System

Abstract

A system is provided.SOLUTION: A system comprising: means for analyzing post content using a natural language processing engine to detect inappropriate expressions; means for generating and notifying a user of correction suggestions based on the detected inappropriate expressions; and means for re-analyzing the corrected post content and finally approving and publishing the corrected post content if no problem is found.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] In recent years, systems that allow anyone to freely post comments and reviews on the Internet have become widespread, but this has also led to problems with slander and inappropriate language. These inappropriate comments not only cause psychological harm to the victim, but also put the poster at risk of being subject to online lynching. Therefore, to promote the free exchange of opinions while realizing safe and constructive communication, a system is needed that checks the appropriateness of posted content and provides suggestions for improvement as needed. However, with current systems, there are numerous cases where posters are unaware that their comments are inappropriate and end up making them public. An effective method to resolve this issue is needed. [Means for solving the problem]

[0005] To solve the above problems, the present invention proposes a post content analysis system that uses a natural language processing engine to detect inappropriate language and make correction suggestions. In this system, a user first creates a post and sends it to a server via their device. The server then sends the received post to the natural language processing engine, which scans it for slander and inappropriate language. Based on the detected inappropriate language, a correction suggestion is generated and notified to the user via their device. The user then corrects the post in accordance with the provided feedback and resubmits it. The server then reanalyzes the resubmitted content, confirms that the inappropriate language has been removed, and finally approves and publishes it. This series of processes enables posters to express their opinions safely and constructively while preventing inappropriate language from being used.

[0006] "Inappropriate expressions" are expressions that have a negative impact on others, such as slander, insults, or discrimination.

[0007] A "natural language processing engine" is software that refers to algorithms and techniques for understanding, analyzing, and generating human language.

[0008] "Posted content" refers to text information entered by a user to be published on social media, review sites, etc.

[0009] "Analysis" is the process of structuring the content of a submitted post and understanding its meaning, intent, and context.

[0010] A "correction suggestion" is a suggestion to correct a post that contains inappropriate language so that the language becomes appropriate and constructive.

[0011] "User" refers to an individual or corporation that posts on social media, review sites, etc.

[0012] A "terminal" is a device that a user uses to input content to post and communicate with the server.

[0013] A "server" is a computer system that receives and analyzes user submissions and generates and sends suggested revisions.

[0014] "Re-analysis" is a second analysis process that is performed when the posted content that has received suggested corrections is resubmitted.

[0015] "Approval" is the process of finally verifying that the revised post is appropriate and allowing it to be published. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] As an embodiment of the present invention, a system for analyzing internet posts that detects inappropriate expressions and suggests corrections is shown below. This system is composed of a terminal where users input and send their posts, a server that receives and analyzes the posts and generates feedback, and a natural language processing engine.

[0038] First, a user creates a post on a social networking site or review site. When the user clicks the post button, the device sends the post to the server. The server then sends the received post to a natural language processing engine for analysis. The natural language processing engine analyzes the post and scans it for slander or inappropriate language.

[0039] For example, if a user posts "This product is completely useless. It's not worth buying," the device sends the content to the server. The server's natural language processing engine detects inappropriate expressions such as "completely useless" and "worthless." When an inappropriate expression is detected, the server generates a correction suggestion based on that. For example, in response to the expression "completely useless," the server suggests an amendment such as, "Let me point out specifically what needs to be improved."

[0040] The server then sends these revision suggestions to the device, which then displays them to the user. The user accepts the suggestions and modifies the content of the post. For example, a suggested change might be, "There are some areas for improvement in this product. In particular, I felt that the balance between quality and price was not right."

[0041] The corrected post is then sent from the device to the server again. The server reanalyzes the corrected post to ensure it does not contain any inappropriate language. If there are no problems, the server finally approves it and publishes it on social media and review sites. The device notifies the user that the post was successful.

[0042] Through this series of processes, the system helps users avoid inappropriate language and engage in safe and constructive exchanges of opinions, thereby preventing slanderous comments on the Internet and realizing a healthy communication environment.

[0043] The processing flow will be explained below.

[0044] Step 1:

[0045] A user creates a post on a social networking site or review site. The user enters the post content and clicks the post button to publish it.

[0046] Step 2:

[0047] The device temporarily saves the entered post content, and then when it detects a click on the post button, it sends the saved post content to the server.

[0048] Step 3:

[0049] The server receives the content posted from the device and sends it to a natural language processing engine for analysis.

[0050] Step 4:

[0051] The server's natural language processing engine analyzes the content of posts and scans them for slander or inappropriate language, such as "it's no good at all" or "it's not worth buying."

[0052] Step 5:

[0053] When an inappropriate expression is detected, the server generates a suggestion for correction based on the detected expression. For example, for the expression "not good at all," the server generates a suggestion for correction such as "Let me point out specifically what needs to be improved."

[0054] Step 6:

[0055] The server sends the generated revision suggestions to the terminal, which displays the revision suggestions to the user.

[0056] Step 7:

[0057] The user checks the suggested revisions and modifies the post. For example, they might change it to, "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was not right."

[0058] Step 8:

[0059] The device saves the revised post again and resends it to the server. The revised post is then sent to the server.

[0060] Step 9:

[0061] The server receives the revised post again and sends it to the natural language processing engine for re-analysis, which verifies that the revised post does not contain any inappropriate language.

[0062] Step 10:

[0063] If it is confirmed that the post does not contain any inappropriate content, the server will finally approve the post, which will then be published on social media and review sites.

[0064] Step 11:

[0065] The terminal displays a notification to the user that the posting was successful.

[0066] Example 1

[0067] 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."

[0068] The Internet is prone to abusive and inappropriate language, hindering healthy communication between users. Existing systems do not fully automate the process of detecting and correcting these inappropriate expressions, requiring a great deal of manual effort from users. Therefore, there is a need for a system that can efficiently detect inappropriate expressions in real time and provide appropriate correction suggestions.

[0069] 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.

[0070] In this invention, the server includes a means for allowing a user to create a post and send it to a terminal, a means for the terminal to send the post to the server, and a means for sending the post received by the server to a natural language processing engine for analysis. This allows the server to analyze the content when a user posts, detect slanderous or inappropriate language in real time, automatically generate correction suggestions, and present them to the user. This reduces the user's manual work and promotes healthy communication on the Internet.

[0071] "User" means a person who creates and submits Posts using the System.

[0072] "Terminal" refers to the device (e.g., PC, smartphone) used by a user to input post content and send it to the server.

[0073] "Server" refers to the computer system responsible for analyzing received posts, generating suggested revisions, and ultimately publishing the posts.

[0074] "Natural language processing engine" refers to a machine learning algorithm that analyzes posts, detects inappropriate language, and generates suggested edits.

[0075] "Posted content" refers to the text information that users send to social media or review sites.

[0076] "Profanity" refers to words or phrases that may offend others, including defamatory or offensive content.

[0077] "Suggested revisions" refers to feedback that provides advice on how to revise posts containing inappropriate language to more appropriate language.

[0078] "Parsing" refers to the process by which a natural language processing engine tokenizes a post and understands its grammatical structure and meaning.

[0079] "Publication" refers to posting the final approved post on social media or review sites on the Internet.

[0080] "Authentication Token" means a unique digital code used to authenticate API requests.

[0081] An "HTTP request" refers to a message used to send information from a terminal to a server using a web communication protocol.

[0082] "Tokenization" refers to the process by which a natural language processing engine breaks up a post into words and phrases.

[0083] "Feedback" refers to advice or suggestions for corrections provided to users regarding their posted content.

[0084] "Healthy communication" refers to a constructive and friendly exchange of information that is free from slander and inappropriate language.

[0085] A "machine learning model" refers to a system that uses algorithms to learn patterns from given data and perform classification and prediction.

[0086] As an embodiment of the present invention, a system for analyzing internet posts that detects inappropriate expressions and suggests corrections is shown below. This system is composed of a terminal where users input and send their posts, a server that receives and analyzes the posts and generates feedback, and a natural language processing engine.

[0087] First, a user creates a post on a social networking site or review site. The user then enters the post content using their own device (PC, smartphone, etc.) and clicks the "Post" button. At this point, the device sends the post content to the server.

[0088] The server then analyzes the received post. To do this, the server sends the post to a natural language processing engine. A specific example of a natural language processing engine is "OpenAI GPT-3." This engine analyzes the post and scans it for slander or inappropriate language.

[0089] For example, if a user posts "This product is completely useless. It's not worth buying," the device sends the content to the server. The server's natural language processing engine detects inappropriate expressions such as "completely useless" and "worthless." When an inappropriate expression is detected, the server generates a correction suggestion based on that. For example, in response to the expression "completely useless," the server suggests an amendment such as, "Let me point out specifically what needs to be improved."

[0090] Here are some example prompts for suggested revisions:

[0091] plaintext

[0092] When a user posts, "This product is completely useless. It's not worth buying," create a correction suggestion.

[0093] The server then sends these revision suggestions to the device, which then displays them to the user. The user accepts the suggestions and modifies the content of the post. For example, a suggested change might be, "There are some areas for improvement in this product. In particular, I felt that the balance between quality and price was not right."

[0094] The corrected post is then sent from the device to the server again. The server reanalyzes the corrected post to ensure it does not contain any inappropriate language. If there are no problems, the server finally approves it and publishes it on social media and review sites. The device notifies the user that the post was successful.

[0095] Through this series of processes, the system helps users avoid inappropriate language and engage in safe and constructive exchanges of opinions, thereby preventing slanderous comments on the Internet and realizing a healthy communication environment.

[0096] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0097] Step 1:

[0098] The user creates the content to post and sends it to the terminal.

[0099] Specific operation: The user enters the content to post on a social networking site or review site and clicks the "Post" button.

[0100] Input: Text data entered by the user.

[0101] Output: The post sent by the device.

[0102] Step 2:

[0103] The device sends the post content to the server.

[0104] Specific operation: The device uses an HTTP POST request to send the post content to the server.

[0105] Input: User-generated posts.

[0106] Output: The post sent to the server.

[0107] Step 3:

[0108] The server receives the post.

[0109] Specific operation: The server receives the post content at the configured API endpoint and temporarily stores it.

[0110] Input: Post content sent from the device.

[0111] Output: The post content ready to be sent to the natural language processing engine.

[0112] Step 4:

[0113] The server sends the received post content to a natural language processing engine for analysis.

[0114] How it works: The server formats the post and sends it via an API request to a natural language processing engine, which uses a machine learning model such as OpenAI GPT-3.

[0115] Input: The post received by the server.

[0116] Output: The post content sent to the natural language processing engine.

[0117] Step 5:

[0118] A natural language processing engine analyzes the content of posts and detects inappropriate expressions.

[0119] What it does: A natural language processing engine tokenizes posts, analyzes each word and phrase, and scans them for profanity.

[0120] Input: The post content sent from the server.

[0121] Output: Profanity analysis results.

[0122] Step 6:

[0123] The server generates correction suggestions based on the detected profanity.

[0124] Specific operation: The server creates a correction suggestion based on the analysis results returned by the natural language processing engine. The correction suggestion includes suggestions for improvement or more appropriate expression. For example, in response to "It's no good at all," the server might suggest, "Let me point out specifically what needs to be improved."

[0125] Input: Analysis results from the natural language processing engine.

[0126] Output: Fix suggestions.

[0127] Step 7:

[0128] The server sends the revision suggestions to the device.

[0129] Specific operation: The server sends the generated revision proposal to the terminal as an HTTP response.

[0130] Input: The generated correction suggestions.

[0131] Output: Correction suggestions sent to the terminal.

[0132] Step 8:

[0133] The terminal displays suggested revisions to the user.

[0134] Specific operation: The device displays the received correction suggestions on the screen. The user checks the correction suggestions and corrects the post content as necessary.

[0135] Input: The correction suggestion sent by the server.

[0136] Output: The correction suggestions displayed to the user.

[0137] Step 9:

[0138] The user accepts the suggested revision, corrects the post, and resubmits it.

[0139] Specific operation: After the user checks the suggested corrections, they modify the post as necessary and click the "Post" button again to submit the modified content.

[0140] Input: The user's modified post.

[0141] Output: The revised submission that was resubmitted.

[0142] Step 10:

[0143] The server then re-analyzes the revised post, verifies that it contains no inappropriate language, and finally approves and publishes it.

[0144] What happens: The server sends the revised post back to the natural language processing engine for a final check, confirming that it contains no inappropriate language, and finally approves it.

[0145] Input: The user's modified post.

[0146] Output: The published, approved post.

[0147] Step 11:

[0148] The terminal notifies the user that posting was successful.

[0149] Specific operation: The device receives a success response from the server and notifies the user that "the post was successful."

[0150] Input: A successful response from the server.

[0151] Output: A notification to the user that the submission was successful.

[0152] (Application example 1)

[0153] 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."

[0154] In today's online communications, many comments contain slander and inappropriate language. Comments on video streaming platforms, in particular, can cause trouble and misunderstandings among viewers. Providing a healthy communication environment by detecting such inappropriate comments in advance and correcting them appropriately is required. Furthermore, a system that can automate this process to reduce the burden on users and respond quickly is needed.

[0155] 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.

[0156] In this invention, the server includes means for analyzing posted content using a natural language processing engine to detect inappropriate expressions, means for generating correction suggestions using a generative AI model based on the detected inappropriate expressions and notifying the user, means for reanalyzing the corrected posted content and finally approving and publishing it if there are no problems, and means for monitoring the content of comments posted by users on a video distribution platform. This makes it possible to automatically detect inappropriate expressions in comments and generate correction suggestions, even on different types of posting platforms, thereby realizing a healthy communication environment.

[0157] "Inappropriate language" refers to inappropriate words or phrases that a user uses in their posts or comments that may slander or offend others.

[0158] A "natural language processing engine" refers to a collection of algorithms and software that analyzes input text data and understands its meaning and grammatical structure.

[0159] "Generative AI model" refers to an artificial intelligence model used to generate new text or suggested revisions based on input data and prompts.

[0160] "Suggested corrections" refer to specific suggestions for correcting detected inappropriate language to make it more appropriate and constructive.

[0161] "Terminal" refers to a device (smartphone, PC, tablet, etc.) that a user uses to input content for posting, connect to the Internet, and send it.

[0162] "Server" refers to a central computer that receives data sent by users and processes it, such as analyzing it, generating suggested modifications, reanalyzing it, and final approval.

[0163] "Video distribution platform" refers to a website or application that allows users to upload video content and allows other users to view and comment on it.

[0164] As an embodiment of the present invention, an analysis system for detecting inappropriate expressions and making correction suggestions will be described. This system detects inappropriate expressions in advance when a user posts a comment on a video distribution platform, and makes correction suggestions using a generative AI model.

[0165] The server first receives the comment content sent by the user. Because this comment content may contain inappropriate language, the server analyzes it using a natural language processing engine. The natural language processing engine scans the comment content and detects inappropriate language while referring to a defined list of inappropriate language.

[0166] For example, if a user sends a comment saying, "This video is completely no good," the server detects the inappropriate expression "completely no good." The server then uses a generative AI model to generate correction suggestions for the detected inappropriate expression. The generative AI model generates specific correction suggestions based on the prompt sentence.

[0167] Example prompt sentence:

[0168] text

[0169] Please turn your comments below into constructive words: This video is absolutely lame.

[0170] After the generative AI model generates correction suggestions, the server notifies the user of the suggestions. For example, a correction suggestion might be generated that says, "There are some areas for improvement in this video. In particular, I feel that the content and editing quality need to be improved."

[0171] When the user accepts the correction suggestion, corrects the comment, and resubmits it, the server again analyzes the corrected comment using a natural language processing engine to confirm that it does not contain any inappropriate language. If there are no problems, the server finally approves the comment and publishes it on the video distribution platform. This helps to provide a healthy communication environment.

[0172] This system is equipped with a network interface for exchanging data between the user-operated device and the server. The server uses OpenAI's API (e.g., text-davinci-003) as a generative AI model. The natural language processing engine includes commonly used Python libraries.

[0173] As described above, the system embodying the present invention automates comment moderation on video distribution platforms and converts inappropriate language into constructive language, thereby promoting healthy communication.

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

[0175] Step 1:

[0176] A user enters a comment into the comment section of a video distribution platform and submits it. A specific example of a user input might be, "This video is absolutely terrible." The input includes the comment text entered by the user. Receiving that comment text is the output.

[0177] Step 2:

[0178] The terminal sends the comment entered by the user to the server. The terminal communicates with the server using a network interface. The input includes the user's comment text, and the server receives the comment text as output.

[0179] Step 3:

[0180] The server passes the received comment text to a natural language processing engine to begin detecting profanity. The input includes the user's comment text. The server scans the text using a defined profanity list and extracts profanity. The output includes the presence or absence of profanity and its location.

[0181] Step 4:

[0182] The server uses a generative AI model to generate correction suggestions based on the detected inappropriate language. The input includes the comment text containing the inappropriate language and a prompt. For example, the prompt is "Please change the following comment to something more constructive: This video is completely bad." The generative AI model generates correction text based on this prompt, and the output includes specific correction suggestions.

[0183] Step 5:

[0184] The server sends the generated revision suggestions to the user's device. The input includes the revision suggestion text, and the user's device receives the suggestions as output. For example, a revision suggestion such as "There are some areas for improvement in this video. I feel that the content and editing quality in particular need to be improved."

[0185] Step 6:

[0186] The user checks the correction suggestions sent from the server, corrects the comment based on them, and resubmits it. The input includes the correction suggestion text, and the output is the corrected comment text sent from the terminal to the server. For example, the user may correct the comment by saying, "There are some areas for improvement in this video. I feel that the content and editing quality in particular need to be improved," and resubmit the comment.

[0187] Step 7:

[0188] The server analyzes the resubmitted corrected comment again using a natural language processing engine to check whether it contains any inappropriate language. The input contains the corrected comment text. The natural language processing engine reanalyzes the comment and outputs a result confirming that there are no problems.

