system

Abstract

We provide the system. [Solution] To assess the risks of online content when children use mobile devices, A means of identifying the harmfulness of content based on data collected using generative AI, Means for controlling access to content based on the results of risk assessment, A means of sending notifications to parents or administrators regarding access deemed to be high-risk, A means of recording children's usage history and displaying it as statistical information, 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 persona chatbot control method 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 One of the problems that modern children face when using mobile devices is access to harmful content on the Internet. This increases the risk that children will see inappropriate information or get involved in troubles on SNS. Also, excessive application billing is a problem for families. In the conventional method, there is no effective countermeasure against these problems other than restricting the access itself. Furthermore, the range of harmful content and troubles changes daily, making it difficult to cope with them. 【Means for Solving the Problems】 【0005】 This invention provides a system using generative AI for when children use mobile devices. This system includes means for identifying the harmfulness of content based on collected data, means for controlling access to content based on the results of risk assessment, means for sending notifications to parents or administrators about access deemed high-risk, and means for recording the child's usage history and displaying it as statistical information. This makes it possible to proactively protect children from harmful content and troubles, and provides support for parents and administrators to appropriately supervise their children. 【0006】 "Generative AI" is an artificial intelligence technology that uses machine learning techniques to generate new information based on collected data. 【0007】 "Risk assessment" is the process of analyzing and determining, using numerical values ​​and indicators, the likelihood that specific content may be harmful or dangerous. 【0008】 "Access control" is a mechanism that allows or denies users access to specific content or services. 【0009】 "Notification sending" is a procedure that informs a user of information when certain conditions are met. 【0010】 "Usage history" refers to data that shows a record of what actions and activities a user has taken. 【0011】 "Statistical information" refers to information that is obtained by analyzing collected data and expressing it in numerical values, graphs, and other forms. [Brief explanation of the drawing] 【0012】 [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]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. 【Mode for Carrying Out the Invention】 【0013】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings. 【0014】 First, the language used in the following description will be explained. [[ID=*]] [[ID=*]] 【0015】 [[ID=*]]<* 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. 【0016】 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. 【0017】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0018】 In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like. 【0019】 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." 【0020】 [First Embodiment] 【0021】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0022】 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. 【0023】 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). 【0024】 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. 【0025】 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. 【0026】 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. 【0027】 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. 【0028】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0029】 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. 【0030】 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. 【0031】 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. 【0032】 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". 【0033】 This invention is a system for evaluating the risks associated with online content and supporting children's safe smartphone use. The system consists of three main components: a server, a terminal, and a user (parent or administrator). 【0034】 Server operation 【0035】 The server continuously collects data from the internet and uses generated AI to identify the harmfulness and risk of problems associated with content. The server stores the collected information in a database and constantly updates the evaluation model. It also assigns a risk score to each piece of content, serving as a criterion for determining whether it is appropriate for children to use. This information is provided upon request from the device. 【0036】 Terminal operation 【0037】 The device monitors all requests for websites, applications, and social media that the child accesses in real time. These requests are sent to a server, and access is determined based on the results of a risk assessment. If access is not permitted, the device immediately blocks access to the content and displays a visual warning message to the user. In addition, the device records the child's usage history, which the user can later refer to in the management screen. 【0038】 User actions 【0039】 The user (parent or administrator) communicates with the device and server through a dedicated application. Users can customize filtering levels and notification settings during the initial setup. This allows for flexible responses tailored to the child's usage. If an anomaly is detected by the server, a notification is immediately sent to the device, which the user can then review. Based on the notification, the user can discuss the issue with their child and take necessary actions. 【0040】 Specific example 【0041】 For example, if a child starts using a new social media application, the device sends all its actions to a server, which then analyzes the application's content. If the generating AI detects potentially harmful content or problems, the server determines it's high-risk and instructs the device to restrict access. Simultaneously, this information is notified to the parent or administrator's device, enabling early intervention. Through this entire system, safety and peace of mind regarding children's smartphone use can be ensured. 【0042】 The following describes the processing flow. 【0043】 Step 1: 【0044】 The server collects data from various information sources on the internet, including diverse media types such as text, images, and videos. The collected data forms the basis for risk analysis. 【0045】 Step 2: 【0046】 Based on the collected data, the server trains and updates a model that uses generative AI to determine the harmfulness of content. This model employs natural language processing and image recognition to assess whether the content is harmful. 【0047】 Step 3: 【0048】 The device monitors all requests accessed by the child and sends each request to the server. This includes website URLs and application activity logs. 【0049】 Step 4: 【0050】 The server receives requests sent from terminals and performs a risk assessment of the content based on them. If the assessment determines that the content is harmful, a "high risk" flag is added. 【0051】 Step 5: 【0052】 The device receives a risk assessment from the server and decides whether to allow or block access. Content deemed high-risk is immediately denied access, and a warning message is displayed on the device screen. 【0053】 Step 6: 【0054】 The device records all usage data and generates statistical information. This information is later sent to a management screen for the parent to refer to. 【0055】 Step 7: 【0056】 Users (parents or administrators) can check the status of devices and servers through the management app and adjust settings as needed. If a risk is detected, a notification will be sent to the parent's device. 【0057】 Step 8: 【0058】 After reviewing the notification, users should discuss the matter with their child and take necessary measures. Furthermore, the system's adaptability can be improved by readjusting filtering criteria and notification settings. 【0059】 (Example 1) 【0060】 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." 【0061】 In today's digital environment, there is a need to effectively avoid harmful internet content and high-risk troubles that children may encounter when using mobile devices. However, it is difficult for parents and administrators to manually manage all risks, and providing an environment in which children can use digital devices safely and with peace of mind remains a challenge. 【0062】 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. 【0063】 In this invention, the server includes data collection means for evaluating the dangers of content on a child's mobile device based on information on the network, means for identifying the harmfulness of the content using generated artificial intelligence, and means for managing access to the content based on the danger evaluation results. This makes it possible to automatically provide an environment in which children can safely use digital content and to significantly reduce the burden on parents and administrators. 【0064】 "Information on a network" refers to a collection of data accessible via digital communication networks such as the internet and intranets. 【0065】 "Portable devices used by children" refers to portable electronic devices such as smartphones and tablets that children use on a daily basis. 【0066】 "Content hazard" is a concept that indicates the degree to which digital content may have a harmful effect on children or pose a risk of causing trouble. 【0067】 "Data collection methods" refer to functions and technologies for acquiring and storing information from a network. 【0068】 "Generated artificial intelligence" refers to an AI model that has been trained through machine learning or deep learning techniques and is capable of performing specific tasks. 【0069】 "Means of identifying hazards" refers to technologies used to analyze collected data and perform a process to assess safety. 【0070】 "Means of managing access to content based on risk assessment results" refers to technologies and methods for dynamically setting permissions or restrictions on access based on a risk assessment of the content. 【0071】 The "modes for carrying out the invention" of this invention are shown below. 【0072】 This invention is a system that assesses the risks of network content when children use mobile devices and supports safe use. This system mainly consists of three elements: a server, a terminal, and a user (parent or administrator). 【0073】 The server is primarily responsible for data collection and evaluation. Specifically, it uses a web crawler to collect various data from the internet. The collected data is then analyzed using a generative AI model to identify harmfulness. This AI model employs natural language processing techniques and performs analysis based on prompts such as, "Evaluate whether this content is safe for children." The analyzed data is managed in a database, and access to content is dynamically configured based on the risk assessment. The evaluation model is also regularly updated using machine learning libraries. 【0074】 The device monitors all website and application requests accessed by the child, communicating with the server to verify their security. Based on the evaluation results received from the server, the device controls access to the content. A network monitoring library is used, and if access is deemed dangerous, it is immediately blocked using the firewall. Furthermore, the device records the child's usage history and displays visual warnings to parents or administrators via a GUI, or sends push notifications. 【0075】 Parents or administrators can customize and manage the entire system through a dedicated application. By adjusting filtering levels and notification settings, flexible responses can be tailored to the child's usage. For example, if the server detects a high risk, an immediate notification is sent, allowing the user to take appropriate action. The management screen provides access to statistical information based on the child's usage history, which can be used to support digital education at home. 【0076】 In this way, the system provides comprehensive support to facilitate user management while keeping children's usage environments safe. 【0077】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0078】 Step 1: 【0079】 The server uses a web crawler to collect information from the internet. The input is a specified list of URLs. The crawler uses this list to visit web pages and retrieve content such as text, images, and audio. The output is the collected data, which is then passed on to the next analysis step. 【0080】 Step 2: 【0081】 The server preprocesses the collected data and then inputs prompts into the generated AI model. Specifically, data cleaning and formatting are performed. The input is the raw data collected in step 1, and the output is data converted into a format suitable for AI analysis. For example, the prompt is passed to the model in the form of a message such as "Evaluate whether this content is safe for children." 【0082】 Step 3: 【0083】 The server receives a response from the generating AI model and evaluates the harmfulness of the content. The input is the analysis results obtained from the AI ​​model in step 2. Based on these results, a risk score is calculated using a specific algorithm. The output is the risk score, which is stored in the database. 【0084】 Step 4: 【0085】 The device monitors requests for websites and applications that the user (child) attempts to access. The input is the user's access request. The device sends this to the server and compares it to a configured risk score. The output is the server's decision to allow or deny access. 【0086】 Step 5: 【0087】 The terminal receives risk assessment results from the server and controls access based on those results. If the risk is determined to be high, the terminal blocks access using the network monitoring library and firewall. The input is the server's assessment result, and the output is whether access is permitted or blocked. 【0088】 Step 6: 【0089】 Users customize settings and receive notifications through a dedicated application. Inputs include user actions and server notifications. The application uses a GUI to visually display information to the user, prompting appropriate setting changes and actions. Outputs include updated settings and warning notifications to the user. 【0090】 (Application Example 1) 【0091】 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." 【0092】 When children use electronic communication devices, there is a risk that they may unintentionally access harmful information or highly dangerous content on the internet. Traditional methods make it difficult to identify these risks in advance, and it has been challenging for parents and administrators to respond immediately. Furthermore, existing systems lack flexibility, preventing administrators from responding individually to different situations. Therefore, there was a need for an innovative system that could ensure children's safety while reducing the burden on administrators. 【0093】 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. 【0094】 In this invention, the server includes means for identifying the degree of harmfulness of content based on information collected using a generative AI, means for controlling access to information based on the results of a risk assessment, and means for sending notifications to the administrator regarding access that is judged to have a high risk score calculated by the generative AI model. This makes it possible to assess the dangers of content used by children in advance and respond flexibly and quickly, thereby ensuring the safety of children and reducing the burden on administrators. 【0095】 "Electronic communication equipment" refers to terminal devices used for digital communication, and devices that allow users to access the internet. 【0096】 "Online information" is a general term for digital content that exists on the internet and is accessible to users. 【0097】 "Risk assessment" is the process of identifying potential risks contained in online information and calculating a risk score based on that. 【0098】 "Generative AI" is a technology that uses artificial intelligence to analyze data and calculate risk scores and levels of harm. 【0099】 "Harmfulness" is an indicator that shows the potential degree of negative impact that content may have on minors and other minors. 【0100】 "Notification" refers to a means of communication used to inform an administrator about a specific event or situation. 【0101】 "Usage history" refers to a record of online information accessed using electronic communication devices, and is information used to understand past usage patterns. 【0102】 This invention is an online safety management system that operates between electronic communication devices used by children and monitoring devices used by parents or administrators. The system aims to pre-assess the risks of online content and notify parents or administrators in real time. 【0103】 The server analyzes online information using a generative AI model and calculates a risk score. Specifically, it uses machine learning frameworks such as TENSORFLOW® and PyTorch to run the generative AI model based on data collected from the internet. This model applies natural language processing technology to evaluate the harmfulness of the content. If the risk score exceeds a set threshold, the server sends a command to restrict access by electronic communication devices. 【0104】 The device monitors the actions of the child user and determines whether or not to grant access based on risk assessment information provided by the server. Furthermore, the device records usage history, which can be later referenced by parents or administrators as usage statistics. 【0105】 Users can use a dedicated monitoring application to check device risk assessment results and their child's usage history in real time. This application is developed using ReactJS and Flutter®, and implements notification functions using technologies such as Google FI® rebase Cloud Messaging. Users can freely customize filtering levels and notification settings through the application. 【0106】 For example, if a child starts using a new video-sharing platform, the server uses a generative AI model to analyze content trends on that platform and extract potentially harmful content. If this analysis shows a high risk score, the server sends a notification to the parent's or administrator's device to encourage early action. An example of a prompt to the generative AI model would be, "Analyze content trends on the new social media platform and extract topics that may be harmful to minors." 【0107】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0108】 Step 1: 【0109】 The server collects content data from the internet. The input is online information, which is then stored in a database. The data is retrieved in text format or as metadata. This prepares the basic data for use in subsequent processing. 