system

Abstract

We provide the system. [Solution] Data collection methods for monitoring online activity, AI processing means for analyzing the aforementioned data and detecting anomalies, A notification means for sending an alert to a guardian based on the aforementioned abnormality, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure pertains to a system. [[ID=* ]]<[]>[[]{]]**[[END}}]

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the spread of the Internet, as many minors use SNS, online games, etc., crimes committed by malicious third parties and access to inappropriate content have become serious problems. In response to such risks, it is very difficult for guardians to directly monitor all online activities and take prompt and appropriate measures. Therefore, it is required to more effectively manage and monitor minors' online activities and send warnings to guardians as needed to protect the safety of children.

Means for Solving the Problems

[0005] This invention solves the above problem by providing a system for monitoring and analyzing the online activities of minors in real time. Specifically, it collects the user's access history and usage time using data collection means, and detects anomalies by analyzing the data using AI processing means. If an anomaly is detected, an alert is sent to the guardian via a notification means to prompt action. This system can effectively protect minors from online risks.

[0006] "Online activities" refer to all actions and communications conducted via the internet, including browsing websites, using social media, and playing online games.

[0007] "Data collection means" refers to the technical processes and devices used to acquire and record information about users' online activities.

[0008] "AI processing means" refers to systems and methods that use artificial intelligence technology to analyze collected data and identify specific patterns or anomalies.

[0009] "Abnormal" refers to online activity that deviates from normal patterns or expected behavior and requires special attention.

[0010] "Notification means" refers to communication technologies and devices used to convey the analysis results and detected anomalies to users and guardians.

[0011] An "alert" refers to a message or warning designed to alert a user when a specific situation or anomaly occurs.

[0012] A "guardian" refers to an adult who has the responsibility to manage and protect the life and safety of a minor.

[0013] A "system" refers to a set of devices or methods in which related elements or processes work together to achieve a specific purpose. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0028] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention provides a system for monitoring the online activities of minors, detecting abnormal behavior, and issuing warnings to parents as needed. This system consists of three main components: a server, a terminal, and a user.

[0036] The server is responsible for receiving and analyzing online activity data and detecting anomalies. First, the server receives data such as access history and usage time sent from the device. Next, it analyzes this data using an AI algorithm to check for any anomalies that deviate from normal behavior patterns. Detected anomalies include unusually long usage times at night and access to dangerous websites. Upon detecting an anomaly, the server immediately generates an alert message and sends it to the parent or guardian through appropriate communication channels.

[0037] The device monitors the user's online activity in real time and sends necessary data to the server. Specifically, the device records website visit history, social media usage, and online game play time. This data is collected periodically and encrypted to ensure security before being sent to the server. The device also has a function to directly display minor warnings to the user, for example, a warning message will be displayed when usage time becomes excessive.

[0038] Users (parents) can review alerts sent from the server and manage their children's online activities as needed. Through the alert messages, users can identify specific problems and take measures such as blocking certain websites or limiting usage time. Furthermore, using the situation-specific recommendations provided by the system allows for more effective responses.

[0039] This embodiment can reduce online risks for minors in modern society and provide a safer internet environment.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device monitors the user's online activity in real time and collects access history, usage time, and application usage logs. This data is recorded at regular intervals.

[0043] Step 2:

[0044] The terminal encrypts the collected data and prepares it for transmission to the server. Data transmission is performed in batches to minimize network load.

[0045] Step 3:

[0046] The server receives data sent from the terminal. The received data is stored in a database for analysis.

[0047] Step 4:

[0048] The server uses AI algorithms to analyze incoming data and detect abnormal behavior and suspicious patterns. This analysis can, for example, detect access to inappropriate websites or unusually long usage periods at night.

[0049] Step 5:

[0050] If an anomaly is detected based on the analysis results, the server generates an alert message. The generated alert includes details of the anomaly along with recommended countermeasures.

[0051] Step 6:

[0052] The server sends the generated alerts to the parents. This is done via email or push notifications through a dedicated app.

[0053] Step 7:

[0054] The user (parent / guardian) receives an alert sent from the server and reviews its contents. They understand the details of the anomaly and consider countermeasures.

[0055] Step 8:

[0056] Users take specific actions based on the alerts. For example, they may restrict access to certain websites or review usage rules through conversations with their children.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] Today's minors have access to a wide range of information through the internet, but this may include harmful content or health risks from prolonged use. Parents are required to properly manage their children's online activities and respond quickly when necessary. However, traditional methods present challenges in terms of real-time monitoring and rapid detection of anomalies, which are time-consuming and laborious.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes information acquisition means for monitoring user behavior, AI analysis means for analyzing the information and identifying anomalies, notification means for sending an alert to a monitor based on the anomalies, security means for encrypting the information before transmission, and warning display means for displaying warnings in real time based on the collected information. This makes it possible to efficiently monitor the online activities of minors and to quickly detect and notify of abnormal behavior.

[0062] "Information acquisition methods" refer to technologies that monitor users' online activities in real time and collect data such as network visit history and usage period.

[0063] "AI analysis methods" refer to technologies that use artificial intelligence to identify anomalies that deviate from normal behavioral patterns, based on collected information.

[0064] A "notification method" is a technology for sending an alert to a monitor based on detected anomalies.

[0065] "Security measures" refer to technologies that ensure the safety of data by encrypting collected information before it is transmitted.

[0066] A "warning display method" is a technology that displays warnings to users in real time based on collected data.

[0067] This invention is a system for securely managing minors' internet use and quickly detecting abnormal behavior. This system consists of three main components: a server, a terminal, and a user.

[0068] The server is a powerful computing system that can operate via cloud services. It uses data processing software such as Hadoop and Apache Spark to analyze large amounts of online activity data in real time. As an AI analysis tool, it utilizes machine learning frameworks such as Tensorflow and PyTorch to detect deviations from normal behavioral patterns. Based on the analysis results, if an anomaly is detected, the information is immediately sent to the guardian via a notification system. In this process, the server ensures the secure transmission of data using the HTTPS protocol.

[0069] The device is a smart device typically used by minors, and is implemented as an application on the Android® or iOS operating system. The device monitors the user's network visit history and application usage time, and collects necessary data using information acquisition methods. The collected data is encrypted using the AES encryption algorithm within the device and prepared for transmission to the server. The device also provides warning display methods using a UI framework, allowing it to display visual warnings to the user.

[0070] Users (parents / guardians) receive alerts sent by the server on their smartphones or computers. Based on these alerts, they can use a dedicated application to manage settings such as blacklisting websites accessed by minors and limiting usage time. This application provides a user interface that allows for easy policy changes, offering an intuitive and convenient user experience.

[0071] For example, if a minor is playing online games at night beyond the normal usage time, the device detects this usage, encrypts the data, and sends it to the server. The server analyzes the data, determines that the behavior is abnormal, and sends a notification to the parent stating, "It has been detected that your child is playing games for an extended period at night." Based on this information, the parent can easily set limits on usage time.

[0072] An example of a prompt would be: "Please describe a specific function of a system that monitors the online activity of minors, including a concrete scenario. For example, please explain the process if access to an inappropriate website is detected during a certain period of time."

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The device monitors the user's online activity in real time. Specifically, the data recorded on the device includes network visit history and application usage time. The device periodically retrieves this data and encrypts it using the AES encryption algorithm within the device. This protects data privacy and prepares it for secure transmission. The input is the user's online activity data, and the output is encrypted data.

[0076] Step 2:

[0077] The terminal sends encrypted data to the server. This data is transmitted using a secure communication protocol, such as HTTPS. The server decrypts the received data and prepares it for analysis. The input is the encrypted data sent from the terminal, and the output is the user's activity data decrypted by the server.

[0078] Step 3:

[0079] The server processes the decrypted data using AI analysis tools. Specifically, AI algorithms are used to analyze the data and detect deviations from normal behavioral patterns. The server identifies abnormal behavior and determines whether it is excessive nighttime activity or access to dangerous websites. The input is decrypted activity data, and the output is the anomaly detection result.

[0080] Step 4:

[0081] If the server detects an anomaly, it immediately generates and sends an alert to the parent / guardian using a notification system. This alert includes specific details about the anomaly and recommended actions. For example, it might include information such as, "Access to an inappropriate website was detected at 11 PM," along with recommended actions. The input is the anomaly detection result, and the output is the generated alert message.

[0082] Step 5:

[0083] Users (parents) review received alerts and manage their children's online activities as needed. Parents can take action using a dedicated application, such as blocking specific websites or limiting usage time. Input is the alert message, and output is the management policy set by the parent.

[0084] This process allows the system to effectively monitor the online activities of minors and provide a safe environment for use.

[0085] (Application Example 1)

[0086] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0087] In recent years, internet use among minors has increased, along with a corresponding rise in online risks. In particular, accessing dangerous websites unknowingly and disrupting lifestyles due to prolonged internet use late at night are becoming significant problems. Currently, there is a lack of effective means for parents to monitor their children's online activities and intervene appropriately. Therefore, there is a need for effective monitoring of minors' online activities and the rapid detection and notification of abnormal behavior.

[0088] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0089] In this invention, the server includes data collection means for monitoring online activity, AI processing means for analyzing the data and detecting anomalies, notification means for sending alerts to parents based on the anomalies, and communication means for pushing the alerts to smart devices. This enables the rapid detection of abnormal online activity by minors, allowing parents to take immediate action.

[0090] "Online activities" refer to activities conducted via the internet, such as web browsing, social media use, and playing online games.

[0091] "Data collection means" refers to a function for acquiring and recording data related to a user's online behavior.

[0092] "AI processing means" refers to artificial intelligence algorithms used to analyze collected data and detect anomalies that deviate from normal behavioral patterns.

[0093] "Notification means" refers to communication technology used to convey specific information to parents or guardians based on detected anomalies.

[0094] A "smart device" refers to a mobile or wearable device that has an internet connection and can operate various applications.

[0095] "Push notifications" are a technology that sends information from a server to a device in order to display important information on the screen of the smart device in real time.

[0096] This invention aims to build a system that monitors the online activities of minors, detects anomalies, and notifies parents. It consists of three main components: a server, a terminal, and a user.

[0097] server

[0098] The server receives online activity data and analyzes it using AI algorithms. This analysis utilizes cloud computing resources and employs software such as Python and TensorFlow. The collected data is stored in a comprehensive database and used for anomaly detection. The server is responsible for immediately sending push notifications to parents' smart devices when abnormal behavior is detected.

[0099] terminal

[0100] The device is responsible for monitoring the user's online activity in real time. Specifically, it records data such as website visit history, social media usage, and online game playtime. The recorded data is encrypted and periodically sent to the server. The device also has a warning display function to directly notify the user of minor anomalies in online activity.

[0101] User

[0102] Parents can review alerts received from the server and appropriately manage their children's online activities as needed. Users can take specific measures, such as restricting certain websites or setting usage time limits. Furthermore, the system provides situation-specific recommendations, allowing users to intervene effectively.

[0103] Use as a concrete example

[0104] If the system detects that a minor is frequently accessing dangerous websites they don't normally visit on weekday nights, it identifies the anomaly and quickly generates a notification. An alert stating "Unusual behavior has occurred" appears on the parent's smartphone, providing information to take immediate action.

[0105] Example of a prompt

[0106] "Please tell me how to design an AI model to detect risks in online activities of minors."

[0107] "What are the key elements in building a machine learning algorithm to identify abnormal internet usage behavior?"

[0108] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0109] Step 1:

[0110] The device monitors the user's online activity in real time. Specifically, the device collects data on website visits, social media usage time, and online game play time. This data is used as input, and is encrypted internally to protect privacy before being output.

[0111] Step 2:

[0112] The device periodically sends encrypted online activity data to the server. Specifically, it sends data to the server using a secure communication protocol, which then serves as input for the next processing step.