[0189] Step 8:

[0190] The server finally approves the corrected comment and publishes it to the video streaming platform. The input includes the corrected comment text that was found to be correct by the reanalysis. The output is the comment text published on the video streaming platform. Finally, the device notifies the user that the comment was successfully posted.

[0191] The above is a specific processing procedure of the system for implementing the present invention.

[0192] 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.

[0193] As an embodiment of the present invention, a system for detecting inappropriate expressions from posted content, suggesting corrections, and recognizing the user's emotions is described below. This system analyzes the posted content entered by the user, detects inappropriate expressions such as slander, provides appropriate feedback, and suggests corrections based on emotions as necessary.

[0194] First, a user creates a post on a social networking site or review site. When the user clicks the post button, the device temporarily saves the post and sends it to the server. The server then sends the received post to a natural language processing engine and emotion engine for analysis.

[0195] The natural language processing engine analyzes the content of posts and scans them for slander or inappropriate language. For example, if a user posts something like, "This product is completely useless. It's not worth buying," the device sends the content to the server, where the server's natural language processing engine detects inappropriate language like "completely useless" or "worthless."

[0196] At the same time, an emotion engine recognizes the emotions in users' posts. The system distinguishes between positive, negative, and neutral emotions and customizes suggestions accordingly. For example, if the user expresses strong negative emotions, the system will suggest suggestions that encourage positive feedback, such as, "I'm not satisfied with this product. Specific improvements could be made to balance the quality and price."

[0197] Based on the detected inappropriate expressions and the recognized emotions, the server generates correction suggestions and notifies the user. For example, for the expression "not good at all," the server suggests "Let's point out specifically what needs to be improved," and further suggests "Negative emotions are being read. By calmly pointing out specific points, your opinion will be easier for the other person to understand."

[0198] The user can review the provided feedback and revise their post. For example, a suggested change might be, "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was not right."

[0199] The corrected post is saved again and resubmitted to the server. The server then reanalyzes the corrected post to ensure it does not contain any inappropriate content. If there are no problems, the server finally approves the post and publishes it on social media or review sites. The device then displays a notification to the user that the post was successful.

[0200] Through this series of processes, the system helps users avoid inappropriate language and engage in safe and constructive exchanges of opinions. Furthermore, by taking the user's feelings into consideration, the system can provide more effective feedback that reduces emotional burden. This can prevent slanderous comments on the Internet and create a healthy communication environment.

[0201] The processing flow will be explained below.

[0202] Understood. Now, the specific processing flow of the system is shown below.

[0203] Step 1:

[0204] A user creates a post on a social networking site or review site. The user enters the post content and clicks the post button to publish it.

[0205] Step 2:

[0206] The device temporarily saves the entered post content, and when it detects a click on the post button, it sends the saved post content to the server.

[0207] Step 3:

[0208] The server receives the content posted from the device, sends it to a natural language processing engine, and scans it for slander and inappropriate language.

[0209] Step 4:

[0210] The server's natural language processing engine analyzes the content of the post and detects inappropriate expressions such as "completely useless" and "worthless."

[0211] Step 5:

[0212] At the same time, the server's emotion engine analyzes the user's emotions from the posted content, distinguishing between positive, negative, and neutral emotions, and generating emotion information based on those.

[0213] Step 6:

[0214] The server combines the results of the natural language processing engine and the emotion engine to generate suggestions for correction based on the location of the inappropriate expression and the emotion. For example, in response to the expression "not good at all," the server will suggest, "Let's point out specifically what needs to be improved," and in response to negative emotions, it will generate additional feedback such as, "By calmly pointing out specific points, your opinion will be easier for the other person to understand."

[0215] Step 7:

[0216] The server sends the generated revision suggestions and emotional feedback to the device, which displays them to the user and provides an interface to encourage them to revise the posted content.

[0217] Step 8:

[0218] The user reviews the suggested revisions and emotional feedback and revises the post. For example, they might change it to, "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was not right."

[0219] Step 9:

[0220] The device saves the revised post again and resends it to the server. The revised post is then sent to the server.

[0221] Step 10:

[0222] The server receives the revised post again and sends it to the natural language processing engine for re-analysis, which verifies that the revised post does not contain any inappropriate language.

[0223] Step 11:

[0224] If it is confirmed that the post does not contain any inappropriate content, the server will finally approve the post, which will then be published on social media and review sites.

[0225] Step 12:

[0226] The terminal displays a notification to the user that the posting was successful.

[0227] The above are the specific processing steps of the present invention. Through this series of processes, constructive communication that avoids inappropriate expressions and takes into consideration the feelings of users is realized.

[0228] Example 2

[0229] 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."

[0230] Currently, communication on the Internet is rife with inappropriate language and slander, making it difficult to maintain a safe and healthy communication environment. Furthermore, current systems do not adequately analyze the emotions expressed in users' posts and provide appropriate feedback based on that analysis. To address these issues, a system is needed that can analyze users' posts, detect inappropriate language, and suggest corrections, as well as identify users' emotions from their posts and provide feedback based on that analysis.

[0231] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for analyzing the posted content using a natural language processing engine to detect inappropriate expressions, means for generating correction suggestions based on the detected inappropriate expressions and notifying the user, emotion engine means for identifying the user's emotions from the posted content, means for customizing the correction suggestions based on the emotions and notifying the user, and means for reanalyzing the corrected posted content and finally approving and publishing it if there are no problems. This allows users to avoid inappropriate expressions and engage in safe and constructive exchanges of opinions. Furthermore, providing feedback that takes the user's emotions into consideration can be more effective and reduce emotional burdens.

[0232] A "natural language processing engine" is software that analyzes posted content and detects inappropriate expressions and slander.

[0233] The "emotion engine" is software that identifies a user's emotions from the content of their posts and generates appropriate feedback based on those emotions.

[0234] "Inappropriate expressions" are problematic expressions contained in posts, such as slander, offensive language, and expressions that are socially unacceptable.

[0235] "Suggested corrections" are suggestions or feedback for improving the content of a post that are generated based on detected inappropriate expressions and provided to the user.

[0236] "Posted content" refers to text data that a user enters and attempts to send on a social networking site, review site, etc.

[0237] "User" means an individual or organization that creates posts on social media or review sites and sends them through the system.

[0238] A "terminal" is an electronic device that a user uses to input and send content to post.

[0239] A "server" is a central control device that receives posts, analyzes them, and generates feedback.

[0240] A "machine learning model" is an algorithmic system that is trained using past data to detect inappropriate language and recognize emotions with high accuracy.

[0241] "Reanalysis" is the process of reanalyzing the edited post to ensure it does not contain any inappropriate language.

[0242] "Publication" is the process of finally displaying the approved post on social media or review sites.

[0243] The system of this invention detects inappropriate expressions when users post to social networking sites or review sites, suggests corrections, and recognizes the user's emotions to provide appropriate feedback. This system consists of three main components: the user, the terminal, and the server.

[0244] First, a user enters text into a social networking site or review site and clicks the post button. The device temporarily saves the post and sends the text data to a server. The device can be a regular personal computer or smartphone.

[0245] The posted content received by the server is analyzed by a natural language processing engine and an emotion engine. The natural language processing engine scans the posted content based on a specific algorithm to detect whether it contains inappropriate language or slander. For example, if a user posts a sentence such as "This product is completely useless. Not worth buying," the engine will extract inappropriate language such as "completely useless" and "not worthless."

[0246] At the same time, a sentiment engine determines the user's sentiment from the post. This engine uses natural language processing technology to capture emotional nuances in the text. Sentiments are generally classified as positive, negative, or neutral. If the sentiment is strongly negative, the system suggests revising it to a more positive expression.

[0247] Based on the detected inappropriate expressions and emotions, the server generates correction suggestions and notifies the user. For example, for the expression "not good at all," the server may send suggestions such as "Let's point out specifically what needs to be improved" or "Negative emotions are being read. By calmly pointing out specific points, your opinion will be easier for the other person to understand." These suggestions are sent to the device and displayed to the user.

[0248] The user checks the suggested corrections and modifies the post. The modified text is then sent from the device to the server again. The server re-analyzes the text to ensure it does not contain any inappropriate language. If there are no problems, the server finally approves the post and publishes it on social media and review sites. The device then displays a notification to the user that the post was successful.

[0249] A specific example is shown below. If a user posts, "This product is completely useless. It's not worth buying," the inappropriate expressions "completely useless" and "worthless" are detected, and the server will suggest specific improvements to the user, such as, "Let's suggest specific areas for improvement. For example, 'There are several areas for improvement with this product. In particular, I felt that the balance between quality and price was not right.'"

[0250] This system allows users to avoid inappropriate language and engage in safe and constructive exchanges. Furthermore, by taking into account the user's feelings, it is possible to provide more effective and less emotionally taxing feedback.

[0251] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0252] Step 1: User creates and submits a post

[0253] Input: User-generated posts (e.g., "This product is completely useless. Not worth buying.")

[0254] How it works: A user enters the content to post on a social networking site or review site and clicks the post button.

[0255] Output: Text data that the device temporarily saves the posted content and sends it to the server

[0256] Step 2: Prepare the server to receive and parse the post

[0257] Input: Post content sent from the device (e.g., "This product is completely useless. It's not worth buying.")

[0258] How it works: The server accepts text data received from the device and prepares it to be sent to the natural language processing engine and emotion engine for analysis.

[0259] Output: Analysis requests to the natural language processing engine and emotion engine

[0260] Step 3: Detect profanity

[0261] Input: Post content (e.g. "This product is completely useless. Not worth buying.")

[0262] How it works: The server's natural language processing engine scans posts and detects inappropriate language based on specific algorithms.

[0263] Output: A list of detected profanities (e.g., "not good at all" or "worthless")

[0264] Step 4: Recognize emotions

[0265] Input: Post content (e.g. "This product is completely useless. Not worth buying.")

[0266] How it works: The server's emotion engine determines the emotion of the post, using an emotion recognition algorithm to classify it as positive, negative, or neutral.

[0267] Output: Emotional judgment result (e.g., negative)

[0268] Step 5: Generate and notify correction suggestions

[0269] Input: List of detected inappropriate expressions (e.g., "not good at all" or "worthless"), emotion judgment result (e.g., negative)

[0270] How it works: The server generates suggestions based on the inappropriate expressions and the emotions they identify. For example, it generates suggestions such as "Point out specific areas that need improvement" and "By calmly pointing out specific points, your opinion will be easier for the other person to understand."

[0271] Output: Generated suggestions for correction (e.g., "There are some areas for improvement in this product. In particular, I felt that the balance between quality and price was not right.")

[0272] Step 6: User reviews the suggested revisions and edits their post

[0273] Input: The suggested revisions displayed on the device (e.g., "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was not right.")

[0274] Action: The user reviews the suggested revisions and corrects their post.

[0275] Output: Revised post (e.g. "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was off.")

[0276] Step 7: Resubmit your revised submission

[0277] Input: Revised post (e.g., "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was off.")

[0278] How it works: The user resubmits the revised post from their device to the server.

[0279] Output: Resent post

[0280] Step 8: The server re-parses the revised post

[0281] Input: Resent post (e.g., "This product has some room for improvement. In particular, I felt the balance between quality and price was off.")

[0282] Action: The server reparses the page to ensure it does not contain profanity.

[0283] Output: Analysis results (no problems)

[0284] Step 9: Approve and publish your submission

[0285] Input: Analysis results (no problems)

[0286] How it works: The server ultimately approves the post and publishes it on social media and review sites.

[0287] Output: Published post

[0288] Step 10: Notification of successful submission

[0289] Input: Published post

[0290] Behavior: The device displays a notification to the user that the post was successful.

[0291] Output: Submission success notification message

[0292] (Application example 2)

[0293] 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."

[0294] Conventional systems are limited to detecting inappropriate content in user posts and are unable to provide correction suggestions that fully consider the user's feelings. This can lead to an increase in inappropriate posts by users, potentially damaging the integrity of online platforms. Furthermore, even if inappropriate content is corrected, suggestions that are not based on the user's feelings make it difficult to improve the quality of the posts.

[0295] 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.

[0296] In this invention, the server includes means for analyzing posted content using a natural language processing engine to detect inappropriate expressions, means for generating revision suggestions based on the detected inappropriate expressions and the user's emotions and notifying the user, means for reanalyzing the revised posted content and finally approving and publishing it if there are no problems, and means for using a machine learning model to detect inappropriate expressions and analyze emotions. This makes it possible to generate appropriate revision suggestions that take user emotions into consideration, improve the quality of posted content on online platforms, and maintain a healthy communication environment.

[0297] A "natural language processing engine" is a technology for analyzing text data and understanding its meaning and context.

[0298] "Profanity" refers to words or phrases that are hurtful, offensive or discriminatory.

[0299] "Correction suggestions" refer to suggestions for improvements or alternative expressions provided to users in response to detected inappropriate expressions.

[0300] "User emotions" refers to the classification of emotions that users put into the content they post, such as positive, negative, or neutral.

[0301] A "machine learning model" is an algorithm that is trained using large amounts of data to automatically perform a specific task.

[0302] "Terminal" refers to a device through which a user inputs data and communicates with a server.

[0303] "Server means" refers to a computer system that processes and stores data in response to requests from clients.

[0304] As an embodiment of the present invention, a system incorporating a review management application for an online shopping site will be described. This system detects inappropriate expressions, analyzes user sentiment, and makes appropriate correction suggestions.

[0305] First, a user enters a product review using a review management application on their smartphone. When the user clicks the submit button, the review content is temporarily saved and sent to a server. The server receives the review content and sends it to a natural language processing engine. This natural language processing engine runs an algorithm using a machine learning model to detect inappropriate language. If inappropriate language is detected, further sentiment analysis is performed to determine whether the review content should be classified as positive, negative, or neutral.

[0306] Based on the detected inappropriate expressions and the results of the sentiment analysis, the server generates a correction suggestion. The suggestion is notified to the user and displayed on the smartphone screen. The user can confirm the suggested correction and revise the review. The revised review content is sent back to the server for re-analysis. If this re-analysis confirms that no inappropriate expressions are present, the review is finally approved and published on the online shopping site. After publication, the user receives a notification on their smartphone that the review has been successfully posted.

[0307] Each process in this system uses the Python programming language and the TextBlob library, which includes a natural language processing engine and the NaiveBayesAnalyzer for sentiment analysis, to detect profanity and perform sentiment analysis.

[0308] As a specific example, consider the case where a user enters a review as follows:

[0309] "This product is completely useless. Not worth buying."

[0310] When this review is submitted, the server detects inappropriate expressions such as "not good at all" and "not worth it" and determines the sentiment as "negative." As a result, the following sentence is displayed as a suggestion for correction:

[0311] "Point out specifically what needs to be improved." "By calmly pointing out specific points, your opinion will be easier for the other person to understand."

[0312] An example of a prompt is as follows:

[0313] "Analyze user-written reviews like 'This product is terrible. Not worth buying.' and generate correction suggestions based on profanity and sentiment."

[0314] These processes enable users to communicate in a healthy manner and improve the quality of reviews on online shopping sites, which is expected to maintain the integrity of online platforms.

[0315] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0316] Step 1:

[0317] User enters and submits review

[0318] The user enters a review into the smartphone application and presses the submit button. The review content is received as text data. This text data is temporarily stored and sent to the server.

[0319] Step 2:

[0320] The server receives the review content.

[0321] The server receives the text data of the review content sent by the user. The text data from the user is received as input. This prepares the server for the next analysis process.

[0322] Step 3:

[0323] Analysis of review content using a natural language processing engine

[0324] The server's natural language processing engine analyzes the review content. The input to be analyzed is the received text data. Specifically, the text data is scanned to see if it contains any inappropriate language. An AI model is then used to detect inappropriate language. The detection results are output as a list of inappropriate language.

[0325] Step 4:

[0326] Emotional analysis of users using an emotion engine

[0327] The server's emotion engine analyzes the sentiment of text data. It receives the text data of the review as input. Specifically, it uses the NaiveBayesAnalyzer from the TextBlob library to classify the sentiment of the text as positive, negative, or neutral. The result is the type of sentiment.

[0328] Step 5:

[0329] Generate correction suggestions

[0330] The server generates correction suggestions based on the detected inappropriate expressions and the results of sentiment analysis. It receives a list of inappropriate expressions and the type of sentiment as input. Specifically, it retrieves improvement suggestions corresponding to the detected inappropriate expressions from a dictionary and generates additional suggestions according to the sentiment. The output is the text data of the correction suggestions.

[0331] Step 6:

[0332] Notification of proposed amendments

[0333] The server notifies the user of the revision suggestions it has generated. It receives the text data of the revision suggestions as input. Specifically, it sends a notification message to the smartphone application, which displays it on the screen. The output is the text of the revision suggestions that the user can confirm.

[0334] Step 7:

[0335] User Review Modifications

[0336] The system corrects the review content based on the correction suggestions provided by the user. The system receives correction suggestions from the server as input. The specific operation is that the user corrects the review on their smartphone and submits it again. The corrected text data is output.

[0337] Step 8:

[0338] Re-analysis of revised review content

[0339] The server re-analyzes the revised review content, receiving the revised text data as input. Specifically, it uses the natural language processing engine again to check whether inappropriate language is included. As a result of this check, the review content that does not contain inappropriate language is output.

[0340] Step 9:

[0341] Final review approval and publication

[0342] The server finally approves the corrected review and publishes it on the shopping site. The input is the verified text data. The specific operation is to save the review in the database and display it on the public page of the shopping site. The output is the review published on the shopping site.

[0343] Step 10:

[0344] Review submission success notification

[0345] The device notifies the user that the review has been successfully posted. The input is a notification of the completion of publishing from the server. Specifically, the smartphone application displays a notification message on the screen. The output is a message to the user that the review has been successfully posted.

