System

Abstract

A system is provided.SOLUTION: A system comprising: means for automatically extracting slander comments from the Internet; means for summarizing the extracted slander comments using a natural language processing technique; means for notifying a user of the summarized slander comments; means for generating and transmitting a deletion request for the slander comments; means for managing and notifying progress of the deletion request; and means for collecting and storing evidence data of the slander comments.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] Defamatory comments posted online place a significant mental burden on victims. Victims want the comments to be deleted, but doing so themselves places a heavy burden on both their mind and body. Furthermore, the process for requesting deletion is complicated, and it is often difficult to respond quickly. Furthermore, given the possibility of future litigation, proper evidence storage is also necessary. Given these circumstances, there is a need for a system that can quickly and efficiently extract, summarize, request deletion of defamatory comments, manage progress, and automatically store evidence. [Means for solving the problem]

[0005] The present invention provides the following means.

[0006] A method for automatically extracting defamatory comments from the Internet,

[0007] A means for summarizing the extracted defamatory comments using natural language processing technology;

[0008] means for notifying the user of the summarized abusive comments;

[0009] A means for generating and sending a request to remove abusive comments;

[0010] A means of managing and notifying you about the progress of your removal request;

[0011] The system includes a means for collecting and storing evidence data of abusive comments.

[0012] This will enable us to respond to defamatory comments quickly and efficiently, reducing the psychological burden on victims. Furthermore, proper storage of evidence will also enable future legal action.

[0013] "Defamatory comments" are comments or statements written with malicious intent to defame an individual or organization.

[0014] The "Internet" is a global information and communications network that interconnects computer networks around the world.

[0015] "Extraction" is the process of selecting specific data or information from a large number of sources.

[0016] "Natural language processing technology" is a technical field for processing human language using computers, and includes methods for analyzing text and voice data.

[0017] "Summarizing" is the act of summarizing a long piece of text or story in a concise manner, extracting and expressing only the main points.

[0018] "User" means an individual or organization that uses the system or service.

[0019] A "notification" is a message or alert sent to inform you of specific information.

[0020] A "removal request" is a formal request to remove inappropriate content, such as abusive comments.

[0021] "Progress management" is the process of checking whether work or projects are progressing as planned and making adjustments or instructions as necessary.

[0022] "Evidential data" refers to information or materials collected and stored to prove a specific fact, and is used in legal proceedings, etc. [Brief explanation of the drawings]

[0023] [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

[0024] 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.

[0025] First, the terms used in the following description will be explained.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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."

[0031] [First embodiment]

[0032] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0033] 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.

[0034] 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).

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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."

[0044] The system of the present invention aims to reduce the psychological burden by efficiently extracting defamatory comments, notifying users, and requesting their deletion. This system is realized by combining a server, user terminals, and data sources on the Internet. The specific operation of the system is explained below.

[0045] Research and summary functions

[0046] First, the server automatically extracts abusive comments from the Internet. It periodically sends requests to the APIs of targeted websites and social media sites to collect the latest comment data. For example, it can use the Twitter API to collect replies and mentions of specific users.

[0047] The server applies keyword filtering to the collected comment data, extracting comments containing negative keywords, such as "gross," "die," and "scammer."

[0048] The server then analyzes and summarizes the extracted comments using natural language processing (NLP) techniques, including tokenization, morphological analysis, and sentiment analysis. This allows the server to understand the context and emotional level of the comment, summarizing a comment like "XX is a terrible person" as "strong criticism of XX."

[0049] Deletion request proxy function

[0050] The server notifies the user of the summarized abusive comments via email or in-app notification, making it easy for the user to check.

[0051] Next, the server automatically generates a deletion request for the defamatory comments it finds. The deletion request is generated using a template that includes information such as the comment content, posting date and time, and poster ID. The generated deletion request is sent to the user's device, where the user confirms the content and then approves it.

[0052] Once the user approves the deletion request, the server automatically sends the request to the administrator of the relevant website or social networking site. This process uses the platform's API endpoint or an administrator's contact form. The server also manages the progress of the deletion request and periodically reports to the user, notifying them of the status, such as "request in progress," "processing," or "deletion completed."

[0053] Evidence storage function

[0054] Finally, the server collects and stores evidence of the abusive comments as files. This evidence includes screenshots of the comments, text data, timestamps, and poster IDs. This data is stored in a secure database and can be accessed by lawyers and other parties if necessary. Security is also ensured using data encryption and access control.

[0055] These features allow for a swift and efficient response to defamatory comments, significantly reducing the user's psychological burden. Appropriate evidence storage also enables future legal action. This system provides comprehensive support for combating defamatory comments on the Internet.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] The server periodically sends requests to the APIs of designated websites and social media sites to collect the latest comment data. The data collected through the API is temporarily stored in a database on the server.

[0059] Step 2:

[0060] The server applies keyword filtering to the collected comment data, extracting comments containing negative keywords such as "gross" and "die." This filtering process is implemented using Python scripts and regular expressions.

[0061] Step 3:

[0062] The server applies an algorithm that reviews the filtering results to filter out unusual cases. The algorithm uses past filtering results and heuristics to minimize false positives.

[0063] Step 4:

[0064] The server analyzes the extracted comments using natural language processing (NLP) techniques, including tokenization, morphological analysis, and sentiment analysis. For example, it uses Python libraries such as NLTK and SpaCy to analyze text data.

[0065] Step 5:

[0066] The server summarizes the comments based on the analysis results. In the summarization process, only the main points are extracted, and a comment such as "XX is a terrible person" is summarized as "Strong criticism of XX."

[0067] Step 6:

[0068] The server notifies the user of the summarized comment. The notification is sent to the user's device via email or in-app notification, allowing the user to immediately check the details of the comment.

[0069] Step 7:

[0070] The server automatically generates a deletion request for defamatory comments. The deletion request includes information such as the comment content, posting date and time, and poster ID. This information is saved in template format.

[0071] Step 8:

[0072] The user must review and approve the deletion request via the device, either through the app or the web interface.

[0073] Step 9:

[0074] The server receives the user's approval and sends the deletion request to the administrator of the relevant website or social networking site, who formally submits the request via an API endpoint or an administrator contact form.

[0075] Step 10:

[0076] The server periodically checks the status to manage the progress of the deletion request. For example, it tracks the status such as "request in progress," "processing," or "deletion completed," and notifies the user.

[0077] Step 11:

[0078] The server collects and stores evidence of defamatory comments as files, including screenshots of the comments, text data, timestamps, and poster IDs.

[0079] Step 12:

[0080] The server stores the collected evidence data in a secure database, uses data encryption and access control to ensure the security of the information, and provides an appropriate interface for lawyers and other parties to access it.

[0081] Example 1

[0082] 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."

[0083] Defamatory comments on the Internet can cause significant psychological stress and have a social impact on individuals and organizations. However, it is extremely difficult and time-consuming to quickly and accurately detect defamatory comments from the vast number of comments posted daily and to manually respond to them. In addition, the process of requesting the removal of defamatory comments is complicated, and managing the progress and preserving evidence are also issues.

[0084] 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.

[0085] In this invention, the server includes a means for automatically extracting defamatory content from a computer network, a means for summarizing the extracted defamatory content using natural language processing technology, and a means for notifying the user of the summarized defamatory content, thereby enabling the rapid and accurate extraction and summarization of defamatory comments.

[0086] Furthermore, the present invention includes a means for generating and transmitting a request to remove abusive content, a means for managing and notifying the progress of the removal request, and a means for collecting and storing evidence of the abusive content, thereby simplifying the process of requesting the removal of abusive comments, managing the progress, and ensuring the preservation of evidence.

[0087] "Abusive content" means comments or posts intended to harm or insult others.

[0088] A "computer network" is a communications network, such as the Internet, in which multiple computer terminals are interconnected.

[0089] "Means of extraction" refers to the function of collecting and selecting data based on specific conditions or keywords.

[0090] "Natural language processing technology" is a general term for a series of algorithms and technologies for processing and understanding human language.

[0091] "Summarization methods" are processes or techniques used to summarize collected data succinctly.

[0092] "User" means any individual or organization that uses this system.

[0093] "Means of notification" refers to the communication method or device that the system uses to convey specific information to the user.

[0094] A "removal request" is a formal request to a website or service operator to remove defamatory content.

[0095] "Means for generating and sending" refers to the functionality for automatically generating and sending deletion requests to the appropriate parties (websites and service operators).

[0096] "Progress management and notification means" refers to the functionality for tracking the current state of a deletion request and informing the user of its progress as appropriate.

[0097] "Evidential information" refers to data such as screenshots of comments, text data, timestamps, and poster IDs collected to prove the existence of defamatory content.

[0098] "Collection and storage means" refers to the functions or technologies used to obtain and securely store evidentiary information.

[0099] The system of the present invention aims to reduce the psychological burden by efficiently extracting defamatory content, notifying the user, and requesting its deletion on their behalf. This system is realized by combining a server, user terminals, and data sources on a computer network.

[0100] Research and summary functions

[0101] First, the server periodically sends requests to specific websites or social networking sites (e.g., Twitter) on the Internet to collect comment data. This can be done using standard frameworks such as the Twitter API. For example, replies and mentions for a specific user can be collected via the Twitter API. The comment data obtained by the server is then stored in a database.

[0102] The server then applies keyword filtering to the collected comment data. This filtering process is performed using scripts written in programming languages ​​such as Python and Java. For example, comments containing negative keywords (e.g., "gross," "die," "scammer," etc.) are extracted.

[0103] The server then analyzes and summarizes the extracted comments using natural language processing (NLP) techniques. It leverages NLP libraries (e.g., NLTK and spaCy) to perform tokenization, morphological analysis, and sentiment analysis to understand the context and emotional intensity of the comment. It summarizes a comment like "XX is a horrible person" as "Strong criticism of XX."

[0104] Deletion request proxy function

[0105] The server notifies the user of the summarized defamatory content. The notification is sent via email or in-app notification, so that the user can easily check it. The email system can use the SMTP protocol or REST API.

[0106] Next, the server automatically generates a request to remove the defamatory content that was found. This request is generated using a template that includes information such as the comment content, posting date and time, and poster ID. Template engines that can be used include Jinja2 and Thymeleaf. The generated request is sent to the user's device, where the user confirms the content and then approves it.

[0107] Once the user approves the deletion request, the server automatically sends the request to the administrator of the relevant website or social networking site. This process uses each platform's API endpoint or administrator's contact form. The server also manages the progress of the deletion request and periodically notifies the user of the status, such as "request in progress," "processing," or "deletion completed."

[0108] Evidence storage function

[0109] Finally, the server collects and stores evidence of the defamatory content as files. This evidence includes screenshots of comments, text data, timestamps, and poster IDs. This data is stored in a secure database and can be accessed by lawyers and other parties if necessary. The server ensures security by encrypting the data and controlling access.

[0110] Examples and prompts

[0111] For example, if a comment such as "XX is the worst person" is posted on Twitter, the system's processing would proceed as follows: The server collects comments via Twitter's API and filters out comments containing the keyword "worst." The server summarizes the comment as "strong criticism of XX" and notifies the user. The user approves the deletion request, and the server sends it to an administrator. The server tracks progress and notifies the user of the status. The server also stores the comment and related information as evidence.

[0112] Example prompt for a generative AI model:

[0113] "Please explain the steps you would take to generate an appropriate removal request for the Twitter comment '____ is a horrible person', notify the user, and seek their approval. Also, please provide a summary of this comment."

[0114] The above is an embodiment of the present invention.

[0115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0116] Step 1: Collecting comment data

[0117] 1. The server periodically sends a request to a specific website or social networking site (e.g., Twitter) on the Internet.

[0118] 2. The server uses Twitter's API to collect replies and mentions for a specific user.

[0119] Input: Twitter API endpoint, ID of a specific user.

[0120] Data processing: Receive the response from the API and extract the comment data in JSON format.

[0121] Output: Comment data in JSON format.

[0122] Specific operation: The server sends an HTTP GET request to the Twitter API and stores the comment data obtained in response in a database.

[0123] Step 2: Keyword filtering

[0124] 1. Apply keyword filtering to the comment data collected by the server.

[0125] Input: JSON-formatted comment data, a list of negative keywords (e.g., "gross," "die," "scammer," etc.).

[0126] Data processing: Check whether comments contain negative keywords and extract comments that contain them.

[0127] Output: A filtered comment data list.

[0128] Specific operation: The server uses a Python script to perform keyword matching on the comment data list and adds comments that match the filter to the list.

[0129] Step 3: Summarizing comments

[0130] 1. The server analyzes and summarizes the extracted comments using natural language processing (NLP) technology.

[0131] Input: The filtered comment data list.

[0132] Data processing: Tokenization, morphological analysis, and sentiment analysis are performed to understand the context and sentiment of comments.

[0133] Output: A summarized comment data list.

[0134] What it does: The server uses an NLP engine (e.g. spaCy) to analyze each comment and generate a summary from each comment.

[0135] Step 4: Notify users

[0136] 1. The server notifies the user of the summarized abusive comments.

[0137] Input: A summarized comment data list.

[0138] Data processing: Formatting data for email or in-app notifications.

[0139] Output: Informational message.

[0140] Specific behavior: The server sends a notification message to the user using the SMTP protocol or a push notification service (e.g., Firebase Cloud Messaging).

[0141] Step 5: Generate a removal request

[0142] 1. The server automatically generates a request to delete any abusive comments it finds.

[0143] Input: Summary of comment, posting date and time, and poster ID.

[0144] Data processing: Embed this information in the deletion request template.

[0145] Output: An automatically generated deletion request.

[0146] Specific operation: The server creates a deletion request using a template engine (e.g., Jinja2) and sends it to the user's terminal.

[0147] Step 6: User Authorization

[0148] 1. The user reviews and approves the deletion request.

[0149] Input: Auto-generated removal request.

[0150] Data processing: Record user authorization.

[0151] Output: Approved removal request.

[0152] Specific operation: The user's device displays a confirmation screen, records the user's actions, and sends them to the server.

[0153] Step 7: Submitting and tracking your removal request

[0154] 1. The server automatically sends a deletion request to the administrator of the relevant website or social networking site.

[0155] Input: Approved removal request.

[0156] Data processing: Convert the deletion request into the appropriate format and send it to the specified endpoint.

[0157] Output: The submission status of the deletion request.

[0158] Specific operation: The server sends a deletion request using each platform's API endpoint or contact form and updates the status in the database.

[0159] Step 8: Communicate progress

[0160] 1. The server manages the progress of the deletion request and notifies the user periodically.

[0161] Input: The progress of the removal request.

[0162] Data transformation: Converting progress into notification messages.

[0163] Output: Progress notification messages.

[0164] Specific operation: The server will notify the user of the status such as "Request in progress," "Processing," or "Deletion completed" using a push notification service or email.

[0165] Step 9: Collect and store evidence

[0166] 1. The server collects evidence of defamatory comments and stores them as files.

[0167] Input: Comment screenshot, text data, timestamp, and author ID.

[0168] Data processing: storing these data in a secure format.

[0169] Output: Encrypted evidence file.

[0170] What it does: The server runs a process to collect evidence data, encrypts it, stores it in a database, and sets access controls as needed.

[0171] The above are the specific processing steps of the program of this system.

[0172] (Application example 1)

[0173] 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."

[0174] Many abusive comments are posted on the Internet, which can not only cause users psychological stress but also damage their personal reputation. Manually monitoring and deleting such comments is extremely time-consuming, so there is a need for a system that can efficiently extract, notify, delete, and store evidence of abusive comments. Furthermore, a system that can run on smartphones and has an easy-to-use interface is also required.

[0175] 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.

[0176] In this invention, the server includes means for automatically extracting abusive comments from the Internet, means for summarizing the extracted abusive comments using natural language processing technology, means for notifying users of the summarized abusive comments, means for generating and sending requests to delete the abusive comments, means for managing and notifying the progress of the deletion requests, means for collecting and storing evidence data of the abusive comments, means for providing a user interface that operates on a smartphone, means for filtering abusive comments that include negative keywords, means for generating summaries of the abusive comments using a generative AI model, and means for automatically collecting screenshots and related metadata, thereby enabling a fast and efficient response to abusive comments.

[0177] "Defamatory comments" are comments posted online that criticize and damage the reputation of specific individuals or groups.

[0178] "Means of extraction" refers to technology or devices that automatically collect specific information from the Internet.

[0179] "Natural language processing technology" is a series of technologies that use computers to understand, analyze, and generate human language.

[0180] A "means of summarizing" is a technique or device that compresses information, extracts the main points, and summarizes them briefly.

[0181] "Means for notifying" refers to a technique or device that conveys specific information to a user.

[0182] "Means for generating and sending a deletion request" refers to technology or devices that create a document requesting the deletion of abusive comments and send it to the administrator.

[0183] "Means for managing and notifying the progress of deletion requests" refers to techniques and devices that track the status of deletion requests and communicate that progress to users.

[0184] "Means for collecting and storing evidential data" refers to technology and devices that collect and safely store evidence related to defamatory comments.

[0185] A "smartphone" is a portable electronic device that, in addition to the functions of a mobile phone, has advanced computing power and Internet connectivity.

[0186] A "user interface" is the means by which a user interacts with a computer system or application.

[0187] "Filtering means" refers to technology or devices that select information based on specific criteria and remove unnecessary parts.

[0188] A "generative AI model" is an algorithmic model that is trained to perform a specific task using artificial intelligence.

[0189] A "screenshot" is a technique for saving the contents displayed on a computer or smartphone screen as a still image.

[0190] "Metadata" is data that describes information about data, and includes information such as the creation time and creator.

[0191] This invention is a system that efficiently extracts defamatory comments from the Internet, summarizes them, notifies users, requests their deletion, and stores necessary evidence data. It is realized by combining a server, user terminals (smartphones), and data sources on the Internet.

[0192] First, the server periodically collects comment data from social media sites and websites on the Internet. Specifically, it sends a request to the social media site's API to retrieve comments and replies for the target user. At this time, the server-side program communicates with the API using the "requests" library. The collected comment data is temporarily stored on the server.

[0193] Next, the server filters the collected comment data, automatically extracting comments containing negative keywords (e.g., "gross," "die," "scammer," etc.). This filtering process is also performed on the server side, using an efficient search algorithm.

[0194] Natural language processing technology utilizing a generative AI model is applied to the filtered comments to create a summary of the comments. Specifically, the collected comments are processed in order through processes such as tokenization, morphological analysis, and sentiment analysis to extract important information. Natural language processing libraries such as "simplicity-nlp" are used. The summary results are used in notifications, which will be described later.

[0195] The summarized comments are sent to the user's smartphone. A dedicated application running on the user's device receives the summary data sent from the server and sends the notification. The most common notification methods are push notifications and in-app notifications.

[0196] After the user checks the notification, they can approve the deletion request if necessary. After receiving the user's approval, the server automatically generates a deletion request and sends it to the API endpoint of the target social media or website. The server also manages the progress of the deletion request and notifies the user. The progress status includes statuses such as "request in progress," "processing," and "deletion completed."

[0197] Furthermore, evidence data of defamatory comments is automatically collected and stored by the server. This evidence data includes screenshots of the comments, text data, and metadata (posting date and time, poster ID, etc.). This data is encrypted as necessary and stored in a secure database.

[0198] As a concrete example, let's say the following abusive comment is posted online:

[0199] "○○ is a truly horrible person, no different from a con man."

[0200] These comments will be automatically extracted, filtered and summarized by the system.

[0201] The generative AI model is given a prompt like this:

[0202] "Please summarize the comments below and generate a concise statement for notification:

[0203] "○○ is a truly horrible person, no different from a con man."

[0204] Expected output:

[0205] "A strong criticism of ○○."

[0206] In this way, the present invention provides a system that can respond quickly and efficiently to abusive comments, thereby reducing the psychological burden on users.

[0207] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0208] Step 1:

[0209] The server collects comment data from social media sites and websites on the Internet. Specifically, the server-side program periodically sends API requests using the "requests" library to obtain comments and replies from target users. The input is the comment data received as an API response, and the output is the comment data temporarily stored on the server.

[0210] Step 2:

[0211] The server filters the collected comment data using negative keywords. Specifically, it executes a process to automatically extract comments containing specific keywords (e.g., "gross," "die," "scammer"). The input is the comment data stored on the server, and the output is a list of filtered negative comments.

[0212] Step 3:

[0213] The server uses a generative AI model to create a summary of the filtered negative comments. Specifically, it uses natural language processing technology to "tokenize," "morphologically analyze," and "sentimentally analyze" the comments to extract important information. The generative AI model uses the following prompt:

[0214] "Please summarize the comments below and generate a concise statement for notification:

[0215] "○○ is a truly horrible person, no different from a con man."

[0216] Expected output:

[0217] "A strong criticism of ○○."

[0218] The input is the filtered negative comments and the output is the generated comment summary.

[0219] Step 4:

[0220] The server notifies the user of the summarized comments on their smartphone. The user receives the notification and displays it to the user through a user interface. Notification methods include push notifications and in-app notifications. The input is the summarized comment data, and the output is a notification displayed on the user's smartphone.

[0221] Step 5:

[0222] The user checks the notification and approves the deletion request as necessary. Operation is simple via the user interface. The input is the deletion request approval after user confirmation, and the output is the deletion request approval information sent to the server.

[0223] Step 6:

[0224] The server automatically generates a deletion request message after receiving user approval and sends it to the API endpoint of the target social networking site or website. The deletion request message contains information such as the comment content, posting date and time, and poster ID. The input is the deletion request approval information, and the output is the deletion request sent to the social networking site or website.

[0225] Step 7:

[0226] Furthermore, the server automatically captures screenshots of defamatory comments as evidence, and collects and stores text data and metadata (posting date and time, poster ID). The input is negative comment data, and the output is encrypted evidence data. This data is stored in a secure database in preparation for future legal action.

[0227] Step 8:

[0228] The progress of the deletion request is managed by the server and periodically notified to the user. The progress status includes "request in progress," "processing," "deletion completed," etc., and can be checked by the user on their smartphone. The input is the progress status of the deletion request, and the output is a status notification to the user.

