converts this input information into a data format such as JSON, and prepares it for transmission to the server.

[1007] Step 3:

[1008] The device sends the prepared JSON format text data to the server as a POST request.

[1009] Step 4:

[1010] The server receives the request from the device, parses the JSON data, and extracts the text.

[1011] Step 5:

[1012] The server uses generative AI tools to analyze the extracted text using natural language processing (NLP) algorithms to detect offensive language, inflammatory language, and legal and ethical issues.

[1013] Step 6:

[1014] The server uses an emotion engine to analyze the user's emotions from the text, which identifies emotions from keywords, context, and specific expressions in the text.

[1015] Step 7:

[1016] The server generates correction suggestions based on the analysis results of the generation AI means and the emotion analysis results of the emotion engine.

[1017] Examples:

[1018] The server determines from the analysis results of the generated AI that the expression "too stupid" is offensive.

[1019] The server determines from the analysis results of the emotion engine that the user's emotion is "anger."

[1020] The server generates the correction suggestion "I sometimes feel that I lack understanding."

[1021] Step 8:

[1022] The server generates correction suggestions and compiles them in JSON format, which is then sent to the device as a POST request.

[1023] Step 9:

[1024] The device receives the correction suggestions from the server and displays them visually to the user, such as in the form of a pop-up or notification.

[1025] Step 10:

[1026] The user checks the proposed corrections and selects whether to approve or re-edit. For example, the user agrees with the proposal and clicks the "Approve" button.

[1027] Step 11:

[1028] After receiving the user's approval, the terminal converts the revised text into JSON format and sends it again to the server as a POST request.

[1029] Step 12:

[1030] The server checks the received corrections and issues a posting request using the API of various SNS services.

[1031] Step 13:

[1032] The SNS receives the posting request from the server and posts the corrected sentence to the user's account. Finally, the sentence "I sometimes feel that I lack understanding" is displayed on the SNS.

[1033] As described above, this system analyzes user input and generates appropriate correction suggestions by combining an emotion engine and generative AI methods, preventing inappropriate posts before they occur. This promotes smooth communication on network services.

[1034] Example 2

[1035] 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."

[1036] In today's network services, users may post inappropriate or misleading content, which can cause trouble with other users. Furthermore, emotional posts that users did not intend to post may be published, which can have social repercussions and cause psychological stress. There is a need for a method to address these issues and ensure that posting content is appropriate.

[1037] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving a sentence input by a user using a terminal, means including a generative AI model for analyzing the received sentence, means for generating a revision suggestion using an emotion engine based on the analysis result by the generative AI model, means for displaying the generated revision suggestion to the user, and means for posting the revised sentence to various network services based on the user's approval. This allows the user to optimize the content of the sentence and control emotions before posting, making it possible to prevent inappropriate expressions and trouble.

[1038] "User" refers to an individual or organization that accesses the network service, inputs text, and posts it.

[1039] A "terminal" is a device that a user uses to access network services, and examples include smartphones, tablets, and personal computers.

[1040] A "server" refers to a computer system that receives and processes data sent from a terminal via a network.

[1041] A "generative AI model" refers to an artificial intelligence algorithm that uses natural language processing (NLP) to analyze text.

[1042] An "emotion engine" refers to a system that identifies and analyzes a user's emotions from keywords and context within a sentence.

[1043] "Correction Suggestions" refers to suggestions for correcting inappropriate or misleading expressions in sentences entered by users, based on the results of analysis by the generative AI model and emotion engine.

[1044] "Network services" refers to various online platforms and social networking services provided via the Internet.

[1045] This invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. This system is characterized by improving the accuracy of analysis and the appropriateness of suggested corrections by combining it with an emotion engine that recognizes the user's emotions.

[1046] System configuration

[1047] 1. Terminal

[1048] The terminal is the device into which the user inputs the text they wish to post. Specifically, this includes smartphones, tablets, and PCs. The terminal is responsible for receiving input from the user and sending that data to the server.

[1049] 2. Server

[1050] The server is the center of the system, receiving and analyzing text data sent from the device. The server uses a generative AI model and emotion engine to analyze the text and identify problem areas.

[1051] System Operation

[1052] User input and submission

[1053] When a user uses a device to enter text into the SNS posting screen and clicks the "Post" button, the device converts the entered text into JSON format or similar and sends a POST request to the server.

[1054] Text analysis

[1055] The server extracts the received text data and analyzes it using a natural language processing (NLP) algorithm with a generative AI model. This analysis detects offensive or inflammatory language, as well as legal and ethical issues. The emotion engine also analyzes the user's emotions from keywords and context within the text, identifying emotions such as "anger" or "sadness."

[1056] Generate correction suggestions

[1057] The server generates appropriate correction suggestions based on the analysis results of the generative AI model and emotion engine. For example, it suggests correcting an offensive expression such as "too stupid" to "I sometimes feel that you lack understanding."

[1058] Submitting and Viewing Suggested Revisions

[1059] The server sends the generated revision suggestions in JSON format to the terminal. The terminal receives the revision suggestions and displays them to the user. The user checks the displayed revision suggestions and approves or re-edits them. For example, if the user approves the suggestion that "I feel there are some gaps in understanding," he or she clicks the "Approve" button.

[1060] Last Post

[1061] Once the user approves the revised text, it is sent back to the server. After final confirmation, the server issues a posting request to the appropriate network service via the API of each SNS. For example, the final text, "I sometimes feel that I lack understanding," is posted to the SNS.

[1062] Specific examples

[1063] Prompt Sentence Examples

[1064] "Enter the text you're planning to post on social media below and our system will suggest ways to edit inappropriate or misleading content."

[1065] This system allows users to prevent posts containing inappropriate or misleading language, thereby avoiding trouble on network services. Furthermore, by using an emotion engine, appropriate revision suggestions are provided that take the user's emotions into consideration, promoting smoother communication.

[1066] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1067] Step 1:

[1068] The user uses the device to input text into the SNS posting screen. For example, the user might input "My boss is too stupid to handle." This input text is the input data.

[1069] Step 2:

[1070] When the user clicks the "Post" button, the device converts the entered text data into JSON format. This conversion process results in JSON format text data. The device then sends the obtained text data to the server as an HTTP POST request. The input data is the text entered by the user, and the output data is the data converted into JSON format.

[1071] Step 3:

[1072] The server receives an HTTP POST request from the device. The server extracts the text data from the request and prepares it for analysis. The input data is the JSON format data sent from the device, and the output data is the extracted text.

[1073] Step 4:

[1074] The server analyzes the extracted text using a generative AI model. The generative AI model detects offensive language, inflammatory language, and legal and ethical issues in the text. It also uses an emotion engine to identify the user's emotions from keywords and context within the text. The input data is the extracted text, and the output data is the results of the analysis and emotion analysis.