[0346] 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.

[0347] 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.

[0348] 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.

[0349] [Second embodiment]

[0350] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0351] 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.

[0352] 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).

[0353] 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.

[0354] 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.

[0355] 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).

[0356] 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.

[0357] 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.

[0358] 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.

[0359] 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.

[0360] 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.

[0361] 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."

[0362] As an embodiment of the present invention, a system for analyzing internet posts that detects inappropriate expressions and suggests corrections is shown below. This system is composed of a terminal where users input and send their posts, a server that receives and analyzes the posts and generates feedback, and a natural language processing engine.

[0363] First, a user creates a post on a social networking site or review site. When the user clicks the post button, the device sends the post to the server. The server then sends the received post to a natural language processing engine for analysis. The natural language processing engine analyzes the post and scans it for slander or inappropriate language.

[0364] For example, if a user posts "This product is completely useless. It's not worth buying," the device sends the content to the server. The server's natural language processing engine detects inappropriate expressions such as "completely useless" and "worthless." When an inappropriate expression is detected, the server generates a correction suggestion based on that. For example, in response to the expression "completely useless," the server suggests an amendment such as, "Let me point out specifically what needs to be improved."

[0365] The server then sends these revision suggestions to the device, which then displays them to the user. The user accepts the suggestions and modifies the content of the post. For example, a suggested change might be, "There are some areas for improvement in this product. In particular, I felt that the balance between quality and price was not right."

[0366] The corrected post is then sent from the device to the server again. The server reanalyzes the corrected post to ensure it does not contain any inappropriate language. If there are no problems, the server finally approves it and publishes it on social media and review sites. The device notifies the user that the post was successful.

[0367] Through this series of processes, the system helps users avoid inappropriate language and engage in safe and constructive exchanges of opinions, thereby preventing slanderous comments on the Internet and realizing a healthy communication environment.

[0368] The processing flow will be explained below.

[0369] Step 1:

[0370] A user creates a post on a social networking site or review site. The user enters the post content and clicks the post button to publish it.

[0371] Step 2:

[0372] The device temporarily saves the entered post content, and then when it detects a click on the post button, it sends the saved post content to the server.

[0373] Step 3:

[0374] The server receives the content posted from the device and sends it to a natural language processing engine for analysis.

[0375] Step 4:

[0376] The server's natural language processing engine analyzes the content of posts and scans them for slander or inappropriate language, such as "it's no good at all" or "it's not worth buying."

[0377] Step 5:

[0378] When an inappropriate expression is detected, the server generates a suggestion for correction based on the detected expression. For example, for the expression "not good at all," the server generates a suggestion for correction such as "Let me point out specifically what needs to be improved."

[0379] Step 6:

[0380] The server sends the generated revision suggestions to the terminal, which displays the revision suggestions to the user.

[0381] Step 7:

[0382] The user checks the suggested revisions and modifies the post. For example, they might change it to, "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was not right."

[0383] Step 8:

[0384] The device saves the revised post again and resends it to the server. The revised post is then sent to the server.

[0385] Step 9:

[0386] The server receives the revised post again and sends it to the natural language processing engine for re-analysis, which verifies that the revised post does not contain any inappropriate language.

[0387] Step 10:

[0388] If it is confirmed that the post does not contain any inappropriate content, the server will finally approve the post, which will then be published on social media and review sites.

[0389] Step 11:

[0390] The terminal displays a notification to the user that the posting was successful.

[0391] Example 1

[0392] 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."

[0393] The Internet is prone to abusive and inappropriate language, hindering healthy communication between users. Existing systems do not fully automate the process of detecting and correcting these inappropriate expressions, requiring a great deal of manual effort from users. Therefore, there is a need for a system that can efficiently detect inappropriate expressions in real time and provide appropriate correction suggestions.

[0394] 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.

[0395] In this invention, the server includes a means for allowing a user to create a post and send it to a terminal, a means for the terminal to send the post to the server, and a means for sending the post received by the server to a natural language processing engine for analysis. This allows the server to analyze the content when a user posts, detect slanderous or inappropriate language in real time, automatically generate correction suggestions, and present them to the user. This reduces the user's manual work and promotes healthy communication on the Internet.

[0396] "User" means a person who creates and submits Posts using the System.

[0397] "Terminal" refers to the device (e.g., PC, smartphone) used by a user to input post content and send it to the server.

[0398] "Server" refers to the computer system responsible for analyzing received posts, generating suggested revisions, and ultimately publishing the posts.

[0399] "Natural language processing engine" refers to a machine learning algorithm that analyzes posts, detects inappropriate language, and generates suggested edits.

[0400] "Posted content" refers to the text information that users send to social media or review sites.

[0401] "Profanity" refers to words or phrases that may offend others, including defamatory or offensive content.

[0402] "Suggested revisions" refers to feedback that provides advice on how to revise posts containing inappropriate language to more appropriate language.

[0403] "Parsing" refers to the process by which a natural language processing engine tokenizes a post and understands its grammatical structure and meaning.

[0404] "Publication" refers to posting the final approved post on social media or review sites on the Internet.

[0405] "Authentication Token" means a unique digital code used to authenticate API requests.

[0406] An "HTTP request" refers to a message used to send information from a terminal to a server using a web communication protocol.

[0407] "Tokenization" refers to the process by which a natural language processing engine breaks up a post into words and phrases.

[0408] "Feedback" refers to advice or suggestions for corrections provided to users regarding their posted content.

[0409] "Healthy communication" refers to a constructive and friendly exchange of information that is free from slander and inappropriate language.

[0410] A "machine learning model" refers to a system that uses algorithms to learn patterns from given data and perform classification and prediction.

[0411] As an embodiment of the present invention, a system for analyzing internet posts that detects inappropriate expressions and suggests corrections is shown below. This system is composed of a terminal where users input and send their posts, a server that receives and analyzes the posts and generates feedback, and a natural language processing engine.

[0412] First, a user creates a post on a social networking site or review site. The user then enters the post content using their own device (PC, smartphone, etc.) and clicks the "Post" button. At this point, the device sends the post content to the server.

[0413] The server then analyzes the received post. To do this, the server sends the post to a natural language processing engine. A specific example of a natural language processing engine is "OpenAI GPT-3." This engine analyzes the post and scans it for slander or inappropriate language.

[0414] For example, if a user posts "This product is completely useless. It's not worth buying," the device sends the content to the server. The server's natural language processing engine detects inappropriate expressions such as "completely useless" and "worthless." When an inappropriate expression is detected, the server generates a correction suggestion based on that. For example, in response to the expression "completely useless," the server suggests an amendment such as, "Let me point out specifically what needs to be improved."

[0415] Here are some example prompts for suggested revisions:

[0416] plaintext

[0417] When a user posts, "This product is completely useless. It's not worth buying," create a correction suggestion.

[0418] The server then sends these revision suggestions to the device, which then displays them to the user. The user accepts the suggestions and modifies the content of the post. For example, a suggested change might be, "There are some areas for improvement in this product. In particular, I felt that the balance between quality and price was not right."

[0419] The corrected post is then sent from the device to the server again. The server reanalyzes the corrected post to ensure it does not contain any inappropriate language. If there are no problems, the server finally approves it and publishes it on social media and review sites. The device notifies the user that the post was successful.

[0420] Through this series of processes, the system helps users avoid inappropriate language and engage in safe and constructive exchanges of opinions, thereby preventing slanderous comments on the Internet and realizing a healthy communication environment.

[0421] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0422] Step 1:

[0423] The user creates the content to post and sends it to the terminal.

[0424] Specific operation: The user enters the content to post on a social networking site or review site and clicks the "Post" button.

[0425] Input: Text data entered by the user.

[0426] Output: The post sent by the device.

[0427] Step 2:

[0428] The device sends the post content to the server.

[0429] Specific operation: The device uses an HTTP POST request to send the post content to the server.

[0430] Input: User-generated posts.

[0431] Output: The post sent to the server.

[0432] Step 3:

[0433] The server receives the post.

[0434] Specific operation: The server receives the post content at the configured API endpoint and temporarily stores it.

[0435] Input: Post content sent from the device.

[0436] Output: The post content ready to be sent to the natural language processing engine.

[0437] Step 4:

[0438] The server sends the received post content to a natural language processing engine for analysis.

[0439] How it works: The server formats the post and sends it via an API request to a natural language processing engine, which uses a machine learning model such as OpenAI GPT-3.

[0440] Input: The post received by the server.

[0441] Output: The post content sent to the natural language processing engine.

[0442] Step 5:

[0443] A natural language processing engine analyzes the content of posts and detects inappropriate expressions.

[0444] What it does: A natural language processing engine tokenizes posts, analyzes each word and phrase, and scans them for profanity.

[0445] Input: The post content sent from the server.

[0446] Output: Profanity analysis results.

[0447] Step 6:

[0448] The server generates correction suggestions based on the detected profanity.

[0449] Specific operation: The server creates a correction suggestion based on the analysis results returned by the natural language processing engine. The correction suggestion includes suggestions for improvement or more appropriate expression. For example, in response to "It's no good at all," the server might suggest, "Let me point out specifically what needs to be improved."

[0450] Input: Analysis results from the natural language processing engine.

[0451] Output: Fix suggestions.

[0452] Step 7:

[0453] The server sends the revision suggestions to the device.

[0454] Specific operation: The server sends the generated revision proposal to the terminal as an HTTP response.

[0455] Input: The generated correction suggestions.

[0456] Output: Correction suggestions sent to the terminal.

[0457] Step 8:

[0458] The terminal displays suggested revisions to the user.

[0459] Specific operation: The device displays the received correction suggestions on the screen. The user checks the correction suggestions and corrects the post content as necessary.

[0460] Input: The correction suggestion sent by the server.

[0461] Output: The correction suggestions displayed to the user.

[0462] Step 9:

[0463] The user accepts the suggested revision, corrects the post, and resubmits it.

[0464] Specific operation: After the user checks the suggested corrections, they modify the post as necessary and click the "Post" button again to submit the modified content.

[0465] Input: The user's modified post.

[0466] Output: The revised submission that was resubmitted.

[0467] Step 10:

[0468] The server then re-analyzes the revised post, verifies that it contains no inappropriate language, and finally approves and publishes it.

[0469] What happens: The server sends the revised post back to the natural language processing engine for a final check, confirming that it contains no inappropriate language, and finally approves it.

[0470] Input: The user's modified post.

[0471] Output: The published, approved post.

[0472] Step 11:

[0473] The terminal notifies the user that posting was successful.

[0474] Specific operation: The device receives a success response from the server and notifies the user that "the post was successful."

[0475] Input: A successful response from the server.

[0476] Output: A notification to the user that the submission was successful.

[0477] (Application example 1)

[0478] 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."

[0479] In today's online communications, many comments contain slander and inappropriate language. Comments on video streaming platforms, in particular, can cause trouble and misunderstandings among viewers. Providing a healthy communication environment by detecting such inappropriate comments in advance and correcting them appropriately is required. Furthermore, a system that can automate this process to reduce the burden on users and respond quickly is needed.

[0480] 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.

[0481] In this invention, the server includes means for analyzing posted content using a natural language processing engine to detect inappropriate expressions, means for generating correction suggestions using a generative AI model based on the detected inappropriate expressions and notifying the user, means for reanalyzing the corrected posted content and finally approving and publishing it if there are no problems, and means for monitoring the content of comments posted by users on a video distribution platform. This makes it possible to automatically detect inappropriate expressions in comments and generate correction suggestions, even on different types of posting platforms, thereby realizing a healthy communication environment.

[0482] "Inappropriate language" refers to inappropriate words or phrases that a user uses in their posts or comments that may slander or offend others.

[0483] A "natural language processing engine" refers to a collection of algorithms and software that analyzes input text data and understands its meaning and grammatical structure.

[0484] "Generative AI model" refers to an artificial intelligence model used to generate new text or suggested revisions based on input data and prompts.

[0485] "Suggested corrections" refer to specific suggestions for correcting detected inappropriate language to make it more appropriate and constructive.

[0486] "Terminal" refers to a device (smartphone, PC, tablet, etc.) that a user uses to input content for posting, connect to the Internet, and send it.

[0487] "Server" refers to a central computer that receives data sent by users and processes it, such as analyzing it, generating suggested modifications, reanalyzing it, and final approval.

[0488] "Video distribution platform" refers to a website or application that allows users to upload video content and allows other users to view and comment on it.

[0489] As an embodiment of the present invention, an analysis system for detecting inappropriate expressions and making correction suggestions will be described. This system detects inappropriate expressions in advance when a user posts a comment on a video distribution platform, and makes correction suggestions using a generative AI model.

[0490] The server first receives the comment content sent by the user. Because this comment content may contain inappropriate language, the server analyzes it using a natural language processing engine. The natural language processing engine scans the comment content and detects inappropriate language while referring to a defined list of inappropriate language.

[0491] For example, if a user sends a comment saying, "This video is completely no good," the server detects the inappropriate expression "completely no good." The server then uses a generative AI model to generate correction suggestions for the detected inappropriate expression. The generative AI model generates specific correction suggestions based on the prompt sentence.

[0492] Example prompt sentence:

[0493] text

[0494] Please turn your comments below into constructive words: This video is absolutely lame.

[0495] After the generative AI model generates correction suggestions, the server notifies the user of the suggestions. For example, a correction suggestion might be generated that says, "There are some areas for improvement in this video. In particular, I feel that the content and editing quality need to be improved."

[0496] When the user accepts the correction suggestion, corrects the comment, and resubmits it, the server again analyzes the corrected comment using a natural language processing engine to confirm that it does not contain any inappropriate language. If there are no problems, the server finally approves the comment and publishes it on the video distribution platform. This helps to provide a healthy communication environment.

[0497] This system is equipped with a network interface for exchanging data between the user-operated device and the server. The server uses OpenAI's API (e.g., text-davinci-003) as a generative AI model. The natural language processing engine includes commonly used Python libraries.

[0498] As described above, the system embodying the present invention automates comment moderation on video distribution platforms and converts inappropriate language into constructive language, thereby promoting healthy communication.

[0499] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0500] Step 1:

[0501] A user enters a comment into the comment section of a video distribution platform and submits it. A specific example of a user input might be, "This video is absolutely terrible." The input includes the comment text entered by the user. Receiving that comment text is the output.

[0502] Step 2:

[0503] The terminal sends the comment entered by the user to the server. The terminal communicates with the server using a network interface. The input includes the user's comment text, and the server receives the comment text as output.

[0504] Step 3:

[0505] The server passes the received comment text to a natural language processing engine to begin detecting profanity. The input includes the user's comment text. The server scans the text using a defined profanity list and extracts profanity. The output includes the presence or absence of profanity and its location.

[0506] Step 4:

[0507] The server uses a generative AI model to generate correction suggestions based on the detected inappropriate language. The input includes the comment text containing the inappropriate language and a prompt. For example, the prompt is "Please change the following comment to something more constructive: This video is completely bad." The generative AI model generates correction text based on this prompt, and the output includes specific correction suggestions.

[0508] Step 5:

[0509] The server sends the generated revision suggestions to the user's device. The input includes the revision suggestion text, and the user's device receives the suggestions as output. For example, a revision suggestion such as "There are some areas for improvement in this video. I feel that the content and editing quality in particular need to be improved."

[0510] Step 6:

[0511] The user checks the correction suggestions sent from the server, corrects the comment based on them, and resubmits it. The input includes the correction suggestion text, and the output is the corrected comment text sent from the terminal to the server. For example, the user may correct the comment by saying, "There are some areas for improvement in this video. I feel that the content and editing quality in particular need to be improved," and resubmit the comment.

[0512] Step 7:

[0513] The server analyzes the resubmitted corrected comment again using a natural language processing engine to check whether it contains any inappropriate language. The input contains the corrected comment text. The natural language processing engine reanalyzes the comment and outputs a result confirming that there are no problems.

[0514] Step 8:

[0515] The server finally approves the corrected comment and publishes it to the video streaming platform. The input includes the corrected comment text that was found to be correct by the reanalysis. The output is the comment text published on the video streaming platform. Finally, the device notifies the user that the comment was successfully posted.

[0516] The above is a specific processing procedure of the system for implementing the present invention.

[0517] 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.

[0518] As an embodiment of the present invention, a system for detecting inappropriate expressions from posted content, suggesting corrections, and recognizing the user's emotions is described below. This system analyzes the posted content entered by the user, detects inappropriate expressions such as slander, provides appropriate feedback, and suggests corrections based on emotions as necessary.

[0519] First, a user creates a post on a social networking site or review site. When the user clicks the post button, the device temporarily saves the post and sends it to the server. The server then sends the received post to a natural language processing engine and emotion engine for analysis.

[0520] The natural language processing engine analyzes the content of posts and scans them for slander or inappropriate language. For example, if a user posts something like, "This product is completely useless. It's not worth buying," the device sends the content to the server, where the server's natural language processing engine detects inappropriate language like "completely useless" or "worthless."

[0521] At the same time, an emotion engine recognizes the emotions in users' posts. The system distinguishes between positive, negative, and neutral emotions and customizes suggestions accordingly. For example, if the user expresses strong negative emotions, the system will suggest suggestions that encourage positive feedback, such as, "I'm not satisfied with this product. Specific improvements could be made to balance the quality and price."

[0522] Based on the detected inappropriate expressions and the recognized emotions, the server generates correction suggestions and notifies the user. For example, for the expression "not good at all," the server suggests "Let's point out specifically what needs to be improved," and further suggests "Negative emotions are being read. By calmly pointing out specific points, your opinion will be easier for the other person to understand."

[0523] The user can review the provided feedback and revise their post. For example, a suggested change might be, "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was not right."