[0229] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0230] The system of the present invention aims to reduce the psychological burden by efficiently extracting abusive comments, notifying users, and requesting their deletion. This system is realized by combining a server, user terminals, an internet data source, and an emotion engine. The specific operation of the system is explained below.

[0231] Research and summary functions

[0232] First, the server automatically extracts abusive comments from the Internet. It periodically sends requests to the APIs of targeted websites and social media sites to collect the latest comment data. For example, it can use the Twitter API to collect replies and mentions of specific users.

[0233] The server applies keyword filtering to the collected comment data, extracting comments containing negative keywords such as "the worst" or "die." For example, a comment such as "XX is the worst person" would be picked up.

[0234] The server then analyzes and summarizes the extracted comments using natural language processing (NLP) techniques, including tokenization, morphological analysis, and sentiment analysis. This allows the server to understand the context and emotional level of the comment, summarizing a comment like "XX is a terrible person" as "strong criticism of XX."

[0235] Deletion request proxy function

[0236] The server then notifies the user of the summarized abusive comments. The emotion engine then analyzes the user's emotions and adjusts the notification content based on their emotional state. For example, if the user is under high stress, the notification may be delayed or the content may be softened.

[0237] Next, the server automatically generates a deletion request for the defamatory comments it finds. The deletion request is generated using a template that includes information such as the comment content, posting date and time, and poster ID. The generated deletion request is sent to the user's device, where the user confirms the content and then approves it.

[0238] Once the user approves the deletion request, the server automatically sends the request to the administrator of the relevant website or social networking site. This process uses the platform's API endpoint or an administrator's contact form. The server also manages the progress of the deletion request and periodically reports to the user, notifying them of the status, such as "request in progress," "processing," or "deletion completed."

[0239] Evidence storage function

[0240] Finally, the server collects and stores evidence of the abusive comments as files. This evidence includes screenshots of the comments, text data, timestamps, and poster IDs. This data is stored in a secure database and can be accessed by lawyers and other parties if necessary. Security is also ensured using data encryption and access control.

[0241] Emotion Engine Functions

[0242] The emotion engine recognizes the user's emotional state and adjusts the system's behavior accordingly. It analyzes emotions using user input data and biosensor data (e.g., heart rate and electrodermal activity). If the user is under high stress, notifications are delayed or suppressed to reduce the user's psychological burden. It can also automatically adjust the priority of deletion requests and change the order in which they are sent to administrators depending on the user's emotional state.

[0243] These features allow for a swift and efficient response to defamatory comments, significantly reducing the user's psychological burden. Appropriate evidence storage also enables future legal action. This system provides comprehensive support for combating defamatory comments on the Internet.

[0244] The processing flow will be explained below.

[0245] The system of the present invention aims to reduce the psychological burden by efficiently extracting abusive comments, notifying users, and requesting their deletion. This system is realized by combining a server, user terminals, an internet data source, and an emotion engine. The specific operation of the system is explained below.

[0246] System-wide processing steps

[0247] Step 1:

[0248] The server periodically sends requests to the APIs of designated websites and social media sites to collect the latest comment data. The data collected through the API is temporarily stored in a database on the server.

[0249] Step 2:

[0250] The server applies keyword filtering to the collected comment data, extracting comments containing negative keywords such as "gross" and "die." This filtering process is implemented using Python scripts and regular expressions.

[0251] Step 3:

[0252] The server applies an algorithm to filter out outlier cases to filtered comments, using past filtering results and heuristics to minimize false positives.

[0253] Step 4:

[0254] The server further analyzes the filtered results using natural language processing (NLP) techniques, including tokenization, morphological analysis, and sentiment analysis, using Python libraries such as NLTK and SpaCy to analyze the text data.

[0255] Step 5:

[0256] The server summarizes the comments based on the analysis results. The summarization process extracts only the main points. For example, a comment such as "XX is a terrible person" can be summarized as "Strong criticism of XX."

[0257] Step 6:

[0258] The server notifies the user of the summarized comments, and the emotion engine analyzes the user's emotions and adjusts the notification content based on their emotional state. For example, if the user is under high stress, the notification may be delayed.

[0259] Step 7:

[0260] The server automatically generates a deletion request for defamatory comments. The deletion request includes information such as the comment content, posting date and time, and poster ID. This information is saved in template format.

[0261] Step 8:

[0262] The server sends the deletion request to the user's device, where the user can review and approve the request via an app or web interface.

[0263] Step 9:

[0264] Once the user has approved the request, the server sends the deletion request to the administrator of the relevant website or social networking site, using an API endpoint or a contact form for the administrator.

[0265] Step 10:

[0266] The server periodically checks the status to manage the progress of the deletion request. For example, it tracks the status such as "request in progress," "processing," or "deletion completed," and notifies the user.

[0267] Step 11:

[0268] The server collects and stores evidence of defamatory comments as files, including screenshots of the comments, text data, timestamps, and poster IDs.

[0269] Step 12:

[0270] The server stores the collected evidence data in a secure database, uses data encryption and access control to ensure the security of the information, and provides an appropriate interface for lawyers and other parties to access it.

[0271] Emotion Engine Processing Procedure

[0272] Step 1:

[0273] It collects user input data and biometric sensor data (such as heart rate and electrodermal activity), and transmits this data to a server via the user's device.

[0274] Step 2:

[0275] The server uses an emotion engine to analyze the collected data and determine the user's emotional state, for example, analyzing fluctuations in heart rate and electrodermal activity to determine whether the user is under stress.

[0276] Step 3:

[0277] The server selects appropriate actions based on the user's emotional state based on the analysis results of the emotion engine. For example, if the user is in a high stress state, the server may delay notifications or notify them with less stressful content.

[0278] Step 4:

[0279] The emotion engine automatically adjusts the priority of deletion requests and changes the order in which they are sent to the administrator depending on the user's emotional state. For example, it gives priority to deletion requests from users who are particularly stressed.

[0280] These features allow for a swift and efficient response to defamatory comments, significantly reducing the user's psychological burden. Appropriate evidence storage also enables future legal action. This system provides comprehensive support for combating defamatory comments on the Internet.

[0281] Example 2

[0282] 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."

[0283] Defamatory comments on the Internet can cause significant psychological stress for users and, in some cases, can lead to legal issues. Therefore, there is a need for a system that can efficiently detect and quickly respond to defamatory comments. However, current systems require manual review and response, placing a significant burden on users. Furthermore, there is a lack of systems that can properly store evidence of comments and provide it when needed. Furthermore, responses are not tailored to the user's emotional state, which rarely reduces the psychological burden.

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

[0285] In this invention, the server includes means for automatically extracting defamatory comments from the Internet, means for summarizing the extracted defamatory comments using natural language processing technology, means for notifying users of the summarized defamatory comments, means for analyzing the user's emotional state and adjusting the content and timing of the notification, means for generating and sending requests to delete the defamatory comments, means for managing and notifying the progress of the deletion request, and means for collecting and storing evidence data of the defamatory comments. This enables swift and efficient responses to defamatory comments, significantly reducing the psychological burden on users, and enabling early responses to legal issues and appropriate evidence storage.

[0286] "Defamatory comments" are comments that contain content that belittles, insults, or damages the reputation of others.

[0287] "Automated internet extraction methods" refers to algorithms or functions that programmatically collect data from specific websites or social networking services.

[0288] "Natural language processing technology" refers to the technology of analyzing and understanding human language using a computer, and includes tasks such as tokenization, morphological analysis, and sentiment analysis.

[0289] "Summarization methods" refers to algorithms or functions that concisely summarize extracted data and provide important information in a concise format.

[0290] "Means for notifying the user" refers to a function for notifying the user of the processing results via email, application notification, etc.

[0291] "Means for analyzing emotional state" refers to algorithms or functions that analyze user input data or biometric sensor data to estimate the user's current emotional state.

[0292] "Means for adjusting the content and timing of notifications" refers to a function for changing the content of the message to be notified and the time of notification based on the user's emotional state.

[0293] "Means for generating and sending removal requests" refers to a function that automatically creates a request for removal of abusive comments and sends it to the administrator of the corresponding website or social networking service.

[0294] "Means for managing and notifying the progress of deletion requests" refers to a function for monitoring the status of submitted deletion requests and reporting the status to the user.

[0295] "Means for collecting and storing evidentiary data" refers to the ability to collect screenshots, text data, timestamps, poster identification information, etc. related to defamatory comments and store them in a secure database.

[0296] The system of the present invention aims to reduce the psychological burden on users by efficiently extracting defamatory comments from the Internet, notifying them, and requesting their deletion. This system is realized by combining a server, user terminals, data sources on the Internet, and an emotion engine.

[0297] System Configuration

[0298] 1. Server:

[0299] The server is responsible for key processes such as extracting abusive comments, summarizing them, sending notifications, generating and sending deletion requests, and collecting and storing evidence data. The server requires a high-performance CPU, sufficient memory, and storage. Software used includes NLP models for natural language processing (e.g., spaCy or NLTK), sentiment analysis engines (e.g., Google Cloud Natural Language API), and databases (e.g., PostgreSQL).

[0300] 2. User Device:

[0301] The user's device receives the notification from the server, confirms the contents of the deletion request, and approves it. Common devices such as smartphones, tablets, and PCs can be used.

[0302] 3. Internet data sources:

[0303] Social media platforms (e.g., Twitter, Facebook) are used as data sources. Comment data is collected from these platforms via APIs.

[0304] 4. Emotion Engine:

[0305] The emotion engine analyzes the user's emotional state and adjusts the system's behavior by analyzing biometric sensors (e.g., heart rate sensor, electrodermal activity sensor) and user input data.

[0306] Specific examples of system processing

[0307] Examples of data collection include:

[0308] The server uses the Twitter API to execute a shell script to retrieve replies and mentions for "@example_user." Specifically, the following prompt is input into the generative AI model:

[0309] Please use Twitter's API to collect abusive comments about @example_user. Extract comments containing keywords such as "disgusting" and "die," summarize them, and report them.

[0310] Examples of keyword filtering:

[0311] The server extracts comments such as "XX should die" from the list of collected comments and summarizes them as "strong criticism" using a natural language processing API.

[0312] Examples of user notifications:

[0313] The server generates a notification and sends it to the user via email, stating, "The following abusive comment was found: Strong criticism of ____." The emotion engine analyzes the user's heart rate and stress level, and delays the notification if the user is in a high stress state.

[0314] An example of generating and approving a deletion request:

[0315] The user clicks the link in the email, confirms the deletion request, and clicks the approval button. The server then uses the Twitter API to send the deletion request to the administrator, saves the status in the database, and notifies the user by email.

[0316] Examples of evidence collection and storage:

[0317] The server uses web scraping technology to capture screenshots of comments and stores them in an encrypted format in a database, which can then be used as legal evidence if needed.

[0318] In this way, the system of the present invention can quickly and efficiently respond to defamatory comments, reducing the psychological burden on users. In addition, by properly storing evidence, it can also respond to legal action if necessary.

[0319] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0320] Step 1:

[0321] Data collection:

[0322] The server accesses the API of a specific internet data source (e.g., a social media platform) to collect replies and mentions for the target user. The target user's ID and API access key are required as input. The server sends a request based on this input data and obtains the collected comment data as output. Specifically, the server uses the Twitter API to execute a query to obtain replies for "@example_user." This retrieves all comments related to "@example_user" in JSON format.

[0323] Step 2:

[0324] Keyword filtering:

[0325] The server filters the collected comment data using a negative keyword list. The collected comment data and the negative keyword list are required as input. The server uses these to analyze the text in the comments and generates a list of comments containing negative keywords as output. Specifically, the server extracts comments containing keywords such as "the worst" and "die." For example, a comment such as "XX is the worst person" would be added to the list.

[0326] Step 3:

[0327] Natural language processing summarization:

[0328] The server summarizes the filtered comment data using natural language processing (NLP) technology. The filtered comment list is required as input. The server performs tokenization, morphological analysis, and sentiment analysis on this, and generates summarized comments as output. Specifically, the server uses an NLP model (e.g., spaCy) to summarize a comment such as "XX is a horrible person" as "Strong criticism of XX."

[0329] Step 4:

[0330] User Notice:

[0331] The server notifies the user of the generated summary comment. The input required is the summarized comment and the user's notification settings. Based on these, the server outputs a notification to the user via email or a notification system. Specifically, the server sends the user an email stating, "The following abusive comment was found: Strong criticism of XX." The emotion engine delays notifications if the user's biosensor data (e.g., heart rate) indicates a high level of stress.

[0332] Step 5:

[0333] Generate a removal request:

[0334] The server automatically generates a deletion request based on abusive comments. The input required is a summary of the comment, the posting date and time, and the poster's identification information. The server applies this data to a template and generates a deletion request as output. Specifically, the server creates a deletion request for the comment "XX is the worst kind of person" and uses the template to fill in the necessary information.

[0335] Step 6:

[0336] User verification and authorization:

[0337] The user checks the deletion request sent from the server. The content of the deletion request sent from the server is required as input. The user checks the content of the deletion request based on this and sends an "approval" or "rejection" response to the server as output. Specifically, the user clicks the link in the email, checks the content of the deletion request, and clicks the "approval" button.

[0338] Step 7:

[0339] Submit a removal request:

[0340] The server receives the user's approval and sends the deletion request to the relevant platform administrator. An approved deletion request form is required as input. Based on this, the server sends the deletion request via the SNS API or administrator inquiry form, and stores the status of the deletion request in a database as output. Specifically, the server uses Twitter's API to send the deletion request and stores the status as "request pending."

[0341] Step 8:

[0342] Progress tracking and reporting:

[0343] The server periodically checks the progress of the deletion request and reports the status to the user. As input, it requires the current status of the deletion request. The server updates the status based on this and sends a notification message to the user as output. Specifically, the server notifies the user by email with a status message such as "Your deletion request is being processed."

[0344] Step 9:

[0345] Evidence collection and preservation:

[0346] The server collects evidence of abusive comments and stores it as a file. The input required is the comment data and associated metadata. The server then takes screenshots based on this, collects comment text, timestamps, and author identification, and stores this data in a secure database as output. Specifically, the server uses a screenshot capture tool to generate screenshots of the comments and stores them in an encrypted format.

[0347] Step 10:

[0348] Emotional State Analysis:

[0349] The emotion engine analyzes user input data and biometric sensor data to estimate the user's emotional state. As input, biometric sensor data (e.g., heart rate, electrodermal activity) and data entered by the user are required. The emotion engine analyzes the emotional state based on these and determines the user's stress level and emotional state as output. Specifically, the emotion engine analyzes heart rate sensor data to determine whether the user is in a high-stress state and notifies the server of the result.

[0350] (Application example 2)

[0351] 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."

[0352] When advertising campaigns are conducted online, slanderous and negative comments often have a negative impact on the effectiveness of the advertisement. Until now, there has been no system that can efficiently monitor and analyze these comments, notify advertising managers, and, if necessary, immediately request their deletion. Another problem is that these comments increase the psychological burden on advertising managers. Therefore, there is a need to solve these problems, minimize the negative impact of slanderous comments on advertising campaigns, and reduce the psychological burden on advertising managers.

[0353] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for automatically extracting abusive comments from the Internet, means for summarizing the extracted abusive comments using natural language processing technology, means for notifying users of the summarized abusive comments, means for generating and sending a request to delete the abusive comments, means for managing and notifying the progress of the deletion request, means for collecting and storing evidence data of the abusive comments, means for monitoring comments related to advertisements in real time, means for analyzing negative comments and notifying advertising personnel in a manner that reduces their psychological burden, and means for sending deletion requests to an administrator and reporting the progress of the request to the advertising personnel. This enables rapid monitoring of negative comments in advertising campaigns, reduces the psychological burden on advertising personnel, and minimizes the negative impact of abusive comments on advertising effectiveness.

[0354] "Defamatory comments" are negative comments made on the Internet with the intention of damaging the reputation or credibility of others.

[0355] "Automatic extraction" means using a program or algorithm to collect the target data without human intervention.

[0356] "Natural language processing technology" is a technology that allows computers to analyze and understand human language (natural language).

[0357] "Summarization methods" are techniques and methods for summarizing long comments or sentences and extracting important information.

[0358] "Notifying users" means notifying specific users of the collected and analyzed information.

[0359] "Generating and sending a removal request" means creating and sending a request to ask an administrator to remove a specific abusive comment.

[0360] "Manage and notify the progress of removal requests" means tracking the progress of removal requests and informing users of their status.

[0361] "Collecting and storing evidentiary data" means collecting information about abusive comments and storing it in a safe place.

[0362] "Comments about advertisements" are user opinions and impressions about a particular advertisement on the Internet.

[0363] "Real-time monitoring" means checking and collecting data immediately the moment it is generated.

[0364] "Analyzing negative comments" means analyzing the content of the comments and identifying negative or critical content within them.

[0365] "Notifying in a way that reduces psychological burden" means conveying information in a way that takes into consideration the mental and emotional burden on the user and minimizes stress.

[0366] "Send to administrator and report progress" means sending the generated deletion request to an administrator, and then tracking and reporting the progress of the request.

[0367] The system of the present invention aims to efficiently extract defamatory and negative comments about online advertising campaigns and minimize their negative impact on advertising effectiveness. It also provides support functions for notifications and removal requests to reduce the psychological burden on advertising personnel.

[0368] This system is composed of the following means.

[0369] 1. A method for automatically extracting defamatory comments from the Internet:

[0370] The server periodically collects comment data from social media sites and websites using APIs, making it possible to collect comments related to advertisements in real time or periodically.

[0371] 2. A method for summarizing extracted abusive comments using natural language processing technology:

[0372] The server applies natural language processing (NLP) technology to the collected comment data, specifically tokenizing, morphological analysis, and sentiment analysis, to summarize negative comments.

[0373] 3. Means of notifying users of summarized abusive comments:

[0374] The server then notifies the advertiser of the summarized negative comments, using an emotion engine to analyze the advertiser's psychological state and tailor the content of the notification to reduce stress, using email or app notifications.

[0375] 4. How to generate and submit a request to remove abusive comments:

[0376] A deletion request is generated for the collected negative comments and sent to the administrator of the advertising platform. A deletion request containing the necessary information is generated using a template, and is sent after obtaining the user's approval.

[0377] 5. How we manage and notify you about the progress of your removal request:

[0378] Track the progress of generated and submitted removal requests and periodically report their status to the advertising manager, so that the advertising manager is always aware of the status of the removal request.

[0379] 6. Means of collecting and storing evidence data of defamatory comments:

[0380] The server collects evidence data, such as screenshots of comments, text data, timestamps, and poster IDs, and stores it in a secure database. Security is also ensured using data encryption and access control.

[0381] 7. How to monitor comments about your ads in real time:

[0382] The server monitors comments related to ads in real time based on the specified keywords, allowing for immediate response to advertising campaigns.

[0383] 8. Analyze negative comments and notify advertisers in a way that minimizes their emotional burden:

[0384] The server identifies negative comments based on the results of sentiment analysis and notifies advertising managers at the appropriate time and with the appropriate content.

[0385] 9. How to submit a removal request to the Administrator and report its progress to the Advertiser:

[0386] The removal request will be sent to the administrator and the progress will be reported to the advertising manager, allowing for more efficient management of the advertising campaign.

[0387] Examples of concrete examples and prompts

[0388] For example, if a social media API is used to collect comments about a new product advertisement and a comment such as "this is the worst product" is detected, the system will analyze the psychological state of the person in charge of advertising using an emotion engine and notify them in a way that reduces their psychological burden. It will also automatically generate a deletion request and send it to the administrator.

[0389] Example prompt sentence:

[0390] Please create a specific program that extracts specific negative comments from social media comments about a new product advertisement, notifies the person in charge of advertising in a way that reduces the psychological burden, and generates a request to remove the comment. Also, please explain in natural language what the program does.

[0391] By implementing the above measures, the impact of negative comments on advertising campaigns can be minimized and the psychological burden on advertising personnel can be reduced.

[0392] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0393] Step 1:

[0394] Automatically extracts defamatory comments from the Internet.

[0395] The server periodically collects comment data from social media platforms and websites using APIs, specifically filtering and collecting comments containing keywords related to targeted advertising campaigns.

[0396] Input: Social media or website API endpoints, target keywords

[0397] Output: Filtered comment data

[0398] Step 2:

[0399] The extracted abusive comments are summarized using natural language processing technology.

[0400] The server performs tokenization, morphological analysis, and sentiment analysis on the collected comments to identify and summarize negative comments.

[0401] Input: Collected comment data

[0402] Output: Summarized negative comments

[0403] Step 3:

[0404] Notify the user of the summarized abusive comments.

[0405] The server then notifies the advertising manager of the summarized negative comments, using an emotion engine to analyze the advertising manager's psychological state and adjust the notification content to reduce stress.

[0406] Input: Summarized negative comments, advertising manager's psychological state data

[0407] Output: Adjusted notification content

[0408] Step 4:

[0409] Generate and submit a request to remove abusive comments.

[0410] The server automatically generates a deletion request based on the summarized abusive comments. It generates the necessary information (comment content, posting date and time, poster ID, etc.) based on a template and sends the deletion request after obtaining the user's approval.

[0411] Input: Summary of abusive comment, removal request template, user approval

[0412] Output: Generated deletion request, sending status

[0413] Step 5:

[0414] Manage and notify you of the progress of your removal request.

[0415] The server tracks the progress of the submitted removal request and periodically reports the status to the advertising manager, managing the status of the removal request and notifying the status such as "request in progress," "processing," or "removal completed."

[0416] Input: Status data of the deletion request submitted

[0417] Output: Progress report

[0418] Step 6:

[0419] Collect and store evidence of abusive comments.

[0420] The server collects evidence data related to defamatory comments, such as screenshots, text data, timestamps, and poster IDs, and stores it in a secure database. Security is ensured using data encryption and access control.

[0421] Input: Negative comments and associated metadata

[0422] Output: Stored evidence data

[0423] Step 7:

[0424] Monitor comments about your ads in real time.

[0425] The server monitors comments related to advertisements in real time based on the specified keywords and extracts immediate negative comments for a particular advertising campaign.