[0113] Step 3:

[0114] The server analyzes the encrypted data it receives. Upon receiving data, it decrypts and preprocesses it, then formats it into a format suitable for the AI ​​algorithm. Using a generative AI model, it detects anomalies that deviate from normal behavioral patterns, and outputs information about any anomalies it finds.

[0115] Step 4:

[0116] When the server detects abnormal behavior, it generates an alert based on that information. The generated alert includes the type and details of the abnormality, which then serves as input for the next step.

[0117] Step 5:

[0118] The server uses alert information to send push notifications to the parent's smart device. Specifically, it uses a communication method to send information to the parent's device in real time, which is the output. The smart device that receives the notification then displays the alert message.

[0119] Step 6:

[0120] Parents, as users, can review received alerts and take specific actions. Based on detailed information about the anomaly, they can perform management operations such as restricting access to specific websites or limiting usage time. This allows them to obtain the results of the user's actions.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention relates to a system that monitors the online activities of minors and combines them with an emotion engine that recognizes the user's emotions to enable more advanced anomaly detection and notification to parents. The system consists of three main components: a server, a terminal, and a user.

[0123] The server's role is to analyze collected online activity data and user emotional state data. First, it receives data sent from the device and stores it in a database. Next, it uses AI algorithms and an emotion engine to detect anomalies and emotional changes that deviate from normal behavioral patterns. In particular, it can detect anomalies based on the user's emotional state, and if the anomaly is related to the user's psychological stress or anxiety, it can prioritize alerts.

[0124] The device tracks online activity and measures emotional states. Specifically, it collects data such as access history, usage time, and application usage logs, as well as emotional information by analyzing the user's facial expressions and tone of voice. This allows for real-time monitoring of changes in the user's emotions and the transmission of this data to the server.

[0125] Users (parents) can receive alerts and additional emotional information sent from the server. For example, if the server detects emotional patterns indicating anxiety or stress, parents can receive support information to help them take specific action. In addition to the usual anomaly detection results, alerts include contextual information based on emotional analysis, allowing parents to take appropriate measures considering their child's psychological state.

[0126] This embodiment allows parents not only to address typical online risks but also to detect potential problems arising from emotional shifts and provide more comprehensive support. In this way, the introduction of an emotional engine can further enhance the safety of minors.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The device monitors the user's online activity in real time, collecting access history, usage time, and application logs. It also analyzes the user's facial expressions and voice through the camera and microphone to acquire emotional data.

[0130] Step 2:

[0131] The device encrypts the acquired online activity data and sentiment data and securely transmits it to the server. Transmission is performed periodically in batch mode, taking network load into consideration.

[0132] Step 3:

[0133] The server receives data from the terminal and stores it in a database. This includes information about online activities and data on emotional states obtained through facial expression and voice analysis.

[0134] Step 4:

[0135] The server analyzes the data using AI algorithms and an emotion engine. It evaluates whether any unusual patterns are detected in online activities and whether stress or anxiety can be inferred from the emotional data.

[0136] Step 5:

[0137] The server generates an alert message when it detects abnormal patterns or signs of emotional stress. The alert includes details about the detected anomaly and additional information based on the emotional state.

[0138] Step 6:

[0139] The server sends the generated alerts to parents via email or app notifications. In addition to the usual anomaly detection notifications, contextual information about the user's emotions is provided.

[0140] Step 7:

[0141] The user (parent / guardian) receives alerts sent from the server and reviews their content. They then consider appropriate countermeasures based on the analysis of the child's emotional state, ensuring the child's safety and mental well-being.

[0142] Step 8:

[0143] Users will implement specific countermeasures. For example, if abnormal behavior caused by stress is detected, they may create a space for dialogue with the child, provide a relaxing environment, or re-establish rules for online activities.

[0144] (Example 2)

[0145] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0146] To enhance the safety of minors' online activities, it is necessary to not only monitor access history and usage time, but also to detect anomalies that take into account changes in emotional state. However, current systems are insufficient for comprehensive anomaly detection based on emotional changes and related alert notifications to parents, thus requiring more advanced monitoring and response.

[0147] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0148] In this invention, the server includes data collection means for monitoring online behavior, artificial intelligence processing means for analyzing the data and detecting anomalies, and notification means for sending alerts to parents based on the anomalies and the user's emotional state. This enables comprehensive anomaly detection and rapid notification to parents in the online activities of minors, taking into account not only abnormal behavioral patterns but also emotional changes.

[0149] "Data collection methods for monitoring online behavior" refer to devices and software that have the function of recording a user's website visit history, digital device usage time, and changes in facial expressions and voice. This collection allows for a detailed understanding of the user's activity.

[0150] "Artificial intelligence processing means" refers to algorithms and computational techniques for analyzing collected data to identify anomalies that deviate from normal behavioral patterns, as well as emotional changes related to the user's psychological stress and anxiety. This improves the accuracy of anomaly detection.

[0151] "Notification means" refers to devices or software that have the function of sending alerts to parents based on detected anomalies and the user's emotional state. This means allows parents to respond quickly.

[0152] One embodiment of this invention is to provide a system that comprehensively monitors the online activities of minors, detects abnormalities considering their emotional state, and promptly notifies their guardians.

[0153] The server utilizes data collection tools for monitoring online behavior and processing tools that integrate artificial intelligence and emotion engines. Specifically, the server can analyze the collected data using machine learning frameworks such as TensorFlow and PyTorch. In this process, it identifies deviations from normal behavior patterns and changes in emotions related to the user's psychological stress and anxiety. The server incorporates database management systems such as MySQL® and MongoDB, enabling secure data storage and rapid access.

[0154] The device collects the user's online activity data and emotional state data. Specifically, the device collects web browser history and application usage logs, and uses the camera and microphone to acquire facial expressions and voice data. This information is immediately transferred to the server.

[0155] Users (parents) can manage the safety of minors' online activities through alerts sent from the server. These alerts include detection results of abnormal behavior and contextual information about emotional state, allowing parents to take appropriate action. For example, if a child shows signs of anxiety on a particular website, parents can encourage them to take time to relax.

[0156] A concrete example of a prompt question might be, "How do you design an AI model that analyzes user behavior patterns and detects anomalies based on emotional changes?" This prompt is useful as a reference for deepening one's understanding of system algorithm design.

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The device collects the user's online activity data and emotional state data. Specifically, the device retrieves browser history and records URLs, and collects application usage logs. It also captures the user's facial expressions and voice data using the camera and microphone. Inputs include website visit history, app usage time, facial image data, and voice data. Outputs are these data converted into a format that can be sent to the server.

[0160] Step 2:

[0161] The terminal sends the collected data to the server. Specifically, it bundles the data into packets and securely transfers them to the server using an encryption protocol. The input is the data generated in step 1, and the output is in a format that the server receives and can use in the next analysis step.

[0162] Step 3:

[0163] The server saves the received data to the database. Specifically, it performs data validation to confirm data integrity before saving it to the database. The input is data sent from the terminal, and the output is data in a well-organized database.

[0164] Step 4:

[0165] The server analyzes data using AI algorithms and an emotion engine. Specifically, it utilizes a generative AI model to detect anomalies that deviate from normal behavioral patterns and changes in emotion. The input is user behavior and emotion data stored in a database, and the output is the detection results of specific patterns of anomalies and the results of emotion state analysis.

[0166] Step 5:

[0167] The server generates alerts based on the analysis results and notifies the user. Specifically, when psychological stress or anxiety is detected, an alert of appropriate importance is sent to the parent via email or push notification. The input is the analysis results from step 4, and the output generates content including notification information and countermeasures.

[0168] Step 6:

[0169] Based on the alerts received, users take action regarding their children's online activities. Specific actions include engaging in conversations with children and restricting the use of digital devices, thereby improving the online safety of minors. Input is alert information from the server, and output is the actions taken and their results.

[0170] (Application Example 2)

[0171] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0172] To ensure the safety of minors' online activities, it is necessary not only to monitor online history but also to accurately understand users' emotional states and detect potential dangers associated with emotional changes early on. However, existing systems are insufficient in detecting anomalies that take emotional states into account, which poses a challenge as it may lead to missing cases that require immediate attention.

[0173] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0174] In this invention, the server includes data acquisition means for monitoring online activity, AI analysis means for analyzing the user's facial expression information and detecting anomalies accompanied by emotional changes, and communication means for notifying parents of detailed additional information based on emotional changes. This makes it possible to comprehensively monitor the user's online activity and emotional state, detect potential dangers early, and notify parents.

[0175] "Online activities" is a general term for a series of actions and operations that users perform using the internet.

[0176] "Data acquisition means" refers to a device or software used to collect information related to a user's online activities.

[0177] "Facial expression information" refers to visual data that indicates emotions and psychological states obtained through the movements of a user's face.

[0178] "AI analysis means" refers to a program or system that processes collected data and uses artificial intelligence technology to identify patterns and anomalies.

[0179] "Emotional change" refers to the phenomenon where a user's emotional state changes over time.

[0180] "Communication means" refers to a system that includes protocols and interfaces for transmitting information to other devices or users.

[0181] "Guardian" refers to a person who has the responsibility to protect the user and to oversee their safety.

[0182] "Additional information" refers to information that provides supplementary data or context related to the detected anomaly.

[0183] This invention is a system that detects abnormalities in the online activities and emotional state of minors and notifies parents of necessary information. The system consists of three main components: a server, a terminal, and a user. The functions and processing of each component are described in detail below.

[0184] First, the device is equipped with a camera sensor and microphone to collect online activity and user facial expression information. The software embedded in the device uses OpenCV for facial recognition, monitors the user's emotional state in real time, and an AI model using TensorFlow analyzes changes in emotion. This data is acquired along with the user's access history and usage time information and sent to the server.

[0185] Next, the server processes the collected data using AI analysis tools. The AI ​​analysis tools detect anomalies accompanied by emotional changes, and if a user shows stress or anxiety associated with specific risky behaviors, that information is given priority for evaluation. For example, if a user has been accessing inappropriate content for an extended period and their facial expression indicates anxiety, the emotion engine will immediately detect that event as an anomaly.

[0186] Finally, the server sends a detailed notification to the parent / guardian via communication means based on the detected anomaly. This notification includes additional information about the emotional changes and their context, and parents / guardians can receive it in real time on their smartphones or computers. This improves the online safety of minors while also enabling appropriate support.

[0187] For example, if a user shows signs of stress while visiting a particular site, a notification will be sent to the parent stating, "Your child may be experiencing anxiety while viewing certain content." An example of a prompt using a generative AI model might be, "Please explain how this system detects emotional changes in minors' online activities and communicates that information to parents."

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The device captures the user's face using its camera sensor and acquires facial expression image data. This acquired image is used as input for facial region recognition using OpenCV. As a result of processing with OpenCV, facial feature point data is output. This data is passed to the Emotion AI model.

[0191] Step 2:

[0192] The device collects online activity data, including access history and usage time. This data is combined with facial feature point data to prepare it for AI analysis. This process connects the user's behavior and emotions, preparing the data for input to the server. This data is then transmitted to the server.

[0193] Step 3:

[0194] The server processes data received from the terminal using AI analysis tools. The input data includes online activity and facial feature point data, and an emotional state is evaluated using a TensorFlow model. This process outputs results for detecting abnormal behavior accompanied by changes in emotional state.

[0195] Step 4:

[0196] The server analyzes the anomaly detection results based on emotional states and generates information to notify parents with a corresponding priority. For example, if a user exhibits certain risky behavior and displays an anxious expression, the notification priority will be set higher. This information is then transmitted as a notification message.

[0197] Step 5:

[0198] The server sends the generated notification message to the parent via a communication method. The notification includes the results of a situational sentiment analysis and additional information, which the parent receives and reviews. This step ensures the user's safety and allows for necessary actions to be taken in real time.

