system

Abstract

A system is provided.SOLUTION: A system comprising: means for monitoring all posts on an SNS platform and acquiring post data; means for analyzing the acquired post data using a natural language processing technique and determining whether the post data is criminal; means for sending an alert to an administrator when it is determined that the post data is criminal; and means for deleting a corresponding post and executing a sanction action on a user based on an instruction from the administrator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The risk of criminal activity being posted on social media platforms is increasing. This could threaten the safety of users and undermine the credibility of the platform itself. The objective of this invention is to provide a safe and reliable social media platform by quickly and accurately detecting criminal posts and taking appropriate measures. [Means for solving the problem]

[0005] The present invention is a system that includes a means for monitoring all posts on a social media platform and acquiring post data, a means for analyzing the acquired post data using natural language processing technology to determine whether the post is criminal, a means for issuing an alert to an administrator if the post is determined to be criminal, and a means for deleting the relevant post and implementing sanctions against the user based on instructions from the administrator. Furthermore, by providing a means for adding post data to a queue in real time and passing the queue to an AI module, and a means for analyzing keywords and context in the post data when determining criminality, the system achieves advanced analysis and rapid response.

[0006] An "SNS platform" is an online service that allows a large number of users to participate and share information and communicate.

[0007] A "post" is content such as text, images, or videos that a user creates and publishes on a social media platform.

[0008] "Monitoring" refers to the constant checking of all posts made on social media platforms to detect inappropriate content or behavior.

[0009] "Posted Data" refers to information related to content such as text, images, and videos posted by users on social media platforms.

[0010] "Acquiring" refers to the act of the system collecting data posted on a social media platform.

[0011] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language.

[0012] "Analysis" is the process of examining the acquired posted data in detail and understanding its contents.

[0013] "Criminality" refers to the characteristics or characteristics of a post that may involve illegal or fraudulent activity.

[0014] "Judge" means to decide whether the content of the post is criminal based on the analysis results.

[0015] An "administrator" is a person in charge of managing a social media platform, supervising the content posted and taking measures against fraudulent activity.

[0016] An "alert" is a warning or warning notification sent to an administrator when the system detects an abnormality or problem.

[0017] "Deletion" means removing specific posted data from a social media platform.

[0018] "Sanctions" refer to the imposition of penalties, such as suspending or deleting accounts, on users who violate the rules.

[0019] "Real-time" refers to a state in which processing or response is carried out immediately without delay.

[0020] A "queue" is a waiting line in which data waiting to be processed is stored in order and processed in sequence.

[0021] An "AI module" is a software component that uses artificial intelligence techniques to perform a specific task.

[0022] "Keywords" are words that are considered important for indicating a particular theme or content.

[0023] "Context" is information that indicates the situation or background in which a sentence or part of a sentence is placed. [Brief explanation of the drawings]

[0024] [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

[0025] 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.

[0026] First, the terms used in the following description will be explained.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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."

[0032] [First embodiment]

[0033] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0034] 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.

[0035] 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).

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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."

[0045] An embodiment of the present invention provides a mechanism for monitoring user posts on a social networking platform and detecting criminal activity in real time, thereby ensuring the reliability of users and the platform.

[0046] Watch and get posts

[0047] When a user logs in to a social networking platform and posts a new post, the post data is immediately sent to the server, which captures the post content, the poster's information, and the post timestamp, and stores them in a database, ready for immediate analysis.

[0048] Analysis using natural language processing

[0049] The server passes the posted data acquired in real time to an AI module. The AI ​​module uses natural language processing technology to analyze the content of the post. Specifically, it performs morphological analysis of the text, extracts keywords, and analyzes the context to determine whether the post is criminal. For example, if a post contains a keyword such as "illegal part-time job," the context is also analyzed to assess whether it represents illegal activity.

[0050] Criminality determination and alert issuing

[0051] If the AI ​​module detects any criminal activity in the post, the server will send an alert to the administrator based on the results of the assessment. The alert will be sent via email or as a dashboard notification for the administrator. The administrator's device will immediately receive the alert and notify the administrator.

[0052] Management Actions and Sanctions

[0053] Administrators will review the alert and check the relevant posts. If deemed necessary, they will instruct the server to delete the posts and take disciplinary action against the violating users. For example, they may delete specific violating posts and suspend the accounts of the users.

[0054] Specific examples

[0055] For example, if a user posts something containing the phrase "Introducing illegal part-time jobs that will earn you money easily," the posted data is immediately sent to the server. The server passes the data to an AI module, which recognizes "illegal part-time jobs" as a keyword and analyzes the context. If illegality is detected as a result, an alert is sent to an administrator. The administrator checks the alert and instructs them on the necessary countermeasures. Based on the instructions, the server deletes the post and suspends the user's account.

[0056] This invention is a system for quickly and accurately detecting illegal activities on social media platforms and ensuring user safety. The operation of this system will make social media platforms safer and more reliable.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] A user creates a new post on a social media platform, which can contain text, images, and videos.

[0060] Step 2:

[0061] The server instantly retrieves user-created posts using the API. The retrieved data includes details such as the post content, poster information, and posting date and time.

[0062] Step 3:

[0063] The server stores the acquired submission data in a database, and simultaneously adds the submission data to a queue in real time to prepare for analysis.

[0064] Step 4:

[0065] The server passes the queued data to the AI ​​module, where it also schedules it for sequential processing.

[0066] Step 5:

[0067] The AI ​​module analyzes the received post data using natural language processing technology, which includes morphological analysis of the text, keyword extraction, and context analysis.

[0068] Step 6:

[0069] The AI ​​module uses the analysis results to determine whether a post is criminal. For example, it detects keywords such as "illegal part-time jobs" or "drug sales" and evaluates whether they constitute illegal activity. Contextual analysis is also performed to make a more accurate judgment.

[0070] Step 7:

[0071] The server receives the criminality judgment results from the AI ​​module, stores the results in a database, and simultaneously sends an alert to the administrator if a criminality is judged to have occurred.

[0072] Step 8:

[0073] The administrator's terminal receives alerts from the server, which are displayed as notifications via email and on the administrator's dashboard.

[0074] Step 9:

[0075] Administrators will review the alert, review the content of the relevant post, and decide on sanctions against the poster, if necessary.

[0076] Step 10:

[0077] The administrator will instruct the server to remove the post and take disciplinary action against the user, which may include suspending or permanently terminating the account.

[0078] Step 11:

[0079] The server will then delete the relevant post from the database based on the administrator's instructions, as well as remove it from inventory and log the violation.

[0080] Step 12:

[0081] The server updates account status and sets access restrictions to enforce sanctions against violating users.

[0082] Step 13:

[0083] The server will notify the user of the details of the sanctions, which will be sent via email or messaging within the social media platform.

[0084] Example 1

[0085] 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."

[0086] The number of posts related to fraudulent activities and crimes on social media platforms is increasing. Such posts undermine the reliability of the platforms and threaten the safety of users, so they need to be detected and addressed quickly and accurately. However, current systems mainly rely on manual monitoring and periodic checks, and while real-time monitoring and rapid response are required, there are still issues that cannot be fully addressed.

[0087] 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.

[0088] In this invention, the server includes a means for acquiring data posted by users to the platform, a means for transmitting the acquired data to an AI module and analyzing it using natural language processing technology, and a means for sending an alert to an administrator terminal in real time when criminal activity is detected. This makes it possible to quickly and accurately detect posts related to fraudulent activities or crimes on the SNS platform and take appropriate measures to ensure the reliability of the platform and the safety of users.

[0089] "User" refers to any individual or corporation that uses the SNS Platform.

[0090] "Platform" refers to an online system that provides services for users to post and communicate.

[0091] "Posted Data" refers to content such as text, images, and videos that users create and submit on the Platform.

[0092] "Server" refers to a computer system that receives user posted data, processes it, and stores it in a database.

[0093] "AI module" refers to a software component that analyzes posted data and uses natural language processing technology to detect criminal activity.

[0094] "Natural language processing technology" refers to the technology used by AI modules to perform morphological analysis of text data, extract keywords, analyze context, and so on.

[0095] "Administrator" refers to the individual or organization responsible for operating the platform, monitoring posted data, and responding to fraudulent activity.

[0096] "Alert" refers to a notification sent to an Administrator when criminal activity is detected.

[0097] "Real-time" refers to the fact that processing and notification are carried out almost immediately after the user's posted data is sent.

[0098] The present invention describes a system for monitoring user posts on a social networking platform in real time to detect criminal activity, with the aim of improving the safety and reliability of the platform.

[0099] Watch and get posts

[0100] When a user logs in to a social networking platform and posts a new post, the post data is immediately sent from the device to the server. The server retrieves the post content, author information, and post timestamp, and stores them in a database. The device does this through an HTTP POST request, and the server's API endpoint receives the request and processes the data.

[0101] Data analysis

[0102] The server sends the newly acquired post data to the AI ​​module, which uses a natural language processing library (e.g., spaCy or NLTK) to:

[0103] Morphological analysis of text

[0104] Keyword extraction

[0105] Contextual Analysis

[0106] The AI ​​module uses these analyses to determine whether a post is criminal. For example, if a post contains the keyword "illegal part-time job," it will also analyze the context to assess whether it is illegal activity.

[0107] Criminal activity detection and alerting

[0108] If the AI ​​module detects criminal activity in a post, it returns the result to the server. The server then sends an alert to the administrator based on the result. The alert is sent via email or a dashboard notification for the administrator. The server uses an SMTP server to send emails and Websockets to send real-time notifications to the dashboard.

[0109] Management Actions and Sanctions

[0110] The administrator's terminal receives the alert and notifies the administrator. The administrator confirms the alert and checks the relevant post. If necessary, the administrator instructs the server to delete the relevant post and impose sanctions on the violating user. The server then receives the instruction, deletes the relevant data from the database, and updates the status of the user's account.

[0111] Specific examples

[0112] For example, if a user posts something containing the phrase "introducing illegal part-time jobs that will earn you money easily," the posted data is immediately sent to the server. The server passes the data to an AI module, which recognizes "illegal part-time jobs" as a keyword and analyzes the context. If illegality is detected as a result, an alert is sent to an administrator. The administrator checks the alert and instructs them on the necessary countermeasures. Through this process, the post in question is deleted and the user's account is suspended.

[0113] This system will enable the rapid and accurate detection of illegal activities on social media platforms and ensure the safety of users. This system aims to increase the reliability and safety of the platform and provide an environment where users can use it with peace of mind.

[0114] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0115] Step 1:

[0116] A user logs in to a social media platform and posts a new post. When the user presses the post button, the post data is sent from the device to the server. Specifically, the device generates an HTTP POST request and sends data including the post content, poster information, and a timestamp to the server's API endpoint.

[0117] Input: User post content, poster information, timestamp

[0118] Output: HTTP POST request sent

[0119] Step 2:

[0120] The server receives the posted data from the device and stores it in a database. The server converts this data into the required format and stores it in the database, making it ready for immediate analysis.

[0121] Input: HTTP POST request (user submitted data)

[0122] Output: Post data saved in the database

[0123] Step 3:

[0124] The server sends the saved post data to the AI ​​module, which then sends the post data as a request to the API endpoint for the AI ​​module. The AI ​​module then analyzes the post content using natural language processing technology.

[0125] Input: Post data retrieved from the database

[0126] Output: Send request to AI module

[0127] Step 4:

[0128] The AI ​​module uses a natural language processing library to analyze the received post data. Specifically, it performs morphological analysis, keyword extraction, and context analysis to determine whether the post is criminal.

[0129] Input: Post data sent from the server

[0130] Output: Analysis results (whether or not there is criminal activity)

[0131] Step 5:

[0132] If the AI ​​module detects any criminal activity in the post, it returns the result to the server in JSON format, which the server receives.

[0133] Input: Analysis results (whether or not there is a criminal offense)

[0134] Output: Response to the server (data indicating whether or not there is criminal activity)

[0135] Step 6:

[0136] The server sends alerts to administrators based on the results of the AI ​​module. The server retrieves the administrator's contact information from a database, sends emails using an SMTP server, and also sends real-time notifications to an administrator's dashboard using Websockets.

[0137] Input: Criminal data, administrator contact information

[0138] Output: Alert notification to administrator (email, dashboard notification)

[0139] Step 7:

[0140] The administrator device receives the alert and notifies the administrator, who then checks the content of the alert and the relevant posts.

[0141] Input: Alert notification from the server

[0142] Output: Administrator confirmation process begins

[0143] Step 8:

[0144] Based on the alert notification, administrators will check the relevant posts and, if necessary, issue instructions for disciplinary action, such as deleting the post or suspending the user's account.

[0145] Input: Check the content of the relevant post

[0146] Output: Instructions for action

[0147] Step 9:

[0148] The server, upon receiving instructions from the administrator, will remove the post from the database and, if necessary, update the status of the user's account, ensuring that the violation is removed promptly.

[0149] Input: Instructions for corrective action from administrator

[0150] Output: Database update (delete post, change user account status)

[0151] (Application example 1)

[0152] 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."

[0153] There is a need to efficiently detect potentially criminal posts on social media platforms and respond quickly. However, with current technology, it has been difficult to detect potentially criminal posts in real time, and there have often been delays in notifying administrators and responding to them. In addition, it is labor-intensive and unrealistic for administrators to constantly monitor the platform. In particular, there has been an issue with the lack of integration with smart devices, making it difficult to respond quickly on-site.

[0154] 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.

[0155] In this invention, the server includes means for monitoring all posts on the SNS platform and acquiring post data, means for analyzing the acquired post data using natural language processing technology and determining whether the post is criminal, means for issuing an alert to an administrator if the post is determined to be criminal, means for deleting the post and implementing sanctions against the user based on instructions from the administrator, and means for sending a visual alert to the wearable device if criminality is detected. This makes it possible to detect criminal posts in real time and respond quickly.

[0156] An "SNS platform" is an online service that allows users to communicate over the Internet.

[0157] "Posted data" refers to information such as text, images, and videos that users publish on social media platforms.

[0158] "Natural language processing technology" is a technology that allows computers to analyze and understand human language, and includes morphological analysis and contextual analysis.

[0159] "Criminality" is a quality that determines whether posted data suggests illegal activity.

[0160] An "alert" is a warning notification that the system automatically issues to the administrator.

[0161] A "wearable device" is a computing device that is worn on the body, such as smart glasses or a smart watch.

[0162] A "visual alert" is a visual warning message displayed on a wearable device.

[0163] "Administrator" means a person or organization that operates or monitors a social media platform.

[0164] A "queue" is a data structure for processing posted data in order.

[0165] An "AI module" is a software component that uses artificial intelligence technology to analyze data and determine certain attributes, such as criminality.

[0166] "Sanctions" are measures that impose penalties on users who make illegal posts, such as suspending their accounts or deleting posts.

[0167] An embodiment of the present invention is a system for monitoring posts on a social networking platform, detecting potentially criminal posts in real time, and responding promptly. The system includes a means for monitoring all posts on the social networking platform, analyzing them using natural language processing technology, and sending an alert to an administrator and a means for sending a visual alert to a wearable device.

[0168] System Program

[0169] The server retrieves posted data in real time and stores it in a database. The server adds the posted data to a queue and passes it to the AI ​​module. The AI ​​module uses natural language processing technology to perform morphological and contextual analysis of the posted data to determine whether it is criminal. Specifically, it uses the Google Cloud Natural Language API. If criminal activity is detected, the server sends an alert to the administrator and also sends a visual alert to the wearable device.

[0170] Specific examples

[0171] For example, if a user posts something containing the phrase "Introduction to illegal part-time jobs that allow you to easily earn money," the posted data is sent to the server. The server then passes the posted data to an AI module, which recognizes keywords such as "illegal part-time jobs" and analyzes the context. If the analysis detects any criminal activity, the server will send an alert to the administrator and also send a visual alert to the wearable device worn by the security officer.

[0172] Hardware and software used

[0173] Server: Collects and analyzes data posted on SNS platforms and notifies the administrator.

[0174] Wearable devices: Devices worn by security personnel that can receive visual alerts, such as smart glasses.

[0175] Google Cloud Natural Language API: Provides natural language processing technology to analyze posted data and determine criminality.

[0176] Prompt Sentence Examples

[0177] Please analyze the content of the post below and determine whether it is criminal. If so, please also provide specific keywords and context.

[0178] "I have work to do in a dangerous location tonight, and I'm looking for someone to come."

[0179] This will enable the detection and rapid response of criminal activity on social media platforms, improving the safety and reliability of the system.

[0180] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0181] Step 1:

[0182] A user logs in to a social networking platform and posts a new message. The message data is sent from the user's device to the server. The input here is the message data created by the user, and the output is the message data sent to the server.

[0183] Step 2:

[0184] The server receives the submission data and stores it in a database, where it is ready to be added to the queue. The input here is the submission data sent to the server, and the output is the submission data stored in the database and the submission data ready to be added to the queue.

[0185] Step 3:

[0186] The server adds the submitted data to a queue and passes the queue to the AI ​​module. The input here is the submitted data added to the queue, and the output is the submitted data passed to the AI ​​module.

[0187] Step 4:

[0188] The AI ​​module analyzes the posted data using natural language processing technology. Specifically, it performs morphological analysis, keyword extraction, and contextual analysis to determine whether the post is criminal. The technology used is the Google Cloud Natural Language API. The input here is the posted data passed to the AI ​​module, and the output is the analysis results.

[0189] Step 5:

[0190] The server receives the analysis results from the AI ​​module and checks whether criminal activity has been detected. If criminal activity is detected, the server sends an alert to the administrator. The input here is the analysis result of the AI ​​module, and the output is an alert notification to the administrator.

[0191] Step 6:

[0192] If the server detects criminal activity, it sends a visual alert to the wearable device. The input here is the analysis result that criminal activity was detected, and the output is the visual alert sent to the wearable device.

[0193] Step 7:

[0194] An administrator checks the alert, deletes the relevant post if necessary, and takes sanctions against the user. The input here is the alert sent to the administrator, and the output is the deleted post data and the user data on which sanctions were taken.

[0195] This series of processes makes it possible to detect potentially criminal posts on social media platforms in real time and respond quickly.

[0196] 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.

[0197] An embodiment of the present invention provides a system for monitoring user posts on a social media platform and analyzing criminality and user sentiment in real time, which improves the trust between users and the platform and enables early detection and response to criminal or inappropriate behavior.

[0198] Watch and get posts

[0199] When a user creates a new post on a social networking platform, the post data is immediately sent to the server, which retrieves details such as the post content, author information, and the posting date and time, and stores them in a database. This allows the post content to be immediately analyzed.

