system

Abstract

We provide the system. [Solution] Means of monitoring children's use of information devices, A means of sending data acquired from a terminal to a server, A means of analyzing transmitted data and using data processing techniques to detect abnormal behavioral patterns, A means for performing an evaluation and generating information when an anomaly is detected, A means of notifying the user of the generated information, A means of communication with a terminal, installed on a home device, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] In recent years, with the spread of the Internet and social networking services (SNS), children are increasingly exposed to various risks, such as inappropriate online content, contact with dangerous people, and health effects due to long-term use. Under such circumstances, it is required that guardians effectively monitor children's online activities and intervene at appropriate times. However, there is a problem that the current manual monitoring has limitations and it is difficult to respond efficiently and quickly.

Means for Solving the Problems

[0005] To address this challenge, a system is provided that monitors children's use of information devices in real time and transmits the acquired data to a server. The server analyzes the transmitted data using artificial intelligence to detect abnormal behavioral patterns. If an anomaly is detected, the system includes a mechanism to generate and notify alerts that assess the risk and suggest specific countermeasures to parents, enabling parents to quickly ensure their child's safety.

[0006] The term "child" refers to minors below a certain age and is defined as someone who should be protected.

[0007] An "information terminal" is an electronic device capable of connecting to the internet, including smartphones, tablets, and computers.

[0008] "Means of monitoring" refers to a process or device for recording the activities of a specific subject and collecting data in a format that can be analyzed in real time or retrospectively.

[0009] A "server" refers to a computer system that receives, processes, and stores data via a network, and is responsible for managing the data sent from client devices.

[0010] "Means of analyzing data" refers to a system or method for evaluating collected data using specific algorithms or methods and extracting insights from it.

[0011] "Methods using artificial intelligence" refers to technologies that utilize machine learning and data mining to predict patterns and detect anomalies.

[0012] "Abnormal behavioral patterns" refer to specific behaviors that differ from what is normally expected or that may threaten safety.

[0013] "Means of risk assessment" refers to a process or system for quantitatively or qualitatively evaluating the risks and impacts associated with a particular event or pattern.

[0014] "Means of generating alerts" refers to a system or method that creates a notification when specific conditions are met and communicates that information to the appropriate person.

[0015] "Means of notification" refers to a process or device for transmitting generated information or warnings to designated recipients. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This 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 Embodiment 2 when the 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 the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] 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.

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

[0019] In the following embodiments, the 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.

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

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

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system for monitoring children's use of information terminals and providing appropriate information to parents. The following describes the details of an embodiment of the system.

[0038] First, dedicated monitoring software is installed on the device used by the child. This device continuously collects data such as internet connection activity, application usage, and message sending and receiving history. For example, the device records the time it was used at night and the URLs of websites visited, accumulating basic data to detect abnormal behavioral patterns.

[0039] This data is periodically sent to a server in an encrypted format. The server analyzes the received data using artificial intelligence. This AI model is trained to identify abnormal behavioral patterns, such as "prolonged use at night" or "communication containing specific keywords considered dangerous."

[0040] If the server detects an anomaly, it performs a risk assessment based on that information. If a risk exceeding a certain threshold is detected, the server automatically generates an alert. For example, if a specific risk score is reached, a message is created that includes specific actions such as, "Your child is accessing dangerous content. We recommend setting restrictions."

[0041] The user (parent / guardian) will receive this alert on their device. The notification will be sent via push, ensuring it is delivered immediately and securely. Based on this information, the user can take specific actions, such as adjusting filtering software settings or discussing the issue with their child.

[0042] Thus, this system is designed to protect children's safety and provides parents with a means to support their children quickly and effectively. Specifically, the server can monitor internet usage patterns during certain nighttime hours and take swift action based on the results.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The device monitors the user's internet activity. Specifically, it collects encrypted logs of URLs of websites visited, history of apps used, and communication content.

[0046] Step 2:

[0047] The device sends the collected data to the server at regular intervals. For security reasons, the transmitted data is encrypted.

[0048] Step 3:

[0049] The server saves the data received from the terminal to a database. During this process, it checks whether the data is complete and prompts the user to resend it if there are any problems.

[0050] Step 4:

[0051] The server analyzes the stored data using an artificial intelligence model. This model performs pattern recognition to detect abnormal behavior.

[0052] Step 5:

[0053] When abnormal behavior is detected, the server performs a risk assessment. Based on the assessment results, a specific risk score is calculated to determine the severity of the problem.

[0054] Step 6:

[0055] Based on the risk assessment results, the server generates an alert for parents. This alert includes specific issues and recommended actions.

[0056] Step 7:

[0057] The server quickly pushes the generated alerts to the user's device. These notifications are sent in real time to immediately alert the user.

[0058] Step 8:

[0059] The user checks the alerts received on their device and, based on the content, takes necessary actions such as changing filter settings or talking to their child.

[0060] (Example 1)

[0061] 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."

[0062] In today's digital society, ensuring the safety of children when using information processing devices is crucial, but it is not practical for supervisors to directly monitor all their activities. Therefore, there is a need for an effective system that allows supervisors to remotely and appropriately monitor children's digital activities, detect potential dangers in a timely manner, and take appropriate action.

[0063] 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.

[0064] In this invention, the server includes means for monitoring a child's use of an information processing device, means for transferring information acquired from the processing device to a storage device, and means for analyzing the transferred information and using machine learning to detect abnormal behavior patterns. This allows supervisors to effectively monitor a child's behavior and immediately conduct risk assessments and take countermeasures when an anomaly is detected.

[0065] An "information processing device" is an electronic device used for processing and communicating digital data, and includes computers and smartphones.

[0066] A "storage device" refers to a mechanical or electronic device used to store data, and includes hard disks and cloud storage.

[0067] "Transfer" refers to the act of moving data from one location to another, specifically the process of sending information to a server via a network.

[0068] "Analysis" refers to the process of examining acquired data in detail to reveal underlying patterns and anomalies.

[0069] An "abnormal behavior pattern" refers to irregular actions that deviate from normal usage or past usage history, and is subject to risk assessment.

[0070] "Machine learning" refers to the technology that uses algorithms based on large amounts of data to learn patterns and perform predictions and identifications.

[0071] "Monitoring" refers to the act of continuously observing and recording the usage and activity of a device.

[0072] A "supervisor" refers to a person responsible for managing a child's digital activities and ensuring their safety; this is usually a parent or guardian.

[0073] "Risk assessment" is the process of determining the potential dangers associated with detected anomalies and considering appropriate countermeasures.

[0074] "Notifications" are short messages that convey important information or warnings to users.

[0075] This invention is a system for safely managing children's internet use, and specific embodiments are shown below.

[0076] First, dedicated monitoring software is installed on the device used by the child user. This software runs on an information processing device that processes digital data and collects information such as internet usage and application usage history. Specifically, the device periodically retrieves a list of applications used and a history of websites visited, and stores this data in a storage device while maintaining confidentiality using AES encryption technology.

[0077] Next, the device transfers the collected data to the server via the internet connection. After receiving this data, the server uses a generative AI model to analyze it. This model learns patterns based on a large amount of data and can identify abnormal patterns such as "unusually long usage times at night" or "communication containing dangerous keywords." When the server detects such anomalies, it performs a risk assessment and generates a notification that includes specific countermeasures if necessary.

[0078] Notifications are immediately pushed to the parent's device. This allows parents to stay informed of the situation through a secure means of communication and, if necessary, change device settings or communicate with their child. For example, in response to the prompt "What criteria should I use to determine if my child's internet use is safe?", the server can generate appropriate risk assessments and suggested countermeasures.

[0079] The technologies and methods used in implementing this invention are diverse, effectively combining information security, data analysis, and real-time communication. This system configuration ensures the safety of the digital environment surrounding children, allowing parents to use its protective features with peace of mind.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The device monitors the digital activities of the child who is the user. Specifically, it collects data every minute, such as internet connection status, applications being used, and website visit history. For example, the device logs the name of the currently active application and the URLs of the websites visited. This becomes the input data.

[0083] Step 2:

[0084] The device securely converts the collected data using AES encryption technology. This encrypted data is then ready for transmission to the server while protecting privacy. The device's role is to ensure accurate data transmission while preventing the leakage of user information. The encrypted data becomes the output data.

[0085] Step 3:

[0086] The terminal sends encrypted data to the server over the internet. The transmission uses a secure communication protocol and is performed in batches every hour. This allows the server to process a large amount of data at once. The transmitted encrypted data becomes the server's input data.

[0087] Step 4:

[0088] After decrypting the received data, the server performs analysis using a generating AI model. The purpose of the analysis is to identify abnormal operating patterns, such as long-term use or communications containing dangerous keywords. In this step, the AI ​​compares the data with past data to identify anomalies. The analysis results become the output data.

[0089] Step 5:

[0090] The server performs a risk assessment based on the analysis results and generates notifications if necessary. Risk assessment is the process of creating alert messages when certain thresholds are exceeded. Notifications include information such as "Your usage time is too long" or "You are accessing a dangerous site." The generated notifications are the output data.

[0091] Step 6:

[0092] The server pushes generated notifications to the user's device (the parent / guardian). These notifications are sent in real time, allowing parents to immediately consider appropriate actions. Receiving notifications in real time enables parents to instantly understand their child's activities. The notifications serve as input data for the user.

[0093] Step 7:

[0094] Parents, as users, manage their children's devices based on the notifications they receive. Specifically, they take measures such as strengthening filter settings and communicating with their children as needed. This helps keep children's digital activities safe. Parental actions become the overall output of the system.

[0095] (Application Example 1)

[0096] 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."

[0097] In today's world, where children's online activities using information devices are increasing, parents are required to appropriately monitor their children's activities and ensure their safety. However, current systems lack mechanisms to effectively detect abnormal behavior and respond quickly. Furthermore, there are problems with the difficulty of utilizing these systems within the home.

[0098] 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.

[0099] In this invention, the server includes means for monitoring a child's use of an information terminal, means for being installed on a home device and communicating with the terminal, and means for using data processing technology to detect abnormal behavioral patterns. This enables parents to properly understand their child's use of the information terminal, quickly detect abnormalities, and take appropriate measures.

[0100] "Means for monitoring children's use of information devices" refers to technical means for continuously tracking and recording activity on information devices used by children.

[0101] "Means of sending data acquired from a terminal to a server" refers to communication protocols and infrastructure for sending monitored information to an external data management system.

[0102] "Means of using data processing techniques to detect abnormal behavioral patterns" refer to analytical methods and algorithms that analyze acquired data and identify actions that deviate from predefined norms.

[0103] "Means for evaluating and generating information when an anomaly is detected" refers to means for evaluating the risk based on the detected anomaly and creating information to warn parents as necessary.

[0104] "Means of notifying users of generated information" refers to communication methods or notification systems for immediately conveying created warnings and notices to parents.

[0105] "A means of installing software on home devices and communicating with terminals" refers to a method of aggregating and relaying data by incorporating software into devices installed in homes and communicating with information terminals.

[0106] The system that implements this application consists of a series of processes that monitor the use of information devices used by children, analyze the collected data on a server to manage their activities, and notify parents. Software installed on home devices works in conjunction with information devices to track their usage and communication content in real time. The obtained data is encrypted and transmitted to a remote server via the home network.

[0107] The server incorporates data processing technology and artificial intelligence to detect abnormal behavioral patterns. Specifically, it analyzes the usage time of information terminals, the types of websites accessed, and the content of applications used. For example, if it detects situations such as usage exceeding normal hours at night or access to potentially harmful websites, the server analyzes the information and performs a risk assessment.

[0108] Based on the detected anomalies, the server generates a warning message as an evaluation result. This message is immediately displayed as a push notification on the parent's device via home equipment. This allows parents to monitor their child's online activity in real time and take appropriate action.

[0109] As a concrete example, here is an example of a prompt message that the server might generate. For example, it might say, "Generate a warning message to be generated when a robot that monitors a child's digital device usage detects an abnormal usage pattern. Specifically, let's say a large amount of data was used at night." This prompt message triggers an AI model to generate an appropriate warning message, which is then sent to the parent or guardian.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The device collects usage and communication history data in real time. Specifically, monitoring software on the device logs application usage time, URLs of visited websites, and message sending and receiving history. The input data obtained through this process is prepared as the device's operation log.

[0113] Step 2:

[0114] The terminal encrypts the collected data and sends it to the server. Specifically, the terminal encrypts the acquired log data using an encryption algorithm and transfers it to the server using a secure communication protocol. Through this process, the log data is delivered to the server as encrypted security data.