[0199] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0200] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0201] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0205] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0206] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0207] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0208] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0209] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0210] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0211] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0212] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0213] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0214] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0215] This invention provides a system for monitoring the online activities of minors, detecting abnormal behavior, and issuing warnings to parents as needed. This system consists of three main components: a server, a terminal, and a user.

[0216] The server is responsible for receiving and analyzing online activity data and detecting anomalies. First, the server receives data such as access history and usage time sent from the device. Next, it analyzes this data using an AI algorithm to check for any anomalies that deviate from normal behavior patterns. Detected anomalies include unusually long usage times at night and access to dangerous websites. Upon detecting an anomaly, the server immediately generates an alert message and sends it to the parent or guardian through appropriate communication channels.

[0217] The device monitors the user's online activity in real time and sends necessary data to the server. Specifically, the device records website visit history, social media usage, and online game play time. This data is collected periodically and encrypted to ensure security before being sent to the server. The device also has a function to directly display minor warnings to the user, for example, a warning message will be displayed when usage time becomes excessive.

[0218] Users (parents) can review alerts sent from the server and manage their children's online activities as needed. Through the alert messages, users can identify specific problems and take measures such as blocking certain websites or limiting usage time. Furthermore, using the situation-specific recommendations provided by the system allows for more effective responses.

[0219] This embodiment can reduce online risks for minors in modern society and provide a safer internet environment.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The device monitors the user's online activity in real time and collects access history, usage time, and application usage logs. This data is recorded at regular intervals.

[0223] Step 2:

[0224] The terminal encrypts the collected data and prepares it for transmission to the server. Data transmission is performed in batches to minimize network load.

[0225] Step 3:

[0226] The server receives data sent from the terminal. The received data is stored in a database for analysis.

[0227] Step 4:

[0228] The server uses AI algorithms to analyze incoming data and detect abnormal behavior and suspicious patterns. This analysis can, for example, detect access to inappropriate websites or unusually long usage periods at night.

[0229] Step 5:

[0230] If an anomaly is detected based on the analysis results, the server generates an alert message. The generated alert includes details of the anomaly along with recommended countermeasures.

[0231] Step 6:

[0232] The server sends the generated alerts to the parents. This is done via email or push notifications through a dedicated app.

[0233] Step 7:

[0234] The user (parent / guardian) receives an alert sent from the server and reviews its contents. They understand the details of the anomaly and consider countermeasures.

[0235] Step 8:

[0236] Users take specific actions based on the alerts. For example, they may restrict access to certain websites or review usage rules through conversations with their children.

[0237] (Example 1)

[0238] Next, we will describe Example 1. 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."

[0239] Today's minors have access to a wide range of information through the internet, but this may include harmful content or health risks from prolonged use. Parents are required to properly manage their children's online activities and respond quickly when necessary. However, traditional methods present challenges in terms of real-time monitoring and rapid detection of anomalies, which are time-consuming and laborious.

[0240] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0241] In this invention, the server includes information acquisition means for monitoring user behavior, AI analysis means for analyzing the information and identifying anomalies, notification means for sending an alert to a monitor based on the anomalies, security means for encrypting the information before transmission, and warning display means for displaying warnings in real time based on the collected information. This makes it possible to efficiently monitor the online activities of minors and to quickly detect and notify of abnormal behavior.

[0242] "Information acquisition methods" refer to technologies that monitor users' online activities in real time and collect data such as network visit history and usage period.

[0243] "AI analysis methods" refer to technologies that use artificial intelligence to identify anomalies that deviate from normal behavioral patterns, based on collected information.

[0244] A "notification method" is a technology for sending an alert to a monitor based on detected anomalies.

[0245] "Security measures" refer to technologies that ensure the safety of data by encrypting collected information before it is transmitted.

[0246] A "warning display method" is a technology that displays warnings to users in real time based on collected data.

[0247] This invention is a system for securely managing minors' internet use and quickly detecting abnormal behavior. This system consists of three main components: a server, a terminal, and a user.

[0248] The server is a powerful computing system that can operate via cloud services. It uses data processing software such as Hadoop and Apache Spark to analyze large amounts of online activity data in real time. For AI analysis, it utilizes machine learning frameworks like TensorFlow and PyTorch to detect deviations from normal behavioral patterns. Based on the analysis results, if an anomaly is detected, it immediately sends the information to the guardian via a notification system. In this process, the server uses the HTTPS protocol to ensure the secure transmission of data.

[0249] The device is a smart device typically used by minors, implemented as an application on Android or iOS operating systems. The device monitors the user's network visit history and application usage time, collecting necessary data using information acquisition methods. The collected data is encrypted within the device using the AES encryption algorithm and prepared for transmission to the server. Furthermore, the device utilizes a UI framework to provide warning display mechanisms, allowing for the display of visual warnings to the user.

[0250] Users (parents / guardians) receive alerts sent by the server on their smartphones or computers. Based on these alerts, they can use a dedicated application to manage settings such as blacklisting websites accessed by minors and limiting usage time. This application provides a user interface that allows for easy policy changes, offering an intuitive and convenient user experience.

[0251] For example, if a minor is playing online games at night beyond the normal usage time, the device detects this usage, encrypts the data, and sends it to the server. The server analyzes the data, determines that the behavior is abnormal, and sends a notification to the parent stating, "It has been detected that your child is playing games for an extended period at night." Based on this information, the parent can easily set limits on usage time.

[0252] An example of a prompt would be: "Please describe a specific function of a system that monitors the online activity of minors, including a concrete scenario. For example, please explain the process if access to an inappropriate website is detected during a certain period of time."

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] The device monitors the user's online activity in real time. Specifically, the data recorded on the device includes network visit history and application usage time. The device periodically retrieves this data and encrypts it using the AES encryption algorithm within the device. This protects data privacy and prepares it for secure transmission. The input is the user's online activity data, and the output is encrypted data.

[0256] Step 2:

[0257] The terminal sends encrypted data to the server. This data is transmitted using a secure communication protocol, such as HTTPS. The server decrypts the received data and prepares it for analysis. The input is the encrypted data sent from the terminal, and the output is the user's activity data decrypted by the server.

[0258] Step 3:

[0259] The server processes the decrypted data using AI analysis tools. Specifically, AI algorithms are used to analyze the data and detect deviations from normal behavioral patterns. The server identifies abnormal behavior and determines whether it is excessive nighttime activity or access to dangerous websites. The input is decrypted activity data, and the output is the anomaly detection result.

[0260] Step 4:

[0261] If the server detects an anomaly, it immediately generates and sends an alert to the parent / guardian using a notification system. This alert includes specific details about the anomaly and recommended actions. For example, it might include information such as, "Access to an inappropriate website was detected at 11 PM," along with recommended actions. The input is the anomaly detection result, and the output is the generated alert message.

[0262] Step 5:

[0263] Users (parents) review received alerts and manage their children's online activities as needed. Parents can take action using a dedicated application, such as blocking specific websites or limiting usage time. Input is the alert message, and output is the management policy set by the parent.

[0264] This process allows the system to effectively monitor the online activities of minors and provide a safe environment for use.

[0265] (Application Example 1)

[0266] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0267] In recent years, internet use among minors has increased, along with a corresponding rise in online risks. In particular, accessing dangerous websites unknowingly and disrupting lifestyles due to prolonged internet use late at night are becoming significant problems. Currently, there is a lack of effective means for parents to monitor their children's online activities and intervene appropriately. Therefore, there is a need for effective monitoring of minors' online activities and the rapid detection and notification of abnormal behavior.

[0268] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0269] In this invention, the server includes data collection means for monitoring online activity, AI processing means for analyzing the data and detecting anomalies, notification means for sending alerts to parents based on the anomalies, and communication means for pushing the alerts to smart devices. This enables the rapid detection of abnormal online activity by minors, allowing parents to take immediate action.

[0270] "Online activities" refer to activities conducted via the internet, such as web browsing, social media use, and playing online games.

[0271] "Data collection means" refers to a function for acquiring and recording data related to a user's online behavior.

[0272] "AI processing means" refers to artificial intelligence algorithms used to analyze collected data and detect anomalies that deviate from normal behavioral patterns.

[0273] "Notification means" refers to communication technology used to convey specific information to parents or guardians based on detected anomalies.

[0274] A "smart device" refers to a mobile or wearable device that has an internet connection and can operate various applications.

[0275] "Push notifications" are a technology that sends information from a server to a device in order to display important information on the screen of the smart device in real time.

[0276] This invention aims to build a system that monitors the online activities of minors, detects anomalies, and notifies parents. It consists of three main components: a server, a terminal, and a user.

[0277] server

[0278] The server receives online activity data and analyzes it using AI algorithms. This analysis utilizes cloud computing resources and employs software such as Python and TensorFlow. The collected data is stored in a comprehensive database and used for anomaly detection. The server is responsible for immediately sending push notifications to parents' smart devices when abnormal behavior is detected.

[0279] terminal

[0280] The terminal has the role of monitoring the user's online activities in real time. Specifically, it records as data the website visit history, SNS usage status, and online game usage time. The recorded data is encrypted and then periodically sent to the server. The terminal also has a warning display function for directly notifying the user of minor abnormalities in online activities.

[0281] User

[0282] The user, who is a guardian, can check the alerts received from the server and appropriately manage the child's online activities as needed. The user can take specific measures such as restricting access to specific websites or setting usage time. Furthermore, since this system presents recommended measures according to the situation, the user can utilize this to achieve effective intervention.

[0283] Use as a specific example

[0284] If it is detected that a minor frequently accesses dangerous websites that they do not usually access on weekday nights, the server discriminates the abnormality and quickly generates a notification. An alert saying "There has been unusual behavior" is displayed on the guardian's smartphone, and information for immediate response is provided.

[0285] Example of a prompt sentence

[0286] "Please teach me the design method of an AI model for detecting risks in minors' online activities."

[0287] "What are the important elements in constructing a machine learning algorithm for judging abnormal Internet usage behavior?"

[0288] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0289] Step 1:

[0290] The device monitors the user's online activity in real time. Specifically, the device collects data on website visits, social media usage time, and online game play time. This data is used as input, and is encrypted internally to protect privacy before being output.

[0291] Step 2:

[0292] The device periodically sends encrypted online activity data to the server. Specifically, it sends data to the server using a secure communication protocol, which then serves as input for the next processing step.

[0293] Step 3:

[0294] The server analyzes the encrypted data it receives. Upon receiving data, it decrypts and preprocesses it, then formats it into a format suitable for the AI ​​algorithm. Using a generative AI model, it detects anomalies that deviate from normal behavioral patterns, and outputs information about any anomalies it finds.

[0295] Step 4:

[0296] When the server detects abnormal behavior, it generates an alert based on that information. The generated alert includes the type and details of the abnormality, which then serves as input for the next step.

[0297] Step 5:

[0298] The server uses alert information to send push notifications to the parent's smart device. Specifically, it uses a communication method to send information to the parent's device in real time, which is the output. The smart device that receives the notification then displays the alert message.

[0299] Step 6:

[0300] Parents, as users, can review received alerts and take specific actions. Based on detailed information about the anomaly, they can perform management operations such as restricting access to specific websites or limiting usage time. This allows them to obtain the results of the user's actions.

[0301] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0302] This invention relates to a system that monitors the online activities of minors and combines them with an emotion engine that recognizes the user's emotions to enable more advanced anomaly detection and notification to parents. The system consists of three main components: a server, a terminal, and a user.

[0303] The server's role is to analyze collected online activity data and user emotional state data. First, it receives data sent from the device and stores it in a database. Next, it uses AI algorithms and an emotion engine to detect anomalies and emotional changes that deviate from normal behavioral patterns. In particular, it can detect anomalies based on the user's emotional state, and if the anomaly is related to the user's psychological stress or anxiety, it can prioritize alerts.