[0426] Inputs: Keywords related to your ad campaign, real-time comment data

[0427] Output: Real-time monitored comment data

[0428] Step 8:

[0429] Negative comments are analyzed and notified in a way that reduces the psychological burden on advertising staff.

[0430] The server notifies the advertiser of negative comments identified based on the results of sentiment analysis, taking into account the advertiser's psychological state. The system adjusts the content of the notification to reduce the advertiser's psychological burden.

[0431] Input: Sentiment analysis results, advertising manager's psychological state data

[0432] Output: Adjusted notification content

[0433] Step 9:

[0434] Send the removal request to the administrator and report the progress to the advertising manager.

[0435] The server sends the generated removal request to an administrator, tracks the progress of the removal request, and reports the status to the advertising representative.

[0436] Input: Generated deletion request, progress data

[0437] Output: Progress report of removal request

[0438] 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.

[0439] 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.

[0440] 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.

[0441] [Second embodiment]

[0442] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0443] 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.

[0444] 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).

[0445] 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.

[0446] 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.

[0447] 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).

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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."

[0454] The system of the present invention aims to reduce the psychological burden by efficiently extracting defamatory comments, notifying users, and requesting their deletion. This system is realized by combining a server, user terminals, and data sources on the Internet. The specific operation of the system is explained below.

[0455] Research and summary functions

[0456] First, the server automatically extracts abusive comments from the Internet. It periodically sends requests to the APIs of targeted websites and social media sites to collect the latest comment data. For example, it can use the Twitter API to collect replies and mentions of specific users.

[0457] The server applies keyword filtering to the collected comment data, extracting comments containing negative keywords, such as "gross," "die," and "scammer."

[0458] The server then analyzes and summarizes the extracted comments using natural language processing (NLP) techniques, including tokenization, morphological analysis, and sentiment analysis. This allows the server to understand the context and emotional level of the comment, summarizing a comment like "XX is a terrible person" as "strong criticism of XX."

[0459] Deletion request proxy function

[0460] The server notifies the user of the summarized abusive comments via email or in-app notification, making it easy for the user to check.

[0461] Next, the server automatically generates a deletion request for the defamatory comments it finds. The deletion request is generated using a template that includes information such as the comment content, posting date and time, and poster ID. The generated deletion request is sent to the user's device, where the user confirms the content and then approves it.

[0462] Once the user approves the deletion request, the server automatically sends the request to the administrator of the relevant website or social networking site. This process uses the platform's API endpoint or an administrator's contact form. The server also manages the progress of the deletion request and periodically reports to the user, notifying them of the status, such as "request in progress," "processing," or "deletion completed."

[0463] Evidence storage function

[0464] Finally, the server collects and stores evidence of the abusive comments as files. This evidence includes screenshots of the comments, text data, timestamps, and poster IDs. This data is stored in a secure database and can be accessed by lawyers and other parties if necessary. Security is also ensured using data encryption and access control.

[0465] These features allow for a swift and efficient response to defamatory comments, significantly reducing the user's psychological burden. Appropriate evidence storage also enables future legal action. This system provides comprehensive support for combating defamatory comments on the Internet.

[0466] The processing flow will be explained below.

[0467] Step 1:

[0468] The server periodically sends requests to the APIs of designated websites and social media sites to collect the latest comment data. The data collected through the API is temporarily stored in a database on the server.

[0469] Step 2:

[0470] The server applies keyword filtering to the collected comment data, extracting comments containing negative keywords such as "gross" and "die." This filtering process is implemented using Python scripts and regular expressions.

[0471] Step 3:

[0472] The server applies an algorithm that reviews the filtering results to filter out unusual cases. The algorithm uses past filtering results and heuristics to minimize false positives.

[0473] Step 4:

[0474] The server analyzes the extracted comments using natural language processing (NLP) techniques, including tokenization, morphological analysis, and sentiment analysis. For example, it uses Python libraries such as NLTK and SpaCy to analyze text data.

[0475] Step 5:

[0476] The server summarizes the comments based on the analysis results. In the summarization process, only the main points are extracted, and a comment such as "XX is a terrible person" is summarized as "Strong criticism of XX."

[0477] Step 6:

[0478] The server notifies the user of the summarized comment. The notification is sent to the user's device via email or in-app notification, allowing the user to immediately check the details of the comment.

[0479] Step 7:

[0480] The server automatically generates a deletion request for defamatory comments. The deletion request includes information such as the comment content, posting date and time, and poster ID. This information is saved in template format.

[0481] Step 8:

[0482] The user must review and approve the deletion request via the device, either through the app or the web interface.

[0483] Step 9:

[0484] The server receives the user's approval and sends the deletion request to the administrator of the relevant website or social networking site, who formally submits the request via an API endpoint or an administrator contact form.

[0485] Step 10:

[0486] The server periodically checks the status to manage the progress of the deletion request. For example, it tracks the status such as "request in progress," "processing," or "deletion completed," and notifies the user.

[0487] Step 11:

[0488] The server collects and stores evidence of defamatory comments as files, including screenshots of the comments, text data, timestamps, and poster IDs.

[0489] Step 12:

[0490] The server stores the collected evidence data in a secure database, uses data encryption and access control to ensure the security of the information, and provides an appropriate interface for lawyers and other parties to access it.

[0491] Example 1

[0492] 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."

[0493] Defamatory comments on the Internet can cause significant psychological stress and have a social impact on individuals and organizations. However, it is extremely difficult and time-consuming to quickly and accurately detect defamatory comments from the vast number of comments posted daily and to manually respond to them. In addition, the process of requesting the removal of defamatory comments is complicated, and managing the progress and preserving evidence are also issues.

[0494] 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.

[0495] In this invention, the server includes a means for automatically extracting defamatory content from a computer network, a means for summarizing the extracted defamatory content using natural language processing technology, and a means for notifying the user of the summarized defamatory content, thereby enabling the rapid and accurate extraction and summarization of defamatory comments.

[0496] Furthermore, the present invention includes a means for generating and transmitting a request to remove abusive content, a means for managing and notifying the progress of the removal request, and a means for collecting and storing evidence of the abusive content, thereby simplifying the process of requesting the removal of abusive comments, managing the progress, and ensuring the preservation of evidence.

[0497] "Abusive content" means comments or posts intended to harm or insult others.

[0498] A "computer network" is a communications network, such as the Internet, in which multiple computer terminals are interconnected.

[0499] "Means of extraction" refers to the function of collecting and selecting data based on specific conditions or keywords.

[0500] "Natural language processing technology" is a general term for a series of algorithms and technologies for processing and understanding human language.

[0501] "Summarization methods" are processes or techniques used to summarize collected data succinctly.

[0502] "User" means any individual or organization that uses this system.

[0503] "Means of notification" refers to the communication method or device that the system uses to convey specific information to the user.

[0504] A "removal request" is a formal request to a website or service operator to remove defamatory content.

[0505] "Means for generating and sending" refers to the functionality for automatically generating and sending deletion requests to the appropriate parties (websites and service operators).

[0506] "Progress management and notification means" refers to the functionality for tracking the current state of a deletion request and informing the user of its progress as appropriate.

[0507] "Evidential information" refers to data such as screenshots of comments, text data, timestamps, and poster IDs collected to prove the existence of defamatory content.

[0508] "Collection and storage means" refers to the functions or technologies used to obtain and securely store evidentiary information.

[0509] The system of the present invention aims to reduce the psychological burden by efficiently extracting defamatory content, notifying the user, and requesting its deletion on their behalf. This system is realized by combining a server, user terminals, and data sources on a computer network.

[0510] Research and summary functions

[0511] First, the server periodically sends requests to specific websites or social networking sites (e.g., Twitter) on the Internet to collect comment data. This can be done using standard frameworks such as the Twitter API. For example, replies and mentions for a specific user can be collected via the Twitter API. The comment data obtained by the server is then stored in a database.

[0512] The server then applies keyword filtering to the collected comment data. This filtering process is performed using scripts written in programming languages ​​such as Python and Java. For example, comments containing negative keywords (e.g., "gross," "die," "scammer," etc.) are extracted.

[0513] The server then analyzes and summarizes the extracted comments using natural language processing (NLP) techniques. It leverages NLP libraries (e.g., NLTK and spaCy) to perform tokenization, morphological analysis, and sentiment analysis to understand the context and emotional intensity of the comment. It summarizes a comment like "XX is a horrible person" as "Strong criticism of XX."

[0514] Deletion request proxy function

[0515] The server notifies the user of the summarized defamatory content. The notification is sent via email or in-app notification, so that the user can easily check it. The email system can use the SMTP protocol or REST API.

[0516] Next, the server automatically generates a request to remove the defamatory content that was found. This request is generated using a template that includes information such as the comment content, posting date and time, and poster ID. Template engines that can be used include Jinja2 and Thymeleaf. The generated request is sent to the user's device, where the user confirms the content and then approves it.

[0517] Once the user approves the deletion request, the server automatically sends the request to the administrator of the relevant website or social networking site. This process uses each platform's API endpoint or administrator's contact form. The server also manages the progress of the deletion request and periodically notifies the user of the status, such as "request in progress," "processing," or "deletion completed."

[0518] Evidence storage function

[0519] Finally, the server collects and stores evidence of the defamatory content as files. This evidence includes screenshots of comments, text data, timestamps, and poster IDs. This data is stored in a secure database and can be accessed by lawyers and other parties if necessary. The server ensures security by encrypting the data and controlling access.

[0520] Examples and prompts

[0521] For example, if a comment such as "XX is the worst person" is posted on Twitter, the system's processing would proceed as follows: The server collects comments via Twitter's API and filters out comments containing the keyword "worst." The server summarizes the comment as "strong criticism of XX" and notifies the user. The user approves the deletion request, and the server sends it to an administrator. The server tracks progress and notifies the user of the status. The server also stores the comment and related information as evidence.

[0522] Example prompt for a generative AI model:

[0523] "Please explain the steps you would take to generate an appropriate removal request for the Twitter comment '____ is a horrible person', notify the user, and seek their approval. Also, please provide a summary of this comment."

[0524] The above is an embodiment of the present invention.

[0525] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0526] Step 1: Collecting comment data

[0527] 1. The server periodically sends a request to a specific website or social networking site (e.g., Twitter) on the Internet.

[0528] 2. The server uses Twitter's API to collect replies and mentions for a specific user.

[0529] Input: Twitter API endpoint, ID of a specific user.

[0530] Data processing: Receive the response from the API and extract the comment data in JSON format.

[0531] Output: Comment data in JSON format.

[0532] Specific operation: The server sends an HTTP GET request to the Twitter API and stores the comment data obtained in response in a database.

[0533] Step 2: Keyword filtering

[0534] 1. Apply keyword filtering to the comment data collected by the server.

[0535] Input: JSON-formatted comment data, a list of negative keywords (e.g., "gross," "die," "scammer," etc.).

[0536] Data processing: Check whether comments contain negative keywords and extract comments that contain them.

[0537] Output: A filtered comment data list.

[0538] Specific operation: The server uses a Python script to perform keyword matching on the comment data list and adds comments that match the filter to the list.

[0539] Step 3: Summarizing comments

[0540] 1. The server analyzes and summarizes the extracted comments using natural language processing (NLP) technology.

[0541] Input: The filtered comment data list.

[0542] Data processing: Tokenization, morphological analysis, and sentiment analysis are performed to understand the context and sentiment of comments.

[0543] Output: A summarized comment data list.

[0544] What it does: The server uses an NLP engine (e.g. spaCy) to analyze each comment and generate a summary from each comment.

[0545] Step 4: Notify users

[0546] 1. The server notifies the user of the summarized abusive comments.

[0547] Input: A summarized comment data list.

[0548] Data processing: Formatting data for email or in-app notifications.

[0549] Output: Informational message.

[0550] Specific behavior: The server sends a notification message to the user using the SMTP protocol or a push notification service (e.g., Firebase Cloud Messaging).

[0551] Step 5: Generate a removal request

[0552] 1. The server automatically generates a request to delete any abusive comments it finds.

[0553] Input: Summary of comment, posting date and time, and poster ID.

[0554] Data processing: Embed this information in the deletion request template.

[0555] Output: An automatically generated deletion request.

[0556] Specific operation: The server creates a deletion request using a template engine (e.g., Jinja2) and sends it to the user's terminal.

[0557] Step 6: User Authorization

[0558] 1. The user reviews and approves the deletion request.

[0559] Input: Auto-generated removal request.

[0560] Data processing: Record user authorization.

[0561] Output: Approved removal request.

[0562] Specific operation: The user's device displays a confirmation screen, records the user's actions, and sends them to the server.

[0563] Step 7: Submitting and tracking your removal request

[0564] 1. The server automatically sends a deletion request to the administrator of the relevant website or social networking site.

[0565] Input: Approved removal request.

[0566] Data processing: Convert the deletion request into the appropriate format and send it to the specified endpoint.

[0567] Output: The submission status of the deletion request.

[0568] Specific operation: The server sends a deletion request using each platform's API endpoint or contact form and updates the status in the database.

[0569] Step 8: Communicate progress

[0570] 1. The server manages the progress of the deletion request and notifies the user periodically.

[0571] Input: The progress of the removal request.

[0572] Data transformation: Converting progress into notification messages.

[0573] Output: Progress notification messages.

[0574] Specific operation: The server will notify the user of the status such as "Request in progress," "Processing," or "Deletion completed" using a push notification service or email.

[0575] Step 9: Collect and store evidence

[0576] 1. The server collects evidence of defamatory comments and stores them as files.

[0577] Input: Comment screenshot, text data, timestamp, and author ID.

[0578] Data processing: storing these data in a secure format.

[0579] Output: Encrypted evidence file.

[0580] What it does: The server runs a process to collect evidence data, encrypts it, stores it in a database, and sets access controls as needed.

[0581] The above are the specific processing steps of the program of this system.

[0582] (Application example 1)

[0583] 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."

[0584] Many abusive comments are posted on the Internet, which can not only cause users psychological stress but also damage their personal reputation. Manually monitoring and deleting such comments is extremely time-consuming, so there is a need for a system that can efficiently extract, notify, delete, and store evidence of abusive comments. Furthermore, a system that can run on smartphones and has an easy-to-use interface is also required.

[0585] 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.

[0586] In this invention, the server includes means for automatically extracting abusive comments from the Internet, means for summarizing the extracted abusive comments using natural language processing technology, means for notifying users of the summarized abusive comments, means for generating and sending requests to delete the abusive comments, means for managing and notifying the progress of the deletion requests, means for collecting and storing evidence data of the abusive comments, means for providing a user interface that operates on a smartphone, means for filtering abusive comments that include negative keywords, means for generating summaries of the abusive comments using a generative AI model, and means for automatically collecting screenshots and related metadata, thereby enabling a fast and efficient response to abusive comments.

[0587] "Defamatory comments" are comments posted online that criticize and damage the reputation of specific individuals or groups.

[0588] "Means of extraction" refers to technology or devices that automatically collect specific information from the Internet.

[0589] "Natural language processing technology" is a series of technologies that use computers to understand, analyze, and generate human language.

[0590] A "means of summarizing" is a technique or device that compresses information, extracts the main points, and summarizes them briefly.

[0591] "Means for notifying" refers to a technique or device that conveys specific information to a user.

[0592] "Means for generating and sending a deletion request" refers to technology or devices that create a document requesting the deletion of abusive comments and send it to the administrator.

[0593] "Means for managing and notifying the progress of deletion requests" refers to techniques and devices that track the status of deletion requests and communicate that progress to users.

[0594] "Means for collecting and storing evidential data" refers to technology and devices that collect and safely store evidence related to defamatory comments.

[0595] A "smartphone" is a portable electronic device that, in addition to the functions of a mobile phone, has advanced computing power and Internet connectivity.

[0596] A "user interface" is the means by which a user interacts with a computer system or application.

[0597] "Filtering means" refers to technology or devices that select information based on specific criteria and remove unnecessary parts.

[0598] A "generative AI model" is an algorithmic model that is trained to perform a specific task using artificial intelligence.

[0599] A "screenshot" is a technique for saving the contents displayed on a computer or smartphone screen as a still image.

[0600] "Metadata" is data that describes information about data, and includes information such as the creation time and creator.

[0601] This invention is a system that efficiently extracts defamatory comments from the Internet, summarizes them, notifies users, requests their deletion, and stores necessary evidence data. It is realized by combining a server, user terminals (smartphones), and data sources on the Internet.

[0602] First, the server periodically collects comment data from social media sites and websites on the Internet. Specifically, it sends a request to the social media site's API to retrieve comments and replies for the target user. At this time, the server-side program communicates with the API using the "requests" library. The collected comment data is temporarily stored on the server.

[0603] Next, the server filters the collected comment data, automatically extracting comments containing negative keywords (e.g., "gross," "die," "scammer," etc.). This filtering process is also performed on the server side, using an efficient search algorithm.

[0604] Natural language processing technology utilizing a generative AI model is applied to the filtered comments to create a summary of the comments. Specifically, the collected comments are processed in order through processes such as tokenization, morphological analysis, and sentiment analysis to extract important information. Natural language processing libraries such as "simplicity-nlp" are used. The summary results are used in notifications, which will be described later.

[0605] The summarized comments are sent to the user's smartphone. A dedicated application running on the user's device receives the summary data sent from the server and sends the notification. The most common notification methods are push notifications and in-app notifications.

[0606] After the user checks the notification, they can approve the deletion request if necessary. After receiving the user's approval, the server automatically generates a deletion request and sends it to the API endpoint of the target social media or website. The server also manages the progress of the deletion request and notifies the user. The progress status includes statuses such as "request in progress," "processing," and "deletion completed."

[0607] Furthermore, evidence data of defamatory comments is automatically collected and stored by the server. This evidence data includes screenshots of the comments, text data, and metadata (posting date and time, poster ID, etc.). This data is encrypted as necessary and stored in a secure database.

[0608] As a concrete example, let's say the following abusive comment is posted online:

[0609] "○○ is a truly horrible person, no different from a con man."

[0610] These comments will be automatically extracted, filtered and summarized by the system.

[0611] The generative AI model is given a prompt like this:

[0612] "Please summarize the comments below and generate a concise statement for notification:

[0613] "○○ is a truly horrible person, no different from a con man."

[0614] Expected output:

[0615] "A strong criticism of ○○."

[0616] In this way, the present invention provides a system that can respond quickly and efficiently to abusive comments, thereby reducing the psychological burden on users.

[0617] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0618] Step 1:

[0619] The server collects comment data from social media sites and websites on the Internet. Specifically, the server-side program periodically sends API requests using the "requests" library to obtain comments and replies from target users. The input is the comment data received as an API response, and the output is the comment data temporarily stored on the server.

[0620] Step 2:

[0621] The server filters the collected comment data using negative keywords. Specifically, it executes a process to automatically extract comments containing specific keywords (e.g., "gross," "die," "scammer"). The input is the comment data stored on the server, and the output is a list of filtered negative comments.

[0622] Step 3:

[0623] The server uses a generative AI model to create a summary of the filtered negative comments. Specifically, it uses natural language processing technology to "tokenize," "morphologically analyze," and "sentimentally analyze" the comments to extract important information. The generative AI model uses the following prompt:

[0624] "Please summarize the comments below and generate a concise statement for notification:

[0625] "○○ is a truly horrible person, no different from a con man."

[0626] Expected output:

[0627] "A strong criticism of ○○."

[0628] The input is the filtered negative comments and the output is the generated comment summary.

[0629] Step 4:

[0630] The server notifies the user of the summarized comments on their smartphone. The user receives the notification and displays it to the user through a user interface. Notification methods include push notifications and in-app notifications. The input is the summarized comment data, and the output is a notification displayed on the user's smartphone.

[0631] Step 5:

[0632] The user checks the notification and approves the deletion request as necessary. Operation is simple via the user interface. The input is the deletion request approval after user confirmation, and the output is the deletion request approval information sent to the server.

[0633] Step 6:

[0634] The server automatically generates a deletion request message after receiving user approval and sends it to the API endpoint of the target social networking site or website. The deletion request message contains information such as the comment content, posting date and time, and poster ID. The input is the deletion request approval information, and the output is the deletion request sent to the social networking site or website.

[0635] Step 7:

[0636] Furthermore, the server automatically captures screenshots of defamatory comments as evidence, and collects and stores text data and metadata (posting date and time, poster ID). The input is negative comment data, and the output is encrypted evidence data. This data is stored in a secure database in preparation for future legal action.

[0637] Step 8:

[0638] The progress of the deletion request is managed by the server and periodically notified to the user. The progress status includes "request in progress," "processing," "deletion completed," etc., and can be checked by the user on their smartphone. The input is the progress status of the deletion request, and the output is a status notification to the user.

[0639] 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.

[0640] The system of the present invention aims to reduce the psychological burden by efficiently extracting abusive comments, notifying users, and requesting their deletion. This system is realized by combining a server, user terminals, an internet data source, and an emotion engine. The specific operation of the system is explained below.

[0641] Research and summary functions

[0642] First, the server automatically extracts abusive comments from the Internet. It periodically sends requests to the APIs of targeted websites and social media sites to collect the latest comment data. For example, it can use the Twitter API to collect replies and mentions of specific users.

[0643] The server applies keyword filtering to the collected comment data, extracting comments containing negative keywords such as "the worst" or "die." For example, a comment such as "XX is the worst person" would be picked up.

[0644] The server then analyzes and summarizes the extracted comments using natural language processing (NLP) techniques, including tokenization, morphological analysis, and sentiment analysis. This allows the server to understand the context and emotional level of the comment, summarizing a comment like "XX is a terrible person" as "strong criticism of XX."

[0645] Deletion request proxy function

[0646] The server then notifies the user of the summarized abusive comments. The emotion engine then analyzes the user's emotions and adjusts the notification content based on their emotional state. For example, if the user is under high stress, the notification may be delayed or the content may be softened.

[0647] Next, the server automatically generates a deletion request for the defamatory comments it finds. The deletion request is generated using a template that includes information such as the comment content, posting date and time, and poster ID. The generated deletion request is sent to the user's device, where the user confirms the content and then approves it.