[0115] Step 3:

[0116] The server decrypts the received encrypted data to prepare it for analysis. It receives encrypted child usage data as input and securely decrypts it using a dedicated decryption algorithm. This process makes the decrypted usage data available for analysis on the server.

[0117] Step 4:

[0118] The server applies data processing techniques to detect abnormal behavioral patterns using the decrypted data. Specifically, it uses an AI model to analyze the data and pattern recognition techniques to identify unusual behavior. This process yields anomaly detection analysis results.

[0119] Step 5:

[0120] The server performs a risk assessment and generates information when an anomaly is detected. Specifically, it calculates a risk score for the detected abnormal behavior and creates a warning message based on that score. This process generates warning information to notify parents.

[0121] Step 6:

[0122] The server notifies the user of the generated warning information via home devices. Specifically, the server sends the warning message as a push notification to the user's mobile device. This process allows the user to receive the warning immediately and consider appropriate action.

[0123] 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.

[0124] This invention is a system that combines an emotion engine to monitor and analyze children's use of information terminals and detect abnormal behavioral patterns. This enables more advanced risk assessment and notification to users (parents).

[0125] In addition to conventional monitoring software, the device will be equipped with an emotion engine that identifies the user's emotional state. This engine uses facial recognition technology and voice analysis to analyze the user's facial expressions and voice while using the device and identify their emotions at that time. For example, it can identify emotions such as joy, anger, and sadness from facial expressions in real time.

[0126] The usage and emotional data collected by the device are sent to the server in an encrypted format. Upon receiving this data, the server analyzes the emotional data in addition to the conventional online activity data. The artificial intelligence model is designed to place particular importance on emotional changes that occur during abnormal behavior. For example, it identifies patterns such as emotional instability that may occur when a child has prolonged access to inappropriate content.

[0127] Emotional data assists the server in detecting anomalies and assessing risks. The emotional engine incorporates recognized emotional changes, enabling more accurate risk assessments. For example, a sudden change in emotion may indicate a stressful state, leading to a high-risk assessment and prompting a high-priority notification to parents.

[0128] Users (parents) can receive these alerts in real time. The notifications include specific situations and recommended actions, such as, "We have detected emotional fluctuations due to content your child recently accessed. We recommend talking to your child."

[0129] Thus, the present invention is a system that utilizes both online activity and emotional data to enhance child protection and enable appropriate information provision and prompt response to parents. For example, if a message a child receives is accompanied by an emotional change, parents can immediately understand the situation and take appropriate measures.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The device collects data on the user's online activity and emotions. This process involves identifying emotions from the user's facial expressions and voice using the camera and microphone, in addition to website visit history and app usage.

[0133] Step 2:

[0134] The device encrypts the collected online activity data and sentiment data and sends it to the server using a secure communication protocol.

[0135] Step 3:

[0136] The server stores the received data in the database. During storage, it verifies the data's integrity and requests retransmission of any missing data if necessary.

[0137] Step 4:

[0138] The server begins analysis using an artificial intelligence model based on the stored data. This model specifically analyzes the relationship between online activity and sentiment data acquired simultaneously.

[0139] Step 5:

[0140] When the server detects abnormal behavioral patterns or sudden emotional changes, it performs a risk assessment based on these findings. This assessment also takes into account the magnitude of the emotional changes to calculate an overall risk score.

[0141] Step 6:

[0142] Based on the risk assessment results, the server generates an alert that includes specific countermeasures. This alert describes the details of the detected anomaly and recommended actions for parents.

[0143] Step 7:

[0144] The server sends alerts to the user's device via push notifications. These notifications are timely and serve to immediately communicate the situation.

[0145] Step 8:

[0146] Users can check alerts on their devices. This allows them to understand the specific situation and take appropriate action, such as adjusting settings or talking to their children, as needed.

[0147] (Example 2)

[0148] 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".

[0149] In modern society, there is a need to effectively monitor children's computer usage and to quickly detect and respond to potential abnormal behavior and the impact of inappropriate content. However, conventional systems are limited to superficial analysis of usage data and lack the ability to conduct deep risk assessments based on the user's emotional state. Furthermore, notifications to parents often fail to consider changes in emotions, resulting in a lack of concrete suggestions for countermeasures.

[0150] 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.

[0151] In this invention, the server includes means for using machine intelligence to analyze transmitted information and detect abnormal behavioral characteristics, means including an emotion analysis function for identifying emotional states, and means for receiving emotional data and analyzing it in combination with behavioral characteristics. This makes it possible to improve the accuracy of risk assessment, including changes in a child's emotions, and to notify parents of specific coping methods.

[0152] "Computer equipment" refers to devices used for data processing, or computer equipment in general.

[0153] A "data aggregation device" refers to an information processing system for centrally storing and managing data.

[0154] "Information" refers to all data related to the use of computer devices and user activity logs.

[0155] "Machine intelligence" refers to algorithms that use artificial intelligence technology to perform pattern recognition and anomaly detection.

[0156] A "warning" refers to a cautionary message sent to parents or guardians when an abnormality is detected.

[0157] "Emotion analysis function" refers to technology that identifies a user's emotional state from their voice and facial expressions and collects that data.

[0158] This system is equipped with advanced analytical capabilities to monitor children's use of computer devices and detect abnormal behavior.

[0159] Terminal:

[0160] In addition to conventional monitoring software, the terminal is equipped with an emotion analysis function to identify the user's emotional state. This function uses facial recognition and voice analysis technologies to acquire emotional data from the user's facial expressions and voice. Specifically, it uses a general facial recognition library for facial recognition and a speech-to-text service for voice analysis. The terminal encrypts this information and transmits the data to the integrator using a secure protocol.

[0161] server:

[0162] The server is responsible for receiving and analyzing data transmitted from terminals. The received information is analyzed using machine intelligence algorithms to detect abnormal behavioral characteristics and assess risks. This analysis includes correlation analysis of behavioral and emotional data, and alerts are generated if significant emotional changes are detected. This analysis is made more efficient by using generative AI models.

[0163] User (parent / guardian):

[0164] Users receive alerts from the server. These notifications include situation-specific action recommendations based on analysis of sentiment data. For example, a message might read, "Significant changes in your child's emotions were observed during their browsing activity. We recommend reviewing the content and having a discussion."

[0165] Specific example:

[0166] For example, if a child is experiencing stress from in-game chat, the emotion analysis function can detect this change. The server can then identify it as an anomaly and send a notification to the parent. In this way, it becomes possible to comprehensively manage a child's emotional changes while using a computer.

[0167] Example of a prompt:

[0168] "This sentiment analysis system monitors access to inappropriate content and changes in users' emotions. Could you please explain the specific criteria used for making these judgments?"

[0169] This invention can ensure children's safe online activities and provide support to help parents understand the situation appropriately.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] Device: The device collects input data regarding the user's usage and emotional state. This data includes application usage history on the device, as well as facial and voice data acquired from the camera and microphone. The emotion analysis function processes this data and determines emotions such as joy and anger in real time from facial expressions. The emotional data obtained from this analysis is integrated with usage data.

[0173] Step 2:

[0174] Terminal: The integrated data is encrypted using the AES-256 encryption algorithm. The encrypted data is securely transmitted to the server via the HTTPS communication protocol. Encryption is to prevent external eavesdropping and tampering with the data.

[0175] Step 3:

[0176] Server: The server decrypts the received dataset and stores it in the database. The data includes online behavioral history and sentiment ratings. An AI algorithm uses this data to analyze behavioral characteristics and patterns of sentiment change to determine whether abnormal behavior is detected. The output here is the risk level if an anomaly is detected.

[0177] Step 4:

[0178] Server: When the risk level is high, a generative AI model is used to suggest specific recommended actions and countermeasures. This process uses predictive analytics to identify anomalies and refine risk assessments by comparing them with historical data. The output is information that includes recommendations tailored to individual cases.

[0179] Step 5:

[0180] Server: The server generates final alerts and recommendations and notifies the user (parent / guardian). These notifications include real-time changes in sentiment and behavioral patterns, as well as recommended actions. Notification methods include email and push notifications via mobile apps.

[0181] Step 6:

[0182] User (Parent): Based on the alerts and recommendations received, users can communicate with their children and set up management policies. This allows them to respond quickly to potential risks in their children's online activities.

[0183] (Application Example 2)

[0184] 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".

[0185] There is a need to monitor inappropriate use of information processing devices and impulsive electronic payment behavior by children and minors in real time, and to promptly and effectively notify parents or guardians. In particular, a mechanism is needed to prevent unsafe spending by considering the influence of emotional changes on purchasing behavior.

[0186] 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.

[0187] In this invention, the server includes means for monitoring the user's activity of the information processing device, means for using facial expression and voice analysis technology to determine the acquired emotional state, and means for detecting abnormal behavior based on the activity time of the information processing device, the type of access destination, and emotional fluctuations. This prevents inappropriate electronic payments by children and minors, enabling them to engage in digital activities with peace of mind.

[0188] "User" refers to an individual who operates an information processing device or a person who supervises the actions of an individual acting on their behalf.

[0189] An "information processing device" is a computing device used for inputting, processing, and outputting data.

[0190] "Activity" refers to a series of actions or operations performed on an information processing device.

[0191] A "server" is a computer system that processes and analyzes information and provides services to clients.

[0192] "Emotional state" refers to the emotional situation or changes in that state that the user expresses through facial expressions and voice.

[0193] "Facial expression analysis technology" is a technique that uses digital image processing to identify emotions from an individual's facial movements.

[0194] "Voice analysis technology" is a technology that analyzes the characteristics of voice to identify emotions and intentions.

[0195] "Activity time" refers to the length of time that an information processing device is in use.

[0196] "Type of access destination" refers to the category and characteristics of external resources that can be reached from the information processing device.

[0197] "Emotional fluctuation" refers to the movement of a user's emotional state over time.

[0198] "Abnormal behavior" refers to activity on an information processing device that deviates from the normally acceptable range.

[0199] "Notification" refers to the act or means of quickly conveying certain information to others.

[0200] To implement this invention, three main entities are involved: a server, a terminal, and a user. The server receives data transmitted from the terminal, which is an information processing device, and identifies abnormal behavior based on that data. Furthermore, the server has an emotion analysis function built in, and uses facial expression analysis technology and voice analysis technology to identify emotional states in real time. The identified emotion data is analyzed together with the device's activity data using machine learning technology to detect abnormal behavior.

[0201] The terminal is an information processing device operated by the user, and it uses a camera and microphone to record the user's facial expressions and voice in real time. The acquired data is temporarily processed within the terminal and sent to the server in an encoded format. Encryption of this data is important to maintain the security of the communication.

[0202] When abnormal behavior is detected by the server, the user receives an immediate notification. The notification includes specific countermeasures based on the user's emotional state and behavioral data. This allows the user to prevent themselves from engaging in inappropriate behavior and to take necessary actions quickly.

[0203] For example, in an electronic payment service, when a user attempts an impulse purchase, the server detects the emotional fluctuation and automatically issues a warning as an anomaly. A specific example of a prompt message would be: "Analyze the child's emotional fluctuation patterns and current payment activity to identify potential unsafe purchases. For example, if the child impulsively attempts to purchase an expensive item they don't normally buy." Based on this prompt, a generative AI model performs an analysis to obtain the optimal result.

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The device records the user's facial expressions and voice in real time using its camera and microphone. It receives video data from the camera and audio data from the microphone as input. As output, this data is temporarily stored within the device and then encrypted. Encryption is performed to ensure privacy and improve communication security.

[0207] Step 2:

[0208] The device sends encrypted data to the server. This data includes the user's facial expressions and voice data, and processing begins when the server receives it. The input is the encoded data sent from the device, and the output is the data awaiting analysis that has arrived at the server.

[0209] Step 3:

[0210] The server decodes the received data and analyzes the emotional state using facial expression analysis and voice analysis technologies. The input is the decoded data; the AI ​​model identifies emotions from facial expressions and evaluates the emotional state through voice analysis. The output is the analyzed emotional data.

[0211] Step 4:

[0212] The server uses machine learning techniques to integrate emotional data and activity data from the device and analyzes it to detect abnormal behavior. The input consists of emotional and activity data, and it automatically detects abnormal behavior based on indicators. The output is data indicating the presence or absence of abnormal behavior and its nature.

[0213] Step 5:

[0214] The server converts detected abnormal behavior into alerts based on risk assessments and generates them. Inputs include data on the abnormal behavior and information on the risk level. Outputs are specific alert messages to be sent to parents.

[0215] Step 6:

[0216] Users receive alerts sent from the server and view notifications in real time. These notifications include specific actions based on emotional fluctuations and behavioral data. Input is the alert message from the server, and output is the notification displayed to the user along with specific action instructions.

