system

Abstract

The system according to this embodiment aims to monitor children's smartphone use in real time, warn of access to inappropriate content or suspicious accounts, and provide digital literacy education. [Solution] The system according to the embodiment comprises an analysis unit, a warning unit, an education unit, a provision unit, a response unit, and a visualization unit. The analysis unit analyzes the child's smartphone usage in real time. The warning unit warns of inappropriate content or access to suspicious accounts based on the results analyzed by the analysis unit. The education unit teaches digital literacy based on the content warned by the warning unit. The provision unit provides learning content provided by the education unit. The response unit supports initial response when trouble occurs online. The visualization unit visualizes the child's smartphone usage and learning progress.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to monitor in real time inappropriate content and access to suspicious accounts in children's use of smartphones and to provide appropriate education and responses.

[0005] The system according to the embodiment aims to monitor in real time children's use of smartphones, warn of access to inappropriate content and suspicious accounts, and provide digital literacy education.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a warning unit, an education unit, a provision unit, a response unit, and a visualization unit. The analysis unit analyzes children's smartphone usage in real time. The warning unit warns of inappropriate content or access to suspicious accounts based on the results analyzed by the analysis unit. The education unit teaches digital literacy based on the content warned by the warning unit. The provision unit provides learning content provided by the education unit. The response unit supports initial response when problems occur online. The visualization unit visualizes children's smartphone usage and learning progress. [Effects of the Invention]

[0007] The system according to this embodiment can monitor children's smartphone use in real time, warn of access to inappropriate content or suspicious accounts, and provide digital literacy education. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8]This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memories (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that supports and manages smartphone use by children and young people. This AI agent system provides a service that offers a safe and healthy digital environment by performing risk prevention and digital literacy education within the smartphone. The AI ​​agent system analyzes children's smartphone use in real time and warns of access to inappropriate content and suspicious accounts. For example, if a child attempts to access inappropriate content such as fraud, violent content, or defamation on social media or the web, it will immediately display a warning. It also automatically analyzes messages from suspicious accounts and evaluates and notifies the user of the danger. Next, the AI ​​agent system is equipped with a digital literacy learning mode. In this mode, the AI ​​interactively teaches digital literacy through daily smartphone use. For example, it incorporates quiz formats and gamification elements such as "What should you check before clicking this link?" and "How can you keep your password secure?" to make learning fun. Furthermore, the AI ​​agent system provides learning content on digital skills and safety knowledge according to age and interests. For example, the service offers content such as "How to Make Friends Online" for elementary school students and "How to Avoid Phishing Scams" for middle school students and above. Furthermore, if a child encounters trouble online (bullying, harassment, fraud, etc.), the AI ​​will notify parents and schools and provide initial support. If necessary, it will provide contact information for specialized organizations and instructions. The AI ​​agent system also provides a dashboard that visualizes and shares children's smartphone usage and learning progress with parents. This allows parents to understand what their children are learning and what risks they are aware of, creating opportunities for conversation. This service aims to alleviate anxieties about increasing smartphone use and online troubles, and to create a society where children can safely utilize the digital environment. By utilizing generative AI to update personalized educational programs and advice in real time, it enables flexible responses to the diverse needs of each family.This allows the AI ​​agent system to support and manage smartphone use among children and young people, providing a safe and healthy digital environment.

[0029] The AI ​​agent system according to this embodiment comprises an analysis unit, a warning unit, an education unit, a provision unit, a response unit, and a visualization unit. The analysis unit analyzes a child's smartphone usage in real time. The analysis unit monitors, for example, the smartphone usage history and application usage, and collects data in real time. The analysis unit can use AI to analyze the collected data and identify patterns in the child's smartphone usage. For example, the analysis unit can use AI to identify which applications the child uses most frequently. The analysis unit can also use AI to identify when the child uses their smartphone. Furthermore, the analysis unit can use AI to identify what kind of content the child is accessing. The warning unit warns of inappropriate content and access to suspicious accounts based on the results analyzed by the analysis unit. For example, the warning unit displays a warning message if the child attempts to access inappropriate content. The warning unit can use AI to automatically analyze messages from suspicious accounts and evaluate and notify of the risks. For example, the warning unit uses AI to analyze the content of messages from suspicious accounts and evaluate the risks. Furthermore, the warning unit can use AI to display warning messages if it determines that something is highly dangerous. The Ministry of Education will teach digital literacy based on the warnings issued by the warning unit. The Ministry of Education will teach digital literacy, for example, by incorporating quiz formats and gamification elements. The Ministry of Education can use AI to understand children's learning progress in real time and provide appropriate educational content. For example, the Ministry of Education can use AI to monitor children's learning progress and update educational content as needed. The Ministry of Education can also use AI to provide educational content tailored to children's interests and age. The provision unit will provide the learning content provided by the Ministry of Education. The provision unit will provide, for example, video materials and interactive quizzes. The provision unit can use AI to provide learning content tailored to children's interests and age. For example, the provision unit will use AI to recommend learning content based on children's interests.Furthermore, the provisioning unit can use AI to provide learning content tailored to the child's age. The response unit supports initial response when problems occur online. For example, if bullying or harassment occurs online, the response unit will notify parents and schools. The response unit can use AI to analyze the nature of the problem and suggest appropriate response methods. For example, the response unit can use AI to provide contact information for specialized organizations and action procedures depending on the nature of the problem. The response unit can also use AI to support initial response depending on the nature of the problem. The visualization unit visualizes the child's smartphone usage and learning progress. For example, the visualization unit can provide a dashboard so that parents can understand their child's smartphone usage and learning progress. The visualization unit can use AI to visualize the child's smartphone usage and learning progress in real time. For example, the visualization unit can use AI to display the child's smartphone usage in graphs and charts. The visualization unit can also use AI to visualize the child's learning progress. As a result, the AI ​​agent system according to this embodiment can analyze children's smartphone usage in real time, warn of access to inappropriate content or suspicious accounts, educate them on digital literacy, provide learning content, support initial response when problems occur, and visualize usage status and learning progress.

[0030] The analytics department analyzes children's smartphone use in real time. Specifically, it monitors smartphone usage history and application usage, collecting data in real time. For example, it collects data such as smartphone browser history, app launch time, usage frequency, and time of day. This data is analyzed using AI to identify patterns in children's smartphone use. The AI ​​uses machine learning algorithms to identify from the collected data which applications children use frequently, at what times of day they use their smartphones, and what kind of content they access. For example, the AI ​​may detect that a child is using a particular game app for extended periods and determine that this may be affecting their study time. The AI ​​may also detect that a child is using their smartphone late into the night and determine that there is a risk of sleep deprivation. Furthermore, the AI ​​can detect that a child is accessing inappropriate content and issue a warning. This allows the analytics department to understand detailed patterns of children's smartphone use and provide information to take appropriate measures.

[0031] The warning unit warns of inappropriate content and suspicious accounts based on the results of analysis by the analysis unit. Specifically, it displays a warning message if a child attempts to access inappropriate content. For example, if a child attempts to access violent videos or adult websites, the warning unit will immediately display a warning message and restrict access. The warning unit can also use AI to automatically analyze messages from suspicious accounts, assess the danger, and notify users. The AI ​​uses natural language processing technology to analyze the content of messages and detect dangerous keywords and phrases. For example, the AI ​​can detect threatening words or requests for personal information in messages, and if it determines that the danger is high, it will display a warning message. Furthermore, the warning unit can notify parents and schools before a child is in a dangerous situation. This allows the warning unit to ensure the safety of children and take appropriate action quickly.

[0032] The Ministry of Education will teach digital literacy based on the warnings issued by the Warning Department. Specifically, it will incorporate quizzes and gamification elements into its teaching. For example, if a child attempts to access inappropriate content, they will be taught the reasons and risks through a quiz. By incorporating game elements, children can learn while having fun. The Ministry of Education can use AI to monitor children's learning progress in real time and provide appropriate educational content. The AI ​​will monitor children's learning progress and update the educational content as needed. For example, if a child lacks understanding of a particular topic, the AI ​​will provide additional learning content related to that topic. The AI ​​can also provide educational content tailored to the child's interests and age. This will enable the Ministry of Education to effectively improve children's digital literacy.

[0033] The service provider will provide learning content provided by the Ministry of Education. Specifically, it will provide video learning materials and interactive quizzes. For example, it will provide video learning materials for children to learn digital literacy, conveying information in a visually easy-to-understand format. In addition, children can check what they have learned and deepen their understanding through interactive quizzes. The service provider can use AI to provide learning content tailored to children's interests and age. The AI ​​analyzes children's past learning history and interests and recommends the most suitable learning content. For example, if a child is interested in a particular topic, the AI ​​will prioritize providing learning content related to that topic. Furthermore, by providing learning content appropriate to the child's age, the AI ​​can provide learning materials of appropriate difficulty. In this way, the service provider can increase children's motivation to learn and support effective learning.