[0524] The corrected post is saved again and resubmitted to the server. The server then reanalyzes the corrected post to ensure it does not contain any inappropriate content. If there are no problems, the server finally approves the post and publishes it on social media or review sites. The device then displays a notification to the user that the post was successful.

[0525] Through this series of processes, the system helps users avoid inappropriate language and engage in safe and constructive exchanges of opinions. Furthermore, by taking the user's feelings into consideration, the system can provide more effective feedback that reduces emotional burden. This can prevent slanderous comments on the Internet and create a healthy communication environment.

[0526] The processing flow will be explained below.

[0527] Understood. Now, the specific processing flow of the system is shown below.

[0528] Step 1:

[0529] A user creates a post on a social networking site or review site. The user enters the post content and clicks the post button to publish it.

[0530] Step 2:

[0531] The device temporarily saves the entered post content, and when it detects a click on the post button, it sends the saved post content to the server.

[0532] Step 3:

[0533] The server receives the content posted from the device, sends it to a natural language processing engine, and scans it for slander and inappropriate language.

[0534] Step 4:

[0535] The server's natural language processing engine analyzes the content of the post and detects inappropriate expressions such as "completely useless" and "worthless."

[0536] Step 5:

[0537] At the same time, the server's emotion engine analyzes the user's emotions from the posted content, distinguishing between positive, negative, and neutral emotions, and generating emotion information based on those.

[0538] Step 6:

[0539] The server combines the results of the natural language processing engine and the emotion engine to generate suggestions for correction based on the location of the inappropriate expression and the emotion. For example, in response to the expression "not good at all," the server will suggest, "Let's point out specifically what needs to be improved," and in response to negative emotions, it will generate additional feedback such as, "By calmly pointing out specific points, your opinion will be easier for the other person to understand."

[0540] Step 7:

[0541] The server sends the generated revision suggestions and emotional feedback to the device, which displays them to the user and provides an interface to encourage them to revise the posted content.

[0542] Step 8:

[0543] The user reviews the suggested revisions and emotional feedback and revises the post. For example, they might change it to, "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was not right."

[0544] Step 9:

[0545] The device saves the revised post again and resends it to the server. The revised post is then sent to the server.

[0546] Step 10:

[0547] The server receives the revised post again and sends it to the natural language processing engine for re-analysis, which verifies that the revised post does not contain any inappropriate language.

[0548] Step 11:

[0549] If it is confirmed that the post does not contain any inappropriate content, the server will finally approve the post, which will then be published on social media and review sites.

[0550] Step 12:

[0551] The terminal displays a notification to the user that the posting was successful.

[0552] The above are the specific processing steps of the present invention. Through this series of processes, constructive communication that avoids inappropriate expressions and takes into consideration the feelings of users is realized.

[0553] Example 2

[0554] 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."

[0555] Currently, communication on the Internet is rife with inappropriate language and slander, making it difficult to maintain a safe and healthy communication environment. Furthermore, current systems do not adequately analyze the emotions expressed in users' posts and provide appropriate feedback based on that analysis. To address these issues, a system is needed that can analyze users' posts, detect inappropriate language, and suggest corrections, as well as identify users' emotions from their posts and provide feedback based on that analysis.

[0556] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for analyzing the posted content using a natural language processing engine to detect inappropriate expressions, means for generating correction suggestions based on the detected inappropriate expressions and notifying the user, emotion engine means for identifying the user's emotions from the posted content, means for customizing the correction suggestions based on the emotions and notifying the user, and means for reanalyzing the corrected posted content and finally approving and publishing it if there are no problems. This allows users to avoid inappropriate expressions and engage in safe and constructive exchanges of opinions. Furthermore, providing feedback that takes the user's emotions into consideration can be more effective and reduce emotional burdens.

[0557] A "natural language processing engine" is software that analyzes posted content and detects inappropriate expressions and slander.

[0558] The "emotion engine" is software that identifies a user's emotions from the content of their posts and generates appropriate feedback based on those emotions.

[0559] "Inappropriate expressions" are problematic expressions contained in posts, such as slander, offensive language, and expressions that are socially unacceptable.

[0560] "Suggested corrections" are suggestions or feedback for improving the content of a post that are generated based on detected inappropriate expressions and provided to the user.

[0561] "Posted content" refers to text data that a user enters and attempts to send on a social networking site, review site, etc.

[0562] "User" means an individual or organization that creates posts on social media or review sites and sends them through the system.

[0563] A "terminal" is an electronic device that a user uses to input and send content to post.

[0564] A "server" is a central control device that receives posts, analyzes them, and generates feedback.

[0565] A "machine learning model" is an algorithmic system that is trained using past data to detect inappropriate language and recognize emotions with high accuracy.

[0566] "Reanalysis" is the process of reanalyzing the edited post to ensure it does not contain any inappropriate language.

[0567] "Publication" is the process of finally displaying the approved post on social media or review sites.

[0568] The system of this invention detects inappropriate expressions when users post to social networking sites or review sites, suggests corrections, and recognizes the user's emotions to provide appropriate feedback. This system consists of three main components: the user, the terminal, and the server.

[0569] First, a user enters text into a social networking site or review site and clicks the post button. The device temporarily saves the post and sends the text data to a server. The device can be a regular personal computer or smartphone.

[0570] The posted content received by the server is analyzed by a natural language processing engine and an emotion engine. The natural language processing engine scans the posted content based on a specific algorithm to detect whether it contains inappropriate language or slander. For example, if a user posts a sentence such as "This product is completely useless. Not worth buying," the engine will extract inappropriate language such as "completely useless" and "not worthless."

[0571] At the same time, a sentiment engine determines the user's sentiment from the post. This engine uses natural language processing technology to capture emotional nuances in the text. Sentiments are generally classified as positive, negative, or neutral. If the sentiment is strongly negative, the system suggests revising it to a more positive expression.

[0572] Based on the detected inappropriate expressions and emotions, the server generates correction suggestions and notifies the user. For example, for the expression "not good at all," the server may send suggestions such as "Let's point out specifically what needs to be improved" or "Negative emotions are being read. By calmly pointing out specific points, your opinion will be easier for the other person to understand." These suggestions are sent to the device and displayed to the user.

[0573] The user checks the suggested corrections and modifies the post. The modified text is then sent from the device to the server again. The server re-analyzes the text to ensure it does not contain any inappropriate language. If there are no problems, the server finally approves the post and publishes it on social media and review sites. The device then displays a notification to the user that the post was successful.

[0574] A specific example is shown below. If a user posts, "This product is completely useless. It's not worth buying," the inappropriate expressions "completely useless" and "worthless" are detected, and the server will suggest specific improvements to the user, such as, "Let's suggest specific areas for improvement. For example, 'There are several areas for improvement with this product. In particular, I felt that the balance between quality and price was not right.'"

[0575] This system allows users to avoid inappropriate language and engage in safe and constructive exchanges. Furthermore, by taking into account the user's feelings, it is possible to provide more effective and less emotionally taxing feedback.

[0576] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0577] Step 1: User creates and submits a post

[0578] Input: User-generated posts (e.g., "This product is completely useless. Not worth buying.")

[0579] How it works: A user enters the content to post on a social networking site or review site and clicks the post button.

[0580] Output: Text data that the device temporarily saves the posted content and sends it to the server

[0581] Step 2: Prepare the server to receive and parse the post

[0582] Input: Post content sent from the device (e.g., "This product is completely useless. It's not worth buying.")

[0583] How it works: The server accepts text data received from the device and prepares it to be sent to the natural language processing engine and emotion engine for analysis.

[0584] Output: Analysis requests to the natural language processing engine and emotion engine

[0585] Step 3: Detect profanity

[0586] Input: Post content (e.g. "This product is completely useless. Not worth buying.")

[0587] How it works: The server's natural language processing engine scans posts and detects inappropriate language based on specific algorithms.

[0588] Output: A list of detected profanities (e.g., "not good at all" or "worthless")

[0589] Step 4: Recognize emotions

[0590] Input: Post content (e.g. "This product is completely useless. Not worth buying.")

[0591] How it works: The server's emotion engine determines the emotion of the post, using an emotion recognition algorithm to classify it as positive, negative, or neutral.

[0592] Output: Emotional judgment result (e.g., negative)

[0593] Step 5: Generate and notify correction suggestions

[0594] Input: List of detected inappropriate expressions (e.g., "not good at all" or "worthless"), emotion judgment result (e.g., negative)

[0595] How it works: The server generates suggestions based on the inappropriate expressions and the emotions they identify. For example, it generates suggestions such as "Point out specific areas that need improvement" and "By calmly pointing out specific points, your opinion will be easier for the other person to understand."

[0596] Output: Generated suggestions for correction (e.g., "There are some areas for improvement in this product. In particular, I felt that the balance between quality and price was not right.")

[0597] Step 6: User reviews the suggested revisions and edits their post

[0598] Input: The suggested revisions displayed on the device (e.g., "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was not right.")

[0599] Action: The user reviews the suggested revisions and corrects their post.

[0600] Output: Revised post (e.g. "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was off.")

[0601] Step 7: Resubmit your revised submission

[0602] Input: Revised post (e.g., "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was off.")

[0603] How it works: The user resubmits the revised post from their device to the server.

[0604] Output: Resent post

[0605] Step 8: The server re-parses the revised post

[0606] Input: Resent post (e.g., "This product has some room for improvement. In particular, I felt the balance between quality and price was off.")

[0607] Action: The server reparses the page to ensure it does not contain profanity.

[0608] Output: Analysis results (no problems)

[0609] Step 9: Approve and publish your submission

[0610] Input: Analysis results (no problems)

[0611] How it works: The server ultimately approves the post and publishes it on social media and review sites.

[0612] Output: Published post

[0613] Step 10: Notification of successful submission

[0614] Input: Published post

[0615] Behavior: The device displays a notification to the user that the post was successful.

[0616] Output: Submission success notification message

[0617] (Application example 2)

[0618] 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."

[0619] Conventional systems are limited to detecting inappropriate content in user posts and are unable to provide correction suggestions that fully consider the user's feelings. This can lead to an increase in inappropriate posts by users, potentially damaging the integrity of online platforms. Furthermore, even if inappropriate content is corrected, suggestions that are not based on the user's feelings make it difficult to improve the quality of the posts.

[0620] 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.

[0621] In this invention, the server includes means for analyzing posted content using a natural language processing engine to detect inappropriate expressions, means for generating revision suggestions based on the detected inappropriate expressions and the user's emotions and notifying the user, means for reanalyzing the revised posted content and finally approving and publishing it if there are no problems, and means for using a machine learning model to detect inappropriate expressions and analyze emotions. This makes it possible to generate appropriate revision suggestions that take user emotions into consideration, improve the quality of posted content on online platforms, and maintain a healthy communication environment.

[0622] A "natural language processing engine" is a technology for analyzing text data and understanding its meaning and context.

[0623] "Profanity" refers to words or phrases that are hurtful, offensive or discriminatory.

[0624] "Correction suggestions" refer to suggestions for improvements or alternative expressions provided to users in response to detected inappropriate expressions.

[0625] "User emotions" refers to the classification of emotions that users put into the content they post, such as positive, negative, or neutral.

[0626] A "machine learning model" is an algorithm that is trained using large amounts of data to automatically perform a specific task.

[0627] "Terminal" refers to a device through which a user inputs data and communicates with a server.

[0628] "Server means" refers to a computer system that processes and stores data in response to requests from clients.

[0629] As an embodiment of the present invention, a system incorporating a review management application for an online shopping site will be described. This system detects inappropriate expressions, analyzes user sentiment, and makes appropriate correction suggestions.

[0630] First, a user enters a product review using a review management application on their smartphone. When the user clicks the submit button, the review content is temporarily saved and sent to a server. The server receives the review content and sends it to a natural language processing engine. This natural language processing engine runs an algorithm using a machine learning model to detect inappropriate language. If inappropriate language is detected, further sentiment analysis is performed to determine whether the review content should be classified as positive, negative, or neutral.

[0631] Based on the detected inappropriate expressions and the results of the sentiment analysis, the server generates a correction suggestion. The suggestion is notified to the user and displayed on the smartphone screen. The user can confirm the suggested correction and revise the review. The revised review content is sent back to the server for re-analysis. If this re-analysis confirms that no inappropriate expressions are present, the review is finally approved and published on the online shopping site. After publication, the user receives a notification on their smartphone that the review has been successfully posted.

[0632] Each process in this system uses the Python programming language and the TextBlob library, which includes a natural language processing engine and the NaiveBayesAnalyzer for sentiment analysis, to detect profanity and perform sentiment analysis.

[0633] As a specific example, consider the case where a user enters a review as follows:

[0634] "This product is completely useless. Not worth buying."

[0635] When this review is submitted, the server detects inappropriate expressions such as "not good at all" and "not worth it" and determines the sentiment as "negative." As a result, the following sentence is displayed as a suggestion for correction:

[0636] "Point out specifically what needs to be improved." "By calmly pointing out specific points, your opinion will be easier for the other person to understand."

[0637] An example of a prompt is as follows:

[0638] "Analyze user-written reviews like 'This product is terrible. Not worth buying.' and generate correction suggestions based on profanity and sentiment."

[0639] These processes enable users to communicate in a healthy manner and improve the quality of reviews on online shopping sites, which is expected to maintain the integrity of online platforms.

[0640] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0641] Step 1:

[0642] User enters and submits review

[0643] The user enters a review into the smartphone application and presses the submit button. The review content is received as text data. This text data is temporarily stored and sent to the server.

[0644] Step 2:

[0645] The server receives the review content.

[0646] The server receives the text data of the review content sent by the user. The text data from the user is received as input. This prepares the server for the next analysis process.

[0647] Step 3:

[0648] Analysis of review content using a natural language processing engine

[0649] The server's natural language processing engine analyzes the review content. The input to be analyzed is the received text data. Specifically, the text data is scanned to see if it contains any inappropriate language. An AI model is then used to detect inappropriate language. The detection results are output as a list of inappropriate language.

[0650] Step 4:

[0651] Emotional analysis of users using an emotion engine

[0652] The server's emotion engine analyzes the sentiment of text data. It receives the text data of the review as input. Specifically, it uses the NaiveBayesAnalyzer from the TextBlob library to classify the sentiment of the text as positive, negative, or neutral. The result is the type of sentiment.

[0653] Step 5:

[0654] Generate correction suggestions

[0655] The server generates correction suggestions based on the detected inappropriate expressions and the results of sentiment analysis. It receives a list of inappropriate expressions and the type of sentiment as input. Specifically, it retrieves improvement suggestions corresponding to the detected inappropriate expressions from a dictionary and generates additional suggestions according to the sentiment. The output is the text data of the correction suggestions.

[0656] Step 6:

[0657] Notification of proposed amendments

[0658] The server notifies the user of the revision suggestions it has generated. It receives the text data of the revision suggestions as input. Specifically, it sends a notification message to the smartphone application, which displays it on the screen. The output is the text of the revision suggestions that the user can confirm.

[0659] Step 7:

[0660] User Review Modifications

[0661] The system corrects the review content based on the correction suggestions provided by the user. The system receives correction suggestions from the server as input. The specific operation is that the user corrects the review on their smartphone and submits it again. The corrected text data is output.

[0662] Step 8:

[0663] Re-analysis of revised review content

[0664] The server re-analyzes the revised review content, receiving the revised text data as input. Specifically, it uses the natural language processing engine again to check whether inappropriate language is included. As a result of this check, the review content that does not contain inappropriate language is output.

[0665] Step 9:

[0666] Final review approval and publication

[0667] The server finally approves the corrected review and publishes it on the shopping site. The input is the verified text data. The specific operation is to save the review in the database and display it on the public page of the shopping site. The output is the review published on the shopping site.

[0668] Step 10:

[0669] Review submission success notification

[0670] The device notifies the user that the review has been successfully posted. The input is a notification of the completion of publishing from the server. Specifically, the smartphone application displays a notification message on the screen. The output is a message to the user that the review has been successfully posted.

[0671] 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.

[0672] 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.

[0673] 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.

[0674] [Third embodiment]

[0675] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0676] 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.

[0677] 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).

[0678] 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.

[0679] 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.

[0680] 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).

[0681] 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.

[0682] 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.

[0683] 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.

[0684] 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.

[0685] 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.

[0686] 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."

[0687] As an embodiment of the present invention, a system for analyzing internet posts that detects inappropriate expressions and suggests corrections is shown below. This system is composed of a terminal where users input and send their posts, a server that receives and analyzes the posts and generates feedback, and a natural language processing engine.

[0688] First, a user creates a post on a social networking site or review site. When the user clicks the post button, the device sends the post to the server. The server then sends the received post to a natural language processing engine for analysis. The natural language processing engine analyzes the post and scans it for slander or inappropriate language.

[0689] For example, if a user posts "This product is completely useless. It's not worth buying," the device sends the content to the server. The server's natural language processing engine detects inappropriate expressions such as "completely useless" and "worthless." When an inappropriate expression is detected, the server generates a correction suggestion based on that. For example, in response to the expression "completely useless," the server suggests an amendment such as, "Let me point out specifically what needs to be improved."

[0690] The server then sends these revision suggestions to the device, which then displays them to the user. The user accepts the suggestions and modifies the content of the post. For example, a suggested change might be, "There are some areas for improvement in this product. In particular, I felt that the balance between quality and price was not right."

[0691] The corrected post is then sent from the device to the server again. The server reanalyzes the corrected post to ensure it does not contain any inappropriate language. If there are no problems, the server finally approves it and publishes it on social media and review sites. The device notifies the user that the post was successful.

[0692] Through this series of processes, the system helps users avoid inappropriate language and engage in safe and constructive exchanges of opinions, thereby preventing slanderous comments on the Internet and realizing a healthy communication environment.

[0693] The processing flow will be explained below.

[0694] Step 1:

[0695] A user creates a post on a social networking site or review site. The user enters the post content and clicks the post button to publish it.