[0200] Analysis using natural language processing and emotion engine

[0201] The server adds the acquired post data to a queue in real time and passes it to the AI ​​module and emotion engine. The AI ​​module analyzes the post content using natural language processing technology. Specifically, it performs morphological analysis of the text, keyword extraction, and context analysis, while the emotion engine analyzes the user's emotions (e.g., anger, sadness, joy, etc.).

[0202] Judging criminality and emotions

[0203] The AI ​​module uses the results of the analysis to determine whether a post is criminal. It also uses the results of the emotion engine to assess whether the post is related to any criminal activity. For example, posts that express strong feelings of anger or aggression are evaluated with particular attention.

[0204] Sending alerts

[0205] The server sends an alert to the administrator based on the results of the AI ​​module and emotion engine. It will not only issue an alert if criminal activity is detected, but also alerts for high-risk posts, taking into account the results of user sentiment analysis. Alerts are sent via email and to the administrator's dedicated dashboard.

[0206] Management Actions and Sanctions

[0207] Administrators will review the alert and review the details of the relevant post. If criminal activity is confirmed, administrators will decide to remove the post or impose sanctions on the user. This may include additional actions based on sentiment analysis results.

[0208] Specific examples

[0209] For example, if a user posts something like "threatening with angry language," the posted data is sent to the server. The server passes it to the AI ​​module and emotion engine, where the AI ​​module determines it is a "threat," and the emotion engine analyzes it for the emotion of "anger." If the results indicate criminal activity, the server sends an alert to an administrator. The administrator reviews the alert and instructs disciplinary action, such as deleting the post and suspending the user's account.

[0210] The system of the present invention not only detects criminal activity on social media platforms quickly and accurately and ensures user safety, but also enables more sophisticated responses by analyzing user sentiment, making social media platforms safer and more reliable.

[0211] The processing flow will be explained below.

[0212] Step 1:

[0213] A user creates a new post on a social media platform, which can consist of text, images, videos, etc.

[0214] Step 2:

[0215] The server instantly retrieves user-created post data via the API, including metadata such as the post content, poster information, and posting date and time.

[0216] Step 3:

[0217] The server stores the acquired posting data in a database and simultaneously adds the posting data to a queue in real time.

[0218] Step 4:

[0219] The server schedules sequential processing to pass the posted data added to the queue to the AI ​​module and emotion engine.

[0220] Step 5:

[0221] The AI ​​module analyzes the received post data using natural language processing technology, specifically by performing morphological analysis of the text, keyword extraction, and contextual analysis.

[0222] Step 6:

[0223] The emotion engine analyzes the same posted data and evaluates the user's emotions, identifying emotions such as anger, sadness, and joy in the process.

[0224] Step 7:

[0225] The AI ​​module uses natural language processing to determine the criminality of posts, for example by detecting keywords such as "threat" and "violence" and evaluating their meaning and context.

[0226] Step 8:

[0227] If an emotion that is deemed to be criminal or serious (such as "anger" or "fear") is detected, the emotion engine provides the evaluation result to the AI ​​module, which can then accurately determine the criminality of the post.

[0228] Step 9:

[0229] The server acquires the results of the AI ​​module and emotion engine and stores them in a database. At the same time, if it determines that there is criminal activity or a high level of risk, it will send an alert to the administrator.

[0230] Step 10:

[0231] The administrator's terminal receives alerts from the server, which are displayed as notifications via email and on the administrator's dashboard.

[0232] Step 11:

[0233] Administrators will review the alert and details of the relevant posts, and after reviewing the content, will determine the necessary sanctions.

[0234] Step 12:

[0235] The administrator will instruct the server to remove the post and take disciplinary action against the user, which may include suspending or permanently terminating the account.

[0236] Step 13:

[0237] The server will delete the relevant posts from the database based on the administrator's instructions, and if necessary, record a log of the violation.

[0238] Step 14:

[0239] The server updates account status and sets access restrictions to enforce sanctions against violating users.

[0240] Step 15:

[0241] The server will notify the user of the details of the sanctions, which will be sent via email or messaging within the social media platform.

[0242] Example 2

[0243] 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."

[0244] Conventional social media platforms lack the means to properly monitor user posts and detect criminal or inappropriate emotional expressions in real time. As a result, it is difficult to detect and respond to criminal or inappropriate behavior early on. While there is also hope for sophisticated responses based on user emotion analysis, conventional systems have not fully realized this. Therefore, there is a need for a system that can improve the trust between users and the platform and enable early detection and response to criminal or inappropriate behavior.

[0245] 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.

[0246] In this invention, the server includes means for monitoring all posts on the SNS platform and acquiring post data, means for storing the acquired post data in a data storage device, means for adding the acquired post data to a message queue in real time, means for passing the message queue to an AI module and a sentiment analysis engine using natural language processing technology, means for analyzing the post data and analyzing sentiment using natural language processing technology, means for determining the criminality and sentiment of the post based on the analysis results, means for issuing an alert to an administrator if a post is determined to be criminal, and means for deleting the relevant post and implementing sanctions against the user based on instructions from the administrator. This enables real-time analysis of post data, enabling early detection and response to criminal or inappropriate behavior. Furthermore, analyzing user sentiment allows for more advanced responses, further improving the reliability and safety of the platform.

[0247] "SNS Platform" refers to an online service that enables users to post content and interact with other users.

[0248] "Posted data" refers to information such as text, images, and videos that users create and publish on social media platforms.

[0249] "Server" refers to a computer system that processes, stores, and manages data on a network.

[0250] A "data storage device" is a device or system for storing data, including hard disk drives, SSDs, and cloud storage.

[0251] A "message queue" is a system for temporarily storing data and processing it sequentially, and examples include RabbitMQ and Apache Kafka.

[0252] "Natural language processing technology" is a technology for understanding and analyzing human language, and includes morphological analysis, keyword extraction, contextual analysis, etc.

[0253] An "AI module" is a software component that performs analysis using artificial intelligence, and specifically includes natural language processing and machine learning models.

[0254] An "emotion analysis engine" is a system that analyzes user emotions from text data and calculates emotional scores such as anger, sadness, and joy.

[0255] "Analysis results" refers to information based on the analysis of posted data obtained by an AI module or sentiment analysis engine.

[0256] "Criminal" refers to the characteristics or content of posted data that violate or have the potential to violate laws or regulations.

[0257] An "alert" refers to a warning message or notification sent to an administrator when a specific condition is met.

[0258] "Administrator" refers to the person or organization responsible for operating a social media platform, monitoring user posts, and responding to violations.

[0259] "Sanctions" refers to penalties or restrictions imposed on users when violations of the terms of service or criminal activity are confirmed.

[0260] An embodiment of the present invention provides a system that monitors user posts on a social media platform and analyzes criminality and user sentiment in real time, thereby improving the trust between users and the platform and enabling early detection and response to criminal or inappropriate behavior.

[0261] When a user creates a new post on a social networking platform, the post data is immediately sent from the device to the server, which then retrieves details such as the post content, poster information, and posting date and time, and stores the data in a data storage device.

[0262] The server adds the received post data to a message queue in real time. In this case, a message broker such as RabbitMQ or Apache Kafka is used for the message queue. The data added to this message queue is then subjected to subsequent analysis processing.

[0263] The server then extracts the posted data from the message queue and passes it to an AI module that uses natural language processing technology and a sentiment analysis engine. The AI ​​module uses the BERT model, which also uses natural language processing technology, and the sentiment analysis engine uses IBM Watson's sentiment analysis API. The AI ​​module performs morphological analysis of the posted content, keyword extraction, and contextual analysis, while the sentiment analysis engine analyzes the user's emotions (anger, sadness, joy, etc.) in real time.

[0264] The AI ​​module uses the analysis results to determine the criminality and emotion of the post and sends it back to the server. For example, posts that are judged to be "threatening" or that express strong emotions such as "anger" are flagged as high risk.

[0265] The server sends an alert to the administrator based on the results of the AI ​​module and sentiment analysis engine. The alert is sent as a notification via email or to a dashboard dedicated to the administrator. For example, an email may be sent stating, "User ID: 12345's post has been determined to be high risk. Anger score: 90%."

[0266] The administrator will check the alert and review the details of the relevant post. In some cases, the administrator may decide to delete the post or impose sanctions on the user (such as suspending the account). For example, the administrator may take the following action: "Check the post by user ID: 12345 and instruct them to delete it and suspend the account."

[0267] Specific examples

[0268] For example, if a user makes a post saying "I'm going to kill someone," the post will be processed by the system as follows:

[0269] 1. Your post is sent to the server immediately.

[0270] 2. The server stores the post in a database.

[0271] 3. The posted data is added to the message queue.

[0272] 4. The server takes the data from the queue and passes it to the BERT model and IBM Watson sentiment analysis API.

[0273] 5. The AI ​​module determines it to be a "threat" and the emotion engine analyzes the emotion score for "anger."

[0274] 6. The server sends an alert email to the administrator stating, "User ID: 12345's post has been deemed high risk. Anger score: 90%."

[0275] 7. The administrator will check the content of the post on the dashboard and take disciplinary action, such as deleting the content posted by user ID: 12345 and temporarily suspending the account.

[0276] Prompt Sentence Examples

[0277] 1. "Please rate the criminal nature of the following post: 'I want to hit someone.'"

[0278] 2. "Do a sentiment analysis of the following post: 'I'm so tired today.'"

[0279] This invention enables early detection and response to criminal and inappropriate behavior on social media platforms, ensuring the safety of users and the platform and improving its reliability. Furthermore, analyzing user emotions enables more advanced responses, making social media platforms safer and more reliable.

[0280] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0281] Step 1:

[0282] A user creates a new post on a social media platform.

[0283] Input: The user types the content to post.

[0284] Specific behavior: A user logs into a social networking application, enters text such as "kill someone," and clicks the post button.

[0285] Output: The post content is sent from the device to the server.

[0286] Step 2:

[0287] The terminal transmits the user's posted data to the server.

[0288] Input: User-generated submitted data.

[0289] Specific operation: The device sends the text data entered by the user, such as "kill someone," to the server as an HTTP request.

[0290] Output: Posted data reaches the server.

[0291] Step 3:

[0292] The server stores the received posting data in a data storage device.

[0293] Input: Post data sent from the device.

[0294] Specific operation: The server uses the INSERT statement to store data including the post content, poster ID, and posting date and time in the database. Specifically, "INSERT INTO posts (content, user_id, timestamp) VALUES ('Kill someone', 12345, '2023-10-01');"

[0295] Output: Posted data is saved in the database.

[0296] Step 4:

[0297] The server adds the posted data stored in the database to a message queue in real time.

[0298] Input: Post data stored in a database.

[0299] Specific operation: The server adds the post data to a message queue (e.g., RabbitMQ). It is added to the queue in the format "Post content: kill someone, Poster ID: 12345, Post date: 2023-10-01".

[0300] Output: Posted data is added to the message queue.

[0301] Step 5:

[0302] The server retrieves the posted data from the message queue and passes it to the AI ​​module and sentiment analysis engine.

[0303] Input: Post data added to the message queue.

[0304] Specific operation: The server inputs the data retrieved from the queue into an AI module (BERT model) that uses natural language processing technology and a sentiment analysis engine (IBM Watson sentiment analysis API).

[0305] Output: The data is passed to the AI ​​module and sentiment analysis engine.

[0306] Step 6:

[0307] The AI ​​module analyzes the posted data and analyzes emotions.

[0308] Input: Post data passed from the server.

[0309] Specific operation: The AI ​​module performs morphological analysis of the text, keyword extraction, and contextual analysis, and the sentiment analysis engine analyzes emotions (anger, sadness, joy, etc.). The text "kill someone" is analyzed as "Anger score: 90%" and falls into the "Threat" category.

[0310] Output: Analysis results (e.g., "Anger score: 90%", "Threat" category) are obtained.

[0311] Step 7:

[0312] The server sends an alert to the administrator based on the analysis results of the AI ​​module and emotion analysis engine.

[0313] Input: Analysis results from the AI ​​module and sentiment analysis engine.

[0314] Specific operation: The server sends an alert email or a notification to the administrator's dashboard stating something like "User ID: 12345's post has been deemed high risk. Anger score: 90%."

[0315] Output: An alert is sent to the administrator.

[0316] Step 8:

[0317] The administrator will confirm the alert sent and review the details of the relevant post.

[0318] Input: The alert content sent from the server.

[0319] Specific actions: The administrator will log in to the dashboard and take action such as "checking the relevant post and the post by user ID: 12345, and instructing them to delete the post and suspend the account."

[0320] Output: Decision and implementation of action, which may include deleting the relevant post and suspending the user's account.

[0321] (Application example 2)

[0322] 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."

[0323] Conventional social networking platforms have systems that monitor user posts and determine whether they are criminal, but they lack the ability to analyze user emotions. This makes it difficult to quickly detect posts that express negative user emotions and to respond appropriately, resulting in a lack of deterrent power for inappropriate behavior. Furthermore, administrators' responses when criminal activity is detected are inefficient, requiring a rapid response. The objective of this invention is to solve these problems and improve the safety and reliability of social networking platforms.

[0324] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for monitoring all posts on the SNS platform and acquiring post data, means for analyzing the acquired post data using natural language processing technology to determine whether the post is criminal, means for analyzing the emotions in the acquired post data and generating an emotion score for the user, means for issuing an alert to an administrator if the post is determined to be criminal, means for deleting the post and implementing sanctions against the user based on instructions from the administrator, and means including an administrator dashboard for the administrator to check and respond to the alert. This enables early detection and rapid response to inappropriate posts, and also enables appropriate measures to be taken for posts that express the user's negative emotions.

[0325] A "SNS platform" is an online service that allows users to share posts, comments, images, and videos.

[0326] "Posted data" refers to information such as text, images, and videos that users post on social media platforms.

[0327] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and includes morphological analysis, keyword extraction, contextual analysis, and more.

[0328] "Criminality" refers to whether the posted data contains content that may violate the law.

[0329] The "emotion score" is a numerical representation of the user's emotions contained in the posted data, and evaluates emotions such as "anger," "sadness," and "joy."

[0330] "Administrator" refers to the person or team responsible for operating a social media platform, monitoring content, and taking action as necessary.

[0331] An "alert" is a warning message that is sent to an administrator when a criminal offense is detected.

[0332] The "Admin Dashboard" is a dedicated management screen that allows administrators to monitor posts on social media platforms, check alerts, and take action.

[0333] "Sanctions" are measures taken against posts that are deemed to be criminal, and include deleting posts and suspending users' accounts.

[0334] To implement this invention, a system is required to monitor posts on social media platforms in real time and analyze their criminality and sentiment scores. This system uses the following hardware and software:

[0335] Hardware and software used:

[0336] 1. Hardware:

[0337] High-performance server: Required to process large amounts of submitted data quickly.

[0338] Database server: Required to store and manage submitted data.

[0339] 2. Software:

[0340] Natural Language Processing (NLP) libraries: For example, use spaCy or NLTK.

[0341] Sentiment analysis tools: For example, using TextBlob.

[0342] Database: For example, use MySQL or PostgreSQL.

[0343] System Details:

[0344] 1. The server monitors all posts on the SNS platform in real time and immediately retrieves new post data, including the post content, poster information, and post date and time.

[0345] 2. The server analyzes the acquired posted data using natural language processing technology. Specifically, it performs morphological analysis of the text, extracts keywords, and analyzes context. For example, it detects whether keywords such as "threat" or "attack" are included.

[0346] 3. The server also simultaneously performs emotion analysis and generates an emotion score for the user. For example, it analyzes whether the content of the post corresponds to "anger," "sadness," or "happiness," and quantifies the degree of that emotion.

[0347] 4. If the server determines that the activity is highly criminal, it will send an alert to the administrator. The alert will be sent as a notification via email or to the administrator's dashboard.

[0348] 5. The administrator will check the alert and review the details of the relevant post. If a criminal offense is confirmed, the administrator will delete the post and / or take disciplinary action against the user (such as suspending the account).

[0349] Examples:

[0350] If a user posts on a social media platform something like "threatening with angry language," the post data is sent to a server. The server passes it to an AI module and emotion engine, which uses natural language processing technology to determine whether it is a "threat," and the emotion engine analyzes it for the emotion of "anger." If the results indicate criminal activity, the server sends an alert to an administrator. The administrator reviews the alert and instructs sanctions such as deleting the post and suspending the user's account.

[0351] Example prompt sentence:

[0352] "Please monitor corporate social media for angry posts posted on September 3, 2023, and analyze potential threats."

[0353] "Analyze inappropriate content posted by students on educational institution social media and check for any dangerous posts."

[0354] In this way, the system of the present invention can identify inappropriate posts on social media platforms early and respond quickly.

[0355] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0356] Step 1:

[0357] The server retrieves new post data from the SNS platform in real time. This post data includes the text content, poster information, posting date and time, etc. Specifically, it uses the SNS platform's API to fetch the data every time a new post is made.

[0358] Input: New post data

[0359] Output: Retrieved post data

[0360] Step 2:

[0361] The server stores the acquired post data in a database. This allows the data to be retained for subsequent analysis. Storing data in the database is done using INSERT statements.

[0362] Input: Retrieved post data

[0363] Output: Post data stored in the database

[0364] Step 3:

[0365] The server adds the submitted data stored in the database to a queue, which is a collection of data waiting to be analyzed. For example, it uses a message queue system such as Redis.

[0366] Input: Post data stored in the database

[0367] Output: Post data added to the queue

[0368] Step 4:

[0369] The server retrieves the posted data from the queue and analyzes the text using natural language processing techniques, such as morphological analysis, keyword extraction, and context analysis. For example, it uses the spaCy or NLTK library.

[0370] Input: Post data added to the queue

[0371] Output: Analysis results (morphological analysis, keyword extraction, context analysis)

[0372] Step 5:

[0373] The server simultaneously performs emotion analysis and generates an emotion score for the user, which is a numerical representation of emotions such as "anger," "sadness," and "happiness." For example, it uses the TextBlob library.

[0374] Input: Post data added to the queue

[0375] Output: Sentiment score

[0376] Step 6:

[0377] The server determines whether a post is criminal based on the results of natural language processing analysis and the emotion score. For example, if a post contains keywords such as "threat" or "attack" and the emotion score indicates a high value such as "anger," it is determined to be criminal.

[0378] Input: Analysis results (morphological analysis, keyword extraction, context analysis), sentiment score

[0379] Output: Criminal conduct determination result

[0380] Step 7:

[0381] If the server determines that the activity is highly criminal, it will send an alert to the administrator. The alert will be sent via email or to a dedicated administrator dashboard. Specifically, it uses the SMTP protocol and WebSocket communication.