【0110】 Step 2: 【0111】 The server feeds the collected data into a generating AI model to determine its level of harmfulness. The input is the online information collected in step 1. The model uses natural language processing techniques to analyze the data and outputs a risk score. This output is a numerical value indicating how dangerous the online information is to the user. 【0112】 Step 3: 【0113】 The server determines whether to grant access to specific online information based on the risk score. The input is the risk score from step 2. A threshold is set to evaluate the score, and if it exceeds the threshold, it is judged as "high risk." Based on this result, it is determined whether access is permitted or restricted. 【0114】 Step 4: 【0115】 The terminal receives an access restriction command from the server. The input is the result of the access permission / denial determination in step 3. Access to information determined to be high-risk is blocked, and a warning message is displayed to the user. This operation ensures the safety of children. 【0116】 Step 5: 【0117】 The terminal records usage history and provides it to the user for later reference. Input is the child's operation log, which is recorded in a database and converted into statistical information. Output is the usage history and statistical information that the user can review. 【0118】 Step 6: 【0119】 Users can check device usage history and risk assessment status through a dedicated application and adjust access settings. Inputs are the history data output from step 5 and the server's risk notification. Based on this, users adjust filtering levels and notification settings to optimize the system. This allows administrators to manage children's internet usage in real time. 【0120】 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. 【0121】 This invention provides a system that not only assesses the risks of online content and ensures safety when children use mobile devices, but also offers a higher level of protection by understanding the user's emotions. This system has a configuration that combines a server, a terminal, and an emotion engine. 【0122】 Server operation 【0123】 The server collects data from the internet and analyzes it using generative AI to identify the harmfulness and risk of content. The results of this analysis are stored in the server's database and updated as needed. Furthermore, the server receives data from an emotion engine and performs emotion-based risk assessments to generate more precise risk scores. 【0124】 Terminal operation 【0125】 The device monitors all content the user accesses. Each request is sent to a server, which returns a risk assessment and user sentiment data analyzed by an emotion engine. Based on this information, the device decides whether to allow access to the content. In addition, the device is equipped with sensors that capture the user's emotions, thereby collecting sentiment data in real time. 【0126】 User actions 【0127】 Parents or administrators can manage various system functions through the application. Specifically, they can customize filtering settings and notification options, and view their child's access history and emotional changes. If the server issues an alert regarding any risk, a notification is sent to the device, including emotional information from the emotion engine. 【0128】 Emotional engine integration 【0129】 The emotion engine recognizes the user's emotions using voice analysis, facial expression analysis, or biosensors. This information is transmitted to the server and considered as part of a risk assessment. For example, if the system determines that the user is experiencing stress, it immediately notifies the parent and conducts a more thorough risk assessment. 【0130】 Specific example 【0131】 For example, if a child attempts to view harmful videos online, the device sends an access request to the server. The server uses a generative AI and emotion engine to assess the video's harmfulness and the child's current emotional state. If the video is high-risk and the child is also experiencing stress, the device blocks access to the video and notifies the parent of the details. This allows parents to detect the problem early and take appropriate action. In this way, the system goes beyond simple content filtering, achieving advanced security management that even considers the user's emotions. 【0132】 The following describes the processing flow. 【0133】 Step 1: 【0134】 The server begins collecting vast amounts of content data from the internet. This data includes text, images, and video information. The server stores this data in a database. 【0135】 Step 2: 【0136】 The server uses generative AI to learn and update a model that assesses the risk of content from the collected data. Specifically, it uses natural language processing techniques to determine whether the content is harmful. 【0137】 Step 3: 【0138】 The device monitors the user's content access. Each time the user accesses new content, it sends a request to the server. This request includes the content's URL and metadata. 【0139】 Step 4: 【0140】 The server analyzes the request received from the terminal and performs a risk assessment using a pre-trained model. If the assessment determines that the request is high-risk, the server returns that information to the terminal. 【0141】 Step 5: 【0142】 The device receives the risk assessment results from the server and, based on that, allows or denies access to the content. If access is denied, the device displays a warning message to the user. 【0143】 Step 6: 【0144】 The emotion engine installed in the device analyzes the user's biometric data and facial expressions to recognize emotions. The acquired emotion data is then sent to a server. 【0145】 Step 7: 【0146】 The server incorporates the received sentiment data into its risk assessment and recalculates the content's risk score. If it detects that the user is experiencing anxiety or stress, a more rigorous assessment is performed. 【0147】 Step 8: 【0148】 Users can view their child's access history and emotional state through the management application. If a high-risk situation is detected, a notification is immediately sent to the user's device. Users can adjust settings and take appropriate measures as needed. 【0149】 Step 9: 【0150】 Users can check notifications, interact with their children, and provide feedback on content usage and emotional states. By changing settings and reviewing rules, they can flexibly support their children. 【0151】 (Example 2) 【0152】 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 will be referred to as the "terminal." 【0153】 When children access the internet using mobile devices, there is a challenge in properly assessing the risks of the content and ensuring their safety. Furthermore, conventional systems only filter content and fail to consider the user's feelings in risk management, thus not adequately ensuring safety. In addition, when parents or administrators are involved in protecting children, it often requires many manual steps, which is inefficient. 【0154】 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. 【0155】 This invention includes a server that utilizes generative AI to analyze internet information and determine the risk of its content, a means of integrating speech recognition and facial expression analysis technologies to acquire user emotional data, and a means of calculating a risk score based on the collected emotional data to improve the accuracy of access control. This enables accurate assessment of content risks and flexible risk management according to the user's emotional state, allowing parents and administrators to protect their children more effectively. 【0156】 "Generative AI" refers to a system that uses artificial intelligence technology to analyze and predict data, and is particularly capable of discovering patterns from large-scale data. 【0157】 "Internet information" refers to all data accessible online, including in various formats such as text, images, videos, and audio. 【0158】 "Speech recognition" is a technology that uses computers to analyze human speech and convert it into text data, enabling the understanding of spoken content. 【0159】 "Facial expression analysis" is a technology that recognizes a person's facial expressions from an image and infers their emotional state, making judgments based on facial features. 【0160】 "Emotional data" refers to data that expresses an individual's emotional state using numerical values ​​or categories, and is obtained from voice, facial expressions, and other biometric information. 【0161】 A "risk score" is a numerical evaluation that represents the level of risk associated with a particular situation or content, and is used to support risk management. 【0162】 Access control is a technology that restricts which resources and content a user can access, and it is crucial for ensuring the security of a system. 【0163】 "Parent or administrator" refers to a person who has the authority to supervise a child's online activities and intervene as needed, and typically refers to users in the home or educational institution. 【0164】 "Statistical information" refers to a unified set of data analyzed from collected data, which facilitates the analysis of trends and the discovery of patterns. 【0165】 This invention is a system that assesses the risks associated with online content when children use mobile devices, thereby ensuring safety. Specifically, it utilizes generative AI models and natural language processing technology to achieve precise risk assessments that include user sentiment data. 【0166】 The server implements generative AI models using Python and other programming languages ​​to collect and analyze internet information. For example, it uses BeautifulSoup and Scrapy to collect information and applies natural language processing techniques to text data. Deep learning frameworks such as TensorFlow and PyTorch are used for image and video analysis. 【0167】 The device is equipped with hardware such as a camera and microphone to collect emotional data by capturing the user's facial expressions and voice in real time. The voice is analyzed through speech recognition software such as Google Cloud Speech-to-Text, and OpenCV is used for facial expression analysis. 【0168】 Users (parents or administrators) can view their child's risk assessment and emotional data via a smartphone app. The application is built with frameworks such as React Native, and through it, parents and administrators can adjust filtering and notification settings and view detailed risk reports. 【0169】 Specifically, when a child attempts to access a video on the internet, the device sends the request to a server. The server then inputs the following prompt into a generating AI model to assess the risk: "Please provide a risk and sentiment-based score for the content the user is currently trying to access." If the server determines the content to be harmful, the device blocks access to that content and notifies the parent. In this way, the safety of children's online activities can be highly controlled. 【0170】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0171】 Step 1: 【0172】 The server collects information from the internet. It receives URLs and keywords from specific websites as input. Using data collection tools (e.g., BeautifulSoup or Scrapy), it parses this information to obtain text, image, and video data. The output is the collected raw dataset, which the server then feeds to the next analysis step. 【0173】 Step 2: 【0174】 The server applies a generative AI model based on the collected data and analyzes its content. It receives the dataset obtained in step 1 as input. The generative AI model (for example, a model built using TensorFlow) analyzes the text using natural language processing techniques and assesses its harmfulness. Images and videos are analyzed similarly. The output is an evaluation result including a risk score for each data point. 【0175】 Step 3: 【0176】 The device captures user emotion data. It receives a real-time emotion data stream from the camera and microphone as input. It identifies the emotional state by performing facial expression analysis using OpenCV and speech analysis using Google Cloud Speech-to-Text. The output is a dataset showing the user's emotional state, which is sent to the server as information useful for risk assessment. 【0177】 Step 4: 【0178】 The server inputs a prompt message into the generating AI model to calculate a revised risk score that takes sentiment data into account. The prompt message is: "Please provide a risk and sentiment-based score for the content the user is currently accessing." The input is the risk assessment from Step 2 and the sentiment data from Step 3. Integrated data analysis outputs a more accurate risk score. 【0179】 Step 5: 【0180】 The device determines whether to allow access to the content based on the risk score received from the server. It receives a revision risk score sent from the server as input. An action is triggered to restrict access if the risk is high, and to allow access if it is low. The output is whether the user is granted or restricted access to the content. 【0181】 Step 6: 【0182】 The user (parent or administrator) receives notifications from the device and monitors system operation. Inputs include notifications from the server containing alert information and sentiment data. Through the application, they adjust filtering and notification settings to ensure the child's safety. Outputs include changes to system settings and additional administrative actions. 【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】 Current internet security systems for children primarily rely on static information to assess the harmfulness of content, failing to consider real-time emotional changes. Therefore, more dynamic and individually tailored security management is needed. Furthermore, a system capable of accurate risk assessment by appropriately monitoring children's emotional states is required. 【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 identifying the harmfulness of digital information based on data collected using a generative AI, means for detecting the user's emotional state and analyzing the emotional data to reflect it in the risk assessment, and means for controlling access to digital information based on the results of the risk assessment and the user's emotional state. This enables adaptive safety management that responds to real-time emotional changes for each user. 【0188】 An "information processing device" is a computer system used for processing and managing digital information. 【0189】 An "information network" is a communication structure that enables the exchange of digital information among users. 【0190】 "Digital information" refers to all types of electrical information handled on information networks. 【0191】 "Generative AI" is an artificial intelligence technology that enables pattern recognition and data generation through artificial learning. 【0192】 "Harmfulness" is an evaluation index that refers to the degree to which digital information is likely to have a negative impact on users. 【0193】 "Emotional data" refers to data that indicates the user's psychological state, and is obtained through voice analysis and facial expression analysis. 【0194】 "Access control" is a method for managing users' ability to connect to and view digital information on an information network. 【0195】 A "supervisor" is a person or entity responsible for monitoring user activity and ensuring safety. 【0196】 "Visualized statistical information" refers to information that presents collected data in the form of graphs, charts, and other visual representations, making it easy to understand. 【0197】 "Analysis techniques" are methods for understanding digital information in detail and systematically. 【0198】 "Recognition technology" refers to the technology of finding specific patterns or features from digital information and then understanding and judging them. 【0199】 This invention is a system for performing risk assessment of digital information on an information network in an information processing device used by children. The system is implemented by the following components. 【0200】 The server collects digital information from the information network and uses generative AI to assess its harmfulness. The generative AI model classifies digital information according to its risk by analyzing a vast dataset. For example, it learns patterns from previously reported harmful information and uses that to evaluate new digital information. This makes it possible to proactively identify information that could negatively impact users. 【0201】 The device is equipped with sensors that monitor the user's emotional state in real time. An emotion engine, which utilizes voice analysis and facial expression analysis technologies, detects the user's psychological state and sends it to the server as emotion data. This emotion data is analyzed on the server side and reflected in the risk assessment of digital information. For example, if the user is experiencing stress, stricter access control is implemented using the emotion data. 【0202】 Users can monitor and control the system's operation through an intuitive interface. Usage history and changes in emotional state are displayed as visualized statistics on the dashboard, allowing supervisors to make further safety adjustments based on this information. 【0203】 For example, when a child tries to install a new online game, the server analyzes the game's chat function and restricts installation if it determines it poses a risk. Furthermore, if the emotion engine detects signs of anxiety from the user's facial expressions, a notification is sent to the parent, recommending appropriate action. In this way, the system achieves dynamic and personalized security. 【0204】 As an example of a prompt, the AI ​​can be given a command such as, "Analyze the harmfulness of this content, and if the user is currently stressed, block it based on that risk score," which will then be used to determine whether to allow the content. 【0205】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0206】 Step 1: 【0207】 The device acquires the user's voice and video data in real time. This data is collected through sensors built into the device and transmitted to voice analysis and facial expression analysis devices. The analysis devices detect the user's emotional state from this data and generate emotion data. The output is emotion data in which the user's specific emotions are represented numerically. 【0208】 Step 2: 【0209】 The terminal sends the generated sentiment data to the server. The server receives this sentiment data and then uses generative AI to analyze the digital information collected from the information network. Based on past datasets, the generative AI model receives prompt sentences that evaluate the harmfulness of the digital information as input and outputs a specific risk score. 【0210】 Step 3: 【0211】 The server decides whether to allow or deny digital information based on risk scores and emotional data. The server then generates a final access control instruction as output by applying algorithms that increase or decrease the risk score, for example, if the user's emotions are stressed. 