[0696] Step 2:

[0697] The device temporarily saves the entered post content, and then when it detects a click on the post button, it sends the saved post content to the server.

[0698] Step 3:

[0699] The server receives the content posted from the device and sends it to a natural language processing engine for analysis.

[0700] Step 4:

[0701] The server's natural language processing engine analyzes the content of posts and scans them for slander or inappropriate language, such as "it's no good at all" or "it's not worth buying."

[0702] Step 5:

[0703] When an inappropriate expression is detected, the server generates a suggestion for correction based on the detected expression. For example, for the expression "not good at all," the server generates a suggestion for correction such as "Let me point out specifically what needs to be improved."

[0704] Step 6:

[0705] The server sends the generated revision suggestions to the terminal, which displays the revision suggestions to the user.

[0706] Step 7:

[0707] The user checks the suggested revisions and modifies the post. For example, they might change it to, "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was not right."

[0708] Step 8:

[0709] The device saves the revised post again and resends it to the server. The revised post is then sent to the server.

[0710] Step 9:

[0711] The server receives the revised post again and sends it to the natural language processing engine for re-analysis, which verifies that the revised post does not contain any inappropriate language.

[0712] Step 10:

[0713] If it is confirmed that the post does not contain any inappropriate content, the server will finally approve the post, which will then be published on social media and review sites.

[0714] Step 11:

[0715] The terminal displays a notification to the user that the posting was successful.

[0716] Example 1

[0717] 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."

[0718] The Internet is prone to abusive and inappropriate language, hindering healthy communication between users. Existing systems do not fully automate the process of detecting and correcting these inappropriate expressions, requiring a great deal of manual effort from users. Therefore, there is a need for a system that can efficiently detect inappropriate expressions in real time and provide appropriate correction suggestions.

[0719] 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.

[0720] In this invention, the server includes a means for allowing a user to create a post and send it to a terminal, a means for the terminal to send the post to the server, and a means for sending the post received by the server to a natural language processing engine for analysis. This allows the server to analyze the content when a user posts, detect slanderous or inappropriate language in real time, automatically generate correction suggestions, and present them to the user. This reduces the user's manual work and promotes healthy communication on the Internet.

[0721] "User" means a person who creates and submits Posts using the System.

[0722] "Terminal" refers to the device (e.g., PC, smartphone) used by a user to input post content and send it to the server.

[0723] "Server" refers to the computer system responsible for analyzing received posts, generating suggested revisions, and ultimately publishing the posts.

[0724] "Natural language processing engine" refers to a machine learning algorithm that analyzes posts, detects inappropriate language, and generates suggested edits.

[0725] "Posted content" refers to the text information that users send to social media or review sites.

[0726] "Profanity" refers to words or phrases that may offend others, including defamatory or offensive content.

[0727] "Suggested revisions" refers to feedback that provides advice on how to revise posts containing inappropriate language to more appropriate language.

[0728] "Parsing" refers to the process by which a natural language processing engine tokenizes a post and understands its grammatical structure and meaning.

[0729] "Publication" refers to posting the final approved post on social media or review sites on the Internet.

[0730] "Authentication Token" means a unique digital code used to authenticate API requests.

[0731] An "HTTP request" refers to a message used to send information from a terminal to a server using a web communication protocol.

[0732] "Tokenization" refers to the process by which a natural language processing engine breaks up a post into words and phrases.

[0733] "Feedback" refers to advice or suggestions for corrections provided to users regarding their posted content.

[0734] "Healthy communication" refers to a constructive and friendly exchange of information that is free from slander and inappropriate language.

[0735] A "machine learning model" refers to a system that uses algorithms to learn patterns from given data and perform classification and prediction.

[0736] As an embodiment of the present invention, a system for analyzing internet posts that detects inappropriate expressions and suggests corrections is shown below. This system is composed of a terminal where users input and send their posts, a server that receives and analyzes the posts and generates feedback, and a natural language processing engine.

[0737] First, a user creates a post on a social networking site or review site. The user then enters the post content using their own device (PC, smartphone, etc.) and clicks the "Post" button. At this point, the device sends the post content to the server.

[0738] The server then analyzes the received post. To do this, the server sends the post to a natural language processing engine. A specific example of a natural language processing engine is "OpenAI GPT-3." This engine analyzes the post and scans it for slander or inappropriate language.

[0739] For example, if a user posts "This product is completely useless. It's not worth buying," the device sends the content to the server. The server's natural language processing engine detects inappropriate expressions such as "completely useless" and "worthless." When an inappropriate expression is detected, the server generates a correction suggestion based on that. For example, in response to the expression "completely useless," the server suggests an amendment such as, "Let me point out specifically what needs to be improved."

[0740] Here are some example prompts for suggested revisions:

[0741] plaintext

[0742] When a user posts, "This product is completely useless. It's not worth buying," create a correction suggestion.

[0743] The server then sends these revision suggestions to the device, which then displays them to the user. The user accepts the suggestions and modifies the content of the post. For example, a suggested change might be, "There are some areas for improvement in this product. In particular, I felt that the balance between quality and price was not right."

[0744] The corrected post is then sent from the device to the server again. The server reanalyzes the corrected post to ensure it does not contain any inappropriate language. If there are no problems, the server finally approves it and publishes it on social media and review sites. The device notifies the user that the post was successful.

[0745] Through this series of processes, the system helps users avoid inappropriate language and engage in safe and constructive exchanges of opinions, thereby preventing slanderous comments on the Internet and realizing a healthy communication environment.

[0746] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0747] Step 1:

[0748] The user creates the content to post and sends it to the terminal.

[0749] Specific operation: The user enters the content to post on a social networking site or review site and clicks the "Post" button.

[0750] Input: Text data entered by the user.

[0751] Output: The post sent by the device.

[0752] Step 2:

[0753] The device sends the post content to the server.

[0754] Specific operation: The device uses an HTTP POST request to send the post content to the server.

[0755] Input: User-generated posts.

[0756] Output: The post sent to the server.

[0757] Step 3:

[0758] The server receives the post.

[0759] Specific operation: The server receives the post content at the configured API endpoint and temporarily stores it.

[0760] Input: Post content sent from the device.

[0761] Output: The post content ready to be sent to the natural language processing engine.

[0762] Step 4:

[0763] The server sends the received post content to a natural language processing engine for analysis.

[0764] How it works: The server formats the post and sends it via an API request to a natural language processing engine, which uses a machine learning model such as OpenAI GPT-3.

[0765] Input: The post received by the server.

[0766] Output: The post content sent to the natural language processing engine.

[0767] Step 5:

[0768] A natural language processing engine analyzes the content of posts and detects inappropriate expressions.

[0769] What it does: A natural language processing engine tokenizes posts, analyzes each word and phrase, and scans them for profanity.

[0770] Input: The post content sent from the server.

[0771] Output: Profanity analysis results.

[0772] Step 6:

[0773] The server generates correction suggestions based on the detected profanity.

[0774] Specific operation: The server creates a correction suggestion based on the analysis results returned by the natural language processing engine. The correction suggestion includes suggestions for improvement or more appropriate expression. For example, in response to "It's no good at all," the server might suggest, "Let me point out specifically what needs to be improved."

[0775] Input: Analysis results from the natural language processing engine.

[0776] Output: Fix suggestions.

[0777] Step 7:

[0778] The server sends the revision suggestions to the device.

[0779] Specific operation: The server sends the generated revision proposal to the terminal as an HTTP response.

[0780] Input: The generated correction suggestions.

[0781] Output: Correction suggestions sent to the terminal.

[0782] Step 8:

[0783] The terminal displays suggested revisions to the user.

[0784] Specific operation: The device displays the received correction suggestions on the screen. The user checks the correction suggestions and corrects the post content as necessary.

[0785] Input: The correction suggestion sent by the server.

[0786] Output: The correction suggestions displayed to the user.

[0787] Step 9:

[0788] The user accepts the suggested revision, corrects the post, and resubmits it.

[0789] Specific operation: After the user checks the suggested corrections, they modify the post as necessary and click the "Post" button again to submit the modified content.

[0790] Input: The user's modified post.

[0791] Output: The revised submission that was resubmitted.

[0792] Step 10:

[0793] The server then re-analyzes the revised post, verifies that it contains no inappropriate language, and finally approves and publishes it.

[0794] What happens: The server sends the revised post back to the natural language processing engine for a final check, confirming that it contains no inappropriate language, and finally approves it.

[0795] Input: The user's modified post.

[0796] Output: The published, approved post.

[0797] Step 11:

[0798] The terminal notifies the user that posting was successful.

[0799] Specific operation: The device receives a success response from the server and notifies the user that "the post was successful."

[0800] Input: A successful response from the server.

[0801] Output: A notification to the user that the submission was successful.

[0802] (Application example 1)

[0803] 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."

[0804] In today's online communications, many comments contain slander and inappropriate language. Comments on video streaming platforms, in particular, can cause trouble and misunderstandings among viewers. Providing a healthy communication environment by detecting such inappropriate comments in advance and correcting them appropriately is required. Furthermore, a system that can automate this process to reduce the burden on users and respond quickly is needed.

[0805] 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.

[0806] In this invention, the server includes means for analyzing posted content using a natural language processing engine to detect inappropriate expressions, means for generating correction suggestions using a generative AI model based on the detected inappropriate expressions and notifying the user, means for reanalyzing the corrected posted content and finally approving and publishing it if there are no problems, and means for monitoring the content of comments posted by users on a video distribution platform. This makes it possible to automatically detect inappropriate expressions in comments and generate correction suggestions, even on different types of posting platforms, thereby realizing a healthy communication environment.

[0807] "Inappropriate language" refers to inappropriate words or phrases that a user uses in their posts or comments that may slander or offend others.

[0808] A "natural language processing engine" refers to a collection of algorithms and software that analyzes input text data and understands its meaning and grammatical structure.

[0809] "Generative AI model" refers to an artificial intelligence model used to generate new text or suggested revisions based on input data and prompts.

[0810] "Suggested corrections" refer to specific suggestions for correcting detected inappropriate language to make it more appropriate and constructive.

[0811] "Terminal" refers to a device (smartphone, PC, tablet, etc.) that a user uses to input content for posting, connect to the Internet, and send it.

[0812] "Server" refers to a central computer that receives data sent by users and processes it, such as analyzing it, generating suggested modifications, reanalyzing it, and final approval.

[0813] "Video distribution platform" refers to a website or application that allows users to upload video content and allows other users to view and comment on it.

[0814] As an embodiment of the present invention, an analysis system for detecting inappropriate expressions and making correction suggestions will be described. This system detects inappropriate expressions in advance when a user posts a comment on a video distribution platform, and makes correction suggestions using a generative AI model.

[0815] The server first receives the comment content sent by the user. Because this comment content may contain inappropriate language, the server analyzes it using a natural language processing engine. The natural language processing engine scans the comment content and detects inappropriate language while referring to a defined list of inappropriate language.

[0816] For example, if a user sends a comment saying, "This video is completely no good," the server detects the inappropriate expression "completely no good." The server then uses a generative AI model to generate correction suggestions for the detected inappropriate expression. The generative AI model generates specific correction suggestions based on the prompt sentence.

[0817] Example prompt sentence:

[0818] text

[0819] Please turn your comments below into constructive words: This video is absolutely lame.

[0820] After the generative AI model generates correction suggestions, the server notifies the user of the suggestions. For example, a correction suggestion might be generated that says, "There are some areas for improvement in this video. In particular, I feel that the content and editing quality need to be improved."

[0821] When the user accepts the correction suggestion, corrects the comment, and resubmits it, the server again analyzes the corrected comment using a natural language processing engine to confirm that it does not contain any inappropriate language. If there are no problems, the server finally approves the comment and publishes it on the video distribution platform. This helps to provide a healthy communication environment.

[0822] This system is equipped with a network interface for exchanging data between the user-operated device and the server. The server uses OpenAI's API (e.g., text-davinci-003) as a generative AI model. The natural language processing engine includes commonly used Python libraries.

[0823] As described above, the system embodying the present invention automates comment moderation on video distribution platforms and converts inappropriate language into constructive language, thereby promoting healthy communication.

[0824] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0825] Step 1:

[0826] A user enters a comment into the comment section of a video distribution platform and submits it. A specific example of a user input might be, "This video is absolutely terrible." The input includes the comment text entered by the user. Receiving that comment text is the output.

[0827] Step 2:

[0828] The terminal sends the comment entered by the user to the server. The terminal communicates with the server using a network interface. The input includes the user's comment text, and the server receives the comment text as output.

[0829] Step 3:

[0830] The server passes the received comment text to a natural language processing engine to begin detecting profanity. The input includes the user's comment text. The server scans the text using a defined profanity list and extracts profanity. The output includes the presence or absence of profanity and its location.

[0831] Step 4:

[0832] The server uses a generative AI model to generate correction suggestions based on the detected inappropriate language. The input includes the comment text containing the inappropriate language and a prompt. For example, the prompt is "Please change the following comment to something more constructive: This video is completely bad." The generative AI model generates correction text based on this prompt, and the output includes specific correction suggestions.

[0833] Step 5:

[0834] The server sends the generated revision suggestions to the user's device. The input includes the revision suggestion text, and the user's device receives the suggestions as output. For example, a revision suggestion such as "There are some areas for improvement in this video. I feel that the content and editing quality in particular need to be improved."

[0835] Step 6:

[0836] The user checks the correction suggestions sent from the server, corrects the comment based on them, and resubmits it. The input includes the correction suggestion text, and the output is the corrected comment text sent from the terminal to the server. For example, the user may correct the comment by saying, "There are some areas for improvement in this video. I feel that the content and editing quality in particular need to be improved," and resubmit the comment.

[0837] Step 7:

[0838] The server analyzes the resubmitted corrected comment again using a natural language processing engine to check whether it contains any inappropriate language. The input contains the corrected comment text. The natural language processing engine reanalyzes the comment and outputs a result confirming that there are no problems.

[0839] Step 8:

[0840] The server finally approves the corrected comment and publishes it to the video streaming platform. The input includes the corrected comment text that was found to be correct by the reanalysis. The output is the comment text published on the video streaming platform. Finally, the device notifies the user that the comment was successfully posted.

[0841] The above is a specific processing procedure of the system for implementing the present invention.

[0842] 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.

[0843] As an embodiment of the present invention, a system for detecting inappropriate expressions from posted content, suggesting corrections, and recognizing the user's emotions is described below. This system analyzes the posted content entered by the user, detects inappropriate expressions such as slander, provides appropriate feedback, and suggests corrections based on emotions as necessary.

[0844] First, a user creates a post on a social networking site or review site. When the user clicks the post button, the device temporarily saves the post and sends it to the server. The server then sends the received post to a natural language processing engine and emotion engine for analysis.

[0845] The natural language processing engine analyzes the content of posts and scans them for slander or inappropriate language. For example, if a user posts something like, "This product is completely useless. It's not worth buying," the device sends the content to the server, where the server's natural language processing engine detects inappropriate language like "completely useless" or "worthless."

[0846] At the same time, an emotion engine recognizes the emotions in users' posts. The system distinguishes between positive, negative, and neutral emotions and customizes suggestions accordingly. For example, if the user expresses strong negative emotions, the system will suggest suggestions that encourage positive feedback, such as, "I'm not satisfied with this product. Specific improvements could be made to balance the quality and price."

[0847] Based on the detected inappropriate expressions and the recognized emotions, the server generates correction suggestions and notifies the user. For example, for the expression "not good at all," the server suggests "Let's point out specifically what needs to be improved," and further suggests "Negative emotions are being read. By calmly pointing out specific points, your opinion will be easier for the other person to understand."

[0848] The user can review the provided feedback and revise their post. For example, a suggested change might be, "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was not right."

[0849] The corrected post is saved again and resubmitted to the server. The server then reanalyzes the corrected post to ensure it does not contain any inappropriate content. If there are no problems, the server finally approves the post and publishes it on social media or review sites. The device then displays a notification to the user that the post was successful.

[0850] Through this series of processes, the system helps users avoid inappropriate language and engage in safe and constructive exchanges of opinions. Furthermore, by taking the user's feelings into consideration, the system can provide more effective feedback that reduces emotional burden. This can prevent slanderous comments on the Internet and create a healthy communication environment.

[0851] The processing flow will be explained below.

[0852] Understood. Now, the specific processing flow of the system is shown below.

[0853] Step 1:

[0854] A user creates a post on a social networking site or review site. The user enters the post content and clicks the post button to publish it.

[0855] Step 2:

[0856] The device temporarily saves the entered post content, and when it detects a click on the post button, it sends the saved post content to the server.

[0857] Step 3:

[0858] The server receives the content posted from the device, sends it to a natural language processing engine, and scans it for slander and inappropriate language.

[0859] Step 4:

[0860] The server's natural language processing engine analyzes the content of the post and detects inappropriate expressions such as "completely useless" and "worthless."

[0861] Step 5:

[0862] At the same time, the server's emotion engine analyzes the user's emotions from the posted content, distinguishing between positive, negative, and neutral emotions, and generating emotion information based on those.

[0863] Step 6:

[0864] The server combines the results of the natural language processing engine and the emotion engine to generate suggestions for correction based on the location of the inappropriate expression and the emotion. For example, in response to the expression "not good at all," the server will suggest, "Let's point out specifically what needs to be improved," and in response to negative emotions, it will generate additional feedback such as, "By calmly pointing out specific points, your opinion will be easier for the other person to understand."

[0865] Step 7:

[0866] The server sends the generated revision suggestions and emotional feedback to the device, which displays them to the user and provides an interface to encourage them to revise the posted content.

[0867] Step 8:

[0868] The user reviews the suggested revisions and emotional feedback and revises the post. For example, they might change it to, "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was not right."

[0869] Step 9:

[0870] The device saves the revised post again and resends it to the server. The revised post is then sent to the server.