[0034] The response department provides initial support when problems occur online. Specifically, it notifies parents and schools if bullying or harassment occurs online. For example, if a child is bullied online, the response department analyzes the situation and notifies parents and schools. The response department can also use AI to analyze the nature of the problem and suggest appropriate countermeasures. The AI ​​will provide contact information for specialized organizations and action procedures depending on the nature of the problem. For example, if a child is harassed online, the AI ​​will analyze the situation and suggest appropriate countermeasures. The AI ​​can also support initial response depending on the nature of the problem. For example, if a child is involved in trouble online, the AI ​​will analyze the situation and suggest appropriate countermeasures. This allows the response department to respond quickly and appropriately to online problems and ensure the safety of children.

[0035] The visualization unit visualizes children's smartphone usage and learning progress. Specifically, it provides a dashboard that allows parents to understand their children's smartphone usage and learning progress. For example, parents can use the dashboard to see which applications their children are using, for how long, and at what times of day they are using their smartphones. Parents can also use the dashboard to check their children's learning progress and understand which topics their children are struggling with. The visualization unit uses AI to visualize children's smartphone usage and learning progress in real time. The AI ​​analyzes the collected data and displays it in graphs and charts. For example, the AI ​​displays children's smartphone usage in graphs and charts, allowing parents to understand the situation at a glance. The AI ​​also visualizes children's learning progress, allowing parents to understand their children's learning situation. As a result, the visualization unit can provide parents with information to understand their children's smartphone usage and learning progress in real time and take appropriate action.

[0036] The warning unit can automatically analyze messages from suspicious accounts and assess and notify users of their risks. For example, the warning unit can use AI to analyze messages from suspicious accounts and assess their risks. The warning unit can use AI to analyze the content of messages and sender information to identify spam accounts and fake accounts. For example, the warning unit can use AI to assess whether the content of a message is potentially fraudulent or phishing. The warning unit can also use AI to display a warning message if it determines that the risk is high. This ensures the safety of children by automatically analyzing messages from suspicious accounts and assessing and notifying users of their risks. Some or all of the above processes in the warning unit may be performed using AI or not using AI. For example, the warning unit can input messages from suspicious accounts into a generating AI and have the generating AI perform the analysis of the message content.

[0037] The Ministry of Education can teach digital literacy by incorporating quiz formats and gamification elements. For example, the Ministry of Education could ask questions in a quiz format, such as, "What should you check before clicking this link?" The Ministry of Education could use AI to analyze children's answers and provide appropriate feedback. For example, the Ministry of Education could use AI to determine whether a child's answer is correct or incorrect and present the correct answer. The Ministry of Education could also incorporate gamification elements to make learning digital literacy fun. For example, the Ministry of Education could use AI to award points or badges according to a child's learning progress. This would allow children to learn digital literacy in an enjoyable way by incorporating quiz formats and gamification elements. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education could input quiz-format questions into a generating AI and have the generating AI generate appropriate feedback.

[0038] The service provider can offer learning content related to digital skills and safety knowledge tailored to age and interests. For example, it can provide content such as "How to make friends online" for elementary school students and "How to avoid phishing scams" for middle school students and older. The service provider can use AI to recommend learning content based on a child's age and interests. For example, it can use AI to suggest relevant learning content based on a child's interests. The service provider can also use AI to provide learning content appropriate to a child's age. By providing learning content tailored to age and interests, it can capture children's interest and promote effective learning. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input data on a child's age and interests into a generating AI and have the generating AI recommend the most suitable learning content.

[0039] The response unit can notify parents and schools when problems occur online and provide contact information for specialized organizations and procedures for action. For example, if bullying or harassment occurs online, the response unit will notify parents and schools. The response unit can use AI to analyze the nature of the problem and suggest appropriate countermeasures. For example, the response unit can use AI to provide contact information for specialized organizations and procedures for action depending on the nature of the problem. The response unit can also use AI to support initial responses depending on the nature of the problem. This allows for a swift response and appropriate support when problems occur online. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input the nature of the problem into a generating AI and have the generating AI suggest appropriate countermeasures.

[0040] The visualization unit can provide a dashboard that shares a child's smartphone usage and learning progress with parents. For example, the visualization unit can provide a dashboard that allows parents to understand their child's smartphone usage and learning progress. The visualization unit can use AI to visualize a child's smartphone usage and learning progress in real time. For example, the visualization unit can use AI to display a child's smartphone usage in graphs and charts. The visualization unit can also use AI to visualize a child's learning progress. This allows parents to understand their child's situation and provide appropriate support by sharing information about their child's smartphone usage and learning progress. Some or all of the above-described processes in the visualization unit may be performed using AI or not. For example, the visualization unit can input data on a child's smartphone usage and learning progress into a generating AI and have the generating AI perform the generation of the visualization.

[0041] The analysis unit can select the optimal analysis algorithm by referring to the child's past usage history during analysis. For example, the analysis unit can analyze the usage patterns of a specific application based on the applications the child has frequently used in the past. The analysis unit can use AI to analyze past usage history and select the optimal analysis algorithm. For example, the analysis unit can use AI to analyze usage trends during specific time periods from past usage history. The analysis unit can also use AI to refer to past usage history and analyze the level of interest in specific content. By referring to past usage history, the analysis unit can select the optimal analysis algorithm and perform highly accurate analysis. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past usage history data into a generating AI and have the generating AI select the optimal analysis algorithm.

[0042] The analysis unit can filter data based on a child's current lifestyle and areas of interest during analysis. For example, if a child uses a smartphone during school lessons, the analysis unit will prioritize analyzing learning-related activities. The analysis unit can also use AI to analyze and filter data based on a child's current lifestyle and areas of interest. For example, if a child uses a smartphone on weekends, the analysis unit will prioritize analyzing entertainment-related activities. Furthermore, if a child has a particular hobby, the analysis unit can also prioritize analyzing activities related to that hobby. This allows for more relevant analysis results by filtering data based on the child's current lifestyle and areas of interest. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the child's current lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0043] The analysis unit can prioritize the analysis of highly relevant data by considering the child's geographical location during the analysis process. For example, if the child is in a specific location, the analysis unit will prioritize the analysis of activities related to that location. The analysis unit can use AI to analyze geographical location information and prioritize the analysis of highly relevant data. For example, if the child is traveling, the analysis unit will use AI to prioritize the analysis of activities at the travel destination. The analysis unit can also use AI to prioritize the analysis of activities at school if the child is at school. By considering geographical location information, the analysis unit can prioritize the analysis of highly relevant data and obtain highly accurate results. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical location data into a generating AI and have the generating AI perform the prioritization analysis of highly relevant data.

[0044] The analysis unit can analyze a child's social media activity and obtain relevant data during the analysis process. For example, if a child frequently uses a particular social media platform, the analysis unit will prioritize analyzing activity on that platform. The analysis unit can also use AI to analyze social media activity and obtain relevant data. For example, if a child uses a particular hashtag, the analysis unit will prioritize analyzing activity related to that hashtag using AI. Furthermore, if a child belongs to a particular group, the analysis unit can also prioritize analyzing activity within that group using AI. This allows for the acquisition of relevant data and more comprehensive analysis by analyzing social media activity. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input social media activity data into a generating AI and have the generating AI perform the acquisition of relevant data.

[0045] The warning unit can adjust the level of detail of a warning based on the importance of the content when a warning is issued. For example, the warning unit provides detailed warning information for high-importance content. The warning unit can use AI to evaluate the importance of content and adjust the level of detail of the warning. For example, the warning unit can use AI to evaluate the risk level of content and change the level of detail of the warning according to its importance. The warning unit can also use AI to display a concise warning for low-importance content. This allows for appropriate warnings by adjusting the level of detail of the warning based on the importance of the content. Some or all of the above processes in the warning unit may be performed using AI or not. For example, the warning unit can input content importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the warning.

[0046] The warning unit can apply different warning algorithms depending on the content category when issuing a warning. For example, the warning unit can apply a warning algorithm specific to social media for social media content. The warning unit can use AI to analyze the content category and apply an appropriate warning algorithm. For example, the warning unit can use AI to apply a web-specific warning algorithm to web content. The warning unit can also use AI to apply a message-specific warning algorithm to message content. By applying different warning algorithms depending on the content category, more effective warnings can be provided. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input content category data into a generating AI and have the generating AI execute the application of an appropriate warning algorithm.