[0648] Once the user approves the deletion request, the server automatically sends the request to the administrator of the relevant website or social networking site. This process uses the platform's API endpoint or an administrator's contact form. The server also manages the progress of the deletion request and periodically reports to the user, notifying them of the status, such as "request in progress," "processing," or "deletion completed."

[0649] Evidence storage function

[0650] Finally, the server collects and stores evidence of the abusive comments as files. This evidence includes screenshots of the comments, text data, timestamps, and poster IDs. This data is stored in a secure database and can be accessed by lawyers and other parties if necessary. Security is also ensured using data encryption and access control.

[0651] Emotion Engine Functions

[0652] The emotion engine recognizes the user's emotional state and adjusts the system's behavior accordingly. It analyzes emotions using user input data and biosensor data (e.g., heart rate and electrodermal activity). If the user is under high stress, notifications are delayed or suppressed to reduce the user's psychological burden. It can also automatically adjust the priority of deletion requests and change the order in which they are sent to administrators depending on the user's emotional state.

[0653] These features allow for a swift and efficient response to defamatory comments, significantly reducing the user's psychological burden. Appropriate evidence storage also enables future legal action. This system provides comprehensive support for combating defamatory comments on the Internet.

[0654] The processing flow will be explained below.

[0655] The system of the present invention aims to reduce the psychological burden by efficiently extracting abusive comments, notifying users, and requesting their deletion. This system is realized by combining a server, user terminals, an internet data source, and an emotion engine. The specific operation of the system is explained below.

[0656] System-wide processing steps

[0657] Step 1:

[0658] The server periodically sends requests to the APIs of designated websites and social media sites to collect the latest comment data. The data collected through the API is temporarily stored in a database on the server.

[0659] Step 2:

[0660] The server applies keyword filtering to the collected comment data, extracting comments containing negative keywords such as "gross" and "die." This filtering process is implemented using Python scripts and regular expressions.

[0661] Step 3:

[0662] The server applies an algorithm to filter out outlier cases to filtered comments, using past filtering results and heuristics to minimize false positives.

[0663] Step 4:

[0664] The server further analyzes the filtered results using natural language processing (NLP) techniques, including tokenization, morphological analysis, and sentiment analysis, using Python libraries such as NLTK and SpaCy to analyze the text data.

[0665] Step 5:

[0666] The server summarizes the comments based on the analysis results. The summarization process extracts only the main points. For example, a comment such as "XX is a terrible person" can be summarized as "Strong criticism of XX."

[0667] Step 6:

[0668] The server notifies the user of the summarized comments, and the emotion engine analyzes the user's emotions and adjusts the notification content based on their emotional state. For example, if the user is under high stress, the notification may be delayed.

[0669] Step 7:

[0670] The server automatically generates a deletion request for defamatory comments. The deletion request includes information such as the comment content, posting date and time, and poster ID. This information is saved in template format.

[0671] Step 8:

[0672] The server sends the deletion request to the user's device, where the user can review and approve the request via an app or web interface.

[0673] Step 9:

[0674] Once the user has approved the request, the server sends the deletion request to the administrator of the relevant website or social networking site, using an API endpoint or a contact form for the administrator.

[0675] Step 10:

[0676] The server periodically checks the status to manage the progress of the deletion request. For example, it tracks the status such as "request in progress," "processing," or "deletion completed," and notifies the user.

[0677] Step 11:

[0678] The server collects and stores evidence of defamatory comments as files, including screenshots of the comments, text data, timestamps, and poster IDs.

[0679] Step 12:

[0680] The server stores the collected evidence data in a secure database, uses data encryption and access control to ensure the security of the information, and provides an appropriate interface for lawyers and other parties to access it.

[0681] Emotion Engine Processing Procedure

[0682] Step 1:

[0683] It collects user input data and biometric sensor data (such as heart rate and electrodermal activity), and transmits this data to a server via the user's device.

[0684] Step 2:

[0685] The server uses an emotion engine to analyze the collected data and determine the user's emotional state, for example, analyzing fluctuations in heart rate and electrodermal activity to determine whether the user is under stress.

[0686] Step 3:

[0687] The server selects appropriate actions based on the user's emotional state based on the analysis results of the emotion engine. For example, if the user is in a high stress state, the server may delay notifications or notify them with less stressful content.

[0688] Step 4:

[0689] The emotion engine automatically adjusts the priority of deletion requests and changes the order in which they are sent to the administrator depending on the user's emotional state. For example, it gives priority to deletion requests from users who are particularly stressed.

[0690] These features allow for a swift and efficient response to defamatory comments, significantly reducing the user's psychological burden. Appropriate evidence storage also enables future legal action. This system provides comprehensive support for combating defamatory comments on the Internet.

[0691] Example 2

[0692] 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."

[0693] Defamatory comments on the Internet can cause significant psychological stress for users and, in some cases, can lead to legal issues. Therefore, there is a need for a system that can efficiently detect and quickly respond to defamatory comments. However, current systems require manual review and response, placing a significant burden on users. Furthermore, there is a lack of systems that can properly store evidence of comments and provide it when needed. Furthermore, responses are not tailored to the user's emotional state, which rarely reduces the psychological burden.

[0694] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0695] In this invention, the server includes means for automatically extracting defamatory comments from the Internet, means for summarizing the extracted defamatory comments using natural language processing technology, means for notifying users of the summarized defamatory comments, means for analyzing the user's emotional state and adjusting the content and timing of the notification, means for generating and sending requests to delete the defamatory comments, means for managing and notifying the progress of the deletion request, and means for collecting and storing evidence data of the defamatory comments. This enables swift and efficient responses to defamatory comments, significantly reducing the psychological burden on users, and enabling early responses to legal issues and appropriate evidence storage.

[0696] "Defamatory comments" are comments that contain content that belittles, insults, or damages the reputation of others.

[0697] "Automated internet extraction methods" refers to algorithms or functions that programmatically collect data from specific websites or social networking services.

[0698] "Natural language processing technology" refers to the technology of analyzing and understanding human language using a computer, and includes tasks such as tokenization, morphological analysis, and sentiment analysis.

[0699] "Summarization methods" refers to algorithms or functions that concisely summarize extracted data and provide important information in a concise format.

[0700] "Means for notifying the user" refers to a function for notifying the user of the processing results via email, application notification, etc.

[0701] "Means for analyzing emotional state" refers to algorithms or functions that analyze user input data or biometric sensor data to estimate the user's current emotional state.

[0702] "Means for adjusting the content and timing of notifications" refers to a function for changing the content of the message to be notified and the time of notification based on the user's emotional state.

[0703] "Means for generating and sending removal requests" refers to a function that automatically creates a request for removal of abusive comments and sends it to the administrator of the corresponding website or social networking service.

[0704] "Means for managing and notifying the progress of deletion requests" refers to a function for monitoring the status of submitted deletion requests and reporting the status to the user.

[0705] "Means for collecting and storing evidentiary data" refers to the ability to collect screenshots, text data, timestamps, poster identification information, etc. related to defamatory comments and store them in a secure database.

[0706] The system of the present invention aims to reduce the psychological burden on users by efficiently extracting defamatory comments from the Internet, notifying them, and requesting their deletion. This system is realized by combining a server, user terminals, data sources on the Internet, and an emotion engine.

[0707] System Configuration

[0708] 1. Server:

[0709] The server is responsible for key processes such as extracting abusive comments, summarizing them, sending notifications, generating and sending deletion requests, and collecting and storing evidence data. The server requires a high-performance CPU, sufficient memory, and storage. Software used includes NLP models for natural language processing (e.g., spaCy or NLTK), sentiment analysis engines (e.g., Google Cloud Natural Language API), and databases (e.g., PostgreSQL).

[0710] 2. User Device:

[0711] The user's device receives the notification from the server, confirms the contents of the deletion request, and approves it. Common devices such as smartphones, tablets, and PCs can be used.

[0712] 3. Internet data sources:

[0713] Social media platforms (e.g., Twitter, Facebook) are used as data sources. Comment data is collected from these platforms via APIs.

[0714] 4. Emotion Engine:

[0715] The emotion engine analyzes the user's emotional state and adjusts the system's behavior by analyzing biometric sensors (e.g., heart rate sensor, electrodermal activity sensor) and user input data.

[0716] Specific examples of system processing

[0717] Examples of data collection include:

[0718] The server uses the Twitter API to execute a shell script to retrieve replies and mentions for "@example_user." Specifically, the following prompt is input into the generative AI model:

[0719] Please use Twitter's API to collect abusive comments about @example_user. Extract comments containing keywords such as "disgusting" and "die," summarize them, and report them.

[0720] Examples of keyword filtering:

[0721] The server extracts comments such as "XX should die" from the list of collected comments and summarizes them as "strong criticism" using a natural language processing API.

[0722] Examples of user notifications:

[0723] The server generates a notification and sends it to the user via email, stating, "The following abusive comment was found: Strong criticism of ____." The emotion engine analyzes the user's heart rate and stress level, and delays the notification if the user is in a high stress state.

[0724] An example of generating and approving a deletion request:

[0725] The user clicks the link in the email, confirms the deletion request, and clicks the approval button. The server then uses the Twitter API to send the deletion request to the administrator, saves the status in the database, and notifies the user by email.

[0726] Examples of evidence collection and storage:

[0727] The server uses web scraping technology to capture screenshots of comments and stores them in an encrypted format in a database, which can then be used as legal evidence if needed.

[0728] In this way, the system of the present invention can quickly and efficiently respond to defamatory comments, reducing the psychological burden on users. In addition, by properly storing evidence, it can also respond to legal action if necessary.

[0729] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0730] Step 1:

[0731] Data collection:

[0732] The server accesses the API of a specific internet data source (e.g., a social media platform) to collect replies and mentions for the target user. The target user's ID and API access key are required as input. The server sends a request based on this input data and obtains the collected comment data as output. Specifically, the server uses the Twitter API to execute a query to obtain replies for "@example_user." This retrieves all comments related to "@example_user" in JSON format.

[0733] Step 2:

[0734] Keyword filtering:

[0735] The server filters the collected comment data using a negative keyword list. The collected comment data and the negative keyword list are required as input. The server uses these to analyze the text in the comments and generates a list of comments containing negative keywords as output. Specifically, the server extracts comments containing keywords such as "the worst" and "die." For example, a comment such as "XX is the worst person" would be added to the list.

[0736] Step 3:

[0737] Natural language processing summarization:

[0738] The server summarizes the filtered comment data using natural language processing (NLP) technology. The filtered comment list is required as input. The server performs tokenization, morphological analysis, and sentiment analysis on this, and generates summarized comments as output. Specifically, the server uses an NLP model (e.g., spaCy) to summarize a comment such as "XX is a horrible person" as "Strong criticism of XX."

[0739] Step 4:

[0740] User Notice:

[0741] The server notifies the user of the generated summary comment. The input required is the summarized comment and the user's notification settings. Based on these, the server outputs a notification to the user via email or a notification system. Specifically, the server sends the user an email stating, "The following abusive comment was found: Strong criticism of XX." The emotion engine delays notifications if the user's biosensor data (e.g., heart rate) indicates a high level of stress.

[0742] Step 5:

[0743] Generate a removal request:

[0744] The server automatically generates a deletion request based on abusive comments. The input required is a summary of the comment, the posting date and time, and the poster's identification information. The server applies this data to a template and generates a deletion request as output. Specifically, the server creates a deletion request for the comment "XX is the worst kind of person" and uses the template to fill in the necessary information.

[0745] Step 6:

[0746] User verification and authorization:

[0747] The user checks the deletion request sent from the server. The content of the deletion request sent from the server is required as input. The user checks the content of the deletion request based on this and sends an "approval" or "rejection" response to the server as output. Specifically, the user clicks the link in the email, checks the content of the deletion request, and clicks the "approval" button.

[0748] Step 7:

[0749] Submit a removal request:

[0750] The server receives the user's approval and sends the deletion request to the relevant platform administrator. An approved deletion request form is required as input. Based on this, the server sends the deletion request via the SNS API or administrator inquiry form, and stores the status of the deletion request in a database as output. Specifically, the server uses Twitter's API to send the deletion request and stores the status as "request pending."

[0751] Step 8:

[0752] Progress tracking and reporting:

[0753] The server periodically checks the progress of the deletion request and reports the status to the user. As input, it requires the current status of the deletion request. The server updates the status based on this and sends a notification message to the user as output. Specifically, the server notifies the user by email with a status message such as "Your deletion request is being processed."

[0754] Step 9:

[0755] Evidence collection and preservation:

[0756] The server collects evidence of abusive comments and stores it as a file. The input required is the comment data and associated metadata. The server then takes screenshots based on this, collects comment text, timestamps, and author identification, and stores this data in a secure database as output. Specifically, the server uses a screenshot capture tool to generate screenshots of the comments and stores them in an encrypted format.

[0757] Step 10:

[0758] Emotional State Analysis:

[0759] The emotion engine analyzes user input data and biometric sensor data to estimate the user's emotional state. As input, biometric sensor data (e.g., heart rate, electrodermal activity) and data entered by the user are required. The emotion engine analyzes the emotional state based on these and determines the user's stress level and emotional state as output. Specifically, the emotion engine analyzes heart rate sensor data to determine whether the user is in a high-stress state and notifies the server of the result.

[0760] (Application example 2)

[0761] 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."

[0762] When advertising campaigns are conducted online, slanderous and negative comments often have a negative impact on the effectiveness of the advertisement. Until now, there has been no system that can efficiently monitor and analyze these comments, notify advertising managers, and, if necessary, immediately request their deletion. Another problem is that these comments increase the psychological burden on advertising managers. Therefore, there is a need to solve these problems, minimize the negative impact of slanderous comments on advertising campaigns, and reduce the psychological burden on advertising managers.

[0763] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for automatically extracting abusive comments from the Internet, means for summarizing the extracted abusive comments using natural language processing technology, means for notifying users of the summarized abusive comments, means for generating and sending a request to delete the abusive comments, means for managing and notifying the progress of the deletion request, means for collecting and storing evidence data of the abusive comments, means for monitoring comments related to advertisements in real time, means for analyzing negative comments and notifying advertising personnel in a manner that reduces their psychological burden, and means for sending deletion requests to an administrator and reporting the progress of the request to the advertising personnel. This enables rapid monitoring of negative comments in advertising campaigns, reduces the psychological burden on advertising personnel, and minimizes the negative impact of abusive comments on advertising effectiveness.

[0764] "Defamatory comments" are negative comments made on the Internet with the intention of damaging the reputation or credibility of others.

[0765] "Automatic extraction" means using a program or algorithm to collect the target data without human intervention.

[0766] "Natural language processing technology" is a technology that allows computers to analyze and understand human language (natural language).

[0767] "Summarization methods" are techniques and methods for summarizing long comments or sentences and extracting important information.

[0768] "Notifying users" means notifying specific users of the collected and analyzed information.

[0769] "Generating and sending a removal request" means creating and sending a request to ask an administrator to remove a specific abusive comment.

[0770] "Manage and notify the progress of removal requests" means tracking the progress of removal requests and informing users of their status.

[0771] "Collecting and storing evidentiary data" means collecting information about abusive comments and storing it in a safe place.

[0772] "Comments about advertisements" are user opinions and impressions about a particular advertisement on the Internet.

[0773] "Real-time monitoring" means checking and collecting data immediately the moment it is generated.

[0774] "Analyzing negative comments" means analyzing the content of the comments and identifying negative or critical content within them.

[0775] "Notifying in a way that reduces psychological burden" means conveying information in a way that takes into consideration the mental and emotional burden on the user and minimizes stress.

[0776] "Send to administrator and report progress" means sending the generated deletion request to an administrator, and then tracking and reporting the progress of the request.

[0777] The system of the present invention aims to efficiently extract defamatory and negative comments about online advertising campaigns and minimize their negative impact on advertising effectiveness. It also provides support functions for notifications and removal requests to reduce the psychological burden on advertising personnel.

[0778] This system is composed of the following means.

[0779] 1. A method for automatically extracting defamatory comments from the Internet:

[0780] The server periodically collects comment data from social media sites and websites using APIs, making it possible to collect comments related to advertisements in real time or periodically.

[0781] 2. A method for summarizing extracted abusive comments using natural language processing technology:

[0782] The server applies natural language processing (NLP) technology to the collected comment data, specifically tokenizing, morphological analysis, and sentiment analysis, to summarize negative comments.

[0783] 3. Means of notifying users of summarized abusive comments:

[0784] The server then notifies the advertiser of the summarized negative comments, using an emotion engine to analyze the advertiser's psychological state and tailor the content of the notification to reduce stress, using email or app notifications.

[0785] 4. How to generate and submit a request to remove abusive comments:

[0786] A deletion request is generated for the collected negative comments and sent to the administrator of the advertising platform. A deletion request containing the necessary information is generated using a template, and is sent after obtaining the user's approval.

[0787] 5. How we manage and notify you about the progress of your removal request:

[0788] Track the progress of generated and submitted removal requests and periodically report their status to the advertising manager, so that the advertising manager is always aware of the status of the removal request.

[0789] 6. Means of collecting and storing evidence data of defamatory comments:

[0790] The server collects evidence data, such as screenshots of comments, text data, timestamps, and poster IDs, and stores it in a secure database. Security is also ensured using data encryption and access control.

[0791] 7. How to monitor comments about your ads in real time:

[0792] The server monitors comments related to ads in real time based on the specified keywords, allowing for immediate response to advertising campaigns.

[0793] 8. Analyze negative comments and notify advertisers in a way that minimizes their emotional burden:

[0794] The server identifies negative comments based on the results of sentiment analysis and notifies advertising managers at the appropriate time and with the appropriate content.

[0795] 9. How to submit a removal request to the Administrator and report its progress to the Advertiser:

[0796] The removal request will be sent to the administrator and the progress will be reported to the advertising manager, allowing for more efficient management of the advertising campaign.

[0797] Examples of concrete examples and prompts

[0798] For example, if a social media API is used to collect comments about a new product advertisement and a comment such as "this is the worst product" is detected, the system will analyze the psychological state of the person in charge of advertising using an emotion engine and notify them in a way that reduces their psychological burden. It will also automatically generate a deletion request and send it to the administrator.

[0799] Example prompt sentence:

[0800] Please create a specific program that extracts specific negative comments from social media comments about a new product advertisement, notifies the person in charge of advertising in a way that reduces the psychological burden, and generates a request to remove the comment. Also, please explain in natural language what the program does.

[0801] By implementing the above measures, the impact of negative comments on advertising campaigns can be minimized and the psychological burden on advertising personnel can be reduced.

[0802] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0803] Step 1:

[0804] Automatically extracts defamatory comments from the Internet.

[0805] The server periodically collects comment data from social media platforms and websites using APIs, specifically filtering and collecting comments containing keywords related to targeted advertising campaigns.

[0806] Input: Social media or website API endpoints, target keywords

[0807] Output: Filtered comment data

[0808] Step 2:

[0809] The extracted abusive comments are summarized using natural language processing technology.

[0810] The server performs tokenization, morphological analysis, and sentiment analysis on the collected comments to identify and summarize negative comments.

[0811] Input: Collected comment data

[0812] Output: Summarized negative comments

[0813] Step 3:

[0814] Notify the user of the summarized abusive comments.

[0815] The server then notifies the advertising manager of the summarized negative comments, using an emotion engine to analyze the advertising manager's psychological state and adjust the notification content to reduce stress.

[0816] Input: Summarized negative comments, advertising manager's psychological state data

[0817] Output: Adjusted notification content

[0818] Step 4:

[0819] Generate and submit a request to remove abusive comments.

[0820] The server automatically generates a deletion request based on the summarized abusive comments. It generates the necessary information (comment content, posting date and time, poster ID, etc.) based on a template and sends the deletion request after obtaining the user's approval.

[0821] Input: Summary of abusive comment, removal request template, user approval

[0822] Output: Generated deletion request, sending status

[0823] Step 5:

[0824] Manage and notify you of the progress of your removal request.

[0825] The server tracks the progress of the submitted removal request and periodically reports the status to the advertising manager, managing the status of the removal request and notifying the status such as "request in progress," "processing," or "removal completed."

[0826] Input: Status data of the deletion request submitted

[0827] Output: Progress report

[0828] Step 6:

[0829] Collect and store evidence of abusive comments.

[0830] The server collects evidence data related to defamatory comments, such as screenshots, text data, timestamps, and poster IDs, and stores it in a secure database. Security is ensured using data encryption and access control.

[0831] Input: Negative comments and associated metadata

[0832] Output: Stored evidence data

[0833] Step 7:

[0834] Monitor comments about your ads in real time.

[0835] The server monitors comments related to advertisements in real time based on the specified keywords and extracts immediate negative comments for a particular advertising campaign.

[0836] Inputs: Keywords related to your ad campaign, real-time comment data

[0837] Output: Real-time monitored comment data

[0838] Step 8:

[0839] Negative comments are analyzed and notified in a way that reduces the psychological burden on advertising staff.

[0840] The server notifies the advertiser of negative comments identified based on the results of sentiment analysis, taking into account the advertiser's psychological state. The system adjusts the content of the notification to reduce the advertiser's psychological burden.

[0841] Input: Sentiment analysis results, advertising manager's psychological state data

[0842] Output: Adjusted notification content

[0843] Step 9:

[0844] Send the removal request to the administrator and report the progress to the advertising manager.

[0845] The server sends the generated removal request to an administrator, tracks the progress of the removal request, and reports the status to the advertising representative.

[0846] Input: Generated deletion request, progress data

[0847] Output: Progress report of removal request

[0848] 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.

[0849] 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.

[0850] 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.

[0851] [Third embodiment]

[0852] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0853] 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.

[0854] 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).

[0855] 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.

[0856] 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.

[0857] 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).

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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."

[0864] The system of the present invention aims to reduce the psychological burden by efficiently extracting defamatory comments, notifying users, and requesting their deletion. This system is realized by combining a server, user terminals, and data sources on the Internet. The specific operation of the system is explained below.

[0865] Research and summary functions

[0866] First, the server automatically extracts abusive comments from the Internet. It periodically sends requests to the APIs of targeted websites and social media sites to collect the latest comment data. For example, it can use the Twitter API to collect replies and mentions of specific users.