[0382] Input: Criminal conduct determination result

[0383] Output: Alert sent to administrator

[0384] Step 8:

[0385] Administrators can view alerts on a dedicated dashboard and review details of the affected posts, including the text content, author information, and sentiment score.

[0386] Input: Alert sent to administrator

[0387] Output: Post details displayed

[0388] Step 9:

[0389] If a criminal offense is confirmed, the administrator will delete the post or take sanctions against the user. For example, the administrator can delete the post using an API call, or suspend the user's account by updating the database.

[0390] Input: Post details displayed

[0391] Output: Sanctions taken

[0392] This series of processes enables early detection and swift response to inappropriate posts on social media platforms.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] [Second embodiment]

[0397] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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.

[0408] 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."

[0409] An embodiment of the present invention provides a mechanism for monitoring user posts on a social networking platform and detecting criminal activity in real time, thereby ensuring the reliability of users and the platform.

[0410] Watch and get posts

[0411] When a user logs in to a social networking platform and posts a new post, the post data is immediately sent to the server, which captures the post content, the poster's information, and the post timestamp, and stores them in a database, ready for immediate analysis.

[0412] Analysis using natural language processing

[0413] The server passes the posted data acquired in real time to an AI module. The AI ​​module uses natural language processing technology to analyze the content of the post. Specifically, it performs morphological analysis of the text, extracts keywords, and analyzes the context to determine whether the post is criminal. For example, if a post contains a keyword such as "illegal part-time job," the context is also analyzed to assess whether it represents illegal activity.

[0414] Criminality determination and alert issuing

[0415] If the AI ​​module detects any criminal activity in the post, the server will send an alert to the administrator based on the results of the assessment. The alert will be sent via email or as a dashboard notification for the administrator. The administrator's device will immediately receive the alert and notify the administrator.

[0416] Management Actions and Sanctions

[0417] Administrators will review the alert and check the relevant posts. If deemed necessary, they will instruct the server to delete the posts and take disciplinary action against the violating users. For example, they may delete specific violating posts and suspend the accounts of the users.

[0418] Specific examples

[0419] For example, if a user posts something containing the phrase "Introducing illegal part-time jobs that will earn you money easily," the posted data is immediately sent to the server. The server passes the data to an AI module, which recognizes "illegal part-time jobs" as a keyword and analyzes the context. If illegality is detected as a result, an alert is sent to an administrator. The administrator checks the alert and instructs them on the necessary countermeasures. Based on the instructions, the server deletes the post and suspends the user's account.

[0420] This invention is a system for quickly and accurately detecting illegal activities on social media platforms and ensuring user safety. The operation of this system will make social media platforms safer and more reliable.

[0421] The processing flow will be explained below.

[0422] Step 1:

[0423] A user creates a new post on a social media platform, which can contain text, images, and videos.

[0424] Step 2:

[0425] The server instantly retrieves user-created posts using the API. The retrieved data includes details such as the post content, poster information, and posting date and time.

[0426] Step 3:

[0427] The server stores the acquired submission data in a database, and simultaneously adds the submission data to a queue in real time to prepare for analysis.

[0428] Step 4:

[0429] The server passes the queued data to the AI ​​module, where it also schedules it for sequential processing.

[0430] Step 5:

[0431] The AI ​​module analyzes the received post data using natural language processing technology, which includes morphological analysis of the text, keyword extraction, and context analysis.

[0432] Step 6:

[0433] The AI ​​module uses the analysis results to determine whether a post is criminal. For example, it detects keywords such as "illegal part-time jobs" or "drug sales" and evaluates whether they constitute illegal activity. Contextual analysis is also performed to make a more accurate judgment.

[0434] Step 7:

[0435] The server receives the criminality judgment results from the AI ​​module, stores the results in a database, and simultaneously sends an alert to the administrator if a criminality is judged to have occurred.

[0436] Step 8:

[0437] The administrator's terminal receives alerts from the server, which are displayed as notifications via email and on the administrator's dashboard.

[0438] Step 9:

[0439] Administrators will review the alert, review the content of the relevant post, and decide on sanctions against the poster, if necessary.

[0440] Step 10:

[0441] The administrator will instruct the server to remove the post and take disciplinary action against the user, which may include suspending or permanently terminating the account.

[0442] Step 11:

[0443] The server will then delete the relevant post from the database based on the administrator's instructions, as well as remove it from inventory and log the violation.

[0444] Step 12:

[0445] The server updates account status and sets access restrictions to enforce sanctions against violating users.

[0446] Step 13:

[0447] The server will notify the user of the details of the sanctions, which will be sent via email or messaging within the social media platform.

[0448] Example 1

[0449] 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."

[0450] The number of posts related to fraudulent activities and crimes on social media platforms is increasing. Such posts undermine the reliability of the platforms and threaten the safety of users, so they need to be detected and addressed quickly and accurately. However, current systems mainly rely on manual monitoring and periodic checks, and while real-time monitoring and rapid response are required, there are still issues that cannot be fully addressed.

[0451] 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.

[0452] In this invention, the server includes a means for acquiring data posted by users to the platform, a means for transmitting the acquired data to an AI module and analyzing it using natural language processing technology, and a means for sending an alert to an administrator terminal in real time when criminal activity is detected. This makes it possible to quickly and accurately detect posts related to fraudulent activities or crimes on the SNS platform and take appropriate measures to ensure the reliability of the platform and the safety of users.

[0453] "User" refers to any individual or corporation that uses the SNS Platform.

[0454] "Platform" refers to an online system that provides services for users to post and communicate.

[0455] "Posted Data" refers to content such as text, images, and videos that users create and submit on the Platform.

[0456] "Server" refers to a computer system that receives user posted data, processes it, and stores it in a database.

[0457] "AI module" refers to a software component that analyzes posted data and uses natural language processing technology to detect criminal activity.

[0458] "Natural language processing technology" refers to the technology used by AI modules to perform morphological analysis of text data, extract keywords, analyze context, and so on.

[0459] "Administrator" refers to the individual or organization responsible for operating the platform, monitoring posted data, and responding to fraudulent activity.

[0460] "Alert" refers to a notification sent to an Administrator when criminal activity is detected.

[0461] "Real-time" refers to the fact that processing and notification are carried out almost immediately after the user's posted data is sent.

[0462] The present invention describes a system for monitoring user posts on a social networking platform in real time to detect criminal activity, with the aim of improving the safety and reliability of the platform.

[0463] Watch and get posts

[0464] When a user logs in to a social networking platform and posts a new post, the post data is immediately sent from the device to the server. The server retrieves the post content, author information, and post timestamp, and stores them in a database. The device does this through an HTTP POST request, and the server's API endpoint receives the request and processes the data.

[0465] Data analysis

[0466] The server sends the newly acquired post data to the AI ​​module, which uses a natural language processing library (e.g., spaCy or NLTK) to:

[0467] Morphological analysis of text

[0468] Keyword extraction

[0469] Contextual Analysis

[0470] The AI ​​module uses these analyses to determine whether a post is criminal. For example, if a post contains the keyword "illegal part-time job," it will also analyze the context to assess whether it is illegal activity.

[0471] Criminal activity detection and alerting

[0472] If the AI ​​module detects criminal activity in a post, it returns the result to the server. The server then sends an alert to the administrator based on the result. The alert is sent via email or a dashboard notification for the administrator. The server uses an SMTP server to send emails and Websockets to send real-time notifications to the dashboard.

[0473] Management Actions and Sanctions

[0474] The administrator's terminal receives the alert and notifies the administrator. The administrator confirms the alert and checks the relevant post. If necessary, the administrator instructs the server to delete the relevant post and impose sanctions on the violating user. The server then receives the instruction, deletes the relevant data from the database, and updates the status of the user's account.

[0475] Specific examples

[0476] For example, if a user posts something containing the phrase "introducing illegal part-time jobs that will earn you money easily," the posted data is immediately sent to the server. The server passes the data to an AI module, which recognizes "illegal part-time jobs" as a keyword and analyzes the context. If illegality is detected as a result, an alert is sent to an administrator. The administrator checks the alert and instructs them on the necessary countermeasures. Through this process, the post in question is deleted and the user's account is suspended.

[0477] This system will enable the rapid and accurate detection of illegal activities on social media platforms and ensure the safety of users. This system aims to increase the reliability and safety of the platform and provide an environment where users can use it with peace of mind.

[0478] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0479] Step 1:

[0480] A user logs in to a social media platform and posts a new post. When the user presses the post button, the post data is sent from the device to the server. Specifically, the device generates an HTTP POST request and sends data including the post content, poster information, and a timestamp to the server's API endpoint.

[0481] Input: User post content, poster information, timestamp

[0482] Output: HTTP POST request sent

[0483] Step 2:

[0484] The server receives the posted data from the device and stores it in a database. The server converts this data into the required format and stores it in the database, making it ready for immediate analysis.

[0485] Input: HTTP POST request (user submitted data)

[0486] Output: Post data saved in the database

[0487] Step 3:

[0488] The server sends the saved post data to the AI ​​module, which then sends the post data as a request to the API endpoint for the AI ​​module. The AI ​​module then analyzes the post content using natural language processing technology.

[0489] Input: Post data retrieved from the database

[0490] Output: Send request to AI module

[0491] Step 4:

[0492] The AI ​​module uses a natural language processing library to analyze the received post data. Specifically, it performs morphological analysis, keyword extraction, and context analysis to determine whether the post is criminal.

[0493] Input: Post data sent from the server

[0494] Output: Analysis results (whether or not there is criminal activity)

[0495] Step 5:

[0496] If the AI ​​module detects any criminal activity in the post, it returns the result to the server in JSON format, which the server receives.

[0497] Input: Analysis results (whether or not there is a criminal offense)

[0498] Output: Response to the server (data indicating whether or not there is criminal activity)

[0499] Step 6:

[0500] The server sends alerts to administrators based on the results of the AI ​​module. The server retrieves the administrator's contact information from a database, sends emails using an SMTP server, and also sends real-time notifications to an administrator's dashboard using Websockets.

[0501] Input: Criminal data, administrator contact information

[0502] Output: Alert notification to administrator (email, dashboard notification)

[0503] Step 7:

[0504] The administrator device receives the alert and notifies the administrator, who then checks the content of the alert and the relevant posts.

[0505] Input: Alert notification from the server

[0506] Output: Administrator confirmation process begins

[0507] Step 8:

[0508] Based on the alert notification, administrators will check the relevant posts and, if necessary, issue instructions for disciplinary action, such as deleting the post or suspending the user's account.

[0509] Input: Check the content of the relevant post

[0510] Output: Instructions for action

[0511] Step 9:

[0512] The server, upon receiving instructions from the administrator, will remove the post from the database and, if necessary, update the status of the user's account, ensuring that the violation is removed promptly.

[0513] Input: Instructions for corrective action from administrator

[0514] Output: Database update (delete post, change user account status)

[0515] (Application example 1)

[0516] 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."

[0517] There is a need to efficiently detect potentially criminal posts on social media platforms and respond quickly. However, with current technology, it has been difficult to detect potentially criminal posts in real time, and there have often been delays in notifying administrators and responding to them. In addition, it is labor-intensive and unrealistic for administrators to constantly monitor the platform. In particular, there has been an issue with the lack of integration with smart devices, making it difficult to respond quickly on-site.

[0518] 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.

[0519] In this invention, the server includes means for monitoring all posts on the SNS platform and acquiring post data, means for analyzing the acquired post data using natural language processing technology and determining whether the post is criminal, means for issuing an alert to an administrator if the post is determined to be criminal, means for deleting the post and implementing sanctions against the user based on instructions from the administrator, and means for sending a visual alert to the wearable device if criminality is detected. This makes it possible to detect criminal posts in real time and respond quickly.

[0520] An "SNS platform" is an online service that allows users to communicate over the Internet.

[0521] "Posted data" refers to information such as text, images, and videos that users publish on social media platforms.

[0522] "Natural language processing technology" is a technology that allows computers to analyze and understand human language, and includes morphological analysis and contextual analysis.

[0523] "Criminality" is a quality that determines whether posted data suggests illegal activity.

[0524] An "alert" is a warning notification that the system automatically issues to the administrator.

[0525] A "wearable device" is a computing device that is worn on the body, such as smart glasses or a smart watch.

[0526] A "visual alert" is a visual warning message displayed on a wearable device.

[0527] "Administrator" means a person or organization that operates or monitors a social media platform.

[0528] A "queue" is a data structure for processing posted data in order.

[0529] An "AI module" is a software component that uses artificial intelligence technology to analyze data and determine certain attributes, such as criminality.

[0530] "Sanctions" are measures that impose penalties on users who make illegal posts, such as suspending their accounts or deleting posts.

[0531] An embodiment of the present invention is a system for monitoring posts on a social networking platform, detecting potentially criminal posts in real time, and responding promptly. The system includes a means for monitoring all posts on the social networking platform, analyzing them using natural language processing technology, and sending an alert to an administrator and a means for sending a visual alert to a wearable device.

[0532] System Program

[0533] The server retrieves posted data in real time and stores it in a database. The server adds the posted data to a queue and passes it to the AI ​​module. The AI ​​module uses natural language processing technology to perform morphological and contextual analysis of the posted data to determine whether it is criminal. Specifically, it uses the Google Cloud Natural Language API. If criminal activity is detected, the server sends an alert to the administrator and also sends a visual alert to the wearable device.

[0534] Specific examples

[0535] For example, if a user posts something containing the phrase "Introduction to illegal part-time jobs that allow you to easily earn money," the posted data is sent to the server. The server then passes the posted data to an AI module, which recognizes keywords such as "illegal part-time jobs" and analyzes the context. If the analysis detects any criminal activity, the server will send an alert to the administrator and also send a visual alert to the wearable device worn by the security officer.

[0536] Hardware and software used

[0537] Server: Collects and analyzes data posted on SNS platforms and notifies the administrator.

[0538] Wearable devices: Devices worn by security personnel that can receive visual alerts, such as smart glasses.

[0539] Google Cloud Natural Language API: Provides natural language processing technology to analyze posted data and determine criminality.

[0540] Prompt Sentence Examples

[0541] Please analyze the content of the post below and determine whether it is criminal. If so, please also provide specific keywords and context.

[0542] "I have work to do in a dangerous location tonight, and I'm looking for someone to come."

[0543] This will enable the detection and rapid response of criminal activity on social media platforms, improving the safety and reliability of the system.

[0544] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0545] Step 1:

[0546] A user logs in to a social networking platform and posts a new message. The message data is sent from the user's device to the server. The input here is the message data created by the user, and the output is the message data sent to the server.

[0547] Step 2:

[0548] The server receives the submission data and stores it in a database, where it is ready to be added to the queue. The input here is the submission data sent to the server, and the output is the submission data stored in the database and the submission data ready to be added to the queue.

[0549] Step 3:

[0550] The server adds the submitted data to a queue and passes the queue to the AI ​​module. The input here is the submitted data added to the queue, and the output is the submitted data passed to the AI ​​module.

[0551] Step 4:

[0552] The AI ​​module analyzes the posted data using natural language processing technology. Specifically, it performs morphological analysis, keyword extraction, and contextual analysis to determine whether the post is criminal. The technology used is the Google Cloud Natural Language API. The input here is the posted data passed to the AI ​​module, and the output is the analysis results.

[0553] Step 5:

[0554] The server receives the analysis results from the AI ​​module and checks whether criminal activity has been detected. If criminal activity is detected, the server sends an alert to the administrator. The input here is the analysis result of the AI ​​module, and the output is an alert notification to the administrator.

[0555] Step 6:

[0556] If the server detects criminal activity, it sends a visual alert to the wearable device. The input here is the analysis result that criminal activity was detected, and the output is the visual alert sent to the wearable device.

[0557] Step 7:

[0558] An administrator checks the alert, deletes the relevant post if necessary, and takes sanctions against the user. The input here is the alert sent to the administrator, and the output is the deleted post data and the user data on which sanctions were taken.

[0559] This series of processes makes it possible to detect potentially criminal posts on social media platforms in real time and respond quickly.

[0560] 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.

[0561] An embodiment of the present invention provides a system for monitoring user posts on a social media platform and analyzing criminality and user sentiment in real time, which improves the trust between users and the platform and enables early detection and response to criminal or inappropriate behavior.

[0562] Watch and get posts

[0563] When a user creates a new post on a social networking platform, the post data is immediately sent to the server, which retrieves details such as the post content, author information, and the posting date and time, and stores them in a database. This allows the post content to be immediately analyzed.

[0564] Analysis using natural language processing and emotion engine

[0565] The server adds the acquired post data to a queue in real time and passes it to the AI ​​module and emotion engine. The AI ​​module analyzes the post content using natural language processing technology. Specifically, it performs morphological analysis of the text, keyword extraction, and context analysis, while the emotion engine analyzes the user's emotions (e.g., anger, sadness, joy, etc.).

[0566] Judging criminality and emotions

[0567] The AI ​​module uses the results of the analysis to determine whether a post is criminal. It also uses the results of the emotion engine to assess whether the post is related to any criminal activity. For example, posts that express strong feelings of anger or aggression are evaluated with particular attention.

[0568] Sending alerts

[0569] The server sends an alert to the administrator based on the results of the AI ​​module and emotion engine. It will not only issue an alert if criminal activity is detected, but also alerts for high-risk posts, taking into account the results of user sentiment analysis. Alerts are sent via email and to the administrator's dedicated dashboard.

[0570] Management Actions and Sanctions

[0571] Administrators will review the alert and review the details of the relevant post. If criminal activity is confirmed, administrators will decide to remove the post or impose sanctions on the user. This may include additional actions based on sentiment analysis results.

[0572] Specific examples

[0573] For example, if a user posts something like "threatening with angry language," the posted data is sent to the server. The server passes it to the AI ​​module and emotion engine, where the AI ​​module determines it is a "threat," and the emotion engine analyzes it for the emotion of "anger." If the results indicate criminal activity, the server sends an alert to an administrator. The administrator reviews the alert and instructs disciplinary action, such as deleting the post and suspending the user's account.

[0574] The system of the present invention not only detects criminal activity on social media platforms quickly and accurately and ensures user safety, but also enables more sophisticated responses by analyzing user sentiment, making social media platforms safer and more reliable.

[0575] The processing flow will be explained below.

[0576] Step 1:

[0577] A user creates a new post on a social media platform, which can consist of text, images, videos, etc.

[0578] Step 2:

[0579] The server instantly retrieves user-created post data via the API, including metadata such as the post content, poster information, and posting date and time.