[0871] Step 10:

[0872] The server receives the revised post again and sends it to the natural language processing engine for re-analysis, which verifies that the revised post does not contain any inappropriate language.

[0873] Step 11:

[0874] If it is confirmed that the post does not contain any inappropriate content, the server will finally approve the post, which will then be published on social media and review sites.

[0875] Step 12:

[0876] The terminal displays a notification to the user that the posting was successful.

[0877] The above are the specific processing steps of the present invention. Through this series of processes, constructive communication that avoids inappropriate expressions and takes into consideration the feelings of users is realized.

[0878] Example 2

[0879] 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."

[0880] Currently, communication on the Internet is rife with inappropriate language and slander, making it difficult to maintain a safe and healthy communication environment. Furthermore, current systems do not adequately analyze the emotions expressed in users' posts and provide appropriate feedback based on that analysis. To address these issues, a system is needed that can analyze users' posts, detect inappropriate language, and suggest corrections, as well as identify users' emotions from their posts and provide feedback based on that analysis.

[0881] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for analyzing the posted content using a natural language processing engine to detect inappropriate expressions, means for generating correction suggestions based on the detected inappropriate expressions and notifying the user, emotion engine means for identifying the user's emotions from the posted content, means for customizing the correction suggestions based on the emotions and notifying the user, and means for reanalyzing the corrected posted content and finally approving and publishing it if there are no problems. This allows users to avoid inappropriate expressions and engage in safe and constructive exchanges of opinions. Furthermore, providing feedback that takes the user's emotions into consideration can be more effective and reduce emotional burdens.

[0882] A "natural language processing engine" is software that analyzes posted content and detects inappropriate expressions and slander.

[0883] The "emotion engine" is software that identifies a user's emotions from the content of their posts and generates appropriate feedback based on those emotions.

[0884] "Inappropriate expressions" are problematic expressions contained in posts, such as slander, offensive language, and expressions that are socially unacceptable.

[0885] "Suggested corrections" are suggestions or feedback for improving the content of a post that are generated based on detected inappropriate expressions and provided to the user.

[0886] "Posted content" refers to text data that a user enters and attempts to send on a social networking site, review site, etc.

[0887] "User" means an individual or organization that creates posts on social media or review sites and sends them through the system.

[0888] A "terminal" is an electronic device that a user uses to input and send content to post.

[0889] A "server" is a central control device that receives posts, analyzes them, and generates feedback.

[0890] A "machine learning model" is an algorithmic system that is trained using past data to detect inappropriate language and recognize emotions with high accuracy.

[0891] "Reanalysis" is the process of reanalyzing the edited post to ensure it does not contain any inappropriate language.

[0892] "Publication" is the process of finally displaying the approved post on social media or review sites.

[0893] The system of this invention detects inappropriate expressions when users post to social networking sites or review sites, suggests corrections, and recognizes the user's emotions to provide appropriate feedback. This system consists of three main components: the user, the terminal, and the server.

[0894] First, a user enters text into a social networking site or review site and clicks the post button. The device temporarily saves the post and sends the text data to a server. The device can be a regular personal computer or smartphone.

[0895] The posted content received by the server is analyzed by a natural language processing engine and an emotion engine. The natural language processing engine scans the posted content based on a specific algorithm to detect whether it contains inappropriate language or slander. For example, if a user posts a sentence such as "This product is completely useless. Not worth buying," the engine will extract inappropriate language such as "completely useless" and "not worthless."

[0896] At the same time, a sentiment engine determines the user's sentiment from the post. This engine uses natural language processing technology to capture emotional nuances in the text. Sentiments are generally classified as positive, negative, or neutral. If the sentiment is strongly negative, the system suggests revising it to a more positive expression.

[0897] Based on the detected inappropriate expressions and emotions, the server generates correction suggestions and notifies the user. For example, for the expression "not good at all," the server may send suggestions such as "Let's point out specifically what needs to be improved" or "Negative emotions are being read. By calmly pointing out specific points, your opinion will be easier for the other person to understand." These suggestions are sent to the device and displayed to the user.

[0898] The user checks the suggested corrections and modifies the post. The modified text is then sent from the device to the server again. The server re-analyzes the text to ensure it does not contain any inappropriate language. If there are no problems, the server finally approves the post and publishes it on social media and review sites. The device then displays a notification to the user that the post was successful.

[0899] A specific example is shown below. If a user posts, "This product is completely useless. It's not worth buying," the inappropriate expressions "completely useless" and "worthless" are detected, and the server will suggest specific improvements to the user, such as, "Let's suggest specific areas for improvement. For example, 'There are several areas for improvement with this product. In particular, I felt that the balance between quality and price was not right.'"

[0900] This system allows users to avoid inappropriate language and engage in safe and constructive exchanges. Furthermore, by taking into account the user's feelings, it is possible to provide more effective and less emotionally taxing feedback.

[0901] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0902] Step 1: User creates and submits a post

[0903] Input: User-generated posts (e.g., "This product is completely useless. Not worth buying.")

[0904] How it works: A user enters the content to post on a social networking site or review site and clicks the post button.

[0905] Output: Text data that the device temporarily saves the posted content and sends it to the server

[0906] Step 2: Prepare the server to receive and parse the post

[0907] Input: Post content sent from the device (e.g., "This product is completely useless. It's not worth buying.")

[0908] How it works: The server accepts text data received from the device and prepares it to be sent to the natural language processing engine and emotion engine for analysis.

[0909] Output: Analysis requests to the natural language processing engine and emotion engine

[0910] Step 3: Detect profanity

[0911] Input: Post content (e.g. "This product is completely useless. Not worth buying.")

[0912] How it works: The server's natural language processing engine scans posts and detects inappropriate language based on specific algorithms.

[0913] Output: A list of detected profanities (e.g., "not good at all" or "worthless")

[0914] Step 4: Recognize emotions

[0915] Input: Post content (e.g. "This product is completely useless. Not worth buying.")

[0916] How it works: The server's emotion engine determines the emotion of the post, using an emotion recognition algorithm to classify it as positive, negative, or neutral.

[0917] Output: Emotional judgment result (e.g., negative)

[0918] Step 5: Generate and notify correction suggestions

[0919] Input: List of detected inappropriate expressions (e.g., "not good at all" or "worthless"), emotion judgment result (e.g., negative)

[0920] How it works: The server generates suggestions based on the inappropriate expressions and the emotions they identify. For example, it generates suggestions such as "Point out specific areas that need improvement" and "By calmly pointing out specific points, your opinion will be easier for the other person to understand."

[0921] Output: Generated suggestions for correction (e.g., "There are some areas for improvement in this product. In particular, I felt that the balance between quality and price was not right.")

[0922] Step 6: User reviews the suggested revisions and edits their post

[0923] Input: The suggested revisions displayed on the device (e.g., "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was not right.")

[0924] Action: The user reviews the suggested revisions and corrects their post.

[0925] Output: Revised post (e.g. "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was off.")

[0926] Step 7: Resubmit your revised submission

[0927] Input: Revised post (e.g., "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was off.")

[0928] How it works: The user resubmits the revised post from their device to the server.

[0929] Output: Resent post

[0930] Step 8: The server re-parses the revised post

[0931] Input: Resent post (e.g., "This product has some room for improvement. In particular, I felt the balance between quality and price was off.")

[0932] Action: The server reparses the page to ensure it does not contain profanity.

[0933] Output: Analysis results (no problems)

[0934] Step 9: Approve and publish your submission

[0935] Input: Analysis results (no problems)

[0936] How it works: The server ultimately approves the post and publishes it on social media and review sites.

[0937] Output: Published post

[0938] Step 10: Notification of successful submission

[0939] Input: Published post

[0940] Behavior: The device displays a notification to the user that the post was successful.

[0941] Output: Submission success notification message

[0942] (Application example 2)

[0943] 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."

[0944] Conventional systems are limited to detecting inappropriate content in user posts and are unable to provide correction suggestions that fully consider the user's feelings. This can lead to an increase in inappropriate posts by users, potentially damaging the integrity of online platforms. Furthermore, even if inappropriate content is corrected, suggestions that are not based on the user's feelings make it difficult to improve the quality of the posts.

[0945] 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.

[0946] In this invention, the server includes means for analyzing posted content using a natural language processing engine to detect inappropriate expressions, means for generating revision suggestions based on the detected inappropriate expressions and the user's emotions and notifying the user, means for reanalyzing the revised posted content and finally approving and publishing it if there are no problems, and means for using a machine learning model to detect inappropriate expressions and analyze emotions. This makes it possible to generate appropriate revision suggestions that take user emotions into consideration, improve the quality of posted content on online platforms, and maintain a healthy communication environment.

[0947] A "natural language processing engine" is a technology for analyzing text data and understanding its meaning and context.

[0948] "Profanity" refers to words or phrases that are hurtful, offensive or discriminatory.

[0949] "Correction suggestions" refer to suggestions for improvements or alternative expressions provided to users in response to detected inappropriate expressions.

[0950] "User emotions" refers to the classification of emotions that users put into the content they post, such as positive, negative, or neutral.

[0951] A "machine learning model" is an algorithm that is trained using large amounts of data to automatically perform a specific task.

[0952] "Terminal" refers to a device through which a user inputs data and communicates with a server.

[0953] "Server means" refers to a computer system that processes and stores data in response to requests from clients.

[0954] As an embodiment of the present invention, a system incorporating a review management application for an online shopping site will be described. This system detects inappropriate expressions, analyzes user sentiment, and makes appropriate correction suggestions.

[0955] First, a user enters a product review using a review management application on their smartphone. When the user clicks the submit button, the review content is temporarily saved and sent to a server. The server receives the review content and sends it to a natural language processing engine. This natural language processing engine runs an algorithm using a machine learning model to detect inappropriate language. If inappropriate language is detected, further sentiment analysis is performed to determine whether the review content should be classified as positive, negative, or neutral.

[0956] Based on the detected inappropriate expressions and the results of the sentiment analysis, the server generates a correction suggestion. The suggestion is notified to the user and displayed on the smartphone screen. The user can confirm the suggested correction and revise the review. The revised review content is sent back to the server for re-analysis. If this re-analysis confirms that no inappropriate expressions are present, the review is finally approved and published on the online shopping site. After publication, the user receives a notification on their smartphone that the review has been successfully posted.

[0957] Each process in this system uses the Python programming language and the TextBlob library, which includes a natural language processing engine and the NaiveBayesAnalyzer for sentiment analysis, to detect profanity and perform sentiment analysis.

[0958] As a specific example, consider the case where a user enters a review as follows:

[0959] "This product is completely useless. Not worth buying."

[0960] When this review is submitted, the server detects inappropriate expressions such as "not good at all" and "not worth it" and determines the sentiment as "negative." As a result, the following sentence is displayed as a suggestion for correction:

[0961] "Point out specifically what needs to be improved." "By calmly pointing out specific points, your opinion will be easier for the other person to understand."

[0962] An example of a prompt is as follows:

[0963] "Analyze user-written reviews like 'This product is terrible. Not worth buying.' and generate correction suggestions based on profanity and sentiment."

[0964] These processes enable users to communicate in a healthy manner and improve the quality of reviews on online shopping sites, which is expected to maintain the integrity of online platforms.

[0965] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0966] Step 1:

[0967] User enters and submits review

[0968] The user enters a review into the smartphone application and presses the submit button. The review content is received as text data. This text data is temporarily stored and sent to the server.

[0969] Step 2:

[0970] The server receives the review content.

[0971] The server receives the text data of the review content sent by the user. The text data from the user is received as input. This prepares the server for the next analysis process.

[0972] Step 3:

[0973] Analysis of review content using a natural language processing engine

[0974] The server's natural language processing engine analyzes the review content. The input to be analyzed is the received text data. Specifically, the text data is scanned to see if it contains any inappropriate language. An AI model is then used to detect inappropriate language. The detection results are output as a list of inappropriate language.

[0975] Step 4:

[0976] Emotional analysis of users using an emotion engine

[0977] The server's emotion engine analyzes the sentiment of text data. It receives the text data of the review as input. Specifically, it uses the NaiveBayesAnalyzer from the TextBlob library to classify the sentiment of the text as positive, negative, or neutral. The result is the type of sentiment.

[0978] Step 5:

[0979] Generate correction suggestions

[0980] The server generates correction suggestions based on the detected inappropriate expressions and the results of sentiment analysis. It receives a list of inappropriate expressions and the type of sentiment as input. Specifically, it retrieves improvement suggestions corresponding to the detected inappropriate expressions from a dictionary and generates additional suggestions according to the sentiment. The output is the text data of the correction suggestions.

[0981] Step 6:

[0982] Notification of proposed amendments

[0983] The server notifies the user of the revision suggestions it has generated. It receives the text data of the revision suggestions as input. Specifically, it sends a notification message to the smartphone application, which displays it on the screen. The output is the text of the revision suggestions that the user can confirm.

[0984] Step 7:

[0985] User Review Modifications

[0986] The system corrects the review content based on the correction suggestions provided by the user. The system receives correction suggestions from the server as input. The specific operation is that the user corrects the review on their smartphone and submits it again. The corrected text data is output.

[0987] Step 8:

[0988] Re-analysis of revised review content

[0989] The server re-analyzes the revised review content, receiving the revised text data as input. Specifically, it uses the natural language processing engine again to check whether inappropriate language is included. As a result of this check, the review content that does not contain inappropriate language is output.

[0990] Step 9:

[0991] Final review approval and publication

[0992] The server finally approves the corrected review and publishes it on the shopping site. The input is the verified text data. The specific operation is to save the review in the database and display it on the public page of the shopping site. The output is the review published on the shopping site.

[0993] Step 10:

[0994] Review submission success notification

[0995] The device notifies the user that the review has been successfully posted. The input is a notification of the completion of publishing from the server. Specifically, the smartphone application displays a notification message on the screen. The output is a message to the user that the review has been successfully posted.

[0996] 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.

[0997] 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.

[0998] 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.

[0999] [Fourth embodiment]

[1000] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1001] 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.

[1002] 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).

[1003] 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.

[1004] 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.

[1005] 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).

[1006] 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.

[1007] 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.

[1008] 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.

[1009] 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.

[1010] 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.

[1011] 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.

[1012] 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."

[1013] As an embodiment of the present invention, a system for analyzing internet posts that detects inappropriate expressions and suggests corrections is shown below. This system is composed of a terminal where users input and send their posts, a server that receives and analyzes the posts and generates feedback, and a natural language processing engine.

[1014] First, a user creates a post on a social networking site or review site. When the user clicks the post button, the device sends the post to the server. The server then sends the received post to a natural language processing engine for analysis. The natural language processing engine analyzes the post and scans it for slander or inappropriate language.

[1015] For example, if a user posts "This product is completely useless. It's not worth buying," the device sends the content to the server. The server's natural language processing engine detects inappropriate expressions such as "completely useless" and "worthless." When an inappropriate expression is detected, the server generates a correction suggestion based on that. For example, in response to the expression "completely useless," the server suggests an amendment such as, "Let me point out specifically what needs to be improved."

[1016] The server then sends these revision suggestions to the device, which then displays them to the user. The user accepts the suggestions and modifies the content of the post. For example, a suggested change might be, "There are some areas for improvement in this product. In particular, I felt that the balance between quality and price was not right."

[1017] The corrected post is then sent from the device to the server again. The server reanalyzes the corrected post to ensure it does not contain any inappropriate language. If there are no problems, the server finally approves it and publishes it on social media and review sites. The device notifies the user that the post was successful.

[1018] Through this series of processes, the system helps users avoid inappropriate language and engage in safe and constructive exchanges of opinions, thereby preventing slanderous comments on the Internet and realizing a healthy communication environment.

[1019] The processing flow will be explained below.

[1020] Step 1:

[1021] A user creates a post on a social networking site or review site. The user enters the post content and clicks the post button to publish it.

[1022] Step 2:

[1023] The device temporarily saves the entered post content, and then when it detects a click on the post button, it sends the saved post content to the server.

[1024] Step 3:

[1025] The server receives the content posted from the device and sends it to a natural language processing engine for analysis.

[1026] Step 4:

[1027] The server's natural language processing engine analyzes the content of posts and scans them for slander or inappropriate language, such as "it's no good at all" or "it's not worth buying."

[1028] Step 5:

[1029] When an inappropriate expression is detected, the server generates a suggestion for correction based on the detected expression. For example, for the expression "not good at all," the server generates a suggestion for correction such as "Let me point out specifically what needs to be improved."

[1030] Step 6:

[1031] The server sends the generated revision suggestions to the terminal, which displays the revision suggestions to the user.

[1032] Step 7:

[1033] The user checks the suggested revisions and modifies the post. For example, they might change it to, "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was not right."

[1034] Step 8:

[1035] The device saves the revised post again and resends it to the server. The revised post is then sent to the server.

[1036] Step 9:

[1037] The server receives the revised post again and sends it to the natural language processing engine for re-analysis, which verifies that the revised post does not contain any inappropriate language.

[1038] Step 10:

[1039] If it is confirmed that the post does not contain any inappropriate content, the server will finally approve the post, which will then be published on social media and review sites.

[1040] Step 11:

[1041] The terminal displays a notification to the user that the posting was successful.

[1042] Example 1

[1043] 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."

[1044] The Internet is prone to abusive and inappropriate language, hindering healthy communication between users. Existing systems do not fully automate the process of detecting and correcting these inappropriate expressions, requiring a great deal of manual effort from users. Therefore, there is a need for a system that can efficiently detect inappropriate expressions in real time and provide appropriate correction suggestions.

[1045] 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.

[1046] In this invention, the server includes a means for allowing a user to create a post and send it to a terminal, a means for the terminal to send the post to the server, and a means for sending the post received by the server to a natural language processing engine for analysis. This allows the server to analyze the content when a user posts, detect slanderous or inappropriate language in real time, automatically generate correction suggestions, and present them to the user. This reduces the user's manual work and promotes healthy communication on the Internet.