[0047] The warning unit can determine the priority of warnings based on the submission date of the content when a warning is issued. For example, the warning unit can prioritize warnings for recently submitted content. The warning unit can use AI to analyze the submission date of content and determine the priority of warnings. For example, the warning unit can use AI to lower the priority of warnings for older content. The warning unit can also use AI to display warnings with an appropriate priority for content whose submission date is unknown. This allows important warnings to be prioritized by determining the priority of warnings based on the submission date of the content. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input content submission date data into a generating AI and have the generating AI perform the determination of warning priorities.

[0048] The warning unit can adjust the order of warnings based on the relevance of the content when a warning is issued. For example, the warning unit can prioritize displaying warnings for highly relevant content. The warning unit can use AI to evaluate the relevance of content and adjust the order of warnings. For example, the warning unit can use AI to prioritize displaying highly relevant content. The warning unit can also use AI to lower the priority of warnings for less relevant content. This allows important warnings to be prioritized by adjusting the order of warnings based on the relevance of the content. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input content relevance data into a generating AI and have the generating AI perform the adjustment of the warning order.

[0049] The Ministry of Education can adjust the level of detail in educational materials based on the importance of the learning content. For example, the Ministry of Education can provide detailed educational content for highly important learning content. The Ministry of Education can also use AI to assess the importance of learning content and adjust the level of detail. For example, the Ministry of Education can use AI to assess the risk level of learning content and change the level of detail according to its importance. The Ministry of Education can also use AI to provide concise educational content for less important learning content. By adjusting the level of detail based on the importance of the learning content, appropriate education can be provided. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input learning content importance data into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0050] The Ministry of Education can apply different educational algorithms during education depending on the category of learning content. For example, the Ministry of Education can apply a digital literacy-specific educational algorithm to learning content related to digital literacy. The Ministry of Education can use AI to analyze the category of learning content and apply an appropriate educational algorithm. For example, the Ministry of Education can use AI to apply a security-specific educational algorithm to learning content related to security. The Ministry of Education can also use AI to apply a privacy-specific educational algorithm to learning content related to privacy. By applying different educational algorithms depending on the category of learning content, more effective education can be provided. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input learning content category data into a generating AI and have the generating AI execute the application of an appropriate educational algorithm.

[0051] The Ministry of Education can prioritize education based on the submission timing of learning materials. For example, the Ministry of Education can prioritize providing educational content to recently submitted learning materials. The Ministry of Education can use AI to analyze the submission timing of learning materials and determine educational priorities. For example, the Ministry of Education can use AI to lower the educational priority of older learning materials. The Ministry of Education can also use AI to provide educational content with an appropriate priority for learning materials whose submission timing is unknown. This allows important education to be prioritized by determining educational priorities based on the submission timing of learning materials. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input learning material submission timing data into a generating AI and have the generating AI perform the determination of educational priorities.

[0052] The Ministry of Education can adjust the order of lessons based on the relevance of the learning content during lessons. For example, the Ministry of Education can prioritize providing educational content for highly relevant learning topics. The Ministry of Education can use AI to evaluate the relevance of learning content and adjust the order of lessons. For example, the Ministry of Education can use AI to prioritize the display of highly relevant learning content. The Ministry of Education can also use AI to lower the priority of lessons for less relevant learning content. In this way, important lessons can be prioritized by adjusting the order of lessons based on the relevance of the learning content. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input data on the relevance of learning content into a generating AI and have the generating AI perform the adjustment of the order of lessons.

[0053] The content delivery unit can improve the accuracy of its delivery by considering the interrelationships of the content at the time of delivery. For example, the delivery unit can group related content and deliver it together. The delivery unit can use AI to analyze the interrelationships of content and improve the accuracy of its delivery. For example, the delivery unit can use AI to prioritize the delivery of highly relevant content. The delivery unit can also use AI to provide content tailored to the user's interests by considering the interrelationships of the content. In this way, the accuracy of delivery can be improved by considering the interrelationships of the content. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input content interrelationship data into a generating AI and have the generating AI perform the improvement of delivery accuracy.

[0054] The content provider can provide content while considering the attribute information of the content submitter. For example, the provider can provide highly reliable content by considering the submitter's expertise. The provider can use AI to analyze the submitter's attribute information and provide content. For example, the provider can use AI to provide highly-rated content by considering the submitter's past performance. The provider can also use AI to analyze the submitter's attribute information and provide content that matches the user's interests. This allows for more appropriate content provision by considering the attribute information of the content submitter. Some or all of the above processing in the provider can be performed using AI or not. For example, the provider can input the submitter's attribute information data into a generating AI and have the generating AI perform the provision.

[0055] The content delivery unit can consider the geographical distribution of content when providing it. For example, the delivery unit can prioritize providing local content based on the user's current location. The delivery unit can use AI to analyze the geographical distribution of content and provide it accordingly. For example, the delivery unit can use AI to provide content relevant to the user's travel destination if the user is traveling. The delivery unit can also use AI to analyze the geographical distribution of users and provide the most suitable content for each region. This allows for more appropriate content delivery by considering the geographical distribution of content. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input geographical distribution data of content into a generating AI and have the generating AI perform the delivery.

[0056] The content delivery unit can improve the accuracy of its delivery by referring to related literature at the time of delivery. For example, the delivery unit can provide highly reliable content based on related literature. The delivery unit can use AI to analyze related literature and improve the accuracy of its delivery. For example, the delivery unit can use AI to refer to related literature and provide content that includes detailed information. The delivery unit can also use AI to analyze related literature and provide content that matches the user's interests. This improves the accuracy of delivery by referring to related literature for the content. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input related literature data into a generating AI and have the generating AI perform the delivery.

[0057] The response unit can select the optimal response method by referring to past response history when responding. For example, the response unit can select the optimal response method based on past successful response methods. The response unit can use AI to analyze past response history and select the optimal response method. For example, the response unit can use AI to avoid failed response methods from past response history. The response unit can also use AI to analyze past response history and select the most effective response method. In this way, by referring to past response history, the optimal response method can be selected and an effective response can be taken. Some or all of the above processing in the response unit may be performed using AI or not using AI. For example, the response unit can input past response history data into a generating AI and have the generating AI perform the selection of the optimal response method.

[0058] The response unit can customize its response methods based on the current situation when responding. For example, the response unit can select the optimal response method based on the current situation. The response unit can use AI to analyze the current situation and customize the response methods. For example, the response unit can use AI to analyze the current situation and provide effective response methods. The response unit can also use AI to flexibly customize response methods according to the current situation. This allows for more effective responses by customizing response methods based on the current situation. Some or all of the above-described processes in the response unit may be performed using AI or not. For example, the response unit can input current situation data into a generating AI and have the generating AI perform the customization of response methods.

[0059] The response unit can select the optimal response method by considering geographical location information when responding. For example, if a child is in a specific location, the response unit will select a response method related to that location. The response unit can use AI to analyze geographical location information and select the optimal response method. For example, if a child is traveling, the response unit can use AI to provide a response method related to the travel destination. The response unit can also use AI to analyze the child's geographical location information and select the optimal response method. This allows for the selection of the optimal response method and effective response by considering geographical location information. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input geographical location data into a generating AI and have the generating AI select the optimal response method.

[0060] The response unit can analyze social media activity and propose response measures when responding. For example, if a child frequently uses a particular social media platform, the response unit will propose response measures on that platform. The response unit can use AI to analyze social media activity and propose response measures. For example, if a child uses a particular hashtag, the response unit will use AI to propose response measures related to that hashtag. The response unit can also use AI to propose response measures within a particular group if the child belongs to that group. By analyzing social media activity, the response unit can propose the most appropriate response measures and implement effective responses. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input social media activity data into a generating AI and have the generating AI execute the proposal of response measures.

[0061] The visualization unit can select the optimal visualization method by referring to past usage history during visualization. For example, the visualization unit can select the optimal visualization method based on past successful visualization methods. The visualization unit can also use AI to analyze past usage history and select the optimal visualization method. For example, the visualization unit can use AI to avoid failed visualization methods from past usage history. The visualization unit can also use AI to analyze past usage history and select the most effective visualization method. This allows for the selection of the optimal visualization method and effective visualization by referring to past usage history. Some or all of the above-described processes in the visualization unit may be performed using AI or not. For example, the visualization unit can input past usage history data into a generating AI and have the generating AI select the optimal visualization method.