[0580] Step 3:

[0581] The server stores the acquired posting data in a database and simultaneously adds the posting data to a queue in real time.

[0582] Step 4:

[0583] The server schedules sequential processing to pass the posted data added to the queue to the AI ​​module and emotion engine.

[0584] Step 5:

[0585] The AI ​​module analyzes the received post data using natural language processing technology, specifically by performing morphological analysis of the text, keyword extraction, and contextual analysis.

[0586] Step 6:

[0587] The emotion engine analyzes the same posted data and evaluates the user's emotions, identifying emotions such as anger, sadness, and joy in the process.

[0588] Step 7:

[0589] The AI ​​module uses natural language processing to determine the criminality of posts, for example by detecting keywords such as "threat" and "violence" and evaluating their meaning and context.

[0590] Step 8:

[0591] If an emotion that is deemed to be criminal or serious (such as "anger" or "fear") is detected, the emotion engine provides the evaluation result to the AI ​​module, which can then accurately determine the criminality of the post.

[0592] Step 9:

[0593] The server acquires the results of the AI ​​module and emotion engine and stores them in a database. At the same time, if it determines that there is criminal activity or a high level of risk, it will send an alert to the administrator.

[0594] Step 10:

[0595] The administrator's terminal receives alerts from the server, which are displayed as notifications via email and on the administrator's dashboard.

[0596] Step 11:

[0597] Administrators will review the alert and details of the relevant posts, and after reviewing the content, will determine the necessary sanctions.

[0598] Step 12:

[0599] The administrator will instruct the server to remove the post and take disciplinary action against the user, which may include suspending or permanently terminating the account.

[0600] Step 13:

[0601] The server will delete the relevant posts from the database based on the administrator's instructions, and if necessary, record a log of the violation.

[0602] Step 14:

[0603] The server updates account status and sets access restrictions to enforce sanctions against violating users.

[0604] Step 15:

[0605] The server will notify the user of the details of the sanctions, which will be sent via email or messaging within the social media platform.

[0606] Example 2

[0607] 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."

[0608] Conventional social media platforms lack the means to properly monitor user posts and detect criminal or inappropriate emotional expressions in real time. As a result, it is difficult to detect and respond to criminal or inappropriate behavior early on. While there is also hope for sophisticated responses based on user emotion analysis, conventional systems have not fully realized this. Therefore, there is a need for a system that can improve the trust between users and the platform and enable early detection and response to criminal or inappropriate behavior.

[0609] 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.

[0610] In this invention, the server includes means for monitoring all posts on the SNS platform and acquiring post data, means for storing the acquired post data in a data storage device, means for adding the acquired post data to a message queue in real time, means for passing the message queue to an AI module and a sentiment analysis engine using natural language processing technology, means for analyzing the post data and analyzing sentiment using natural language processing technology, means for determining the criminality and sentiment of the post based on the analysis results, means for issuing an alert to an administrator if a post is determined to be criminal, and means for deleting the relevant post and implementing sanctions against the user based on instructions from the administrator. This enables real-time analysis of post data, enabling early detection and response to criminal or inappropriate behavior. Furthermore, analyzing user sentiment allows for more advanced responses, further improving the reliability and safety of the platform.

[0611] "SNS Platform" refers to an online service that enables users to post content and interact with other users.

[0612] "Posted data" refers to information such as text, images, and videos that users create and publish on social media platforms.

[0613] "Server" refers to a computer system that processes, stores, and manages data on a network.

[0614] A "data storage device" is a device or system for storing data, including hard disk drives, SSDs, and cloud storage.

[0615] A "message queue" is a system for temporarily storing data and processing it sequentially, and examples include RabbitMQ and Apache Kafka.

[0616] "Natural language processing technology" is a technology for understanding and analyzing human language, and includes morphological analysis, keyword extraction, contextual analysis, etc.

[0617] An "AI module" is a software component that performs analysis using artificial intelligence, and specifically includes natural language processing and machine learning models.

[0618] An "emotion analysis engine" is a system that analyzes user emotions from text data and calculates emotional scores such as anger, sadness, and joy.

[0619] "Analysis results" refers to information based on the analysis of posted data obtained by an AI module or sentiment analysis engine.

[0620] "Criminal" refers to the characteristics or content of posted data that violate or have the potential to violate laws or regulations.

[0621] An "alert" refers to a warning message or notification sent to an administrator when a specific condition is met.

[0622] "Administrator" refers to the person or organization responsible for operating a social media platform, monitoring user posts, and responding to violations.

[0623] "Sanctions" refers to penalties or restrictions imposed on users when violations of the terms of service or criminal activity are confirmed.

[0624] An embodiment of the present invention provides a system that monitors user posts on a social media platform and analyzes criminality and user sentiment in real time, thereby improving the trust between users and the platform and enabling early detection and response to criminal or inappropriate behavior.

[0625] When a user creates a new post on a social networking platform, the post data is immediately sent from the device to the server, which then retrieves details such as the post content, poster information, and posting date and time, and stores the data in a data storage device.

[0626] The server adds the received post data to a message queue in real time. In this case, a message broker such as RabbitMQ or Apache Kafka is used for the message queue. The data added to this message queue is then subjected to subsequent analysis processing.

[0627] The server then extracts the posted data from the message queue and passes it to an AI module that uses natural language processing technology and a sentiment analysis engine. The AI ​​module uses the BERT model, which also uses natural language processing technology, and the sentiment analysis engine uses IBM Watson's sentiment analysis API. The AI ​​module performs morphological analysis of the posted content, keyword extraction, and contextual analysis, while the sentiment analysis engine analyzes the user's emotions (anger, sadness, joy, etc.) in real time.

[0628] The AI ​​module uses the analysis results to determine the criminality and emotion of the post and sends it back to the server. For example, posts that are judged to be "threatening" or that express strong emotions such as "anger" are flagged as high risk.

[0629] The server sends an alert to the administrator based on the results of the AI ​​module and sentiment analysis engine. The alert is sent as a notification via email or to a dashboard dedicated to the administrator. For example, an email may be sent stating, "User ID: 12345's post has been determined to be high risk. Anger score: 90%."

[0630] The administrator will check the alert and review the details of the relevant post. In some cases, the administrator may decide to delete the post or impose sanctions on the user (such as suspending the account). For example, the administrator may take the following action: "Check the post by user ID: 12345 and instruct them to delete it and suspend the account."

[0631] Specific examples

[0632] For example, if a user makes a post saying "I'm going to kill someone," the post will be processed by the system as follows:

[0633] 1. Your post is sent to the server immediately.

[0634] 2. The server stores the post in a database.

[0635] 3. The posted data is added to the message queue.

[0636] 4. The server takes the data from the queue and passes it to the BERT model and IBM Watson sentiment analysis API.

[0637] 5. The AI ​​module determines it to be a "threat" and the emotion engine analyzes the emotion score for "anger."

[0638] 6. The server sends an alert email to the administrator stating, "User ID: 12345's post has been deemed high risk. Anger score: 90%."

[0639] 7. The administrator will check the content of the post on the dashboard and take disciplinary action, such as deleting the content posted by user ID: 12345 and temporarily suspending the account.

[0640] Prompt Sentence Examples

[0641] 1. "Please rate the criminal nature of the following post: 'I want to hit someone.'"

[0642] 2. "Do a sentiment analysis of the following post: 'I'm so tired today.'"

[0643] This invention enables early detection and response to criminal and inappropriate behavior on social media platforms, ensuring the safety of users and the platform and improving its reliability. Furthermore, analyzing user emotions enables more advanced responses, making social media platforms safer and more reliable.

[0644] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0645] Step 1:

[0646] A user creates a new post on a social media platform.

[0647] Input: The user types the content to post.

[0648] Specific behavior: A user logs into a social networking application, enters text such as "kill someone," and clicks the post button.

[0649] Output: The post content is sent from the device to the server.

[0650] Step 2:

[0651] The terminal transmits the user's posted data to the server.

[0652] Input: User-generated submitted data.

[0653] Specific operation: The device sends the text data entered by the user, such as "kill someone," to the server as an HTTP request.

[0654] Output: Posted data reaches the server.

[0655] Step 3:

[0656] The server stores the received posting data in a data storage device.

[0657] Input: Post data sent from the device.

[0658] Specific operation: The server uses the INSERT statement to store data including the post content, poster ID, and posting date and time in the database. Specifically, "INSERT INTO posts (content, user_id, timestamp) VALUES ('Kill someone', 12345, '2023-10-01');"

[0659] Output: Posted data is saved in the database.

[0660] Step 4:

[0661] The server adds the posted data stored in the database to a message queue in real time.

[0662] Input: Post data stored in a database.

[0663] Specific operation: The server adds the post data to a message queue (e.g., RabbitMQ). It is added to the queue in the format "Post content: kill someone, Poster ID: 12345, Post date: 2023-10-01".

[0664] Output: Posted data is added to the message queue.

[0665] Step 5:

[0666] The server retrieves the posted data from the message queue and passes it to the AI ​​module and sentiment analysis engine.

[0667] Input: Post data added to the message queue.

[0668] Specific operation: The server inputs the data retrieved from the queue into an AI module (BERT model) that uses natural language processing technology and a sentiment analysis engine (IBM Watson sentiment analysis API).

[0669] Output: The data is passed to the AI ​​module and sentiment analysis engine.

[0670] Step 6:

[0671] The AI ​​module analyzes the posted data and analyzes emotions.

[0672] Input: Post data passed from the server.

[0673] Specific operation: The AI ​​module performs morphological analysis of the text, keyword extraction, and contextual analysis, and the sentiment analysis engine analyzes emotions (anger, sadness, joy, etc.). The text "kill someone" is analyzed as "Anger score: 90%" and falls into the "Threat" category.

[0674] Output: Analysis results (e.g., "Anger score: 90%", "Threat" category) are obtained.

[0675] Step 7:

[0676] The server sends an alert to the administrator based on the analysis results of the AI ​​module and emotion analysis engine.

[0677] Input: Analysis results from the AI ​​module and sentiment analysis engine.

[0678] Specific operation: The server sends an alert email or a notification to the administrator's dashboard stating something like "User ID: 12345's post has been deemed high risk. Anger score: 90%."

[0679] Output: An alert is sent to the administrator.

[0680] Step 8:

[0681] The administrator will confirm the alert sent and review the details of the relevant post.

[0682] Input: The alert content sent from the server.

[0683] Specific actions: The administrator will log in to the dashboard and take action such as "checking the relevant post and the post by user ID: 12345, and instructing them to delete the post and suspend the account."

[0684] Output: Decision and implementation of action, which may include deleting the relevant post and suspending the user's account.

[0685] (Application example 2)

[0686] 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."

[0687] Conventional social networking platforms have systems that monitor user posts and determine whether they are criminal, but they lack the ability to analyze user emotions. This makes it difficult to quickly detect posts that express negative user emotions and to respond appropriately, resulting in a lack of deterrent power for inappropriate behavior. Furthermore, administrators' responses when criminal activity is detected are inefficient, requiring a rapid response. The objective of this invention is to solve these problems and improve the safety and reliability of social networking platforms.

[0688] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for monitoring all posts on the SNS platform and acquiring post data, means for analyzing the acquired post data using natural language processing technology to determine whether the post is criminal, means for analyzing the emotions in the acquired post data and generating an emotion score for the user, means for issuing an alert to an administrator if the post is determined to be criminal, means for deleting the post and implementing sanctions against the user based on instructions from the administrator, and means including an administrator dashboard for the administrator to check and respond to the alert. This enables early detection and rapid response to inappropriate posts, and also enables appropriate measures to be taken for posts that express the user's negative emotions.

[0689] A "SNS platform" is an online service that allows users to share posts, comments, images, and videos.

[0690] "Posted data" refers to information such as text, images, and videos that users post on social media platforms.

[0691] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and includes morphological analysis, keyword extraction, contextual analysis, and more.

[0692] "Criminality" refers to whether the posted data contains content that may violate the law.

[0693] The "emotion score" is a numerical representation of the user's emotions contained in the posted data, and evaluates emotions such as "anger," "sadness," and "joy."

[0694] "Administrator" refers to the person or team responsible for operating a social media platform, monitoring content, and taking action as necessary.

[0695] An "alert" is a warning message that is sent to an administrator when a criminal offense is detected.

[0696] The "Admin Dashboard" is a dedicated management screen that allows administrators to monitor posts on social media platforms, check alerts, and take action.

[0697] "Sanctions" are measures taken against posts that are deemed to be criminal, and include deleting posts and suspending users' accounts.

[0698] To implement this invention, a system is required to monitor posts on social media platforms in real time and analyze their criminality and sentiment scores. This system uses the following hardware and software:

[0699] Hardware and software used:

[0700] 1. Hardware:

[0701] High-performance server: Required to process large amounts of submitted data quickly.

[0702] Database server: Required to store and manage submitted data.

[0703] 2. Software:

[0704] Natural Language Processing (NLP) libraries: For example, use spaCy or NLTK.

[0705] Sentiment analysis tools: For example, using TextBlob.

[0706] Database: For example, use MySQL or PostgreSQL.

[0707] System Details:

[0708] 1. The server monitors all posts on the SNS platform in real time and immediately retrieves new post data, including the post content, poster information, and post date and time.

[0709] 2. The server analyzes the acquired posted data using natural language processing technology. Specifically, it performs morphological analysis of the text, extracts keywords, and analyzes context. For example, it detects whether keywords such as "threat" or "attack" are included.

[0710] 3. The server also simultaneously performs emotion analysis and generates an emotion score for the user. For example, it analyzes whether the content of the post corresponds to "anger," "sadness," or "happiness," and quantifies the degree of that emotion.

[0711] 4. If the server determines that the activity is highly criminal, it will send an alert to the administrator. The alert will be sent as a notification via email or to the administrator's dashboard.

[0712] 5. The administrator will check the alert and review the details of the relevant post. If a criminal offense is confirmed, the administrator will delete the post and / or take disciplinary action against the user (such as suspending the account).

[0713] Examples:

[0714] If a user posts on a social media platform something like "threatening with angry language," the post data is sent to a server. The server passes it to an AI module and emotion engine, which uses natural language processing technology to determine whether it is a "threat," and the emotion engine analyzes it for the emotion of "anger." If the results indicate criminal activity, the server sends an alert to an administrator. The administrator reviews the alert and instructs sanctions such as deleting the post and suspending the user's account.

[0715] Example prompt sentence:

[0716] "Please monitor corporate social media for angry posts posted on September 3, 2023, and analyze potential threats."

[0717] "Analyze inappropriate content posted by students on educational institution social media and check for any dangerous posts."

[0718] In this way, the system of the present invention can identify inappropriate posts on social media platforms early and respond quickly.

[0719] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0720] Step 1:

[0721] The server retrieves new post data from the SNS platform in real time. This post data includes the text content, poster information, posting date and time, etc. Specifically, it uses the SNS platform's API to fetch the data every time a new post is made.

[0722] Input: New post data

[0723] Output: Retrieved post data

[0724] Step 2:

[0725] The server stores the acquired post data in a database. This allows the data to be retained for subsequent analysis. Storing data in the database is done using INSERT statements.

[0726] Input: Retrieved post data

[0727] Output: Post data stored in the database

[0728] Step 3:

[0729] The server adds the submitted data stored in the database to a queue, which is a collection of data waiting to be analyzed. For example, it uses a message queue system such as Redis.

[0730] Input: Post data stored in the database

[0731] Output: Post data added to the queue

[0732] Step 4:

[0733] The server retrieves the posted data from the queue and analyzes the text using natural language processing techniques, such as morphological analysis, keyword extraction, and context analysis. For example, it uses the spaCy or NLTK library.

[0734] Input: Post data added to the queue

[0735] Output: Analysis results (morphological analysis, keyword extraction, context analysis)

[0736] Step 5:

[0737] The server simultaneously performs emotion analysis and generates an emotion score for the user, which is a numerical representation of emotions such as "anger," "sadness," and "happiness." For example, it uses the TextBlob library.

[0738] Input: Post data added to the queue

[0739] Output: Sentiment score

[0740] Step 6:

[0741] The server determines whether a post is criminal based on the results of natural language processing analysis and the emotion score. For example, if a post contains keywords such as "threat" or "attack" and the emotion score indicates a high value such as "anger," it is determined to be criminal.

[0742] Input: Analysis results (morphological analysis, keyword extraction, context analysis), sentiment score

[0743] Output: Criminal conduct determination result

[0744] Step 7:

[0745] If the server determines that the activity is highly criminal, it will send an alert to the administrator. The alert will be sent via email or to a dedicated administrator dashboard. Specifically, it uses the SMTP protocol and WebSocket communication.

[0746] Input: Criminal conduct determination result

[0747] Output: Alert sent to administrator

[0748] Step 8:

[0749] Administrators can view alerts on a dedicated dashboard and review details of the affected posts, including the text content, author information, and sentiment score.

[0750] Input: Alert sent to administrator

[0751] Output: Post details displayed

[0752] Step 9:

[0753] If a criminal offense is confirmed, the administrator will delete the post or take sanctions against the user. For example, the administrator can delete the post using an API call, or suspend the user's account by updating the database.

[0754] Input: Post details displayed

[0755] Output: Sanctions taken

[0756] This series of processes enables early detection and swift response to inappropriate posts on social media platforms.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] [Third embodiment]

[0761] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0762] 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.

[0763] 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).

[0764] 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.

[0765] 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.

[0766] 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).

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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."

[0773] An embodiment of the present invention provides a mechanism for monitoring user posts on a social networking platform and detecting criminal activity in real time, thereby ensuring the reliability of users and the platform.

[0774] Watch and get posts

[0775] When a user logs in to a social networking platform and posts a new post, the post data is immediately sent to the server, which captures the post content, the poster's information, and the post timestamp, and stores them in a database, ready for immediate analysis.

[0776] Analysis using natural language processing

[0777] The server passes the posted data acquired in real time to an AI module. The AI ​​module uses natural language processing technology to analyze the content of the post. Specifically, it performs morphological analysis of the text, extracts keywords, and analyzes the context to determine whether the post is criminal. For example, if a post contains a keyword such as "illegal part-time job," the context is also analyzed to assess whether it represents illegal activity.

[0778] Criminality determination and alert issuing

[0779] If the AI ​​module detects any criminal activity in the post, the server will send an alert to the administrator based on the results of the assessment. The alert will be sent via email or as a dashboard notification for the administrator. The administrator's device will immediately receive the alert and notify the administrator.

[0780] Management Actions and Sanctions

[0781] Administrators will check the alert and review the posts in question. If deemed necessary, they will instruct the server to delete the posts in question and take disciplinary action against the violating user. For example, they may delete the specific violating post and suspend the account of the user in question.