【0212】 Step 4: 【0213】 Based on access control commands from the server, users are granted or denied access to digital information on their devices. After completing a task, the device records usage history and changes in emotional state, which are displayed as visualized statistics on a supervisory dashboard. This allows supervisors to monitor users' internet usage and adjust system settings as needed. 【0214】 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. 【0215】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include 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. 【0216】 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. 【0217】 [Second Embodiment] 【0218】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0219】 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. 【0220】 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). 【0221】 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. 【0222】 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. 【0223】 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). 【0224】 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. 【0225】 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. 【0226】 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. 【0227】 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. 【0228】 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. 【0229】 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". 【0230】 This invention is a system for evaluating the risks associated with online content and supporting children's safe smartphone use. The system consists of three main components: a server, a terminal, and a user (parent or administrator). 【0231】 Server operation 【0232】 The server continuously collects data from the internet and uses generated AI to identify the harmfulness and risk of problems associated with content. The server stores the collected information in a database and constantly updates the evaluation model. It also assigns a risk score to each piece of content, serving as a criterion for determining whether it is appropriate for children to use. This information is provided upon request from the device. 【0233】 Terminal operation 【0234】 The device monitors all requests for websites, applications, and social media that the child accesses in real time. These requests are sent to a server, and access is determined based on the results of a risk assessment. If access is not permitted, the device immediately blocks access to the content and displays a visual warning message to the user. In addition, the device records the child's usage history, which the user can later refer to in the management screen. 【0235】 User actions 【0236】 The user (parent or administrator) communicates with the device and server through a dedicated application. Users can customize filtering levels and notification settings during the initial setup. This allows for flexible responses tailored to the child's usage. If an anomaly is detected by the server, a notification is immediately sent to the device, which the user can then review. Based on the notification, the user can discuss the issue with their child and take necessary actions. 【0237】 Specific example 【0238】 For example, if a child starts using a new social media application, the device sends all its actions to a server, which then analyzes the application's content. If the generating AI detects potentially harmful content or problems, the server determines it's high-risk and instructs the device to restrict access. Simultaneously, this information is notified to the parent or administrator's device, enabling early intervention. Through this entire system, safety and peace of mind regarding children's smartphone use can be ensured. 【0239】 The following describes the processing flow. 【0240】 Step 1: 【0241】 The server collects data from various information sources on the internet, including diverse media types such as text, images, and videos. The collected data forms the basis for risk analysis. 【0242】 Step 2: 【0243】 Based on the collected data, the server trains and updates a model that uses generative AI to determine the harmfulness of content. This model employs natural language processing and image recognition to assess whether the content is harmful. 【0244】 Step 3: 【0245】 The device monitors all requests accessed by the child and sends each request to the server. This includes website URLs and application activity logs. 【0246】 Step 4: 【0247】 The server receives requests sent from terminals and performs a risk assessment of the content based on them. If the assessment determines that the content is harmful, a "high risk" flag is added. 【0248】 Step 5: 【0249】 The device receives a risk assessment from the server and decides whether to allow or block access. Content deemed high-risk is immediately denied access, and a warning message is displayed on the device screen. 【0250】 Step 6: 【0251】 The device records all usage data and generates statistical information. This information is later sent to a management screen for the parent to refer to. 【0252】 Step 7: 【0253】 Users (parents or administrators) can check the status of devices and servers through the management app and adjust settings as needed. If a risk is detected, a notification will be sent to the parent's device. 【0254】 Step 8: 【0255】 After reviewing the notification, users should discuss the matter with their child and take necessary measures. Furthermore, the system's adaptability can be improved by readjusting filtering criteria and notification settings. 【0256】 (Example 1) 【0257】 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." 【0258】 In today's digital environment, there is a need to effectively avoid harmful internet content and high-risk troubles that children may encounter when using mobile devices. However, it is difficult for parents and administrators to manually manage all risks, and providing an environment in which children can use digital devices safely and with peace of mind remains a challenge. 【0259】 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. 【0260】 In this invention, the server includes data collection means for evaluating the dangers of content on a child's mobile device based on information on the network, means for identifying the harmfulness of the content using generated artificial intelligence, and means for managing access to the content based on the danger evaluation results. This makes it possible to automatically provide an environment in which children can safely use digital content and to significantly reduce the burden on parents and administrators. 【0261】 "Information on a network" refers to a collection of data accessible via digital communication networks such as the internet and intranets. 【0262】 "Portable devices used by children" refers to portable electronic devices such as smartphones and tablets that children use on a daily basis. 【0263】 "Content hazard" is a concept that indicates the degree to which digital content may have a harmful effect on children or pose a risk of causing trouble. 【0264】 "Data collection methods" refer to functions and technologies for acquiring and storing information from a network. 【0265】 "Generated artificial intelligence" refers to an AI model that has been trained through machine learning or deep learning techniques and is capable of performing specific tasks. 【0266】 "Means of identifying hazards" refers to technologies used to analyze collected data and perform a process to assess safety. 【0267】 "Means of managing access to content based on risk assessment results" refers to technologies and methods for dynamically setting permissions or restrictions on access based on a risk assessment of the content. 【0268】 The "modes for carrying out the invention" of this invention are shown below. 【0269】 This invention is a system that assesses the risks of network content when children use mobile devices and supports safe use. This system mainly consists of three elements: a server, a terminal, and a user (parent or administrator). 【0270】 The server is primarily responsible for data collection and evaluation. Specifically, it uses a web crawler to collect various data from the internet. The collected data is then analyzed using a generative AI model to identify harmfulness. This AI model employs natural language processing techniques and performs analysis based on prompts such as, "Evaluate whether this content is safe for children." The analyzed data is managed in a database, and access to content is dynamically configured based on the risk assessment. The evaluation model is also regularly updated using machine learning libraries. 【0271】 The device monitors all website and application requests accessed by the child, communicating with the server to verify their security. Based on the evaluation results received from the server, the device controls access to the content. A network monitoring library is used, and if access is deemed dangerous, it is immediately blocked using the firewall. Furthermore, the device records the child's usage history and displays visual warnings to parents or administrators via a GUI, or sends push notifications. 【0272】 Parents or administrators can customize and manage the entire system through a dedicated application. By adjusting filtering levels and notification settings, flexible responses can be tailored to the child's usage. For example, if the server detects a high risk, an immediate notification is sent, allowing the user to take appropriate action. The management screen provides access to statistical information based on the child's usage history, which can be used to support digital education at home. 【0273】 In this way, the system provides comprehensive support to facilitate user management while keeping children's usage environments safe. 【0274】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0275】 Step 1: 【0276】 The server uses a web crawler to collect information from the internet. The input is a specified list of URLs. The crawler uses this list to visit web pages and retrieve content such as text, images, and audio. The output is the collected data, which is then passed on to the next analysis step. 【0277】 Step 2: 【0278】 The server preprocesses the collected data and then inputs prompts into the generated AI model. Specifically, data cleaning and formatting are performed. The input is the raw data collected in step 1, and the output is data converted into a format suitable for AI analysis. For example, the prompt is passed to the model in the form of a message such as "Evaluate whether this content is safe for children." 【0279】 Step 3: 【0280】 The server receives the response from the generative AI model and evaluates the harmfulness of the content. The input includes the analysis results obtained from the AI model in Step 2. Based on this result, a risk score is calculated using a specific algorithm. The output is the risk score, which is stored in the database. 【0281】 Step 4: 【0282】 The terminal monitors the requests of websites or applications that the user (child) attempts to access. The input is the user's access request. The terminal sends this to the server and compares it with the set risk score. The output is the access permission or rejection determination from the server. 【0283】 Step 5: 【0284】 The terminal receives the risk assessment result from the server and controls access based on its content. If it is determined that the risk is high, the terminal blocks access using the network monitoring library and firewall. The input is the server's determination result, and the output is the permission or block of access. 【0285】 Step 6: 【0286】 The user customizes the settings and receives notifications through a dedicated application. The input is the user's operations and the server's notification information. The application visually displays information to the user using the GUI and prompts appropriate setting changes and responses. The output is the updated settings and warning notifications to the user. 【0287】 (Application Example 1) 【0288】 Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0289】 When children use electronic communication devices, there is a risk that they may unintentionally access harmful information or highly dangerous content on the internet. Traditional methods make it difficult to identify these risks in advance, and it has been challenging for parents and administrators to respond immediately. Furthermore, existing systems lack flexibility, preventing administrators from responding individually to different situations. Therefore, there was a need for an innovative system that could ensure children's safety while reducing the burden on administrators. 【0290】 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. 【0291】 In this invention, the server includes means for identifying the degree of harmfulness of content based on information collected using a generative AI, means for controlling access to information based on the results of a risk assessment, and means for sending notifications to the administrator regarding access that is judged to have a high risk score calculated by the generative AI model. This makes it possible to assess the dangers of content used by children in advance and respond flexibly and quickly, thereby ensuring the safety of children and reducing the burden on administrators. 【0292】 "Electronic communication equipment" refers to terminal devices used for digital communication, and devices that allow users to access the internet. 【0293】 "Online information" is a general term for digital content that exists on the internet and is accessible to users. 【0294】 "Risk assessment" is the process of identifying potential risks contained in online information and calculating a risk score based on that. 【0295】 "Generative AI" is a technology that uses artificial intelligence to analyze data and calculate risk scores and levels of harm. 【0296】 "Harmfulness" is an indicator that shows the potential degree of negative impact that content may have on minors and other minors. 【0297】 "Notification" refers to a means of communication used to inform an administrator about a specific event or situation. 【0298】 "Usage history" refers to a record of online information accessed using electronic communication devices, and is information used to understand past usage patterns. 【0299】 This invention is an online safety management system that operates between electronic communication devices used by children and monitoring devices used by parents or administrators. The system aims to pre-assess the risks of online content and notify parents or administrators in real time. 【0300】 The server analyzes online information using a generative AI model and calculates a risk score. Specifically, it uses machine learning frameworks such as TensorFlow and PyTorch to run the generative AI model based on data collected from the internet. This model applies natural language processing techniques to evaluate the harmfulness of the content. If the risk score exceeds a set threshold, the server sends a command to restrict access by electronic communication devices. 【0301】 The device monitors the actions of the child user and determines whether or not to grant access based on risk assessment information provided by the server. Furthermore, the device records usage history, which can be later referenced by parents or administrators as usage statistics. 【0302】 Users can use a dedicated monitoring application to view the risk assessment results of the device and the usage history of children in real time. This application is developed using technologies such as ReactJS and Flutter, and uses technologies such as Google Firebase Cloud Messaging to implement the notification function. Users can freely customize the filtering level and notification settings through the application. 【0303】 As a specific example, when a child starts using a new video sharing platform, the server uses a generative AI model to analyze the content trends on this platform and extract content that may be harmful. If the analysis result shows a high risk score, the server sends a notification to the parent or administrator's device to prompt early action. An example of a prompt sentence for the generative AI model is, "Analyze the content trends on a new social media platform and extract topics that may be harmful to minors." 【0304】 The flow of the specific process in Application Example 1 will be described using FIG. 12. 【0305】 Step 1: 【0306】 The server collects content data from the Internet. The input is online information, which is stored in a database. The data is obtained in text format or as metadata. This prepares the basic data for subsequent processing. 【0307】 Step 2: 【0308】 The server inputs the collected data into the generative AI model to determine the harm level. The input is the online information collected in Step 1. The model analyzes the data using natural language processing technology and outputs a risk score. This output is a numerical value indicating the degree of risk that the online information poses to the user. 【0309】 Step 3: 【0310】 The server determines whether to grant access to specific online information based on the risk score. The input is the risk score from step 2. A threshold is set to evaluate the score, and if it exceeds the threshold, it is judged as "high risk." Based on this result, it is determined whether access is permitted or restricted. 【0311】 Step 4: 【0312】 The terminal receives an access restriction command from the server. The input is the result of the access permission / denial determination in step 3. Access to information determined to be high-risk is blocked, and a warning message is displayed to the user. This operation ensures the safety of children. 【0313】 Step 5: 【0314】 The terminal records usage history and provides it to the user for later reference. Input is the child's operation log, which is recorded in a database and converted into statistical information. Output is the usage history and statistical information that the user can review. 【0315】 Step 6: 【0316】 Users can check device usage history and risk assessment status through a dedicated application and adjust access settings. Inputs are the history data output from step 5 and the server's risk notification. Based on this, users adjust filtering levels and notification settings to optimize the system. This allows administrators to manage children's internet usage in real time. 【0317】 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. 【0318】 This invention provides a system that not only assesses the risks of online content and ensures safety when children use mobile devices, but also offers a higher level of protection by understanding the user's emotions. This system has a configuration that combines a server, a terminal, and an emotion engine. 【0319】 Server operation 【0320】 The server collects data from the internet and analyzes it using generative AI to identify the harmfulness and risk of content. The results of this analysis are stored in the server's database and updated as needed. Furthermore, the server receives data from an emotion engine and performs emotion-based risk assessments to generate more precise risk scores. 