[1075] Step 5:

[1076] The server generates appropriate correction suggestions based on the analysis results and the emotion engine results. For example, the expression "too stupid" is corrected to "I sometimes feel that it lacks understanding." The input data are the analysis results and emotion analysis results, and the output data are correction suggestions.

[1077] Step 6:

[1078] The server converts the generated revision suggestions into JSON format and sends them to the terminal as an HTTP response. The input data is the revision suggestions, and the output data is the JSON-formatted revision suggestions.

[1079] Step 7:

[1080] The terminal displays the JSON-formatted correction suggestions received from the server on the user interface. The user can then review the correction suggestions and approve or re-edit them. The input data is the correction suggestions received from the server, and the output data is the result of the user's approval or re-editing.

[1081] Step 8:

[1082] If the user approves the proposed revision, the terminal sends the approved revised text to the server again. The input data is the approved revised text, and the output data is a POST request sent to the server.

[1083] Step 9:

[1084] The server then performs a final check of the received revised text data. If there are no problems with the content, it issues a posting request through the API of the various SNS. The input data is the approved revised text, and the output data is the posting request sent to the SNS. For example, the text "Sometimes I feel I lack understanding" is posted to the SNS.

[1085] This series of processes allows users to correct posts containing inappropriate language in advance and engage in appropriate communication.

[1086] (Application example 2)

[1087] 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."

[1088] In current network services, inappropriate or misleading expressions are often posted, which can lead to communication problems and trouble. Furthermore, appropriate suggestions for correction based on the user's feelings are rarely made, further complicating the problem. The objective of this invention is to solve these problems and promote smooth communication online.

[1089] 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.

[1090] In this invention, the server includes means for receiving text entered by a user, means for analyzing the received text, means for generating a revision proposal based on the analysis result by the generation means, means for analyzing the user's emotions using an emotion engine, means for optimizing the revision proposal based on the emotion analysis result, means for displaying the revision proposal to the user, and means for posting the revised text to various network services based on the user's approval. This allows appropriate revision proposals that take the user's emotions into consideration to be provided in real time, making it possible to prevent problems on network services and ensure smooth communication.

[1091] The "means for receiving text input by the user" is a communication means for transmitting text data input by the user from the terminal to the server.

[1092] The "means for generating and analyzing received text" refers to a means for analyzing text data received by the server using natural language processing and identifying inappropriate or misleading expressions.

[1093] The "means for generating correction proposals" is a means for generating specific proposals for correcting identified problem areas based on the analysis results.

[1094] "Means for analyzing user emotions using an emotion engine" refers to a means for analyzing emotions from text input by the user and identifying emotional states such as joy, anger, sadness, etc.

[1095] The "means for optimizing revision suggestions based on emotion analysis results" is a means for making revision suggestions adapted to the emotional state of the user based on the emotion analysis results of the emotion engine.

[1096] The "means for displaying the revision suggestions to the user" refers to a screen display means for displaying the generated revision suggestions on the user's terminal so that the user can confirm the revision suggestions.

[1097] "Means for posting revised text based on the user's approval to various network services" refers to a communication means for posting revised text to network services such as SNS and chat services after the user approves the proposed revision.

[1098] The present invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. Specific operations of this system and an embodiment thereof will be described below.

[1099] System configuration

[1100] 1. Terminal

[1101] This is the device through which the user enters the text they wish to post. This includes smartphones, tablets, and PCs.

[1102] The terminal is responsible for receiving input from the user and transmitting that data to the server.

[1103] 2. Server

[1104] It is the central system that receives and analyzes text sent from the terminal.

[1105] The server uses a generative method and an emotion engine to analyze the text and identify problem areas.

[1106] The system optimizes revision suggestions based on the results of sentiment analysis, generating and providing the most appropriate revision suggestions for users.

[1107] Hardware and software used

[1108] Hardware

[1109] User devices such as smartphones, smart glasses, tablets, and personal computers.

[1110] Server (using a high-performance data processing server).

[1111] software

[1112] Use TextBlob as a natural language processing (NLP) library.

[1113] We use a hypothetical EmotionEngine as an emotion analysis library.

[1114] The JSON format and HTTP protocol are used to format and communicate the data.

[1115] Specific examples of embodiments

[1116] A user enters the message they wish to post on social media into the input field on their device. For example, the user might type, "Today's meeting was a complete waste of time." This sentence is sent from the device to the server, where it is analyzed. A natural language processing algorithm (TextBlob) determines whether the sentence contains positive or negative expressions, and an emotion engine (EmotionEngine) analyzes the user's emotion as "anger." Based on this, the server generates a correction suggestion, suggesting, "Let's change this to a more positive expression." This suggestion is displayed on the user's device, and if the user accepts the suggestion, the corrected sentence is changed to, "I learned a lot from today's meeting." The corrected sentence is finally posted to the social media platform.

[1117] Prompt Sentence Examples

[1118] The prompt text to be input to the generative AI model is set as follows:

[1119] Please revise the following sentences to make them more appropriate and positive:

[1120] "Today's meeting was a complete waste of time."

[1121] In this way, this system makes appropriate correction suggestions in real time, taking into account the user's feelings, thereby preventing problems on network services and realizing smooth communication.

[1122] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1123] Step 1:

[1124] The user enters text into the SNS posting screen. For example, the user enters, "Today's meeting was a complete waste of time." The input data is saved in text format on the device.

[1125] Step 2:

[1126] The terminal converts the text entered by the user into an appropriate data format, such as JSON, and sends a POST request to the server. At this stage, the input data is the text entered by the user. The output data is the text data converted into JSON format.

[1127] Step 3:

[1128] The server receives a POST request from the terminal and extracts the text data contained therein. The server analyzes the received data format (JSON format) and extracts the input text. The input data at this stage is text data in JSON format. The output data is text in text format.

[1129] Step 4:

[1130] The server analyzes the received text using a natural language processing (NLP) algorithm (TextBlob). This analysis determines whether the text has a positive or negative tendency, or whether it contains offensive or inflammatory language. The input data is text in text format, and the output data is the analysis result, which includes the tendency of the text and whether it contains offensive language.

[1131] Step 5:

[1132] The server uses an emotion engine to analyze the user's emotions from the received text. The emotion engine identifies emotions from keywords, context, and specific expressions in the text. For example, it detects emotions such as "anger," "sadness," and "joy." The input data is text in text format, and the output data is the emotion analysis results.

[1133] Step 6:

[1134] The server generates appropriate correction suggestions based on the problem areas identified by the generation means and the sentiment analysis results. For example, it suggests correcting a negative expression such as "it was a waste" to something like "I learned very little." The input data are the analysis results and sentiment analysis results, and the output data are the correction suggestions.