[0782] Specific examples

[0783] For example, if a user posts something containing the phrase "Introducing illegal part-time jobs that will earn you money easily," the posted data is immediately sent to the server. The server passes the data to an AI module, which recognizes "illegal part-time jobs" as a keyword and analyzes the context. If illegality is detected as a result, an alert is sent to an administrator. The administrator checks the alert and instructs them on the necessary countermeasures. Based on the instructions, the server deletes the post and suspends the user's account.

[0784] This invention is a system for quickly and accurately detecting illegal activities on social media platforms and ensuring user safety. The operation of this system will make social media platforms safer and more reliable.

[0785] The processing flow will be explained below.

[0786] Step 1:

[0787] A user creates a new post on a social media platform, which can contain text, images, and videos.

[0788] Step 2:

[0789] The server instantly retrieves user-created posts using the API. The retrieved data includes details such as the post content, poster information, and posting date and time.

[0790] Step 3:

[0791] The server stores the acquired submission data in a database, and simultaneously adds the submission data to a queue in real time to prepare for analysis.

[0792] Step 4:

[0793] The server passes the queued data to the AI ​​module, where it also schedules it for sequential processing.

[0794] Step 5:

[0795] The AI ​​module analyzes the received post data using natural language processing technology, which includes morphological analysis of the text, keyword extraction, and context analysis.

[0796] Step 6:

[0797] The AI ​​module uses the analysis results to determine whether a post is criminal. For example, it detects keywords such as "illegal part-time jobs" or "drug sales" and evaluates whether they constitute illegal activity. Contextual analysis is also performed to make a more accurate judgment.

[0798] Step 7:

[0799] The server receives the criminality judgment results from the AI ​​module, stores the results in a database, and simultaneously sends an alert to the administrator if a criminality is judged to have occurred.

[0800] Step 8:

[0801] The administrator's terminal receives alerts from the server, which are displayed as notifications via email and on the administrator's dashboard.

[0802] Step 9:

[0803] Administrators will review the alert, review the content of the relevant post, and decide on sanctions against the poster, if necessary.

[0804] Step 10:

[0805] The administrator will instruct the server to remove the post and take disciplinary action against the user, which may include suspending or permanently terminating the account.

[0806] Step 11:

[0807] The server will then delete the relevant post from the database based on the administrator's instructions, as well as remove it from inventory and log the violation.

[0808] Step 12:

[0809] The server updates account status and sets access restrictions to enforce sanctions against violating users.

[0810] Step 13:

[0811] The server will notify the user of the details of the sanctions, which will be sent via email or messaging within the social media platform.

[0812] Example 1

[0813] 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."

[0814] The number of posts related to fraudulent activities and crimes on social media platforms is increasing. Such posts undermine the reliability of the platforms and threaten the safety of users, so they need to be detected and addressed quickly and accurately. However, current systems mainly rely on manual monitoring and periodic checks, and while real-time monitoring and rapid response are required, there are still issues that cannot be fully addressed.

[0815] 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.

[0816] In this invention, the server includes a means for acquiring data posted by users to the platform, a means for transmitting the acquired data to an AI module and analyzing it using natural language processing technology, and a means for sending an alert to an administrator terminal in real time when criminal activity is detected. This makes it possible to quickly and accurately detect posts related to fraudulent activities or crimes on the SNS platform and take appropriate measures to ensure the reliability of the platform and the safety of users.

[0817] "User" refers to any individual or legal entity that uses the SNS Platform.

[0818] "Platform" refers to an online system that provides services for users to post and communicate.

[0819] "Posted Data" refers to content such as text, images, and videos that users create and submit on the Platform.

[0820] "Server" refers to a computer system that receives user posted data, processes it, and stores it in a database.

[0821] "AI module" refers to a software component that analyzes posted data and uses natural language processing technology to detect criminal activity.

[0822] "Natural language processing technology" refers to the technology used by AI modules to perform morphological analysis of text data, extract keywords, analyze context, and so on.

[0823] "Administrator" refers to the individual or organization responsible for operating the platform, monitoring posted data, and responding to fraudulent activity.

[0824] "Alert" refers to a notification sent to an Administrator when criminal activity is detected.

[0825] "Real-time" refers to the fact that processing and notification are carried out almost immediately after the user's posted data is sent.

[0826] The present invention describes a system for monitoring user posts on a social networking platform in real time to detect criminal activity, with the aim of improving the safety and reliability of the platform.

[0827] Watch and get posts

[0828] When a user logs in to a social networking platform and posts a new post, the post data is immediately sent from the device to the server. The server retrieves the post content, author information, and post timestamp, and stores them in a database. The device does this through an HTTP POST request, and the server's API endpoint receives the request and processes the data.

[0829] Data analysis

[0830] The server sends the newly acquired post data to the AI ​​module, which uses a natural language processing library (e.g., spaCy or NLTK) to:

[0831] Morphological analysis of text

[0832] Keyword extraction

[0833] Contextual Analysis

[0834] The AI ​​module uses these analyses to determine whether a post is criminal. For example, if a post contains the keyword "illegal part-time job," it will also analyze the context to assess whether it is illegal activity.

[0835] Criminal activity detection and alerting

[0836] If the AI ​​module detects criminal activity in a post, it returns the result to the server. The server then sends an alert to the administrator based on the result. The alert is sent via email or a dashboard notification for the administrator. The server uses an SMTP server to send emails and Websockets to send real-time notifications to the dashboard.

[0837] Management Actions and Sanctions

[0838] The administrator's terminal receives the alert and notifies the administrator. The administrator confirms the alert and checks the relevant post. If necessary, the administrator instructs the server to delete the relevant post and impose sanctions on the violating user. The server then receives the instruction, deletes the relevant data from the database, and updates the status of the user's account.

[0839] Specific examples

[0840] For example, if a user posts something containing the phrase "introducing illegal part-time jobs that will earn you money easily," the posted data is immediately sent to the server. The server passes the data to an AI module, which recognizes "illegal part-time jobs" as a keyword and analyzes the context. If illegality is detected as a result, an alert is sent to an administrator. The administrator checks the alert and instructs them on the necessary countermeasures. Through this process, the post in question is deleted and the user's account is suspended.

[0841] This system will enable the rapid and accurate detection of illegal activities on social media platforms and ensure the safety of users. This system aims to increase the reliability and safety of the platform and provide an environment where users can use it with peace of mind.

[0842] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0843] Step 1:

[0844] A user logs in to a social media platform and posts a new post. When the user presses the post button, the post data is sent from the device to the server. Specifically, the device generates an HTTP POST request and sends data including the post content, poster information, and a timestamp to the server's API endpoint.

[0845] Input: User post content, poster information, timestamp

[0846] Output: HTTP POST request sent

[0847] Step 2:

[0848] The server receives the posted data from the device and stores it in a database. The server converts this data into the required format and stores it in the database, making it ready for immediate analysis.

[0849] Input: HTTP POST request (user submitted data)

[0850] Output: Post data saved in the database

[0851] Step 3:

[0852] The server sends the saved post data to the AI ​​module, which then sends the post data as a request to the API endpoint for the AI ​​module. The AI ​​module then analyzes the post content using natural language processing technology.

[0853] Input: Post data retrieved from the database

[0854] Output: Send request to AI module

[0855] Step 4:

[0856] The AI ​​module uses a natural language processing library to analyze the received post data. Specifically, it performs morphological analysis, keyword extraction, and context analysis to determine whether the post is criminal.

[0857] Input: Post data sent from the server

[0858] Output: Analysis results (whether or not there is criminal activity)

[0859] Step 5:

[0860] If the AI ​​module detects any criminal activity in the post, it returns the result to the server in JSON format, which the server receives.

[0861] Input: Analysis results (whether or not there is a criminal offense)

[0862] Output: Response to the server (data indicating whether or not there is criminal activity)

[0863] Step 6:

[0864] The server sends alerts to administrators based on the results of the AI ​​module. The server retrieves the administrator's contact information from a database, sends emails using an SMTP server, and also sends real-time notifications to an administrator's dashboard using Websockets.

[0865] Input: Criminal data, administrator contact information

[0866] Output: Alert notification to administrator (email, dashboard notification)

[0867] Step 7:

[0868] The administrator device receives the alert and notifies the administrator, who then checks the content of the alert and the relevant posts.

[0869] Input: Alert notification from the server

[0870] Output: Administrator confirmation process begins

[0871] Step 8:

[0872] Based on the alert notification, administrators will check the relevant posts and, if necessary, instruct them to take countermeasures, such as deleting the posts or suspending the user's account.

[0873] Input: Check the content of the relevant post

[0874] Output: Instructions for action

[0875] Step 9:

[0876] The server, upon receiving instructions from the administrator, will remove the post from the database and, if necessary, update the status of the user's account, ensuring that the violation is removed promptly.

[0877] Input: Instructions for corrective action from administrator

[0878] Output: Database update (delete post, change user account status)

[0879] (Application example 1)

[0880] 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."

[0881] There is a need to efficiently detect potentially criminal posts on social media platforms and respond quickly. However, with current technology, it has been difficult to detect potentially criminal posts in real time, and there have often been delays in notifying administrators and responding to them. In addition, it is labor-intensive and unrealistic for administrators to constantly monitor the platform. In particular, there has been an issue with the lack of integration with smart devices, making it difficult to respond quickly on-site.

[0882] 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.

[0883] In this invention, the server includes means for monitoring all posts on the SNS platform and acquiring post data, means for analyzing the acquired post data using natural language processing technology and determining whether the post is criminal, means for issuing an alert to an administrator if the post is determined to be criminal, means for deleting the post and implementing sanctions against the user based on instructions from the administrator, and means for sending a visual alert to the wearable device if criminality is detected. This makes it possible to detect criminal posts in real time and respond quickly.

[0884] An "SNS platform" is an online service that allows users to communicate over the Internet.

[0885] "Posted data" refers to information such as text, images, and videos that users publish on social media platforms.

[0886] "Natural language processing technology" is a technology that allows computers to analyze and understand human language, and includes morphological analysis and contextual analysis.

[0887] "Criminality" is a quality that determines whether posted data suggests illegal activity.

[0888] An "alert" is a warning notification that the system automatically issues to the administrator.

[0889] A "wearable device" is a computing device that is worn on the body, such as smart glasses or a smart watch.

[0890] A "visual alert" is a visual warning message displayed on a wearable device.

[0891] "Administrator" means a person or organization that operates or monitors a social media platform.

[0892] A "queue" is a data structure for processing posted data in order.

[0893] An "AI module" is a software component that uses artificial intelligence technology to analyze data and determine certain attributes, such as criminality.

[0894] "Sanctions" are measures that impose penalties on users who make illegal posts, such as suspending their accounts or deleting posts.

[0895] An embodiment of the present invention is a system for monitoring posts on a social networking platform, detecting potentially criminal posts in real time, and responding promptly. The system includes a means for monitoring all posts on the social networking platform, analyzing them using natural language processing technology, and sending an alert to an administrator and a means for sending a visual alert to a wearable device.

[0896] System Program

[0897] The server retrieves posted data in real time and stores it in a database. The server adds the posted data to a queue and passes it to the AI ​​module. The AI ​​module uses natural language processing technology to perform morphological and contextual analysis of the posted data to determine whether it is criminal. Specifically, it uses the Google Cloud Natural Language API. If criminal activity is detected, the server sends an alert to the administrator and also sends a visual alert to the wearable device.

[0898] Specific examples

[0899] For example, if a user posts something containing the phrase "Introduction to illegal part-time jobs that allow you to easily earn money," the posted data is sent to the server. The server then passes the posted data to an AI module, which recognizes keywords such as "illegal part-time jobs" and analyzes the context. If the analysis detects any criminal activity, the server will send an alert to the administrator and also send a visual alert to the wearable device worn by the security officer.

[0900] Hardware and software used

[0901] Server: Collects and analyzes data posted on SNS platforms and notifies the administrator.

[0902] Wearable devices: Devices worn by security personnel that can receive visual alerts, such as smart glasses.

[0903] Google Cloud Natural Language API: Provides natural language processing technology to analyze posted data and determine criminality.

[0904] Prompt Sentence Examples

[0905] Please analyze the content of the post below and determine whether it is criminal. If so, please also provide specific keywords and context.

[0906] "I have work to do in a dangerous location tonight, and I'm looking for someone to come."

[0907] This will enable the detection and rapid response of criminal activity on social media platforms, improving the safety and reliability of the system.

[0908] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0909] Step 1:

[0910] A user logs in to a social networking platform and posts a new message. The message data is sent from the user's device to the server. The input here is the message data created by the user, and the output is the message data sent to the server.

[0911] Step 2:

[0912] The server receives the submission data and stores it in a database, where it is ready to be added to the queue. The input here is the submission data sent to the server, and the output is the submission data stored in the database and the submission data ready to be added to the queue.

[0913] Step 3:

[0914] The server adds the submitted data to a queue and passes the queue to the AI ​​module. The input here is the submitted data added to the queue, and the output is the submitted data passed to the AI ​​module.

[0915] Step 4:

[0916] The AI ​​module analyzes the posted data using natural language processing technology. Specifically, it performs morphological analysis, keyword extraction, and contextual analysis to determine whether the post is criminal. The technology used is the Google Cloud Natural Language API. The input here is the posted data passed to the AI ​​module, and the output is the analysis results.

[0917] Step 5:

[0918] The server receives the analysis results from the AI ​​module and checks whether criminal activity has been detected. If criminal activity is detected, the server sends an alert to the administrator. The input here is the analysis result of the AI ​​module, and the output is an alert notification to the administrator.

[0919] Step 6:

[0920] If the server detects criminal activity, it sends a visual alert to the wearable device. The input here is the analysis result that criminal activity was detected, and the output is the visual alert sent to the wearable device.

[0921] Step 7:

[0922] An administrator checks the alert, deletes the relevant post if necessary, and takes sanctions against the user. The input here is the alert sent to the administrator, and the output is the deleted post data and the user data on which sanctions were taken.

[0923] This series of processes makes it possible to detect potentially criminal posts on social media platforms in real time and respond quickly.

[0924] 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.

[0925] An embodiment of the present invention provides a system for monitoring user posts on a social media platform and analyzing criminality and user sentiment in real time, which improves the trust between users and the platform and enables early detection and response to criminal or inappropriate behavior.

[0926] Watch and get posts

[0927] When a user creates a new post on a social networking platform, the post data is immediately sent to the server, which retrieves details such as the post content, author information, and the posting date and time, and stores them in a database. This allows the post content to be immediately analyzed.

[0928] Analysis using natural language processing and emotion engine

[0929] The server adds the acquired post data to a queue in real time and passes it to the AI ​​module and emotion engine. The AI ​​module analyzes the post content using natural language processing technology. Specifically, it performs morphological analysis of the text, keyword extraction, and context analysis, while the emotion engine analyzes the user's emotions (e.g., anger, sadness, joy, etc.).

[0930] Judging criminality and emotions

[0931] The AI ​​module uses the analysis results to determine whether a post is criminal. It also uses the results of the emotion engine to assess whether the post is related to any criminal activity. For example, posts that express strong feelings of anger or aggression are evaluated with particular attention.

[0932] Sending alerts

[0933] The server sends an alert to the administrator based on the results of the AI ​​module and emotion engine. It will not only issue an alert if criminal activity is detected, but also alerts for high-risk posts, taking into account the results of user sentiment analysis. Alerts are sent via email and to the administrator's dedicated dashboard.

[0934] Management Actions and Sanctions

[0935] Administrators will review the alert and review the details of the relevant post. If criminal activity is confirmed, administrators will decide to remove the post or impose sanctions on the user. This may include additional actions based on sentiment analysis results.

[0936] Specific examples

[0937] For example, if a user posts something like "threatening with angry language," the posted data is sent to the server. The server passes it to the AI ​​module and emotion engine, where the AI ​​module determines it is a "threat," and the emotion engine analyzes it for the emotion of "anger." If the results indicate criminal activity, the server sends an alert to an administrator. The administrator reviews the alert and instructs disciplinary action, such as deleting the post and suspending the user's account.

[0938] The system of the present invention not only detects criminal activity on social media platforms quickly and accurately and ensures user safety, but also enables more sophisticated responses by analyzing user sentiment, making social media platforms safer and more reliable.

[0939] The processing flow will be explained below.

[0940] Step 1:

[0941] A user creates a new post on a social media platform, which can consist of text, images, videos, etc.

[0942] Step 2:

[0943] The server instantly retrieves user-created post data via the API, including metadata such as the post content, poster information, and posting date and time.

[0944] Step 3:

[0945] The server stores the acquired posting data in a database and simultaneously adds the posting data to a queue in real time.

[0946] Step 4:

[0947] The server schedules sequential processing to pass the posted data added to the queue to the AI ​​module and emotion engine.

[0948] Step 5:

[0949] The AI ​​module analyzes the received post data using natural language processing technology, specifically by performing morphological analysis of the text, keyword extraction, and contextual analysis.

[0950] Step 6:

[0951] The emotion engine analyzes the same posted data and evaluates the user's emotions, identifying emotions such as anger, sadness, and joy in the process.

[0952] Step 7:

[0953] The AI ​​module uses natural language processing to determine the criminality of posts, for example by detecting keywords such as "threat" and "violence" and evaluating their meaning and context.

[0954] Step 8:

[0955] If an emotion that is deemed to be criminal or serious (such as "anger" or "fear") is detected, the emotion engine provides the evaluation result to the AI ​​module, which can then accurately determine the criminality of the post.

[0956] Step 9:

[0957] The server acquires the results of the AI ​​module and emotion engine and stores them in a database. At the same time, if it determines that there is criminal activity or a high level of risk, it will send an alert to the administrator.

[0958] Step 10:

[0959] The administrator's terminal receives alerts from the server, which are displayed as notifications via email and on the administrator's dashboard.

[0960] Step 11:

[0961] Administrators will review the alert and details of the relevant posts, and after reviewing the content, will determine the necessary sanctions.

[0962] Step 12:

[0963] The administrator will instruct the server to remove the post and take disciplinary action against the user, which may include suspending or permanently terminating the account.

[0964] Step 13:

[0965] The server will delete the relevant posts from the database based on the administrator's instructions, and if necessary, record a log of the violation.

[0966] Step 14:

[0967] The server updates account status and sets access restrictions to enforce sanctions against violating users.

[0968] Step 15:

[0969] The server will notify the user of the details of the sanctions, which will be sent via email or messaging within the social media platform.

[0970] Example 2

[0971] 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."