[0217] Step 7:

[0218] Users take appropriate action based on the notification content. This includes actions such as providing instructions to the user or changing settings. The input is the choice of action in accordance with the instructions in the notification, and the output is the prevention or correction of the resulting inappropriate behavior.

[0219] 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.

[0220] Data generation model 58 is a type of 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 the following. 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 indicated 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention is a system for monitoring children's use of information terminals and providing appropriate information to parents. The following describes the details of an embodiment of the system.

[0236] First, dedicated monitoring software is installed on the device used by the child. This device continuously collects data such as internet connection activity, application usage, and message sending and receiving history. For example, the device records the time it was used at night and the URLs of websites visited, accumulating basic data to detect abnormal behavioral patterns.

[0237] This data is periodically sent to a server in an encrypted format. The server analyzes the received data using artificial intelligence. This AI model is trained to identify abnormal behavioral patterns, such as "prolonged use at night" or "communication containing specific keywords considered dangerous."

[0238] If the server detects an anomaly, it performs a risk assessment based on that information. If a risk exceeding a certain threshold is detected, the server automatically generates an alert. For example, if a specific risk score is reached, a message is created that includes specific actions such as, "Your child is accessing dangerous content. We recommend setting restrictions."

[0239] The user (parent / guardian) will receive this alert on their device. The notification will be sent via push, ensuring it is delivered immediately and securely. Based on this information, the user can take specific actions, such as adjusting filtering software settings or discussing the issue with their child.

[0240] Thus, this system is designed to protect children's safety and provides parents with a means to support their children quickly and effectively. Specifically, the server can monitor internet usage patterns during certain nighttime hours and take swift action based on the results.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The device monitors the user's internet activity. Specifically, it collects encrypted logs of URLs of websites visited, history of apps used, and communication content.

[0244] Step 2:

[0245] The device sends the collected data to the server at regular intervals. For security reasons, the transmitted data is encrypted.

[0246] Step 3:

[0247] The server saves the data received from the terminal to a database. During this process, it checks whether the data is complete and prompts the user to resend it if there are any problems.

[0248] Step 4:

[0249] The server analyzes the stored data using an artificial intelligence model. This model performs pattern recognition to detect abnormal behavior.

[0250] Step 5:

[0251] When abnormal behavior is detected, the server performs a risk assessment. Based on the assessment results, a specific risk score is calculated to determine the severity of the problem.

[0252] Step 6:

[0253] Based on the risk assessment results, the server generates an alert for parents. This alert includes specific issues and recommended actions.

[0254] Step 7:

[0255] The server quickly pushes the generated alerts to the user's device. These notifications are sent in real time to immediately alert the user.

[0256] Step 8:

[0257] The user checks the alerts received on their device and, based on the content, takes necessary actions such as changing filter settings or talking to their child.

[0258] (Example 1)

[0259] 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."

[0260] In today's digital society, ensuring the safety of children when using information processing devices is crucial, but it is not practical for supervisors to directly monitor all their activities. Therefore, there is a need for an effective system that allows supervisors to remotely and appropriately monitor children's digital activities, detect potential dangers in a timely manner, and take appropriate action.

[0261] 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.

[0262] In this invention, the server includes means for monitoring a child's use of an information processing device, means for transferring information acquired from the processing device to a storage device, and means for analyzing the transferred information and using machine learning to detect abnormal behavior patterns. This allows supervisors to effectively monitor a child's behavior and immediately conduct risk assessments and take countermeasures when an anomaly is detected.

[0263] An "information processing device" is an electronic device used for processing and communicating digital data, and includes computers and smartphones.

[0264] A "storage device" refers to a mechanical or electronic device used to store data, and includes hard disks and cloud storage.

[0265] "Transfer" refers to the act of moving data from one location to another, specifically the process of sending information to a server via a network.

[0266] "Analysis" refers to the process of examining acquired data in detail to reveal underlying patterns and anomalies.

[0267] An "abnormal behavior pattern" refers to irregular actions that deviate from normal usage or past usage history, and is subject to risk assessment.

[0268] "Machine learning" refers to the technology that uses algorithms based on large amounts of data to learn patterns and perform predictions and identifications.

[0269] "Monitoring" refers to the act of continuously observing and recording the usage and activity of a device.

[0270] A "supervisor" refers to a person responsible for managing a child's digital activities and ensuring their safety; this is usually a parent or guardian.

[0271] "Risk assessment" is the process of determining the potential dangers associated with detected anomalies and considering appropriate countermeasures.

[0272] "Notifications" are short messages that convey important information or warnings to users.

[0273] This invention is a system for safely managing children's internet use, and specific embodiments are shown below.

[0274] First, dedicated monitoring software is installed on the device used by the child user. This software runs on an information processing device that processes digital data and collects information such as internet usage and application usage history. Specifically, the device periodically retrieves a list of applications used and a history of websites visited, and stores this data in a storage device while maintaining confidentiality using AES encryption technology.

[0275] Next, the device transfers the collected data to the server via the internet connection. After receiving this data, the server uses a generative AI model to analyze it. This model learns patterns based on a large amount of data and can identify abnormal patterns such as "unusually long usage times at night" or "communication containing dangerous keywords." When the server detects such anomalies, it performs a risk assessment and generates a notification that includes specific countermeasures if necessary.

[0276] Notifications are immediately pushed to the parent's device. This allows parents to stay informed of the situation through a secure means of communication and, if necessary, change device settings or communicate with their child. For example, in response to the prompt "What criteria should I use to determine if my child's internet use is safe?", the server can generate appropriate risk assessments and suggested countermeasures.

[0277] The technologies and methods used in implementing this invention are diverse, effectively combining information security, data analysis, and real-time communication. This system configuration ensures the safety of the digital environment surrounding children, allowing parents to use its protective features with peace of mind.

[0278] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0279] Step 1:

[0280] The device monitors the digital activities of the child who is the user. Specifically, it collects data every minute, such as internet connection status, applications being used, and website visit history. For example, the device logs the name of the currently active application and the URLs of the websites visited. This becomes the input data.

[0281] Step 2:

[0282] The terminal securely converts the collected data using AES encryption technology. This encrypted data is then ready to be sent to the server while protecting privacy. The role of the terminal is to accurately send the data while preventing the leakage of user information. The encrypted data becomes the output data.

[0283] Step 3:

[0284] The terminal sends the encrypted data to the server via the Internet. The transmission uses a secure communication protocol and is executed in batch form every hour. This enables the server to process a large amount of data at once. The transmitted encrypted data becomes the input data for the server.

[0285] Step 4:

[0286] After decrypting the received data, the server performs analysis using a generated AI model. The purpose of the analysis is to identify abnormal operation patterns, such as long-term usage or communications containing dangerous keywords. In this step, the AI compares with past data to identify anomalies. The analysis results become the output data.

[0287] Step 5:

[0288] Based on the analysis results, the server conducts a risk assessment and generates a notification if necessary. Risk assessment is a process of creating an alert message when a specific threshold is exceeded. The notification includes information such as "Too much usage time" or "Accessing a dangerous site". The generated notification is the output data.

[0289] Step 6:

[0290] The server pushes generated notifications to the user's device (the parent / guardian). These notifications are sent in real time, allowing parents to immediately consider appropriate actions. Receiving notifications in real time enables parents to instantly understand their child's activities. The notifications serve as input data for the user.

[0291] Step 7:

[0292] Parents, as users, manage their children's devices based on the notifications they receive. Specifically, they take measures such as strengthening filter settings and communicating with their children as needed. This helps keep children's digital activities safe. Parental actions become the overall output of the system.

[0293] (Application Example 1)

[0294] 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."

[0295] In today's world, where children's online activities using information devices are increasing, parents are required to appropriately monitor their children's activities and ensure their safety. However, current systems lack mechanisms to effectively detect abnormal behavior and respond quickly. Furthermore, there are problems with the difficulty of utilizing these systems within the home.

[0296] 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.

[0297] In this invention, the server includes means for monitoring a child's use of an information terminal, means for being installed on a home device and communicating with the terminal, and means for using data processing technology to detect abnormal behavioral patterns. This enables parents to properly understand their child's use of the information terminal, quickly detect abnormalities, and take appropriate measures.

[0298] "Means for monitoring children's use of information devices" refers to technical means for continuously tracking and recording activity on information devices used by children.

[0299] "Means of sending data acquired from a terminal to a server" refers to communication protocols and infrastructure for sending monitored information to an external data management system.

[0300] "Means of using data processing techniques to detect abnormal behavioral patterns" refer to analytical methods and algorithms that analyze acquired data and identify actions that deviate from predefined norms.

[0301] "Means for evaluating and generating information when an anomaly is detected" refers to means for evaluating the risk based on the detected anomaly and creating information to warn parents as necessary.

[0302] "Means of notifying users of generated information" refers to communication methods or notification systems for immediately conveying created warnings and notices to parents.

[0303] "A means of installing software on home devices and communicating with terminals" refers to a method of aggregating and relaying data by incorporating software into devices installed in homes and communicating with information terminals.

[0304] The system that implements this application consists of a series of processes that monitor the use of information devices used by children, analyze the collected data on a server to manage their activities, and notify parents. Software installed on home devices works in conjunction with information devices to track their usage and communication content in real time. The obtained data is encrypted and transmitted to a remote server via the home network.

[0305] The server is equipped with data processing technology and artificial intelligence to detect abnormal behavior patterns. Specifically, it analyzes the usage time of the information terminal, the types of websites accessed, and the content of the applications used. For example, when a situation where the usage time exceeds the normal level at night or access to harmful sites is detected, the server analyzes the information and conducts a risk assessment.

[0306] Based on the detected anomalies, the server generates a warning message as the evaluation result. This message is immediately displayed as a push notification on the guardian's device via the home device. As a result, the guardian can monitor the child's online activities in real time and take appropriate actions.

[0307] As a specific example, an example of the prompt text generated by the server is shown. For example, "Please generate a warning message that a robot for monitoring children's digital device usage generates when it detects an abnormal usage pattern. As a specific situation, assume that a large amount of data is being used at night." This is the mechanism by which the AI model generates an appropriate warning message and notifies the guardian.

[0308] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0309] Step 1:

[0310] The terminal collects data on usage status and communication history in real time. As a specific operation, the monitoring software in the terminal records the application usage time, the URL of the website visited, and the message sending and receiving history in a log. The input data obtained through this process is prepared as the operation log of the terminal.

[0311] Step 2:

[0312] The terminal encrypts the collected data and sends it to the server. Specifically, the terminal encrypts the acquired log data using an encryption algorithm and transfers it to the server using a secure communication protocol. Through this process, the log data is delivered to the server as encrypted security data.

[0313] Step 3:

[0314] The server decrypts the received encrypted data to prepare it for analysis. It receives encrypted child usage data as input and securely decrypts it using a dedicated decryption algorithm. This process makes the decrypted usage data available for analysis on the server.

[0315] Step 4:

[0316] The server applies data processing techniques to detect abnormal behavioral patterns using the decrypted data. Specifically, it uses an AI model to analyze the data and pattern recognition techniques to identify unusual behavior. This process yields anomaly detection analysis results.

[0317] Step 5:

[0318] The server performs a risk assessment and generates information when an anomaly is detected. Specifically, it calculates a risk score for the detected abnormal behavior and creates a warning message based on that score. This process generates warning information to notify parents.

[0319] Step 6:

[0320] The server notifies the user of the generated warning information via home devices. Specifically, the server sends the warning message as a push notification to the user's mobile device. This process allows the user to receive the warning immediately and consider appropriate action.

[0321] 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.

[0322] This invention is a system that combines an emotion engine to monitor and analyze children's use of information terminals and detect abnormal behavioral patterns. This enables more advanced risk assessment and notification to users (parents).

[0323] In addition to conventional monitoring software, the device will be equipped with an emotion engine that identifies the user's emotional state. This engine uses facial recognition technology and voice analysis to analyze the user's facial expressions and voice while using the device and identify their emotions at that time. For example, it can identify emotions such as joy, anger, and sadness from facial expressions in real time.

[0324] The usage and emotional data collected by the device are sent to the server in an encrypted format. Upon receiving this data, the server analyzes the emotional data in addition to the conventional online activity data. The artificial intelligence model is designed to place particular importance on emotional changes that occur during abnormal behavior. For example, it identifies patterns such as emotional instability that may occur when a child has prolonged access to inappropriate content.