[0304] The device tracks online activity and measures emotional states. Specifically, it collects data such as access history, usage time, and application usage logs, as well as emotional information by analyzing the user's facial expressions and tone of voice. This allows for real-time monitoring of changes in the user's emotions and the transmission of this data to the server.

[0305] The user (guardian) can receive alerts and additional information regarding emotions sent from the server. For example, when the server detects an emotional pattern indicating uneasiness or stress, the guardian can receive support information for taking specific countermeasures. In addition to the normal anomaly detection results, the alerts include context information based on emotion analysis, enabling the guardian to take appropriate measures considering the child's psychological state.

[0306] With this embodiment, the guardian can not only handle normal online risks but also detect potential problems due to emotional changes and provide more comprehensive support. Thus, by introducing the emotion engine, it becomes possible to further improve the safety of minors.

[0307] The processing flow will be described below.

[0308] Step 1:

[0309] The terminal monitors the user's online activities in real time and collects access history, usage time, and application logs. Also, it analyzes the user's facial expressions and voice through the camera and microphone to obtain emotion data.

[0310] Step 2:

[0311] The terminal encrypts the obtained online activity data and emotion data and securely transmits them to the server. The transmission is performed in batch mode at regular intervals considering the network load.

[0312] Step 3:

[0313] The server receives the data from the terminal and stores it in the database. This includes information regarding online activities and data on the emotional state from facial expression and voice analysis.

[0314] Step 4:

[0315] The server analyzes the data using AI algorithms and an emotion engine. It evaluates whether any unusual patterns are detected in online activities and whether stress or anxiety can be inferred from the emotional data.

[0316] Step 5:

[0317] The server generates an alert message when it detects abnormal patterns or signs of emotional stress. The alert includes details about the detected anomaly and additional information based on the emotional state.

[0318] Step 6:

[0319] The server sends the generated alerts to parents via email or app notifications. In addition to the usual anomaly detection notifications, contextual information about the user's emotions is provided.

[0320] Step 7:

[0321] The user (parent / guardian) receives alerts sent from the server and reviews their content. They then consider appropriate countermeasures based on the analysis of the child's emotional state, ensuring the child's safety and mental well-being.

[0322] Step 8:

[0323] Users will implement specific countermeasures. For example, if abnormal behavior caused by stress is detected, they may create a space for dialogue with the child, provide a relaxing environment, or re-establish rules for online activities.

[0324] (Example 2)

[0325] Next, we will describe Example 2. 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".

[0326] To enhance the safety of minors' online activities, it is necessary to not only monitor access history and usage time, but also to detect anomalies that take into account changes in emotional state. However, current systems are insufficient for comprehensive anomaly detection based on emotional changes and related alert notifications to parents, thus requiring more advanced monitoring and response.

[0327] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0328] In this invention, the server includes data collection means for monitoring online behavior, artificial intelligence processing means for analyzing the data and detecting anomalies, and notification means for sending alerts to parents based on the anomalies and the user's emotional state. This enables comprehensive anomaly detection and rapid notification to parents in the online activities of minors, taking into account not only abnormal behavioral patterns but also emotional changes.

[0329] "Data collection methods for monitoring online behavior" refer to devices and software that have the function of recording a user's website visit history, digital device usage time, and changes in facial expressions and voice. This collection allows for a detailed understanding of the user's activity.

[0330] "Artificial intelligence processing means" refers to algorithms and computational techniques for analyzing collected data to identify anomalies that deviate from normal behavioral patterns, as well as emotional changes related to the user's psychological stress and anxiety. This improves the accuracy of anomaly detection.

[0331] "Notification means" refers to devices or software that have the function of sending alerts to parents based on detected anomalies and the user's emotional state. This means allows parents to respond quickly.

[0332] One embodiment of this invention is to provide a system that comprehensively monitors the online activities of minors, detects abnormalities considering their emotional state, and promptly notifies their guardians.

[0333] The server utilizes data collection tools for monitoring online behavior and processing tools that integrate artificial intelligence and emotion engines. Specifically, the server can analyze the collected data using machine learning frameworks such as TensorFlow and PyTorch. In this process, it identifies deviations from normal behavior patterns and changes in emotions related to the user's psychological stress and anxiety. The server incorporates database management systems such as MySQL and MongoDB, enabling secure data storage and rapid access.

[0334] The device collects the user's online activity data and emotional state data. Specifically, the device collects web browser history and application usage logs, and uses the camera and microphone to acquire facial expressions and voice data. This information is immediately transferred to the server.

[0335] Users (parents) can manage the safety of minors' online activities through alerts sent from the server. These alerts include detection results of abnormal behavior and contextual information about emotional state, allowing parents to take appropriate action. For example, if a child shows signs of anxiety on a particular website, parents can encourage them to take time to relax.

[0336] A concrete example of a prompt question might be, "How do you design an AI model that analyzes user behavior patterns and detects anomalies based on emotional changes?" This prompt is useful as a reference for deepening one's understanding of system algorithm design.

[0337] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0338] Step 1:

[0339] The device collects the user's online activity data and emotional state data. Specifically, the device retrieves browser history and records URLs, and collects application usage logs. It also captures the user's facial expressions and voice data using the camera and microphone. Inputs include website visit history, app usage time, facial image data, and voice data. Outputs are these data converted into a format that can be sent to the server.

[0340] Step 2:

[0341] The terminal sends the collected data to the server. Specifically, it bundles the data into packets and securely transfers them to the server using an encryption protocol. The input is the data generated in step 1, and the output is in a format that the server receives and can use in the next analysis step.

[0342] Step 3:

[0343] The server saves the received data to the database. Specifically, it performs data validation to confirm data integrity before saving it to the database. The input is data sent from the terminal, and the output is data in a well-organized database.

[0344] Step 4:

[0345] The server analyzes data using AI algorithms and an emotion engine. Specifically, it utilizes a generative AI model to detect anomalies that deviate from normal behavioral patterns and changes in emotion. The input is user behavior and emotion data stored in a database, and the output is the detection results of specific patterns of anomalies and the results of emotion state analysis.

[0346] Step 5:

[0347] The server generates alerts based on the analysis results and notifies the user. Specifically, when psychological stress or anxiety is detected, an alert of appropriate importance is sent to the parent via email or push notification. The input is the analysis results from step 4, and the output generates content including notification information and countermeasures.

[0348] Step 6:

[0349] Based on the alerts received, users take action regarding their children's online activities. Specific actions include engaging in conversations with children and restricting the use of digital devices, thereby improving the online safety of minors. Input is alert information from the server, and output is the actions taken and their results.

[0350] (Application Example 2)

[0351] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0352] To ensure the safety of minors' online activities, it is necessary not only to monitor online history but also to accurately understand users' emotional states and detect potential dangers associated with emotional changes early on. However, existing systems are insufficient in detecting anomalies that take emotional states into account, which poses a challenge as it may lead to missing cases that require immediate attention.

[0353] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0354] In this invention, the server includes data acquisition means for monitoring online activity, AI analysis means for analyzing the user's facial expression information and detecting anomalies accompanied by emotional changes, and communication means for notifying parents of detailed additional information based on emotional changes. This makes it possible to comprehensively monitor the user's online activity and emotional state, detect potential dangers early, and notify parents.

[0355] "Online activities" is a general term for a series of actions and operations that users perform using the internet.

[0356] "Data acquisition means" refers to a device or software used to collect information related to a user's online activities.

[0357] "Facial expression information" refers to visual data that indicates emotions and psychological states obtained through the movements of a user's face.

[0358] "AI analysis means" refers to a program or system that processes collected data and uses artificial intelligence technology to identify patterns and anomalies.

[0359] "Emotional change" refers to the phenomenon where a user's emotional state changes over time.

[0360] "Communication means" refers to a system that includes protocols and interfaces for transmitting information to other devices or users.

[0361] "Guardian" refers to a person who has the responsibility to protect the user and to oversee their safety.

[0362] "Additional information" refers to information that provides supplementary data or context related to the detected anomaly.

[0363] This invention is a system that detects abnormalities in the online activities and emotional state of minors and notifies parents of necessary information. The system consists of three main components: a server, a terminal, and a user. The functions and processing of each component are described in detail below.

[0364] First, the device is equipped with a camera sensor and microphone to collect online activity and user facial expression information. The software embedded in the device uses OpenCV for facial recognition, monitors the user's emotional state in real time, and an AI model using TensorFlow analyzes changes in emotion. This data is acquired along with the user's access history and usage time information and sent to the server.

[0365] Next, the server processes the collected data using AI analysis tools. The AI ​​analysis tools detect anomalies accompanied by emotional changes, and if a user shows stress or anxiety associated with specific risky behaviors, that information is given priority for evaluation. For example, if a user has been accessing inappropriate content for an extended period and their facial expression indicates anxiety, the emotion engine will immediately detect that event as an anomaly.

[0366] Finally, the server sends a detailed notification to the parent / guardian via communication means based on the detected anomaly. This notification includes additional information about the emotional changes and their context, and parents / guardians can receive it in real time on their smartphones or computers. This improves the online safety of minors while also enabling appropriate support.

[0367] For example, if a user shows signs of stress while visiting a particular site, a notification will be sent to the parent stating, "Your child may be experiencing anxiety while viewing certain content." An example of a prompt using a generative AI model might be, "Please explain how this system detects emotional changes in minors' online activities and communicates that information to parents."

[0368] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0369] Step 1:

[0370] The device captures the user's face using its camera sensor and acquires facial expression image data. This acquired image is used as input for facial region recognition using OpenCV. As a result of processing with OpenCV, facial feature point data is output. This data is passed to the Emotion AI model.

[0371] Step 2:

[0372] The device collects online activity data, including access history and usage time. This data is combined with facial feature point data to prepare it for AI analysis. This process connects the user's behavior and emotions, preparing the data for input to the server. This data is then transmitted to the server.

[0373] Step 3:

[0374] The server processes data received from the terminal using AI analysis tools. The input data includes online activity and facial feature point data, and an emotional state is evaluated using a TensorFlow model. This process outputs results for detecting abnormal behavior accompanied by changes in emotional state.

[0375] Step 4:

[0376] The server analyzes the anomaly detection results based on emotional states and generates information to notify parents with a corresponding priority. For example, if a user exhibits certain risky behavior and displays an anxious expression, the notification priority will be set higher. This information is then transmitted as a notification message.

[0377] Step 5:

[0378] The server sends the generated notification message to the parent via a communication method. The notification includes the results of a situational sentiment analysis and additional information, which the parent receives and reviews. This step ensures the user's safety and allows for necessary actions to be taken in real time.

[0379] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0380] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0381] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0382] [Third Embodiment]

[0383] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0384] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0385] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0386] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0387] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0388] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0389] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0390] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0391] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0392] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0393] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0394] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0395] This invention provides a system for monitoring the online activities of minors, detecting abnormal behavior, and issuing warnings to parents as needed. This system consists of three main components: a server, a terminal, and a user.

[0396] The server is responsible for receiving and analyzing online activity data and detecting anomalies. First, the server receives data such as access history and usage time sent from the device. Next, it analyzes this data using an AI algorithm to check for any anomalies that deviate from normal behavior patterns. Detected anomalies include unusually long usage times at night and access to dangerous websites. Upon detecting an anomaly, the server immediately generates an alert message and sends it to the parent or guardian through appropriate communication channels.

[0397] The device monitors the user's online activity in real time and sends necessary data to the server. Specifically, the device records website visit history, social media usage, and online game play time. This data is collected periodically and encrypted to ensure security before being sent to the server. The device also has a function to directly display minor warnings to the user, for example, a warning message will be displayed when usage time becomes excessive.

[0398] Users (parents) can review alerts sent from the server and manage their children's online activities as needed. Through the alert messages, users can identify specific problems and take measures such as blocking certain websites or limiting usage time. Furthermore, using the situation-specific recommendations provided by the system allows for more effective responses.