[1135] Step 7:

[1136] The server compiles the revision suggestions into JSON format and sends them to the device. The device receives the revision suggestions from the server and displays them to the user. The input data is the revision suggestions, and the output data is the revision suggestions displayed to the user.

[1137] Step 8:

[1138] The user checks the proposed revisions and approves or re-edits them. For example, the user approves the suggestion that "there was little learning." At this stage, the input data is the proposed revisions, and the output data is the user's approval result.

[1139] Step 9:

[1140] The device resends the approved revised text to the server. The server performs a final check of the revised text and issues a posting request through the API of the various SNS. The input data is the approved revised text, and the output data is the result of posting to the SNS.

[1141] 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.

[1142] 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.

[1143] 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.

[1144] [Fourth embodiment]

[1145] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1146] 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.

[1147] 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).

[1148] 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.

[1149] 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.

[1150] 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).

[1151] 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.

[1152] 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.

[1153] 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.

[1154] 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.

[1155] 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.

[1156] 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.

[1157] 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."

[1158] The present invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. This system is implemented using a terminal and a server. The specific operation and processing of this system are described below.

[1159] System configuration

[1160] 1. Terminal

[1161] This is the device through which the user enters the text they wish to post. This includes smartphones, tablets, and PCs.

[1162] The terminal is responsible for receiving input from the user and transmitting that data to the server.

[1163] 2. Server

[1164] It is the central system that receives and analyzes text sent from the terminal.

[1165] The server uses a generative ant method to analyze the text and identify problematic sections.

[1166] System program processing

[1167] 1. User Input

[1168] A user uses a device to input text into a social networking site posting screen. For example, the user inputs the text, "My boss is so stupid I can't stand it."

[1169] 2. Sending text

[1170] The terminal sends the text entered by the user to the server. Specifically, it converts the text data into an appropriate data format such as JSON and sends a POST request to the server.

[1171] 3. Receiving and analyzing text

[1172] The server receives requests from devices and extracts the text data contained within. The generation AI analyzes the received text using natural language processing technology and evaluates the content of the text. This analysis identifies offensive language, inflammatory language, and legal and ethical issues.

[1173] For example, the expression "too stupid" is judged to be offensive.

[1174] 4. Generating correction suggestions

[1175] The server generates appropriate correction suggestions based on the analysis results. The generation AI creates a suggestion to correct the expression "too stupid" to "I sometimes feel that I lack understanding."

[1176] 5. Submitting a revision suggestion

[1177] The server compiles the proposed changes and sends them to the device, which sends the changes in JSON format as a POST request.

[1178] 6. User Verification and Authorization

[1179] The terminal receives the proposed revisions and displays them to the user. The user checks the proposed revisions and either approves or re-edits them. If the user agrees with the proposal, he or she clicks the "Approve" button.

[1180] Example: Acknowledge the user's suggestion that "I sometimes feel I lack understanding."

[1181] 7. Final Post

[1182] The device resends the approved revised text to the server, which then posts the revised text via the API of the various SNS services.

[1183] Example: Finally, the sentence "I sometimes feel like I lack understanding" is posted on social media.

[1184] This will provide an environment where users can post on network services with peace of mind. By implementing this system, misunderstandings and trouble caused by inappropriate expressions can be prevented, and smooth communication will be promoted.

[1185] The processing flow will be explained below.

[1186] Step 1:

[1187] A user uses a device to input text into a social media posting screen. For example, the user types, "My boss is so stupid I can't stand it."

[1188] Step 2:

[1189] The device receives text input from the user, converts this input information into a data format such as JSON, and prepares it for transmission to the server.

[1190] Step 3:

[1191] The device sends the prepared JSON format text data to the server as a POST request.

[1192] Step 4:

[1193] The server receives the request from the device, parses the JSON data, and extracts the text.

[1194] Step 5:

[1195] The server uses generative AI tools to analyze the extracted text using natural language processing (NLP) algorithms to detect offensive language, inflammatory language, and legal and ethical issues.

[1196] Step 6:

[1197] Based on the analysis results, the generative AI generates suggested corrections. For example, it creates a suggestion to correct the expression "too stupid" to "I sometimes feel that I lack understanding."

[1198] Step 7:

[1199] The server generates correction suggestions and compiles them in JSON format, which is then sent to the device as a POST request.

[1200] Step 8:

[1201] The device receives the correction suggestions from the server and displays them visually to the user, such as in the form of a pop-up or notification.

[1202] Step 9:

[1203] The user checks the proposed corrections and selects whether to approve or re-edit. For example, the user agrees with the proposal and clicks the "Approve" button.

[1204] Step 10:

[1205] After receiving the user's approval, the terminal converts the revised text into JSON format and sends it again to the server as a POST request.

[1206] Step 11:

[1207] The server makes a final check of the revised text received and issues a posting request using the API of various SNS services.

[1208] Step 12:

[1209] The various SNS services receive the posting request from the server and post the corrected sentence to the user's account. Finally, the sentence "I sometimes feel that I lack understanding" is displayed on the SNS as the user's post.

[1210] This will prevent users from posting inappropriate content and avoid misunderstandings and problems on network services.

[1211] Example 1

[1212] 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."

[1213] In modern network services, posts by users often contain offensive or misleading language, which can result in trouble or misunderstandings. This can be detrimental to users and other stakeholders. Conventional systems lacked the means to detect such problems in real time and to appropriately correct them, which sometimes hindered smooth communication.

[1214] 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.

[1215] In this invention, the server includes means for receiving text entered by a user, means for converting the received text into JSON format and transmitting it to the server, a generation AI means for analyzing the text received by the server, means for generating revision suggestions based on the results of the analysis by the generation AI means, means for compiling the revision suggestions in JSON format and transmitting them to the terminal, means for displaying the revision suggestions to the user and obtaining the user's approval, and means for resending the approved revised text to the server and posting it to various network services. This allows users to check the revision suggestions before posting text and to correct misleading or offensive language in advance. This facilitates communication on network services and prevents problems caused by inappropriate language.

[1216] A "user" is an entity that uses a network service and inputs, transmits, and checks text through a terminal.

[1217] A "terminal" is a device that allows a user to input text and communicate data with a server. Examples of such devices include smartphones, tablets, and PCs.

[1218] The "server" is a central system that receives text sent by users, analyzes it using a generative AI model, and generates and sends correction suggestions.

[1219] A "generative AI model" is an algorithm or system that uses natural language processing techniques to analyze text and generate appropriate correction suggestions.

[1220] The "JSON format" is a lightweight data exchange format for structuring and representing data, and is an abbreviation for JavaScript Object Notation.

[1221] A "POST request" is a method in the HTTP protocol for a client to send data to a server.