[0325] Emotional data assists the server in detecting anomalies and assessing risks. The emotional engine incorporates recognized emotional changes, enabling more accurate risk assessments. For example, a sudden change in emotion may indicate a stressful state, leading to a high-risk assessment and prompting a high-priority notification to parents.

[0326] Users (parents) can receive these alerts in real time. The notifications include specific situations and recommended actions, such as, "We have detected emotional fluctuations due to content your child recently accessed. We recommend talking to your child."

[0327] Thus, the present invention is a system that utilizes both online activity and emotional data to enhance child protection and enable appropriate information provision and prompt response to parents. For example, if a message a child receives is accompanied by an emotional change, parents can immediately understand the situation and take appropriate measures.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] The device collects data on the user's online activity and emotions. This process involves identifying emotions from the user's facial expressions and voice using the camera and microphone, in addition to website visit history and app usage.

[0331] Step 2:

[0332] The device encrypts the collected online activity data and sentiment data and sends it to the server using a secure communication protocol.

[0333] Step 3:

[0334] The server stores the received data in the database. During storage, it verifies the data's integrity and requests retransmission of any missing data if necessary.

[0335] Step 4:

[0336] The server begins analysis using an artificial intelligence model based on the stored data. This model specifically analyzes the relationship between online activity and sentiment data acquired simultaneously.

[0337] Step 5:

[0338] When the server detects abnormal behavioral patterns or sudden emotional changes, it performs a risk assessment based on these findings. This assessment also takes into account the magnitude of the emotional changes to calculate an overall risk score.

[0339] Step 6:

[0340] Based on the risk assessment results, the server generates an alert that includes specific countermeasures. This alert describes the details of the detected anomaly and recommended actions for parents.

[0341] Step 7:

[0342] The server sends alerts to the user's device via push notifications. These notifications are timely and serve to immediately communicate the situation.

[0343] Step 8:

[0344] Users can check alerts on their devices. This allows them to understand the specific situation and take appropriate action, such as adjusting settings or talking to their children, as needed.

[0345] (Example 2)

[0346] 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".

[0347] In modern society, there is a need to effectively monitor children's computer usage and to quickly detect and respond to potential abnormal behavior and the impact of inappropriate content. However, conventional systems are limited to superficial analysis of usage data and lack the ability to conduct deep risk assessments based on the user's emotional state. Furthermore, notifications to parents often fail to consider changes in emotions, resulting in a lack of concrete suggestions for countermeasures.

[0348] 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.

[0349] In this invention, the server includes means for using machine intelligence to analyze transmitted information and detect abnormal behavioral characteristics, means including an emotion analysis function for identifying emotional states, and means for receiving emotional data and analyzing it in combination with behavioral characteristics. This makes it possible to improve the accuracy of risk assessment, including changes in a child's emotions, and to notify parents of specific coping methods.

[0350] "Computer equipment" refers to devices used for data processing, or computer equipment in general.

[0351] A "data aggregation device" refers to an information processing system for centrally storing and managing data.

[0352] "Information" refers to all data related to the use of computer devices and user activity logs.

[0353] "Machine intelligence" refers to algorithms that use artificial intelligence technology to perform pattern recognition and anomaly detection.

[0354] A "warning" refers to a cautionary message sent to parents or guardians when an abnormality is detected.

[0355] "Emotion analysis function" refers to technology that identifies a user's emotional state from their voice and facial expressions and collects that data.

[0356] This system is equipped with advanced analytical capabilities to monitor children's use of computer devices and detect abnormal behavior.

[0357] Terminal:

[0358] In addition to conventional monitoring software, the terminal is equipped with an emotion analysis function to identify the user's emotional state. This function uses facial recognition and voice analysis technologies to acquire emotional data from the user's facial expressions and voice. Specifically, it uses a general facial recognition library for facial recognition and a speech-to-text service for voice analysis. The terminal encrypts this information and transmits the data to the integrator using a secure protocol.

[0359] server:

[0360] The server is responsible for receiving and analyzing data transmitted from terminals. The received information is analyzed using machine intelligence algorithms to detect abnormal behavioral characteristics and assess risks. This analysis includes correlation analysis of behavioral and emotional data, and alerts are generated if significant emotional changes are detected. This analysis is made more efficient by using generative AI models.

[0361] User (parent / guardian):

[0362] Users receive alerts from the server. These notifications include situation-specific action recommendations based on analysis of sentiment data. For example, a message might read, "Significant changes in your child's emotions were observed during their browsing activity. We recommend reviewing the content and having a discussion."

[0363] Specific example:

[0364] For example, if a child is experiencing stress from in-game chat, the emotion analysis function can detect this change. The server can then identify it as an anomaly and send a notification to the parent. In this way, it becomes possible to comprehensively manage a child's emotional changes while using a computer.

[0365] Example of a prompt:

[0366] "This sentiment analysis system monitors access to inappropriate content and changes in users' emotions. Could you please explain the specific criteria used for making these judgments?"

[0367] This invention can ensure children's safe online activities and provide support to help parents understand the situation appropriately.

[0368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0369] Step 1:

[0370] Device: The device collects input data regarding the user's usage and emotional state. This data includes application usage history on the device, as well as facial and voice data acquired from the camera and microphone. The emotion analysis function processes this data and determines emotions such as joy and anger in real time from facial expressions. The emotional data obtained from this analysis is integrated with usage data.

[0371] Step 2:

[0372] Terminal: The integrated data is encrypted using the AES-256 encryption algorithm. The encrypted data is securely transmitted to the server via the HTTPS communication protocol. Encryption is to prevent external eavesdropping and tampering with the data.

[0373] Step 3:

[0374] Server: The server decrypts the received dataset and stores it in the database. The data includes online behavioral history and sentiment ratings. An AI algorithm uses this data to analyze behavioral characteristics and patterns of sentiment change to determine whether abnormal behavior is detected. The output here is the risk level if an anomaly is detected.

[0375] Step 4:

[0376] Server: When the risk level is high, a generative AI model is used to suggest specific recommended actions and countermeasures. This process uses predictive analytics to identify anomalies and refine risk assessments by comparing them with historical data. The output is information that includes recommendations tailored to individual cases.

[0377] Step 5:

[0378] Server: The server generates final alerts and recommendations and notifies the user (parent / guardian). These notifications include real-time changes in sentiment and behavioral patterns, as well as recommended actions. Notification methods include email and push notifications via mobile apps.

[0379] Step 6:

[0380] User (Parent): Based on the alerts and recommendations received, users can communicate with their children and set up management policies. This allows them to respond quickly to potential risks in their children's online activities.

[0381] (Application Example 2)

[0382] 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."

[0383] There is a need to monitor inappropriate use of information processing devices and impulsive electronic payment behavior by children and minors in real time, and to promptly and effectively notify parents or guardians. In particular, a mechanism is needed to prevent unsafe spending by considering the influence of emotional changes on purchasing behavior.

[0384] 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.

[0385] In this invention, the server includes means for monitoring the user's activity of the information processing device, means for using facial expression and voice analysis technology to determine the acquired emotional state, and means for detecting abnormal behavior based on the activity time of the information processing device, the type of access destination, and emotional fluctuations. This prevents inappropriate electronic payments by children and minors, enabling them to engage in digital activities with peace of mind.

[0386] "User" refers to an individual who operates an information processing device or a person who supervises the actions of an individual acting on their behalf.

[0387] An "information processing device" is a computing device used for inputting, processing, and outputting data.

[0388] "Activity" refers to a series of actions or operations performed on an information processing device.

[0389] A "server" is a computer system that processes and analyzes information and provides services to clients.

[0390] "Emotional state" refers to the emotional situation or changes in that state that the user expresses through facial expressions and voice.

[0391] "Facial expression analysis technology" is a technique that uses digital image processing to identify emotions from an individual's facial movements.

[0392] "Voice analysis technology" is a technology that analyzes the characteristics of voice to identify emotions and intentions.

[0393] "Activity time" refers to the length of time that an information processing device is in use.

[0394] "Type of access destination" refers to the category and characteristics of external resources that can be reached from the information processing device.

[0395] "Emotional fluctuation" refers to the movement of a user's emotional state over time.

[0396] "Abnormal behavior" refers to activity on an information processing device that deviates from the normally acceptable range.

[0397] "Notification" refers to the act or means of quickly conveying certain information to others.

[0398] To implement this invention, three main entities are involved: a server, a terminal, and a user. The server receives data transmitted from the terminal, which is an information processing device, and identifies abnormal behavior based on that data. Furthermore, the server has an emotion analysis function built in, and uses facial expression analysis technology and voice analysis technology to identify emotional states in real time. The identified emotion data is analyzed together with the device's activity data using machine learning technology to detect abnormal behavior.

[0399] The terminal is an information processing device operated by the user, and it uses a camera and microphone to record the user's facial expressions and voice in real time. The acquired data is temporarily processed within the terminal and sent to the server in an encoded format. Encryption of this data is important to maintain the security of the communication.

[0400] When abnormal behavior is detected by the server, the user receives an immediate notification. The notification includes specific countermeasures based on the user's emotional state and behavioral data. This allows the user to prevent themselves from engaging in inappropriate behavior and to take necessary actions quickly.

[0401] For example, in an electronic payment service, when a user attempts an impulse purchase, the server detects the emotional fluctuation and automatically issues a warning as an anomaly. A specific example of a prompt message would be: "Analyze the child's emotional fluctuation patterns and current payment activity to identify potential unsafe purchases. For example, if the child impulsively attempts to purchase an expensive item they don't normally buy." Based on this prompt, a generative AI model performs an analysis to obtain the optimal result.

[0402] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0403] Step 1:

[0404] The device records the user's facial expressions and voice in real time using its camera and microphone. It receives video data from the camera and audio data from the microphone as input. As output, this data is temporarily stored within the device and then encrypted. Encryption is performed to ensure privacy and improve communication security.

[0405] Step 2:

[0406] The device sends encrypted data to the server. This data includes the user's facial expressions and voice data, and processing begins when the server receives it. The input is the encoded data sent from the device, and the output is the data awaiting analysis that has arrived at the server.

[0407] Step 3:

[0408] The server decodes the received data and analyzes the emotional state using facial expression analysis and voice analysis technologies. The input is the decoded data; the AI ​​model identifies emotions from facial expressions and evaluates the emotional state through voice analysis. The output is the analyzed emotional data.

[0409] Step 4:

[0410] The server uses machine learning techniques to integrate emotional data and activity data from the device and analyzes it to detect abnormal behavior. The input consists of emotional and activity data, and it automatically detects abnormal behavior based on indicators. The output is data indicating the presence or absence of abnormal behavior and its nature.

[0411] Step 5:

[0412] The server converts detected abnormal behavior into alerts based on risk assessments and generates them. Inputs include data on the abnormal behavior and information on the risk level. Outputs are specific alert messages to be sent to parents.

[0413] Step 6:

[0414] Users receive alerts sent from the server and view notifications in real time. These notifications include specific actions based on emotional fluctuations and behavioral data. Input is the alert message from the server, and output is the notification displayed to the user along with specific action instructions.

[0415] Step 7:

[0416] Users take appropriate action based on the notification content. This includes actions such as providing instructions to the user or changing settings. The input is the choice of action in accordance with the instructions in the notification, and the output is the prevention or correction of the resulting inappropriate behavior.

[0417] 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.

[0418] 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 the following. 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 indicated 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.

[0419] 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.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] 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.

[0423] 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).

[0424] 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.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] 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".

[0433] This invention is a system for monitoring children's use of information terminals and providing appropriate information to parents. The following describes the details of an embodiment of the system.

[0434] First, dedicated monitoring software is installed on the device used by the child. This device continuously collects data such as internet connection activity, application usage, and message sending and receiving history. For example, the device records the time it was used at night and the URLs of websites visited, accumulating basic data to detect abnormal behavioral patterns.

[0435] This data is periodically sent to a server in an encrypted format. The server analyzes the received data using artificial intelligence. This AI model is trained to identify abnormal behavioral patterns, such as "prolonged use at night" or "communication containing specific keywords considered dangerous."

[0436] If the server detects an anomaly, it performs a risk assessment based on that information. If a risk exceeding a certain threshold is detected, the server automatically generates an alert. For example, if a specific risk score is reached, a message is created that includes specific actions such as, "Your child is accessing dangerous content. We recommend setting restrictions."

[0437] The user (parent / guardian) will receive this alert on their device. The notification will be sent via push, ensuring it is delivered immediately and securely. Based on this information, the user can take specific actions, such as adjusting filtering software settings or discussing the issue with their child.

[0438] Thus, this system is designed to protect children's safety and provides parents with a means to support their children quickly and effectively. Specifically, the server can monitor internet usage patterns during certain nighttime hours and take swift action based on the results.