[0867] The server applies keyword filtering to the collected comment data, extracting comments containing negative keywords, such as "gross," "die," and "scammer."

[0868] The server then analyzes and summarizes the extracted comments using natural language processing (NLP) techniques, including tokenization, morphological analysis, and sentiment analysis. This allows the server to understand the context and emotional level of the comment, summarizing a comment like "XX is a terrible person" as "strong criticism of XX."

[0869] Deletion request proxy function

[0870] The server notifies the user of the summarized abusive comments via email or in-app notification, making it easy for the user to check.

[0871] Next, the server automatically generates a deletion request for the defamatory comments it finds. The deletion request is generated using a template that includes information such as the comment content, posting date and time, and poster ID. The generated deletion request is sent to the user's device, where the user confirms the content and then approves it.

[0872] Once the user approves the deletion request, the server automatically sends the request to the administrator of the relevant website or social networking site. This process uses the platform's API endpoint or an administrator's contact form. The server also manages the progress of the deletion request and periodically reports to the user, notifying them of the status, such as "request in progress," "processing," or "deletion completed."

[0873] Evidence storage function

[0874] Finally, the server collects and stores evidence of the abusive comments as files. This evidence includes screenshots of the comments, text data, timestamps, and poster IDs. This data is stored in a secure database and can be accessed by lawyers and other parties if necessary. Security is also ensured using data encryption and access control.

[0875] These features allow for a swift and efficient response to defamatory comments, significantly reducing the user's psychological burden. Appropriate evidence storage also enables future legal action. This system provides comprehensive support for combating defamatory comments on the Internet.

[0876] The processing flow will be explained below.

[0877] Step 1:

[0878] The server periodically sends requests to the APIs of designated websites and social media sites to collect the latest comment data. The data collected through the API is temporarily stored in a database on the server.

[0879] Step 2:

[0880] The server applies keyword filtering to the collected comment data, extracting comments containing negative keywords such as "gross" and "die." This filtering process is implemented using Python scripts and regular expressions.

[0881] Step 3:

[0882] The server applies an algorithm that reviews the filtering results to filter out unusual cases. The algorithm uses past filtering results and heuristics to minimize false positives.

[0883] Step 4:

[0884] The server analyzes the extracted comments using natural language processing (NLP) techniques, including tokenization, morphological analysis, and sentiment analysis. For example, it uses Python libraries such as NLTK and SpaCy to analyze text data.

[0885] Step 5:

[0886] The server summarizes the comments based on the analysis results. In the summarization process, only the main points are extracted, and a comment such as "XX is a terrible person" is summarized as "Strong criticism of XX."

[0887] Step 6:

[0888] The server notifies the user of the summarized comment. The notification is sent to the user's device via email or in-app notification, allowing the user to immediately check the details of the comment.

[0889] Step 7:

[0890] The server automatically generates a deletion request for defamatory comments. The deletion request includes information such as the comment content, posting date and time, and poster ID. This information is saved in template format.

[0891] Step 8:

[0892] The user must review and approve the deletion request via the device, either through the app or the web interface.

[0893] Step 9:

[0894] The server receives the user's approval and sends the deletion request to the administrator of the relevant website or social networking site, who formally submits the request via an API endpoint or an administrator contact form.

[0895] Step 10:

[0896] The server periodically checks the status to manage the progress of the deletion request. For example, it tracks the status such as "request in progress," "processing," or "deletion completed," and notifies the user.

[0897] Step 11:

[0898] The server collects and stores evidence of defamatory comments as files, including screenshots of the comments, text data, timestamps, and poster IDs.

[0899] Step 12:

[0900] The server stores the collected evidence data in a secure database, uses data encryption and access control to ensure the security of the information, and provides an appropriate interface for lawyers and other parties to access it.

[0901] Example 1

[0902] 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."

[0903] Defamatory comments on the Internet can cause significant psychological stress and have a social impact on individuals and organizations. However, it is extremely difficult and time-consuming to quickly and accurately detect defamatory comments from the vast number of comments posted daily and to manually respond to them. In addition, the process of requesting the removal of defamatory comments is complicated, and managing the progress and preserving evidence are also issues.

[0904] 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.

[0905] In this invention, the server includes a means for automatically extracting defamatory content from a computer network, a means for summarizing the extracted defamatory content using natural language processing technology, and a means for notifying the user of the summarized defamatory content, thereby enabling the rapid and accurate extraction and summarization of defamatory comments.

[0906] Furthermore, the present invention includes a means for generating and transmitting a request to remove abusive content, a means for managing and notifying the progress of the removal request, and a means for collecting and storing evidence of the abusive content, thereby simplifying the process of requesting the removal of abusive comments, managing the progress, and ensuring the preservation of evidence.

[0907] "Abusive content" means comments or posts intended to harm or insult others.

[0908] A "computer network" is a communications network, such as the Internet, in which multiple computer terminals are interconnected.

[0909] "Means of extraction" refers to the function of collecting and selecting data based on specific conditions or keywords.

[0910] "Natural language processing technology" is a general term for a series of algorithms and technologies for processing and understanding human language.

[0911] "Summarization methods" are processes or techniques used to summarize collected data succinctly.

[0912] "User" means any individual or organization that uses this system.

[0913] "Means of notification" refers to the communication method or device that the system uses to convey specific information to the user.

[0914] A "removal request" is a formal request to a website or service operator to remove defamatory content.

[0915] "Means for generating and sending" refers to the functionality for automatically generating and sending deletion requests to the appropriate parties (websites and service operators).

[0916] "Progress management and notification means" refers to the functionality for tracking the current state of a deletion request and informing the user of its progress as appropriate.

[0917] "Evidential information" refers to data such as screenshots of comments, text data, timestamps, and poster IDs collected to prove the existence of defamatory content.

[0918] "Collection and storage means" refers to the functions or technologies used to obtain and securely store evidentiary information.

[0919] The system of the present invention aims to reduce the psychological burden by efficiently extracting defamatory content, notifying the user, and requesting its deletion on their behalf. This system is realized by combining a server, user terminals, and data sources on a computer network.

[0920] Research and summary functions

[0921] First, the server periodically sends requests to specific websites or social networking sites (e.g., Twitter) on the Internet to collect comment data. This can be done using standard frameworks such as the Twitter API. For example, replies and mentions for a specific user can be collected via the Twitter API. The comment data obtained by the server is then stored in a database.

[0922] The server then applies keyword filtering to the collected comment data. This filtering process is performed using scripts written in programming languages ​​such as Python and Java. For example, comments containing negative keywords (e.g., "gross," "die," "scammer," etc.) are extracted.

[0923] The server then analyzes and summarizes the extracted comments using natural language processing (NLP) techniques. It leverages NLP libraries (e.g., NLTK and spaCy) to perform tokenization, morphological analysis, and sentiment analysis to understand the context and emotional intensity of the comment. It summarizes a comment like "XX is a horrible person" as "Strong criticism of XX."

[0924] Deletion request proxy function

[0925] The server notifies the user of the summarized defamatory content. The notification is sent via email or in-app notification, so that the user can easily check it. The email system can use the SMTP protocol or REST API.

[0926] Next, the server automatically generates a request to remove the defamatory content that was found. This request is generated using a template that includes information such as the comment content, posting date and time, and poster ID. Template engines that can be used include Jinja2 and Thymeleaf. The generated request is sent to the user's device, where the user confirms the content and then approves it.

[0927] Once the user approves the deletion request, the server automatically sends the request to the administrator of the relevant website or social networking site. This process uses each platform's API endpoint or administrator's contact form. The server also manages the progress of the deletion request and periodically notifies the user of the status, such as "request in progress," "processing," or "deletion completed."

[0928] Evidence storage function

[0929] Finally, the server collects and stores evidence of the defamatory content as files. This evidence includes screenshots of comments, text data, timestamps, and poster IDs. This data is stored in a secure database and can be accessed by lawyers and other parties if necessary. The server ensures security by encrypting the data and controlling access.

[0930] Examples and prompts

[0931] For example, if a comment such as "XX is the worst person" is posted on Twitter, the system's processing would proceed as follows: The server collects comments via Twitter's API and filters out comments containing the keyword "worst." The server summarizes the comment as "strong criticism of XX" and notifies the user. The user approves the deletion request, and the server sends it to an administrator. The server tracks progress and notifies the user of the status. The server also stores the comment and related information as evidence.

[0932] Example prompt for a generative AI model:

[0933] "Please explain the steps you would take to generate an appropriate removal request for the Twitter comment '____ is a horrible person', notify the user, and seek their approval. Also, please provide a summary of this comment."

[0934] The above is an embodiment of the present invention.

[0935] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0936] Step 1: Collecting comment data

[0937] 1. The server periodically sends a request to a specific website or social networking site (e.g., Twitter) on the Internet.

[0938] 2. The server uses Twitter's API to collect replies and mentions for a specific user.

[0939] Input: Twitter API endpoint, ID of a specific user.

[0940] Data processing: Receive the response from the API and extract the comment data in JSON format.

[0941] Output: Comment data in JSON format.

[0942] Specific operation: The server sends an HTTP GET request to the Twitter API and stores the comment data obtained in response in a database.

[0943] Step 2: Keyword filtering

[0944] 1. Apply keyword filtering to the comment data collected by the server.

[0945] Input: JSON-formatted comment data, a list of negative keywords (e.g., "gross," "die," "scammer," etc.).

[0946] Data processing: Check whether comments contain negative keywords and extract comments that contain them.

[0947] Output: A filtered comment data list.

[0948] Specific operation: The server uses a Python script to perform keyword matching on the comment data list and adds comments that match the filter to the list.

[0949] Step 3: Summarizing comments

[0950] 1. The server analyzes and summarizes the extracted comments using natural language processing (NLP) technology.

[0951] Input: The filtered comment data list.

[0952] Data processing: Tokenization, morphological analysis, and sentiment analysis are performed to understand the context and sentiment of comments.

[0953] Output: A summarized comment data list.

[0954] What it does: The server uses an NLP engine (e.g. spaCy) to analyze each comment and generate a summary from each comment.

[0955] Step 4: Notify users

[0956] 1. The server notifies the user of the summarized abusive comments.

[0957] Input: A summarized comment data list.

[0958] Data processing: Formatting data for email or in-app notifications.

[0959] Output: Informational message.

[0960] Specific behavior: The server sends a notification message to the user using the SMTP protocol or a push notification service (e.g., Firebase Cloud Messaging).

[0961] Step 5: Generate a removal request

[0962] 1. The server automatically generates a request to delete any abusive comments it finds.

[0963] Input: Summary of comment, posting date and time, and poster ID.

[0964] Data processing: Embed this information in the deletion request template.

[0965] Output: An automatically generated deletion request.

[0966] Specific operation: The server creates a deletion request using a template engine (e.g., Jinja2) and sends it to the user's terminal.

[0967] Step 6: User Authorization

[0968] 1. The user reviews and approves the deletion request.

[0969] Input: Auto-generated removal request.

[0970] Data processing: Record user authorization.

[0971] Output: Approved removal request.

[0972] Specific operation: The user's device displays a confirmation screen, records the user's actions, and sends them to the server.

[0973] Step 7: Submitting and tracking your removal request

[0974] 1. The server automatically sends a deletion request to the administrator of the relevant website or social networking site.

[0975] Input: Approved removal request.

[0976] Data processing: Convert the deletion request into the appropriate format and send it to the specified endpoint.

[0977] Output: The submission status of the deletion request.

[0978] Specific operation: The server sends a deletion request using each platform's API endpoint or contact form and updates the status in the database.

[0979] Step 8: Communicate progress

[0980] 1. The server manages the progress of the deletion request and notifies the user periodically.

[0981] Input: The progress of the removal request.

[0982] Data transformation: Converting progress into notification messages.

[0983] Output: Progress notification messages.

[0984] Specific operation: The server will notify the user of the status such as "Request in progress," "Processing," or "Deletion completed" using a push notification service or email.

[0985] Step 9: Collect and store evidence

[0986] 1. The server collects evidence of defamatory comments and stores them as files.

[0987] Input: Comment screenshot, text data, timestamp, and author ID.

[0988] Data processing: storing these data in a secure format.

[0989] Output: Encrypted evidence file.

[0990] What it does: The server runs a process to collect evidence data, encrypts it, stores it in a database, and sets access controls as needed.

[0991] The above are the specific processing steps of the program of this system.

[0992] (Application example 1)

[0993] 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."

[0994] Many abusive comments are posted on the Internet, which can not only cause users psychological stress but also damage their personal reputation. Manually monitoring and deleting such comments is extremely time-consuming, so there is a need for a system that can efficiently extract, notify, delete, and store evidence of abusive comments. Furthermore, a system that can run on smartphones and has an easy-to-use interface is also required.

[0995] 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.

[0996] In this invention, the server includes means for automatically extracting abusive comments from the Internet, means for summarizing the extracted abusive comments using natural language processing technology, means for notifying users of the summarized abusive comments, means for generating and sending requests to delete the abusive comments, means for managing and notifying the progress of the deletion requests, means for collecting and storing evidence data of the abusive comments, means for providing a user interface that operates on a smartphone, means for filtering abusive comments that include negative keywords, means for generating summaries of the abusive comments using a generative AI model, and means for automatically collecting screenshots and related metadata, thereby enabling a fast and efficient response to abusive comments.

[0997] "Defamatory comments" are comments posted online that criticize and damage the reputation of specific individuals or groups.

[0998] "Means of extraction" refers to technology or devices that automatically collect specific information from the Internet.

[0999] "Natural language processing technology" is a series of technologies that use computers to understand, analyze, and generate human language.

[1000] A "means of summarizing" is a technique or device that compresses information, extracts the main points, and summarizes them briefly.

[1001] "Means for notifying" refers to a technique or device that conveys specific information to a user.

[1002] "Means for generating and sending a deletion request" refers to technology or devices that create a document requesting the deletion of abusive comments and send it to the administrator.

[1003] "Means for managing and notifying the progress of deletion requests" refers to techniques and devices that track the status of deletion requests and communicate that progress to users.

[1004] "Means for collecting and storing evidential data" refers to technology and devices that collect and safely store evidence related to defamatory comments.

[1005] A "smartphone" is a portable electronic device that, in addition to the functions of a mobile phone, has advanced computing power and Internet connectivity.

[1006] A "user interface" is the means by which a user interacts with a computer system or application.

[1007] "Filtering means" refers to technology or devices that select information based on specific criteria and remove unnecessary parts.

[1008] A "generative AI model" is an algorithmic model that is trained to perform a specific task using artificial intelligence.

[1009] A "screenshot" is a technique for saving the contents displayed on a computer or smartphone screen as a still image.

[1010] "Metadata" is data that describes information about data, and includes information such as the creation time and creator.

[1011] This invention is a system that efficiently extracts defamatory comments from the Internet, summarizes them, notifies users, requests their deletion, and stores necessary evidence data. It is realized by combining a server, user terminals (smartphones), and data sources on the Internet.

[1012] First, the server periodically collects comment data from social media sites and websites on the Internet. Specifically, it sends a request to the social media site's API to retrieve comments and replies for the target user. At this time, the server-side program communicates with the API using the "requests" library. The collected comment data is temporarily stored on the server.

[1013] Next, the server filters the collected comment data, automatically extracting comments containing negative keywords (e.g., "gross," "die," "scammer," etc.). This filtering process is also performed on the server side, using an efficient search algorithm.

[1014] Natural language processing technology utilizing a generative AI model is applied to the filtered comments to create a summary of the comments. Specifically, the collected comments are processed in order through processes such as tokenization, morphological analysis, and sentiment analysis to extract important information. Natural language processing libraries such as "simplicity-nlp" are used. The summary results are used in notifications, which will be described later.

[1015] The summarized comments are sent to the user's smartphone. A dedicated application running on the user's device receives the summary data sent from the server and sends the notification. The most common notification methods are push notifications and in-app notifications.

[1016] After the user checks the notification, they can approve the deletion request if necessary. After receiving the user's approval, the server automatically generates a deletion request and sends it to the API endpoint of the target social media or website. The server also manages the progress of the deletion request and notifies the user. The progress status includes statuses such as "request in progress," "processing," and "deletion completed."

[1017] Furthermore, evidence data of defamatory comments is automatically collected and stored by the server. This evidence data includes screenshots of the comments, text data, and metadata (posting date and time, poster ID, etc.). This data is encrypted as necessary and stored in a secure database.

[1018] As a concrete example, let's say the following abusive comment is posted online:

[1019] "○○ is a truly horrible person, no different from a con man."

[1020] These comments will be automatically extracted, filtered and summarized by the system.

[1021] The generative AI model is given a prompt like this:

[1022] "Please summarize the comments below and generate a concise statement for notification:

[1023] "○○ is a truly horrible person, no different from a con man."

[1024] Expected output:

[1025] "A strong criticism of ○○."

[1026] In this way, the present invention provides a system that can respond quickly and efficiently to abusive comments, thereby reducing the psychological burden on users.

[1027] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1028] Step 1:

[1029] The server collects comment data from social media sites and websites on the Internet. Specifically, the server-side program periodically sends API requests using the "requests" library to obtain comments and replies from target users. The input is the comment data received as an API response, and the output is the comment data temporarily stored on the server.

[1030] Step 2:

[1031] The server filters the collected comment data using negative keywords. Specifically, it executes a process to automatically extract comments containing specific keywords (e.g., "gross," "die," "scammer"). The input is the comment data stored on the server, and the output is a list of filtered negative comments.

[1032] Step 3:

[1033] The server uses a generative AI model to create a summary of the filtered negative comments. Specifically, it uses natural language processing technology to "tokenize," "morphologically analyze," and "sentimentally analyze" the comments to extract important information. The generative AI model uses the following prompt:

[1034] "Please summarize the comments below and generate a concise statement for notification:

[1035] "○○ is a truly horrible person, no different from a con man."

[1036] Expected output:

[1037] "A strong criticism of ○○."

[1038] The input is the filtered negative comments and the output is the generated comment summary.

[1039] Step 4:

[1040] The server notifies the user of the summarized comments on their smartphone. The user receives the notification and displays it to the user through a user interface. Notification methods include push notifications and in-app notifications. The input is the summarized comment data, and the output is a notification displayed on the user's smartphone.

[1041] Step 5:

[1042] The user checks the notification and approves the deletion request as necessary. Operation is simple via the user interface. The input is the deletion request approval after user confirmation, and the output is the deletion request approval information sent to the server.

[1043] Step 6:

[1044] The server automatically generates a deletion request message after receiving user approval and sends it to the API endpoint of the target social networking site or website. The deletion request message contains information such as the comment content, posting date and time, and poster ID. The input is the deletion request approval information, and the output is the deletion request sent to the social networking site or website.

[1045] Step 7:

[1046] Furthermore, the server automatically captures screenshots of defamatory comments as evidence, and collects and stores text data and metadata (posting date and time, poster ID). The input is negative comment data, and the output is encrypted evidence data. This data is stored in a secure database in preparation for future legal action.

[1047] Step 8:

[1048] The progress of the deletion request is managed by the server and periodically notified to the user. The progress status includes "request in progress," "processing," "deletion completed," etc., and can be checked by the user on their smartphone. The input is the progress status of the deletion request, and the output is a status notification to the user.

[1049] 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.

[1050] The system of the present invention aims to reduce the psychological burden by efficiently extracting abusive comments, notifying users, and requesting their deletion. This system is realized by combining a server, user terminals, an internet data source, and an emotion engine. The specific operation of the system is explained below.

[1051] Research and summary functions

[1052] First, the server automatically extracts abusive comments from the Internet. It periodically sends requests to the APIs of targeted websites and social media sites to collect the latest comment data. For example, it can use the Twitter API to collect replies and mentions of specific users.

[1053] The server applies keyword filtering to the collected comment data, extracting comments containing negative keywords such as "the worst" or "die." For example, a comment such as "XX is the worst person" would be picked up.

[1054] The server then analyzes and summarizes the extracted comments using natural language processing (NLP) techniques, including tokenization, morphological analysis, and sentiment analysis. This allows the server to understand the context and emotional level of the comment, summarizing a comment like "XX is a terrible person" as "strong criticism of XX."

[1055] Deletion request proxy function

[1056] The server then notifies the user of the summarized abusive comments. The emotion engine then analyzes the user's emotions and adjusts the notification content based on their emotional state. For example, if the user is under high stress, the notification may be delayed or the content may be softened.

[1057] Next, the server automatically generates a deletion request for the defamatory comments it finds. The deletion request is generated using a template that includes information such as the comment content, posting date and time, and poster ID. The generated deletion request is sent to the user's device, where the user confirms the content and then approves it.

[1058] Once the user approves the deletion request, the server automatically sends the request to the administrator of the relevant website or social networking site. This process uses the platform's API endpoint or an administrator's contact form. The server also manages the progress of the deletion request and periodically reports to the user, notifying them of the status, such as "request in progress," "processing," or "deletion completed."

[1059] Evidence storage function

[1060] Finally, the server collects and stores evidence of the abusive comments as files. This evidence includes screenshots of the comments, text data, timestamps, and poster IDs. This data is stored in a secure database and can be accessed by lawyers and other parties if necessary. Security is also ensured using data encryption and access control.

[1061] Emotion Engine Functions

[1062] The emotion engine recognizes the user's emotional state and adjusts the system's behavior accordingly. It analyzes emotions using user input data and biosensor data (e.g., heart rate and electrodermal activity). If the user is under high stress, notifications are delayed or suppressed to reduce the user's psychological burden. It can also automatically adjust the priority of deletion requests and change the order in which they are sent to administrators depending on the user's emotional state.

[1063] These features allow for a swift and efficient response to defamatory comments, significantly reducing the user's psychological burden. Appropriate evidence storage also enables future legal action. This system provides comprehensive support for combating defamatory comments on the Internet.

[1064] The processing flow will be explained below.

[1065] The system of the present invention aims to reduce the psychological burden by efficiently extracting abusive comments, notifying users, and requesting their deletion. This system is realized by combining a server, user terminals, an internet data source, and an emotion engine. The specific operation of the system is explained below.