[0062] The visualization unit can customize the visualization method based on the current situation during visualization. For example, the visualization unit can select the optimal visualization method based on the current situation. The visualization unit can use AI to analyze the current situation and customize the visualization method. For example, the visualization unit can use AI to analyze the current situation and provide an effective visualization method. The visualization unit can also use AI to flexibly customize the visualization method according to the current situation. This allows for more effective visualization by customizing the visualization method based on the current situation. Some or all of the above-described processes in the visualization unit may be performed using AI or not. For example, the visualization unit can input current situation data into a generating AI and have the generating AI perform the customization of the visualization method.

[0063] The visualization unit can select the optimal visualization method by considering geographical location information during visualization. For example, if a child is in a specific location, the visualization unit will select a visualization method related to that location. The visualization unit can use AI to analyze geographical location information and select the optimal visualization method. For example, if a child is traveling, the visualization unit can use AI to provide a visualization method related to the travel destination. The visualization unit can also use AI to analyze the child's geographical location information and select the optimal visualization method. This allows for the selection of the optimal visualization method and effective visualization by considering geographical location information. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input geographical location data into a generating AI and have the generating AI select the optimal visualization method.

[0064] The visualization unit can analyze social media activity and propose visualization methods during visualization. For example, if a child frequently uses a particular social media platform, the visualization unit can propose visualization methods on that platform. The visualization unit can use AI to analyze social media activity and propose visualization methods. For example, if a child uses a particular hashtag, the visualization unit can use AI to propose visualization methods related to that hashtag. The visualization unit can also use AI to propose visualization methods for a particular group if the child belongs to that group. This allows for the proposal of optimal visualization methods and effective visualization by analyzing social media activity. Some or all of the above-described processes in the visualization unit may be performed using AI or not. For example, the visualization unit can input social media activity data into a generating AI and have the generating AI execute the proposal of visualization methods.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The analysis department can take into account a child's health condition when analyzing their smartphone use in real time. For example, if a child is unwell, the analysis results can be adjusted to limit smartphone usage time. The analysis department can use AI to estimate a child's health condition and adjust the analysis method accordingly. For example, the analysis department can use AI to analyze a child's body temperature and heart rate to estimate their health condition. Furthermore, the analysis department can use AI to adjust the smartphone usage analysis method based on the estimated health condition. This allows for more appropriate analysis results by adjusting the analysis method according to the child's health condition.

[0067] The Ministry of Education can adjust the format of educational content according to each child's learning style. For example, it can provide video materials to children who prefer visual learning and audio materials to children who prefer auditory learning. The Ministry of Education can also use AI to analyze children's learning styles and provide appropriate educational content formats. For example, it can use AI to analyze children's learning history and select the optimal format of educational content. Furthermore, the Ministry of Education can use AI to adjust the format of educational content according to each child's learning style. By adjusting the format of educational content according to each child's learning style, more effective education can be provided.

[0068] The visualization unit can display detailed data for each time of day when analyzing a child's smartphone usage. For example, it can display usage data for the morning, noon, and evening in separate graphs, making it easier for parents to understand their child's usage patterns. The visualization unit uses AI to analyze data for each time of day and generate detailed graphs and charts. For example, the visualization unit uses AI to classify a child's smartphone usage by time of day and display it visually. The visualization unit can also use AI to update data for each time of day in real time. This allows parents to understand their child's smartphone usage in detail and provide appropriate support.

[0069] The analytics department can categorize data by application type when analyzing children's smartphone usage. For example, it can analyze usage patterns by category, such as educational apps, game apps, and social networking apps, making it easier for parents to understand their children's usage trends. The analytics department uses AI to analyze the types of applications used and categorize the data. For example, the analytics department can use AI to automatically categorize the applications children are using and visually display their usage patterns. Furthermore, the analytics department can use AI to update data by application type in real time. This allows parents to understand their children's smartphone usage trends in detail and provide appropriate support.

[0070] The warning unit allows for customization of how warning messages are displayed. For example, it can display warnings using a combination of text messages, voice messages, and visual alerts. The warning unit can use AI to analyze how warning messages are displayed and select the optimal method. For instance, it can use AI to analyze a child's reaction and select the most effective way to display the warning message. Furthermore, the warning unit can use AI to adjust the display method of warning messages in real time. This allows for more effective warnings to children.

[0071] The Ministry of Education can customize the methods of delivering educational content. For example, it can provide content tailored to each child's learning style, such as text materials, video materials, and interactive quizzes. The Ministry of Education can use AI to analyze children's learning styles and select the most optimal method of delivering educational content. For instance, it can use AI to analyze children's learning history and select the most effective method of delivering educational content. Furthermore, the Ministry of Education can use AI to adjust the delivery method of educational content in real time. This enables effective education for children.

[0072] The service provider can diversify the types of content they offer. For example, they can provide not only educational content but also entertainment and relaxation content. The service provider can use AI to analyze children's interests and needs and provide the most suitable content. For example, they can use AI to analyze children's usage history and recommend content based on their interests. Furthermore, the service provider can use AI to adjust the types of content offered in real time. This allows them to provide children with a variety of content and keep them interested.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The analysis department analyzes children's smartphone usage in real time. For example, it monitors smartphone usage history and application usage, collecting data in real time. Using AI, it analyzes the collected data to identify patterns in children's smartphone usage. Step 2: The warning unit warns of inappropriate content and suspicious account access based on the results analyzed by the analysis unit. For example, if a child attempts to access inappropriate content, a warning message will be displayed. Using AI, messages from suspicious accounts are automatically analyzed, and the risk is assessed and notified. Step 3: The Ministry of Education will teach digital literacy based on the warnings issued by the Warning Department. For example, they will incorporate quiz formats and gamification elements into the teaching of digital literacy. They will use AI to monitor children's learning progress in real time and provide appropriate educational content. Step 4: The providing department will provide learning content provided by the Ministry of Education. For example, they will provide video materials and interactive quizzes. They will also use AI to provide learning content tailored to the child's interests and age. Step 5: The response team provides initial support when problems occur online. For example, if bullying or harassment occurs online, it will notify parents or schools. Using AI, it analyzes the nature of the problem and suggests appropriate response methods. Step 6: The visualization section visualizes children's smartphone usage and learning progress. For example, it provides a dashboard so that parents can understand their children's smartphone usage and learning progress. AI is used to visualize children's smartphone usage and learning progress in real time.

[0075] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that supports and manages smartphone use by children and young people. This AI agent system provides a service that offers a safe and healthy digital environment by performing risk prevention and digital literacy education within the smartphone. The AI ​​agent system analyzes children's smartphone use in real time and warns of access to inappropriate content and suspicious accounts. For example, if a child attempts to access inappropriate content such as fraud, violent content, or defamation on social media or the web, it will immediately display a warning. It also automatically analyzes messages from suspicious accounts and evaluates and notifies the user of the danger. Next, the AI ​​agent system is equipped with a digital literacy learning mode. In this mode, the AI ​​interactively teaches digital literacy through daily smartphone use. For example, it incorporates quiz formats and gamification elements such as "What should you check before clicking this link?" and "How can you keep your password secure?" to make learning fun. Furthermore, the AI ​​agent system provides learning content on digital skills and safety knowledge according to age and interests. For example, the service offers content such as "How to Make Friends Online" for elementary school students and "How to Avoid Phishing Scams" for middle school students and above. Furthermore, if a child encounters trouble online (bullying, harassment, fraud, etc.), the AI ​​will notify parents and schools and provide initial support. If necessary, it will provide contact information for specialized organizations and instructions. The AI ​​agent system also provides a dashboard that visualizes and shares children's smartphone usage and learning progress with parents. This allows parents to understand what their children are learning and what risks they are aware of, creating opportunities for conversation. This service aims to alleviate anxieties about increasing smartphone use and online troubles, and to create a society where children can safely utilize the digital environment. By utilizing generative AI to update personalized educational programs and advice in real time, it enables flexible responses to the diverse needs of each family.This allows the AI ​​agent system to support and manage smartphone use among children and young people, providing a safe and healthy digital environment.