[1222] "Suggested modifications" are suggestions for modifying offensive or misleading language identified through analysis to make it more appropriate.

[1223] "Network services" is a general term for various online platforms and services used via the Internet, including social networking sites, blogs, and forums.

[1224] "Natural language processing technology" refers to technology that enables computers to understand, interpret, and generate human language. Specifically, it includes technologies such as morphological analysis, sentence analysis, and machine translation.

[1225] The "HTTP protocol" is a communication protocol for transferring data over the Internet and is an abbreviation for Hypertext Transfer Protocol.

[1226] "Various network services" refers to multiple different online platforms and services, and means a collection of web services provided for specific fields or purposes.

[1227] These definitions clarify each component of the system and its role.

[1228] This invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. The system is primarily composed of a terminal and a server. This section describes the detailed configuration and operation of the system.

[1229] System configuration

[1230] 1. Terminal

[1231] A terminal is a device that allows a user to input text and send it to a server. Examples include smartphones, tablets, and PCs. The terminal converts the text data input by the user into JSON format and sends it to the server. The terminal uses a library for HTTP communication (e.g., JavaScript's fetch function or Python's requests library).

[1232] 2. Server

[1233] The server is a central system that receives and analyzes text sent from the device. The server analyzes the text using a generative AI model (e.g., OpenAI GPT-3). This analysis uses natural language processing libraries such as the Natural Language Toolkit (NLTK) and spaCy. The server generates appropriate correction suggestions based on the analysis results and sends them to the device in JSON format. Web frameworks such as Flask and Django are used for communication on the server side.

[1234] Example of operation

[1235] A specific example of the operation of this system is given below.

[1236] 1. User Input

[1237] The user uses the device to input text into the SNS posting screen. For example, they input the text "My boss is so stupid I can't stand it." At this point, an input field and a send button are displayed on the device screen.

[1238] 2. Sending text

[1239] The device sends the text entered by the user to the server. Specifically, a program on the device converts this text into JSON format and sends it to the server as an HTTP POST request.

[1240] 3. Text Analysis

[1241] The server receives the POST request sent from the device and extracts the text data from the request body. The generative AI model analyzes the received text and determines that the expression "too stupid" is offensive.

[1242] 4. Generating correction suggestions

[1243] The server generates appropriate correction suggestions based on the analysis results, for example, suggesting that the expression "too stupid" be changed to "I sometimes feel that I lack understanding."

[1244] An example of a prompt sentence is as follows:

[1245] Input sentence: "My boss is so stupid I can't stand it."

[1246] Prompts for generating example revision suggestions:

[1247] "Please revise the following sentence to make it less offensive and less misleading.

[1248] "My boss is so stupid I can't handle it"

[1249] 5. Submit and review your proposed revisions

[1250] The server compiles the proposed revisions in JSON format and sends them to the device. The device receives the proposed revisions and displays them to the user. The user can then review the displayed proposals and approve or re-edit them.

[1251] 6. Final Post

[1252] If the user approves the suggestion, the device sends the revised text back to the server, which passes it to the APIs of various network services and ultimately posts it to the social networking site.

[1253] This system allows users to check and correct offensive or misleading language before posting, thereby facilitating smoother communication on network services and preventing problems caused by inappropriate language.

[1254] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1255] Step 1:

[1256] The user uses the device to input text into the SNS posting screen. For example, they might input the phrase "My boss is so stupid I can't stand it." At this point, the input text is displayed in the input field on the device.

[1257] Input: User input: "My boss is so stupid I can't do my job"

[1258] Output: Text that appears in the terminal input field

[1259] Step 2:

[1260] The terminal converts the text entered by the user into JSON format, using the JSON.stringify function in JavaScript or the json.dumps method in Python to structure the text data, and then sends it to the server using an HTTP POST request.

[1261] Input: Raw text data entered by the user

[1262] Output: JSON format data ({"text": "My boss is so stupid I can't stand it"})

[1263] Step 3:

[1264] The server receives the POST request from the device, extracts the JSON-formatted text data from the body, and then analyzes the text using a generative AI model (e.g., OpenAI GPT-3). This analysis uses natural language processing techniques to identify offensive or misleading language.

[1265] Input: JSON data sent from the terminal

[1266] Output: Analysis results (identification of offensive or misleading expressions)

[1267] Step 4:

[1268] The server generates appropriate correction suggestions based on the analysis results. For example, using a generative AI model, it suggests correcting the expression "too stupid" to "I sometimes feel that I lack understanding." A specific example of a generated correction suggestion is shown below.

[1269] Input: Analysis result (problematic expression)

[1270] Output: Suggested correction ("I sometimes feel I lack understanding")

[1271] Step 5:

[1272] The server then compiles the proposed changes in JSON format and sends them to the device as a POST request, using a web framework such as Flask or Django to generate an HTTP response.

[1273] Input: Proposed text

[1274] Output: JSON format correction suggestion data ({"suggestion": "Sometimes I feel I don't understand"})

[1275] Step 6:

[1276] The terminal receives the proposed corrections and displays them to the user. The user can then review the displayed proposal and approve or re-edit it. If the user agrees with the proposal, they click the "Approve" button.

[1277] Input: JSON formatted correction suggestions received from the server

[1278] Output: The suggested corrections shown to the user, and the user's approval action.

[1279] Step 7:

[1280] The device then sends the revised text, approved by the user, back to the server. The server then passes the revised text to the APIs of various network services, and ultimately posts it to social networking sites. Specifically, posting is done using the Twitter API, Facebook API, etc.

[1281] Input: User-approved revised sentence ("I sometimes feel I lack understanding.")

[1282] Output: Corrected text posted to the network service

[1283] (Application example 1)

[1284] 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."

[1285] In today's Internet environment, it is important for users to be able to freely express their opinions, but inappropriate or misleading expressions can be included, which can lead to disputes between users and legal issues.In addition, there is a lack of means to provide appropriate correction suggestions in real time, making it difficult to provide a safe and smooth communication environment.

[1286] 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.

[1287] In this invention, the server includes means for receiving text entered by a user, means for analyzing the received text, means for generating revision suggestions based on the results of the analysis by the means for generating revision suggestions, means for displaying the revision suggestions to the user, means for posting the revised text to various network services based on the user's approval, and means for generating revision suggestions in real time and instantly and securely processing the user's input. This makes it possible to provide a safe and smooth communication environment by checking the appropriateness of the text entered by the user in advance and suggesting revisions.

[1288] "User" refers to a user who uses a system or service to input text.

[1289] "Means for receiving input text" refers to a function for receiving text input by a user from a terminal.

[1290] "Generative ant method" is a technology that uses generative AI to analyze input text and identify problem areas.

[1291] The "means for generating correction suggestions" is a function that suggests appropriate corrections based on the problem areas identified by the generation AI means.