[1066] System-wide processing steps

[1067] Step 1:

[1068] The server periodically sends requests to the APIs of designated websites and social media sites to collect the latest comment data. The data collected through the API is temporarily stored in a database on the server.

[1069] Step 2:

[1070] The server applies keyword filtering to the collected comment data, extracting comments containing negative keywords such as "gross" and "die." This filtering process is implemented using Python scripts and regular expressions.

[1071] Step 3:

[1072] The server applies an algorithm to filter out outlier cases to filtered comments, using past filtering results and heuristics to minimize false positives.

[1073] Step 4:

[1074] The server further analyzes the filtered results using natural language processing (NLP) techniques, including tokenization, morphological analysis, and sentiment analysis, using Python libraries such as NLTK and SpaCy to analyze the text data.

[1075] Step 5:

[1076] The server summarizes the comments based on the analysis results. The summarization process extracts only the main points. For example, a comment such as "XX is a terrible person" can be summarized as "Strong criticism of XX."

[1077] Step 6:

[1078] The server notifies the user of the summarized comments, and the emotion engine analyzes the user's emotions and adjusts the notification content based on their emotional state. For example, if the user is under high stress, the notification may be delayed.

[1079] Step 7:

[1080] The server automatically generates a deletion request for defamatory comments. The deletion request includes information such as the comment content, posting date and time, and poster ID. This information is saved in template format.

[1081] Step 8:

[1082] The server sends the deletion request to the user's device, where the user can review and approve the request via an app or web interface.

[1083] Step 9:

[1084] Once the user has approved the request, the server sends the deletion request to the administrator of the relevant website or social networking site, using an API endpoint or a contact form for the administrator.

[1085] Step 10:

[1086] The server periodically checks the status to manage the progress of the deletion request. For example, it tracks the status such as "request in progress," "processing," or "deletion completed," and notifies the user.

[1087] Step 11:

[1088] The server collects and stores evidence of defamatory comments as files, including screenshots of the comments, text data, timestamps, and poster IDs.

[1089] Step 12:

[1090] The server stores the collected evidence data in a secure database, uses data encryption and access control to ensure the security of the information, and provides an appropriate interface for lawyers and other parties to access it.

[1091] Emotion Engine Processing Procedure

[1092] Step 1:

[1093] It collects user input data and biometric sensor data (such as heart rate and electrodermal activity), and transmits this data to a server via the user's device.

[1094] Step 2:

[1095] The server uses an emotion engine to analyze the collected data and determine the user's emotional state, for example, analyzing fluctuations in heart rate and electrodermal activity to determine whether the user is under stress.

[1096] Step 3:

[1097] The server selects appropriate actions based on the user's emotional state based on the analysis results of the emotion engine. For example, if the user is in a high stress state, the server may delay notifications or notify them with less stressful content.

[1098] Step 4:

[1099] The emotion engine automatically adjusts the priority of deletion requests and changes the order in which they are sent to the administrator depending on the user's emotional state. For example, it gives priority to deletion requests from users who are particularly stressed.

[1100] These features allow for a swift and efficient response to defamatory comments, significantly reducing the user's psychological burden. Appropriate evidence storage also enables future legal action. This system provides comprehensive support for combating defamatory comments on the Internet.

[1101] Example 2

[1102] 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."

[1103] Defamatory comments on the Internet can cause significant psychological stress for users and, in some cases, can lead to legal issues. Therefore, there is a need for a system that can efficiently detect and quickly respond to defamatory comments. However, current systems require manual review and response, placing a significant burden on users. Furthermore, there is a lack of systems that can properly store evidence of comments and provide it when needed. Furthermore, responses are not tailored to the user's emotional state, which rarely reduces the psychological burden.

[1104] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1105] In this invention, the server includes means for automatically extracting defamatory comments from the Internet, means for summarizing the extracted defamatory comments using natural language processing technology, means for notifying users of the summarized defamatory comments, means for analyzing the user's emotional state and adjusting the content and timing of the notification, means for generating and sending requests to delete the defamatory comments, means for managing and notifying the progress of the deletion request, and means for collecting and storing evidence data of the defamatory comments. This enables swift and efficient responses to defamatory comments, significantly reducing the psychological burden on users, and enabling early responses to legal issues and appropriate evidence storage.

[1106] "Defamatory comments" are comments that contain content that belittles, insults, or damages the reputation of others.

[1107] "Automated internet extraction methods" refers to algorithms or functions that programmatically collect data from specific websites or social networking services.

[1108] "Natural language processing technology" refers to the technology of analyzing and understanding human language using a computer, and includes tasks such as tokenization, morphological analysis, and sentiment analysis.

[1109] "Summarization methods" refers to algorithms or functions that concisely summarize extracted data and provide important information in a concise format.

[1110] "Means for notifying the user" refers to a function for notifying the user of the processing results via email, application notification, etc.

[1111] "Means for analyzing emotional state" refers to algorithms or functions that analyze user input data or biometric sensor data to estimate the user's current emotional state.

[1112] "Means for adjusting the content and timing of notifications" refers to a function for changing the content of the message to be notified and the time of notification based on the user's emotional state.

[1113] "Means for generating and sending removal requests" refers to a function that automatically creates a request for removal of abusive comments and sends it to the administrator of the corresponding website or social networking service.

[1114] "Means for managing and notifying the progress of deletion requests" refers to a function for monitoring the status of submitted deletion requests and reporting the status to the user.

[1115] "Means for collecting and storing evidentiary data" refers to the ability to collect screenshots, text data, timestamps, poster identification information, etc. related to defamatory comments and store them in a secure database.

[1116] The system of the present invention aims to reduce the psychological burden on users by efficiently extracting defamatory comments from the Internet, notifying them, and requesting their deletion. This system is realized by combining a server, user terminals, data sources on the Internet, and an emotion engine.

[1117] System Configuration

[1118] 1. Server:

[1119] The server is responsible for key processes such as extracting abusive comments, summarizing them, sending notifications, generating and sending deletion requests, and collecting and storing evidence data. The server requires a high-performance CPU, sufficient memory, and storage. Software used includes NLP models for natural language processing (e.g., spaCy or NLTK), sentiment analysis engines (e.g., Google Cloud Natural Language API), and databases (e.g., PostgreSQL).

[1120] 2. User Device:

[1121] The user's device receives the notification from the server, confirms the contents of the deletion request, and approves it. Common devices such as smartphones, tablets, and PCs can be used.

[1122] 3. Internet data sources:

[1123] Social media platforms (e.g., Twitter, Facebook) are used as data sources. Comment data is collected from these platforms via APIs.

[1124] 4. Emotion Engine:

[1125] The emotion engine analyzes the user's emotional state and adjusts the system's behavior by analyzing biometric sensors (e.g., heart rate sensor, electrodermal activity sensor) and user input data.

[1126] Specific examples of system processing

[1127] Examples of data collection include:

[1128] The server uses the Twitter API to execute a shell script to retrieve replies and mentions for "@example_user." Specifically, the following prompt is input into the generative AI model:

[1129] Please use Twitter's API to collect abusive comments about @example_user. Extract comments containing keywords such as "disgusting" and "die," summarize them, and report them.

[1130] Examples of keyword filtering:

[1131] The server extracts comments such as "XX should die" from the list of collected comments and summarizes them as "strong criticism" using a natural language processing API.

[1132] Examples of user notifications:

[1133] The server generates a notification and sends it to the user via email, stating, "The following abusive comment was found: Strong criticism of ____." The emotion engine analyzes the user's heart rate and stress level, and delays the notification if the user is in a high stress state.

[1134] An example of generating and approving a deletion request:

[1135] The user clicks the link in the email, confirms the deletion request, and clicks the approval button. The server then uses the Twitter API to send the deletion request to the administrator, saves the status in the database, and notifies the user by email.

[1136] Examples of evidence collection and storage:

[1137] The server uses web scraping technology to capture screenshots of comments and stores them in an encrypted format in a database, which can then be used as legal evidence if needed.

[1138] In this way, the system of the present invention can quickly and efficiently respond to defamatory comments, reducing the psychological burden on users. In addition, by properly storing evidence, it can also respond to legal action if necessary.

[1139] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1140] Step 1:

[1141] Data collection:

[1142] The server accesses the API of a specific internet data source (e.g., a social media platform) to collect replies and mentions for the target user. The target user's ID and API access key are required as input. The server sends a request based on this input data and obtains the collected comment data as output. Specifically, the server uses the Twitter API to execute a query to obtain replies for "@example_user." This retrieves all comments related to "@example_user" in JSON format.

[1143] Step 2:

[1144] Keyword filtering:

[1145] The server filters the collected comment data using a negative keyword list. The collected comment data and the negative keyword list are required as input. The server uses these to analyze the text in the comments and generates a list of comments containing negative keywords as output. Specifically, the server extracts comments containing keywords such as "the worst" and "die." For example, a comment such as "XX is the worst person" would be added to the list.

[1146] Step 3:

[1147] Natural language processing summarization:

[1148] The server summarizes the filtered comment data using natural language processing (NLP) technology. The filtered comment list is required as input. The server performs tokenization, morphological analysis, and sentiment analysis on this, and generates summarized comments as output. Specifically, the server uses an NLP model (e.g., spaCy) to summarize a comment such as "XX is a horrible person" as "Strong criticism of XX."

[1149] Step 4:

[1150] User Notice:

[1151] The server notifies the user of the generated summary comment. The input required is the summarized comment and the user's notification settings. Based on these, the server outputs a notification to the user via email or a notification system. Specifically, the server sends the user an email stating, "The following abusive comment was found: Strong criticism of XX." The emotion engine delays notifications if the user's biosensor data (e.g., heart rate) indicates a high level of stress.

[1152] Step 5:

[1153] Generate a removal request:

[1154] The server automatically generates a deletion request based on abusive comments. The input required is a summary of the comment, the posting date and time, and the poster's identification information. The server applies this data to a template and generates a deletion request as output. Specifically, the server creates a deletion request for the comment "XX is the worst kind of person" and uses the template to fill in the necessary information.

[1155] Step 6:

[1156] User verification and authorization:

[1157] The user checks the deletion request sent from the server. The content of the deletion request sent from the server is required as input. The user checks the content of the deletion request based on this and sends an "approval" or "rejection" response to the server as output. Specifically, the user clicks the link in the email, checks the content of the deletion request, and clicks the "approval" button.

[1158] Step 7:

[1159] Submit a removal request:

[1160] The server receives the user's approval and sends the deletion request to the relevant platform administrator. An approved deletion request form is required as input. Based on this, the server sends the deletion request via the SNS API or administrator inquiry form, and stores the status of the deletion request in a database as output. Specifically, the server uses Twitter's API to send the deletion request and stores the status as "request pending."

[1161] Step 8:

[1162] Progress tracking and reporting:

[1163] The server periodically checks the progress of the deletion request and reports the status to the user. As input, it requires the current status of the deletion request. The server updates the status based on this and sends a notification message to the user as output. Specifically, the server notifies the user by email with a status message such as "Your deletion request is being processed."

[1164] Step 9:

[1165] Evidence collection and preservation:

[1166] The server collects evidence of abusive comments and stores it as a file. The input required is the comment data and associated metadata. The server then takes screenshots based on this, collects comment text, timestamps, and author identification, and stores this data in a secure database as output. Specifically, the server uses a screenshot capture tool to generate screenshots of the comments and stores them in an encrypted format.

[1167] Step 10:

[1168] Emotional State Analysis:

[1169] The emotion engine analyzes user input data and biometric sensor data to estimate the user's emotional state. As input, biometric sensor data (e.g., heart rate, electrodermal activity) and data entered by the user are required. The emotion engine analyzes the emotional state based on these and determines the user's stress level and emotional state as output. Specifically, the emotion engine analyzes heart rate sensor data to determine whether the user is in a high-stress state and notifies the server of the result.

[1170] (Application example 2)

[1171] 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."

[1172] When advertising campaigns are conducted online, slanderous and negative comments often have a negative impact on the effectiveness of the advertisement. Until now, there has been no system that can efficiently monitor and analyze these comments, notify advertising managers, and, if necessary, immediately request their deletion. Another problem is that these comments increase the psychological burden on advertising managers. Therefore, there is a need to solve these problems, minimize the negative impact of slanderous comments on advertising campaigns, and reduce the psychological burden on advertising managers.

[1173] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for automatically extracting abusive comments from the Internet, means for summarizing the extracted abusive comments using natural language processing technology, means for notifying users of the summarized abusive comments, means for generating and sending a request to delete the abusive comments, means for managing and notifying the progress of the deletion request, means for collecting and storing evidence data of the abusive comments, means for monitoring comments related to advertisements in real time, means for analyzing negative comments and notifying advertising personnel in a manner that reduces their psychological burden, and means for sending deletion requests to an administrator and reporting the progress of the request to the advertising personnel. This enables rapid monitoring of negative comments in advertising campaigns, reduces the psychological burden on advertising personnel, and minimizes the negative impact of abusive comments on advertising effectiveness.

[1174] "Defamatory comments" are negative comments made on the Internet with the intention of damaging the reputation or credibility of others.

[1175] "Automatic extraction" means using a program or algorithm to collect the target data without human intervention.

[1176] "Natural language processing technology" is a technology that allows computers to analyze and understand human language (natural language).

[1177] "Summarization methods" are techniques and methods for summarizing long comments or sentences and extracting important information.

[1178] "Notifying users" means notifying specific users of the collected and analyzed information.

[1179] "Generating and sending a removal request" means creating and sending a request to ask an administrator to remove a specific abusive comment.

[1180] "Manage and notify the progress of removal requests" means tracking the progress of removal requests and informing users of their status.

[1181] "Collecting and storing evidentiary data" means collecting information about abusive comments and storing it in a safe place.

[1182] "Comments about advertisements" are user opinions and impressions about a particular advertisement on the Internet.

[1183] "Real-time monitoring" means checking and collecting data immediately the moment it is generated.

[1184] "Analyzing negative comments" means analyzing the content of the comments and identifying negative or critical content within them.

[1185] "Notifying in a way that reduces psychological burden" means conveying information in a way that takes into consideration the mental and emotional burden on the user and minimizes stress.

[1186] "Send to administrator and report progress" means sending the generated deletion request to an administrator, and then tracking and reporting the progress of the request.

[1187] The system of the present invention aims to efficiently extract defamatory and negative comments about online advertising campaigns and minimize their negative impact on advertising effectiveness. It also provides support functions for notifications and removal requests to reduce the psychological burden on advertising personnel.

[1188] This system is composed of the following means.

[1189] 1. A method for automatically extracting defamatory comments from the Internet:

[1190] The server periodically collects comment data from social media sites and websites using APIs, making it possible to collect comments related to advertisements in real time or periodically.

[1191] 2. A method for summarizing extracted abusive comments using natural language processing technology:

[1192] The server applies natural language processing (NLP) technology to the collected comment data, specifically tokenizing, morphological analysis, and sentiment analysis, to summarize negative comments.

[1193] 3. Means of notifying users of summarized abusive comments:

[1194] The server then notifies the advertiser of the summarized negative comments, using an emotion engine to analyze the advertiser's psychological state and tailor the content of the notification to reduce stress, using email or app notifications.

[1195] 4. How to generate and submit a request to remove abusive comments:

[1196] A deletion request is generated for the collected negative comments and sent to the administrator of the advertising platform. A deletion request containing the necessary information is generated using a template, and is sent after obtaining the user's approval.

[1197] 5. How we manage and notify you about the progress of your removal request:

[1198] Track the progress of generated and submitted removal requests and periodically report their status to the advertising manager, so that the advertising manager is always aware of the status of the removal request.

[1199] 6. Means of collecting and storing evidence data of defamatory comments:

[1200] The server collects evidence data, such as screenshots of comments, text data, timestamps, and poster IDs, and stores it in a secure database. Security is also ensured using data encryption and access control.

[1201] 7. How to monitor comments about your ads in real time:

[1202] The server monitors comments related to ads in real time based on the specified keywords, allowing for immediate response to advertising campaigns.

[1203] 8. Analyze negative comments and notify advertisers in a way that minimizes their emotional burden:

[1204] The server identifies negative comments based on the results of sentiment analysis and notifies advertising managers at the appropriate time and with the appropriate content.

[1205] 9. How to submit a removal request to the Administrator and report its progress to the Advertiser:

[1206] The removal request will be sent to the administrator and the progress will be reported to the advertising manager, allowing for more efficient management of the advertising campaign.

[1207] Examples of concrete examples and prompts

[1208] For example, if a social media API is used to collect comments about a new product advertisement and a comment such as "this is the worst product" is detected, the system will analyze the psychological state of the person in charge of advertising using an emotion engine and notify them in a way that reduces their psychological burden. It will also automatically generate a deletion request and send it to the administrator.

[1209] Example prompt sentence:

[1210] Please create a specific program that extracts specific negative comments from social media comments about a new product advertisement, notifies the person in charge of advertising in a way that reduces the psychological burden, and generates a request to remove the comment. Also, please explain in natural language what the program does.

[1211] By implementing the above measures, the impact of negative comments on advertising campaigns can be minimized and the psychological burden on advertising personnel can be reduced.

[1212] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1213] Step 1:

[1214] Automatically extracts defamatory comments from the Internet.

[1215] The server periodically collects comment data from social media platforms and websites using APIs, specifically filtering and collecting comments containing keywords related to targeted advertising campaigns.

[1216] Input: Social media or website API endpoints, target keywords

[1217] Output: Filtered comment data

[1218] Step 2:

[1219] The extracted abusive comments are summarized using natural language processing technology.

[1220] The server performs tokenization, morphological analysis, and sentiment analysis on the collected comments to identify and summarize negative comments.

[1221] Input: Collected comment data

[1222] Output: Summarized negative comments

[1223] Step 3:

[1224] Notify the user of the summarized abusive comments.

[1225] The server then notifies the advertising manager of the summarized negative comments, using an emotion engine to analyze the advertising manager's psychological state and adjust the notification content to reduce stress.

[1226] Input: Summarized negative comments, advertising manager's psychological state data

[1227] Output: Adjusted notification content

[1228] Step 4:

[1229] Generate and submit a request to remove abusive comments.

[1230] The server automatically generates a deletion request based on the summarized abusive comments. It generates the necessary information (comment content, posting date and time, poster ID, etc.) based on a template and sends the deletion request after obtaining the user's approval.

[1231] Input: Summary of abusive comment, removal request template, user approval

[1232] Output: Generated deletion request, sending status

[1233] Step 5:

[1234] Manage and notify you of the progress of your removal request.

[1235] The server tracks the progress of the submitted removal request and periodically reports the status to the advertising manager, managing the status of the removal request and notifying the status such as "request in progress," "processing," or "removal completed."

[1236] Input: Status data of the deletion request submitted

[1237] Output: Progress report

[1238] Step 6:

[1239] Collect and store evidence of abusive comments.

[1240] The server collects evidence data related to defamatory comments, such as screenshots, text data, timestamps, and poster IDs, and stores it in a secure database. Security is ensured using data encryption and access control.

[1241] Input: Negative comments and associated metadata

[1242] Output: Stored evidence data

[1243] Step 7:

[1244] Monitor comments about your ads in real time.

[1245] The server monitors comments related to advertisements in real time based on the specified keywords and extracts immediate negative comments for a particular advertising campaign.

[1246] Inputs: Keywords related to your ad campaign, real-time comment data

[1247] Output: Real-time monitored comment data

[1248] Step 8:

[1249] Negative comments are analyzed and notified in a way that reduces the psychological burden on advertising staff.

[1250] The server notifies the advertiser of negative comments identified based on the results of sentiment analysis, taking into account the advertiser's psychological state. The system adjusts the content of the notification to reduce the advertiser's psychological burden.

[1251] Input: Sentiment analysis results, advertising manager's psychological state data

[1252] Output: Adjusted notification content

[1253] Step 9:

[1254] Send the removal request to the administrator and report the progress to the advertising manager.

[1255] The server sends the generated removal request to an administrator, tracks the progress of the removal request, and reports the status to the advertising representative.

[1256] Input: Generated deletion request, progress data

[1257] Output: Progress report of removal request

[1258] 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.

[1259] 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.

[1260] 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.

[1261] [Fourth embodiment]

[1262] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1263] 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.

[1264] 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).

[1265] 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.

[1266] 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.

[1267] 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).

[1268] 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.

[1269] 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.

[1270] 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.

[1271] 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.

[1272] 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.

[1273] 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.

[1274] 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."

[1275] The system of the present invention aims to reduce the psychological burden by efficiently extracting defamatory comments, notifying users, and requesting their deletion. This system is realized by combining a server, user terminals, and data sources on the Internet. The specific operation of the system is explained below.

[1276] Research and summary functions

[1277] First, the server automatically extracts abusive comments from the Internet. It periodically sends requests to the APIs of targeted websites and social media sites to collect the latest comment data. For example, it can use the Twitter API to collect replies and mentions of specific users.

[1278] The server applies keyword filtering to the collected comment data, extracting comments containing negative keywords, such as "gross," "die," and "scammer."

[1279] The server then analyzes and summarizes the extracted comments using natural language processing (NLP) techniques, including tokenization, morphological analysis, and sentiment analysis. This allows the server to understand the context and emotional level of the comment, summarizing a comment like "XX is a terrible person" as "strong criticism of XX."

[1280] Deletion request proxy function

[1281] The server notifies the user of the summarized abusive comments via email or in-app notification, making it easy for the user to check.

[1282] Next, the server automatically generates a deletion request for the defamatory comments it finds. The deletion request is generated using a template that includes information such as the comment content, posting date and time, and poster ID. The generated deletion request is sent to the user's device, where the user confirms the content and then approves it.

[1283] Once the user approves the deletion request, the server automatically sends the request to the administrator of the relevant website or social networking site. This process uses the platform's API endpoint or an administrator's contact form. The server also manages the progress of the deletion request and periodically reports to the user, notifying them of the status, such as "request in progress," "processing," or "deletion completed."

[1284] Evidence storage function

[1285] Finally, the server collects and stores evidence of the abusive comments as files. This evidence includes screenshots of the comments, text data, timestamps, and poster IDs. This data is stored in a secure database and can be accessed by lawyers and other parties if necessary. Security is also ensured using data encryption and access control.