[1047] "User" means a person who creates and submits Posts using the System.

[1048] "Terminal" refers to the device (e.g., PC, smartphone) used by a user to input post content and send it to the server.

[1049] "Server" refers to the computer system responsible for analyzing received posts, generating suggested revisions, and ultimately publishing the posts.

[1050] "Natural language processing engine" refers to a machine learning algorithm that analyzes posts, detects inappropriate language, and generates suggested edits.

[1051] "Posted content" refers to the text information that users send to social media or review sites.

[1052] "Profanity" refers to words or phrases that may offend others, including defamatory or offensive content.

[1053] "Suggested revisions" refers to feedback that provides advice on how to revise posts containing inappropriate language to more appropriate language.

[1054] "Parsing" refers to the process by which a natural language processing engine tokenizes a post and understands its grammatical structure and meaning.

[1055] "Publication" refers to posting the final approved post on social media or review sites on the Internet.

[1056] "Authentication Token" means a unique digital code used to authenticate API requests.

[1057] An "HTTP request" refers to a message used to send information from a terminal to a server using a web communication protocol.

[1058] "Tokenization" refers to the process by which a natural language processing engine breaks up a post into words and phrases.

[1059] "Feedback" refers to advice or suggestions for corrections provided to users regarding their posted content.

[1060] "Healthy communication" refers to a constructive and friendly exchange of information that is free from slander and inappropriate language.

[1061] A "machine learning model" refers to a system that uses algorithms to learn patterns from given data and perform classification and prediction.

[1062] As an embodiment of the present invention, a system for analyzing internet posts that detects inappropriate expressions and suggests corrections is shown below. This system is composed of a terminal where users input and send their posts, a server that receives and analyzes the posts and generates feedback, and a natural language processing engine.

[1063] First, a user creates a post on a social networking site or review site. The user then enters the post content using their own device (PC, smartphone, etc.) and clicks the "Post" button. At this point, the device sends the post content to the server.

[1064] The server then analyzes the received post. To do this, the server sends the post to a natural language processing engine. A specific example of a natural language processing engine is "OpenAI GPT-3." This engine analyzes the post and scans it for slander or inappropriate language.

[1065] For example, if a user posts "This product is completely useless. It's not worth buying," the device sends the content to the server. The server's natural language processing engine detects inappropriate expressions such as "completely useless" and "worthless." When an inappropriate expression is detected, the server generates a correction suggestion based on that. For example, in response to the expression "completely useless," the server suggests an amendment such as, "Let me point out specifically what needs to be improved."

[1066] Here are some example prompts for suggested revisions:

[1067] plaintext

[1068] When a user posts, "This product is completely useless. It's not worth buying," create a correction suggestion.

[1069] The server then sends these revision suggestions to the device, which then displays them to the user. The user accepts the suggestions and modifies the content of the post. For example, a suggested change might be, "There are some areas for improvement in this product. In particular, I felt that the balance between quality and price was not right."

[1070] The corrected post is then sent from the device to the server again. The server reanalyzes the corrected post to ensure it does not contain any inappropriate language. If there are no problems, the server finally approves it and publishes it on social media and review sites. The device notifies the user that the post was successful.

[1071] Through this series of processes, the system helps users avoid inappropriate language and engage in safe and constructive exchanges of opinions, thereby preventing slanderous comments on the Internet and realizing a healthy communication environment.

[1072] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1073] Step 1:

[1074] The user creates the content to post and sends it to the terminal.

[1075] Specific operation: The user enters the content to post on a social networking site or review site and clicks the "Post" button.

[1076] Input: Text data entered by the user.

[1077] Output: The post sent by the device.

[1078] Step 2:

[1079] The device sends the post content to the server.

[1080] Specific operation: The device uses an HTTP POST request to send the post content to the server.

[1081] Input: User-generated posts.

[1082] Output: The post sent to the server.

[1083] Step 3:

[1084] The server receives the post.

[1085] Specific operation: The server receives the post content at the configured API endpoint and temporarily stores it.

[1086] Input: Post content sent from the device.

[1087] Output: The post content ready to be sent to the natural language processing engine.

[1088] Step 4:

[1089] The server sends the received post content to a natural language processing engine for analysis.

[1090] How it works: The server formats the post and sends it via an API request to a natural language processing engine, which uses a machine learning model such as OpenAI GPT-3.

[1091] Input: The post received by the server.

[1092] Output: The post content sent to the natural language processing engine.

[1093] Step 5:

[1094] A natural language processing engine analyzes the content of posts and detects inappropriate expressions.

[1095] What it does: A natural language processing engine tokenizes posts, analyzes each word and phrase, and scans them for profanity.

[1096] Input: The post content sent from the server.

[1097] Output: Profanity analysis results.

[1098] Step 6:

[1099] The server generates correction suggestions based on the detected profanity.

[1100] Specific operation: The server creates a correction suggestion based on the analysis results returned by the natural language processing engine. The correction suggestion includes suggestions for improvement or more appropriate expression. For example, in response to "It's no good at all," the server might suggest, "Let me point out specifically what needs to be improved."

[1101] Input: Analysis results from the natural language processing engine.

[1102] Output: Fix suggestions.

[1103] Step 7:

[1104] The server sends the revision suggestions to the device.

[1105] Specific operation: The server sends the generated revision proposal to the terminal as an HTTP response.

[1106] Input: The generated correction suggestions.

[1107] Output: Correction suggestions sent to the terminal.

[1108] Step 8:

[1109] The terminal displays suggested revisions to the user.

[1110] Specific operation: The device displays the received correction suggestions on the screen. The user checks the correction suggestions and corrects the post content as necessary.

[1111] Input: The correction suggestion sent by the server.

[1112] Output: The correction suggestions displayed to the user.

[1113] Step 9:

[1114] The user accepts the suggested revision, corrects the post, and resubmits it.

[1115] Specific operation: After the user checks the suggested corrections, they modify the post as necessary and click the "Post" button again to submit the modified content.

[1116] Input: The user's modified post.

[1117] Output: The revised submission that was resubmitted.

[1118] Step 10:

[1119] The server then re-analyzes the revised post, verifies that it contains no inappropriate language, and finally approves and publishes it.

[1120] What happens: The server sends the revised post back to the natural language processing engine for a final check, confirming that it contains no inappropriate language, and finally approves it.

[1121] Input: The user's modified post.

[1122] Output: The published, approved post.

[1123] Step 11:

[1124] The terminal notifies the user that posting was successful.

[1125] Specific operation: The device receives a success response from the server and notifies the user that "the post was successful."

[1126] Input: A successful response from the server.

[1127] Output: A notification to the user that the submission was successful.

[1128] (Application example 1)

[1129] 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."

[1130] In today's online communications, many comments contain slander and inappropriate language. Comments on video streaming platforms, in particular, can cause trouble and misunderstandings among viewers. Providing a healthy communication environment by detecting such inappropriate comments in advance and correcting them appropriately is required. Furthermore, a system that can automate this process to reduce the burden on users and respond quickly is needed.

[1131] 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.

[1132] In this invention, the server includes means for analyzing posted content using a natural language processing engine to detect inappropriate expressions, means for generating correction suggestions using a generative AI model based on the detected inappropriate expressions and notifying the user, means for reanalyzing the corrected posted content and finally approving and publishing it if there are no problems, and means for monitoring the content of comments posted by users on a video distribution platform. This makes it possible to automatically detect inappropriate expressions in comments and generate correction suggestions, even on different types of posting platforms, thereby realizing a healthy communication environment.

[1133] "Inappropriate language" refers to inappropriate words or phrases that a user uses in their posts or comments that may slander or offend others.

[1134] A "natural language processing engine" refers to a collection of algorithms and software that analyzes input text data and understands its meaning and grammatical structure.

[1135] "Generative AI model" refers to an artificial intelligence model used to generate new text or suggested revisions based on input data and prompts.

[1136] "Suggested corrections" refer to specific suggestions for correcting detected inappropriate language to make it more appropriate and constructive.

[1137] "Terminal" refers to a device (smartphone, PC, tablet, etc.) that a user uses to input content for posting, connect to the Internet, and send it.

[1138] "Server" refers to a central computer that receives data sent by users and processes it, such as analyzing it, generating suggested modifications, reanalyzing it, and final approval.

[1139] "Video distribution platform" refers to a website or application that allows users to upload video content and allows other users to view and comment on it.

[1140] As an embodiment of the present invention, an analysis system for detecting inappropriate expressions and making correction suggestions will be described. This system detects inappropriate expressions in advance when a user posts a comment on a video distribution platform, and makes correction suggestions using a generative AI model.

[1141] The server first receives the comment content sent by the user. Because this comment content may contain inappropriate language, the server analyzes it using a natural language processing engine. The natural language processing engine scans the comment content and detects inappropriate language while referring to a defined list of inappropriate language.

[1142] For example, if a user sends a comment saying, "This video is completely no good," the server detects the inappropriate expression "completely no good." The server then uses a generative AI model to generate correction suggestions for the detected inappropriate expression. The generative AI model generates specific correction suggestions based on the prompt sentence.

[1143] Example prompt sentence:

[1144] text

[1145] Please turn your comments below into constructive words: This video is absolutely lame.

[1146] After the generative AI model generates correction suggestions, the server notifies the user of the suggestions. For example, a correction suggestion might be generated that says, "There are some areas for improvement in this video. In particular, I feel that the content and editing quality need to be improved."

[1147] When the user accepts the correction suggestion, corrects the comment, and resubmits it, the server again analyzes the corrected comment using a natural language processing engine to confirm that it does not contain any inappropriate language. If there are no problems, the server finally approves the comment and publishes it on the video distribution platform. This helps to provide a healthy communication environment.

[1148] This system is equipped with a network interface for exchanging data between the user-operated device and the server. The server uses OpenAI's API (e.g., text-davinci-003) as a generative AI model. The natural language processing engine includes commonly used Python libraries.

[1149] As described above, the system embodying the present invention automates comment moderation on video distribution platforms and converts inappropriate language into constructive language, thereby promoting healthy communication.

[1150] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1151] Step 1:

[1152] A user enters a comment into the comment section of a video distribution platform and submits it. A specific example of a user input might be, "This video is absolutely terrible." The input includes the comment text entered by the user. Receiving that comment text is the output.

[1153] Step 2:

[1154] The terminal sends the comment entered by the user to the server. The terminal communicates with the server using a network interface. The input includes the user's comment text, and the server receives the comment text as output.

[1155] Step 3:

[1156] The server passes the received comment text to a natural language processing engine to begin detecting profanity. The input includes the user's comment text. The server scans the text using a defined profanity list and extracts profanity. The output includes the presence or absence of profanity and its location.

[1157] Step 4:

[1158] The server uses a generative AI model to generate correction suggestions based on the detected inappropriate language. The input includes the comment text containing the inappropriate language and a prompt. For example, the prompt is "Please change the following comment to something more constructive: This video is completely bad." The generative AI model generates correction text based on this prompt, and the output includes specific correction suggestions.

[1159] Step 5:

[1160] The server sends the generated revision suggestions to the user's device. The input includes the revision suggestion text, and the user's device receives the suggestions as output. For example, a revision suggestion such as "There are some areas for improvement in this video. I feel that the content and editing quality in particular need to be improved."

[1161] Step 6:

[1162] The user checks the correction suggestions sent from the server, corrects the comment based on them, and resubmits it. The input includes the correction suggestion text, and the output is the corrected comment text sent from the terminal to the server. For example, the user may correct the comment by saying, "There are some areas for improvement in this video. I feel that the content and editing quality in particular need to be improved," and resubmit the comment.

[1163] Step 7:

[1164] The server analyzes the resubmitted corrected comment again using a natural language processing engine to check whether it contains any inappropriate language. The input contains the corrected comment text. The natural language processing engine reanalyzes the comment and outputs a result confirming that there are no problems.

[1165] Step 8:

[1166] The server finally approves the corrected comment and publishes it to the video streaming platform. The input includes the corrected comment text that was found to be correct by the reanalysis. The output is the comment text published on the video streaming platform. Finally, the device notifies the user that the comment was successfully posted.

[1167] The above is a specific processing procedure of the system for implementing the present invention.

[1168] 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.

[1169] As an embodiment of the present invention, a system for detecting inappropriate expressions from posted content, suggesting corrections, and recognizing the user's emotions is described below. This system analyzes the posted content entered by the user, detects inappropriate expressions such as slander, provides appropriate feedback, and suggests corrections based on emotions as necessary.

[1170] First, a user creates a post on a social networking site or review site. When the user clicks the post button, the device temporarily saves the post and sends it to the server. The server then sends the received post to a natural language processing engine and emotion engine for analysis.

[1171] The natural language processing engine analyzes the content of posts and scans them for slander or inappropriate language. For example, if a user posts something like, "This product is completely useless. It's not worth buying," the device sends the content to the server, where the server's natural language processing engine detects inappropriate language like "completely useless" or "worthless."

[1172] At the same time, an emotion engine recognizes the emotions in users' posts. The system distinguishes between positive, negative, and neutral emotions and customizes suggestions accordingly. For example, if the user expresses strong negative emotions, the system will suggest suggestions that encourage positive feedback, such as, "I'm not satisfied with this product. Specific improvements could be made to balance the quality and price."

[1173] Based on the detected inappropriate expressions and the recognized emotions, the server generates correction suggestions and notifies the user. For example, for the expression "not good at all," the server suggests "Let's point out specifically what needs to be improved," and further suggests "Negative emotions are being read. By calmly pointing out specific points, your opinion will be easier for the other person to understand."

[1174] The user can review the provided feedback and revise their post. For example, a suggested change might be, "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was not right."

[1175] The corrected post is saved again and resubmitted to the server. The server then reanalyzes the corrected post to ensure it does not contain any inappropriate content. If there are no problems, the server finally approves the post and publishes it on social media or review sites. The device then displays a notification to the user that the post was successful.

[1176] Through this series of processes, the system helps users avoid inappropriate language and engage in safe and constructive exchanges of opinions. Furthermore, by taking the user's feelings into consideration, the system can provide more effective feedback that reduces emotional burden. This can prevent slanderous comments on the Internet and create a healthy communication environment.

[1177] The processing flow will be explained below.

[1178] Understood. Now, the specific processing flow of the system is shown below.

[1179] Step 1:

[1180] A user creates a post on a social networking site or review site. The user enters the post content and clicks the post button to publish it.

[1181] Step 2:

[1182] The device temporarily saves the entered post content, and when it detects a click on the post button, it sends the saved post content to the server.

[1183] Step 3:

[1184] The server receives the content posted from the device, sends it to a natural language processing engine, and scans it for slander and inappropriate language.

[1185] Step 4:

[1186] The server's natural language processing engine analyzes the content of the post and detects inappropriate expressions such as "completely useless" and "worthless."

[1187] Step 5:

[1188] At the same time, the server's emotion engine analyzes the user's emotions from the posted content, distinguishing between positive, negative, and neutral emotions, and generating emotion information based on those.

[1189] Step 6:

[1190] The server combines the results of the natural language processing engine and the emotion engine to generate suggestions for correction based on the location of the inappropriate expression and the emotion. For example, in response to the expression "not good at all," the server will suggest, "Let's point out specifically what needs to be improved," and in response to negative emotions, it will generate additional feedback such as, "By calmly pointing out specific points, your opinion will be easier for the other person to understand."

[1191] Step 7:

[1192] The server sends the generated revision suggestions and emotional feedback to the device, which displays them to the user and provides an interface to encourage them to revise the posted content.

[1193] Step 8:

[1194] The user reviews the suggested revisions and emotional feedback and revises the post. For example, they might change it to, "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was not right."

[1195] Step 9:

[1196] The device saves the revised post again and resends it to the server. The revised post is then sent to the server.

[1197] Step 10:

[1198] The server receives the revised post again and sends it to the natural language processing engine for re-analysis, which verifies that the revised post does not contain any inappropriate language.

[1199] Step 11:

[1200] If it is confirmed that the post does not contain any inappropriate content, the server will finally approve the post, which will then be published on social media and review sites.

[1201] Step 12:

[1202] The terminal displays a notification to the user that the posting was successful.

[1203] The above are the specific processing steps of the present invention. Through this series of processes, constructive communication that avoids inappropriate expressions and takes into consideration the feelings of users is realized.

[1204] Example 2

[1205] 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."

[1206] Currently, communication on the Internet is rife with inappropriate language and slander, making it difficult to maintain a safe and healthy communication environment. Furthermore, current systems do not adequately analyze the emotions expressed in users' posts and provide appropriate feedback based on that analysis. To address these issues, a system is needed that can analyze users' posts, detect inappropriate language, and suggest corrections, as well as identify users' emotions from their posts and provide feedback based on that analysis.

[1207] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for analyzing the posted content using a natural language processing engine to detect inappropriate expressions, means for generating correction suggestions based on the detected inappropriate expressions and notifying the user, emotion engine means for identifying the user's emotions from the posted content, means for customizing the correction suggestions based on the emotions and notifying the user, and means for reanalyzing the corrected posted content and finally approving and publishing it if there are no problems. This allows users to avoid inappropriate expressions and engage in safe and constructive exchanges of opinions. Furthermore, providing feedback that takes the user's emotions into consideration can be more effective and reduce emotional burdens.

[1208] A "natural language processing engine" is software that analyzes posted content and detects inappropriate expressions and slander.

[1209] The "emotion engine" is software that identifies a user's emotions from the content of their posts and generates appropriate feedback based on those emotions.

[1210] "Inappropriate expressions" are problematic expressions contained in posts, such as slander, offensive language, and expressions that are socially unacceptable.

[1211] "Suggested corrections" are suggestions or feedback for improving the content of a post that are generated based on detected inappropriate expressions and provided to the user.

[1212] "Posted content" refers to text data that a user enters and attempts to send on a social networking site, review site, etc.