【0321】 Terminal operation 【0322】 The device monitors all content the user accesses. Each request is sent to a server, which returns a risk assessment and user sentiment data analyzed by an emotion engine. Based on this information, the device decides whether to allow access to the content. In addition, the device is equipped with sensors that capture the user's emotions, thereby collecting sentiment data in real time. 【0323】 User actions 【0324】 Parents or administrators can manage various system functions through the application. Specifically, they can customize filtering settings and notification options, and view their child's access history and emotional changes. If the server issues an alert regarding any risk, a notification is sent to the device, including emotional information from the emotion engine. 【0325】 Emotional engine integration 【0326】 The emotion engine recognizes the user's emotions using voice analysis, facial expression analysis, or biosensors. This information is transmitted to the server and considered as part of a risk assessment. For example, if the system determines that the user is experiencing stress, it immediately notifies the parent and conducts a more thorough risk assessment. 【0327】 Specific example 【0328】 For example, if a child attempts to view harmful videos online, the device sends an access request to the server. The server uses a generative AI and emotion engine to assess the video's harmfulness and the child's current emotional state. If the video is high-risk and the child is also experiencing stress, the device blocks access to the video and notifies the parent of the details. This allows parents to detect the problem early and take appropriate action. In this way, the system goes beyond simple content filtering, achieving advanced security management that even considers the user's emotions. 【0329】 The following describes the processing flow. 【0330】 Step 1: 【0331】 The server begins collecting vast amounts of content data from the internet. This data includes text, images, and video information. The server stores this data in a database. 【0332】 Step 2: 【0333】 The server uses generative AI to learn and update a model that assesses the risk of content from the collected data. Specifically, it uses natural language processing techniques to determine whether the content is harmful. 【0334】 Step 3: 【0335】 The device monitors the user's content access. Each time the user accesses new content, it sends a request to the server. This request includes the content's URL and metadata. 【0336】 Step 4: 【0337】 The server analyzes the request received from the terminal and performs a risk assessment using a pre-trained model. If the assessment determines that the request is high-risk, the server returns that information to the terminal. 【0338】 Step 5: 【0339】 The device receives the risk assessment results from the server and, based on that, allows or denies access to the content. If access is denied, the device displays a warning message to the user. 【0340】 Step 6: 【0341】 The emotion engine installed in the device analyzes the user's biometric data and facial expressions to recognize emotions. The acquired emotion data is then sent to a server. 【0342】 Step 7: 【0343】 The server incorporates the received sentiment data into its risk assessment and recalculates the content's risk score. If it detects that the user is experiencing anxiety or stress, a more rigorous assessment is performed. 【0344】 Step 8: 【0345】 Users can view their child's access history and emotional state through the management application. If a high-risk situation is detected, a notification is immediately sent to the user's device. Users can adjust settings and take appropriate measures as needed. 【0346】 Step 9: 【0347】 Users can check notifications, interact with their children, and provide feedback on content usage and emotional states. By changing settings and reviewing rules, they can flexibly support their children. 【0348】 (Example 2) 【0349】 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". 【0350】 When children access the internet using mobile devices, there is a challenge in properly assessing the risks of the content and ensuring their safety. Furthermore, conventional systems only filter content and fail to consider the user's feelings in risk management, thus not adequately ensuring safety. In addition, when parents or administrators are involved in protecting children, it often requires many manual steps, which is inefficient. 【0351】 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. 【0352】 This invention includes a server that utilizes generative AI to analyze internet information and determine the risk of its content, a means of integrating speech recognition and facial expression analysis technologies to acquire user emotional data, and a means of calculating a risk score based on the collected emotional data to improve the accuracy of access control. This enables accurate assessment of content risks and flexible risk management according to the user's emotional state, allowing parents and administrators to protect their children more effectively. 【0353】 "Generative AI" refers to a system that uses artificial intelligence technology to analyze and predict data, and is particularly capable of discovering patterns from large-scale data. 【0354】 "Internet information" refers to all data accessible online, including in various formats such as text, images, videos, and audio. 【0355】 "Speech recognition" is a technology that uses computers to analyze human speech and convert it into text data, enabling the understanding of spoken content. 【0356】 "Facial expression analysis" is a technology that recognizes a person's facial expressions from an image and infers their emotional state, making judgments based on facial features. 【0357】 "Emotional data" refers to data that expresses an individual's emotional state using numerical values ​​or categories, and is obtained from voice, facial expressions, and other biometric information. 【0358】 A "risk score" is a numerical evaluation that represents the level of risk associated with a particular situation or content, and is used to support risk management. 【0359】 Access control is a technology that restricts which resources and content a user can access, and it is crucial for ensuring the security of a system. 【0360】 "Parent or administrator" refers to a person who has the authority to supervise a child's online activities and intervene as needed, and typically refers to users in the home or educational institution. 【0361】 "Statistical information" refers to a unified set of data analyzed from collected data, which facilitates the analysis of trends and the discovery of patterns. 【0362】 This invention is a system that assesses the risks associated with online content when children use mobile devices, thereby ensuring safety. Specifically, it utilizes generative AI models and natural language processing technology to achieve precise risk assessments that include user sentiment data. 【0363】 The server implements generative AI models using Python and other programming languages ​​to collect and analyze internet information. For example, it uses BeautifulSoup and Scrapy to collect information and applies natural language processing techniques to text data. Deep learning frameworks such as TensorFlow and PyTorch are used for image and video analysis. 【0364】 The device is equipped with hardware such as a camera and microphone to collect emotional data by capturing the user's facial expressions and voice in real time. The voice is analyzed through speech recognition software such as Google Cloud Speech-to-Text, and OpenCV is used for facial expression analysis. 【0365】 Users (parents or administrators) can view their child's risk assessment and emotional data via a smartphone app. The application is built with frameworks such as React Native, and through it, parents and administrators can adjust filtering and notification settings and view detailed risk reports. 【0366】 Specifically, when a child attempts to access a video on the internet, the device sends the request to a server. The server then inputs the following prompt into a generating AI model to assess the risk: "Please provide a risk and sentiment-based score for the content the user is currently trying to access." If the server determines the content to be harmful, the device blocks access to that content and notifies the parent. In this way, the safety of children's online activities can be highly controlled. 【0367】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0368】 Step 1: 【0369】 The server collects information from the internet. It receives URLs and keywords from specific websites as input. Using data collection tools (e.g., BeautifulSoup or Scrapy), it parses this information to obtain text, image, and video data. The output is the collected raw dataset, which the server then feeds to the next analysis step. 【0370】 Step 2: 【0371】 The server applies a generative AI model based on the collected data and analyzes its content. It receives the dataset obtained in step 1 as input. The generative AI model (for example, a model built using TensorFlow) analyzes the text using natural language processing techniques and assesses its harmfulness. Images and videos are analyzed similarly. The output is an evaluation result including a risk score for each data point. 【0372】 Step 3: 【0373】 The device captures user emotion data. It receives a real-time emotion data stream from the camera and microphone as input. It identifies the emotional state by performing facial expression analysis using OpenCV and speech analysis using Google Cloud Speech-to-Text. The output is a dataset showing the user's emotional state, which is sent to the server as information useful for risk assessment. 【0374】 Step 4: 【0375】 The server inputs a prompt message into the generating AI model to calculate a revised risk score that takes sentiment data into account. The prompt message is: "Please provide a risk and sentiment-based score for the content the user is currently accessing." The input is the risk assessment from Step 2 and the sentiment data from Step 3. Integrated data analysis outputs a more accurate risk score. 【0376】 Step 5: 【0377】 The device determines whether to allow access to the content based on the risk score received from the server. It receives a revision risk score sent from the server as input. An action is triggered to restrict access if the risk is high, and to allow access if it is low. The output is whether the user is granted or restricted access to the content. 【0378】 Step 6: 【0379】 The user (parent or administrator) receives notifications from the device and monitors system operation. Inputs include notifications from the server containing alert information and sentiment data. Through the application, they adjust filtering and notification settings to ensure the child's safety. Outputs include changes to system settings and additional administrative actions. 【0380】 (Application Example 2) 【0381】 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." 【0382】 Current internet security systems for children primarily rely on static information to assess the harmfulness of content, failing to consider real-time emotional changes. Therefore, more dynamic and individually tailored security management is needed. Furthermore, a system capable of accurate risk assessment by appropriately monitoring children's emotional states is required. 【0383】 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. 【0384】 In this invention, the server includes means for identifying the harmfulness of digital information based on data collected using a generative AI, means for detecting the user's emotional state and analyzing the emotional data to reflect it in the risk assessment, and means for controlling access to digital information based on the results of the risk assessment and the user's emotional state. This enables adaptive safety management that responds to real-time emotional changes for each user. 【0385】 An "information processing device" is a computer system used for processing and managing digital information. 【0386】 An "information network" is a communication structure that enables the exchange of digital information among users. 【0387】 "Digital information" refers to all types of electrical information handled on information networks. 【0388】 "Generative AI" is an artificial intelligence technology that enables pattern recognition and data generation through artificial learning. 【0389】 "Harmfulness" is an evaluation index that refers to the degree to which digital information is likely to have a negative impact on users. 【0390】 "Emotional data" refers to data that indicates the user's psychological state, and is obtained through voice analysis and facial expression analysis. 【0391】 "Access control" is a method for managing users' ability to connect to and view digital information on an information network. 【0392】 A "supervisor" is a person or entity responsible for monitoring user activity and ensuring safety. 【0393】 "Visualized statistical information" refers to information that presents collected data in the form of graphs, charts, and other visual representations, making it easy to understand. 【0394】 "Analysis techniques" are methods for understanding digital information in detail and systematically. 【0395】 "Recognition technology" refers to the technology of finding specific patterns or features from digital information and then understanding and judging them. 【0396】 This invention is a system for performing risk assessment of digital information on an information network in an information processing device used by children. The system is implemented by the following components. 【0397】 The server collects digital information from the information network and uses generative AI to assess its harmfulness. The generative AI model classifies digital information according to its risk by analyzing a vast dataset. For example, it learns patterns from previously reported harmful information and uses that to evaluate new digital information. This makes it possible to proactively identify information that could negatively impact users. 【0398】 The device is equipped with sensors that monitor the user's emotional state in real time. An emotion engine, which utilizes voice analysis and facial expression analysis technologies, detects the user's psychological state and sends it to the server as emotion data. This emotion data is analyzed on the server side and reflected in the risk assessment of digital information. For example, if the user is experiencing stress, stricter access control is implemented using the emotion data. 【0399】 Users can monitor and control the system's operation through an intuitive interface. Usage history and changes in emotional state are displayed as visualized statistics on the dashboard, allowing supervisors to make further safety adjustments based on this information. 【0400】 For example, when a child tries to install a new online game, the server analyzes the game's chat function and restricts installation if it determines it poses a risk. Furthermore, if the emotion engine detects signs of anxiety from the user's facial expressions, a notification is sent to the parent, recommending appropriate action. In this way, the system achieves dynamic and personalized security. 【0401】 As an example of a prompt, the AI ​​can be given a command such as, "Analyze the harmfulness of this content, and if the user is currently stressed, block it based on that risk score," which will then be used to determine whether to allow the content. 【0402】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0403】 Step 1: 【0404】 The device acquires the user's voice and video data in real time. This data is collected through sensors built into the device and transmitted to voice analysis and facial expression analysis devices. The analysis devices detect the user's emotional state from this data and generate emotion data. The output is emotion data in which the user's specific emotions are represented numerically. 【0405】 Step 2: 【0406】 The terminal sends the generated sentiment data to the server. The server receives this sentiment data and then uses generative AI to analyze the digital information collected from the information network. Based on past datasets, the generative AI model receives prompt sentences that evaluate the harmfulness of the digital information as input and outputs a specific risk score. 【0407】 Step 3: 【0408】 The server decides whether to allow or deny digital information based on risk scores and emotional data. The server then generates a final access control instruction as output by applying algorithms that increase or decrease the risk score, for example, if the user's emotions are stressed. 【0409】 Step 4: 【0410】 Based on access control commands from the server, users are granted or denied access to digital information on their devices. After completing a task, the device records usage history and changes in emotional state, which are displayed as visualized statistics on a supervisory dashboard. This allows supervisors to monitor users' internet usage and adjust system settings as needed. 【0411】 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. 【0412】 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. 【0413】 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. 【0414】 [Third Embodiment] 【0415】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0416】 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. 【0417】 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). 【0418】 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. 【0419】 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. 【0420】 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). 【0421】 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. 【0422】 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. 【0423】 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. 【0424】 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. 【0425】 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. 【0426】 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". 【0427】 This invention is a system for evaluating the risks associated with online content and supporting children's safe smartphone use. The system consists of three main components: a server, a terminal, and a user (parent or administrator). 【0428】 Server operation 【0429】 The server continuously collects data from the internet and uses generated AI to identify the harmfulness and risk of problems associated with content. The server stores the collected information in a database and constantly updates the evaluation model. It also assigns a risk score to each piece of content, serving as a criterion for determining whether it is appropriate for children to use. This information is provided upon request from the device. 【0430】 Terminal operation 【0431】 The device monitors all requests for websites, applications, and social media that the child accesses in real time. These requests are sent to a server, and access is determined based on the results of a risk assessment. If access is not permitted, the device immediately blocks access to the content and displays a visual warning message to the user. In addition, the device records the child's usage history, which the user can later refer to in the management screen. 