[0439] The following describes the processing flow.

[0440] Step 1:

[0441] The device monitors the user's internet activity. Specifically, it collects encrypted logs of URLs of websites visited, history of apps used, and communication content.

[0442] Step 2:

[0443] The device sends the collected data to the server at regular intervals. For security reasons, the transmitted data is encrypted.

[0444] Step 3:

[0445] The server saves the data received from the terminal to a database. During this process, it checks whether the data is complete and prompts the user to resend it if there are any problems.

[0446] Step 4:

[0447] The server analyzes the stored data using an artificial intelligence model. This model performs pattern recognition to detect abnormal behavior.

[0448] Step 5:

[0449] When abnormal behavior is detected, the server performs a risk assessment. Based on the assessment results, a specific risk score is calculated to determine the severity of the problem.

[0450] Step 6:

[0451] Based on the risk assessment results, the server generates an alert for parents. This alert includes specific issues and recommended actions.

[0452] Step 7:

[0453] The server quickly pushes the generated alerts to the user's device. These notifications are sent in real time to immediately alert the user.

[0454] Step 8:

[0455] The user checks the alerts received on their device and, based on the content, takes necessary actions such as changing filter settings or talking to their child.

[0456] (Example 1)

[0457] 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."

[0458] In today's digital society, ensuring the safety of children when using information processing devices is crucial, but it is not practical for supervisors to directly monitor all their activities. Therefore, there is a need for an effective system that allows supervisors to remotely and appropriately monitor children's digital activities, detect potential dangers in a timely manner, and take appropriate action.

[0459] 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.

[0460] In this invention, the server includes means for monitoring a child's use of an information processing device, means for transferring information acquired from the processing device to a storage device, and means for analyzing the transferred information and using machine learning to detect abnormal behavior patterns. This allows supervisors to effectively monitor a child's behavior and immediately conduct risk assessments and take countermeasures when an anomaly is detected.

[0461] An "information processing device" is an electronic device used for processing and communicating digital data, and includes computers and smartphones.

[0462] A "storage device" refers to a mechanical or electronic device used to store data, and includes hard disks and cloud storage.

[0463] "Transfer" refers to the act of moving data from one location to another, specifically the process of sending information to a server via a network.

[0464] "Analysis" refers to the process of examining acquired data in detail to reveal underlying patterns and anomalies.

[0465] An "abnormal behavior pattern" refers to irregular actions that deviate from normal usage or past usage history, and is subject to risk assessment.

[0466] "Machine learning" refers to the technology that uses algorithms based on large amounts of data to learn patterns and perform predictions and identifications.

[0467] "Monitoring" refers to the act of continuously observing and recording the usage and activity of a device.

[0468] A "supervisor" refers to a person responsible for managing a child's digital activities and ensuring their safety; this is usually a parent or guardian.

[0469] "Risk assessment" is the process of determining the potential dangers associated with detected anomalies and considering appropriate countermeasures.

[0470] "Notifications" are short messages that convey important information or warnings to users.

[0471] This invention is a system for safely managing children's internet use, and specific embodiments are shown below.

[0472] First, dedicated monitoring software is installed on the device used by the child user. This software runs on an information processing device that processes digital data and collects information such as internet usage and application usage history. Specifically, the device periodically retrieves a list of applications used and a history of websites visited, and stores this data in a storage device while maintaining confidentiality using AES encryption technology.

[0473] Next, the device transfers the collected data to the server via the internet connection. After receiving this data, the server uses a generative AI model to analyze it. This model learns patterns based on a large amount of data and can identify abnormal patterns such as "unusually long usage times at night" or "communication containing dangerous keywords." When the server detects such anomalies, it performs a risk assessment and generates a notification that includes specific countermeasures if necessary.

[0474] Notifications are immediately pushed to the parent's device. This allows parents to stay informed of the situation through a secure means of communication and, if necessary, change device settings or communicate with their child. For example, in response to the prompt "What criteria should I use to determine if my child's internet use is safe?", the server can generate appropriate risk assessments and suggested countermeasures.

[0475] The technologies and methods used in implementing this invention are diverse, effectively combining information security, data analysis, and real-time communication. This system configuration ensures the safety of the digital environment surrounding children, allowing parents to use its protective features with peace of mind.

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] The device monitors the digital activities of the child who is the user. Specifically, it collects data every minute, such as internet connection status, applications being used, and website visit history. For example, the device logs the name of the currently active application and the URLs of the websites visited. This becomes the input data.

[0479] Step 2:

[0480] The device securely converts the collected data using AES encryption technology. This encrypted data is then ready for transmission to the server while protecting privacy. The device's role is to ensure accurate data transmission while preventing the leakage of user information. The encrypted data becomes the output data.

[0481] Step 3:

[0482] The terminal sends encrypted data to the server over the internet. The transmission uses a secure communication protocol and is performed in batches every hour. This allows the server to process a large amount of data at once. The transmitted encrypted data becomes the server's input data.

[0483] Step 4:

[0484] After decrypting the received data, the server performs analysis using a generating AI model. The purpose of the analysis is to identify abnormal operating patterns, such as long-term use or communications containing dangerous keywords. In this step, the AI ​​compares the data with past data to identify anomalies. The analysis results become the output data.

[0485] Step 5:

[0486] The server performs a risk assessment based on the analysis results and generates notifications if necessary. Risk assessment is the process of creating alert messages when certain thresholds are exceeded. Notifications include information such as "Your usage time is too long" or "You are accessing a dangerous site." The generated notifications are the output data.

[0487] Step 6:

[0488] The server pushes generated notifications to the user's device (the parent / guardian). These notifications are sent in real time, allowing parents to immediately consider appropriate actions. Receiving notifications in real time enables parents to instantly understand their child's activities. The notifications serve as input data for the user.

[0489] Step 7:

[0490] Parents, as users, manage their children's devices based on the notifications they receive. Specifically, they take measures such as strengthening filter settings and communicating with their children as needed. This helps keep children's digital activities safe. Parental actions become the overall output of the system.

[0491] (Application Example 1)

[0492] 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."

[0493] In today's world, where children's online activities using information devices are increasing, parents are required to appropriately monitor their children's activities and ensure their safety. However, current systems lack mechanisms to effectively detect abnormal behavior and respond quickly. Furthermore, there are problems with the difficulty of utilizing these systems within the home.

[0494] 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.

[0495] In this invention, the server includes means for monitoring a child's use of an information terminal, means for being installed on a home device and communicating with the terminal, and means for using data processing technology to detect abnormal behavioral patterns. This enables parents to properly understand their child's use of the information terminal, quickly detect abnormalities, and take appropriate measures.

[0496] "Means for monitoring children's use of information devices" refers to technical means for continuously tracking and recording activity on information devices used by children.

[0497] "Means of sending data acquired from a terminal to a server" refers to communication protocols and infrastructure for sending monitored information to an external data management system.

[0498] "Means of using data processing techniques to detect abnormal behavioral patterns" refer to analytical methods and algorithms that analyze acquired data and identify actions that deviate from predefined norms.

[0499] "Means for evaluating and generating information when an anomaly is detected" refers to means for evaluating the risk based on the detected anomaly and creating information to warn parents as necessary.

[0500] "Means of notifying users of generated information" refers to communication methods or notification systems for immediately conveying created warnings and notices to parents.

[0501] "A means of installing software on home devices and communicating with terminals" refers to a method of aggregating and relaying data by incorporating software into devices installed in homes and communicating with information terminals.

[0502] The system that implements this application consists of a series of processes that monitor the use of information devices used by children, analyze the collected data on a server to manage their activities, and notify parents. Software installed on home devices works in conjunction with information devices to track their usage and communication content in real time. The obtained data is encrypted and transmitted to a remote server via the home network.

[0503] The server incorporates data processing technology and artificial intelligence to detect abnormal behavioral patterns. Specifically, it analyzes the usage time of information terminals, the types of websites accessed, and the content of applications used. For example, if it detects situations such as usage exceeding normal hours at night or access to potentially harmful websites, the server analyzes the information and performs a risk assessment.

[0504] Based on the detected anomalies, the server generates a warning message as an evaluation result. This message is immediately displayed as a push notification on the parent's device via home equipment. This allows parents to monitor their child's online activity in real time and take appropriate action.

[0505] As a concrete example, here is an example of a prompt message that the server might generate. For example, it might say, "Generate a warning message to be generated when a robot that monitors a child's digital device usage detects an abnormal usage pattern. Specifically, let's say a large amount of data was used at night." This prompt message triggers an AI model to generate an appropriate warning message, which is then sent to the parent or guardian.

[0506] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0507] Step 1:

[0508] The device collects usage and communication history data in real time. Specifically, monitoring software on the device logs application usage time, URLs of visited websites, and message sending and receiving history. The input data obtained through this process is prepared as the device's operation log.

[0509] Step 2:

[0510] The terminal encrypts the collected data and sends it to the server. Specifically, the terminal encrypts the acquired log data using an encryption algorithm and transfers it to the server using a secure communication protocol. Through this process, the log data is delivered to the server as encrypted security data.

[0511] Step 3:

[0512] The server decrypts the received encrypted data to prepare it for analysis. It receives encrypted child usage data as input and securely decrypts it using a dedicated decryption algorithm. This process makes the decrypted usage data available for analysis on the server.

[0513] Step 4:

[0514] The server applies data processing techniques to detect abnormal behavioral patterns using the decrypted data. Specifically, it uses an AI model to analyze the data and pattern recognition techniques to identify unusual behavior. This process yields anomaly detection analysis results.

[0515] Step 5:

[0516] The server performs a risk assessment and generates information when an anomaly is detected. Specifically, it calculates a risk score for the detected abnormal behavior and creates a warning message based on that score. This process generates warning information to notify parents.

[0517] Step 6:

[0518] The server notifies the user of the generated warning information via home devices. Specifically, the server sends the warning message as a push notification to the user's mobile device. This process allows the user to receive the warning immediately and consider appropriate action.

[0519] 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.

[0520] This invention is a system that combines an emotion engine to monitor and analyze children's use of information terminals and detect abnormal behavioral patterns. This enables more advanced risk assessment and notification to users (parents).

[0521] In addition to conventional monitoring software, the device will be equipped with an emotion engine that identifies the user's emotional state. This engine uses facial recognition technology and voice analysis to analyze the user's facial expressions and voice while using the device and identify their emotions at that time. For example, it can identify emotions such as joy, anger, and sadness from facial expressions in real time.

[0522] The usage and emotional data collected by the device are sent to the server in an encrypted format. Upon receiving this data, the server analyzes the emotional data in addition to the conventional online activity data. The artificial intelligence model is designed to place particular importance on emotional changes that occur during abnormal behavior. For example, it identifies patterns such as emotional instability that may occur when a child has prolonged access to inappropriate content.

[0523] Emotional data assists the server in detecting anomalies and assessing risks. The emotional engine incorporates recognized emotional changes, enabling more accurate risk assessments. For example, a sudden change in emotion may indicate a stressful state, leading to a high-risk assessment and prompting a high-priority notification to parents.

[0524] Users (parents) can receive these alerts in real time. The notifications include specific situations and recommended actions, such as, "We have detected emotional fluctuations due to content your child recently accessed. We recommend talking to your child."

[0525] Thus, the present invention is a system that utilizes both online activity and emotional data to enhance child protection and enable appropriate information provision and prompt response to parents. For example, if a message a child receives is accompanied by an emotional change, parents can immediately understand the situation and take appropriate measures.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] The device collects data on the user's online activity and emotions. This process involves identifying emotions from the user's facial expressions and voice using the camera and microphone, in addition to website visit history and app usage.

[0529] Step 2:

[0530] The device encrypts the collected online activity data and sentiment data and sends it to the server using a secure communication protocol.

[0531] Step 3:

[0532] The server stores the received data in the database. During storage, it verifies the data's integrity and requests retransmission of any missing data if necessary.

[0533] Step 4:

[0534] The server begins analysis using an artificial intelligence model based on the stored data. This model specifically analyzes the relationship between online activity and sentiment data acquired simultaneously.

[0535] Step 5:

[0536] When the server detects abnormal behavioral patterns or sudden emotional changes, it performs a risk assessment based on these findings. This assessment also takes into account the magnitude of the emotional changes to calculate an overall risk score.

[0537] Step 6:

[0538] Based on the risk assessment results, the server generates an alert that includes specific countermeasures. This alert describes the details of the detected anomaly and recommended actions for parents.