[0076] The AI ​​agent system according to this embodiment comprises an analysis unit, a warning unit, an education unit, a provision unit, a response unit, and a visualization unit. The analysis unit analyzes a child's smartphone usage in real time. The analysis unit monitors, for example, the smartphone usage history and application usage, and collects data in real time. The analysis unit can use AI to analyze the collected data and identify patterns in the child's smartphone usage. For example, the analysis unit can use AI to identify which applications the child uses most frequently. The analysis unit can also use AI to identify when the child uses their smartphone. Furthermore, the analysis unit can use AI to identify what kind of content the child is accessing. The warning unit warns of inappropriate content and access to suspicious accounts based on the results analyzed by the analysis unit. For example, the warning unit displays a warning message if the child attempts to access inappropriate content. The warning unit can use AI to automatically analyze messages from suspicious accounts and evaluate and notify of the risks. For example, the warning unit uses AI to analyze the content of messages from suspicious accounts and evaluate the risks. Furthermore, the warning unit can use AI to display warning messages if it determines that something is highly dangerous. The Ministry of Education will teach digital literacy based on the warnings issued by the warning unit. The Ministry of Education will teach digital literacy, for example, by incorporating quiz formats and gamification elements. The Ministry of Education can use AI to understand children's learning progress in real time and provide appropriate educational content. For example, the Ministry of Education can use AI to monitor children's learning progress and update educational content as needed. The Ministry of Education can also use AI to provide educational content tailored to children's interests and age. The provision unit will provide the learning content provided by the Ministry of Education. The provision unit will provide, for example, video materials and interactive quizzes. The provision unit can use AI to provide learning content tailored to children's interests and age. For example, the provision unit will use AI to recommend learning content based on children's interests.Furthermore, the provisioning unit can use AI to provide learning content tailored to the child's age. The response unit supports initial response when problems occur online. For example, if bullying or harassment occurs online, the response unit will notify parents and schools. The response unit can use AI to analyze the nature of the problem and suggest appropriate response methods. For example, the response unit can use AI to provide contact information for specialized organizations and action procedures depending on the nature of the problem. The response unit can also use AI to support initial response depending on the nature of the problem. The visualization unit visualizes the child's smartphone usage and learning progress. For example, the visualization unit can provide a dashboard so that parents can understand their child's smartphone usage and learning progress. The visualization unit can use AI to visualize the child's smartphone usage and learning progress in real time. For example, the visualization unit can use AI to display the child's smartphone usage in graphs and charts. The visualization unit can also use AI to visualize the child's learning progress. As a result, the AI ​​agent system according to this embodiment can analyze children's smartphone usage in real time, warn of access to inappropriate content or suspicious accounts, educate them on digital literacy, provide learning content, support initial response when problems occur, and visualize usage status and learning progress.

[0077] The analytics department analyzes children's smartphone use in real time. Specifically, it monitors smartphone usage history and application usage, collecting data in real time. For example, it collects data such as smartphone browser history, app launch time, usage frequency, and time of day. This data is analyzed using AI to identify patterns in children's smartphone use. The AI ​​uses machine learning algorithms to identify from the collected data which applications children use frequently, at what times of day they use their smartphones, and what kind of content they access. For example, the AI ​​may detect that a child is using a particular game app for extended periods and determine that this may be affecting their study time. The AI ​​may also detect that a child is using their smartphone late into the night and determine that there is a risk of sleep deprivation. Furthermore, the AI ​​can detect that a child is accessing inappropriate content and issue a warning. This allows the analytics department to understand detailed patterns of children's smartphone use and provide information to take appropriate measures.

[0078] The warning unit warns of inappropriate content and suspicious accounts based on the results of analysis by the analysis unit. Specifically, it displays a warning message if a child attempts to access inappropriate content. For example, if a child attempts to access violent videos or adult websites, the warning unit will immediately display a warning message and restrict access. The warning unit can also use AI to automatically analyze messages from suspicious accounts, assess the danger, and notify users. The AI ​​uses natural language processing technology to analyze the content of messages and detect dangerous keywords and phrases. For example, the AI ​​can detect threatening words or requests for personal information in messages, and if it determines that the danger is high, it will display a warning message. Furthermore, the warning unit can notify parents and schools before a child is in a dangerous situation. This allows the warning unit to ensure the safety of children and take appropriate action quickly.

[0079] The Ministry of Education will teach digital literacy based on the warnings issued by the Warning Department. Specifically, it will incorporate quizzes and gamification elements into its teaching. For example, if a child attempts to access inappropriate content, they will be taught the reasons and risks through a quiz. By incorporating game elements, children can learn while having fun. The Ministry of Education can use AI to monitor children's learning progress in real time and provide appropriate educational content. The AI ​​will monitor children's learning progress and update the educational content as needed. For example, if a child lacks understanding of a particular topic, the AI ​​will provide additional learning content related to that topic. The AI ​​can also provide educational content tailored to the child's interests and age. This will enable the Ministry of Education to effectively improve children's digital literacy.

[0080] The service provider will provide learning content provided by the Ministry of Education. Specifically, it will provide video learning materials and interactive quizzes. For example, it will provide video learning materials for children to learn digital literacy, conveying information in a visually easy-to-understand format. In addition, children can check what they have learned and deepen their understanding through interactive quizzes. The service provider can use AI to provide learning content tailored to children's interests and age. The AI ​​analyzes children's past learning history and interests and recommends the most suitable learning content. For example, if a child is interested in a particular topic, the AI ​​will prioritize providing learning content related to that topic. Furthermore, by providing learning content appropriate to the child's age, the AI ​​can provide learning materials of appropriate difficulty. In this way, the service provider can increase children's motivation to learn and support effective learning.

[0081] The response department provides initial support when problems occur online. Specifically, it notifies parents and schools if bullying or harassment occurs online. For example, if a child is bullied online, the response department analyzes the situation and notifies parents and schools. The response department can also use AI to analyze the nature of the problem and suggest appropriate countermeasures. The AI ​​will provide contact information for specialized organizations and action procedures depending on the nature of the problem. For example, if a child is harassed online, the AI ​​will analyze the situation and suggest appropriate countermeasures. The AI ​​can also support initial response depending on the nature of the problem. For example, if a child is involved in trouble online, the AI ​​will analyze the situation and suggest appropriate countermeasures. This allows the response department to respond quickly and appropriately to online problems and ensure the safety of children.

[0082] The visualization unit visualizes children's smartphone usage and learning progress. Specifically, it provides a dashboard that allows parents to understand their children's smartphone usage and learning progress. For example, parents can use the dashboard to see which applications their children are using, for how long, and at what times of day they are using their smartphones. Parents can also use the dashboard to check their children's learning progress and understand which topics their children are struggling with. The visualization unit uses AI to visualize children's smartphone usage and learning progress in real time. The AI ​​analyzes the collected data and displays it in graphs and charts. For example, the AI ​​displays children's smartphone usage in graphs and charts, allowing parents to understand the situation at a glance. The AI ​​also visualizes children's learning progress, allowing parents to understand their children's learning situation. As a result, the visualization unit can provide parents with information to understand their children's smartphone usage and learning progress in real time and take appropriate action.

[0083] The warning unit can automatically analyze messages from suspicious accounts and assess and notify users of their risks. For example, the warning unit can use AI to analyze messages from suspicious accounts and assess their risks. The warning unit can use AI to analyze the content of messages and sender information to identify spam accounts and fake accounts. For example, the warning unit can use AI to assess whether the content of a message is potentially fraudulent or phishing. The warning unit can also use AI to display a warning message if it determines that the risk is high. This ensures the safety of children by automatically analyzing messages from suspicious accounts and assessing and notifying users of their risks. Some or all of the above processes in the warning unit may be performed using AI or not using AI. For example, the warning unit can input messages from suspicious accounts into a generating AI and have the generating AI perform the analysis of the message content.

[0084] The Ministry of Education can teach digital literacy by incorporating quiz formats and gamification elements. For example, the Ministry of Education could ask questions in a quiz format, such as, "What should you check before clicking this link?" The Ministry of Education could use AI to analyze children's answers and provide appropriate feedback. For example, the Ministry of Education could use AI to determine whether a child's answer is correct or incorrect and present the correct answer. The Ministry of Education could also incorporate gamification elements to make learning digital literacy fun. For example, the Ministry of Education could use AI to award points or badges according to a child's learning progress. This would allow children to learn digital literacy in an enjoyable way by incorporating quiz formats and gamification elements. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education could input quiz-format questions into a generating AI and have the generating AI generate appropriate feedback.

[0085] The service provider can offer learning content related to digital skills and safety knowledge tailored to age and interests. For example, it can provide content such as "How to make friends online" for elementary school students and "How to avoid phishing scams" for middle school students and older. The service provider can use AI to recommend learning content based on a child's age and interests. For example, it can use AI to suggest relevant learning content based on a child's interests. The service provider can also use AI to provide learning content appropriate to a child's age. By providing learning content tailored to age and interests, it can capture children's interest and promote effective learning. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input data on a child's age and interests into a generating AI and have the generating AI recommend the most suitable learning content.

[0086] The response unit can notify parents and schools when problems occur online and provide contact information for specialized organizations and procedures for action. For example, if bullying or harassment occurs online, the response unit will notify parents and schools. The response unit can use AI to analyze the nature of the problem and suggest appropriate countermeasures. For example, the response unit can use AI to provide contact information for specialized organizations and procedures for action depending on the nature of the problem. The response unit can also use AI to support initial responses depending on the nature of the problem. This allows for a swift response and appropriate support when problems occur online. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input the nature of the problem into a generating AI and have the generating AI suggest appropriate countermeasures.