[1286] These features allow for a swift and efficient response to defamatory comments, significantly reducing the user's psychological burden. Appropriate evidence storage also enables future legal action. This system provides comprehensive support for combating defamatory comments on the Internet.

[1287] The processing flow will be explained below.

[1288] Step 1:

[1289] The server periodically sends requests to the APIs of designated websites and social media sites to collect the latest comment data. The data collected through the API is temporarily stored in a database on the server.

[1290] Step 2:

[1291] The server applies keyword filtering to the collected comment data, extracting comments containing negative keywords such as "gross" and "die." This filtering process is implemented using Python scripts and regular expressions.

[1292] Step 3:

[1293] The server applies an algorithm that reviews the filtering results to filter out unusual cases. The algorithm uses past filtering results and heuristics to minimize false positives.

[1294] Step 4:

[1295] The server analyzes the extracted comments using natural language processing (NLP) techniques, including tokenization, morphological analysis, and sentiment analysis. For example, it uses Python libraries such as NLTK and SpaCy to analyze text data.

[1296] Step 5:

[1297] The server summarizes the comments based on the analysis results. In the summarization process, only the main points are extracted, and a comment such as "XX is a terrible person" is summarized as "Strong criticism of XX."

[1298] Step 6:

[1299] The server notifies the user of the summarized comment. The notification is sent to the user's device via email or in-app notification, allowing the user to immediately check the details of the comment.

[1300] Step 7:

[1301] The server automatically generates a deletion request for defamatory comments. The deletion request includes information such as the comment content, posting date and time, and poster ID. This information is saved in template format.

[1302] Step 8:

[1303] The user must review and approve the deletion request via the device, either through the app or the web interface.

[1304] Step 9:

[1305] The server receives the user's approval and sends the deletion request to the administrator of the relevant website or social networking site, who formally submits the request via an API endpoint or an administrator contact form.

[1306] Step 10:

[1307] The server periodically checks the status to manage the progress of the deletion request. For example, it tracks the status such as "request in progress," "processing," or "deletion completed," and notifies the user.

[1308] Step 11:

[1309] The server collects and stores evidence of defamatory comments as files, including screenshots of the comments, text data, timestamps, and poster IDs.

[1310] Step 12:

[1311] The server stores the collected evidence data in a secure database, uses data encryption and access control to ensure the security of the information, and provides an appropriate interface for lawyers and other parties to access it.

[1312] Example 1

[1313] 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."

[1314] Defamatory comments on the Internet can cause significant psychological stress and have a social impact on individuals and organizations. However, it is extremely difficult and time-consuming to quickly and accurately detect defamatory comments from the vast number of comments posted daily and to manually respond to them. In addition, the process of requesting the removal of defamatory comments is complicated, and managing the progress and preserving evidence are also issues.

[1315] 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.

[1316] In this invention, the server includes a means for automatically extracting defamatory content from a computer network, a means for summarizing the extracted defamatory content using natural language processing technology, and a means for notifying the user of the summarized defamatory content, thereby enabling the rapid and accurate extraction and summarization of defamatory comments.

[1317] Furthermore, the present invention includes a means for generating and transmitting a request to remove abusive content, a means for managing and notifying the progress of the removal request, and a means for collecting and storing evidence of the abusive content, thereby simplifying the process of requesting the removal of abusive comments, managing the progress, and ensuring the preservation of evidence.

[1318] "Abusive content" means comments or posts intended to harm or insult others.

[1319] A "computer network" is a communications network, such as the Internet, in which multiple computer terminals are interconnected.

[1320] "Means of extraction" refers to the function of collecting and selecting data based on specific conditions or keywords.

[1321] "Natural language processing technology" is a general term for a series of algorithms and technologies for processing and understanding human language.

[1322] "Summarization methods" are processes or techniques used to summarize collected data succinctly.

[1323] "User" means any individual or organization that uses this system.

[1324] "Means of notification" refers to the communication method or device that the system uses to convey specific information to the user.

[1325] A "removal request" is a formal request to a website or service operator to remove defamatory content.

[1326] "Means for generating and sending" refers to the functionality for automatically generating and sending deletion requests to the appropriate parties (websites and service operators).

[1327] "Progress management and notification means" refers to the functionality for tracking the current state of a deletion request and informing the user of its progress as appropriate.

[1328] "Evidential information" refers to data such as screenshots of comments, text data, timestamps, and poster IDs collected to prove the existence of defamatory content.

[1329] "Collection and storage means" refers to the functions or technologies used to obtain and securely store evidentiary information.

[1330] The system of the present invention aims to reduce the psychological burden by efficiently extracting defamatory content, notifying the user, and requesting its deletion on their behalf. This system is realized by combining a server, user terminals, and data sources on a computer network.

[1331] Research and summary functions

[1332] First, the server periodically sends requests to specific websites or social networking sites (e.g., Twitter) on the Internet to collect comment data. This can be done using standard frameworks such as the Twitter API. For example, replies and mentions for a specific user can be collected via the Twitter API. The comment data obtained by the server is then stored in a database.

[1333] The server then applies keyword filtering to the collected comment data. This filtering process is performed using scripts written in programming languages ​​such as Python and Java. For example, comments containing negative keywords (e.g., "gross," "die," "scammer," etc.) are extracted.

[1334] The server then analyzes and summarizes the extracted comments using natural language processing (NLP) techniques. It leverages NLP libraries (e.g., NLTK and spaCy) to perform tokenization, morphological analysis, and sentiment analysis to understand the context and emotional intensity of the comment. It summarizes a comment like "XX is a horrible person" as "Strong criticism of XX."

[1335] Deletion request proxy function

[1336] The server notifies the user of the summarized defamatory content. The notification is sent via email or in-app notification, so that the user can easily check it. The email system can use the SMTP protocol or REST API.

[1337] Next, the server automatically generates a request to remove the defamatory content that was found. This request is generated using a template that includes information such as the comment content, posting date and time, and poster ID. Template engines that can be used include Jinja2 and Thymeleaf. The generated request is sent to the user's device, where the user confirms the content and then approves it.

[1338] Once the user approves the deletion request, the server automatically sends the request to the administrator of the relevant website or social networking site. This process uses each platform's API endpoint or administrator's contact form. The server also manages the progress of the deletion request and periodically notifies the user of the status, such as "request in progress," "processing," or "deletion completed."

[1339] Evidence storage function

[1340] Finally, the server collects and stores evidence of the defamatory content as files. This evidence includes screenshots of comments, text data, timestamps, and poster IDs. This data is stored in a secure database and can be accessed by lawyers and other parties if necessary. The server ensures security by encrypting the data and controlling access.

[1341] Examples and prompts

[1342] For example, if a comment such as "XX is the worst person" is posted on Twitter, the system's processing would proceed as follows: The server collects comments via Twitter's API and filters out comments containing the keyword "worst." The server summarizes the comment as "strong criticism of XX" and notifies the user. The user approves the deletion request, and the server sends it to an administrator. The server tracks progress and notifies the user of the status. The server also stores the comment and related information as evidence.

[1343] Example prompt for a generative AI model:

[1344] "Please explain the steps you would take to generate an appropriate removal request for the Twitter comment '____ is a horrible person', notify the user, and seek their approval. Also, please provide a summary of this comment."

[1345] The above is an embodiment of the present invention.

[1346] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1347] Step 1: Collecting comment data

[1348] 1. The server periodically sends a request to a specific website or social networking site (e.g., Twitter) on the Internet.

[1349] 2. The server uses Twitter's API to collect replies and mentions for a specific user.

[1350] Input: Twitter API endpoint, ID of a specific user.

[1351] Data processing: Receive the response from the API and extract the comment data in JSON format.

[1352] Output: Comment data in JSON format.

[1353] Specific operation: The server sends an HTTP GET request to the Twitter API and stores the comment data obtained in response in a database.

[1354] Step 2: Keyword filtering

[1355] 1. Apply keyword filtering to the comment data collected by the server.

[1356] Input: JSON-formatted comment data, a list of negative keywords (e.g., "gross," "die," "scammer," etc.).

[1357] Data processing: Check whether comments contain negative keywords and extract comments that contain them.

[1358] Output: A filtered comment data list.

[1359] Specific operation: The server uses a Python script to perform keyword matching on the comment data list and adds comments that match the filter to the list.

[1360] Step 3: Summarizing comments

[1361] 1. The server analyzes and summarizes the extracted comments using natural language processing (NLP) technology.

[1362] Input: The filtered comment data list.

[1363] Data processing: Tokenization, morphological analysis, and sentiment analysis are performed to understand the context and sentiment of comments.

[1364] Output: A summarized comment data list.

[1365] What it does: The server uses an NLP engine (e.g. spaCy) to analyze each comment and generate a summary from each comment.

[1366] Step 4: Notify users

[1367] 1. The server notifies the user of the summarized abusive comments.

[1368] Input: A summarized comment data list.

[1369] Data processing: Formatting data for email or in-app notifications.

[1370] Output: Informational message.

[1371] Specific behavior: The server sends a notification message to the user using the SMTP protocol or a push notification service (e.g., Firebase Cloud Messaging).

[1372] Step 5: Generate a removal request

[1373] 1. The server automatically generates a request to delete any abusive comments it finds.

[1374] Input: Summary of comment, posting date and time, and poster ID.

[1375] Data processing: Embed this information in the deletion request template.

[1376] Output: An automatically generated deletion request.

[1377] Specific operation: The server creates a deletion request using a template engine (e.g., Jinja2) and sends it to the user's terminal.

[1378] Step 6: User Authorization

[1379] 1. The user reviews and approves the deletion request.

[1380] Input: Auto-generated removal request.

[1381] Data processing: Record user authorization.

[1382] Output: Approved removal request.

[1383] Specific operation: The user's device displays a confirmation screen, records the user's actions, and sends them to the server.

[1384] Step 7: Submitting and tracking your removal request

[1385] 1. The server automatically sends a deletion request to the administrator of the relevant website or social networking site.

[1386] Input: Approved removal request.

[1387] Data processing: Convert the deletion request into the appropriate format and send it to the specified endpoint.

[1388] Output: The submission status of the deletion request.

[1389] Specific operation: The server sends a deletion request using each platform's API endpoint or contact form and updates the status in the database.

[1390] Step 8: Communicate progress

[1391] 1. The server manages the progress of the deletion request and notifies the user periodically.

[1392] Input: The progress of the removal request.

[1393] Data transformation: Converting progress into notification messages.

[1394] Output: Progress notification messages.

[1395] Specific operation: The server will notify the user of the status such as "Request in progress," "Processing," or "Deletion completed" using a push notification service or email.

[1396] Step 9: Collect and store evidence

[1397] 1. The server collects evidence of defamatory comments and stores them as files.

[1398] Input: Comment screenshot, text data, timestamp, and author ID.

[1399] Data processing: storing these data in a secure format.

[1400] Output: Encrypted evidence file.

[1401] What it does: The server runs a process to collect evidence data, encrypts it, stores it in a database, and sets access controls as needed.

[1402] The above are the specific processing steps of the program of this system.

[1403] (Application example 1)

[1404] 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."

[1405] Many abusive comments are posted on the Internet, which can not only cause users psychological stress but also damage their personal reputation. Manually monitoring and deleting such comments is extremely time-consuming, so there is a need for a system that can efficiently extract, notify, delete, and store evidence of abusive comments. Furthermore, a system that can run on smartphones and has an easy-to-use interface is also required.

[1406] 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.

[1407] In this invention, the server includes means for automatically extracting abusive comments from the Internet, means for summarizing the extracted abusive comments using natural language processing technology, means for notifying users of the summarized abusive comments, means for generating and sending requests to delete the abusive comments, means for managing and notifying the progress of the deletion requests, means for collecting and storing evidence data of the abusive comments, means for providing a user interface that operates on a smartphone, means for filtering abusive comments that include negative keywords, means for generating summaries of the abusive comments using a generative AI model, and means for automatically collecting screenshots and related metadata, thereby enabling a fast and efficient response to abusive comments.

[1408] "Defamatory comments" are comments posted online that criticize and damage the reputation of specific individuals or groups.

[1409] "Means of extraction" refers to technology or devices that automatically collect specific information from the Internet.

[1410] "Natural language processing technology" is a series of technologies that use computers to understand, analyze, and generate human language.

[1411] A "means of summarizing" is a technique or device that compresses information, extracts the main points, and summarizes them briefly.

[1412] "Means for notifying" refers to a technique or device that conveys specific information to a user.

[1413] "Means for generating and sending a deletion request" refers to technology or devices that create a document requesting the deletion of abusive comments and send it to the administrator.

[1414] "Means for managing and notifying the progress of deletion requests" refers to techniques and devices that track the status of deletion requests and communicate that progress to users.

[1415] "Means for collecting and storing evidential data" refers to technology and devices that collect and safely store evidence related to defamatory comments.

[1416] A "smartphone" is a portable electronic device that, in addition to the functions of a mobile phone, has advanced computing power and Internet connectivity.

[1417] A "user interface" is the means by which a user interacts with a computer system or application.

[1418] "Filtering means" refers to technology or devices that select information based on specific criteria and remove unnecessary parts.

[1419] A "generative AI model" is an algorithmic model that is trained to perform a specific task using artificial intelligence.

[1420] A "screenshot" is a technique for saving the contents displayed on a computer or smartphone screen as a still image.

[1421] "Metadata" is data that describes information about data, and includes information such as the creation time and creator.

[1422] This invention is a system that efficiently extracts defamatory comments from the Internet, summarizes them, notifies users, requests their deletion, and stores necessary evidence data. It is realized by combining a server, user terminals (smartphones), and data sources on the Internet.

[1423] First, the server periodically collects comment data from social media sites and websites on the Internet. Specifically, it sends a request to the social media site's API to retrieve comments and replies for the target user. At this time, the server-side program communicates with the API using the "requests" library. The collected comment data is temporarily stored on the server.

[1424] Next, the server filters the collected comment data, automatically extracting comments containing negative keywords (e.g., "gross," "die," "scammer," etc.). This filtering process is also performed on the server side, using an efficient search algorithm.

[1425] Natural language processing technology utilizing a generative AI model is applied to the filtered comments to create a summary of the comments. Specifically, the collected comments are processed in order through processes such as tokenization, morphological analysis, and sentiment analysis to extract important information. Natural language processing libraries such as "simplicity-nlp" are used. The summary results are used in notifications, which will be described later.

[1426] The summarized comments are sent to the user's smartphone. A dedicated application running on the user's device receives the summary data sent from the server and sends the notification. The most common notification methods are push notifications and in-app notifications.

[1427] After the user checks the notification, they can approve the deletion request if necessary. After receiving the user's approval, the server automatically generates a deletion request and sends it to the API endpoint of the target social media or website. The server also manages the progress of the deletion request and notifies the user. The progress status includes statuses such as "request in progress," "processing," and "deletion completed."

[1428] Furthermore, evidence data of defamatory comments is automatically collected and stored by the server. This evidence data includes screenshots of the comments, text data, and metadata (posting date and time, poster ID, etc.). This data is encrypted as necessary and stored in a secure database.

[1429] As a concrete example, let's say the following abusive comment is posted online:

[1430] "○○ is a truly horrible person, no different from a con man."

[1431] These comments will be automatically extracted, filtered and summarized by the system.

[1432] The generative AI model is given a prompt like this:

[1433] "Please summarize the comments below and generate a concise statement for notification:

[1434] "○○ is a truly horrible person, no different from a con man."

[1435] Expected output:

[1436] "A strong criticism of ○○."

[1437] In this way, the present invention provides a system that can respond quickly and efficiently to abusive comments, thereby reducing the psychological burden on users.

[1438] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1439] Step 1:

[1440] The server collects comment data from social media sites and websites on the Internet. Specifically, the server-side program periodically sends API requests using the "requests" library to obtain comments and replies from target users. The input is the comment data received as an API response, and the output is the comment data temporarily stored on the server.

[1441] Step 2:

[1442] The server filters the collected comment data using negative keywords. Specifically, it executes a process to automatically extract comments containing specific keywords (e.g., "gross," "die," "scammer"). The input is the comment data stored on the server, and the output is a list of filtered negative comments.

[1443] Step 3:

[1444] The server uses a generative AI model to create a summary of the filtered negative comments. Specifically, it uses natural language processing technology to "tokenize," "morphologically analyze," and "sentimentally analyze" the comments to extract important information. The generative AI model uses the following prompt:

[1445] "Please summarize the comments below and generate a concise statement for notification:

[1446] "○○ is a truly horrible person, no different from a con man."

[1447] Expected output:

[1448] "A strong criticism of ○○."

[1449] The input is the filtered negative comments and the output is the generated comment summary.

[1450] Step 4:

[1451] The server notifies the user of the summarized comments on their smartphone. The user receives the notification and displays it to the user through a user interface. Notification methods include push notifications and in-app notifications. The input is the summarized comment data, and the output is a notification displayed on the user's smartphone.

[1452] Step 5:

[1453] The user checks the notification and approves the deletion request as necessary. Operation is simple via the user interface. The input is the deletion request approval after user confirmation, and the output is the deletion request approval information sent to the server.

[1454] Step 6:

[1455] The server automatically generates a deletion request message after receiving user approval and sends it to the API endpoint of the target social networking site or website. The deletion request message contains information such as the comment content, posting date and time, and poster ID. The input is the deletion request approval information, and the output is the deletion request sent to the social networking site or website.

[1456] Step 7:

[1457] Furthermore, the server automatically captures screenshots of defamatory comments as evidence, and collects and stores text data and metadata (posting date and time, poster ID). The input is negative comment data, and the output is encrypted evidence data. This data is stored in a secure database in preparation for future legal action.

[1458] Step 8:

[1459] The progress of the deletion request is managed by the server and periodically notified to the user. The progress status includes "request in progress," "processing," "deletion completed," etc., and can be checked by the user on their smartphone. The input is the progress status of the deletion request, and the output is a status notification to the user.

[1460] 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.

[1461] The system of the present invention aims to reduce the psychological burden by efficiently extracting abusive comments, notifying users, and requesting their deletion. This system is realized by combining a server, user terminals, an internet data source, and an emotion engine. The specific operation of the system is explained below.

[1462] Research and summary functions

[1463] First, the server automatically extracts abusive comments from the Internet. It periodically sends requests to the APIs of targeted websites and social media sites to collect the latest comment data. For example, it can use the Twitter API to collect replies and mentions of specific users.

[1464] The server applies keyword filtering to the collected comment data, extracting comments containing negative keywords such as "the worst" or "die." For example, a comment such as "XX is the worst person" would be picked up.

[1465] The server then analyzes and summarizes the extracted comments using natural language processing (NLP) techniques, including tokenization, morphological analysis, and sentiment analysis. This allows the server to understand the context and emotional level of the comment, summarizing a comment like "XX is a terrible person" as "strong criticism of XX."

[1466] Deletion request proxy function

[1467] The server then notifies the user of the summarized abusive comments. The emotion engine then analyzes the user's emotions and adjusts the notification content based on their emotional state. For example, if the user is under high stress, the notification may be delayed or the content may be softened.

[1468] Next, the server automatically generates a deletion request for the defamatory comments it finds. The deletion request is generated using a template that includes information such as the comment content, posting date and time, and poster ID. The generated deletion request is sent to the user's device, where the user confirms the content and then approves it.

[1469] Once the user approves the deletion request, the server automatically sends the request to the administrator of the relevant website or social networking site. This process uses the platform's API endpoint or an administrator's contact form. The server also manages the progress of the deletion request and periodically reports to the user, notifying them of the status, such as "request in progress," "processing," or "deletion completed."

[1470] Evidence storage function

[1471] Finally, the server collects and stores evidence of the abusive comments as files. This evidence includes screenshots of the comments, text data, timestamps, and poster IDs. This data is stored in a secure database and can be accessed by lawyers and other parties if necessary. Security is also ensured using data encryption and access control.

[1472] Emotion Engine Functions

[1473] The emotion engine recognizes the user's emotional state and adjusts the system's behavior accordingly. It analyzes emotions using user input data and biosensor data (e.g., heart rate and electrodermal activity). If the user is under high stress, notifications are delayed or suppressed to reduce the user's psychological burden. It can also automatically adjust the priority of deletion requests and change the order in which they are sent to administrators depending on the user's emotional state.

[1474] These features allow for a swift and efficient response to defamatory comments, significantly reducing the user's psychological burden. Appropriate evidence storage also enables future legal action. This system provides comprehensive support for combating defamatory comments on the Internet.

[1475] The processing flow will be explained below.

[1476] The system of the present invention aims to reduce the psychological burden by efficiently extracting abusive comments, notifying users, and requesting their deletion. This system is realized by combining a server, user terminals, an internet data source, and an emotion engine. The specific operation of the system is explained below.

[1477] System-wide processing steps

[1478] Step 1:

[1479] The server periodically sends requests to the APIs of designated websites and social media sites to collect the latest comment data. The data collected through the API is temporarily stored in a database on the server.

[1480] Step 2:

[1481] The server applies keyword filtering to the collected comment data, extracting comments containing negative keywords such as "gross" and "die." This filtering process is implemented using Python scripts and regular expressions.

[1482] Step 3:

[1483] The server applies an algorithm to filter out outlier cases to filtered comments, using past filtering results and heuristics to minimize false positives.

[1484] Step 4:

[1485] The server further analyzes the filtered results using natural language processing (NLP) techniques, including tokenization, morphological analysis, and sentiment analysis, using Python libraries such as NLTK and SpaCy to analyze the text data.

[1486] Step 5:

[1487] The server summarizes the comments based on the analysis results. The summarization process extracts only the main points. For example, a comment such as "XX is a terrible person" can be summarized as "Strong criticism of XX."

[1488] Step 6:

[1489] The server notifies the user of the summarized comments, and the emotion engine analyzes the user's emotions and adjusts the notification content based on their emotional state. For example, if the user is under high stress, the notification may be delayed.

[1490] Step 7:

[1491] The server automatically generates a deletion request for defamatory comments. The deletion request includes information such as the comment content, posting date and time, and poster ID. This information is saved in template format.