[0539] Step 7:

[0540] The server sends alerts to the user's device via push notifications. These notifications are timely and serve to immediately communicate the situation.

[0541] Step 8:

[0542] Users can check alerts on their devices. This allows them to understand the specific situation and take appropriate action, such as adjusting settings or talking to their children, as needed.

[0543] (Example 2)

[0544] 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."

[0545] In modern society, there is a need to effectively monitor children's computer usage and to quickly detect and respond to potential abnormal behavior and the impact of inappropriate content. However, conventional systems are limited to superficial analysis of usage data and lack the ability to conduct deep risk assessments based on the user's emotional state. Furthermore, notifications to parents often fail to consider changes in emotions, resulting in a lack of concrete suggestions for countermeasures.

[0546] 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.

[0547] In this invention, the server includes means for using machine intelligence to analyze transmitted information and detect abnormal behavioral characteristics, means including an emotion analysis function for identifying emotional states, and means for receiving emotional data and analyzing it in combination with behavioral characteristics. This makes it possible to improve the accuracy of risk assessment, including changes in a child's emotions, and to notify parents of specific coping methods.

[0548] "Computer equipment" refers to devices used for data processing, or computer equipment in general.

[0549] A "data aggregation device" refers to an information processing system for centrally storing and managing data.

[0550] "Information" refers to all data related to the use of computer devices and user activity logs.

[0551] "Machine intelligence" refers to algorithms that use artificial intelligence technology to perform pattern recognition and anomaly detection.

[0552] A "warning" refers to a cautionary message sent to parents or guardians when an abnormality is detected.

[0553] "Emotion analysis function" refers to technology that identifies a user's emotional state from their voice and facial expressions and collects that data.

[0554] This system is equipped with advanced analytical capabilities to monitor children's use of computer devices and detect abnormal behavior.

[0555] Terminal:

[0556] In addition to conventional monitoring software, the terminal is equipped with an emotion analysis function to identify the user's emotional state. This function uses facial recognition and voice analysis technologies to acquire emotional data from the user's facial expressions and voice. Specifically, it uses a general facial recognition library for facial recognition and a speech-to-text service for voice analysis. The terminal encrypts this information and transmits the data to the integrator using a secure protocol.

[0557] server:

[0558] The server is responsible for receiving and analyzing data transmitted from terminals. The received information is analyzed using machine intelligence algorithms to detect abnormal behavioral characteristics and assess risks. This analysis includes correlation analysis of behavioral and emotional data, and alerts are generated if significant emotional changes are detected. This analysis is made more efficient by using generative AI models.

[0559] User (parent / guardian):

[0560] Users receive alerts from the server. These notifications include situation-specific action recommendations based on analysis of sentiment data. For example, a message might read, "Significant changes in your child's emotions were observed during their browsing activity. We recommend reviewing the content and having a discussion."

[0561] Specific example:

[0562] For example, if a child is experiencing stress from in-game chat, the emotion analysis function can detect this change. The server can then identify it as an anomaly and send a notification to the parent. In this way, it becomes possible to comprehensively manage a child's emotional changes while using a computer.

[0563] Example of a prompt:

[0564] "This sentiment analysis system monitors access to inappropriate content and changes in users' emotions. Could you please explain the specific criteria used for making these judgments?"

[0565] This invention can ensure children's safe online activities and provide support to help parents understand the situation appropriately.

[0566] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0567] Step 1:

[0568] Device: The device collects input data regarding the user's usage and emotional state. This data includes application usage history on the device, as well as facial and voice data acquired from the camera and microphone. The emotion analysis function processes this data and determines emotions such as joy and anger in real time from facial expressions. The emotional data obtained from this analysis is integrated with usage data.

[0569] Step 2:

[0570] Terminal: The integrated data is encrypted using the AES-256 encryption algorithm. The encrypted data is securely transmitted to the server via the HTTPS communication protocol. Encryption is to prevent external eavesdropping and tampering with the data.

[0571] Step 3:

[0572] Server: The server decrypts the received dataset and stores it in the database. The data includes online behavioral history and sentiment ratings. An AI algorithm uses this data to analyze behavioral characteristics and patterns of sentiment change to determine whether abnormal behavior is detected. The output here is the risk level if an anomaly is detected.

[0573] Step 4:

[0574] Server: When the risk level is high, a generative AI model is used to suggest specific recommended actions and countermeasures. This process uses predictive analytics to identify anomalies and refine risk assessments by comparing them with historical data. The output is information that includes recommendations tailored to individual cases.

[0575] Step 5:

[0576] Server: The server generates final alerts and recommendations and notifies the user (parent / guardian). These notifications include real-time changes in sentiment and behavioral patterns, as well as recommended actions. Notification methods include email and push notifications via mobile apps.

[0577] Step 6:

[0578] User (Parent): Based on the alerts and recommendations received, users can communicate with their children and set up management policies. This allows them to respond quickly to potential risks in their children's online activities.

[0579] (Application Example 2)

[0580] 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."

[0581] There is a need to monitor inappropriate use of information processing devices and impulsive electronic payment behavior by children and minors in real time, and to promptly and effectively notify parents or guardians. In particular, a mechanism is needed to prevent unsafe spending by considering the influence of emotional changes on purchasing behavior.

[0582] 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.

[0583] In this invention, the server includes means for monitoring the user's activity of the information processing device, means for using facial expression and voice analysis technology to determine the acquired emotional state, and means for detecting abnormal behavior based on the activity time of the information processing device, the type of access destination, and emotional fluctuations. This prevents inappropriate electronic payments by children and minors, enabling them to engage in digital activities with peace of mind.

[0584] "User" refers to an individual who operates an information processing device or a person who supervises the actions of an individual acting on their behalf.

[0585] An "information processing device" is a computing device used for inputting, processing, and outputting data.

[0586] "Activity" refers to a series of actions or operations performed on an information processing device.

[0587] A "server" is a computer system that processes and analyzes information and provides services to clients.

[0588] "Emotional state" refers to the emotional situation or changes in that state that the user expresses through facial expressions and voice.

[0589] "Facial expression analysis technology" is a technique that uses digital image processing to identify emotions from an individual's facial movements.

[0590] "Voice analysis technology" is a technology that analyzes the characteristics of voice to identify emotions and intentions.

[0591] "Activity time" refers to the length of time that an information processing device is in use.

[0592] "Type of access destination" refers to the category and characteristics of external resources that can be reached from the information processing device.

[0593] "Emotional fluctuation" refers to the movement of a user's emotional state over time.

[0594] "Abnormal behavior" refers to activity on an information processing device that deviates from the normally acceptable range.

[0595] "Notification" refers to the act or means of quickly conveying certain information to others.

[0596] To implement this invention, three main entities are involved: a server, a terminal, and a user. The server receives data transmitted from the terminal, which is an information processing device, and identifies abnormal behavior based on that data. Furthermore, the server has an emotion analysis function built in, and uses facial expression analysis technology and voice analysis technology to identify emotional states in real time. The identified emotion data is analyzed together with the device's activity data using machine learning technology to detect abnormal behavior.

[0597] The terminal is an information processing device operated by the user, and it uses a camera and microphone to record the user's facial expressions and voice in real time. The acquired data is temporarily processed within the terminal and sent to the server in an encoded format. Encryption of this data is important to maintain the security of the communication.

[0598] When abnormal behavior is detected by the server, the user receives an immediate notification. The notification includes specific countermeasures based on the user's emotional state and behavioral data. This allows the user to prevent themselves from engaging in inappropriate behavior and to take necessary actions quickly.

[0599] For example, in an electronic payment service, when a user attempts an impulse purchase, the server detects the emotional fluctuation and automatically issues a warning as an anomaly. A specific example of a prompt message would be: "Analyze the child's emotional fluctuation patterns and current payment activity to identify potential unsafe purchases. For example, if the child impulsively attempts to purchase an expensive item they don't normally buy." Based on this prompt, a generative AI model performs an analysis to obtain the optimal result.

[0600] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0601] Step 1:

[0602] The device records the user's facial expressions and voice in real time using its camera and microphone. It receives video data from the camera and audio data from the microphone as input. As output, this data is temporarily stored within the device and then encrypted. Encryption is performed to ensure privacy and improve communication security.

[0603] Step 2:

[0604] The device sends encrypted data to the server. This data includes the user's facial expressions and voice data, and processing begins when the server receives it. The input is the encoded data sent from the device, and the output is the data awaiting analysis that has arrived at the server.

[0605] Step 3:

[0606] The server decodes the received data and analyzes the emotional state using facial expression analysis and voice analysis technologies. The input is the decoded data; the AI ​​model identifies emotions from facial expressions and evaluates the emotional state through voice analysis. The output is the analyzed emotional data.

[0607] Step 4:

[0608] The server uses machine learning techniques to integrate emotional data and activity data from the device and analyzes it to detect abnormal behavior. The input consists of emotional and activity data, and it automatically detects abnormal behavior based on indicators. The output is data indicating the presence or absence of abnormal behavior and its nature.

[0609] Step 5:

[0610] The server converts detected abnormal behavior into alerts based on risk assessments and generates them. Inputs include data on the abnormal behavior and information on the risk level. Outputs are specific alert messages to be sent to parents.

[0611] Step 6:

[0612] Users receive alerts sent from the server and view notifications in real time. These notifications include specific actions based on emotional fluctuations and behavioral data. Input is the alert message from the server, and output is the notification displayed to the user along with specific action instructions.

[0613] Step 7:

[0614] Users take appropriate action based on the notification content. This includes actions such as providing instructions to the user or changing settings. The input is the choice of action in accordance with the instructions in the notification, and the output is the prevention or correction of the resulting inappropriate behavior.

[0615] 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.

[0616] 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 the following. 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 indicated 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.

[0617] 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.

[0618] [Fourth Embodiment]

[0619] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0620] 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.

[0621] 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).

[0622] 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.

[0623] 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.

[0624] 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).

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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.

[0631] 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".

[0632] This invention is a system for monitoring children's use of information terminals and providing appropriate information to parents. The following describes the details of an embodiment of the system.

[0633] First, dedicated monitoring software is installed on the device used by the child. This device continuously collects data such as internet connection activity, application usage, and message sending and receiving history. For example, the device records the time it was used at night and the URLs of websites visited, accumulating basic data to detect abnormal behavioral patterns.

[0634] This data is periodically sent to a server in an encrypted format. The server analyzes the received data using artificial intelligence. This AI model is trained to identify abnormal behavioral patterns, such as "prolonged use at night" or "communication containing specific keywords considered dangerous."

[0635] If the server detects an anomaly, it performs a risk assessment based on that information. If a risk exceeding a certain threshold is detected, the server automatically generates an alert. For example, if a specific risk score is reached, a message is created that includes specific actions such as, "Your child is accessing dangerous content. We recommend setting restrictions."

[0636] The user (parent / guardian) will receive this alert on their device. The notification will be sent via push, ensuring it is delivered immediately and securely. Based on this information, the user can take specific actions, such as adjusting filtering software settings or discussing the issue with their child.

[0637] Thus, this system is designed to protect children's safety and provides parents with a means to support their children quickly and effectively. Specifically, the server can monitor internet usage patterns during certain nighttime hours and take swift action based on the results.

[0638] The following describes the processing flow.

[0639] Step 1:

[0640] The device monitors the user's internet activity. Specifically, it collects encrypted logs of URLs of websites visited, history of apps used, and communication content.

[0641] Step 2:

[0642] The device sends the collected data to the server at regular intervals. For security reasons, the transmitted data is encrypted.

[0643] Step 3:

[0644] The server saves the data received from the terminal to a database. During this process, it checks whether the data is complete and prompts the user to resend it if there are any problems.

[0645] Step 4:

[0646] The server analyzes the stored data using an artificial intelligence model. This model performs pattern recognition to detect abnormal behavior.

[0647] Step 5:

[0648] When abnormal behavior is detected, the server performs a risk assessment. Based on the assessment results, a specific risk score is calculated to determine the severity of the problem.

[0649] Step 6:

[0650] Based on the risk assessment results, the server generates an alert for parents. This alert includes specific issues and recommended actions.

[0651] Step 7:

[0652] The server quickly pushes the generated alerts to the user's device. These notifications are sent in real time to immediately alert the user.

[0653] Step 8:

[0654] The user checks the alerts received on their device and, based on the content, takes necessary actions such as changing filter settings or talking to their child.

[0655] (Example 1)

[0656] 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".

[0657] In today's digital society, ensuring the safety of children when using information processing devices is crucial, but it is not practical for supervisors to directly monitor all their activities. Therefore, there is a need for an effective system that allows supervisors to remotely and appropriately monitor children's digital activities, detect potential dangers in a timely manner, and take appropriate action.