[0087] The visualization unit can provide a dashboard that shares a child's smartphone usage and learning progress with parents. For example, the visualization unit can provide a dashboard that allows parents to understand their child's smartphone usage and learning progress. The visualization unit can use AI to visualize a child's smartphone usage and learning progress in real time. For example, the visualization unit can use AI to display a child's smartphone usage in graphs and charts. The visualization unit can also use AI to visualize a child's learning progress. This allows parents to understand their child's situation and provide appropriate support by sharing information about their child's smartphone usage and learning progress. Some or all of the above-described processes in the visualization unit may be performed using AI or not. For example, the visualization unit can input data on a child's smartphone usage and learning progress into a generating AI and have the generating AI perform the generation of the visualization.

[0088] The analysis unit can estimate a child's emotions and adjust the analysis method of smartphone use based on the estimated emotions. For example, if a child is stressed, the analysis unit can simplify the analysis of smartphone use and extract only the important points. The analysis unit can use AI to estimate a child's emotions and adjust the analysis method according to those emotions. For example, the analysis unit can use AI to analyze a child's facial expressions and voice to estimate their emotions. The analysis unit can also use AI to adjust the analysis method of smartphone use based on the estimated emotions. By adjusting the analysis method according to the child's emotions, more appropriate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input child emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The analysis unit can select the optimal analysis algorithm by referring to the child's past usage history during analysis. For example, the analysis unit can analyze the usage patterns of a specific application based on the applications the child has frequently used in the past. The analysis unit can use AI to analyze past usage history and select the optimal analysis algorithm. For example, the analysis unit can use AI to analyze usage trends during specific time periods from past usage history. The analysis unit can also use AI to refer to past usage history and analyze the level of interest in specific content. By referring to past usage history, the analysis unit can select the optimal analysis algorithm and perform highly accurate analysis. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past usage history data into a generating AI and have the generating AI select the optimal analysis algorithm.

[0090] The analysis unit can filter data based on a child's current lifestyle and areas of interest during analysis. For example, if a child uses a smartphone during school lessons, the analysis unit will prioritize analyzing learning-related activities. The analysis unit can also use AI to analyze and filter data based on a child's current lifestyle and areas of interest. For example, if a child uses a smartphone on weekends, the analysis unit will prioritize analyzing entertainment-related activities. Furthermore, if a child has a particular hobby, the analysis unit can also prioritize analyzing activities related to that hobby. This allows for more relevant analysis results by filtering data based on the child's current lifestyle and areas of interest. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data on the child's current lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0091] The analysis unit can estimate a child's emotions and prioritize the analysis results based on the estimated emotions. For example, if a child is stressed, the analysis unit will prioritize analyzing the activities that cause stress. The analysis unit can use AI to estimate a child's emotions and prioritize the analysis results according to those emotions. For example, the analysis unit can use AI to analyze a child's facial expressions and voice to estimate their emotions. The analysis unit can also use AI to prioritize the analysis results based on the estimated emotions. This allows for the priority of important information to be provided by prioritizing the analysis results according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input child emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The analysis unit can prioritize the analysis of highly relevant data by considering the child's geographical location during the analysis process. For example, if the child is in a specific location, the analysis unit will prioritize the analysis of activities related to that location. The analysis unit can use AI to analyze geographical location information and prioritize the analysis of highly relevant data. For example, if the child is traveling, the analysis unit will use AI to prioritize the analysis of activities at the travel destination. The analysis unit can also use AI to prioritize the analysis of activities at school if the child is at school. By considering geographical location information, the analysis unit can prioritize the analysis of highly relevant data and obtain highly accurate results. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical location data into a generating AI and have the generating AI perform the prioritization analysis of highly relevant data.

[0093] The analysis unit can analyze a child's social media activity and obtain relevant data during the analysis process. For example, if a child frequently uses a particular social media platform, the analysis unit will prioritize analyzing activity on that platform. The analysis unit can also use AI to analyze social media activity and obtain relevant data. For example, if a child uses a particular hashtag, the analysis unit will prioritize analyzing activity related to that hashtag using AI. Furthermore, if a child belongs to a particular group, the analysis unit can also prioritize analyzing activity within that group using AI. This allows for the acquisition of relevant data and more comprehensive analysis by analyzing social media activity. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input social media activity data into a generating AI and have the generating AI perform the acquisition of relevant data.

[0094] The warning unit can estimate a child's emotions and adjust the way it expresses the warning based on those emotions. For example, if a child is stressed, the warning unit will display a warning in gentle language. The warning unit can use AI to estimate a child's emotions and adjust the way it expresses the warning accordingly. For example, the warning unit can use AI to analyze a child's facial expressions and voice to estimate their emotions. The warning unit can also use AI to adjust the way it expresses the warning based on the estimated emotions. This allows for more effective warnings by adjusting the way the warning is expressed according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the warning unit may be performed using AI or not. For example, the warning unit can input child emotion data into a generative AI and have the generative AI perform emotion estimation.

[0095] The warning unit can adjust the level of detail of a warning based on the importance of the content when a warning is issued. For example, the warning unit provides detailed warning information for high-importance content. The warning unit can use AI to evaluate the importance of content and adjust the level of detail of the warning. For example, the warning unit can use AI to evaluate the risk level of content and change the level of detail of the warning according to its importance. The warning unit can also use AI to display a concise warning for low-importance content. This allows for appropriate warnings by adjusting the level of detail of the warning based on the importance of the content. Some or all of the above processes in the warning unit may be performed using AI or not. For example, the warning unit can input content importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the warning.

[0096] The warning unit can apply different warning algorithms depending on the content category when issuing a warning. For example, the warning unit can apply a warning algorithm specific to social media for social media content. The warning unit can use AI to analyze the content category and apply an appropriate warning algorithm. For example, the warning unit can use AI to apply a web-specific warning algorithm to web content. The warning unit can also use AI to apply a message-specific warning algorithm to message content. By applying different warning algorithms depending on the content category, more effective warnings can be provided. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input content category data into a generating AI and have the generating AI execute the application of an appropriate warning algorithm.

[0097] The warning unit can estimate the child's emotions and adjust the length of the warning based on the estimated emotions. For example, if the child is stressed, the warning unit will display a short, concise warning. The warning unit can use AI to estimate the child's emotions and adjust the length of the warning accordingly. For example, the warning unit can use AI to analyze the child's facial expressions and voice to estimate emotions. The warning unit can also use AI to adjust the length of the warning based on the estimated emotions. This allows for more effective warnings by adjusting the length of the warning according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the child's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The warning unit can determine the priority of warnings based on the submission date of the content when a warning is issued. For example, the warning unit can prioritize warnings for recently submitted content. The warning unit can use AI to analyze the submission date of content and determine the priority of warnings. For example, the warning unit can use AI to lower the priority of warnings for older content. The warning unit can also use AI to display warnings with an appropriate priority for content whose submission date is unknown. This allows important warnings to be prioritized by determining the priority of warnings based on the submission date of the content. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input content submission date data into a generating AI and have the generating AI perform the determination of warning priorities.

[0099] The warning unit can adjust the order of warnings based on the relevance of the content when a warning is issued. For example, the warning unit can prioritize displaying warnings for highly relevant content. The warning unit can use AI to evaluate the relevance of content and adjust the order of warnings. For example, the warning unit can use AI to prioritize displaying highly relevant content. The warning unit can also use AI to lower the priority of warnings for less relevant content. This allows important warnings to be prioritized by adjusting the order of warnings based on the relevance of the content. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input content relevance data into a generating AI and have the generating AI perform the adjustment of the warning order.

[0100] The Ministry of Education can estimate children's emotions and adjust educational methods based on those estimated emotions. For example, if a child is stressed, the Ministry of Education can provide educational content in gentle language. The Ministry of Education can use AI to estimate children's emotions and adjust educational methods accordingly. For example, the Ministry of Education can use AI to analyze children's facial expressions and voices to estimate their emotions. The Ministry of Education can also use AI to adjust educational methods based on the estimated emotions. This allows for more effective education by adjusting educational methods according to children's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input children's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0101] The Ministry of Education can adjust the level of detail in educational materials based on the importance of the learning content. For example, the Ministry of Education can provide detailed educational content for highly important learning content. The Ministry of Education can also use AI to assess the importance of learning content and adjust the level of detail. For example, the Ministry of Education can use AI to assess the risk level of learning content and change the level of detail according to its importance. The Ministry of Education can also use AI to provide concise educational content for less important learning content. By adjusting the level of detail based on the importance of the learning content, appropriate education can be provided. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input learning content importance data into a generating AI and have the generating AI perform the adjustment of the level of detail.