[1492] Step 8:

[1493] The server sends the deletion request to the user's device, where the user can review and approve the request via an app or web interface.

[1494] Step 9:

[1495] Once the user has approved the request, the server sends the deletion request to the administrator of the relevant website or social networking site, using an API endpoint or a contact form for the administrator.

[1496] Step 10:

[1497] The server periodically checks the status to manage the progress of the deletion request. For example, it tracks the status such as "request in progress," "processing," or "deletion completed," and notifies the user.

[1498] Step 11:

[1499] The server collects and stores evidence of defamatory comments as files, including screenshots of the comments, text data, timestamps, and poster IDs.

[1500] Step 12:

[1501] The server stores the collected evidence data in a secure database, uses data encryption and access control to ensure the security of the information, and provides an appropriate interface for lawyers and other parties to access it.

[1502] Emotion Engine Processing Procedure

[1503] Step 1:

[1504] It collects user input data and biometric sensor data (such as heart rate and electrodermal activity), and transmits this data to a server via the user's device.

[1505] Step 2:

[1506] The server uses an emotion engine to analyze the collected data and determine the user's emotional state, for example, analyzing fluctuations in heart rate and electrodermal activity to determine whether the user is under stress.

[1507] Step 3:

[1508] The server selects appropriate actions based on the user's emotional state based on the analysis results of the emotion engine. For example, if the user is in a high stress state, the server may delay notifications or notify them with less stressful content.

[1509] Step 4:

[1510] The emotion engine automatically adjusts the priority of deletion requests and changes the order in which they are sent to the administrator depending on the user's emotional state. For example, it gives priority to deletion requests from users who are particularly stressed.

[1511] These features allow for a swift and efficient response to defamatory comments, significantly reducing the user's psychological burden. Appropriate evidence storage also enables future legal action. This system provides comprehensive support for combating defamatory comments on the Internet.

[1512] Example 2

[1513] 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."

[1514] Defamatory comments on the Internet can cause significant psychological stress for users and, in some cases, can lead to legal issues. Therefore, there is a need for a system that can efficiently detect and quickly respond to defamatory comments. However, current systems require manual review and response, placing a significant burden on users. Furthermore, there is a lack of systems that can properly store evidence of comments and provide it when needed. Furthermore, responses are not tailored to the user's emotional state, which rarely reduces the psychological burden.

[1515] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1516] In this invention, the server includes means for automatically extracting defamatory comments from the Internet, means for summarizing the extracted defamatory comments using natural language processing technology, means for notifying users of the summarized defamatory comments, means for analyzing the user's emotional state and adjusting the content and timing of the notification, means for generating and sending requests to delete the defamatory comments, means for managing and notifying the progress of the deletion request, and means for collecting and storing evidence data of the defamatory comments. This enables swift and efficient responses to defamatory comments, significantly reducing the psychological burden on users, and enabling early responses to legal issues and appropriate evidence storage.

[1517] "Defamatory comments" are comments that contain content that belittles, insults, or damages the reputation of others.

[1518] "Automated internet extraction methods" refers to algorithms or functions that programmatically collect data from specific websites or social networking services.

[1519] "Natural language processing technology" refers to the technology of analyzing and understanding human language using a computer, and includes tasks such as tokenization, morphological analysis, and sentiment analysis.

[1520] "Summarization methods" refers to algorithms or functions that concisely summarize extracted data and provide important information in a concise format.

[1521] "Means for notifying the user" refers to a function for notifying the user of the processing results via email, application notification, etc.

[1522] "Means for analyzing emotional state" refers to algorithms or functions that analyze user input data or biometric sensor data to estimate the user's current emotional state.

[1523] "Means for adjusting the content and timing of notifications" refers to a function for changing the content of the message to be notified and the time of notification based on the user's emotional state.

[1524] "Means for generating and sending removal requests" refers to a function that automatically creates a request for removal of abusive comments and sends it to the administrator of the corresponding website or social networking service.

[1525] "Means for managing and notifying the progress of deletion requests" refers to a function for monitoring the status of submitted deletion requests and reporting the status to the user.

[1526] "Means for collecting and storing evidentiary data" refers to the ability to collect screenshots, text data, timestamps, poster identification information, etc. related to defamatory comments and store them in a secure database.

[1527] The system of the present invention aims to reduce the psychological burden on users by efficiently extracting defamatory comments from the Internet, notifying them, and requesting their deletion. This system is realized by combining a server, user terminals, data sources on the Internet, and an emotion engine.

[1528] System Configuration

[1529] 1. Server:

[1530] The server is responsible for key processes such as extracting abusive comments, summarizing them, sending notifications, generating and sending deletion requests, and collecting and storing evidence data. The server requires a high-performance CPU, sufficient memory, and storage. Software used includes NLP models for natural language processing (e.g., spaCy or NLTK), sentiment analysis engines (e.g., Google Cloud Natural Language API), and databases (e.g., PostgreSQL).

[1531] 2. User Device:

[1532] The user's device receives the notification from the server, confirms the contents of the deletion request, and approves it. Common devices such as smartphones, tablets, and PCs can be used.

[1533] 3. Internet data sources:

[1534] Social media platforms (e.g., Twitter, Facebook) are used as data sources. Comment data is collected from these platforms via APIs.

[1535] 4. Emotion Engine:

[1536] The emotion engine analyzes the user's emotional state and adjusts the system's behavior by analyzing biometric sensors (e.g., heart rate sensor, electrodermal activity sensor) and user input data.

[1537] Specific examples of system processing

[1538] Examples of data collection include:

[1539] The server uses the Twitter API to execute a shell script to retrieve replies and mentions for "@example_user." Specifically, the following prompt is input into the generative AI model:

[1540] Please use Twitter's API to collect abusive comments about @example_user. Extract comments containing keywords such as "disgusting" and "die," summarize them, and report them.

[1541] Examples of keyword filtering:

[1542] The server extracts comments such as "XX should die" from the list of collected comments and summarizes them as "strong criticism" using a natural language processing API.

[1543] Examples of user notifications:

[1544] The server generates a notification and sends it to the user via email, stating, "The following abusive comment was found: Strong criticism of ____." The emotion engine analyzes the user's heart rate and stress level, and delays the notification if the user is in a high stress state.

[1545] An example of generating and approving a deletion request:

[1546] The user clicks the link in the email, confirms the deletion request, and clicks the approval button. The server then uses the Twitter API to send the deletion request to the administrator, saves the status in the database, and notifies the user by email.

[1547] Examples of evidence collection and storage:

[1548] The server uses web scraping technology to capture screenshots of comments and stores them in an encrypted format in a database, which can then be used as legal evidence if needed.

[1549] In this way, the system of the present invention can quickly and efficiently respond to defamatory comments, reducing the psychological burden on users. In addition, by properly storing evidence, it can also respond to legal action if necessary.

[1550] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1551] Step 1:

[1552] Data collection:

[1553] The server accesses the API of a specific internet data source (e.g., a social media platform) to collect replies and mentions for the target user. The target user's ID and API access key are required as input. The server sends a request based on this input data and obtains the collected comment data as output. Specifically, the server uses the Twitter API to execute a query to obtain replies for "@example_user." This retrieves all comments related to "@example_user" in JSON format.

[1554] Step 2:

[1555] Keyword filtering:

[1556] The server filters the collected comment data using a negative keyword list. The collected comment data and the negative keyword list are required as input. The server uses these to analyze the text in the comments and generates a list of comments containing negative keywords as output. Specifically, the server extracts comments containing keywords such as "the worst" and "die." For example, a comment such as "XX is the worst person" would be added to the list.

[1557] Step 3:

[1558] Natural language processing summarization:

[1559] The server summarizes the filtered comment data using natural language processing (NLP) technology. The filtered comment list is required as input. The server performs tokenization, morphological analysis, and sentiment analysis on this, and generates summarized comments as output. Specifically, the server uses an NLP model (e.g., spaCy) to summarize a comment such as "XX is a horrible person" as "Strong criticism of XX."

[1560] Step 4:

[1561] User Notice:

[1562] The server notifies the user of the generated summary comment. The input required is the summarized comment and the user's notification settings. Based on these, the server outputs a notification to the user via email or a notification system. Specifically, the server sends the user an email stating, "The following abusive comment was found: Strong criticism of XX." The emotion engine delays notifications if the user's biosensor data (e.g., heart rate) indicates a high level of stress.

[1563] Step 5:

[1564] Generate a removal request:

[1565] The server automatically generates a deletion request based on abusive comments. The input required is a summary of the comment, the posting date and time, and the poster's identification information. The server applies this data to a template and generates a deletion request as output. Specifically, the server creates a deletion request for the comment "XX is the worst kind of person" and uses the template to fill in the necessary information.

[1566] Step 6:

[1567] User verification and authorization:

[1568] The user checks the deletion request sent from the server. The content of the deletion request sent from the server is required as input. The user checks the content of the deletion request based on this and sends an "approval" or "rejection" response to the server as output. Specifically, the user clicks the link in the email, checks the content of the deletion request, and clicks the "approval" button.

[1569] Step 7:

[1570] Submit a removal request:

[1571] The server receives the user's approval and sends the deletion request to the relevant platform administrator. An approved deletion request form is required as input. Based on this, the server sends the deletion request via the SNS API or administrator inquiry form, and stores the status of the deletion request in a database as output. Specifically, the server uses Twitter's API to send the deletion request and stores the status as "request pending."

[1572] Step 8:

[1573] Progress tracking and reporting:

[1574] The server periodically checks the progress of the deletion request and reports the status to the user. As input, it requires the current status of the deletion request. The server updates the status based on this and sends a notification message to the user as output. Specifically, the server notifies the user by email with a status message such as "Your deletion request is being processed."

[1575] Step 9:

[1576] Evidence collection and preservation:

[1577] The server collects evidence of abusive comments and stores it as a file. The input required is the comment data and associated metadata. The server then takes screenshots based on this, collects comment text, timestamps, and author identification, and stores this data in a secure database as output. Specifically, the server uses a screenshot capture tool to generate screenshots of the comments and stores them in an encrypted format.

[1578] Step 10:

[1579] Emotional State Analysis:

[1580] The emotion engine analyzes user input data and biometric sensor data to estimate the user's emotional state. As input, biometric sensor data (e.g., heart rate, electrodermal activity) and data entered by the user are required. The emotion engine analyzes the emotional state based on these and determines the user's stress level and emotional state as output. Specifically, the emotion engine analyzes heart rate sensor data to determine whether the user is in a high-stress state and notifies the server of the result.

[1581] (Application example 2)

[1582] 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."

[1583] When advertising campaigns are conducted online, slanderous and negative comments often have a negative impact on the effectiveness of the advertisement. Until now, there has been no system that can efficiently monitor and analyze these comments, notify advertising managers, and, if necessary, immediately request their deletion. Another problem is that these comments increase the psychological burden on advertising managers. Therefore, there is a need to solve these problems, minimize the negative impact of slanderous comments on advertising campaigns, and reduce the psychological burden on advertising managers.

[1584] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for automatically extracting abusive comments from the Internet, means for summarizing the extracted abusive comments using natural language processing technology, means for notifying users of the summarized abusive comments, means for generating and sending a request to delete the abusive comments, means for managing and notifying the progress of the deletion request, means for collecting and storing evidence data of the abusive comments, means for monitoring comments related to advertisements in real time, means for analyzing negative comments and notifying advertising personnel in a manner that reduces their psychological burden, and means for sending deletion requests to an administrator and reporting the progress of the request to the advertising personnel. This enables rapid monitoring of negative comments in advertising campaigns, reduces the psychological burden on advertising personnel, and minimizes the negative impact of abusive comments on advertising effectiveness.

[1585] "Defamatory comments" are negative comments made on the Internet with the intention of damaging the reputation or credibility of others.

[1586] "Automatic extraction" means using a program or algorithm to collect the target data without human intervention.

[1587] "Natural language processing technology" is a technology that allows computers to analyze and understand human language (natural language).

[1588] "Summarization methods" are techniques and methods for summarizing long comments or sentences and extracting important information.

[1589] "Notifying users" means notifying specific users of the collected and analyzed information.

[1590] "Generating and sending a removal request" means creating and sending a request to ask an administrator to remove a specific abusive comment.

[1591] "Manage and notify the progress of removal requests" means tracking the progress of removal requests and informing users of their status.

[1592] "Collecting and storing evidentiary data" means collecting information about abusive comments and storing it in a safe place.

[1593] "Comments about advertisements" are user opinions and impressions about a particular advertisement on the Internet.

[1594] "Real-time monitoring" means checking and collecting data immediately the moment it is generated.

[1595] "Analyzing negative comments" means analyzing the content of the comments and identifying negative or critical content within them.

[1596] "Notifying in a way that reduces psychological burden" means conveying information in a way that takes into consideration the mental and emotional burden on the user and minimizes stress.

[1597] "Send to administrator and report progress" means sending the generated deletion request to an administrator, and then tracking and reporting the progress of the request.

[1598] The system of the present invention aims to efficiently extract defamatory and negative comments about online advertising campaigns and minimize their negative impact on advertising effectiveness. It also provides support functions for notifications and removal requests to reduce the psychological burden on advertising personnel.

[1599] This system is composed of the following means.

[1600] 1. A method for automatically extracting defamatory comments from the Internet:

[1601] The server periodically collects comment data from social media sites and websites using APIs, making it possible to collect comments related to advertisements in real time or periodically.

[1602] 2. A method for summarizing extracted abusive comments using natural language processing technology:

[1603] The server applies natural language processing (NLP) technology to the collected comment data, specifically tokenizing, morphological analysis, and sentiment analysis, to summarize negative comments.

[1604] 3. Means of notifying users of summarized abusive comments:

[1605] The server then notifies the advertiser of the summarized negative comments, using an emotion engine to analyze the advertiser's psychological state and tailor the content of the notification to reduce stress, using email or app notifications.

[1606] 4. How to generate and submit a request to remove abusive comments:

[1607] A deletion request is generated for the collected negative comments and sent to the administrator of the advertising platform. A deletion request containing the necessary information is generated using a template, and is sent after obtaining the user's approval.

[1608] 5. How we manage and notify you about the progress of your removal request:

[1609] Track the progress of generated and submitted removal requests and periodically report their status to the advertising manager, so that the advertising manager is always aware of the status of the removal request.

[1610] 6. Means of collecting and storing evidence data of defamatory comments:

[1611] The server collects evidence data, such as screenshots of comments, text data, timestamps, and poster IDs, and stores it in a secure database. Security is also ensured using data encryption and access control.

[1612] 7. How to monitor comments about your ads in real time:

[1613] The server monitors comments related to ads in real time based on the specified keywords, allowing for immediate response to advertising campaigns.

[1614] 8. Analyze negative comments and notify advertisers in a way that minimizes their emotional burden:

[1615] The server identifies negative comments based on the results of sentiment analysis and notifies advertising managers at the appropriate time and with the appropriate content.

[1616] 9. How to submit a removal request to the Administrator and report its progress to the Advertiser:

[1617] The removal request will be sent to the administrator and the progress will be reported to the advertising manager, allowing for more efficient management of the advertising campaign.

[1618] Examples of concrete examples and prompts

[1619] For example, if a social media API is used to collect comments about a new product advertisement and a comment such as "this is the worst product" is detected, the system will analyze the psychological state of the person in charge of advertising using an emotion engine and notify them in a way that reduces their psychological burden. It will also automatically generate a deletion request and send it to the administrator.

[1620] Example prompt sentence:

[1621] Please create a specific program that extracts specific negative comments from social media comments about a new product advertisement, notifies the person in charge of advertising in a way that reduces the psychological burden, and generates a request to remove the comment. Also, please explain in natural language what the program does.

[1622] By implementing the above measures, the impact of negative comments on advertising campaigns can be minimized and the psychological burden on advertising personnel can be reduced.

[1623] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1624] Step 1:

[1625] Automatically extracts defamatory comments from the Internet.

[1626] The server periodically collects comment data from social media platforms and websites using APIs, specifically filtering and collecting comments containing keywords related to targeted advertising campaigns.

[1627] Input: Social media or website API endpoints, target keywords

[1628] Output: Filtered comment data

[1629] Step 2:

[1630] The extracted abusive comments are summarized using natural language processing technology.

[1631] The server performs tokenization, morphological analysis, and sentiment analysis on the collected comments to identify and summarize negative comments.

[1632] Input: Collected comment data

[1633] Output: Summarized negative comments

[1634] Step 3:

[1635] Notify the user of the summarized abusive comments.

[1636] The server then notifies the advertising manager of the summarized negative comments, using an emotion engine to analyze the advertising manager's psychological state and adjust the notification content to reduce stress.

[1637] Input: Summarized negative comments, advertising manager's psychological state data

[1638] Output: Adjusted notification content

[1639] Step 4:

[1640] Generate and submit a request to remove abusive comments.

[1641] The server automatically generates a deletion request based on the summarized abusive comments. It generates the necessary information (comment content, posting date and time, poster ID, etc.) based on a template and sends the deletion request after obtaining the user's approval.

[1642] Input: Summary of abusive comment, removal request template, user approval

[1643] Output: Generated deletion request, sending status

[1644] Step 5:

[1645] Manage and notify you of the progress of your removal request.

[1646] The server tracks the progress of the submitted removal request and periodically reports the status to the advertising manager, managing the status of the removal request and notifying the status such as "request in progress," "processing," or "removal completed."

[1647] Input: Status data of the deletion request submitted

[1648] Output: Progress report

[1649] Step 6:

[1650] Collect and store evidence of abusive comments.

[1651] The server collects evidence data related to defamatory comments, such as screenshots, text data, timestamps, and poster IDs, and stores it in a secure database. Security is ensured using data encryption and access control.

[1652] Input: Negative comments and associated metadata

[1653] Output: Stored evidence data

[1654] Step 7:

[1655] Monitor comments about your ads in real time.

[1656] The server monitors comments related to advertisements in real time based on the specified keywords and extracts immediate negative comments for a particular advertising campaign.

[1657] Inputs: Keywords related to your ad campaign, real-time comment data

[1658] Output: Real-time monitored comment data

[1659] Step 8:

[1660] Negative comments are analyzed and notified in a way that reduces the psychological burden on advertising staff.

[1661] The server notifies the advertiser of negative comments identified based on the results of sentiment analysis, taking into account the advertiser's psychological state. The system adjusts the content of the notification to reduce the advertiser's psychological burden.

[1662] Input: Sentiment analysis results, advertising manager's psychological state data

[1663] Output: Adjusted notification content

[1664] Step 9:

[1665] Send the removal request to the administrator and report the progress to the advertising manager.

[1666] The server sends the generated removal request to an administrator, tracks the progress of the removal request, and reports the status to the advertising representative.

[1667] Input: Generated deletion request, progress data

[1668] Output: Progress report of removal request

[1669] 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.

[1670] 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.

[1671] 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.

[1672] 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.

[1673] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1674] 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.

[1675] 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).

[1676] 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.

[1677] 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."

[1678] 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.

[1679] 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).

[1680] 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.

[1681] 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.

[1682] 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.

[1683] 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.

[1684] 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.

[1685] 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.

[1686] 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.

[1687] 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.

[1688] 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.

[1689] 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.

[1690] The following is further disclosed regarding the above embodiment.

[1691] (Claim 1)

[1692] A method for automatically extracting defamatory comments from the Internet,

[1693] A means for summarizing the extracted defamatory comments using natural language processing technology;

[1694] means for notifying the user of the summarized abusive comments;

[1695] A means for generating and sending a request to remove abusive comments;

[1696] A means of managing and notifying you about the progress of your removal request;

[1697] Means of collecting and storing evidence of abusive comments

[1698] A system including:

[1699] (Claim 2)

[1700] 2. The system according to claim 1, wherein the means for generating and transmitting the deletion request transmits the deletion request to the site administrator upon receiving approval from the user.

[1701] (Claim 3)

[1702] The system of claim 1, wherein the means for collecting evidence data of defamatory comments collects evidence including screenshots, comment text, and metadata.

[1703] "Example 1"

[1704] (Claim 1)

[1705] means for automatically extracting libelous material from a computer network;

[1706] A means for summarizing the extracted defamatory content using natural language processing technology;

[1707] a means for notifying the user of the summarized libelous content;

[1708] a means for generating and transmitting a request to remove the abusive material;

[1709] A means of managing and notifying you of the progress of your removal request;

[1710] Means of collecting and storing evidence of defamatory content

[1711] A system including:

[1712] (Claim 2)

[1713] 2. The system according to claim 1, wherein the means for generating and transmitting the deletion request transmits the deletion request to the site administrator upon receiving approval from the user.

[1714] (Claim 3)

[1715] 2. The system of claim 1, wherein the means for collecting evidence of the libelous content collects evidence including image capture and document data, and additional information.

[1716] "Application Example 1"

[1717] (Claim 1)

[1718] A method for automatically extracting defamatory comments from the Internet,

[1719] A means for summarizing the extracted defamatory comments using natural language processing technology;

[1720] means for notifying the user of the summarized abusive comments;

[1721] A means for generating and sending a request to remove abusive comments;

[1722] A means of managing and notifying you about the progress of your removal request;

[1723] A means of collecting and storing evidence data of abusive comments;

[1724] means for providing a user interface on a smartphone;

[1725] A means of filtering abusive comments containing negative keywords;

[1726] A means for generating summaries of abusive comments using a generative AI model;

[1727] Automated means of collecting screenshots and associated metadata

[1728] A system including:

[1729] (Claim 2)

[1730] 2. The system according to claim 1, wherein the means for generating and transmitting the deletion request transmits the deletion request to the admin...

Claims

1. A method for automatically extracting defamatory comments from the Internet, A means for summarizing the extracted defamatory comments using natural language processing technology; means for notifying the user of the summarized abusive comments; A means for generating and sending a request to remove abusive comments; A means of managing and notifying you about the progress of your removal request; Means of collecting and storing evidence of abusive comments A system including:

2. 2. The system according to claim 1, wherein the means for generating and transmitting the deletion request transmits the deletion request to the site administrator upon receiving approval from the user.

3. The system according to claim 1 , wherein the means for collecting evidence data of abusive comments collects evidence including screenshots, comment text, and metadata.

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