【0432】 User actions 【0433】 The user (parent or administrator) communicates with the device and server through a dedicated application. Users can customize filtering levels and notification settings during the initial setup. This allows for flexible responses tailored to the child's usage. If an anomaly is detected by the server, a notification is immediately sent to the device, which the user can then review. Based on the notification, the user can discuss the issue with their child and take necessary actions. 【0434】 Specific example 【0435】 For example, if a child starts using a new social media application, the device sends all its actions to a server, which then analyzes the application's content. If the generating AI detects potentially harmful content or problems, the server determines it's high-risk and instructs the device to restrict access. Simultaneously, this information is notified to the parent or administrator's device, enabling early intervention. Through this entire system, safety and peace of mind regarding children's smartphone use can be ensured. 【0436】 The following describes the processing flow. 【0437】 Step 1: 【0438】 The server collects data from various information sources on the internet, including diverse media types such as text, images, and videos. The collected data forms the basis for risk analysis. 【0439】 Step 2: 【0440】 Based on the collected data, the server trains and updates a model that uses generative AI to determine the harmfulness of content. This model employs natural language processing and image recognition to assess whether the content is harmful. 【0441】 Step 3: 【0442】 The device monitors all requests accessed by the child and sends each request to the server. This includes website URLs and application activity logs. 【0443】 Step 4: 【0444】 The server receives requests sent from terminals and performs a risk assessment of the content based on them. If the assessment determines that the content is harmful, a "high risk" flag is added. 【0445】 Step 5: 【0446】 The device receives a risk assessment from the server and decides whether to allow or block access. Content deemed high-risk is immediately denied access, and a warning message is displayed on the device screen. 【0447】 Step 6: 【0448】 The device records all usage data and generates statistical information. This information is later sent to a management screen for the parent to refer to. 【0449】 Step 7: 【0450】 Users (parents or administrators) can check the status of devices and servers through the management app and adjust settings as needed. If a risk is detected, a notification will be sent to the parent's device. 【0451】 Step 8: 【0452】 After reviewing the notification, users should discuss the matter with their child and take necessary measures. Furthermore, the system's adaptability can be improved by readjusting filtering criteria and notification settings. 【0453】 (Example 1) 【0454】 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." 【0455】 In today's digital environment, there is a need to effectively avoid harmful internet content and high-risk troubles that children may encounter when using mobile devices. However, it is difficult for parents and administrators to manually manage all risks, and providing an environment in which children can use digital devices safely and with peace of mind remains a challenge. 【0456】 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. 【0457】 In this invention, the server includes data collection means for evaluating the dangers of content on a child's mobile device based on information on the network, means for identifying the harmfulness of the content using generated artificial intelligence, and means for managing access to the content based on the danger evaluation results. This makes it possible to automatically provide an environment in which children can safely use digital content and to significantly reduce the burden on parents and administrators. 【0458】 "Information on a network" refers to a collection of data accessible via digital communication networks such as the internet and intranets. 【0459】 "Portable devices used by children" refers to portable electronic devices such as smartphones and tablets that children use on a daily basis. 【0460】 "Content hazard" is a concept that indicates the degree to which digital content may have a harmful effect on children or pose a risk of causing trouble. 【0461】 "Data collection methods" refer to functions and technologies for acquiring and storing information from a network. 【0462】 "Generated artificial intelligence" refers to an AI model that has been trained through machine learning or deep learning techniques and is capable of performing specific tasks. 【0463】 "Means of identifying hazards" refers to technologies used to analyze collected data and perform a process to assess safety. 【0464】 "Means of managing access to content based on risk assessment results" refers to technologies and methods for dynamically setting permissions or restrictions on access based on a risk assessment of the content. 【0465】 The "modes for carrying out the invention" of this invention are shown below. 【0466】 This invention is a system that assesses the risks of network content when children use mobile devices and supports safe use. This system mainly consists of three elements: a server, a terminal, and a user (parent or administrator). 【0467】 The server is primarily responsible for data collection and evaluation. Specifically, it uses a web crawler to collect various data from the internet. The collected data is then analyzed using a generative AI model to identify harmfulness. This AI model employs natural language processing techniques and performs analysis based on prompts such as, "Evaluate whether this content is safe for children." The analyzed data is managed in a database, and access to content is dynamically configured based on the risk assessment. The evaluation model is also regularly updated using machine learning libraries. 【0468】 The device monitors all website and application requests accessed by the child, communicating with the server to verify their security. Based on the evaluation results received from the server, the device controls access to the content. A network monitoring library is used, and if access is deemed dangerous, it is immediately blocked using the firewall. Furthermore, the device records the child's usage history and displays visual warnings to parents or administrators via a GUI, or sends push notifications. 【0469】 Parents or administrators can customize and manage the entire system through a dedicated application. By adjusting filtering levels and notification settings, flexible responses can be tailored to the child's usage. For example, if the server detects a high risk, an immediate notification is sent, allowing the user to take appropriate action. The management screen provides access to statistical information based on the child's usage history, which can be used to support digital education at home. 【0470】 In this way, the system provides comprehensive support to facilitate user management while keeping children's usage environments safe. 【0471】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0472】 Step 1: 【0473】 The server uses a web crawler to collect information from the internet. The input is a specified list of URLs. The crawler uses this list to visit web pages and retrieve content such as text, images, and audio. The output is the collected data, which is then passed on to the next analysis step. 【0474】 Step 2: 【0475】 The server preprocesses the collected data and then inputs prompts into the generated AI model. Specifically, data cleaning and formatting are performed. The input is the raw data collected in step 1, and the output is data converted into a format suitable for AI analysis. For example, the prompt is passed to the model in the form of a message such as "Evaluate whether this content is safe for children." 【0476】 Step 3: 【0477】 The server receives a response from the generating AI model and evaluates the harmfulness of the content. The input is the analysis results obtained from the AI ​​model in step 2. Based on these results, a risk score is calculated using a specific algorithm. The output is the risk score, which is stored in the database. 【0478】 Step 4: 【0479】 The device monitors requests for websites and applications that the user (child) attempts to access. The input is the user's access request. The device sends this to the server and compares it to a configured risk score. The output is the server's decision to allow or deny access. 【0480】 Step 5: 【0481】 The terminal receives risk assessment results from the server and controls access based on those results. If the risk is determined to be high, the terminal blocks access using the network monitoring library and firewall. The input is the server's assessment result, and the output is whether access is permitted or blocked. 【0482】 Step 6: 【0483】 Users customize settings and receive notifications through a dedicated application. Inputs include user actions and server notifications. The application uses a GUI to visually display information to the user, prompting appropriate setting changes and actions. Outputs include updated settings and warning notifications to the user. 【0484】 (Application Example 1) 【0485】 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." 【0486】 When children use electronic communication devices, there is a risk that they may unintentionally access harmful information or highly dangerous content on the internet. Traditional methods make it difficult to identify these risks in advance, and it has been challenging for parents and administrators to respond immediately. Furthermore, existing systems lack flexibility, preventing administrators from responding individually to different situations. Therefore, there was a need for an innovative system that could ensure children's safety while reducing the burden on administrators. 【0487】 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. 【0488】 In this invention, the server includes means for identifying the degree of harmfulness of content based on information collected using a generative AI, means for controlling access to information based on the results of a risk assessment, and means for sending notifications to the administrator regarding access that is judged to have a high risk score calculated by the generative AI model. This makes it possible to assess the dangers of content used by children in advance and respond flexibly and quickly, thereby ensuring the safety of children and reducing the burden on administrators. 【0489】 "Electronic communication equipment" refers to terminal devices used for digital communication, and devices that allow users to access the internet. 【0490】 "Online information" is a general term for digital content that exists on the internet and is accessible to users. 【0491】 "Risk assessment" is the process of identifying potential risks contained in online information and calculating a risk score based on that. 【0492】 "Generative AI" is a technology that uses artificial intelligence to analyze data and calculate risk scores and levels of harm. 【0493】 "Harmfulness" is an indicator that shows the potential degree of negative impact that content may have on minors and other minors. 【0494】 "Notification" refers to a means of communication used to inform an administrator about a specific event or situation. 【0495】 "Usage history" refers to a record of online information accessed using electronic communication devices, and is information used to understand past usage patterns. 【0496】 This invention is an online safety management system that operates between electronic communication devices used by children and monitoring devices used by parents or administrators. The system aims to pre-assess the risks of online content and notify parents or administrators in real time. 【0497】 The server analyzes online information using a generative AI model and calculates a risk score. Specifically, it uses machine learning frameworks such as TensorFlow and PyTorch to run the generative AI model based on data collected from the internet. This model applies natural language processing techniques to evaluate the harmfulness of the content. If the risk score exceeds a set threshold, the server sends a command to restrict access by electronic communication devices. 【0498】 The device monitors the actions of the child user and determines whether or not to grant access based on risk assessment information provided by the server. Furthermore, the device records usage history, which can be later referenced by parents or administrators as usage statistics. 【0499】 Users can use a dedicated monitoring application to check device risk assessment results and their child's usage history in real time. This application is developed using ReactJS and Flutter, and utilizes technologies such as Google Firebase Cloud Messaging to implement notification functions. Users can freely customize filtering levels and notification settings through the application. 【0500】 For example, if a child starts using a new video-sharing platform, the server uses a generative AI model to analyze content trends on that platform and extract potentially harmful content. If this analysis shows a high risk score, the server sends a notification to the parent's or administrator's device to encourage early action. An example of a prompt to the generative AI model would be, "Analyze content trends on the new social media platform and extract topics that may be harmful to minors." 【0501】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0502】 Step 1: 【0503】 The server collects content data from the internet. The input is online information, which is then stored in a database. The data is retrieved in text format or as metadata. This prepares the basic data for use in subsequent processing. 【0504】 Step 2: 【0505】 The server feeds the collected data into a generating AI model to determine its level of harmfulness. The input is the online information collected in step 1. The model uses natural language processing techniques to analyze the data and outputs a risk score. This output is a numerical value indicating how dangerous the online information is to the user. 【0506】 Step 3: 【0507】 The server determines whether to grant access to specific online information based on the risk score. The input is the risk score from step 2. A threshold is set to evaluate the score, and if it exceeds the threshold, it is judged as "high risk." Based on this result, it is determined whether access is permitted or restricted. 【0508】 Step 4: 【0509】 The terminal receives an access restriction command from the server. The input is the result of the access permission / denial determination in step 3. Access to information determined to be high-risk is blocked, and a warning message is displayed to the user. This operation ensures the safety of children. 【0510】 Step 5: 【0511】 The terminal records usage history and provides it to the user for later reference. Input is the child's operation log, which is recorded in a database and converted into statistical information. Output is the usage history and statistical information that the user can review. 【0512】 Step 6: 【0513】 Users can check device usage history and risk assessment status through a dedicated application and adjust access settings. Inputs are the history data output from step 5 and the server's risk notification. Based on this, users adjust filtering levels and notification settings to optimize the system. This allows administrators to manage children's internet usage in real time. 【0514】 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. 【0515】 This invention provides a system that not only assesses the risks of online content and ensures safety when children use mobile devices, but also offers a higher level of protection by understanding the user's emotions. This system has a configuration that combines a server, a terminal, and an emotion engine. 【0516】 Server operation 【0517】 The server collects data from the internet and analyzes it using generative AI to identify the harmfulness and risk of content. The results of this analysis are stored in the server's database and updated as needed. Furthermore, the server receives data from an emotion engine and performs emotion-based risk assessments to generate more precise risk scores. 【0518】 Terminal operation 【0519】 The device monitors all content the user accesses. Each request is sent to a server, which returns a risk assessment and user sentiment data analyzed by an emotion engine. Based on this information, the device decides whether to allow access to the content. In addition, the device is equipped with sensors that capture the user's emotions, thereby collecting sentiment data in real time. 【0520】 User actions 【0521】 Parents or administrators can manage various system functions through the application. Specifically, they can customize filtering settings and notification options, and view their child's access history and emotional changes. If the server issues an alert regarding any risk, a notification is sent to the device, including emotional information from the emotion engine. 【0522】 Emotional engine integration 【0523】 The emotion engine recognizes the user's emotions using voice analysis, facial expression analysis, or biosensors. This information is transmitted to the server and considered as part of a risk assessment. For example, if the system determines that the user is experiencing stress, it immediately notifies the parent and conducts a more thorough risk assessment. 【0524】 Specific example 【0525】 For example, if a child attempts to view harmful videos online, the device sends an access request to the server. The server uses a generative AI and emotion engine to assess the video's harmfulness and the child's current emotional state. If the video is high-risk and the child is also experiencing stress, the device blocks access to the video and notifies the parent of the details. This allows parents to detect the problem early and take appropriate action. In this way, the system goes beyond simple content filtering, achieving advanced security management that even considers the user's emotions. 【0526】 The following describes the processing flow. 【0527】 Step 1: 【0528】 The server begins collecting vast amounts of content data from the internet. This data includes text, images, and video information. The server stores this data in a database. 【0529】 Step 2: 【0530】 The server uses generative AI to learn and update a model that assesses the risk of content from the collected data. Specifically, it uses natural language processing techniques to determine whether the content is harmful. 【0531】 Step 3: 【0532】 The device monitors the user's content access. Each time the user accesses new content, it sends a request to the server. This request includes the content's URL and metadata. 【0533】 Step 4: 【0534】 The server analyzes the request received from the terminal and performs a risk assessment using a pre-trained model. If the assessment determines that the request is high-risk, the server returns that information to the terminal. 