[0102] The Ministry of Education can apply different educational algorithms during education depending on the category of learning content. For example, the Ministry of Education can apply a digital literacy-specific educational algorithm to learning content related to digital literacy. The Ministry of Education can use AI to analyze the category of learning content and apply an appropriate educational algorithm. For example, the Ministry of Education can use AI to apply a security-specific educational algorithm to learning content related to security. The Ministry of Education can also use AI to apply a privacy-specific educational algorithm to learning content related to privacy. By applying different educational algorithms depending on the category of learning content, more effective education can be provided. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input learning content category data into a generating AI and have the generating AI execute the application of an appropriate educational algorithm.

[0103] The Ministry of Education can estimate children's emotions and adjust the length of lessons based on those estimates. For example, if a child is stressed, the Ministry of Education can provide short, concise educational content. The Ministry of Education can use AI to estimate children's emotions and adjust the length of lessons accordingly. For example, the Ministry of Education can use AI to analyze children's facial expressions and voices to estimate their emotions. The Ministry of Education can also use AI to adjust the length of lessons based on the estimated emotions. This allows for more effective education by adjusting the length of lessons according to children's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input children's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0104] The Ministry of Education can prioritize education based on the submission timing of learning materials. For example, the Ministry of Education can prioritize providing educational content to recently submitted learning materials. The Ministry of Education can use AI to analyze the submission timing of learning materials and determine educational priorities. For example, the Ministry of Education can use AI to lower the educational priority of older learning materials. The Ministry of Education can also use AI to provide educational content with an appropriate priority for learning materials whose submission timing is unknown. This allows important education to be prioritized by determining educational priorities based on the submission timing of learning materials. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input learning material submission timing data into a generating AI and have the generating AI perform the determination of educational priorities.

[0105] The Ministry of Education can adjust the order of lessons based on the relevance of the learning content during lessons. For example, the Ministry of Education can prioritize providing educational content for highly relevant learning topics. The Ministry of Education can use AI to evaluate the relevance of learning content and adjust the order of lessons. For example, the Ministry of Education can use AI to prioritize the display of highly relevant learning content. The Ministry of Education can also use AI to lower the priority of lessons for less relevant learning content. In this way, important lessons can be prioritized by adjusting the order of lessons based on the relevance of the learning content. Some or all of the above processes by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input data on the relevance of learning content into a generating AI and have the generating AI perform the adjustment of the order of lessons.

[0106] The service provider can estimate a child's emotions and prioritize the content to be provided based on those estimated emotions. For example, if a child is stressed, the service provider will prioritize providing relaxing content. The service provider can use AI to estimate a child's emotions and prioritize the content to be provided according to those emotions. For example, the service provider can use AI to analyze a child's facial expressions and voice to estimate their emotions. The service provider can also use AI to prioritize the content to be provided based on the estimated emotions. This allows for more effective content delivery by prioritizing content according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input child emotion data into a generative AI and have the generative AI perform emotion estimation.

[0107] The content delivery unit can improve the accuracy of its delivery by considering the interrelationships of the content at the time of delivery. For example, the delivery unit can group related content and deliver it together. The delivery unit can use AI to analyze the interrelationships of content and improve the accuracy of its delivery. For example, the delivery unit can use AI to prioritize the delivery of highly relevant content. The delivery unit can also use AI to provide content tailored to the user's interests by considering the interrelationships of the content. In this way, the accuracy of delivery can be improved by considering the interrelationships of the content. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input content interrelationship data into a generating AI and have the generating AI perform the improvement of delivery accuracy.

[0108] The content provider can provide content while considering the attribute information of the content submitter. For example, the provider can provide highly reliable content by considering the submitter's expertise. The provider can use AI to analyze the submitter's attribute information and provide content. For example, the provider can use AI to provide highly-rated content by considering the submitter's past performance. The provider can also use AI to analyze the submitter's attribute information and provide content that matches the user's interests. This allows for more appropriate content provision by considering the attribute information of the content submitter. Some or all of the above processing in the provider can be performed using AI or not. For example, the provider can input the submitter's attribute information data into a generating AI and have the generating AI perform the provision.

[0109] The service provider can estimate a child's emotions and adjust the display method of the content based on the estimated emotions. For example, if a child is stressed, the service provider can provide a simple and highly visible display method. The service provider can use AI to estimate a child's emotions and adjust the display method of the content according to those emotions. For example, the service provider can use AI to analyze a child's facial expressions and voice to estimate their emotions. The service provider can also use AI to adjust the display method of the content based on the estimated emotions. This allows for more effective content delivery by adjusting the display method of the content according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input child emotion data into a generative AI and have the generative AI perform emotion estimation.

[0110] The content delivery unit can consider the geographical distribution of content when providing it. For example, the delivery unit can prioritize providing local content based on the user's current location. The delivery unit can use AI to analyze the geographical distribution of content and provide it accordingly. For example, the delivery unit can use AI to provide content relevant to the user's travel destination if the user is traveling. The delivery unit can also use AI to analyze the geographical distribution of users and provide the most suitable content for each region. This allows for more appropriate content delivery by considering the geographical distribution of content. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input geographical distribution data of content into a generating AI and have the generating AI perform the delivery.

[0111] The content delivery unit can improve the accuracy of its delivery by referring to related literature at the time of delivery. For example, the delivery unit can provide highly reliable content based on related literature. The delivery unit can use AI to analyze related literature and improve the accuracy of its delivery. For example, the delivery unit can use AI to refer to related literature and provide content that includes detailed information. The delivery unit can also use AI to analyze related literature and provide content that matches the user's interests. This improves the accuracy of delivery by referring to related literature for the content. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input related literature data into a generating AI and have the generating AI perform the delivery.

[0112] The response unit can estimate the child's emotions and adjust its response method based on the estimated emotions. For example, if the child is stressed, the response unit will respond with gentle words. The response unit can use AI to estimate the child's emotions and adjust its response method accordingly. For example, the response unit can use AI to analyze the child's facial expressions and voice to estimate emotions. The response unit can also use AI to adjust its response method based on the estimated emotions. This allows for more effective responses by adjusting the response method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input the child's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0113] The response unit can select the optimal response method by referring to past response history when responding. For example, the response unit can select the optimal response method based on past successful response methods. The response unit can use AI to analyze past response history and select the optimal response method. For example, the response unit can use AI to avoid failed response methods from past response history. The response unit can also use AI to analyze past response history and select the most effective response method. In this way, by referring to past response history, the optimal response method can be selected and an effective response can be taken. Some or all of the above processing in the response unit may be performed using AI or not using AI. For example, the response unit can input past response history data into a generating AI and have the generating AI perform the selection of the optimal response method.

[0114] The response unit can customize its response methods based on the current situation when responding. For example, the response unit can select the optimal response method based on the current situation. The response unit can use AI to analyze the current situation and customize the response methods. For example, the response unit can use AI to analyze the current situation and provide effective response methods. The response unit can also use AI to flexibly customize response methods according to the current situation. This allows for more effective responses by customizing response methods based on the current situation. Some or all of the above-described processes in the response unit may be performed using AI or not. For example, the response unit can input current situation data into a generating AI and have the generating AI perform the customization of response methods.

[0115] The response unit can estimate a child's emotions and determine the priority of responses based on the estimated emotions. For example, if a child is feeling stressed, the response unit will prioritize responding to that child. The response unit can use AI to estimate a child's emotions and determine the priority of responses according to those emotions. For example, the response unit can use AI to analyze a child's facial expressions and voice to estimate their emotions. The response unit can also use AI to determine the priority of responses based on the estimated emotions. This allows important responses to be prioritized by determining the priority of responses according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input child emotion data into a generative AI and have the generative AI perform emotion estimation.

[0116] The response unit can select the optimal response method by considering geographical location information when responding. For example, if a child is in a specific location, the response unit will select a response method related to that location. The response unit can use AI to analyze geographical location information and select the optimal response method. For example, if a child is traveling, the response unit can use AI to provide a response method related to the travel destination. The response unit can also use AI to analyze the child's geographical location information and select the optimal response method. This allows for the selection of the optimal response method and effective response by considering geographical location information. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input geographical location data into a generating AI and have the generating AI select the optimal response method.