[0972] Conventional social media platforms lack the means to properly monitor user posts and detect criminal or inappropriate emotional expressions in real time. As a result, it is difficult to detect and respond to criminal or inappropriate behavior early on. While there is also hope for sophisticated responses based on user emotion analysis, conventional systems have not fully realized this. Therefore, there is a need for a system that can improve the trust between users and the platform and enable early detection and response to criminal or inappropriate behavior.

[0973] 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.

[0974] In this invention, the server includes means for monitoring all posts on the SNS platform and acquiring post data, means for storing the acquired post data in a data storage device, means for adding the acquired post data to a message queue in real time, means for passing the message queue to an AI module and a sentiment analysis engine using natural language processing technology, means for analyzing the post data and analyzing sentiment using natural language processing technology, means for determining the criminality and sentiment of the post based on the analysis results, means for issuing an alert to an administrator if a post is determined to be criminal, and means for deleting the relevant post and implementing sanctions against the user based on instructions from the administrator. This enables real-time analysis of post data, enabling early detection and response to criminal or inappropriate behavior. Furthermore, analyzing user sentiment allows for more advanced responses, further improving the reliability and safety of the platform.

[0975] "SNS Platform" refers to an online service that enables users to post content and interact with other users.

[0976] "Posted data" refers to information such as text, images, and videos that users create and publish on social media platforms.

[0977] "Server" refers to a computer system that processes, stores, and manages data on a network.

[0978] A "data storage device" is a device or system for storing data, including hard disk drives, SSDs, and cloud storage.

[0979] A "message queue" is a system for temporarily storing data and processing it sequentially, and examples include RabbitMQ and Apache Kafka.

[0980] "Natural language processing technology" is a technology for understanding and analyzing human language, and includes morphological analysis, keyword extraction, contextual analysis, etc.

[0981] An "AI module" is a software component that performs analysis using artificial intelligence, and specifically includes natural language processing and machine learning models.

[0982] An "emotion analysis engine" is a system that analyzes user emotions from text data and calculates emotional scores such as anger, sadness, and joy.

[0983] "Analysis results" refers to information based on the analysis of posted data obtained by an AI module or sentiment analysis engine.

[0984] "Criminal" refers to the characteristics or content of posted data that violate or have the potential to violate laws or regulations.

[0985] An "alert" refers to a warning message or notification sent to an administrator when a specific condition is met.

[0986] "Administrator" refers to the person or organization responsible for operating a social media platform, monitoring user posts, and responding to violations.

[0987] "Sanctions" refers to penalties or restrictions imposed on users when violations of the terms of service or criminal activity are confirmed.

[0988] An embodiment of the present invention provides a system that monitors user posts on a social media platform and analyzes criminality and user sentiment in real time, thereby improving the trust between users and the platform and enabling early detection and response to criminal or inappropriate behavior.

[0989] When a user creates a new post on a social networking platform, the post data is immediately sent from the device to the server, which then retrieves details such as the post content, poster information, and posting date and time, and stores the data in a data storage device.

[0990] The server adds the received post data to a message queue in real time. In this case, a message broker such as RabbitMQ or Apache Kafka is used for the message queue. The data added to this message queue is then subjected to subsequent analysis processing.

[0991] The server then extracts the posted data from the message queue and passes it to an AI module that uses natural language processing technology and a sentiment analysis engine. The AI ​​module uses the BERT model, which also uses natural language processing technology, and the sentiment analysis engine uses IBM Watson's sentiment analysis API. The AI ​​module performs morphological analysis of the posted content, keyword extraction, and contextual analysis, while the sentiment analysis engine analyzes the user's emotions (anger, sadness, joy, etc.) in real time.

[0992] The AI ​​module uses the analysis results to determine the criminality and emotion of the post and sends it back to the server. For example, posts that are judged to be "threatening" or that express strong emotions such as "anger" are flagged as high risk.

[0993] The server sends an alert to the administrator based on the results of the AI ​​module and sentiment analysis engine. The alert is sent as a notification via email or to a dashboard dedicated to the administrator. For example, an email may be sent stating, "User ID: 12345's post has been determined to be high risk. Anger score: 90%."

[0994] The administrator will check the alert and review the details of the relevant post. In some cases, the administrator may decide to delete the post or impose sanctions on the user (such as suspending the account). For example, the administrator may take the following action: "Check the post by user ID: 12345 and instruct them to delete it and suspend the account."

[0995] Specific examples

[0996] For example, if a user makes a post saying "I'm going to kill someone," the post will be processed by the system as follows:

[0997] 1. Your post is sent to the server immediately.

[0998] 2. The server stores the post in a database.

[0999] 3. The posted data is added to the message queue.

[1000] 4. The server takes the data from the queue and passes it to the BERT model and IBM Watson sentiment analysis API.

[1001] 5. The AI ​​module determines it to be a "threat" and the emotion engine analyzes the emotion score for "anger."

[1002] 6. The server sends an alert email to the administrator stating, "User ID: 12345's post has been deemed high risk. Anger score: 90%."

[1003] 7. The administrator will check the content of the post on the dashboard and take disciplinary action, such as deleting the content posted by user ID: 12345 and temporarily suspending the account.

[1004] Prompt Sentence Examples

[1005] 1. "Please rate the criminal nature of the following post: 'I want to hit someone.'"

[1006] 2. "Do a sentiment analysis of the following post: 'I'm so tired today.'"

[1007] This invention enables early detection and response to criminal and inappropriate behavior on social media platforms, ensuring the safety of users and the platform and improving its reliability. Furthermore, analyzing user emotions enables more advanced responses, making social media platforms safer and more reliable.

[1008] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1009] Step 1:

[1010] A user creates a new post on a social media platform.

[1011] Input: The user types the content to post.

[1012] Specific behavior: A user logs into a social networking application, enters text such as "kill someone," and clicks the post button.

[1013] Output: The post content is sent from the device to the server.

[1014] Step 2:

[1015] The terminal transmits the user's posted data to the server.

[1016] Input: User-generated submitted data.

[1017] Specific operation: The device sends the text data entered by the user, such as "kill someone," to the server as an HTTP request.

[1018] Output: Posted data reaches the server.

[1019] Step 3:

[1020] The server stores the received posting data in a data storage device.

[1021] Input: Post data sent from the device.

[1022] Specific operation: The server uses the INSERT statement to store data including the post content, poster ID, and posting date and time in the database. Specifically, "INSERT INTO posts (content, user_id, timestamp) VALUES ('Kill someone', 12345, '2023-10-01');"

[1023] Output: Posted data is saved in the database.

[1024] Step 4:

[1025] The server adds the posted data stored in the database to a message queue in real time.

[1026] Input: Post data stored in a database.

[1027] Specific operation: The server adds the post data to a message queue (e.g., RabbitMQ). It is added to the queue in the format "Post content: kill someone, Poster ID: 12345, Post date: 2023-10-01".

[1028] Output: Posted data is added to the message queue.

[1029] Step 5:

[1030] The server retrieves the posted data from the message queue and passes it to the AI ​​module and sentiment analysis engine.

[1031] Input: Post data added to the message queue.

[1032] Specific operation: The server inputs the data retrieved from the queue into an AI module (BERT model) that uses natural language processing technology and a sentiment analysis engine (IBM Watson sentiment analysis API).

[1033] Output: The data is passed to the AI ​​module and sentiment analysis engine.

[1034] Step 6:

[1035] The AI ​​module analyzes the posted data and analyzes emotions.

[1036] Input: Post data passed from the server.

[1037] Specific operation: The AI ​​module performs morphological analysis of the text, keyword extraction, and contextual analysis, and the sentiment analysis engine analyzes emotions (anger, sadness, joy, etc.). The text "kill someone" is analyzed as "Anger score: 90%" and falls into the "Threat" category.

[1038] Output: Analysis results (e.g., "Anger score: 90%", "Threat" category) are obtained.

[1039] Step 7:

[1040] The server sends an alert to the administrator based on the analysis results of the AI ​​module and emotion analysis engine.

[1041] Input: Analysis results from the AI ​​module and sentiment analysis engine.

[1042] Specific operation: The server sends an alert email or a notification to the administrator's dashboard stating something like "User ID: 12345's post has been deemed high risk. Anger score: 90%."

[1043] Output: An alert is sent to the administrator.

[1044] Step 8:

[1045] The administrator will confirm the alert sent and review the details of the relevant post.

[1046] Input: The alert content sent from the server.

[1047] Specific actions: The administrator will log in to the dashboard and take action such as "checking the relevant post and the post by user ID: 12345, and instructing them to delete the post and suspend the account."

[1048] Output: Decision and implementation of action, which may include deleting the relevant post and suspending the user's account.

[1049] (Application example 2)

[1050] 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."

[1051] Conventional social networking platforms have systems that monitor user posts and determine whether they are criminal, but they lack the ability to analyze user emotions. This makes it difficult to quickly detect posts that express negative user emotions and to respond appropriately, resulting in a lack of deterrent power for inappropriate behavior. Furthermore, administrators' responses when criminal activity is detected are inefficient, requiring a rapid response. The objective of this invention is to solve these problems and improve the safety and reliability of social networking platforms.

[1052] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for monitoring all posts on the SNS platform and acquiring post data, means for analyzing the acquired post data using natural language processing technology to determine whether the post is criminal, means for analyzing the emotions in the acquired post data and generating an emotion score for the user, means for issuing an alert to an administrator if the post is determined to be criminal, means for deleting the post and implementing sanctions against the user based on instructions from the administrator, and means including an administrator dashboard for the administrator to check and respond to the alert. This enables early detection and rapid response to inappropriate posts, and also enables appropriate measures to be taken for posts that express the user's negative emotions.

[1053] A "SNS platform" is an online service that allows users to share posts, comments, images, and videos.

[1054] "Posted data" refers to information such as text, images, and videos that users post on social media platforms.

[1055] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and includes morphological analysis, keyword extraction, contextual analysis, and more.

[1056] "Criminality" refers to whether the posted data contains content that may violate the law.

[1057] The "emotion score" is a numerical representation of the user's emotions contained in the posted data, and evaluates emotions such as "anger," "sadness," and "joy."

[1058] "Administrator" refers to the person or team responsible for operating a social media platform, monitoring content, and taking action as necessary.

[1059] An "alert" is a warning message that is sent to an administrator when a criminal offense is detected.

[1060] The "Admin Dashboard" is a dedicated management screen that allows administrators to monitor posts on social media platforms, check alerts, and take action.

[1061] "Sanctions" are measures taken against posts that are deemed to be criminal, and include deleting posts and suspending users' accounts.

[1062] To implement this invention, a system is required to monitor posts on social media platforms in real time and analyze their criminality and sentiment scores. This system uses the following hardware and software:

[1063] Hardware and software used:

[1064] 1. Hardware:

[1065] High-performance server: Required to process large amounts of submitted data quickly.

[1066] Database server: Required to store and manage submitted data.

[1067] 2. Software:

[1068] Natural Language Processing (NLP) libraries: For example, use spaCy or NLTK.

[1069] Sentiment analysis tools: For example, using TextBlob.

[1070] Database: For example, use MySQL or PostgreSQL.

[1071] System Details:

[1072] 1. The server monitors all posts on the SNS platform in real time and immediately retrieves new post data, including the post content, poster information, and post date and time.

[1073] 2. The server analyzes the acquired posted data using natural language processing technology. Specifically, it performs morphological analysis of the text, extracts keywords, and analyzes context. For example, it detects whether keywords such as "threat" or "attack" are included.

[1074] 3. The server also simultaneously performs emotion analysis and generates an emotion score for the user. For example, it analyzes whether the content of the post corresponds to "anger," "sadness," or "happiness," and quantifies the degree of that emotion.

[1075] 4. If the server determines that the activity is highly criminal, it will send an alert to the administrator. The alert will be sent as a notification via email or to the administrator's dashboard.

[1076] 5. The administrator will check the alert and review the details of the relevant post. If a criminal offense is confirmed, the administrator will delete the post and / or take disciplinary action against the user (such as suspending the account).

[1077] Examples:

[1078] If a user posts on a social media platform something like "threatening with angry language," the post data is sent to a server. The server passes it to an AI module and emotion engine, which uses natural language processing technology to determine whether it is a "threat," and the emotion engine analyzes it for the emotion of "anger." If the results indicate criminal activity, the server sends an alert to an administrator. The administrator reviews the alert and instructs sanctions such as deleting the post and suspending the user's account.

[1079] Example prompt sentence:

[1080] "Please monitor corporate social media for angry posts posted on September 3, 2023, and analyze potential threats."

[1081] "Analyze inappropriate content posted by students on educational institution social media and check for any dangerous posts."

[1082] In this way, the system of the present invention can identify inappropriate posts on social media platforms early and respond quickly.

[1083] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1084] Step 1:

[1085] The server retrieves new post data from the SNS platform in real time. This post data includes the text content, poster information, posting date and time, etc. Specifically, it uses the SNS platform's API to fetch the data every time a new post is made.

[1086] Input: New post data

[1087] Output: Retrieved post data

[1088] Step 2:

[1089] The server stores the acquired post data in a database. This allows the data to be retained for subsequent analysis. Storing data in the database is done using INSERT statements.

[1090] Input: Retrieved post data

[1091] Output: Post data stored in the database

[1092] Step 3:

[1093] The server adds the submitted data stored in the database to a queue, which is a collection of data waiting to be analyzed. For example, it uses a message queue system such as Redis.

[1094] Input: Post data stored in the database

[1095] Output: Post data added to the queue

[1096] Step 4:

[1097] The server retrieves the posted data from the queue and analyzes the text using natural language processing techniques, such as morphological analysis, keyword extraction, and context analysis. For example, it uses the spaCy or NLTK library.

[1098] Input: Post data added to the queue

[1099] Output: Analysis results (morphological analysis, keyword extraction, context analysis)

[1100] Step 5:

[1101] The server simultaneously performs emotion analysis and generates an emotion score for the user, which is a numerical representation of emotions such as "anger," "sadness," and "happiness." For example, it uses the TextBlob library.

[1102] Input: Post data added to the queue

[1103] Output: Sentiment score

[1104] Step 6:

[1105] The server determines whether a post is criminal based on the results of natural language processing analysis and the emotion score. For example, if a post contains keywords such as "threat" or "attack" and the emotion score indicates a high value such as "anger," it is determined to be criminal.

[1106] Input: Analysis results (morphological analysis, keyword extraction, context analysis), sentiment score

[1107] Output: Criminal conduct determination result

[1108] Step 7:

[1109] If the server determines that the activity is highly criminal, it will send an alert to the administrator. The alert will be sent via email or to a dedicated administrator dashboard. Specifically, it uses the SMTP protocol and WebSocket communication.

[1110] Input: Criminal conduct determination result

[1111] Output: Alert sent to administrator

[1112] Step 8:

[1113] Administrators can view alerts on a dedicated dashboard and review details of the affected posts, including the text content, author information, and sentiment score.

[1114] Input: Alert sent to administrator

[1115] Output: Post details displayed

[1116] Step 9:

[1117] If a criminal offense is confirmed, the administrator will delete the post or take sanctions against the user. For example, the administrator can delete the post using an API call, or suspend the user's account by updating the database.

[1118] Input: Post details displayed

[1119] Output: Sanctions taken

[1120] This series of processes enables early detection and swift response to inappropriate posts on social media platforms.

[1121] 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.

[1122] 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.

[1123] 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.

[1124] [Fourth embodiment]

[1125] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1126] 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.

[1127] 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).

[1128] 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.

[1129] 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.

[1130] 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).

[1131] 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.

[1132] 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.

[1133] 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.

[1134] 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.

[1135] 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.

[1136] 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.

[1137] 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."

[1138] An embodiment of the present invention provides a mechanism for monitoring user posts on a social networking platform and detecting criminal activity in real time, thereby ensuring the reliability of users and the platform.

[1139] Watch and get posts

[1140] When a user logs in to a social networking platform and posts a new post, the post data is immediately sent to the server, which captures the post content, the poster's information, and the post timestamp, and stores them in a database, ready for immediate analysis.

[1141] Analysis using natural language processing

[1142] The server passes the posted data acquired in real time to an AI module. The AI ​​module uses natural language processing technology to analyze the content of the post. Specifically, it performs morphological analysis of the text, extracts keywords, and analyzes the context to determine whether the post is criminal. For example, if a post contains a keyword such as "illegal part-time job," the context is also analyzed to assess whether it represents illegal activity.

[1143] Criminality determination and alert issuing

[1144] If the AI ​​module detects any criminal activity in the post, the server will send an alert to the administrator based on the results of the assessment. The alert will be sent via email or as a dashboard notification for the administrator. The administrator's device will immediately receive the alert and notify the administrator.

[1145] Management Actions and Sanctions

[1146] Administrators will check the alert and review the posts in question. If deemed necessary, they will instruct the server to delete the posts in question and take disciplinary action against the violating user. For example, they may delete the specific violating post and suspend the account of the user in question.

[1147] Specific examples

[1148] For example, if a user posts something containing the phrase "Introducing illegal part-time jobs that will earn you money easily," the posted data is immediately sent to the server. The server passes the data to an AI module, which recognizes "illegal part-time jobs" as a keyword and analyzes the context. If illegality is detected as a result, an alert is sent to an administrator. The administrator checks the alert and instructs them on the necessary countermeasures. Based on the instructions, the server deletes the post and suspends the user's account.

[1149] This invention is a system for quickly and accurately detecting illegal activities on social media platforms and ensuring user safety. The operation of this system will make social media platforms safer and more reliable.

[1150] The processing flow will be explained below.

[1151] Step 1:

[1152] A user creates a new post on a social media platform, which can contain text, images, and videos.

[1153] Step 2:

[1154] The server instantly retrieves user-created posts using the API. The retrieved data includes details such as the post content, poster information, and posting date and time.

[1155] Step 3:

[1156] The server stores the acquired submission data in a database, and simultaneously adds the submission data to a queue in real time to prepare for analysis.

[1157] Step 4:

[1158] The server passes the queued data to the AI ​​module, where it also schedules it for sequential processing.

[1159] Step 5:

[1160] The AI ​​module analyzes the received post data using natural language processing technology, which includes morphological analysis of the text, keyword extraction, and context analysis.

[1161] Step 6:

[1162] The AI ​​module uses the analysis results to determine whether a post is criminal. For example, it detects keywords such as "illegal part-time jobs" or "drug sales" and evaluates whether they constitute illegal activity. Contextual analysis is also performed to make a more accurate judgment.

[1163] Step 7:

[1164] The server receives the criminality judgment results from the AI ​​module, stores the results in a database, and simultaneously sends an alert to the administrator if a criminality is judged to have occurred.