[1292] The "means for displaying revision suggestions to the user" is a function for displaying the generated revision suggestions on the user's device.

[1293] "Means for posting revised text based on user approval to various network services" refers to a function for posting revised text to network services after the user's suggested revisions have been confirmed and approved.

[1294] "Means for generating correction suggestions in real time and instantly and securely processing user input" refers to a function that instantly analyzes text entered by a user and safely generates correction suggestions.

[1295] This invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. The specific operation and processing of this system are described below.

[1296] System configuration

[1297] 1. Terminal

[1298] This is the device through which the user enters the text they wish to post. This includes smartphones, tablets, and PCs.

[1299] The terminal is responsible for receiving input from the user and transmitting that data to the server.

[1300] 2. Server

[1301] It is the central system that receives and analyzes text sent from the terminal.

[1302] The server uses a generative AI model to analyze the text and identify problem areas.

[1303] Hardware and software used

[1304] Hardware: Server machine (on-premise or cloud server)

[1305] Software: Flask, OpenAI API

[1306] Program Description

[1307] Program processing

[1308] 1. User Input

[1309] The user uses the device to input text into the SNS posting screen. For example, they can input a sentence such as, "My boss is so stupid I can't stand it."

[1310] 2. Sending text

[1311] The terminal sends the text entered by the user to the server. At this time, the text data is converted into an appropriate data format such as JSON format and sent to the server via a POST request.

[1312] 3. Receiving and analyzing text

[1313] The server receives requests from devices and extracts the text data contained within. The generative AI model then analyzes the received text using natural language processing technology and evaluates its content. This analysis identifies offensive, inflammatory, and legally or ethically questionable expressions. For example, the expression "too stupid" is deemed offensive.

[1314] 4. Generating correction suggestions

[1315] The server generates appropriate correction suggestions based on the analysis results. The generative AI model creates a suggestion to correct the expression "too stupid" to "I sometimes feel that I lack understanding."

[1316] 5. Submitting a revision suggestion

[1317] The server compiles and sends correction suggestions to the device, which returns them in JSON format.

[1318] 6. User Verification and Authorization

[1319] The terminal receives the suggested revisions and displays them to the user. The user checks the suggested revisions and approves or re-edits them. If the user agrees with the proposal, he or she clicks the "Approve" button. For example, the user approves the suggestion that "I sometimes feel that I lack understanding."

[1320] 7. Final Post

[1321] The device resends the approved revised sentence to the server, and the server posts the revised sentence via the API of various SNS. For example, the sentence "Sometimes I feel I lack understanding" is posted to the SNS.

[1322] Examples and prompts

[1323] Examples:

[1324] User input: "What Tanaka said in yesterday's meeting was completely meaningless."

[1325] Analysis request: {"text": "What Tanaka said at yesterday's meeting was completely meaningless."}

[1326] Suggested revision: "What Tanaka said in yesterday's meeting was a little hard to understand."

[1327] Example prompts to input to a generative AI model:

[1328] Prompt: "Please revise the following sentence to remove any inappropriate language: What Tanaka said at the meeting yesterday was completely meaningless."

[1329] This invention provides an environment in which users can post on network services with peace of mind. It is expected that smooth communication will be promoted by preventing misunderstandings and trouble caused by inappropriate expressions.

[1330] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1331] Step 1:

[1332] The device receives text entered by the user. At this time, the user enters text into a social media posting screen or chat app. The device temporarily stores this input, and when the user clicks the send button, the text data is sent to the server. At this time, the input data is converted to JSON format.

[1333] Step 2:

[1334] The server receives JSON-formatted text data sent from the device, analyzes the received data, and extracts only the necessary text. The extracted text then becomes the input data for the generative AI model.

[1335] Step 3:

[1336] The server analyzes the received text using a generative AI model. This analysis uses natural language processing technology to check for offensive language, inflammatory language, or legal or ethical issues. As a result of the analysis, any inappropriate content is identified.

[1337] Step 4:

[1338] The server generates appropriate correction suggestions based on the analysis results. The generative AI model uses the prompt "Please correct the following sentence and remove inappropriate expressions: [original sentence]." For example, in response to the sentence "My boss is so stupid I can't stand it," the server generates the correction suggestion "Sometimes I feel he lacks understanding."

[1339] Step 5:

[1340] The server compiles the generated revision suggestions into JSON format and sends them to the device. The device receives the revision suggestions and displays them to the user. The revision suggestions are typically displayed side by side, with the original text and the revised text.

[1341] Step 6:

[1342] The user can review the proposed edits on their device. They can either approve the suggested edits or make further edits themselves. If the approve button is pressed, the device converts the edited text back into JSON format and sends it to the server.

[1343] Step 7:

[1344] The server receives the approved revised text and posts it through the API of various network services. The server then posts the revised text to SNS or chat apps using the necessary authentication information. As a result, a safe text that the user "feels like they don't understand" is posted to the SNS.

[1345] These steps enable us to check and correct the appropriateness of the text entered by users in real time, providing an environment where users can post to network services with peace of mind.

[1346] 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.

[1347] The present invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. This system is combined with an emotion engine that recognizes the user's emotions to improve the accuracy of analysis and the appropriateness of correction suggestions. The specific operation and processing of this system are described below.

[1348] System configuration

[1349] 1. Terminal

[1350] This is the device through which the user enters the text they wish to post. This includes smartphones, tablets, and PCs.

[1351] The terminal is responsible for receiving input from the user and transmitting that data to the server.

[1352] 2. Server

[1353] It is the central system that receives and analyzes text sent from the terminal.

[1354] The server uses a generative ant method and an emotion engine to analyze the text and identify problematic parts.

[1355] System program processing

[1356] 1. User Input

[1357] A user uses a device to input text into a social media posting screen. For example, the user types, "My boss is so stupid I can't stand it."

[1358] 2. Sending text

[1359] The terminal sends the text entered by the user to the server. Specifically, it converts the text data into an appropriate data format such as JSON and sends a POST request to the server.

[1360] 3. Receiving and analyzing text

[1361] The server receives the request from the terminal and extracts the text data contained therein.

[1362] The server uses the generative ant method to analyze the extracted text using natural language processing (NLP) algorithms to detect offensive language, inflammatory language, and legal and ethical issues.

[1363] The emotion engine then analyzes the user's emotions from the received text. The emotion engine identifies emotions from keywords, context, and specific expressions in the text. For example, emotions such as anger, sadness, and joy can be detected.

[1364] 4. Generating correction suggestions

[1365] The server generates appropriate correction suggestions based on the problematic part identified by the generation ant method and the emotion analysis results of the emotion engine. For example, if the emotion engine judges the aggressive expression "too stupid" to be "anger," it will correct it to "I sometimes feel that you lack understanding."