【0535】 Step 5: 【0536】 The device receives the risk assessment results from the server and, based on that, allows or denies access to the content. If access is denied, the device displays a warning message to the user. 【0537】 Step 6: 【0538】 The emotion engine installed in the device analyzes the user's biometric data and facial expressions to recognize emotions. The acquired emotion data is then sent to a server. 【0539】 Step 7: 【0540】 The server incorporates the received sentiment data into its risk assessment and recalculates the content's risk score. If it detects that the user is experiencing anxiety or stress, a more rigorous assessment is performed. 【0541】 Step 8: 【0542】 Users can view their child's access history and emotional state through the management application. If a high-risk situation is detected, a notification is immediately sent to the user's device. Users can adjust settings and take appropriate measures as needed. 【0543】 Step 9: 【0544】 Users can check notifications, interact with their children, and provide feedback on content usage and emotional states. By changing settings and reviewing rules, they can flexibly support their children. 【0545】 (Example 2) 【0546】 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." 【0547】 When children access the internet using mobile devices, there is a challenge in properly assessing the risks of the content and ensuring their safety. Furthermore, conventional systems only filter content and fail to consider the user's feelings in risk management, thus not adequately ensuring safety. In addition, when parents or administrators are involved in protecting children, it often requires many manual steps, which is inefficient. 【0548】 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. 【0549】 This invention includes a server that utilizes generative AI to analyze internet information and determine the risk of its content, a means of integrating speech recognition and facial expression analysis technologies to acquire user emotional data, and a means of calculating a risk score based on the collected emotional data to improve the accuracy of access control. This enables accurate assessment of content risks and flexible risk management according to the user's emotional state, allowing parents and administrators to protect their children more effectively. 【0550】 "Generative AI" refers to a system that uses artificial intelligence technology to analyze and predict data, and is particularly capable of discovering patterns from large-scale data. 【0551】 "Internet information" refers to all data accessible online, including in various formats such as text, images, videos, and audio. 【0552】 "Speech recognition" is a technology that uses computers to analyze human speech and convert it into text data, enabling the understanding of spoken content. 【0553】 "Facial expression analysis" is a technology that recognizes a person's facial expressions from an image and infers their emotional state, making judgments based on facial features. 【0554】 "Emotional data" refers to data that expresses an individual's emotional state using numerical values ​​or categories, and is obtained from voice, facial expressions, and other biometric information. 【0555】 A "risk score" is a numerical evaluation that represents the level of risk associated with a particular situation or content, and is used to support risk management. 【0556】 Access control is a technology that restricts which resources and content a user can access, and it is crucial for ensuring the security of a system. 【0557】 "Parent or administrator" refers to a person who has the authority to supervise a child's online activities and intervene as needed, and typically refers to users in the home or educational institution. 【0558】 "Statistical information" refers to a unified set of data analyzed from collected data, which facilitates the analysis of trends and the discovery of patterns. 【0559】 This invention is a system that assesses the risks associated with online content when children use mobile devices, thereby ensuring safety. Specifically, it utilizes generative AI models and natural language processing technology to achieve precise risk assessments that include user sentiment data. 【0560】 The server implements generative AI models using Python and other programming languages ​​to collect and analyze internet information. For example, it uses BeautifulSoup and Scrapy to collect information and applies natural language processing techniques to text data. Deep learning frameworks such as TensorFlow and PyTorch are used for image and video analysis. 【0561】 The device is equipped with hardware such as a camera and microphone to collect emotional data by capturing the user's facial expressions and voice in real time. The voice is analyzed through speech recognition software such as Google Cloud Speech-to-Text, and OpenCV is used for facial expression analysis. 【0562】 Users (parents or administrators) can view their child's risk assessment and emotional data via a smartphone app. The application is built with frameworks such as React Native, and through it, parents and administrators can adjust filtering and notification settings and view detailed risk reports. 【0563】 Specifically, when a child attempts to access a video on the internet, the device sends the request to a server. The server then inputs the following prompt into a generating AI model to assess the risk: "Please provide a risk and sentiment-based score for the content the user is currently trying to access." If the server determines the content to be harmful, the device blocks access to that content and notifies the parent. In this way, the safety of children's online activities can be highly controlled. 【0564】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0565】 Step 1: 【0566】 The server collects information from the internet. It receives URLs and keywords from specific websites as input. Using data collection tools (e.g., BeautifulSoup or Scrapy), it parses this information to obtain text, image, and video data. The output is the collected raw dataset, which the server then feeds to the next analysis step. 【0567】 Step 2: 【0568】 The server applies a generative AI model based on the collected data and analyzes its content. It receives the dataset obtained in step 1 as input. The generative AI model (for example, a model built using TensorFlow) analyzes the text using natural language processing techniques and assesses its harmfulness. Images and videos are analyzed similarly. The output is an evaluation result including a risk score for each data point. 【0569】 Step 3: 【0570】 The device captures user emotion data. It receives a real-time emotion data stream from the camera and microphone as input. It identifies the emotional state by performing facial expression analysis using OpenCV and speech analysis using Google Cloud Speech-to-Text. The output is a dataset showing the user's emotional state, which is sent to the server as information useful for risk assessment. 【0571】 Step 4: 【0572】 The server inputs a prompt message into the generating AI model to calculate a revised risk score that takes sentiment data into account. The prompt message is: "Please provide a risk and sentiment-based score for the content the user is currently accessing." The input is the risk assessment from Step 2 and the sentiment data from Step 3. Integrated data analysis outputs a more accurate risk score. 【0573】 Step 5: 【0574】 The device determines whether to allow access to the content based on the risk score received from the server. It receives a revision risk score sent from the server as input. An action is triggered to restrict access if the risk is high, and to allow access if it is low. The output is whether the user is granted or restricted access to the content. 【0575】 Step 6: 【0576】 The user (parent or administrator) receives notifications from the device and monitors system operation. Inputs include notifications from the server containing alert information and sentiment data. Through the application, they adjust filtering and notification settings to ensure the child's safety. Outputs include changes to system settings and additional administrative actions. 【0577】 (Application Example 2) 【0578】 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." 【0579】 Current internet security systems for children primarily rely on static information to assess the harmfulness of content, failing to consider real-time emotional changes. Therefore, more dynamic and individually tailored security management is needed. Furthermore, a system capable of accurate risk assessment by appropriately monitoring children's emotional states is required. 【0580】 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. 【0581】 In this invention, the server includes means for identifying the harmfulness of digital information based on data collected using a generative AI, means for detecting the user's emotional state and analyzing the emotional data to reflect it in the risk assessment, and means for controlling access to digital information based on the results of the risk assessment and the user's emotional state. This enables adaptive safety management that responds to real-time emotional changes for each user. 【0582】 An "information processing device" is a computer system used for processing and managing digital information. 【0583】 An "information network" is a communication structure that enables the exchange of digital information among users. 【0584】 "Digital information" refers to all types of electrical information handled on information networks. 【0585】 "Generative AI" is an artificial intelligence technology that enables pattern recognition and data generation through artificial learning. 【0586】 "Harmfulness" is an evaluation index that refers to the degree to which digital information is likely to have a negative impact on users. 【0587】 "Emotional data" refers to data that indicates the user's psychological state, and is obtained through voice analysis and facial expression analysis. 【0588】 "Access control" is a method for managing users' ability to connect to and view digital information on an information network. 【0589】 A "supervisor" is a person or entity responsible for monitoring user activity and ensuring safety. 【0590】 "Visualized statistical information" refers to information that presents collected data in the form of graphs, charts, and other visual representations, making it easy to understand. 【0591】 "Analysis techniques" are methods for understanding digital information in detail and systematically. 【0592】 "Recognition technology" refers to the technology of finding specific patterns or features from digital information and then understanding and judging them. 【0593】 This invention is a system for performing risk assessment of digital information on an information network in an information processing device used by children. The system is implemented by the following components. 【0594】 The server collects digital information from the information network and uses generative AI to assess its harmfulness. The generative AI model classifies digital information according to its risk by analyzing a vast dataset. For example, it learns patterns from previously reported harmful information and uses that to evaluate new digital information. This makes it possible to proactively identify information that could negatively impact users. 【0595】 The device is equipped with sensors that monitor the user's emotional state in real time. An emotion engine, which utilizes voice analysis and facial expression analysis technologies, detects the user's psychological state and sends it to the server as emotion data. This emotion data is analyzed on the server side and reflected in the risk assessment of digital information. For example, if the user is experiencing stress, stricter access control is implemented using the emotion data. 【0596】 Users can monitor and control the system's operation through an intuitive interface. Usage history and changes in emotional state are displayed as visualized statistics on the dashboard, allowing supervisors to make further safety adjustments based on this information. 【0597】 For example, when a child tries to install a new online game, the server analyzes the game's chat function and restricts installation if it determines it poses a risk. Furthermore, if the emotion engine detects signs of anxiety from the user's facial expressions, a notification is sent to the parent, recommending appropriate action. In this way, the system achieves dynamic and personalized security. 【0598】 As an example of a prompt, the AI ​​can be given a command such as, "Analyze the harmfulness of this content, and if the user is currently stressed, block it based on that risk score," which will then be used to determine whether to allow the content. 【0599】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0600】 Step 1: 【0601】 The device acquires the user's voice and video data in real time. This data is collected through sensors built into the device and transmitted to voice analysis and facial expression analysis devices. The analysis devices detect the user's emotional state from this data and generate emotion data. The output is emotion data in which the user's specific emotions are represented numerically. 【0602】 Step 2: 【0603】 The terminal sends the generated sentiment data to the server. The server receives this sentiment data and then uses generative AI to analyze the digital information collected from the information network. Based on past datasets, the generative AI model receives prompt sentences that evaluate the harmfulness of the digital information as input and outputs a specific risk score. 【0604】 Step 3: 【0605】 The server decides whether to allow or deny digital information based on risk scores and emotional data. The server then generates a final access control instruction as output by applying algorithms that increase or decrease the risk score, for example, if the user's emotions are stressed. 【0606】 Step 4: 【0607】 Based on access control commands from the server, users are granted or denied access to digital information on their devices. After completing a task, the device records usage history and changes in emotional state, which are displayed as visualized statistics on a supervisory dashboard. This allows supervisors to monitor users' internet usage and adjust system settings as needed. 【0608】 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. 【0609】 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. 【0610】 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. 【0611】 [Fourth Embodiment] 【0612】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0613】 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. 【0614】 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). 【0615】 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. 【0616】 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. 【0617】 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). 【0618】 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. 【0619】 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. 【0620】 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. 【0621】 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. 【0622】 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. 【0623】 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. 【0624】 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". 【0625】 This invention is a system for evaluating the risks associated with online content and supporting children's safe smartphone use. The system consists of three main components: a server, a terminal, and a user (parent or administrator). 【0626】 Server operation 【0627】 The server continuously collects data from the internet and uses generated AI to identify the harmfulness and risk of problems associated with content. The server stores the collected information in a database and constantly updates the evaluation model. It also assigns a risk score to each piece of content, serving as a criterion for determining whether it is appropriate for children to use. This information is provided upon request from the device. 【0628】 Terminal operation 【0629】 The device monitors all requests for websites, applications, and social media that the child accesses in real time. These requests are sent to a server, and access is determined based on the results of a risk assessment. If access is not permitted, the device immediately blocks access to the content and displays a visual warning message to the user. In addition, the device records the child's usage history, which the user can later refer to in the management screen. 【0630】 User actions 【0631】 The user (parent or administrator) communicates with the device and server through a dedicated application. Users can customize filtering levels and notification settings during the initial setup. This allows for flexible responses tailored to the child's usage. If an anomaly is detected by the server, a notification is immediately sent to the device, which the user can then review. Based on the notification, the user can discuss the issue with their child and take necessary actions. 【0632】 Specific example 【0633】 For example, if a child starts using a new social media application, the device sends all its actions to a server, which then analyzes the application's content. If the generating AI detects potentially harmful content or problems, the server determines it's high-risk and instructs the device to restrict access. Simultaneously, this information is notified to the parent or administrator's device, enabling early intervention. Through this entire system, safety and peace of mind regarding children's smartphone use can be ensured. 【0634】 The following describes the processing flow. 【0635】 Step 1: 【0636】 The server collects data from various information sources on the internet, including diverse media types such as text, images, and videos. The collected data forms the basis for risk analysis. 【0637】 Step 2: 【0638】 Based on the collected data, the server trains and updates a model that uses generative AI to determine the harmfulness of content. This model employs natural language processing and image recognition to assess whether the content is harmful. 【0639】 Step 3: 【0640】 The device monitors all requests accessed by the child and sends each request to the server. This includes website URLs and application activity logs. 【0641】 Step 4: 【0642】 The server receives requests sent from terminals and performs a risk assessment of the content based on them. If the assessment determines that the content is harmful, a "high risk" flag is added. 【0643】 Step 5: 【0644】 The device receives a risk assessment from the server and decides whether to allow or block access. Content deemed high-risk is immediately denied access, and a warning message is displayed on the device screen. 【0645】 Step 6: 【0646】 The device records all usage data and generates statistical information. This information is later sent to a management screen for the parent to refer to. 【0647】 Step 7: 【0648】 Users (parents or administrators) can check the status of devices and servers through the management app and adjust settings as needed. If a risk is detected, a notification will be sent to the parent's device. 