[0399] This embodiment can reduce online risks for minors in modern society and provide a safer internet environment.

[0400] The following describes the processing flow.

[0401] Step 1:

[0402] The device monitors the user's online activity in real time and collects access history, usage time, and application usage logs. This data is recorded at regular intervals.

[0403] Step 2:

[0404] The terminal encrypts the collected data and prepares it for transmission to the server. Data transmission is performed in batches to minimize network load.

[0405] Step 3:

[0406] The server receives data sent from the terminal. The received data is stored in a database for analysis.

[0407] Step 4:

[0408] The server uses AI algorithms to analyze incoming data and detect abnormal behavior and suspicious patterns. This analysis can, for example, detect access to inappropriate websites or unusually long usage periods at night.

[0409] Step 5:

[0410] If an anomaly is detected based on the analysis results, the server generates an alert message. The generated alert includes details of the anomaly along with recommended countermeasures.

[0411] Step 6:

[0412] The server sends the generated alerts to the parents. This is done via email or push notifications through a dedicated app.

[0413] Step 7:

[0414] The user (parent / guardian) receives an alert sent from the server and reviews its contents. They understand the details of the anomaly and consider countermeasures.

[0415] Step 8:

[0416] Users take specific actions based on the alerts. For example, they may restrict access to certain websites or review usage rules through conversations with their children.

[0417] (Example 1)

[0418] Next, we will describe Example 1. 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."

[0419] Today's minors have access to a wide range of information through the internet, but this may include harmful content or health risks from prolonged use. Parents are required to properly manage their children's online activities and respond quickly when necessary. However, traditional methods present challenges in terms of real-time monitoring and rapid detection of anomalies, which are time-consuming and laborious.

[0420] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0421] In this invention, the server includes information acquisition means for monitoring user behavior, AI analysis means for analyzing the information and identifying anomalies, notification means for sending an alert to a monitor based on the anomalies, security means for encrypting the information before transmission, and warning display means for displaying warnings in real time based on the collected information. This makes it possible to efficiently monitor the online activities of minors and to quickly detect and notify of abnormal behavior.

[0422] "Information acquisition methods" refer to technologies that monitor users' online activities in real time and collect data such as network visit history and usage period.

[0423] "AI analysis methods" refer to technologies that use artificial intelligence to identify anomalies that deviate from normal behavioral patterns, based on collected information.

[0424] A "notification method" is a technology for sending an alert to a monitor based on detected anomalies.

[0425] "Security measures" refer to technologies that ensure the safety of data by encrypting collected information before it is transmitted.

[0426] A "warning display method" is a technology that displays warnings to users in real time based on collected data.

[0427] This invention is a system for securely managing minors' internet use and quickly detecting abnormal behavior. This system consists of three main components: a server, a terminal, and a user.

[0428] The server is a powerful computing system that can operate via cloud services. It uses data processing software such as Hadoop and Apache Spark to analyze large amounts of online activity data in real time. For AI analysis, it utilizes machine learning frameworks like TensorFlow and PyTorch to detect deviations from normal behavioral patterns. Based on the analysis results, if an anomaly is detected, it immediately sends the information to the guardian via a notification system. In this process, the server uses the HTTPS protocol to ensure the secure transmission of data.

[0429] The device is a smart device typically used by minors, implemented as an application on Android or iOS operating systems. The device monitors the user's network visit history and application usage time, collecting necessary data using information acquisition methods. The collected data is encrypted within the device using the AES encryption algorithm and prepared for transmission to the server. Furthermore, the device utilizes a UI framework to provide warning display mechanisms, allowing for the display of visual warnings to the user.

[0430] Users (parents / guardians) receive alerts sent by the server on their smartphones or computers. Based on these alerts, they can use a dedicated application to manage settings such as blacklisting websites accessed by minors and limiting usage time. This application provides a user interface that allows for easy policy changes, offering an intuitive and convenient user experience.

[0431] For example, if a minor is playing online games at night beyond the normal usage time, the device detects this usage, encrypts the data, and sends it to the server. The server analyzes the data, determines that the behavior is abnormal, and sends a notification to the parent stating, "It has been detected that your child is playing games for an extended period at night." Based on this information, the parent can easily set limits on usage time.

[0432] An example of a prompt would be: "Please describe a specific function of a system that monitors the online activity of minors, including a concrete scenario. For example, please explain the process if access to an inappropriate website is detected during a certain period of time."

[0433] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0434] Step 1:

[0435] The device monitors the user's online activity in real time. Specifically, the data recorded on the device includes network visit history and application usage time. The device periodically retrieves this data and encrypts it using the AES encryption algorithm within the device. This protects data privacy and prepares it for secure transmission. The input is the user's online activity data, and the output is encrypted data.

[0436] Step 2:

[0437] The terminal sends encrypted data to the server. This data is transmitted using a secure communication protocol, such as HTTPS. The server decrypts the received data and prepares it for analysis. The input is the encrypted data sent from the terminal, and the output is the user's activity data decrypted by the server.

[0438] Step 3:

[0439] The server processes the decrypted data using AI analysis tools. Specifically, AI algorithms are used to analyze the data and detect deviations from normal behavioral patterns. The server identifies abnormal behavior and determines whether it is excessive nighttime activity or access to dangerous websites. The input is decrypted activity data, and the output is the anomaly detection result.

[0440] Step 4:

[0441] If the server detects an anomaly, it immediately generates and sends an alert to the parent / guardian using a notification system. This alert includes specific details about the anomaly and recommended actions. For example, it might include information such as, "Access to an inappropriate website was detected at 11 PM," along with recommended actions. The input is the anomaly detection result, and the output is the generated alert message.

[0442] Step 5:

[0443] Users (parents) review received alerts and manage their children's online activities as needed. Parents can take action using a dedicated application, such as blocking specific websites or limiting usage time. Input is the alert message, and output is the management policy set by the parent.

[0444] This process allows the system to effectively monitor the online activities of minors and provide a safe environment for use.

[0445] (Application Example 1)

[0446] Next, we will explain Application Example 1. In the following explanation, 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."

[0447] In recent years, internet use among minors has increased, along with a corresponding rise in online risks. In particular, accessing dangerous websites unknowingly and disrupting lifestyles due to prolonged internet use late at night are becoming significant problems. Currently, there is a lack of effective means for parents to monitor their children's online activities and intervene appropriately. Therefore, there is a need for effective monitoring of minors' online activities and the rapid detection and notification of abnormal behavior.

[0448] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0449] In this invention, the server includes data collection means for monitoring online activity, AI processing means for analyzing the data and detecting anomalies, notification means for sending alerts to parents based on the anomalies, and communication means for pushing the alerts to smart devices. This enables the rapid detection of abnormal online activity by minors, allowing parents to take immediate action.

[0450] "Online activities" refer to activities conducted via the internet, such as web browsing, social media use, and playing online games.

[0451] "Data collection means" refers to a function for acquiring and recording data related to a user's online behavior.

[0452] "AI processing means" refers to artificial intelligence algorithms used to analyze collected data and detect anomalies that deviate from normal behavioral patterns.

[0453] "Notification means" refers to communication technology used to convey specific information to parents or guardians based on detected anomalies.

[0454] A "smart device" refers to a mobile or wearable device that has an internet connection and can operate various applications.

[0455] "Push notifications" are a technology that sends information from a server to a device in order to display important information on the screen of the smart device in real time.

[0456] This invention aims to build a system that monitors the online activities of minors, detects anomalies, and notifies parents. It consists of three main components: a server, a terminal, and a user.

[0457] server

[0458] The server receives online activity data and analyzes it using AI algorithms. This analysis utilizes cloud computing resources and employs software such as Python and TensorFlow. The collected data is stored in a comprehensive database and used for anomaly detection. The server is responsible for immediately sending push notifications to parents' smart devices when abnormal behavior is detected.

[0459] terminal

[0460] The device is responsible for monitoring the user's online activity in real time. Specifically, it records data such as website visit history, social media usage, and online game playtime. The recorded data is encrypted and periodically sent to the server. The device also has a warning display function to directly notify the user of minor anomalies in online activity.

[0461] User

[0462] Parents can review alerts received from the server and appropriately manage their children's online activities as needed. Users can take specific measures, such as restricting certain websites or setting usage time limits. Furthermore, the system provides situation-specific recommendations, allowing users to intervene effectively.

[0463] Use as a concrete example

[0464] If the system detects that a minor is frequently accessing dangerous websites they don't normally visit on weekday nights, it identifies the anomaly and quickly generates a notification. An alert stating "Unusual behavior has occurred" appears on the parent's smartphone, providing information to take immediate action.

[0465] Example of a prompt

[0466] "Please tell me how to design an AI model to detect risks in online activities of minors."

[0467] "What are the key elements in building a machine learning algorithm to identify abnormal internet usage behavior?"

[0468] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0469] Step 1:

[0470] The device monitors the user's online activity in real time. Specifically, the device collects data on website visits, social media usage time, and online game play time. This data is used as input, and is encrypted internally to protect privacy before being output.

[0471] Step 2:

[0472] The device periodically sends encrypted online activity data to the server. Specifically, it sends data to the server using a secure communication protocol, which then serves as input for the next processing step.

[0473] Step 3:

[0474] The server analyzes the encrypted data it receives. Upon receiving data, it decrypts and preprocesses it, then formats it into a format suitable for the AI ​​algorithm. Using a generative AI model, it detects anomalies that deviate from normal behavioral patterns, and outputs information about any anomalies it finds.

[0475] Step 4:

[0476] When the server detects abnormal behavior, it generates an alert based on that information. The generated alert includes the type and details of the abnormality, which then serves as input for the next step.

[0477] Step 5:

[0478] The server uses alert information to send push notifications to the parent's smart device. Specifically, it uses a communication method to send information to the parent's device in real time, which is the output. The smart device that receives the notification then displays the alert message.

[0479] Step 6:

[0480] Parents, as users, can review received alerts and take specific actions. Based on detailed information about the anomaly, they can perform management operations such as restricting access to specific websites or limiting usage time. This allows them to obtain the results of the user's actions.

[0481] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0482] This invention relates to a system that monitors the online activities of minors and combines them with an emotion engine that recognizes the user's emotions to enable more advanced anomaly detection and notification to parents. The system consists of three main components: a server, a terminal, and a user.

[0483] The server's role is to analyze collected online activity data and user emotional state data. First, it receives data sent from the device and stores it in a database. Next, it uses AI algorithms and an emotion engine to detect anomalies and emotional changes that deviate from normal behavioral patterns. In particular, it can detect anomalies based on the user's emotional state, and if the anomaly is related to the user's psychological stress or anxiety, it can prioritize alerts.

[0484] The device tracks online activity and measures emotional states. Specifically, it collects data such as access history, usage time, and application usage logs, as well as emotional information by analyzing the user's facial expressions and tone of voice. This allows for real-time monitoring of changes in the user's emotions and the transmission of this data to the server.

[0485] Users (parents) can receive alerts and additional emotional information sent from the server. For example, if the server detects emotional patterns indicating anxiety or stress, parents can receive support information to help them take specific action. In addition to the usual anomaly detection results, alerts include contextual information based on emotional analysis, allowing parents to take appropriate measures considering their child's psychological state.

[0486] This embodiment allows parents not only to address typical online risks but also to detect potential problems arising from emotional shifts and provide more comprehensive support. In this way, the introduction of an emotional engine can further enhance the safety of minors.

[0487] The following describes the processing flow.

[0488] Step 1:

[0489] The device monitors the user's online activity in real time, collecting access history, usage time, and application logs. It also analyzes the user's facial expressions and voice through the camera and microphone to acquire emotional data.

[0490] Step 2:

[0491] The device encrypts the acquired online activity data and sentiment data and securely transmits it to the server. Transmission is performed periodically in batch mode, taking network load into consideration.