[0658] 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.

[0659] In this invention, the server includes means for monitoring a child's use of an information processing device, means for transferring information acquired from the processing device to a storage device, and means for analyzing the transferred information and using machine learning to detect abnormal behavior patterns. This allows supervisors to effectively monitor a child's behavior and immediately conduct risk assessments and take countermeasures when an anomaly is detected.

[0660] An "information processing device" is an electronic device used for processing and communicating digital data, and includes computers and smartphones.

[0661] A "storage device" refers to a mechanical or electronic device used to store data, and includes hard disks and cloud storage.

[0662] "Transfer" refers to the act of moving data from one location to another, specifically the process of sending information to a server via a network.

[0663] "Analysis" refers to the process of examining acquired data in detail to reveal underlying patterns and anomalies.

[0664] An "abnormal behavior pattern" refers to irregular actions that deviate from normal usage or past usage history, and is subject to risk assessment.

[0665] "Machine learning" refers to the technology that uses algorithms based on large amounts of data to learn patterns and perform predictions and identifications.

[0666] "Monitoring" refers to the act of continuously observing and recording the usage and activity of a device.

[0667] A "supervisor" refers to a person responsible for managing a child's digital activities and ensuring their safety; this is usually a parent or guardian.

[0668] "Risk assessment" is the process of determining the potential dangers associated with detected anomalies and considering appropriate countermeasures.

[0669] "Notifications" are short messages that convey important information or warnings to users.

[0670] This invention is a system for safely managing children's internet use, and specific embodiments are shown below.

[0671] First, dedicated monitoring software is installed on the device used by the child user. This software runs on an information processing device that processes digital data and collects information such as internet usage and application usage history. Specifically, the device periodically retrieves a list of applications used and a history of websites visited, and stores this data in a storage device while maintaining confidentiality using AES encryption technology.

[0672] Next, the device transfers the collected data to the server via the internet connection. After receiving this data, the server uses a generative AI model to analyze it. This model learns patterns based on a large amount of data and can identify abnormal patterns such as "unusually long usage times at night" or "communication containing dangerous keywords." When the server detects such anomalies, it performs a risk assessment and generates a notification that includes specific countermeasures if necessary.

[0673] Notifications are immediately pushed to the parent's device. This allows parents to stay informed of the situation through a secure means of communication and, if necessary, change device settings or communicate with their child. For example, in response to the prompt "What criteria should I use to determine if my child's internet use is safe?", the server can generate appropriate risk assessments and suggested countermeasures.

[0674] The technologies and methods used in implementing this invention are diverse, effectively combining information security, data analysis, and real-time communication. This system configuration ensures the safety of the digital environment surrounding children, allowing parents to use its protective features with peace of mind.

[0675] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0676] Step 1:

[0677] The device monitors the digital activities of the child who is the user. Specifically, it collects data every minute, such as internet connection status, applications being used, and website visit history. For example, the device logs the name of the currently active application and the URLs of the websites visited. This becomes the input data.

[0678] Step 2:

[0679] The device securely converts the collected data using AES encryption technology. This encrypted data is then ready for transmission to the server while protecting privacy. The device's role is to ensure accurate data transmission while preventing the leakage of user information. The encrypted data becomes the output data.

[0680] Step 3:

[0681] The terminal sends encrypted data to the server over the internet. The transmission uses a secure communication protocol and is performed in batches every hour. This allows the server to process a large amount of data at once. The transmitted encrypted data becomes the server's input data.

[0682] Step 4:

[0683] After decrypting the received data, the server performs analysis using a generating AI model. The purpose of the analysis is to identify abnormal operating patterns, such as long-term use or communications containing dangerous keywords. In this step, the AI ​​compares the data with past data to identify anomalies. The analysis results become the output data.

[0684] Step 5:

[0685] The server performs a risk assessment based on the analysis results and generates notifications if necessary. Risk assessment is the process of creating alert messages when certain thresholds are exceeded. Notifications include information such as "Your usage time is too long" or "You are accessing a dangerous site." The generated notifications are the output data.

[0686] Step 6:

[0687] The server pushes generated notifications to the user's device (the parent / guardian). These notifications are sent in real time, allowing parents to immediately consider appropriate actions. Receiving notifications in real time enables parents to instantly understand their child's activities. The notifications serve as input data for the user.

[0688] Step 7:

[0689] Parents, as users, manage their children's devices based on the notifications they receive. Specifically, they take measures such as strengthening filter settings and communicating with their children as needed. This helps keep children's digital activities safe. Parental actions become the overall output of the system.

[0690] (Application Example 1)

[0691] 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".

[0692] In today's world, where children's online activities using information devices are increasing, parents are required to appropriately monitor their children's activities and ensure their safety. However, current systems lack mechanisms to effectively detect abnormal behavior and respond quickly. Furthermore, there are problems with the difficulty of utilizing these systems within the home.

[0693] 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.

[0694] In this invention, the server includes means for monitoring a child's use of an information terminal, means for being installed on a home device and communicating with the terminal, and means for using data processing technology to detect abnormal behavioral patterns. This enables parents to properly understand their child's use of the information terminal, quickly detect abnormalities, and take appropriate measures.

[0695] "Means for monitoring children's use of information devices" refers to technical means for continuously tracking and recording activity on information devices used by children.

[0696] "Means of sending data acquired from a terminal to a server" refers to communication protocols and infrastructure for sending monitored information to an external data management system.

[0697] "Means of using data processing techniques to detect abnormal behavioral patterns" refer to analytical methods and algorithms that analyze acquired data and identify actions that deviate from predefined norms.

[0698] "Means for evaluating and generating information when an anomaly is detected" refers to means for evaluating the risk based on the detected anomaly and creating information to warn parents as necessary.

[0699] "Means of notifying users of generated information" refers to communication methods or notification systems for immediately conveying created warnings and notices to parents.

[0700] "A means of installing software on home devices and communicating with terminals" refers to a method of aggregating and relaying data by incorporating software into devices installed in homes and communicating with information terminals.

[0701] The system that implements this application consists of a series of processes that monitor the use of information devices used by children, analyze the collected data on a server to manage their activities, and notify parents. Software installed on home devices works in conjunction with information devices to track their usage and communication content in real time. The obtained data is encrypted and transmitted to a remote server via the home network.

[0702] The server incorporates data processing technology and artificial intelligence to detect abnormal behavioral patterns. Specifically, it analyzes the usage time of information terminals, the types of websites accessed, and the content of applications used. For example, if it detects situations such as usage exceeding normal hours at night or access to potentially harmful websites, the server analyzes the information and performs a risk assessment.

[0703] Based on the detected anomalies, the server generates a warning message as an evaluation result. This message is immediately displayed as a push notification on the parent's device via home equipment. This allows parents to monitor their child's online activity in real time and take appropriate action.

[0704] As a concrete example, here is an example of a prompt message that the server might generate. For example, it might say, "Generate a warning message to be generated when a robot that monitors a child's digital device usage detects an abnormal usage pattern. Specifically, let's say a large amount of data was used at night." This prompt message triggers an AI model to generate an appropriate warning message, which is then sent to the parent or guardian.

[0705] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0706] Step 1:

[0707] The device collects usage and communication history data in real time. Specifically, monitoring software on the device logs application usage time, URLs of visited websites, and message sending and receiving history. The input data obtained through this process is prepared as the device's operation log.

[0708] Step 2:

[0709] The terminal encrypts the collected data and sends it to the server. Specifically, the terminal encrypts the acquired log data using an encryption algorithm and transfers it to the server using a secure communication protocol. Through this process, the log data is delivered to the server as encrypted security data.

[0710] Step 3:

[0711] The server decrypts the received encrypted data to prepare it for analysis. It receives encrypted child usage data as input and securely decrypts it using a dedicated decryption algorithm. This process makes the decrypted usage data available for analysis on the server.

[0712] Step 4:

[0713] The server applies data processing techniques to detect abnormal behavioral patterns using the decrypted data. Specifically, it uses an AI model to analyze the data and pattern recognition techniques to identify unusual behavior. This process yields anomaly detection analysis results.

[0714] Step 5:

[0715] The server performs a risk assessment and generates information when an anomaly is detected. Specifically, it calculates a risk score for the detected abnormal behavior and creates a warning message based on that score. This process generates warning information to notify parents.

[0716] Step 6:

[0717] The server notifies the user of the generated warning information via home devices. Specifically, the server sends the warning message as a push notification to the user's mobile device. This process allows the user to receive the warning immediately and consider appropriate action.

[0718] 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.

[0719] This invention is a system that combines an emotion engine to monitor and analyze children's use of information terminals and detect abnormal behavioral patterns. This enables more advanced risk assessment and notification to users (parents).

[0720] In addition to conventional monitoring software, the device will be equipped with an emotion engine that identifies the user's emotional state. This engine uses facial recognition technology and voice analysis to analyze the user's facial expressions and voice while using the device and identify their emotions at that time. For example, it can identify emotions such as joy, anger, and sadness from facial expressions in real time.

[0721] The usage and emotional data collected by the device are sent to the server in an encrypted format. Upon receiving this data, the server analyzes the emotional data in addition to the conventional online activity data. The artificial intelligence model is designed to place particular importance on emotional changes that occur during abnormal behavior. For example, it identifies patterns such as emotional instability that may occur when a child has prolonged access to inappropriate content.

[0722] Emotional data assists the server in detecting anomalies and assessing risks. The emotional engine incorporates recognized emotional changes, enabling more accurate risk assessments. For example, a sudden change in emotion may indicate a stressful state, leading to a high-risk assessment and prompting a high-priority notification to parents.

[0723] Users (parents) can receive these alerts in real time. The notifications include specific situations and recommended actions, such as, "We have detected emotional fluctuations due to content your child recently accessed. We recommend talking to your child."

[0724] Thus, the present invention is a system that utilizes both online activity and emotional data to enhance child protection and enable appropriate information provision and prompt response to parents. For example, if a message a child receives is accompanied by an emotional change, parents can immediately understand the situation and take appropriate measures.

[0725] The following describes the processing flow.

[0726] Step 1:

[0727] The device collects data on the user's online activity and emotions. This process involves identifying emotions from the user's facial expressions and voice using the camera and microphone, in addition to website visit history and app usage.

[0728] Step 2:

[0729] The device encrypts the collected online activity data and sentiment data and sends it to the server using a secure communication protocol.

[0730] Step 3:

[0731] The server stores the received data in the database. During storage, it verifies the data's integrity and requests retransmission of any missing data if necessary.

[0732] Step 4:

[0733] The server begins analysis using an artificial intelligence model based on the stored data. This model specifically analyzes the relationship between online activity and sentiment data acquired simultaneously.

[0734] Step 5:

[0735] When the server detects abnormal behavioral patterns or sudden emotional changes, it performs a risk assessment based on these findings. This assessment also takes into account the magnitude of the emotional changes to calculate an overall risk score.

[0736] Step 6:

[0737] Based on the risk assessment results, the server generates an alert that includes specific countermeasures. This alert describes the details of the detected anomaly and recommended actions for parents.

[0738] Step 7:

[0739] The server sends alerts to the user's device via push notifications. These notifications are timely and serve to immediately communicate the situation.

[0740] Step 8:

[0741] Users can check alerts on their devices. This allows them to understand the specific situation and take appropriate action, such as adjusting settings or talking to their children, as needed.

[0742] (Example 2)

[0743] 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".

[0744] In modern society, there is a need to effectively monitor children's computer usage and to quickly detect and respond to potential abnormal behavior and the impact of inappropriate content. However, conventional systems are limited to superficial analysis of usage data and lack the ability to conduct deep risk assessments based on the user's emotional state. Furthermore, notifications to parents often fail to consider changes in emotions, resulting in a lack of concrete suggestions for countermeasures.

[0745] 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.

[0746] In this invention, the server includes means for using machine intelligence to analyze transmitted information and detect abnormal behavioral characteristics, means including an emotion analysis function for identifying emotional states, and means for receiving emotional data and analyzing it in combination with behavioral characteristics. This makes it possible to improve the accuracy of risk assessment, including changes in a child's emotions, and to notify parents of specific coping methods.

[0747] "Computer equipment" refers to devices used for data processing, or computer equipment in general.

[0748] A "data aggregation device" refers to an information processing system for centrally storing and managing data.

[0749] "Information" refers to all data related to the use of computer devices and user activity logs.

[0750] "Machine intelligence" refers to algorithms that use artificial intelligence technology to perform pattern recognition and anomaly detection.