【0649】 Step 8: 【0650】 After reviewing the notification, users should discuss the matter with their child and take necessary measures. Furthermore, the system's adaptability can be improved by readjusting filtering criteria and notification settings. 【0651】 (Example 1) 【0652】 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". 【0653】 In today's digital environment, there is a need to effectively avoid harmful internet content and high-risk troubles that children may encounter when using mobile devices. However, it is difficult for parents and administrators to manually manage all risks, and providing an environment in which children can use digital devices safely and with peace of mind remains a challenge. 【0654】 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. 【0655】 In this invention, the server includes data collection means for evaluating the dangers of content on a child's mobile device based on information on the network, means for identifying the harmfulness of the content using generated artificial intelligence, and means for managing access to the content based on the danger evaluation results. This makes it possible to automatically provide an environment in which children can safely use digital content and to significantly reduce the burden on parents and administrators. 【0656】 "Information on a network" refers to a collection of data accessible via digital communication networks such as the internet and intranets. 【0657】 "Portable devices used by children" refers to portable electronic devices such as smartphones and tablets that children use on a daily basis. 【0658】 "Content hazard" is a concept that indicates the degree to which digital content may have a harmful effect on children or pose a risk of causing trouble. 【0659】 "Data collection methods" refer to functions and technologies for acquiring and storing information from a network. 【0660】 "Generated artificial intelligence" refers to an AI model that has been trained through machine learning or deep learning techniques and is capable of performing specific tasks. 【0661】 "Means of identifying hazards" refers to technologies used to analyze collected data and perform a process to assess safety. 【0662】 "Means of managing access to content based on risk assessment results" refers to technologies and methods for dynamically setting permissions or restrictions on access based on a risk assessment of the content. 【0663】 The "modes for carrying out the invention" of this invention are shown below. 【0664】 This invention is a system that assesses the risks of network content when children use mobile devices and supports safe use. This system mainly consists of three elements: a server, a terminal, and a user (parent or administrator). 【0665】 The server is primarily responsible for data collection and evaluation. Specifically, it uses a web crawler to collect various data from the internet. The collected data is then analyzed using a generative AI model to identify harmfulness. This AI model employs natural language processing techniques and performs analysis based on prompts such as, "Evaluate whether this content is safe for children." The analyzed data is managed in a database, and access to content is dynamically configured based on the risk assessment. The evaluation model is also regularly updated using machine learning libraries. 【0666】 The device monitors all website and application requests accessed by the child, communicating with the server to verify their security. Based on the evaluation results received from the server, the device controls access to the content. A network monitoring library is used, and if access is deemed dangerous, it is immediately blocked using the firewall. Furthermore, the device records the child's usage history and displays visual warnings to parents or administrators via a GUI, or sends push notifications. 【0667】 Parents or administrators can customize and manage the entire system through a dedicated application. By adjusting filtering levels and notification settings, flexible responses can be tailored to the child's usage. For example, if the server detects a high risk, an immediate notification is sent, allowing the user to take appropriate action. The management screen provides access to statistical information based on the child's usage history, which can be used to support digital education at home. 【0668】 In this way, the system provides comprehensive support to facilitate user management while keeping children's usage environments safe. 【0669】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0670】 Step 1: 【0671】 The server uses a web crawler to collect information from the internet. The input is a specified list of URLs. The crawler uses this list to visit web pages and retrieve content such as text, images, and audio. The output is the collected data, which is then passed on to the next analysis step. 【0672】 Step 2: 【0673】 The server preprocesses the collected data and then inputs prompts into the generated AI model. Specifically, data cleaning and formatting are performed. The input is the raw data collected in step 1, and the output is data converted into a format suitable for AI analysis. For example, the prompt is passed to the model in the form of a message such as "Evaluate whether this content is safe for children." 【0674】 Step 3: 【0675】 The server receives a response from the generating AI model and evaluates the harmfulness of the content. The input is the analysis results obtained from the AI ​​model in step 2. Based on these results, a risk score is calculated using a specific algorithm. The output is the risk score, which is stored in the database. 【0676】 Step 4: 【0677】 The device monitors requests for websites and applications that the user (child) attempts to access. The input is the user's access request. The device sends this to the server and compares it to a configured risk score. The output is the server's decision to allow or deny access. 【0678】 Step 5: 【0679】 The terminal receives risk assessment results from the server and controls access based on those results. If the risk is determined to be high, the terminal blocks access using the network monitoring library and firewall. The input is the server's assessment result, and the output is whether access is permitted or blocked. 【0680】 Step 6: 【0681】 Users customize settings and receive notifications through a dedicated application. Inputs include user actions and server notifications. The application uses a GUI to visually display information to the user, prompting appropriate setting changes and actions. Outputs include updated settings and warning notifications to the user. 【0682】 (Application Example 1) 【0683】 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". 【0684】 When children use electronic communication devices, there is a risk that they may unintentionally access harmful information or highly dangerous content on the internet. Traditional methods make it difficult to identify these risks in advance, and it has been challenging for parents and administrators to respond immediately. Furthermore, existing systems lack flexibility, preventing administrators from responding individually to different situations. Therefore, there was a need for an innovative system that could ensure children's safety while reducing the burden on administrators. 【0685】 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. 【0686】 In this invention, the server includes means for identifying the degree of harmfulness of content based on information collected using a generative AI, means for controlling access to information based on the results of a risk assessment, and means for sending notifications to the administrator regarding access that is judged to have a high risk score calculated by the generative AI model. This makes it possible to assess the dangers of content used by children in advance and respond flexibly and quickly, thereby ensuring the safety of children and reducing the burden on administrators. 【0687】 "Electronic communication equipment" refers to terminal devices used for digital communication, and devices that allow users to access the internet. 【0688】 "Online information" is a general term for digital content that exists on the internet and is accessible to users. 【0689】 "Risk assessment" is the process of identifying potential risks contained in online information and calculating a risk score based on that. 【0690】 "Generative AI" is a technology that uses artificial intelligence to analyze data and calculate risk scores and levels of harm. 【0691】 "Harmfulness" is an indicator that shows the potential degree of negative impact that content may have on minors and other minors. 【0692】 "Notification" refers to a means of communication used to inform an administrator about a specific event or situation. 【0693】 "Usage history" refers to a record of online information accessed using electronic communication devices, and is information used to understand past usage patterns. 【0694】 This invention is an online safety management system that operates between electronic communication devices used by children and monitoring devices used by parents or administrators. The system aims to pre-assess the risks of online content and notify parents or administrators in real time. 【0695】 The server analyzes online information using a generative AI model and calculates a risk score. Specifically, it uses machine learning frameworks such as TensorFlow and PyTorch to run the generative AI model based on data collected from the internet. This model applies natural language processing techniques to evaluate the harmfulness of the content. If the risk score exceeds a set threshold, the server sends a command to restrict access by electronic communication devices. 【0696】 The device monitors the actions of the child user and determines whether or not to grant access based on risk assessment information provided by the server. Furthermore, the device records usage history, which can be later referenced by parents or administrators as usage statistics. 【0697】 Users can use a dedicated monitoring application to check device risk assessment results and their child's usage history in real time. This application is developed using ReactJS and Flutter, and utilizes technologies such as Google Firebase Cloud Messaging to implement notification functions. Users can freely customize filtering levels and notification settings through the application. 【0698】 For example, if a child starts using a new video-sharing platform, the server uses a generative AI model to analyze content trends on that platform and extract potentially harmful content. If this analysis shows a high risk score, the server sends a notification to the parent's or administrator's device to encourage early action. An example of a prompt to the generative AI model would be, "Analyze content trends on the new social media platform and extract topics that may be harmful to minors." 【0699】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0700】 Step 1: 【0701】 The server collects content data from the internet. The input is online information, which is then stored in a database. The data is retrieved in text format or as metadata. This prepares the basic data for use in subsequent processing. 【0702】 Step 2: 【0703】 The server feeds the collected data into a generating AI model to determine its level of harmfulness. The input is the online information collected in step 1. The model uses natural language processing techniques to analyze the data and outputs a risk score. This output is a numerical value indicating how dangerous the online information is to the user. 【0704】 Step 3: 【0705】 The server determines whether to grant access to specific online information based on the risk score. The input is the risk score from step 2. A threshold is set to evaluate the score, and if it exceeds the threshold, it is judged as "high risk." Based on this result, it is determined whether access is permitted or restricted. 【0706】 Step 4: 【0707】 The terminal receives an access restriction command from the server. The input is the result of the access permission / denial determination in step 3. Access to information determined to be high-risk is blocked, and a warning message is displayed to the user. This operation ensures the safety of children. 【0708】 Step 5: 【0709】 The terminal records usage history and provides it to the user for later reference. Input is the child's operation log, which is recorded in a database and converted into statistical information. Output is the usage history and statistical information that the user can review. 【0710】 Step 6: 【0711】 Users can check device usage history and risk assessment status through a dedicated application and adjust access settings. Inputs are the history data output from step 5 and the server's risk notification. Based on this, users adjust filtering levels and notification settings to optimize the system. This allows administrators to manage children's internet usage in real time. 【0712】 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. 【0713】 This invention provides a system that not only assesses the risks of online content and ensures safety when children use mobile devices, but also offers a higher level of protection by understanding the user's emotions. This system has a configuration that combines a server, a terminal, and an emotion engine. 【0714】 Server operation 【0715】 The server collects data from the internet and analyzes it using generative AI to identify the harmfulness and risk of content. The results of this analysis are stored in the server's database and updated as needed. Furthermore, the server receives data from an emotion engine and performs emotion-based risk assessments to generate more precise risk scores. 【0716】 Terminal operation 【0717】 The device monitors all content the user accesses. Each request is sent to a server, which returns a risk assessment and user sentiment data analyzed by an emotion engine. Based on this information, the device decides whether to allow access to the content. In addition, the device is equipped with sensors that capture the user's emotions, thereby collecting sentiment data in real time. 【0718】 User actions 【0719】 Parents or administrators can manage various system functions through the application. Specifically, they can customize filtering settings and notification options, and view their child's access history and emotional changes. If the server issues an alert regarding any risk, a notification is sent to the device, including emotional information from the emotion engine. 【0720】 Emotional engine integration 【0721】 The emotion engine recognizes the user's emotions using voice analysis, facial expression analysis, or biosensors. This information is transmitted to the server and considered as part of a risk assessment. For example, if the system determines that the user is experiencing stress, it immediately notifies the parent and conducts a more thorough risk assessment. 【0722】 Specific example 【0723】 For example, if a child attempts to view harmful videos online, the device sends an access request to the server. The server uses a generative AI and emotion engine to assess the video's harmfulness and the child's current emotional state. If the video is high-risk and the child is also experiencing stress, the device blocks access to the video and notifies the parent of the details. This allows parents to detect the problem early and take appropriate action. In this way, the system goes beyond simple content filtering, achieving advanced security management that even considers the user's emotions. 【0724】 The following describes the processing flow. 【0725】 Step 1: 【0726】 The server begins collecting vast amounts of content data from the internet. This data includes text, images, and video information. The server stores this data in a database. 【0727】 Step 2: 【0728】 The server uses generative AI to learn and update a model that assesses the risk of content from the collected data. Specifically, it uses natural language processing techniques to determine whether the content is harmful. 【0729】 Step 3: 【0730】 The device monitors the user's content access. Each time the user accesses new content, it sends a request to the server. This request includes the content's URL and metadata. 【0731】 Step 4: 【0732】 The server analyzes the request received from the terminal and performs a risk assessment using a pre-trained model. If the assessment determines that the request is high-risk, the server returns that information to the terminal. 【0733】 Step 5: 【0734】 The device receives the risk assessment results from the server and, based on that, allows or denies access to the content. If access is denied, the device displays a warning message to the user. 【0735】 Step 6: 【0736】 The emotion engine installed in the device analyzes the user's biometric data and facial expressions to recognize emotions. The acquired emotion data is then sent to a server. 【0737】 Step 7: 【0738】 The server incorporates the received sentiment data into its risk assessment and recalculates the content's risk score. If it detects that the user is experiencing anxiety or stress, a more rigorous assessment is performed. 【0739】 Step 8: 【0740】 Users can view their child's access history and emotional state through the management application. If a high-risk situation is detected, a notification is immediately sent to the user's device. Users can adjust settings and take appropriate measures as needed. 【0741】 Step 9: 【0742】 Users can check notifications, interact with their children, and provide feedback on content usage and emotional states. By changing settings and reviewing rules, they can flexibly support their children. 【0743】 (Example 2) 【0744】 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". 【0745】 When children access the internet using mobile devices, there is a challenge in properly assessing the risks of the content and ensuring their safety. Furthermore, conventional systems only filter content and fail to consider the user's feelings in risk management, thus not adequately ensuring safety. In addition, when parents or administrators are involved in protecting children, it often requires many manual steps, which is inefficient. 【0746】 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. 【0747】 This invention includes a server that utilizes generative AI to analyze internet information and determine the risk of its content, a means of integrating speech recognition and facial expression analysis technologies to acquire user emotional data, and a means of calculating a risk score based on the collected emotional data to improve the accuracy of access control. This enables accurate assessment of content risks and flexible risk management according to the user's emotional state, allowing parents and administrators to protect their children more effectively. 【0748】 "Generative AI" refers to a system that uses artificial intelligence technology to analyze and predict data, and is particularly capable of discovering patterns from large-scale data. 【0749】 "Internet information" refers to all data accessible online, including in various formats such as text, images, videos, and audio. 