[0492] Step 3:

[0493] The server receives data from the terminal and stores it in a database. This includes information about online activities and data on emotional states obtained through facial expression and voice analysis.

[0494] Step 4:

[0495] The server analyzes the data using AI algorithms and an emotion engine. It evaluates whether any unusual patterns are detected in online activities and whether stress or anxiety can be inferred from the emotional data.

[0496] Step 5:

[0497] The server generates an alert message when it detects abnormal patterns or signs of emotional stress. The alert includes details about the detected anomaly and additional information based on the emotional state.

[0498] Step 6:

[0499] The server sends the generated alerts to parents via email or app notifications. In addition to the usual anomaly detection notifications, contextual information about the user's emotions is provided.

[0500] Step 7:

[0501] The user (parent / guardian) receives alerts sent from the server and reviews their content. They then consider appropriate countermeasures based on the analysis of the child's emotional state, ensuring the child's safety and mental well-being.

[0502] Step 8:

[0503] Users will implement specific countermeasures. For example, if abnormal behavior caused by stress is detected, they may create a space for dialogue with the child, provide a relaxing environment, or re-establish rules for online activities.

[0504] (Example 2)

[0505] Next, we will describe Example 2. 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."

[0506] To enhance the safety of minors' online activities, it is necessary to not only monitor access history and usage time, but also to detect anomalies that take into account changes in emotional state. However, current systems are insufficient for comprehensive anomaly detection based on emotional changes and related alert notifications to parents, thus requiring more advanced monitoring and response.

[0507] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0508] In this invention, the server includes data collection means for monitoring online behavior, artificial intelligence processing means for analyzing the data and detecting anomalies, and notification means for sending alerts to parents based on the anomalies and the user's emotional state. This enables comprehensive anomaly detection and rapid notification to parents in the online activities of minors, taking into account not only abnormal behavioral patterns but also emotional changes.

[0509] "Data collection methods for monitoring online behavior" refer to devices and software that have the function of recording a user's website visit history, digital device usage time, and changes in facial expressions and voice. This collection allows for a detailed understanding of the user's activity.

[0510] "Artificial intelligence processing means" refers to algorithms and computational techniques for analyzing collected data to identify anomalies that deviate from normal behavioral patterns, as well as emotional changes related to the user's psychological stress and anxiety. This improves the accuracy of anomaly detection.

[0511] "Notification means" refers to devices or software that have the function of sending alerts to parents based on detected anomalies and the user's emotional state. This means allows parents to respond quickly.

[0512] One embodiment of this invention is to provide a system that comprehensively monitors the online activities of minors, detects abnormalities considering their emotional state, and promptly notifies their guardians.

[0513] The server utilizes data collection tools for monitoring online behavior and processing tools that integrate artificial intelligence and emotion engines. Specifically, the server can analyze the collected data using machine learning frameworks such as TensorFlow and PyTorch. In this process, it identifies deviations from normal behavior patterns and changes in emotions related to the user's psychological stress and anxiety. The server incorporates database management systems such as MySQL and MongoDB, enabling secure data storage and rapid access.

[0514] The device collects the user's online activity data and emotional state data. Specifically, the device collects web browser history and application usage logs, and uses the camera and microphone to acquire facial expressions and voice data. This information is immediately transferred to the server.

[0515] Users (parents) can manage the safety of minors' online activities through alerts sent from the server. These alerts include detection results of abnormal behavior and contextual information about emotional state, allowing parents to take appropriate action. For example, if a child shows signs of anxiety on a particular website, parents can encourage them to take time to relax.

[0516] A concrete example of a prompt question might be, "How do you design an AI model that analyzes user behavior patterns and detects anomalies based on emotional changes?" This prompt is useful as a reference for deepening one's understanding of system algorithm design.

[0517] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0518] Step 1:

[0519] The device collects the user's online activity data and emotional state data. Specifically, the device retrieves browser history and records URLs, and collects application usage logs. It also captures the user's facial expressions and voice data using the camera and microphone. Inputs include website visit history, app usage time, facial image data, and voice data. Outputs are these data converted into a format that can be sent to the server.

[0520] Step 2:

[0521] The terminal sends the collected data to the server. Specifically, it bundles the data into packets and securely transfers them to the server using an encryption protocol. The input is the data generated in step 1, and the output is in a format that the server receives and can use in the next analysis step.

[0522] Step 3:

[0523] The server saves the received data to the database. Specifically, it performs data validation to confirm data integrity before saving it to the database. The input is data sent from the terminal, and the output is data in a well-organized database.

[0524] Step 4:

[0525] The server analyzes data using AI algorithms and an emotion engine. Specifically, it utilizes a generative AI model to detect anomalies that deviate from normal behavioral patterns and changes in emotion. The input is user behavior and emotion data stored in a database, and the output is the detection results of specific patterns of anomalies and the results of emotion state analysis.

[0526] Step 5:

[0527] The server generates alerts based on the analysis results and notifies the user. Specifically, when psychological stress or anxiety is detected, an alert of appropriate importance is sent to the parent via email or push notification. The input is the analysis results from step 4, and the output generates content including notification information and countermeasures.

[0528] Step 6:

[0529] Based on the alerts received, users take action regarding their children's online activities. Specific actions include engaging in conversations with children and restricting the use of digital devices, thereby improving the online safety of minors. Input is alert information from the server, and output is the actions taken and their results.

[0530] (Application Example 2)

[0531] Next, we will explain Application Example 2. In the following explanation, 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."

[0532] To ensure the safety of minors' online activities, it is necessary not only to monitor online history but also to accurately understand users' emotional states and detect potential dangers associated with emotional changes early on. However, existing systems are insufficient in detecting anomalies that take emotional states into account, which poses a challenge as it may lead to missing cases that require immediate attention.

[0533] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0534] In this invention, the server includes data acquisition means for monitoring online activity, AI analysis means for analyzing the user's facial expression information and detecting anomalies accompanied by emotional changes, and communication means for notifying parents of detailed additional information based on emotional changes. This makes it possible to comprehensively monitor the user's online activity and emotional state, detect potential dangers early, and notify parents.

[0535] "Online activities" is a general term for a series of actions and operations that users perform using the internet.

[0536] "Data acquisition means" refers to a device or software used to collect information related to a user's online activities.

[0537] "Facial expression information" refers to visual data that indicates emotions and psychological states obtained through the movements of a user's face.

[0538] "AI analysis means" refers to a program or system that processes collected data and uses artificial intelligence technology to identify patterns and anomalies.

[0539] "Emotional change" refers to the phenomenon where a user's emotional state changes over time.

[0540] "Communication means" refers to a system that includes protocols and interfaces for transmitting information to other devices or users.

[0541] "Guardian" refers to a person who has the responsibility to protect the user and to oversee their safety.

[0542] "Additional information" refers to information that provides supplementary data or context related to the detected anomaly.

[0543] This invention is a system that detects abnormalities in the online activities and emotional state of minors and notifies parents of necessary information. The system consists of three main components: a server, a terminal, and a user. The functions and processing of each component are described in detail below.

[0544] First, the device is equipped with a camera sensor and microphone to collect online activity and user facial expression information. The software embedded in the device uses OpenCV for facial recognition, monitors the user's emotional state in real time, and an AI model using TensorFlow analyzes changes in emotion. This data is acquired along with the user's access history and usage time information and sent to the server.

[0545] Next, the server processes the collected data using AI analysis tools. The AI ​​analysis tools detect anomalies accompanied by emotional changes, and if a user shows stress or anxiety associated with specific risky behaviors, that information is given priority for evaluation. For example, if a user has been accessing inappropriate content for an extended period and their facial expression indicates anxiety, the emotion engine will immediately detect that event as an anomaly.

[0546] Finally, the server sends a detailed notification to the parent / guardian via communication means based on the detected anomaly. This notification includes additional information about the emotional changes and their context, and parents / guardians can receive it in real time on their smartphones or computers. This improves the online safety of minors while also enabling appropriate support.

[0547] For example, if a user shows signs of stress while visiting a particular site, a notification will be sent to the parent stating, "Your child may be experiencing anxiety while viewing certain content." An example of a prompt using a generative AI model might be, "Please explain how this system detects emotional changes in minors' online activities and communicates that information to parents."

[0548] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0549] Step 1:

[0550] The device captures the user's face using its camera sensor and acquires facial expression image data. This acquired image is used as input for facial region recognition using OpenCV. As a result of processing with OpenCV, facial feature point data is output. This data is passed to the Emotion AI model.

[0551] Step 2:

[0552] The device collects online activity data, including access history and usage time. This data is combined with facial feature point data to prepare it for AI analysis. This process connects the user's behavior and emotions, preparing the data for input to the server. This data is then transmitted to the server.

[0553] Step 3:

[0554] The server processes data received from the terminal using AI analysis tools. The input data includes online activity and facial feature point data, and an emotional state is evaluated using a TensorFlow model. This process outputs results for detecting abnormal behavior accompanied by changes in emotional state.

[0555] Step 4:

[0556] The server analyzes the anomaly detection results based on emotional states and generates information to notify parents with a corresponding priority. For example, if a user exhibits certain risky behavior and displays an anxious expression, the notification priority will be set higher. This information is then transmitted as a notification message.

[0557] Step 5:

[0558] The server sends the generated notification message to the parent via a communication method. The notification includes the results of a situational sentiment analysis and additional information, which the parent receives and reviews. This step ensures the user's safety and allows for necessary actions to be taken in real time.

[0559] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0560] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0561] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0562] [Fourth Embodiment]

[0563] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0564] As shown in Figure 7, the 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.

[0565] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0566] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0567] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0568] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0569] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0570] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0571] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0572] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0573] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0574] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0575] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0576] This invention provides a system for monitoring the online activities of minors, detecting abnormal behavior, and issuing warnings to parents as needed. This system consists of three main components: a server, a terminal, and a user.

[0577] The server is responsible for receiving and analyzing online activity data and detecting anomalies. First, the server receives data such as access history and usage time sent from the device. Next, it analyzes this data using an AI algorithm to check for any anomalies that deviate from normal behavior patterns. Detected anomalies include unusually long usage times at night and access to dangerous websites. Upon detecting an anomaly, the server immediately generates an alert message and sends it to the parent or guardian through appropriate communication channels.

[0578] The device monitors the user's online activity in real time and sends necessary data to the server. Specifically, the device records website visit history, social media usage, and online game play time. This data is collected periodically and encrypted to ensure security before being sent to the server. The device also has a function to directly display minor warnings to the user, for example, a warning message will be displayed when usage time becomes excessive.

[0579] Users (parents) can review alerts sent from the server and manage their children's online activities as needed. Through the alert messages, users can identify specific problems and take measures such as blocking certain websites or limiting usage time. Furthermore, using the situation-specific recommendations provided by the system allows for more effective responses.

[0580] This embodiment can reduce online risks for minors in modern society and provide a safer internet environment.

[0581] The following describes the processing flow.

[0582] Step 1:

[0583] The device monitors the user's online activity in real time and collects access history, usage time, and application usage logs. This data is recorded at regular intervals.

[0584] Step 2:

[0585] The terminal encrypts the collected data and prepares it for transmission to the server. Data transmission is performed in batches to minimize network load.

[0586] Step 3:

[0587] The server receives data sent from the terminal. The received data is stored in a database for analysis.

[0588] Step 4:

[0589] The server uses AI algorithms to analyze incoming data and detect abnormal behavior and suspicious patterns. This analysis can, for example, detect access to inappropriate websites or unusually long usage periods at night.

[0590] Step 5:

[0591] If an anomaly is detected based on the analysis results, the server generates an alert message. The generated alert includes details of the anomaly along with recommended countermeasures.

[0592] Step 6:

[0593] The server sends the generated alerts to the parents. This is done via email or push notifications through a dedicated app.