[1213] "User" means an individual or organization that creates posts on social media or review sites and sends them through the system.

[1214] A "terminal" is an electronic device that a user uses to input and send content to post.

[1215] A "server" is a central control device that receives posts, analyzes them, and generates feedback.

[1216] A "machine learning model" is an algorithmic system that is trained using past data to detect inappropriate language and recognize emotions with high accuracy.

[1217] "Reanalysis" is the process of reanalyzing the edited post to ensure it does not contain any inappropriate language.

[1218] "Publication" is the process of finally displaying the approved post on social media or review sites.

[1219] The system of this invention detects inappropriate expressions when users post to social networking sites or review sites, suggests corrections, and recognizes the user's emotions to provide appropriate feedback. This system consists of three main components: the user, the terminal, and the server.

[1220] First, a user enters text into a social networking site or review site and clicks the post button. The device temporarily saves the post and sends the text data to a server. The device can be a regular personal computer or smartphone.

[1221] The posted content received by the server is analyzed by a natural language processing engine and an emotion engine. The natural language processing engine scans the posted content based on a specific algorithm to detect whether it contains inappropriate language or slander. For example, if a user posts a sentence such as "This product is completely useless. Not worth buying," the engine will extract inappropriate language such as "completely useless" and "not worthless."

[1222] At the same time, a sentiment engine determines the user's sentiment from the post. This engine uses natural language processing technology to capture emotional nuances in the text. Sentiments are generally classified as positive, negative, or neutral. If the sentiment is strongly negative, the system suggests revising it to a more positive expression.

[1223] Based on the detected inappropriate expressions and emotions, the server generates correction suggestions and notifies the user. For example, for the expression "not good at all," the server may send suggestions such as "Let's point out specifically what needs to be improved" or "Negative emotions are being read. By calmly pointing out specific points, your opinion will be easier for the other person to understand." These suggestions are sent to the device and displayed to the user.

[1224] The user checks the suggested corrections and modifies the post. The modified text is then sent from the device to the server again. The server re-analyzes the text to ensure it does not contain any inappropriate language. If there are no problems, the server finally approves the post and publishes it on social media and review sites. The device then displays a notification to the user that the post was successful.

[1225] A specific example is shown below. If a user posts, "This product is completely useless. It's not worth buying," the inappropriate expressions "completely useless" and "worthless" are detected, and the server will suggest specific improvements to the user, such as, "Let's suggest specific areas for improvement. For example, 'There are several areas for improvement with this product. In particular, I felt that the balance between quality and price was not right.'"

[1226] This system allows users to avoid inappropriate language and engage in safe and constructive exchanges. Furthermore, by taking into account the user's feelings, it is possible to provide more effective and less emotionally taxing feedback.

[1227] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1228] Step 1: User creates and submits a post

[1229] Input: User-generated posts (e.g., "This product is completely useless. Not worth buying.")

[1230] How it works: A user enters the content to post on a social networking site or review site and clicks the post button.

[1231] Output: Text data that the device temporarily saves the posted content and sends it to the server

[1232] Step 2: Prepare the server to receive and parse the post

[1233] Input: Post content sent from the device (e.g., "This product is completely useless. It's not worth buying.")

[1234] How it works: The server accepts text data received from the device and prepares it to be sent to the natural language processing engine and emotion engine for analysis.

[1235] Output: Analysis requests to the natural language processing engine and emotion engine

[1236] Step 3: Detect profanity

[1237] Input: Post content (e.g. "This product is completely useless. Not worth buying.")

[1238] How it works: The server's natural language processing engine scans posts and detects inappropriate language based on specific algorithms.

[1239] Output: A list of detected profanities (e.g., "not good at all" or "worthless")

[1240] Step 4: Recognize emotions

[1241] Input: Post content (e.g. "This product is completely useless. Not worth buying.")

[1242] How it works: The server's emotion engine determines the emotion of the post, using an emotion recognition algorithm to classify it as positive, negative, or neutral.

[1243] Output: Emotional judgment result (e.g., negative)

[1244] Step 5: Generate and notify correction suggestions

[1245] Input: List of detected inappropriate expressions (e.g., "not good at all" or "worthless"), emotion judgment result (e.g., negative)

[1246] How it works: The server generates suggestions based on the inappropriate expressions and the emotions they identify. For example, it generates suggestions such as "Point out specific areas that need improvement" and "By calmly pointing out specific points, your opinion will be easier for the other person to understand."

[1247] Output: Generated suggestions for correction (e.g., "There are some areas for improvement in this product. In particular, I felt that the balance between quality and price was not right.")

[1248] Step 6: User reviews the suggested revisions and edits their post

[1249] Input: The suggested revisions displayed on the device (e.g., "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was not right.")

[1250] Action: The user reviews the suggested revisions and corrects their post.

[1251] Output: Revised post (e.g. "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was off.")

[1252] Step 7: Resubmit your revised submission

[1253] Input: Revised post (e.g., "There are some areas for improvement for this product. In particular, I felt that the balance between quality and price was off.")

[1254] How it works: The user resubmits the revised post from their device to the server.

[1255] Output: Resent post

[1256] Step 8: The server re-parses the revised post

[1257] Input: Resent post (e.g., "This product has some room for improvement. In particular, I felt the balance between quality and price was off.")

[1258] Action: The server reparses the page to ensure it does not contain profanity.

[1259] Output: Analysis results (no problems)

[1260] Step 9: Approve and publish your submission

[1261] Input: Analysis results (no problems)

[1262] How it works: The server ultimately approves the post and publishes it on social media and review sites.

[1263] Output: Published post

[1264] Step 10: Notification of successful submission

[1265] Input: Published post

[1266] Behavior: The device displays a notification to the user that the post was successful.

[1267] Output: Submission success notification message

[1268] (Application example 2)

[1269] 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."

[1270] Conventional systems are limited to detecting inappropriate content in user posts and are unable to provide correction suggestions that fully consider the user's feelings. This can lead to an increase in inappropriate posts by users, potentially damaging the integrity of online platforms. Furthermore, even if inappropriate content is corrected, suggestions that are not based on the user's feelings make it difficult to improve the quality of the posts.

[1271] 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.

[1272] In this invention, the server includes means for analyzing posted content using a natural language processing engine to detect inappropriate expressions, means for generating revision suggestions based on the detected inappropriate expressions and the user's emotions and notifying the user, means for reanalyzing the revised posted content and finally approving and publishing it if there are no problems, and means for using a machine learning model to detect inappropriate expressions and analyze emotions. This makes it possible to generate appropriate revision suggestions that take user emotions into consideration, improve the quality of posted content on online platforms, and maintain a healthy communication environment.

[1273] A "natural language processing engine" is a technology for analyzing text data and understanding its meaning and context.

[1274] "Profanity" refers to words or phrases that are hurtful, offensive or discriminatory.

[1275] "Correction suggestions" refer to suggestions for improvements or alternative expressions provided to users in response to detected inappropriate expressions.

[1276] "User emotions" refers to the classification of emotions that users put into the content they post, such as positive, negative, or neutral.

[1277] A "machine learning model" is an algorithm that is trained using large amounts of data to automatically perform a specific task.

[1278] "Terminal" refers to a device through which a user inputs data and communicates with a server.

[1279] "Server means" refers to a computer system that processes and stores data in response to requests from clients.

[1280] As an embodiment of the present invention, a system incorporating a review management application for an online shopping site will be described. This system detects inappropriate expressions, analyzes user sentiment, and makes appropriate correction suggestions.

[1281] First, a user enters a product review using a review management application on their smartphone. When the user clicks the submit button, the review content is temporarily saved and sent to a server. The server receives the review content and sends it to a natural language processing engine. This natural language processing engine runs an algorithm using a machine learning model to detect inappropriate language. If inappropriate language is detected, further sentiment analysis is performed to determine whether the review content should be classified as positive, negative, or neutral.

[1282] Based on the detected inappropriate expressions and the results of the sentiment analysis, the server generates a correction suggestion. The suggestion is notified to the user and displayed on the smartphone screen. The user can confirm the suggested correction and revise the review. The revised review content is sent back to the server for re-analysis. If this re-analysis confirms that no inappropriate expressions are present, the review is finally approved and published on the online shopping site. After publication, the user receives a notification on their smartphone that the review has been successfully posted.

[1283] Each process in this system uses the Python programming language and the TextBlob library, which includes a natural language processing engine and the NaiveBayesAnalyzer for sentiment analysis, to detect profanity and perform sentiment analysis.

[1284] As a specific example, consider the case where a user enters a review as follows:

[1285] "This product is completely useless. Not worth buying."

[1286] When this review is submitted, the server detects inappropriate expressions such as "not good at all" and "not worth it" and determines the sentiment as "negative." As a result, the following sentence is displayed as a suggestion for correction:

[1287] "Point out specifically what needs to be improved." "By calmly pointing out specific points, your opinion will be easier for the other person to understand."

[1288] An example of a prompt is as follows:

[1289] "Analyze user-written reviews like 'This product is terrible. Not worth buying.' and generate correction suggestions based on profanity and sentiment."

[1290] These processes enable users to communicate in a healthy manner and improve the quality of reviews on online shopping sites, which is expected to maintain the integrity of online platforms.

[1291] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1292] Step 1:

[1293] User enters and submits review

[1294] The user enters a review into the smartphone application and presses the submit button. The review content is received as text data. This text data is temporarily stored and sent to the server.

[1295] Step 2:

[1296] The server receives the review content.

[1297] The server receives the text data of the review content sent by the user. The text data from the user is received as input. This prepares the server for the next analysis process.

[1298] Step 3:

[1299] Analysis of review content using a natural language processing engine

[1300] The server's natural language processing engine analyzes the review content. The input to be analyzed is the received text data. Specifically, the text data is scanned to see if it contains any inappropriate language. An AI model is then used to detect inappropriate language. The detection results are output as a list of inappropriate language.

[1301] Step 4:

[1302] Emotional analysis of users using an emotion engine

[1303] The server's emotion engine analyzes the sentiment of text data. It receives the text data of the review as input. Specifically, it uses the NaiveBayesAnalyzer from the TextBlob library to classify the sentiment of the text as positive, negative, or neutral. The result is the type of sentiment.

[1304] Step 5:

[1305] Generate correction suggestions

[1306] The server generates correction suggestions based on the detected inappropriate expressions and the results of sentiment analysis. It receives a list of inappropriate expressions and the type of sentiment as input. Specifically, it retrieves improvement suggestions corresponding to the detected inappropriate expressions from a dictionary and generates additional suggestions according to the sentiment. The output is the text data of the correction suggestions.

[1307] Step 6:

[1308] Notification of proposed amendments

[1309] The server notifies the user of the revision suggestions it has generated. It receives the text data of the revision suggestions as input. Specifically, it sends a notification message to the smartphone application, which displays it on the screen. The output is the text of the revision suggestions that the user can confirm.

[1310] Step 7:

[1311] User Review Modifications

[1312] The system corrects the review content based on the correction suggestions provided by the user. The system receives correction suggestions from the server as input. The specific operation is that the user corrects the review on their smartphone and submits it again. The corrected text data is output.

[1313] Step 8:

[1314] Re-analysis of revised review content

[1315] The server re-analyzes the revised review content, receiving the revised text data as input. Specifically, it uses the natural language processing engine again to check whether inappropriate language is included. As a result of this check, the review content that does not contain inappropriate language is output.

[1316] Step 9:

[1317] Final review approval and publication

[1318] The server finally approves the corrected review and publishes it on the shopping site. The input is the verified text data. The specific operation is to save the review in the database and display it on the public page of the shopping site. The output is the review published on the shopping site.

[1319] Step 10:

[1320] Review submission success notification

[1321] The device notifies the user that the review has been successfully posted. The input is a notification of the completion of publishing from the server. Specifically, the smartphone application displays a notification message on the screen. The output is a message to the user that the review has been successfully posted.

[1322] 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.

[1323] 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.

[1324] 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.

[1325] 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.

[1326] FIG. 9 illustrates 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 behaviors 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.

[1327] 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.

[1328] 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).

[1329] 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.

[1330] 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."

[1331] 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.

[1332] 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).

[1333] 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.

[1334] 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.

[1335] 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.

[1336] 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.

[1337] 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.

[1338] 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.

[1339] 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.

[1340] 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.

[1341] 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.

[1342] 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.

[1343] The following is further disclosed regarding the above embodiment.

[1344] (Claim 1)

[1345] means for analyzing the content of posts using a natural language processing engine to detect inappropriate language;

[1346] means for generating and notifying a user of correction suggestions based on the detected profanity;

[1347] A method to re-analyze the revised submission and, if there are no problems, finally approve and publish it.

[1348] A system including:

[1349] (Claim 2)

[1350] A terminal for users to input and send content to post;

[1351] A server means for analyzing the posted content including inappropriate expressions and generating and transmitting correction suggestions;

[1352] 2. The system according to claim 1, further comprising terminal means for retransmitting the corrected posted content.

[1353] (Claim 3)

[1354] 10. The system of claim 1, wherein the natural language processing algorithm for detecting profanity and generating correction suggestions is trained based on a machine learning model.

[1355] "Example 1"

[1356] Claiming a new invention

[1357] (Claim 1)

[1358] A means for a user to create a posting and transmit it to a terminal;

[1359] A means for the terminal to transmit the posted content to the server;

[1360] A means for transmitting the posted content received by the server to a natural language processing engine for analysis;

[1361] A natural language processing engine analyzes the content of posts and detects inappropriate expressions.

[1362] means for the server to generate and transmit correction suggestions to the terminal based on the detected profanity;

[1363] means for the terminal to display the suggested revisions to the user;

[1364] a means for the user to accept the suggested revision and resubmit the revised submission;

[1365] The server re-analyzes the revised post, verifies that it does not contain any inappropriate content, and finally approves and publishes it.

[1366] A means for the terminal to notify the user of successful posting;

[1367] A system including:

[1368] (Claim 2)

[1369] a terminal means for users to input and send content to be posted;

[1370] A server means for analyzing the posted content including inappropriate expressions and generating and transmitting correction suggestions;

[1371] 2. The system according to claim 1, further comprising terminal means for retransmitting the corrected posted content.

[1372] (Claim 3)

[1373] 10. The system of claim 1, wherein the natural language processing algorithm for detecting profanity and generating correction suggestions is trained based on a machine learning model.

[1374] "Application Example 1"

[1375] (Claim 1)

[1376] means for analyzing the content of posts using a natural language processing engine to detect inappropriate language;

[1377] A means for generating correction suggestions using a generative AI model based on the detected inappropriate expressions and notifying the user of the suggestions;

[1378] A method to re-analyze the revised submission and, if there are no problems, finally approve and publish it.

[1379] A means for monitoring the content of comments posted by users on a video distribution platform;

[1380] A system including:

[1381] (Claim 2)

[1382] A terminal for users to input and send content to post;

[1383] A server means for analyzing the content of a post including inappropriate expressions and generating and transmitting correction suggestions using a generative AI model;

[1384] a terminal means for retransmitting the corrected posting content;

[1385] 2. The system of claim 1, further comprising:

[1386] (Claim 3)

[1387] A natural language processing algorithm for detecting profanity and generating correction suggestions is trained based on a machine learning model; and

[1388] 2. The system of claim 1, wherein the correction suggestions generated using a generative AI model are based on specific prompt sentences.

[1389] "Example 2: Combining Emotion Engines"

[1390] (Claim 1)

[1391] means for analyzing the content of posts using a natural language processing engine to detect inappropriate language;

[1392] means for generating and notifying a user of correction suggestions based on the detected profanity;

[1393] an emotion engine means for identifying a user's emotion from the posted content;

[1394] a means for customizing and notifying the user of a revision suggestion based on the sentiment;

[1395] A method to re-analyze the revised submission and, if there are no problems, finally approve and publish it.

[1396] A system including:

[1397] (Claim 2)

[1398] A terminal for users to input and send content to post;

[1399] A server means for analyzing the posted content including inappropriate expressions and generating and transmitting correction suggestions;

[1400] 2. The system according to claim 1, further comprising terminal means for retransmitting the corrected posted content.

[1401] (Claim 3)

[1402] 10. The system of claim 1, wherein the natural language processing algorithm for detecting profanity and generating correction suggestions is trained based on a machine learning model.

[1403] "Application example 2 when combining emotion engines"

[1404] (Claim 1)

[1405] means for analyzing the content of posts using a natural language processing engine to detect inappropriate language;

[1406] means for generating and notifying a user of correction suggestions based on the detected inappropriate expressions and the user's sentiment;

[1407] A method to re-analyze the revised submission and, if there are no problems, finally approve and publish it.

[1408] a means for using machine learning models to perform profanity detection and sentiment analysis;

[1409] A system including:

[1410] (Claim 2)

[1411] A terminal for users to input and send content to post;

[1412] A server means for analyzing the posted content including inappropriate expressions and emotions, and generating and transmitting correction suggestions;

[1413] 2. The system according to claim 1, further comprising terminal means for retransmitting the corrected posted content.

[1414] (Claim 3)

[1415] 10. The system of claim 1, wherein natural language processing algorithms for detecting profanity, generating correction suggestions, and performing sentiment analysis are trained based on machine learning models. [Explanation of symbols]

[1416] 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 analyzing the content of posts using a natural language processing engine to detect inappropriate language; means for generating and notifying a user of correction suggestions based on the detected profanity; A method to re-analyze the revised submission and, if there are no problems, finally approve and publish it. A system including:

2. A terminal for users to input and send content to post; A server means for analyzing the posted content including inappropriate expressions and generating and transmitting correction suggestions; 2. The system according to claim 1, further comprising terminal means for retransmitting corrected posted content.

3. 10. The system of claim 1, wherein the natural language processing algorithm for detecting profanity and generating correction suggestions is trained based on a machine learning model.

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