[0751] A "warning" refers to a cautionary message sent to parents or guardians when an abnormality is detected.

[0752] "Emotion analysis function" refers to technology that identifies a user's emotional state from their voice and facial expressions and collects that data.

[0753] This system is equipped with advanced analytical capabilities to monitor children's use of computer devices and detect abnormal behavior.

[0754] Terminal:

[0755] In addition to conventional monitoring software, the terminal is equipped with an emotion analysis function to identify the user's emotional state. This function uses facial recognition and voice analysis technologies to acquire emotional data from the user's facial expressions and voice. Specifically, it uses a general facial recognition library for facial recognition and a speech-to-text service for voice analysis. The terminal encrypts this information and transmits the data to the integrator using a secure protocol.

[0756] server:

[0757] The server is responsible for receiving and analyzing data transmitted from terminals. The received information is analyzed using machine intelligence algorithms to detect abnormal behavioral characteristics and assess risks. This analysis includes correlation analysis of behavioral and emotional data, and alerts are generated if significant emotional changes are detected. This analysis is made more efficient by using generative AI models.

[0758] User (parent / guardian):

[0759] Users receive alerts from the server. These notifications include situation-specific action recommendations based on analysis of sentiment data. For example, a message might read, "Significant changes in your child's emotions were observed during their browsing activity. We recommend reviewing the content and having a discussion."

[0760] Specific example:

[0761] For example, if a child is experiencing stress from in-game chat, the emotion analysis function can detect this change. The server can then identify it as an anomaly and send a notification to the parent. In this way, it becomes possible to comprehensively manage a child's emotional changes while using a computer.

[0762] Example of a prompt:

[0763] "This sentiment analysis system monitors access to inappropriate content and changes in users' emotions. Could you please explain the specific criteria used for making these judgments?"

[0764] This invention can ensure children's safe online activities and provide support to help parents understand the situation appropriately.

[0765] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0766] Step 1:

[0767] Device: The device collects input data regarding the user's usage and emotional state. This data includes application usage history on the device, as well as facial and voice data acquired from the camera and microphone. The emotion analysis function processes this data and determines emotions such as joy and anger in real time from facial expressions. The emotional data obtained from this analysis is integrated with usage data.

[0768] Step 2:

[0769] Terminal: The integrated data is encrypted using the AES-256 encryption algorithm. The encrypted data is securely transmitted to the server via the HTTPS communication protocol. Encryption is to prevent external eavesdropping and tampering with the data.

[0770] Step 3:

[0771] Server: The server decrypts the received dataset and stores it in the database. The data includes online behavioral history and sentiment ratings. An AI algorithm uses this data to analyze behavioral characteristics and patterns of sentiment change to determine whether abnormal behavior is detected. The output here is the risk level if an anomaly is detected.

[0772] Step 4:

[0773] Server: When the risk level is high, a generative AI model is used to suggest specific recommended actions and countermeasures. This process uses predictive analytics to identify anomalies and refine risk assessments by comparing them with historical data. The output is information that includes recommendations tailored to individual cases.

[0774] Step 5:

[0775] Server: The server generates final alerts and recommendations and notifies the user (parent / guardian). These notifications include real-time changes in sentiment and behavioral patterns, as well as recommended actions. Notification methods include email and push notifications via mobile apps.

[0776] Step 6:

[0777] User (Parent): Based on the alerts and recommendations received, users can communicate with their children and set up management policies. This allows them to respond quickly to potential risks in their children's online activities.

[0778] (Application Example 2)

[0779] 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".

[0780] There is a need to monitor inappropriate use of information processing devices and impulsive electronic payment behavior by children and minors in real time, and to promptly and effectively notify parents or guardians. In particular, a mechanism is needed to prevent unsafe spending by considering the influence of emotional changes on purchasing behavior.

[0781] 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.

[0782] In this invention, the server includes means for monitoring the user's activity of the information processing device, means for using facial expression and voice analysis technology to determine the acquired emotional state, and means for detecting abnormal behavior based on the activity time of the information processing device, the type of access destination, and emotional fluctuations. This prevents inappropriate electronic payments by children and minors, enabling them to engage in digital activities with peace of mind.

[0783] "User" refers to an individual who operates an information processing device or a person who supervises the actions of an individual acting on their behalf.

[0784] An "information processing device" is a computing device used for inputting, processing, and outputting data.

[0785] "Activity" refers to a series of actions or operations performed on an information processing device.

[0786] A "server" is a computer system that processes and analyzes information and provides services to clients.

[0787] "Emotional state" refers to the emotional situation or changes in that state that the user expresses through facial expressions and voice.

[0788] "Facial expression analysis technology" is a technique that uses digital image processing to identify emotions from an individual's facial movements.

[0789] "Voice analysis technology" is a technology that analyzes the characteristics of voice to identify emotions and intentions.

[0790] "Activity time" refers to the length of time that an information processing device is in use.

[0791] "Type of access destination" refers to the category and characteristics of external resources that can be reached from the information processing device.

[0792] "Emotional fluctuation" refers to the movement of a user's emotional state over time.

[0793] "Abnormal behavior" refers to activity on an information processing device that deviates from the normally acceptable range.

[0794] "Notification" refers to the act or means of quickly conveying certain information to others.

[0795] To implement this invention, three main entities are involved: a server, a terminal, and a user. The server receives data transmitted from the terminal, which is an information processing device, and identifies abnormal behavior based on that data. Furthermore, the server has an emotion analysis function built in, and uses facial expression analysis technology and voice analysis technology to identify emotional states in real time. The identified emotion data is analyzed together with the device's activity data using machine learning technology to detect abnormal behavior.

[0796] The terminal is an information processing device operated by the user, and it uses a camera and microphone to record the user's facial expressions and voice in real time. The acquired data is temporarily processed within the terminal and sent to the server in an encoded format. Encryption of this data is important to maintain the security of the communication.

[0797] When abnormal behavior is detected by the server, the user receives an immediate notification. The notification includes specific countermeasures based on the user's emotional state and behavioral data. This allows the user to prevent themselves from engaging in inappropriate behavior and to take necessary actions quickly.

[0798] For example, in an electronic payment service, when a user attempts an impulse purchase, the server detects the emotional fluctuation and automatically issues a warning as an anomaly. A specific example of a prompt message would be: "Analyze the child's emotional fluctuation patterns and current payment activity to identify potential unsafe purchases. For example, if the child impulsively attempts to purchase an expensive item they don't normally buy." Based on this prompt, a generative AI model performs an analysis to obtain the optimal result.

[0799] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0800] Step 1:

[0801] The device records the user's facial expressions and voice in real time using its camera and microphone. It receives video data from the camera and audio data from the microphone as input. As output, this data is temporarily stored within the device and then encrypted. Encryption is performed to ensure privacy and improve communication security.

[0802] Step 2:

[0803] The device sends encrypted data to the server. This data includes the user's facial expressions and voice data, and processing begins when the server receives it. The input is the encoded data sent from the device, and the output is the data awaiting analysis that has arrived at the server.

[0804] Step 3:

[0805] The server decodes the received data and analyzes the emotional state using facial expression analysis and voice analysis technologies. The input is the decoded data; the AI ​​model identifies emotions from facial expressions and evaluates the emotional state through voice analysis. The output is the analyzed emotional data.

[0806] Step 4:

[0807] The server uses machine learning techniques to integrate emotional data and activity data from the device and analyzes it to detect abnormal behavior. The input consists of emotional and activity data, and it automatically detects abnormal behavior based on indicators. The output is data indicating the presence or absence of abnormal behavior and its nature.

[0808] Step 5:

[0809] The server converts detected abnormal behavior into alerts based on risk assessments and generates them. Inputs include data on the abnormal behavior and information on the risk level. Outputs are specific alert messages to be sent to parents.

[0810] Step 6:

[0811] Users receive alerts sent from the server and view notifications in real time. These notifications include specific actions based on emotional fluctuations and behavioral data. Input is the alert message from the server, and output is the notification displayed to the user along with specific action instructions.

[0812] Step 7:

[0813] Users take appropriate action based on the notification content. This includes actions such as providing instructions to the user or changing settings. The input is the choice of action in accordance with the instructions in the notification, and the output is the prevention or correction of the resulting inappropriate behavior.

[0814] 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.

[0815] 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 the following. 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 indicated 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.

[0816] 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 robot 414.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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."

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] The following is further disclosed regarding the embodiments described above.

[0836] (Claim 1)

[0837] Means of monitoring children's use of information devices,

[0838] A means of sending data acquired from a terminal to a server,

[0839] A means of using artificial intelligence to analyze transmitted data and detect abnormal behavioral patterns,

[0840] A means of evaluating risk and generating alerts when an anomaly is detected,

[0841] A means of notifying parents of the generated alerts,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, further comprising means for presenting specific measures to parents based on acquired data.

[0845] (Claim 3)

[0846] The system according to claim 1, characterized by including means for detecting abnormal behavior based on the terminal's usage time and the type of access destination.

[0847] "Example 1"

[0848] (Claim 1)

[0849] Means for monitoring children's use of information processing devices,

[0850] A means for transferring information acquired from a processing unit to a storage device,

[0851] A means of using machine learning to analyze the transmitted information and detect abnormal operating patterns,

[0852] A means for evaluating the risk and generating a notification when an anomaly is detected,

[0853] A means of sending the generated notification to the supervisor,

[0854] A system that includes this.

[0855] (Claim 2)

[0856] The system according to claim 1, further comprising means for presenting specific countermeasures to the supervisor based on the acquired information.

[0857] (Claim 3)

[0858] The system according to claim 1, characterized by including means for detecting abnormal operation based on the usage time of the processing device and the type of connection destination.

[0859] "Application Example 1"

[0860] (Claim 1)

[0861] Means of monitoring children's use of information devices,

[0862] A means of sending data acquired from a terminal to a server,

[0863] A means of analyzing transmitted data and using data processing techniques to detect abnormal behavioral patterns,

[0864] A means for performing an evaluation and generating information when an anomaly is detected,

[0865] A means of notifying the user of the generated information,

[0866] A means of communication with a terminal, installed on a home device,

[0867] A system that includes this.

[0868] (Claim 2)

[0869] The system according to claim 1, further comprising means for presenting specific countermeasures to the user based on acquired data.

[0870] (Claim 3)

[0871] The system according to claim 1, characterized by including means for detecting abnormal behavior based on the terminal's usage time and the type of access destination.

[0872] "Example 2 of combining an emotion engine"

[0873] (Claim 1)

[0874] Means for monitoring children's use of computer devices,

[0875] Means for transmitting information acquired from the device to the integrating device,

[0876] A means of using machine intelligence to analyze transmitted information and detect abnormal behavioral characteristics,

[0877] A means for evaluating the risk and generating a warning when an anomaly is detected,

[0878] A means including an emotion analysis function for identifying emotional states,

[0879] A means of receiving emotional data and analyzing it in combination with behavioral characteristics,

[0880] A means of notifying parents of the generated warnings,

[0881] A system that includes this.

[0882] (Claim 2)

[0883] The system according to claim 1, further comprising means for suggesting specific countermeasures based on detected changes in emotion.

[0884] (Claim 3)

[0885] The system according to claim 1, characterized by including means for detecting abnormal behavior based on the usage time of the device and the classification of acquired content.

[0886] "Application example 2 when combining with an emotional engine"

[0887] (Claim 1)

[0888] Means for monitoring the user's activity on the information processing device,

[0889] Means for transmitting information acquired from the device to an information processing device,

[0890] A means of analyzing transmitted information and using machine learning techniques to detect abnormal behavioral patterns,

[0891] A means of using facial expression and voice analysis technology to determine the acquired emotional state,

[0892] A means of evaluating risk and generating alerts when an anomaly is detected,

[0893] A means of notifying the user's representative of the generated alerts,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, further comprising means for presenting specific countermeasures to the user's agent based on the acquired information.

[0897] (Claim 3)

[0898] The system according to claim 1, characterized by including means for detecting abnormal behavior based on the activity time of the information processing device, the type of access destination, and emotional fluctuations. [Explanation of Symbols]

[0899] 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. Means of monitoring children's use of information devices, A means of sending data acquired from a terminal to a server, A means of analyzing transmitted data and using data processing techniques to detect abnormal behavioral patterns, A means for performing an evaluation and generating information when an anomaly is detected, A means of notifying the user of the generated information, A means of communication with a terminal, installed on a home device, A system that includes this.

2. The system according to claim 1, further comprising means for presenting specific countermeasures to the user based on acquired data.

3. The system according to claim 1, characterized by including means for detecting abnormal behavior based on the terminal's usage time and the type of access destination.

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