[1165] Step 8:

[1166] The administrator's terminal receives alerts from the server, which are displayed as notifications via email and on the administrator's dashboard.

[1167] Step 9:

[1168] Administrators will review the alert, review the content of the relevant post, and decide on sanctions against the poster, if necessary.

[1169] Step 10:

[1170] The administrator will instruct the server to remove the post and take disciplinary action against the user, which may include suspending or permanently terminating the account.

[1171] Step 11:

[1172] The server will then delete the relevant post from the database based on the administrator's instructions, as well as remove it from inventory and log the violation.

[1173] Step 12:

[1174] The server updates account status and sets access restrictions to enforce sanctions against violating users.

[1175] Step 13:

[1176] The server will notify the user of the details of the sanctions, which will be sent via email or messaging within the social media platform.

[1177] Example 1

[1178] 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."

[1179] The number of posts related to fraudulent activities and crimes on social media platforms is increasing. Such posts undermine the reliability of the platforms and threaten the safety of users, so they need to be detected and addressed quickly and accurately. However, current systems mainly rely on manual monitoring and periodic checks, and while real-time monitoring and rapid response are required, there are still issues that cannot be fully addressed.

[1180] 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.

[1181] In this invention, the server includes a means for acquiring data posted by users to the platform, a means for transmitting the acquired data to an AI module and analyzing it using natural language processing technology, and a means for sending an alert to an administrator terminal in real time when criminal activity is detected. This makes it possible to quickly and accurately detect posts related to fraudulent activities or crimes on the SNS platform and take appropriate measures to ensure the reliability of the platform and the safety of users.

[1182] "User" refers to any individual or legal entity that uses the SNS Platform.

[1183] "Platform" refers to an online system that provides services for users to post and communicate.

[1184] "Posted Data" refers to content such as text, images, and videos that users create and submit on the Platform.

[1185] "Server" refers to a computer system that receives user posted data, processes it, and stores it in a database.

[1186] "AI module" refers to a software component that analyzes posted data and uses natural language processing technology to detect criminal activity.

[1187] "Natural language processing technology" refers to the technology used by AI modules to perform morphological analysis of text data, extract keywords, analyze context, and so on.

[1188] "Administrator" refers to the individual or organization responsible for operating the platform, monitoring posted data, and responding to fraudulent activity.

[1189] "Alert" refers to a notification sent to an Administrator when criminal activity is detected.

[1190] "Real-time" refers to the fact that processing and notification are carried out almost immediately after the user's posted data is sent.

[1191] The present invention describes a system for monitoring user posts on a social networking platform in real time to detect criminal activity, with the aim of improving the safety and reliability of the platform.

[1192] Watch and get posts

[1193] When a user logs in to a social networking platform and posts a new post, the post data is immediately sent from the device to the server. The server retrieves the post content, author information, and post timestamp, and stores them in a database. The device does this through an HTTP POST request, and the server's API endpoint receives the request and processes the data.

[1194] Data analysis

[1195] The server sends the newly acquired post data to the AI ​​module, which uses a natural language processing library (e.g., spaCy or NLTK) to:

[1196] Morphological analysis of text

[1197] Keyword extraction

[1198] Contextual Analysis

[1199] The AI ​​module uses these analyses to determine whether a post is criminal. For example, if a post contains the keyword "illegal part-time job," it will also analyze the context to assess whether it is illegal activity.

[1200] Criminal activity detection and alerting

[1201] If the AI ​​module detects criminal activity in a post, it returns the result to the server. The server then sends an alert to the administrator based on the result. The alert is sent via email or a dashboard notification for the administrator. The server uses an SMTP server to send emails and Websockets to send real-time notifications to the dashboard.

[1202] Management Actions and Sanctions

[1203] The administrator's terminal receives the alert and notifies the administrator. The administrator confirms the alert and checks the relevant post. If necessary, the administrator instructs the server to delete the relevant post and impose sanctions on the violating user. The server then receives the instruction, deletes the relevant data from the database, and updates the status of the user's account.

[1204] Specific examples

[1205] For example, if a user posts something containing the phrase "introducing illegal part-time jobs that will earn you money easily," the posted data is immediately sent to the server. The server passes the data to an AI module, which recognizes "illegal part-time jobs" as a keyword and analyzes the context. If illegality is detected as a result, an alert is sent to an administrator. The administrator checks the alert and instructs them on the necessary countermeasures. Through this process, the post in question is deleted and the user's account is suspended.

[1206] This system will enable the rapid and accurate detection of illegal activities on social media platforms and ensure the safety of users. This system aims to increase the reliability and safety of the platform and provide an environment where users can use it with peace of mind.

[1207] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1208] Step 1:

[1209] A user logs in to a social media platform and posts a new post. When the user presses the post button, the post data is sent from the device to the server. Specifically, the device generates an HTTP POST request and sends data including the post content, poster information, and a timestamp to the server's API endpoint.

[1210] Input: User post content, poster information, timestamp

[1211] Output: HTTP POST request sent

[1212] Step 2:

[1213] The server receives the posted data from the device and stores it in a database. The server converts this data into the required format and stores it in the database, making it ready for immediate analysis.

[1214] Input: HTTP POST request (user submitted data)

[1215] Output: Post data saved in the database

[1216] Step 3:

[1217] The server sends the saved post data to the AI ​​module, which then sends the post data as a request to the API endpoint for the AI ​​module. The AI ​​module then analyzes the post content using natural language processing technology.

[1218] Input: Post data retrieved from the database

[1219] Output: Send request to AI module

[1220] Step 4:

[1221] The AI ​​module uses a natural language processing library to analyze the received post data. Specifically, it performs morphological analysis, keyword extraction, and context analysis to determine whether the post is criminal.

[1222] Input: Post data sent from the server

[1223] Output: Analysis results (whether or not there is criminal activity)

[1224] Step 5:

[1225] If the AI ​​module detects any criminal activity in the post, it returns the result to the server in JSON format, which the server receives.

[1226] Input: Analysis results (whether or not there is a criminal offense)

[1227] Output: Response to the server (data indicating whether or not there is criminal activity)

[1228] Step 6:

[1229] The server sends alerts to administrators based on the results of the AI ​​module. The server retrieves the administrator's contact information from a database, sends emails using an SMTP server, and also sends real-time notifications to an administrator's dashboard using Websockets.

[1230] Input: Criminal data, administrator contact information

[1231] Output: Alert notification to administrator (email, dashboard notification)

[1232] Step 7:

[1233] The administrator device receives the alert and notifies the administrator, who then checks the content of the alert and the relevant posts.

[1234] Input: Alert notification from the server

[1235] Output: Administrator confirmation process begins

[1236] Step 8:

[1237] Based on the alert notification, administrators will check the relevant posts and, if necessary, instruct them to take countermeasures, such as deleting the posts or suspending the user's account.

[1238] Input: Check the content of the relevant post

[1239] Output: Instructions for action

[1240] Step 9:

[1241] The server, upon receiving instructions from the administrator, will remove the post from the database and, if necessary, update the status of the user's account, ensuring that the violation is removed promptly.

[1242] Input: Instructions for corrective action from administrator

[1243] Output: Database update (delete post, change user account status)

[1244] (Application example 1)

[1245] 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."

[1246] There is a need to efficiently detect potentially criminal posts on social media platforms and respond quickly. However, with current technology, it has been difficult to detect potentially criminal posts in real time, and there have often been delays in notifying administrators and responding to them. In addition, it is labor-intensive and unrealistic for administrators to constantly monitor the platform. In particular, there has been an issue with the lack of integration with smart devices, making it difficult to respond quickly on-site.

[1247] 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.

[1248] In this invention, the server includes means for monitoring all posts on the SNS platform and acquiring post data, means for analyzing the acquired post data using natural language processing technology and determining whether the post is criminal, means for issuing an alert to an administrator if the post is determined to be criminal, means for deleting the post and implementing sanctions against the user based on instructions from the administrator, and means for sending a visual alert to the wearable device if criminality is detected. This makes it possible to detect criminal posts in real time and respond quickly.

[1249] An "SNS platform" is an online service that allows users to communicate over the Internet.

[1250] "Posted data" refers to information such as text, images, and videos that users publish on social media platforms.

[1251] "Natural language processing technology" is a technology that allows computers to analyze and understand human language, and includes morphological analysis and contextual analysis.

[1252] "Criminality" is a quality that determines whether posted data suggests illegal activity.

[1253] An "alert" is a warning notification that the system automatically issues to the administrator.

[1254] A "wearable device" is a computing device that is worn on the body, such as smart glasses or a smart watch.

[1255] A "visual alert" is a visual warning message displayed on a wearable device.

[1256] "Administrator" means a person or organization that operates or monitors a social media platform.

[1257] A "queue" is a data structure for processing posted data in order.

[1258] An "AI module" is a software component that uses artificial intelligence technology to analyze data and determine certain attributes, such as criminality.

[1259] "Sanctions" are measures that impose penalties on users who make illegal posts, such as suspending their accounts or deleting posts.

[1260] An embodiment of the present invention is a system for monitoring posts on a social networking platform, detecting potentially criminal posts in real time, and responding promptly. The system includes a means for monitoring all posts on the social networking platform, analyzing them using natural language processing technology, and sending an alert to an administrator and a means for sending a visual alert to a wearable device.

[1261] System Program

[1262] The server retrieves posted data in real time and stores it in a database. The server adds the posted data to a queue and passes it to the AI ​​module. The AI ​​module uses natural language processing technology to perform morphological and contextual analysis of the posted data to determine whether it is criminal. Specifically, it uses the Google Cloud Natural Language API. If criminal activity is detected, the server sends an alert to the administrator and also sends a visual alert to the wearable device.

[1263] Specific examples

[1264] For example, if a user posts something containing the phrase "Introduction to illegal part-time jobs that allow you to easily earn money," the posted data is sent to the server. The server then passes the posted data to an AI module, which recognizes keywords such as "illegal part-time jobs" and analyzes the context. If the analysis detects any criminal activity, the server will send an alert to the administrator and also send a visual alert to the wearable device worn by the security officer.

[1265] Hardware and software used

[1266] Server: Collects and analyzes data posted on SNS platforms and notifies the administrator.

[1267] Wearable devices: Devices worn by security personnel that can receive visual alerts, such as smart glasses.

[1268] Google Cloud Natural Language API: Provides natural language processing technology to analyze posted data and determine criminality.

[1269] Prompt Sentence Examples

[1270] Please analyze the content of the post below and determine whether it is criminal. If so, please also provide specific keywords and context.

[1271] "I have work to do in a dangerous location tonight, and I'm looking for someone to come."

[1272] This will enable the detection and rapid response of criminal activity on social media platforms, improving the safety and reliability of the system.

[1273] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1274] Step 1:

[1275] A user logs in to a social networking platform and posts a new message. The message data is sent from the user's device to the server. The input here is the message data created by the user, and the output is the message data sent to the server.

[1276] Step 2:

[1277] The server receives the submission data and stores it in a database, where it is ready to be added to the queue. The input here is the submission data sent to the server, and the output is the submission data stored in the database and the submission data ready to be added to the queue.

[1278] Step 3:

[1279] The server adds the submitted data to a queue and passes the queue to the AI ​​module. The input here is the submitted data added to the queue, and the output is the submitted data passed to the AI ​​module.

[1280] Step 4:

[1281] The AI ​​module analyzes the posted data using natural language processing technology. Specifically, it performs morphological analysis, keyword extraction, and contextual analysis to determine whether the post is criminal. The technology used is the Google Cloud Natural Language API. The input here is the posted data passed to the AI ​​module, and the output is the analysis results.

[1282] Step 5:

[1283] The server receives the analysis results from the AI ​​module and checks whether criminal activity has been detected. If criminal activity is detected, the server sends an alert to the administrator. The input here is the analysis result of the AI ​​module, and the output is an alert notification to the administrator.

[1284] Step 6:

[1285] If the server detects criminal activity, it sends a visual alert to the wearable device. The input here is the analysis result that criminal activity was detected, and the output is the visual alert sent to the wearable device.

[1286] Step 7:

[1287] An administrator checks the alert, deletes the relevant post if necessary, and takes sanctions against the user. The input here is the alert sent to the administrator, and the output is the deleted post data and the user data on which sanctions were taken.

[1288] This series of processes makes it possible to detect potentially criminal posts on social media platforms in real time and respond quickly.

[1289] 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.

[1290] An embodiment of the present invention provides a system for monitoring user posts on a social media platform and analyzing criminality and user sentiment in real time, which improves the trust between users and the platform and enables early detection and response to criminal or inappropriate behavior.

[1291] Watch and get posts

[1292] When a user creates a new post on a social networking platform, the post data is immediately sent to the server, which retrieves details such as the post content, author information, and the posting date and time, and stores them in a database. This allows the post content to be immediately analyzed.

[1293] Analysis using natural language processing and emotion engine

[1294] The server adds the acquired post data to a queue in real time and passes it to the AI ​​module and emotion engine. The AI ​​module analyzes the post content using natural language processing technology. Specifically, it performs morphological analysis of the text, keyword extraction, and context analysis, while the emotion engine analyzes the user's emotions (e.g., anger, sadness, joy, etc.).

[1295] Judging criminality and emotions

[1296] The AI ​​module uses the analysis results to determine whether a post is criminal. It also uses the results of the emotion engine to assess whether the post is related to any criminal activity. For example, posts that express strong feelings of anger or aggression are evaluated with particular attention.

[1297] Sending alerts

[1298] The server sends an alert to the administrator based on the results of the AI ​​module and emotion engine. It will not only issue an alert if criminal activity is detected, but also alerts for high-risk posts, taking into account the results of user sentiment analysis. Alerts are sent via email and to the administrator's dedicated dashboard.

[1299] Management Actions and Sanctions

[1300] Administrators will review the alert and review the details of the relevant post. If criminal activity is confirmed, administrators will decide to remove the post or impose sanctions on the user. This may include additional actions based on sentiment analysis results.

[1301] Specific examples

[1302] For example, if a user posts something like "threatening with angry language," the posted data is sent to the server. The server passes it to the AI ​​module and emotion engine, where the AI ​​module determines it is a "threat," and the emotion engine analyzes it for the emotion of "anger." If the results indicate criminal activity, the server sends an alert to an administrator. The administrator reviews the alert and instructs disciplinary action, such as deleting the post and suspending the user's account.

[1303] The system of the present invention not only detects criminal activity on social media platforms quickly and accurately and ensures user safety, but also enables more sophisticated responses by analyzing user sentiment, making social media platforms safer and more reliable.

[1304] The processing flow will be explained below.

[1305] Step 1:

[1306] A user creates a new post on a social media platform, which can consist of text, images, videos, etc.

[1307] Step 2:

[1308] The server instantly retrieves user-created post data via the API, including metadata such as the post content, poster information, and posting date and time.

[1309] Step 3:

[1310] The server stores the acquired posting data in a database and simultaneously adds the posting data to a queue in real time.

[1311] Step 4:

[1312] The server schedules sequential processing to pass the posted data added to the queue to the AI ​​module and emotion engine.

[1313] Step 5:

[1314] The AI ​​module analyzes the received post data using natural language processing technology, specifically by performing morphological analysis of the text, keyword extraction, and contextual analysis.

[1315] Step 6:

[1316] The emotion engine analyzes the same posted data and evaluates the user's emotions, identifying emotions such as anger, sadness, and joy in the process.

[1317] Step 7:

[1318] The AI ​​module uses natural language processing to determine the criminality of posts, for example by detecting keywords such as "threat" and "violence" and evaluating their meaning and context.

[1319] Step 8:

[1320] If an emotion that is deemed to be criminal or serious (such as "anger" or "fear") is detected, the emotion engine provides the evaluation result to the AI ​​module, which can then accurately determine the criminality of the post.

[1321] Step 9:

[1322] The server acquires the results of the AI ​​module and emotion engine and stores them in a database. At the same time, if it determines that there is criminal activity or a high level of risk, it will send an alert to the administrator.

[1323] Step 10:

[1324] The administrator's terminal receives alerts from the server, which are displayed as notifications via email and on the administrator's dashboard.

[1325] Step 11:

[1326] Administrators will review the alert and details of the relevant posts, and after reviewing the content, will determine the necessary sanctions.

[1327] Step 12:

[1328] The administrator will instruct the server to remove the post and take disciplinary action against the user, which may include suspending or permanently terminating the account.

[1329] Step 13:

[1330] The server will delete the relevant posts from the database based on the administrator's instructions, and if necessary, record a log of the violation.

[1331] Step 14:

[1332] The server updates account status and sets access restrictions to enforce sanctions against violating users.

[1333] Step 15:

[1334] The server will notify the user of the details of the sanctions, which will be sent via email or messaging within the social media platform.

[1335] Example 2

[1336] 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."

[1337] Conventional social media platforms lack the means to properly monitor user posts and detect criminal or inappropriate emotional expressions in real time. As a result, it is difficult to detect and respond to criminal or inappropriate behavior early on. While there is also hope for sophisticated responses based on user emotion analysis, conventional systems have not fully realized this. Therefore, there is a need for a system that can improve the trust between users and the platform and enable early detection and response to criminal or inappropriate behavior.

[1338] 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.

[1339] In this invention, the server includes means for monitoring all posts on the SNS platform and acquiring post data, means for storing the acquired post data in a data storage device, means for adding the acquired post data to a message queue in real time, means for passing the message queue to an AI module and a sentiment analysis engine using natural language processing technology, means for analyzing the post data and analyzing sentiment using natural language processing technology, means for determining the criminality and sentiment of the post based on the analysis results, means for issuing an alert to an administrator if a post is determined to be criminal, and means for deleting the relevant post and implementing sanctions against the user based on instructions from the administrator. This enables real-time analysis of post data, enabling early detection and response to criminal or inappropriate behavior. Furthermore, analyzing user sentiment allows for more advanced responses, further improving the reliability and safety of the platform.

[1340] "SNS Platform" refers to an online service that enables users to post content and interact with other users.

[1341] "Posted data" refers to information such as text, images, and videos that users create and publish on social media platforms.

[1342] "Server" refers to a computer system that processes, stores, and manages data on a network.

[1343] A "data storage device" is a device or system for storing data, including hard disk drives, SSDs, and cloud storage.

[1344] A "message queue" is a system for temporarily storing data and processing it sequentially, and examples include RabbitMQ and Apache Kafka.

[1345] "Natural language processing technology" is a technology for understanding and analyzing human language, and includes morphological analysis, keyword extraction, contextual analysis, etc.