[1366] 5. Submitting a revision suggestion

[1367] The server compiles the proposed changes and sends them to the device in JSON format.

[1368] 6. User Verification and Authorization

[1369] The terminal receives the proposed revisions from the server and displays them to the user. The user checks the proposed revisions and either approves or re-edits them. If the user agrees with the proposal, he or she clicks the "Approve" button.

[1370] Example: Acknowledge the user's suggestion that "I sometimes feel I lack understanding."

[1371] 7. Final Post

[1372] The device resends the approved revised text to the server, which then performs a final check of the revised text and issues a posting request via the API of the various SNS services.

[1373] Example: Finally, the sentence "I sometimes feel like I lack understanding" is posted on social media.

[1374] This allows users to prevent posts containing inappropriate or misleading language and avoid problems on network services.In addition, the emotion engine provides appropriate revision suggestions that take the user's emotions into consideration, promoting smoother communication.

[1375] The processing flow will be explained below.

[1376] Step 1:

[1377] A user uses a device to input text into a social media posting screen. For example, the user types, "My boss is so stupid I can't stand it."

[1378] Step 2:

[1379] The device receives text input from the user, converts this input information into a data format such as JSON, and prepares it for transmission to the server.

[1380] Step 3:

[1381] The device sends the prepared JSON format text data to the server as a POST request.

[1382] Step 4:

[1383] The server receives the request from the device, parses the JSON data, and extracts the text.

[1384] Step 5:

[1385] The server uses generative AI tools to analyze the extracted text using natural language processing (NLP) algorithms to detect offensive language, inflammatory language, and legal and ethical issues.

[1386] Step 6:

[1387] The server uses an emotion engine to analyze the user's emotions from the text, which identifies emotions from keywords, context, and specific expressions in the text.

[1388] Step 7:

[1389] The server generates correction suggestions based on the analysis results of the generation AI means and the emotion analysis results of the emotion engine.

[1390] Examples:

[1391] The server determines from the analysis results of the generated AI that the expression "too stupid" is offensive.

[1392] The server determines from the analysis results of the emotion engine that the user's emotion is "anger."

[1393] The server generates the correction suggestion "I sometimes feel that I lack understanding."

[1394] Step 8:

[1395] The server generates correction suggestions and compiles them in JSON format, which is then sent to the device as a POST request.

[1396] Step 9:

[1397] The device receives the correction suggestions from the server and displays them visually to the user, such as in the form of a pop-up or notification.

[1398] Step 10:

[1399] The user checks the proposed corrections and selects whether to approve or re-edit. For example, the user agrees with the proposal and clicks the "Approve" button.

[1400] Step 11:

[1401] After receiving the user's approval, the terminal converts the revised text into JSON format and sends it again to the server as a POST request.

[1402] Step 12:

[1403] The server checks the received corrections and issues a posting request using the API of various SNS services.

[1404] Step 13:

[1405] The SNS receives the posting request from the server and posts the corrected sentence to the user's account. Finally, the sentence "I sometimes feel that I lack understanding" is displayed on the SNS.

[1406] As described above, this system analyzes user input and generates appropriate correction suggestions by combining an emotion engine and generative AI methods, preventing inappropriate posts before they occur. This promotes smooth communication on network services.

[1407] Example 2

[1408] 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."

[1409] In today's network services, users may post inappropriate or misleading content, which can cause trouble with other users. Furthermore, emotional posts that users did not intend to post may be published, which can have social repercussions and cause psychological stress. There is a need for a method to address these issues and ensure that posting content is appropriate.

[1410] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving a sentence input by a user using a terminal, means including a generative AI model for analyzing the received sentence, means for generating a revision suggestion using an emotion engine based on the analysis result by the generative AI model, means for displaying the generated revision suggestion to the user, and means for posting the revised sentence to various network services based on the user's approval. This allows the user to optimize the content of the sentence and control emotions before posting, making it possible to prevent inappropriate expressions and trouble.

[1411] "User" refers to an individual or organization that accesses the network service, inputs text, and posts it.

[1412] A "terminal" is a device that a user uses to access network services, and examples include smartphones, tablets, and personal computers.

[1413] A "server" refers to a computer system that receives and processes data sent from a terminal via a network.

[1414] A "generative AI model" refers to an artificial intelligence algorithm that uses natural language processing (NLP) to analyze text.

[1415] An "emotion engine" refers to a system that identifies and analyzes a user's emotions from keywords and context within a sentence.

[1416] "Correction Suggestions" refers to suggestions for correcting inappropriate or misleading expressions in sentences entered by users, based on the results of analysis by the generative AI model and emotion engine.

[1417] "Network services" refers to various online platforms and social networking services provided via the Internet.

[1418] This invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. This system is characterized by improving the accuracy of analysis and the appropriateness of suggested corrections by combining it with an emotion engine that recognizes the user's emotions.

[1419] System configuration

[1420] 1. Terminal

[1421] The terminal is the device into which the user inputs the text they wish to post. Specifically, this includes smartphones, tablets, and PCs. The terminal is responsible for receiving input from the user and sending that data to the server.

[1422] 2. Server

[1423] The server is the center of the system, receiving and analyzing text data sent from the device. The server uses a generative AI model and emotion engine to analyze the text and identify problem areas.

[1424] System Operation

[1425] User input and submission

[1426] When a user uses a device to enter text into the SNS posting screen and clicks the "Post" button, the device converts the entered text into JSON format or similar and sends a POST request to the server.

[1427] Text analysis

[1428] The server extracts the received text data and analyzes it using a natural language processing (NLP) algorithm with a generative AI model. This analysis detects offensive or inflammatory language, as well as legal and ethical issues. The emotion engine also analyzes the user's emotions from keywords and context within the text, identifying emotions such as "anger" or "sadness."

[1429] Generate correction suggestions

[1430] The server generates appropriate correction suggestions based on the analysis results of the generative AI model and emotion engine. For example, it suggests correcting an offensive expression such as "too stupid" to "I sometimes feel that you lack understanding."

[1431] Submitting and Viewing Suggested Revisions

[1432] The server sends the generated revision suggestions in JSON format to the terminal. The terminal receives the revision suggestions and displays them to the user. The user checks the displayed revision suggestions and approves or re-edits them. For example, if the user approves the suggestion that "I feel there are some gaps in understanding," he or she clicks the "Approve" button.

[1433] Last Post

[1434] Once the user approves the revised text, it is sent back to the server. After final confirmation, the server issues a posting request to the appropriate network service via the API of each SNS. For example, the final text, "I sometimes feel that I lack understanding," is posted to the SNS.

[1435] Specific examples

[1436] Prompt Sentence Examples

[1437] "Enter the text you're planning to post on social media below and our system will suggest ways to edit inappropriate or misleading content."