【0750】 "Speech recognition" is a technology that uses computers to analyze human speech and convert it into text data, enabling the understanding of spoken content. 【0751】 "Facial expression analysis" is a technology that recognizes a person's facial expressions from an image and infers their emotional state, making judgments based on facial features. 【0752】 "Emotional data" refers to data that expresses an individual's emotional state using numerical values ​​or categories, and is obtained from voice, facial expressions, and other biometric information. 【0753】 A "risk score" is a numerical evaluation that represents the level of risk associated with a particular situation or content, and is used to support risk management. 【0754】 Access control is a technology that restricts which resources and content a user can access, and it is crucial for ensuring the security of a system. 【0755】 "Parent or administrator" refers to a person who has the authority to supervise a child's online activities and intervene as needed, and typically refers to users in the home or educational institution. 【0756】 "Statistical information" refers to a unified set of data analyzed from collected data, which facilitates the analysis of trends and the discovery of patterns. 【0757】 This invention is a system that assesses the risks associated with online content when children use mobile devices, thereby ensuring safety. Specifically, it utilizes generative AI models and natural language processing technology to achieve precise risk assessments that include user sentiment data. 【0758】 The server implements generative AI models using Python and other programming languages ​​to collect and analyze internet information. For example, it uses BeautifulSoup and Scrapy to collect information and applies natural language processing techniques to text data. Deep learning frameworks such as TensorFlow and PyTorch are used for image and video analysis. 【0759】 The device is equipped with hardware such as a camera and microphone to collect emotional data by capturing the user's facial expressions and voice in real time. The voice is analyzed through speech recognition software such as Google Cloud Speech-to-Text, and OpenCV is used for facial expression analysis. 【0760】 Users (parents or administrators) can view their child's risk assessment and emotional data via a smartphone app. The application is built with frameworks such as React Native, and through it, parents and administrators can adjust filtering and notification settings and view detailed risk reports. 【0761】 Specifically, when a child attempts to access a video on the internet, the device sends the request to a server. The server then inputs the following prompt into a generating AI model to assess the risk: "Please provide a risk and sentiment-based score for the content the user is currently trying to access." If the server determines the content to be harmful, the device blocks access to that content and notifies the parent. In this way, the safety of children's online activities can be highly controlled. 【0762】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0763】 Step 1: 【0764】 The server collects information from the internet. It receives URLs and keywords from specific websites as input. Using data collection tools (e.g., BeautifulSoup or Scrapy), it parses this information to obtain text, image, and video data. The output is the collected raw dataset, which the server then feeds to the next analysis step. 【0765】 Step 2: 【0766】 The server applies a generative AI model based on the collected data and analyzes its content. It receives the dataset obtained in step 1 as input. The generative AI model (for example, a model built using TensorFlow) analyzes the text using natural language processing techniques and assesses its harmfulness. Images and videos are analyzed similarly. The output is an evaluation result including a risk score for each data point. 【0767】 Step 3: 【0768】 The device captures user emotion data. It receives a real-time emotion data stream from the camera and microphone as input. It identifies the emotional state by performing facial expression analysis using OpenCV and speech analysis using Google Cloud Speech-to-Text. The output is a dataset showing the user's emotional state, which is sent to the server as information useful for risk assessment. 【0769】 Step 4: 【0770】 The server inputs a prompt message into the generating AI model to calculate a revised risk score that takes sentiment data into account. The prompt message is: "Please provide a risk and sentiment-based score for the content the user is currently accessing." The input is the risk assessment from Step 2 and the sentiment data from Step 3. Integrated data analysis outputs a more accurate risk score. 【0771】 Step 5: 【0772】 The device determines whether to allow access to the content based on the risk score received from the server. It receives a revision risk score sent from the server as input. An action is triggered to restrict access if the risk is high, and to allow access if it is low. The output is whether the user is granted or restricted access to the content. 【0773】 Step 6: 【0774】 The user (parent or administrator) receives notifications from the device and monitors system operation. Inputs include notifications from the server containing alert information and sentiment data. Through the application, they adjust filtering and notification settings to ensure the child's safety. Outputs include changes to system settings and additional administrative actions. 【0775】 (Application Example 2) 【0776】 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". 【0777】 Current internet security systems for children primarily rely on static information to assess the harmfulness of content, failing to consider real-time emotional changes. Therefore, more dynamic and individually tailored security management is needed. Furthermore, a system capable of accurate risk assessment by appropriately monitoring children's emotional states is required. 【0778】 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. 【0779】 In this invention, the server includes means for identifying the harmfulness of digital information based on data collected using a generative AI, means for detecting the user's emotional state and analyzing the emotional data to reflect it in the risk assessment, and means for controlling access to digital information based on the results of the risk assessment and the user's emotional state. This enables adaptive safety management that responds to real-time emotional changes for each user. 【0780】 An "information processing device" is a computer system used for processing and managing digital information. 【0781】 An "information network" is a communication structure that enables the exchange of digital information among users. 【0782】 "Digital information" refers to all types of electrical information handled on information networks. 【0783】 "Generative AI" is an artificial intelligence technology that enables pattern recognition and data generation through artificial learning. 【0784】 "Harmfulness" is an evaluation index that refers to the degree to which digital information is likely to have a negative impact on users. 【0785】 "Emotional data" refers to data that indicates the user's psychological state, and is obtained through voice analysis and facial expression analysis. 【0786】 "Access control" is a method for managing users' ability to connect to and view digital information on an information network. 【0787】 A "supervisor" is a person or entity responsible for monitoring user activity and ensuring safety. 【0788】 "Visualized statistical information" refers to information that presents collected data in the form of graphs, charts, and other visual representations, making it easy to understand. 【0789】 "Analysis techniques" are methods for understanding digital information in detail and systematically. 【0790】 "Recognition technology" refers to the technology of finding specific patterns or features from digital information and then understanding and judging them. 【0791】 This invention is a system for performing risk assessment of digital information on an information network in an information processing device used by children. The system is implemented by the following components. 【0792】 The server collects digital information from the information network and uses generative AI to assess its harmfulness. The generative AI model classifies digital information according to its risk by analyzing a vast dataset. For example, it learns patterns from previously reported harmful information and uses that to evaluate new digital information. This makes it possible to proactively identify information that could negatively impact users. 【0793】 The device is equipped with sensors that monitor the user's emotional state in real time. An emotion engine, which utilizes voice analysis and facial expression analysis technologies, detects the user's psychological state and sends it to the server as emotion data. This emotion data is analyzed on the server side and reflected in the risk assessment of digital information. For example, if the user is experiencing stress, stricter access control is implemented using the emotion data. 【0794】 Users can monitor and control the system's operation through an intuitive interface. Usage history and changes in emotional state are displayed as visualized statistics on the dashboard, allowing supervisors to make further safety adjustments based on this information. 【0795】 For example, when a child tries to install a new online game, the server analyzes the game's chat function and restricts installation if it determines it poses a risk. Furthermore, if the emotion engine detects signs of anxiety from the user's facial expressions, a notification is sent to the parent, recommending appropriate action. In this way, the system achieves dynamic and personalized security. 【0796】 As an example of a prompt, the AI ​​can be given a command such as, "Analyze the harmfulness of this content, and if the user is currently stressed, block it based on that risk score," which will then be used to determine whether to allow the content. 【0797】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0798】 Step 1: 【0799】 The device acquires the user's voice and video data in real time. This data is collected through sensors built into the device and transmitted to voice analysis and facial expression analysis devices. The analysis devices detect the user's emotional state from this data and generate emotion data. The output is emotion data in which the user's specific emotions are represented numerically. 【0800】 Step 2: 【0801】 The terminal sends the generated sentiment data to the server. The server receives this sentiment data and then uses generative AI to analyze the digital information collected from the information network. Based on past datasets, the generative AI model receives prompt sentences that evaluate the harmfulness of the digital information as input and outputs a specific risk score. 【0802】 Step 3: 【0803】 The server decides whether to allow or deny digital information based on risk scores and emotional data. The server then generates a final access control instruction as output by applying algorithms that increase or decrease the risk score, for example, if the user's emotions are stressed. 【0804】 Step 4: 【0805】 Based on access control commands from the server, users are granted or denied access to digital information on their devices. After completing a task, the device records usage history and changes in emotional state, which are displayed as visualized statistics on a supervisory dashboard. This allows supervisors to monitor users' internet usage and adjust system settings as needed. 【0806】 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. 【0807】 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. 【0808】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0809】 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. 【0810】 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. 【0811】 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. 【0812】 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. 【0813】 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. 【0814】 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." 【0815】 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. 【0816】 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. 【0817】 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. 【0818】 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. 【0819】 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. 【0820】 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. 【0821】 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. 【0822】 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. 【0823】 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. 【0824】 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. 【0825】 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. 【0826】 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 as being incorporated by reference. 【0827】 The following is further disclosed regarding the embodiments described above. 【0828】 (Claim 1) 【0829】 To assess the risks of online content when children use mobile devices, 【0830】 A means of identifying the harmfulness of content based on data collected using generative AI, 【0831】 Means for controlling access to content based on the results of risk assessment, 【0832】 A means of sending notifications to parents or administrators regarding access deemed to be high-risk, 【0833】 A means of recording children's usage history and displaying it as statistical information, 【0834】 A system that includes this. 【0835】 (Claim 2) 【0836】 The system according to claim 1, comprising means of utilizing natural language processing technology to analyze collected data and identify potentially harmful content or problems. 【0837】 (Claim 3) 【0838】 The system according to claim 1, comprising means for making notification settings for a parent or administrator customizable. 【0839】 "Example 1" 【0840】 (Claim 1) 【0841】 To assess the risks of content on mobile devices used by children based on information on the network, 【0842】 Data collection means, 【0843】 A means of identifying the harmfulness of content using generated artificial intelligence, 【0844】 A means of managing access to content based on the results of a risk assessment, 【0845】 A means of sending a notification to the parent or administrator if the evaluation result is determined to be dangerous, 【0846】 A means of saving children's usage history and displaying it as statistical information, 【0847】 A system that includes this. 【0848】 (Claim 2) 【0849】 The system according to claim 1, comprising means for utilizing natural language processing technology to identify the harmfulness and potential problematic nature of content using data analysis means. 【0850】 (Claim 3) 【0851】 The system according to claim 1, comprising adjustment means for customizing notification settings to a parent or administrator. 【0852】 "Application Example 1" 【0853】 (Claim 1) 【0854】 To assess the risks of online information when children use electronic communication devices, 【0855】 A means of identifying the degree of harmfulness of content based on information collected using a generation AI, 【0856】 Means for controlling access to information based on the results of risk assessment, 【0857】 A means of sending a notification to the administrator regarding access that is judged to have a high risk score calculated by a generative AI model, 【0858】 A means for recording the usage history of electronic communication devices and displaying it as usage statistics, 【0859】 A system that includes this. 【0860】 (Claim 2) 【0861】 The system according to claim 1, comprising means for interpreting received information and utilizing natural language processing to identify potentially harmful information or problems. 【0862】 (Claim 3) 【0863】 The system according to claim 1, comprising means for adjusting notification settings for administrators. 【0864】 "Example 2 of combining an emotion engine" 【0865】 (Claim 1) 【0866】 A method that utilizes generation AI to analyze internet information and determine the danger level of its content, 【0867】 A means of acquiring user emotion data by integrating speech recognition and facial expression analysis technologies, 【0868】 A means to improve the accuracy of access control by calculating a risk score based on collected emotional data, 【0869】 Means of notifying parents or administrators about high-risk information access, 【0870】 A means of recording the user's access history and emotional changes, and displaying them as statistical information, 【0871】 A system that includes this. 【0872】 (Claim 2) 【0873】 The system according to claim 1, comprising means for utilizing natural language processing techniques to identify potentially hazardous information and problems. 【0874】 (Claim 3) 【0875】 The system according to claim 1, comprising means for enabling a parent or administrator to adjust notification settings. 【0876】 "Application example 2 when combining with an emotional engine" 【0877】 (Claim 1) 【0878】 To assess the risks of digital information on information networks when children use information processing devices, 【0879】 A means of identifying the harmfulness of digital information based on data collected using generative AI, 【0880】 A means of detecting the user's emotional state and reflecting the emotional data in risk assessment, 【0881】 A means of controlling access to digital information based on the results of risk assessment and the user's emotional state, 【0882】 A means of sending notifications to supervisors regarding access deemed to be high-risk, 【0883】 A means of recording a user's usage history and changes in emotional state, and displaying them as visualized statistical information, 【0884】 A system that includes this. 【0885】 (Claim 2) 【0886】 The system according to claim 1, comprising means of utilizing analytical and recognition techniques to analyze collected data and emotional data and identify potential harmful information or trouble. 【0887】 (Claim 3) 【0888】 The system according to claim 1, comprising means for making notification settings and visualization settings for supervisors customizable. [Explanation of symbols] 【0889】 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

[Claim 1] To assess the risks of online content when children use mobile devices, A means of identifying the harmfulness of content based on data collected using generative AI, Means for controlling access to content based on the results of risk assessment, A means of sending notifications to parents or administrators regarding access deemed to be high-risk, A means of recording children's usage history and displaying it as statistical information, A system that includes this. [Claim 2] The system according to claim 1, comprising means for utilizing natural language processing technology to analyze collected data and identify potentially harmful content or problems. [Claim 3] The system according to claim 1, comprising means for allowing customization of notification settings for a parent or administrator.

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