[0594] Step 7:

[0595] The user (parent / guardian) receives an alert sent from the server and reviews its contents. They understand the details of the anomaly and consider countermeasures.

[0596] Step 8:

[0597] Users take specific actions based on the alerts. For example, they may restrict access to certain websites or review usage rules through conversations with their children.

[0598] (Example 1)

[0599] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0600] Today's minors have access to a wide range of information through the internet, but this may include harmful content or health risks from prolonged use. Parents are required to properly manage their children's online activities and respond quickly when necessary. However, traditional methods present challenges in terms of real-time monitoring and rapid detection of anomalies, which are time-consuming and laborious.

[0601] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0602] In this invention, the server includes information acquisition means for monitoring user behavior, AI analysis means for analyzing the information and identifying anomalies, notification means for sending an alert to a monitor based on the anomalies, security means for encrypting the information before transmission, and warning display means for displaying warnings in real time based on the collected information. This makes it possible to efficiently monitor the online activities of minors and to quickly detect and notify of abnormal behavior.

[0603] "Information acquisition methods" refer to technologies that monitor users' online activities in real time and collect data such as network visit history and usage period.

[0604] "AI analysis methods" refer to technologies that use artificial intelligence to identify anomalies that deviate from normal behavioral patterns, based on collected information.

[0605] A "notification method" is a technology for sending an alert to a monitor based on detected anomalies.

[0606] "Security measures" refer to technologies that ensure the safety of data by encrypting collected information before it is transmitted.

[0607] A "warning display method" is a technology that displays warnings to users in real time based on collected data.

[0608] This invention is a system for securely managing minors' internet use and quickly detecting abnormal behavior. This system consists of three main components: a server, a terminal, and a user.

[0609] The server is a powerful computing system that can operate via cloud services. It uses data processing software such as Hadoop and Apache Spark to analyze large amounts of online activity data in real time. For AI analysis, it utilizes machine learning frameworks like TensorFlow and PyTorch to detect deviations from normal behavioral patterns. Based on the analysis results, if an anomaly is detected, it immediately sends the information to the guardian via a notification system. In this process, the server uses the HTTPS protocol to ensure the secure transmission of data.

[0610] The device is a smart device typically used by minors, implemented as an application on Android or iOS operating systems. The device monitors the user's network visit history and application usage time, collecting necessary data using information acquisition methods. The collected data is encrypted within the device using the AES encryption algorithm and prepared for transmission to the server. Furthermore, the device utilizes a UI framework to provide warning display mechanisms, allowing for the display of visual warnings to the user.

[0611] Users (parents / guardians) receive alerts sent by the server on their smartphones or computers. Based on these alerts, they can use a dedicated application to manage settings such as blacklisting websites accessed by minors and limiting usage time. This application provides a user interface that allows for easy policy changes, offering an intuitive and convenient user experience.

[0612] For example, if a minor is playing online games at night beyond the normal usage time, the device detects this usage, encrypts the data, and sends it to the server. The server analyzes the data, determines that the behavior is abnormal, and sends a notification to the parent stating, "It has been detected that your child is playing games for an extended period at night." Based on this information, the parent can easily set limits on usage time.

[0613] An example of a prompt would be: "Please describe a specific function of a system that monitors the online activity of minors, including a concrete scenario. For example, please explain the process if access to an inappropriate website is detected during a certain period of time."

[0614] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0615] Step 1:

[0616] The device monitors the user's online activity in real time. Specifically, the data recorded on the device includes network visit history and application usage time. The device periodically retrieves this data and encrypts it using the AES encryption algorithm within the device. This protects data privacy and prepares it for secure transmission. The input is the user's online activity data, and the output is encrypted data.

[0617] Step 2:

[0618] The terminal sends encrypted data to the server. This data is transmitted using a secure communication protocol, such as HTTPS. The server decrypts the received data and prepares it for analysis. The input is the encrypted data sent from the terminal, and the output is the user's activity data decrypted by the server.

[0619] Step 3:

[0620] The server processes the decrypted data using AI analysis tools. Specifically, AI algorithms are used to analyze the data and detect deviations from normal behavioral patterns. The server identifies abnormal behavior and determines whether it is excessive nighttime activity or access to dangerous websites. The input is decrypted activity data, and the output is the anomaly detection result.

[0621] Step 4:

[0622] If the server detects an anomaly, it immediately generates and sends an alert to the parent / guardian using a notification system. This alert includes specific details about the anomaly and recommended actions. For example, it might include information such as, "Access to an inappropriate website was detected at 11 PM," along with recommended actions. The input is the anomaly detection result, and the output is the generated alert message.

[0623] Step 5:

[0624] Users (parents) review received alerts and manage their children's online activities as needed. Parents can take action using a dedicated application, such as blocking specific websites or limiting usage time. Input is the alert message, and output is the management policy set by the parent.

[0625] This process allows the system to effectively monitor the online activities of minors and provide a safe environment for use.

[0626] (Application Example 1)

[0627] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0628] In recent years, internet use among minors has increased, along with a corresponding rise in online risks. In particular, accessing dangerous websites unknowingly and disrupting lifestyles due to prolonged internet use late at night are becoming significant problems. Currently, there is a lack of effective means for parents to monitor their children's online activities and intervene appropriately. Therefore, there is a need for effective monitoring of minors' online activities and the rapid detection and notification of abnormal behavior.

[0629] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0630] In this invention, the server includes data collection means for monitoring online activity, AI processing means for analyzing the data and detecting anomalies, notification means for sending alerts to parents based on the anomalies, and communication means for pushing the alerts to smart devices. This enables the rapid detection of abnormal online activity by minors, allowing parents to take immediate action.

[0631] "Online activities" refer to activities conducted via the internet, such as web browsing, social media use, and playing online games.

[0632] "Data collection means" refers to a function for acquiring and recording data related to a user's online behavior.

[0633] "AI processing means" refers to artificial intelligence algorithms used to analyze collected data and detect anomalies that deviate from normal behavioral patterns.

[0634] "Notification means" refers to communication technology used to convey specific information to parents or guardians based on detected anomalies.

[0635] A "smart device" refers to a mobile or wearable device that has an internet connection and can operate various applications.

[0636] "Push notifications" are a technology that sends information from a server to a device in order to display important information on the screen of the smart device in real time.

[0637] This invention aims to build a system that monitors the online activities of minors, detects anomalies, and notifies parents. It consists of three main components: a server, a terminal, and a user.

[0638] server

[0639] The server receives online activity data and analyzes it using AI algorithms. This analysis utilizes cloud computing resources and employs software such as Python and TensorFlow. The collected data is stored in a comprehensive database and used for anomaly detection. The server is responsible for immediately sending push notifications to parents' smart devices when abnormal behavior is detected.

[0640] terminal

[0641] The device is responsible for monitoring the user's online activity in real time. Specifically, it records data such as website visit history, social media usage, and online game playtime. The recorded data is encrypted and periodically sent to the server. The device also has a warning display function to directly notify the user of minor anomalies in online activity.

[0642] User

[0643] Parents can review alerts received from the server and appropriately manage their children's online activities as needed. Users can take specific measures, such as restricting certain websites or setting usage time limits. Furthermore, the system provides situation-specific recommendations, allowing users to intervene effectively.

[0644] Use as a concrete example

[0645] If the system detects that a minor is frequently accessing dangerous websites they don't normally visit on weekday nights, it identifies the anomaly and quickly generates a notification. An alert stating "Unusual behavior has occurred" appears on the parent's smartphone, providing information to take immediate action.

[0646] Example of a prompt

[0647] "Please tell me how to design an AI model to detect risks in online activities of minors."

[0648] "What are the key elements in building a machine learning algorithm to identify abnormal internet usage behavior?"

[0649] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0650] Step 1:

[0651] The device monitors the user's online activity in real time. Specifically, the device collects data on website visits, social media usage time, and online game play time. This data is used as input, and is encrypted internally to protect privacy before being output.

[0652] Step 2:

[0653] The device periodically sends encrypted online activity data to the server. Specifically, it sends data to the server using a secure communication protocol, which then serves as input for the next processing step.

[0654] Step 3:

[0655] The server analyzes the encrypted data it receives. Upon receiving data, it decrypts and preprocesses it, then formats it into a format suitable for the AI ​​algorithm. Using a generative AI model, it detects anomalies that deviate from normal behavioral patterns, and outputs information about any anomalies it finds.

[0656] Step 4:

[0657] When the server detects abnormal behavior, it generates an alert based on that information. The generated alert includes the type and details of the abnormality, which then serves as input for the next step.

[0658] Step 5:

[0659] The server uses alert information to send push notifications to the parent's smart device. Specifically, it uses a communication method to send information to the parent's device in real time, which is the output. The smart device that receives the notification then displays the alert message.

[0660] Step 6:

[0661] Parents, as users, can review received alerts and take specific actions. Based on detailed information about the anomaly, they can perform management operations such as restricting access to specific websites or limiting usage time. This allows them to obtain the results of the user's actions.

[0662] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0663] This invention relates to a system that monitors the online activities of minors and combines them with an emotion engine that recognizes the user's emotions to enable more advanced anomaly detection and notification to parents. The system consists of three main components: a server, a terminal, and a user.

[0664] The server's role is to analyze collected online activity data and user emotional state data. First, it receives data sent from the device and stores it in a database. Next, it uses AI algorithms and an emotion engine to detect anomalies and emotional changes that deviate from normal behavioral patterns. In particular, it can detect anomalies based on the user's emotional state, and if the anomaly is related to the user's psychological stress or anxiety, it can prioritize alerts.

[0665] The device tracks online activity and measures emotional states. Specifically, it collects data such as access history, usage time, and application usage logs, as well as emotional information by analyzing the user's facial expressions and tone of voice. This allows for real-time monitoring of changes in the user's emotions and the transmission of this data to the server.

[0666] Users (parents) can receive alerts and additional emotional information sent from the server. For example, if the server detects emotional patterns indicating anxiety or stress, parents can receive support information to help them take specific action. In addition to the usual anomaly detection results, alerts include contextual information based on emotional analysis, allowing parents to take appropriate measures considering their child's psychological state.

[0667] This embodiment allows parents not only to address typical online risks but also to detect potential problems arising from emotional shifts and provide more comprehensive support. In this way, the introduction of an emotional engine can further enhance the safety of minors.

[0668] The following describes the processing flow.

[0669] Step 1:

[0670] The device monitors the user's online activity in real time, collecting access history, usage time, and application logs. It also analyzes the user's facial expressions and voice through the camera and microphone to acquire emotional data.

[0671] Step 2:

[0672] The device encrypts the acquired online activity data and sentiment data and securely transmits it to the server. Transmission is performed periodically in batch mode, taking network load into consideration.

[0673] Step 3:

[0674] The server receives data from the terminal and stores it in a database. This includes information about online activities and data on emotional states obtained through facial expression and voice analysis.

[0675] Step 4:

[0676] The server analyzes the data using AI algorithms and an emotion engine. It evaluates whether any unusual patterns are detected in online activities and whether stress or anxiety can be inferred from the emotional data.

[0677] Step 5:

[0678] The server generates an alert message when it detects abnormal patterns or signs of emotional stress. The alert includes details about the detected anomaly and additional information based on the emotional state.

[0679] Step 6:

[0680] The server sends the generated alerts to parents via email or app notifications. In addition to the usual anomaly detection notifications, contextual information about the user's emotions is provided.

[0681] Step 7:

[0682] The user (parent / guardian) receives alerts sent from the server and reviews their content. They then consider appropriate countermeasures based on the analysis of the child's emotional state, ensuring the child's safety and mental well-being.

[0683] Step 8:

[0684] Users will implement specific countermeasures. For example, if abnormal behavior caused by stress is detected, they may create a space for dialogue with the child, provide a relaxing environment, or re-establish rules for online activities.