[1346] An "AI module" is a software component that performs analysis using artificial intelligence, and specifically includes natural language processing and machine learning models.

[1347] An "emotion analysis engine" is a system that analyzes user emotions from text data and calculates emotional scores such as anger, sadness, and joy.

[1348] "Analysis results" refers to information based on the analysis of posted data obtained by an AI module or sentiment analysis engine.

[1349] "Criminal" refers to the characteristics or content of posted data that violate or have the potential to violate laws or regulations.

[1350] An "alert" refers to a warning message or notification sent to an administrator when a specific condition is met.

[1351] "Administrator" refers to the person or organization responsible for operating a social media platform, monitoring user posts, and responding to violations.

[1352] "Sanctions" refers to penalties or restrictions imposed on users when violations of the terms of service or criminal activity are confirmed.

[1353] An embodiment of the present invention provides a system that monitors user posts on a social media platform and analyzes criminality and user sentiment in real time, thereby improving the trust between users and the platform and enabling early detection and response to criminal or inappropriate behavior.

[1354] When a user creates a new post on a social networking platform, the post data is immediately sent from the device to the server, which then retrieves details such as the post content, poster information, and posting date and time, and stores the data in a data storage device.

[1355] The server adds the received post data to a message queue in real time. In this case, a message broker such as RabbitMQ or Apache Kafka is used for the message queue. The data added to this message queue is then subjected to subsequent analysis processing.

[1356] The server then extracts the posted data from the message queue and passes it to an AI module that uses natural language processing technology and a sentiment analysis engine. The AI ​​module uses the BERT model, which also uses natural language processing technology, and the sentiment analysis engine uses IBM Watson's sentiment analysis API. The AI ​​module performs morphological analysis of the posted content, keyword extraction, and contextual analysis, while the sentiment analysis engine analyzes the user's emotions (anger, sadness, joy, etc.) in real time.

[1357] The AI ​​module uses the analysis results to determine the criminality and emotion of the post and sends it back to the server. For example, posts that are judged to be "threatening" or that express strong emotions such as "anger" are flagged as high risk.

[1358] The server sends an alert to the administrator based on the results of the AI ​​module and sentiment analysis engine. The alert is sent as a notification via email or to a dashboard dedicated to the administrator. For example, an email may be sent stating, "User ID: 12345's post has been determined to be high risk. Anger score: 90%."

[1359] The administrator will check the alert and review the details of the relevant post. In some cases, the administrator may decide to delete the post or impose sanctions on the user (such as suspending the account). For example, the administrator may take the following action: "Check the post by user ID: 12345 and instruct them to delete it and suspend the account."

[1360] Specific examples

[1361] For example, if a user makes a post saying "I'm going to kill someone," the post will be processed by the system as follows:

[1362] 1. Your post is sent to the server immediately.

[1363] 2. The server stores the post in a database.

[1364] 3. The posted data is added to the message queue.

[1365] 4. The server takes the data from the queue and passes it to the BERT model and IBM Watson sentiment analysis API.

[1366] 5. The AI ​​module determines it to be a "threat" and the emotion engine analyzes the emotion score for "anger."

[1367] 6. The server sends an alert email to the administrator stating, "User ID: 12345's post has been deemed high risk. Anger score: 90%."

[1368] 7. The administrator will check the content of the post on the dashboard and take disciplinary action, such as deleting the content posted by user ID: 12345 and temporarily suspending the account.

[1369] Prompt Sentence Examples

[1370] 1. "Please rate the criminal nature of the following post: 'I want to hit someone.'"

[1371] 2. "Do a sentiment analysis of the following post: 'I'm so tired today.'"

[1372] This invention enables early detection and response to criminal and inappropriate behavior on social media platforms, ensuring the safety of users and the platform and improving its reliability. Furthermore, analyzing user emotions enables more advanced responses, making social media platforms safer and more reliable.

[1373] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1374] Step 1:

[1375] A user creates a new post on a social media platform.

[1376] Input: The user types the content to post.

[1377] Specific behavior: A user logs into a social networking application, enters text such as "kill someone," and clicks the post button.

[1378] Output: The post content is sent from the device to the server.

[1379] Step 2:

[1380] The terminal transmits the user's posted data to the server.

[1381] Input: User-generated submitted data.

[1382] Specific operation: The device sends the text data entered by the user, such as "kill someone," to the server as an HTTP request.

[1383] Output: Posted data reaches the server.

[1384] Step 3:

[1385] The server stores the received posting data in a data storage device.

[1386] Input: Post data sent from the device.

[1387] Specific operation: The server uses the INSERT statement to store data including the post content, poster ID, and posting date and time in the database. Specifically, "INSERT INTO posts (content, user_id, timestamp) VALUES ('Kill someone', 12345, '2023-10-01');"

[1388] Output: Posted data is saved in the database.

[1389] Step 4:

[1390] The server adds the posted data stored in the database to a message queue in real time.

[1391] Input: Post data stored in a database.

[1392] Specific operation: The server adds the post data to a message queue (e.g., RabbitMQ). It is added to the queue in the format "Post content: kill someone, Poster ID: 12345, Post date: 2023-10-01".

[1393] Output: Posted data is added to the message queue.

[1394] Step 5:

[1395] The server retrieves the posted data from the message queue and passes it to the AI ​​module and sentiment analysis engine.

[1396] Input: Post data added to the message queue.

[1397] Specific operation: The server inputs the data retrieved from the queue into an AI module (BERT model) that uses natural language processing technology and a sentiment analysis engine (IBM Watson sentiment analysis API).

[1398] Output: The data is passed to the AI ​​module and sentiment analysis engine.

[1399] Step 6:

[1400] The AI ​​module analyzes the posted data and analyzes emotions.

[1401] Input: Post data passed from the server.

[1402] Specific operation: The AI ​​module performs morphological analysis of the text, keyword extraction, and contextual analysis, and the sentiment analysis engine analyzes emotions (anger, sadness, joy, etc.). The text "kill someone" is analyzed as "Anger score: 90%" and falls into the "Threat" category.

[1403] Output: Analysis results (e.g., "Anger score: 90%", "Threat" category) are obtained.

[1404] Step 7:

[1405] The server sends an alert to the administrator based on the analysis results of the AI ​​module and emotion analysis engine.

[1406] Input: Analysis results from the AI ​​module and sentiment analysis engine.

[1407] Specific operation: The server sends an alert email or a notification to the administrator's dashboard stating something like "User ID: 12345's post has been deemed high risk. Anger score: 90%."

[1408] Output: An alert is sent to the administrator.

[1409] Step 8:

[1410] The administrator will confirm the alert sent and review the details of the relevant post.

[1411] Input: The alert content sent from the server.

[1412] Specific actions: The administrator will log in to the dashboard and take action such as "checking the relevant post and the post by user ID: 12345, and instructing them to delete the post and suspend the account."

[1413] Output: Decision and implementation of action, which may include deleting the relevant post and suspending the user's account.

[1414] (Application example 2)

[1415] 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."

[1416] Conventional social networking platforms have systems that monitor user posts and determine whether they are criminal, but they lack the ability to analyze user emotions. This makes it difficult to quickly detect posts that express negative user emotions and to respond appropriately, resulting in a lack of deterrent power for inappropriate behavior. Furthermore, administrators' responses when criminal activity is detected are inefficient, requiring a rapid response. The objective of this invention is to solve these problems and improve the safety and reliability of social networking platforms.

[1417] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for monitoring all posts on the SNS platform and acquiring post data, means for analyzing the acquired post data using natural language processing technology to determine whether the post is criminal, means for analyzing the emotions in the acquired post data and generating an emotion score for the user, means for issuing an alert to an administrator if the post is determined to be criminal, means for deleting the post and implementing sanctions against the user based on instructions from the administrator, and means including an administrator dashboard for the administrator to check and respond to the alert. This enables early detection and rapid response to inappropriate posts, and also enables appropriate measures to be taken for posts that express the user's negative emotions.

[1418] A "SNS platform" is an online service that allows users to share posts, comments, images, and videos.

[1419] "Posted data" refers to information such as text, images, and videos that users post on social media platforms.

[1420] "Natural language processing technology" is a technology that enables computers to understand and analyze human language, and includes morphological analysis, keyword extraction, contextual analysis, and more.

[1421] "Criminality" refers to whether the posted data contains content that may violate the law.

[1422] The "emotion score" is a numerical representation of the user's emotions contained in the posted data, and evaluates emotions such as "anger," "sadness," and "joy."

[1423] "Administrator" refers to the person or team responsible for operating a social media platform, monitoring content, and taking action as necessary.

[1424] An "alert" is a warning message that is sent to an administrator when a criminal offense is detected.

[1425] The "Admin Dashboard" is a dedicated management screen that allows administrators to monitor posts on social media platforms, check alerts, and take action.

[1426] "Sanctions" are measures taken against posts that are deemed to be criminal, and include specifically deleting posts and suspending users' accounts.

[1427] To implement this invention, a system is required to monitor posts on social media platforms in real time and analyze their criminality and sentiment scores. This system uses the following hardware and software:

[1428] Hardware and software used:

[1429] 1. Hardware:

[1430] High-performance server: Required to process large amounts of submitted data quickly.

[1431] Database server: Required to store and manage submitted data.

[1432] 2. Software:

[1433] Natural Language Processing (NLP) libraries: For example, use spaCy or NLTK.

[1434] Sentiment analysis tools: For example, using TextBlob.

[1435] Database: For example, use MySQL or PostgreSQL.

[1436] System Details:

[1437] 1. The server monitors all posts on the SNS platform in real time and immediately retrieves new post data, including the post content, poster information, and post date and time.

[1438] 2. The server analyzes the acquired posted data using natural language processing technology. Specifically, it performs morphological analysis of the text, extracts keywords, and analyzes context. For example, it detects whether keywords such as "threat" or "attack" are included.

[1439] 3. The server also simultaneously performs emotion analysis and generates an emotion score for the user. For example, it analyzes whether the content of the post corresponds to "anger," "sadness," or "happiness," and quantifies the degree of that emotion.

[1440] 4. If the server determines that the activity is highly criminal, it will send an alert to the administrator. The alert will be sent as a notification via email or to the administrator's dashboard.

[1441] 5. The administrator will check the alert and review the details of the relevant post. If a criminal offense is confirmed, the administrator will delete the post and / or take disciplinary action against the user (such as suspending the account).

[1442] Examples:

[1443] If a user posts on a social media platform something like "threatening with angry language," the post data is sent to a server. The server passes it to an AI module and emotion engine, which uses natural language processing technology to determine whether it is a "threat," and the emotion engine analyzes it for the emotion of "anger." If the results indicate criminal activity, the server sends an alert to an administrator. The administrator reviews the alert and instructs sanctions such as deleting the post and suspending the user's account.

[1444] Example prompt sentence:

[1445] Please monitor corporate social media for angry posts posted on September 3, 2023, and analyze for potential threats.

[1446] "Analyze inappropriate content posted by students on educational institution social media and check for any dangerous posts."

[1447] In this way, the system of the present invention can identify inappropriate posts on social media platforms early and respond quickly.

[1448] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1449] Step 1:

[1450] The server retrieves new post data from the SNS platform in real time. This post data includes the text content, poster information, posting date and time, etc. Specifically, it uses the SNS platform's API to fetch the data every time a new post is made.

[1451] Input: New post data

[1452] Output: Retrieved post data

[1453] Step 2:

[1454] The server stores the acquired post data in a database. This allows the data to be retained for subsequent analysis processing. Storing data in the database is done using INSERT statements.

[1455] Input: Retrieved post data

[1456] Output: Post data stored in the database

[1457] Step 3:

[1458] The server adds the submitted data stored in the database to a queue, which is a collection of data waiting to be analyzed. For example, it uses a message queue system such as Redis.

[1459] Input: Post data stored in the database

[1460] Output: Post data added to the queue

[1461] Step 4:

[1462] The server retrieves the posted data from the queue and analyzes the text using natural language processing techniques, such as morphological analysis, keyword extraction, and context analysis. For example, it uses the spaCy or NLTK library.

[1463] Input: Post data added to the queue

[1464] Output: Analysis results (morphological analysis, keyword extraction, context analysis)

[1465] Step 5:

[1466] The server simultaneously performs sentiment analysis and generates a user sentiment score, which is a numerical representation of emotions such as "anger," "sadness," and "happiness." For example, it uses the TextBlob library.

[1467] Input: Post data added to the queue

[1468] Output: Sentiment score

[1469] Step 6:

[1470] The server determines whether a post is criminal based on the results of natural language processing analysis and the emotion score. For example, if a post contains keywords such as "threat" or "attack" and the emotion score indicates a high value such as "anger," it is determined to be criminal.

[1471] Input: Analysis results (morphological analysis, keyword extraction, context analysis), sentiment score

[1472] Output: Criminal conduct determination result

[1473] Step 7:

[1474] If the server determines that the activity is highly criminal, it will send an alert to the administrator. The alert will be sent via email or a notification on the administrator's dashboard. Specifically, it uses the SMTP protocol and WebSocket communication.

[1475] Input: Criminal conduct determination result

[1476] Output: Alert sent to administrator

[1477] Step 8:

[1478] Administrators can view alerts on a dedicated dashboard and review details of the affected posts, including the post's text content, author information, and sentiment score.

[1479] Input: Alert sent to administrator

[1480] Output: Post details displayed

[1481] Step 9:

[1482] If a criminal offense is confirmed, the administrator will delete the post or take sanctions against the user. For example, the administrator can delete the post using an API call, or suspend the user's account by updating the database.

[1483] Input: Post details displayed

[1484] Output: Sanctions taken

[1485] This series of processes enables early detection and swift response to inappropriate posts on social media platforms.

[1486] 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.

[1487] 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.

[1488] 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.

[1489] 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.

[1490] 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.

[1491] 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.

[1492] 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).

[1493] 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.

[1494] 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."

[1495] 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.

[1496] 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).

[1497] 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.

[1498] 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.

[1499] 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.

[1500] 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.

[1501] 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.

[1502] 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.

[1503] 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.

[1504] 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.

[1505] 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.

[1506] 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.

[1507] The following is further disclosed regarding the above embodiment.

[1508] (Claim 1)

[1509] A means of monitoring all posts on the social media platform and obtaining post data;

[1510] A means of analyzing the acquired posting data using natural language processing technology to determine whether it is criminal in nature;

[1511] If a criminal offense is detected, an alert will be sent to the administrator.

[1512] A system that includes the means to delete relevant posts and take disciplinary action against users based on instructions from administrators.

[1513] (Claim 2)

[1514] 2. The system of claim 1, further comprising means for adding post data to a queue in real time and passing the queue to an AI module.

[1515] (Claim 3)

[1516] 2. The system according to claim 1, further comprising means for analyzing keywords and context of posted data when determining criminality.

[1517] "Example 1"

[1518] (Claim 1)

[1519] A means for acquiring post data posted by users to the platform;

[1520] A means of analyzing the acquired posting data using natural language processing technology to determine whether it is criminal in nature;

[1521] If a criminal offense is detected, an alert will be sent to the administrator.

[1522] A system that includes a means to delete relevant posts and take disciplinary action against users based on instructions from administrators.

[1523] (Claim 2)

[1524] The system according to claim 1, further comprising means for transmitting the acquired posting data to an AI module and analyzing the data using natural language processing technology.

[1525] (Claim 3)

[1526] The system according to claim 1, further comprising a means for sending an alert to an administrator terminal in real time when a criminal activity is detected.

[1527] "Application Example 1"

[1528] (Claim 1)

[1529] A means of monitoring all posts on the social media platform and obtaining post data;

[1530] A means of analyzing the acquired posting data using natural language processing technology to determine whether it is criminal in nature;

[1531] If a criminal offense is detected, an alert will be sent to the administrator.

[1532] The means to remove the posts and take disciplinary action against the user, based on instructions from the administrator;

[1533] means for sending a visual alert to the wearable device if criminal activity is detected;

[1534] A system including:

[1535] (Claim 2)

[1536] 2. The system of claim 1, further comprising means for adding post data to a queue in real time and passing the queue to an AI module.

[1537] (Claim 3)

[1538] 2. The system according to claim 1, further comprising means for analyzing keywords and context of posted data when determining criminality.

[1539] "Example 2: Combining Emotion Engines"

[1540] (Claim 1)

[1541] A means of monitoring all posts on the social media platform and obtaining post data;

[1542] means for storing the acquired posting data in a data storage device;

[1543] A means to add the acquired post data to the message queue in real time,

[1544] A means for passing the message queue to an AI module that uses natural language processing technology and a sentiment analysis engine;

[1545] A means for analyzing posted data and analyzing emotions using natural language processing technology;

[1546] A means for determining the criminality and sentiment of posts based on the analysis results;

[1547] If a criminal offense is detected, an alert will be sent to the administrator.

[1548] A system that includes the means to delete relevant posts and take disciplinary action against users based on instructions from administrators.

[1549] (Claim 2)

[1550] 2. The system according to claim 1, further comprising means for adding the posted data to an analysis queue in real time and passing the analysis queue to the AI ​​module.

[1551] (Claim 3)

[1552] 2. The system according to claim 1, further comprising means for analyzing keywords and context of posted data when determining criminality and emotions.

[1553] "Application example 2 when combining emotion engines"

[1554] (Claim 1)

[1555] A means of monitoring all posts on the social media platform and obtaining post data;

[1556] A means of analyzing the acquired posting data using natural language processing technology to determine whether it is criminal in nature;

[1557] A means for analyzing emotions in the acquired posting data and generating an emotion score for the user;

[1558] If a criminal offense is detected, an alert will be sent to the administrator.

[1559] The means to remove the posts and take disciplinary action against the user, based on instructions from the administrator;

[1560] A system including a means for administrators to view and respond to alerts, including an administrator dashboard.

[1561] (Claim 2)

[1562] 2. The system of claim 1, further comprising means for adding post data to a queue in real time and passing the queue to an AI module.

[1563] (Claim 3)

[1564] 2. The system according to claim 1, further comprising means for analyzing keywords and context of posted data when determining criminality. [Explanation of symbols]

[1565] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of monitoring all posts on the social media platform and obtaining post data; A means of analyzing the acquired posting data using natural language processing technology to determine whether it is criminal in nature; If a criminal offense is detected, an alert will be sent to the administrator. A system that includes the means to delete relevant posts and take disciplinary action against users based on instructions from administrators.

2. 2. The system according to claim 1, further comprising means for adding post data to a queue in real time and passing the queue to an AI module.

3. 2. The system according to claim 1, further comprising means for analyzing keywords and context of posted data when determining whether the posted data is criminal.

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