[1438] This system allows users to prevent posts containing inappropriate or misleading language, thereby avoiding trouble on network services. Furthermore, by using an emotion engine, appropriate revision suggestions are provided that take the user's emotions into consideration, promoting smoother communication.

[1439] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1440] Step 1:

[1441] The user uses the device to input text into the SNS posting screen. For example, the user might input "My boss is too stupid to handle." This input text is the input data.

[1442] Step 2:

[1443] When the user clicks the "Post" button, the device converts the entered text data into JSON format. This conversion process results in JSON format text data. The device then sends the obtained text data to the server as an HTTP POST request. The input data is the text entered by the user, and the output data is the data converted into JSON format.

[1444] Step 3:

[1445] The server receives an HTTP POST request from the device. The server extracts the text data from the request and prepares it for analysis. The input data is the JSON format data sent from the device, and the output data is the extracted text.

[1446] Step 4:

[1447] The server analyzes the extracted text using a generative AI model. The generative AI model detects offensive language, inflammatory language, and legal and ethical issues in the text. It also uses an emotion engine to identify the user's emotions from keywords and context within the text. The input data is the extracted text, and the output data is the results of the analysis and emotion analysis.

[1448] Step 5:

[1449] The server generates appropriate correction suggestions based on the analysis results and the emotion engine results. For example, the expression "too stupid" is corrected to "I sometimes feel that it lacks understanding." The input data are the analysis results and emotion analysis results, and the output data are correction suggestions.

[1450] Step 6:

[1451] The server converts the generated revision suggestions into JSON format and sends them to the terminal as an HTTP response. The input data is the revision suggestions, and the output data is the JSON-formatted revision suggestions.

[1452] Step 7:

[1453] The terminal displays the JSON-formatted correction suggestions received from the server on the user interface. The user can then review the correction suggestions and approve or re-edit them. The input data is the correction suggestions received from the server, and the output data is the result of the user's approval or re-editing.

[1454] Step 8:

[1455] If the user approves the proposed revision, the terminal sends the approved revised text to the server again. The input data is the approved revised text, and the output data is a POST request sent to the server.

[1456] Step 9:

[1457] The server then performs a final check of the received revised text data. If there are no problems with the content, it issues a posting request through the API of the various SNS. The input data is the approved revised text, and the output data is the posting request sent to the SNS. For example, the text "Sometimes I feel I lack understanding" is posted to the SNS.

[1458] This series of processes allows users to correct posts containing inappropriate language in advance and engage in appropriate communication.

[1459] (Application example 2)

[1460] 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."

[1461] In current network services, inappropriate or misleading expressions are often posted, which can lead to communication problems and trouble. Furthermore, appropriate suggestions for correction based on the user's feelings are rarely made, further complicating the problem. The objective of this invention is to solve these problems and promote smooth communication online.

[1462] 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.

[1463] In this invention, the server includes means for receiving text entered by a user, means for analyzing the received text, means for generating a revision proposal based on the analysis result by the generation means, means for analyzing the user's emotions using an emotion engine, means for optimizing the revision proposal based on the emotion analysis result, means for displaying the revision proposal to the user, and means for posting the revised text to various network services based on the user's approval. This allows appropriate revision proposals that take the user's emotions into consideration to be provided in real time, making it possible to prevent problems on network services and ensure smooth communication.

[1464] The "means for receiving text input by the user" is a communication means for transmitting text data input by the user from the terminal to the server.

[1465] The "means for generating and analyzing received text" refers to a means for analyzing text data received by the server using natural language processing and identifying inappropriate or misleading expressions.

[1466] The "means for generating correction proposals" is a means for generating specific proposals for correcting identified problem areas based on the analysis results.

[1467] "Means for analyzing user emotions using an emotion engine" refers to a means for analyzing emotions from text input by the user and identifying emotional states such as joy, anger, sadness, etc.

[1468] The "means for optimizing revision suggestions based on emotion analysis results" is a means for making revision suggestions adapted to the emotional state of the user based on the emotion analysis results of the emotion engine.

[1469] The "means for displaying the revision suggestions to the user" refers to a screen display means for displaying the generated revision suggestions on the user's terminal so that the user can confirm the revision suggestions.

[1470] "Means for posting revised text based on the user's approval to various network services" refers to a communication means for posting revised text to network services such as SNS and chat services after the user approves the proposed revision.

[1471] The present invention relates to a system that automatically analyzes text that a user is about to post to a network service and corrects inappropriate or misleading expressions. Specific operations of this system and an embodiment thereof will be described below.

[1472] System configuration

[1473] 1. Terminal

[1474] This is the device through which the user enters the text they wish to post. This includes smartphones, tablets, and PCs.

[1475] The terminal is responsible for receiving input from the user and transmitting that data to the server.

[1476] 2. Server

[1477] It is the central system that receives and analyzes text sent from the terminal.

[1478] The server uses a generative method and an emotion engine to analyze the text and identify problem areas.

[1479] The system optimizes revision suggestions based on the results of sentiment analysis, generating and providing the most appropriate revision suggestions for users.

[1480] Hardware and software used

[1481] Hardware

[1482] User devices such as smartphones, smart glasses, tablets, and personal computers.

[1483] Server (using a high-performance data processing server).

[1484] software

[1485] Use TextBlob as a natural language processing (NLP) library.

[1486] We use a hypothetical EmotionEngine as an emotion analysis library.

[1487] The JSON format and HTTP protocol are used to format and communicate the data.

[1488] Specific examples of embodiments

[1489] A user enters the message they wish to post on social media into the input field on their device. For example, the user might type, "Today's meeting was a complete waste of time." This sentence is sent from the device to the server, where it is analyzed. A natural language processing algorithm (TextBlob) determines whether the sentence contains positive or negative expressions, and an emotion engine (EmotionEngine) analyzes the user's emotion as "anger." Based on this, the server generates a correction suggestion, suggesting, "Let's change this to a more positive expression." This suggestion is displayed on the user's device, and if the user accepts the suggestion, the corrected sentence is changed to, "I learned a lot from today's meeting." The corrected sentence is finally posted to the social media platform.

[1490] Prompt Sentence Examples

[1491] The prompt text to be input to the generative AI model is set as follows:

[1492] Please revise the following sentences to make them more appropriate and positive:

[1493] "Today's meeting was a complete waste of time."

[1494] In this way, this system makes appropriate correction suggestions in real time, taking into account the user's feelings, thereby preventing problems on network services and realizing smooth communication.

[1495] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1496] Step 1:

[1497] The user enters text into the SNS posting screen. For example, the user enters, "Today's meeting was a complete waste of time." The input data is saved in text format on the device.