[0685] (Example 2)

[0686] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0687] To enhance the safety of minors' online activities, it is necessary to not only monitor access history and usage time, but also to detect anomalies that take into account changes in emotional state. However, current systems are insufficient for comprehensive anomaly detection based on emotional changes and related alert notifications to parents, thus requiring more advanced monitoring and response.

[0688] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0689] In this invention, the server includes data collection means for monitoring online behavior, artificial intelligence processing means for analyzing the data and detecting anomalies, and notification means for sending alerts to parents based on the anomalies and the user's emotional state. This enables comprehensive anomaly detection and rapid notification to parents in the online activities of minors, taking into account not only abnormal behavioral patterns but also emotional changes.

[0690] "Data collection methods for monitoring online behavior" refer to devices and software that have the function of recording a user's website visit history, digital device usage time, and changes in facial expressions and voice. This collection allows for a detailed understanding of the user's activity.

[0691] "Artificial intelligence processing means" refers to algorithms and computational techniques for analyzing collected data to identify anomalies that deviate from normal behavioral patterns, as well as emotional changes related to the user's psychological stress and anxiety. This improves the accuracy of anomaly detection.

[0692] "Notification means" refers to devices or software that have the function of sending alerts to parents based on detected anomalies and the user's emotional state. This means allows parents to respond quickly.

[0693] One embodiment of this invention is to provide a system that comprehensively monitors the online activities of minors, detects abnormalities considering their emotional state, and promptly notifies their guardians.

[0694] The server utilizes data collection tools for monitoring online behavior and processing tools that integrate artificial intelligence and emotion engines. Specifically, the server can analyze the collected data using machine learning frameworks such as TensorFlow and PyTorch. In this process, it identifies deviations from normal behavior patterns and changes in emotions related to the user's psychological stress and anxiety. The server incorporates database management systems such as MySQL and MongoDB, enabling secure data storage and rapid access.

[0695] The device collects the user's online activity data and emotional state data. Specifically, the device collects web browser history and application usage logs, and uses the camera and microphone to acquire facial expressions and voice data. This information is immediately transferred to the server.

[0696] Users (parents) can manage the safety of minors' online activities through alerts sent from the server. These alerts include detection results of abnormal behavior and contextual information about emotional state, allowing parents to take appropriate action. For example, if a child shows signs of anxiety on a particular website, parents can encourage them to take time to relax.

[0697] A concrete example of a prompt question might be, "How do you design an AI model that analyzes user behavior patterns and detects anomalies based on emotional changes?" This prompt is useful as a reference for deepening one's understanding of system algorithm design.

[0698] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0699] Step 1:

[0700] The device collects the user's online activity data and emotional state data. Specifically, the device retrieves browser history and records URLs, and collects application usage logs. It also captures the user's facial expressions and voice data using the camera and microphone. Inputs include website visit history, app usage time, facial image data, and voice data. Outputs are these data converted into a format that can be sent to the server.

[0701] Step 2:

[0702] The terminal sends the collected data to the server. Specifically, it bundles the data into packets and securely transfers them to the server using an encryption protocol. The input is the data generated in step 1, and the output is in a format that the server receives and can use in the next analysis step.

[0703] Step 3:

[0704] The server saves the received data to the database. Specifically, it performs data validation to confirm data integrity before saving it to the database. The input is data sent from the terminal, and the output is data in a well-organized database.

[0705] Step 4:

[0706] The server analyzes data using AI algorithms and an emotion engine. Specifically, it utilizes a generative AI model to detect anomalies that deviate from normal behavioral patterns and changes in emotion. The input is user behavior and emotion data stored in a database, and the output is the detection results of specific patterns of anomalies and the results of emotion state analysis.

[0707] Step 5:

[0708] The server generates alerts based on the analysis results and notifies the user. Specifically, when psychological stress or anxiety is detected, an alert of appropriate importance is sent to the parent via email or push notification. The input is the analysis results from step 4, and the output generates content including notification information and countermeasures.

[0709] Step 6:

[0710] Based on the alerts received, users take action regarding their children's online activities. Specific actions include engaging in conversations with children and restricting the use of digital devices, thereby improving the online safety of minors. Input is alert information from the server, and output is the actions taken and their results.

[0711] (Application Example 2)

[0712] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0713] To ensure the safety of minors' online activities, it is necessary not only to monitor online history but also to accurately understand users' emotional states and detect potential dangers associated with emotional changes early on. However, existing systems are insufficient in detecting anomalies that take emotional states into account, which poses a challenge as it may lead to missing cases that require immediate attention.

[0714] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0715] In this invention, the server includes data acquisition means for monitoring online activity, AI analysis means for analyzing the user's facial expression information and detecting anomalies accompanied by emotional changes, and communication means for notifying parents of detailed additional information based on emotional changes. This makes it possible to comprehensively monitor the user's online activity and emotional state, detect potential dangers early, and notify parents.

[0716] "Online activities" is a general term for a series of actions and operations that users perform using the internet.

[0717] "Data acquisition means" refers to a device or software used to collect information related to a user's online activities.

[0718] "Facial expression information" refers to visual data that indicates emotions and psychological states obtained through the movements of a user's face.

[0719] "AI analysis means" refers to a program or system that processes collected data and uses artificial intelligence technology to identify patterns and anomalies.

[0720] "Emotional change" refers to the phenomenon where a user's emotional state changes over time.

[0721] "Communication means" refers to a system that includes protocols and interfaces for transmitting information to other devices or users.

[0722] "Guardian" refers to a person who has the responsibility to protect the user and to oversee their safety.

[0723] "Additional information" refers to information that provides supplementary data or context related to the detected anomaly.

[0724] This invention is a system that detects abnormalities in the online activities and emotional state of minors and notifies parents of necessary information. The system consists of three main components: a server, a terminal, and a user. The functions and processing of each component are described in detail below.

[0725] First, the device is equipped with a camera sensor and microphone to collect online activity and user facial expression information. The software embedded in the device uses OpenCV for facial recognition, monitors the user's emotional state in real time, and an AI model using TensorFlow analyzes changes in emotion. This data is acquired along with the user's access history and usage time information and sent to the server.

[0726] Next, the server processes the collected data using AI analysis tools. The AI ​​analysis tools detect anomalies accompanied by emotional changes, and if a user shows stress or anxiety associated with specific risky behaviors, that information is given priority for evaluation. For example, if a user has been accessing inappropriate content for an extended period and their facial expression indicates anxiety, the emotion engine will immediately detect that event as an anomaly.

[0727] Finally, the server sends a detailed notification to the parent / guardian via communication means based on the detected anomaly. This notification includes additional information about the emotional changes and their context, and parents / guardians can receive it in real time on their smartphones or computers. This improves the online safety of minors while also enabling appropriate support.

[0728] For example, if a user shows signs of stress while visiting a particular site, a notification will be sent to the parent stating, "Your child may be experiencing anxiety while viewing certain content." An example of a prompt using a generative AI model might be, "Please explain how this system detects emotional changes in minors' online activities and communicates that information to parents."

[0729] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0730] Step 1:

[0731] The device captures the user's face using its camera sensor and acquires facial expression image data. This acquired image is used as input for facial region recognition using OpenCV. As a result of processing with OpenCV, facial feature point data is output. This data is passed to the Emotion AI model.

[0732] Step 2:

[0733] The device collects online activity data, including access history and usage time. This data is combined with facial feature point data to prepare it for AI analysis. This process connects the user's behavior and emotions, preparing the data for input to the server. This data is then transmitted to the server.

[0734] Step 3:

[0735] The server processes data received from the terminal using AI analysis tools. The input data includes online activity and facial feature point data, and an emotional state is evaluated using a TensorFlow model. This process outputs results for detecting abnormal behavior accompanied by changes in emotional state.

[0736] Step 4:

[0737] The server analyzes the anomaly detection results based on emotional states and generates information to notify parents with a corresponding priority. For example, if a user exhibits certain risky behavior and displays an anxious expression, the notification priority will be set higher. This information is then transmitted as a notification message.

[0738] Step 5:

[0739] The server sends the generated notification message to the parent via a communication method. The notification includes the results of a situational sentiment analysis and additional information, which the parent receives and reviews. This step ensures the user's safety and allows for necessary actions to be taken in real time.

[0740] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0741] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include those described above. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions shown by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0742] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0743] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0744] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0745] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0746] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0747] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0748] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0749] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0750] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0751] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0752] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0753] 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.

[0754] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0755] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0756] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0757] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0758] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0759] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0760] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0761] The following is further disclosed regarding the embodiments described above.

[0762] (Claim 1)

[0763] Data collection methods for monitoring online activity,

[0764] AI processing means for analyzing the aforementioned data and detecting anomalies,

[0765] A notification means for sending an alert to a guardian based on the aforementioned abnormality,

[0766] A system that includes this.

[0767] (Claim 2)

[0768] The system according to claim 1, characterized in that the data collection means collects the user's access history and usage time.

[0769] (Claim 3)

[0770] The system according to claim 1, characterized in that the AI ​​processing means detects access to a specific dangerous site.

[0771] "Example 1"

[0772] (Claim 1)

[0773] Means for acquiring information to monitor user behavior,

[0774] AI analysis means for analyzing the aforementioned information and identifying anomalies,

[0775] A notification means for sending an alarm to a monitor based on the aforementioned abnormality,

[0776] A security measure that encrypts the aforementioned information before transmitting it,

[0777] A warning display means for displaying warnings in real time based on collected information,

[0778] A system that includes this.

[0779] (Claim 2)

[0780] The system according to claim 1, characterized in that the information acquisition means acquires the user's network visit history and usage period.

[0781] (Claim 3)

[0782] The system according to claim 1, characterized in that the AI ​​analysis means identifies access to a specific dangerous virtual environment.

[0783] "Application Example 1"

[0784] (Claim 1)

[0785] Data collection methods for monitoring online activity,

[0786] AI processing means for analyzing the aforementioned data and detecting anomalies,

[0787] A notification means for sending an alert to a guardian based on the aforementioned abnormality,

[0788] A communication means for sending the aforementioned alert as a push notification to a smart device,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, characterized in that the data collection means collects the user's visit history and communication network usage time.

[0792] (Claim 3)

[0793] The system according to claim 1, characterized in that the AI ​​processing means learns unusual behavioral patterns and detects unnatural access to websites.

[0794] "Example 2 of combining an emotion engine"

[0795] (Claim 1)

[0796] Data collection methods for monitoring online behavior,

[0797] An artificial intelligence processing means for analyzing the data and detecting anomalies,

[0798] Notification means for sending alerts to parents based on the abnormality and the user's emotional state,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, characterized in that the data collection means collects the user's website visit history, digital device usage time, and changes in facial expressions and voice.

[0802] (Claim 3)

[0803] The system according to claim 1, characterized in that the artificial intelligence processing means detects abnormalities that deviate from normal behavioral patterns and changes in emotions related to the user's psychological stress and anxiety.

[0804] "Application example 2 when combining with an emotional engine"

[0805] (Claim 1)

[0806] A means of acquiring data to monitor online activity,

[0807] An AI analysis means for analyzing the aforementioned data and user facial expression information to detect anomalies accompanied by emotional changes,

[0808] A means of communication for notifying parents of detailed additional information based on the aforementioned emotional changes,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, characterized in that the data acquisition means collects the user's access history, usage time, and facial expression information.

[0812] (Claim 3)

[0813] The system according to claim 1, characterized in that the AI ​​analysis means detects emotional changes in response to specific risky behaviors. [Explanation of Symbols]

[0814] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Data collection methods for monitoring online activity, AI processing means for analyzing the aforementioned data and detecting anomalies, A notification means for sending an alert to a guardian based on the aforementioned abnormality, A system that includes this.

2. The system according to claim 1, characterized in that the data collection means collects the user's access history and usage time.

3. The system according to claim 1, characterized in that the AI ​​processing means detects access to a specific dangerous site.

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