[1498] Step 2:

[1499] The terminal converts the text entered by the user into an appropriate data format, such as JSON, and sends a POST request to the server. At this stage, the input data is the text entered by the user. The output data is the text data converted into JSON format.

[1500] Step 3:

[1501] The server receives a POST request from the terminal and extracts the text data contained therein. The server analyzes the received data format (JSON format) and extracts the input text. The input data at this stage is text data in JSON format. The output data is text in text format.

[1502] Step 4:

[1503] The server analyzes the received text using a natural language processing (NLP) algorithm (TextBlob). This analysis determines whether the text has a positive or negative tendency, or whether it contains offensive or inflammatory language. The input data is text in text format, and the output data is the analysis result, which includes the tendency of the text and whether it contains offensive language.

[1504] Step 5:

[1505] The server uses an emotion engine to analyze the user's emotions from the received text. The emotion engine identifies emotions from keywords, context, and specific expressions in the text. For example, it detects emotions such as "anger," "sadness," and "joy." The input data is text in text format, and the output data is the emotion analysis results.

[1506] Step 6:

[1507] The server generates appropriate correction suggestions based on the problem areas identified by the generation means and the sentiment analysis results. For example, it suggests correcting a negative expression such as "it was a waste" to something like "I learned very little." The input data are the analysis results and sentiment analysis results, and the output data are the correction suggestions.

[1508] Step 7:

[1509] The server compiles the revision suggestions into JSON format and sends them to the device. The device receives the revision suggestions from the server and displays them to the user. The input data is the revision suggestions, and the output data is the revision suggestions displayed to the user.

[1510] Step 8:

[1511] The user checks the proposed revisions and approves or re-edits them. For example, the user approves the suggestion that "there was little learning." At this stage, the input data is the proposed revisions, and the output data is the user's approval result.

[1512] Step 9:

[1513] The device resends the approved revised text to the server. The server performs a final check of the revised text and issues a posting request through the API of the various SNS. The input data is the approved revised text, and the output data is the result of posting to the SNS.

[1514] 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.

[1515] 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.

[1516] 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.

[1517] 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.

[1518] 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.

[1519] 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.

[1520] 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).

[1521] 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.

[1522] 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."

[1523] 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.

[1524] 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).

[1525] 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.

[1526] 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.

[1527] 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.

[1528] 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.

[1529] 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.

[1530] 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.

[1531] 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.

[1532] 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.

[1533] 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.

[1534] 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.

[1535] The following is further disclosed regarding the above embodiment.

[1536] (Claim 1)

[1537] means for receiving text entered by a user;

[1538] generating means for analyzing received text;

[1539] means for generating a modification proposal based on the results analyzed by the generating ant means;

[1540] means for displaying suggested revisions to a user;

[1541] A means for posting the modified text based on the user's approval to various network services;

[1542] A system including:

[1543] (Claim 2)

[1544] The system according to claim 1, further comprising means for checking for the presence of offensive language, inflammatory language, and legal and ethical issues during the analysis.

[1545] (Claim 3)

[1546] The system according to claim 1, further comprising means for generating correction suggestions in real time before posting in cooperation with a network service.

[1547] "Example 1"

[1548] (Claim 1)

[1549] means for receiving text entered by a user;

[1550] A means to convert received text into JSON format and send it to the server,

[1551] A generating AI means for analyzing the text received by the server;

[1552] a means for generating a correction suggestion based on the results analyzed by the generating AI means;

[1553] A means to compile correction suggestions in JSON format and send them to the device,

[1554] a means for displaying the proposed revisions to the user and obtaining the user's approval;

[1555] A means for retransmitting the approved revised text to a server and posting it to various network services;

[1556] A system including:

[1557] (Claim 2)

[1558] The system according to claim 1, further comprising means for checking for the presence of offensive language, inflammatory language, and legal and ethical issues during the analysis.

[1559] (Claim 3)

[1560] 2. The system according to claim 1, further comprising means for converting text entered by a user into JSON format and transmitting the converted text to a server.

[1561] "Application Example 1"

[1562] (Claim 1)

[1563] means for receiving text entered by a user;

[1564] generating means for analyzing received text;

[1565] means for generating a modification proposal based on the results analyzed by the generating ant means;

[1566] means for displaying suggested revisions to a user;

[1567] A means for posting the modified text based on the user's approval to various network services;

[1568] A means to instantly and securely process user input, generating correction suggestions in real time; and

[1569] A system including:

[1570] (Claim 2)

[1571] The system according to claim 1, further comprising means for checking for the presence of offensive language, inflammatory language, and legal and ethical issues during the analysis.

[1572] (Claim 3)

[1573] The system according to claim 1, further comprising means for generating correction suggestions in real time before posting in cooperation with a network service.

[1574] "Example 2: Combining Emotion Engines"

[1575] (Claim 1)

[1576] means for receiving text input by a user using the terminal;

[1577] means including a generative AI model for analyzing the received text;

[1578] A means for generating a correction suggestion using an emotion engine based on the analysis results by the generative AI model;

[1579] means for displaying the generated revision suggestions to a user;

[1580] A means for posting the modified text based on the user's approval to various network services;

[1581] A system including:

[1582] (Claim 2)

[1583] 2. The system according to claim 1, further comprising means for checking for the presence of offensive language, inflammatory language, and legal or ethical issues in analyzing text.

[1584] (Claim 3)

[1585] The system according to claim 1, further comprising means for generating correction suggestions in real time before posting in cooperation with a network service.

[1586] "Application example 2 when combining emotion engines"

[1587] (Claim 1)

[1588] means for receiving text entered by a user;

[1589] generating means for analyzing received text;

[1590] means for generating a revision suggestion based on the analysis result by the generating means;

[1591] means for analyzing a user's emotions by an emotion engine;

[1592] a means for optimizing correction suggestions based on the sentiment analysis results;

[1593] means for displaying suggested revisions to a user;

[1594] A means for posting the modified text based on the user's approval to various network services;

[1595] A system including:

[1596] (Claim 2)

[1597] The system according to claim 1, further comprising means for checking for the presence of offensive language, inflammatory language, and legal and ethical issues during the analysis.

[1598] (Claim 3)

[1599] The system according to claim 1, further comprising means for generating correction suggestions in real time before posting in cooperation with a network service. [Explanation of symbols]

[1600] 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; generating means for analyzing received text; means for generating a modification proposal based on the results analyzed by the generating ant means; means for displaying suggested revisions to a user; A means for posting the modified text based on the user's approval to various network services; A system including:

2. 2. The system according to claim 1, further comprising means for checking for the presence or absence of offensive language, inflammatory language, and legal or ethical issues in the analysis.

3. The system according to claim 1, further comprising means for generating correction suggestions in real time before posting by cooperating with a network service.

Images