[0117] The response unit can analyze social media activity and propose response measures when responding. For example, if a child frequently uses a particular social media platform, the response unit will propose response measures on that platform. The response unit can use AI to analyze social media activity and propose response measures. For example, if a child uses a particular hashtag, the response unit will use AI to propose response measures related to that hashtag. The response unit can also use AI to propose response measures within a particular group if the child belongs to that group. By analyzing social media activity, the response unit can propose the most appropriate response measures and implement effective responses. Some or all of the above processing in the response unit may be performed using AI or not. For example, the response unit can input social media activity data into a generating AI and have the generating AI execute the proposal of response measures.

[0118] The visualization unit can estimate a child's emotions and adjust the visualization method based on the estimated emotions. For example, if a child is stressed, the visualization unit provides a simple and highly visible visualization method. The visualization unit can use AI to estimate a child's emotions and adjust the visualization method according to those emotions. For example, the visualization unit can use AI to analyze a child's facial expressions and voice to estimate their emotions. The visualization unit can also use AI to adjust the visualization method based on the estimated emotions. This allows for more effective visualization by adjusting the visualization method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the visualization unit may be performed using AI or not. For example, the visualization unit can input child emotion data into a generative AI and have the generative AI perform emotion estimation.

[0119] The visualization unit can select the optimal visualization method by referring to past usage history during visualization. For example, the visualization unit can select the optimal visualization method based on past successful visualization methods. The visualization unit can also use AI to analyze past usage history and select the optimal visualization method. For example, the visualization unit can use AI to avoid failed visualization methods from past usage history. The visualization unit can also use AI to analyze past usage history and select the most effective visualization method. This allows for the selection of the optimal visualization method and effective visualization by referring to past usage history. Some or all of the above-described processes in the visualization unit may be performed using AI or not. For example, the visualization unit can input past usage history data into a generating AI and have the generating AI select the optimal visualization method.

[0120] The visualization unit can customize the visualization method based on the current situation during visualization. For example, the visualization unit can select the optimal visualization method based on the current situation. The visualization unit can use AI to analyze the current situation and customize the visualization method. For example, the visualization unit can use AI to analyze the current situation and provide an effective visualization method. The visualization unit can also use AI to flexibly customize the visualization method according to the current situation. This allows for more effective visualization by customizing the visualization method based on the current situation. Some or all of the above-described processes in the visualization unit may be performed using AI or not. For example, the visualization unit can input current situation data into a generating AI and have the generating AI perform the customization of the visualization method.

[0121] The visualization unit can estimate a child's emotions and determine visualization priorities based on the estimated emotions. For example, if a child is experiencing stress, the visualization unit will prioritize visualization. The visualization unit can use AI to estimate a child's emotions and determine visualization priorities according to those emotions. For example, the visualization unit can use AI to analyze a child's facial expressions and voice to estimate their emotions. The visualization unit can also use AI to determine visualization priorities based on the estimated emotions. This allows important information to be visualized preferentially by determining visualization priorities according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the visualization unit may be performed using AI or not. For example, the visualization unit can input child emotion data into a generative AI and have the generative AI perform emotion estimation.

[0122] The visualization unit can select the optimal visualization method by considering geographical location information during visualization. For example, if a child is in a specific location, the visualization unit will select a visualization method related to that location. The visualization unit can use AI to analyze geographical location information and select the optimal visualization method. For example, if a child is traveling, the visualization unit can use AI to provide a visualization method related to the travel destination. The visualization unit can also use AI to analyze the child's geographical location information and select the optimal visualization method. This allows for the selection of the optimal visualization method and effective visualization by considering geographical location information. Some or all of the above processing in the visualization unit may be performed using AI or not. For example, the visualization unit can input geographical location data into a generating AI and have the generating AI select the optimal visualization method.

[0123] The visualization unit can analyze social media activity and propose visualization methods during visualization. For example, if a child frequently uses a particular social media platform, the visualization unit can propose visualization methods on that platform. The visualization unit can use AI to analyze social media activity and propose visualization methods. For example, if a child uses a particular hashtag, the visualization unit can use AI to propose visualization methods related to that hashtag. The visualization unit can also use AI to propose visualization methods for a particular group if the child belongs to that group. This allows for the proposal of optimal visualization methods and effective visualization by analyzing social media activity. Some or all of the above-described processes in the visualization unit may be performed using AI or not. For example, the visualization unit can input social media activity data into a generating AI and have the generating AI execute the proposal of visualization methods.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The analysis department can take into account a child's health condition when analyzing their smartphone use in real time. For example, if a child is unwell, the analysis results can be adjusted to limit smartphone usage time. The analysis department can use AI to estimate a child's health condition and adjust the analysis method accordingly. For example, the analysis department can use AI to analyze a child's body temperature and heart rate to estimate their health condition. Furthermore, the analysis department can use AI to adjust the smartphone usage analysis method based on the estimated health condition. This allows for more appropriate analysis results by adjusting the analysis method according to the child's health condition.

[0126] The warning unit can estimate the child's emotions and adjust the timing of the warning based on those emotions. For example, if the child is relaxed, it will display a warning immediately, but if the child is stressed, it will display the warning after a short delay. The warning unit uses AI to estimate the child's emotions and adjust the timing of the warning accordingly. For example, the warning unit uses AI to analyze the child's facial expressions and voice to estimate their emotions. The warning unit can also use AI to adjust the timing of the warning based on the estimated emotions. This allows for more effective warnings by adjusting the timing of the warning according to the child's emotions.

[0127] The Ministry of Education can adjust the format of educational content according to each child's learning style. For example, it can provide video materials to children who prefer visual learning and audio materials to children who prefer auditory learning. The Ministry of Education can also use AI to analyze children's learning styles and provide appropriate educational content formats. For example, it can use AI to analyze children's learning history and select the optimal format of educational content. Furthermore, the Ministry of Education can use AI to adjust the format of educational content according to each child's learning style. By adjusting the format of educational content according to each child's learning style, more effective education can be provided.

[0128] The service provider can estimate a child's emotions and adjust the difficulty level of the content provided based on those estimates. For example, if a child is stressed, it can provide easy content; if a child is relaxed, it can provide more challenging content. The service provider can use AI to estimate a child's emotions and adjust the difficulty level of the content accordingly. For example, the service provider can use AI to analyze a child's facial expressions and voice to estimate their emotions. The service provider can also use AI to adjust the difficulty level of the content based on the estimated emotions. This allows for more effective content delivery by adjusting the difficulty level of the content according to the child's emotions.

[0129] The response unit can estimate the child's emotions and adjust the frequency of responses based on those estimates. For example, if the child is stressed, it will respond more frequently, and if the child is relaxed, it will respond less frequently. The response unit can use AI to estimate the child's emotions and adjust the frequency of responses accordingly. For example, the response unit can use AI to analyze the child's facial expressions and voice to estimate their emotions. The response unit can also use AI to adjust the frequency of responses based on the estimated emotions. This allows for more effective responses by adjusting the frequency of responses according to the child's emotions.

[0130] The visualization unit can display detailed data for each time of day when analyzing a child's smartphone usage. For example, it can display usage data for the morning, noon, and evening in separate graphs, making it easier for parents to understand their child's usage patterns. The visualization unit uses AI to analyze data for each time of day and generate detailed graphs and charts. For example, the visualization unit uses AI to classify a child's smartphone usage by time of day and display it visually. The visualization unit can also use AI to update data for each time of day in real time. This allows parents to understand their child's smartphone usage in detail and provide appropriate support.

[0131] The analytics department can categorize data by application type when analyzing children's smartphone usage. For example, it can analyze usage patterns by category, such as educational apps, game apps, and social networking apps, making it easier for parents to understand their children's usage trends. The analytics department uses AI to analyze the types of applications used and categorize the data. For example, the analytics department can use AI to automatically categorize the applications children are using and visually display their usage patterns. Furthermore, the analytics department can use AI to update data by application type in real time. This allows parents to understand their children's smartphone usage trends in detail and provide appropriate support.

[0132] The warning unit allows for customization of how warning messages are displayed. For example, it can display warnings using a combination of text messages, voice messages, and visual alerts. The warning unit can use AI to analyze how warning messages are displayed and select the optimal method. For instance, it can use AI to analyze a child's reaction and select the most effective way to display the warning message. Furthermore, the warning unit can use AI to adjust the display method of warning messages in real time. This allows for more effective warnings to children.

[0133] The Ministry of Education can customize the methods of delivering educational content. For example, it can provide content tailored to each child's learning style, such as text materials, video materials, and interactive quizzes. The Ministry of Education can use AI to analyze children's learning styles and select the most optimal method of delivering educational content. For instance, it can use AI to analyze children's learning history and select the most effective method of delivering educational content. Furthermore, the Ministry of Education can use AI to adjust the delivery method of educational content in real time. This enables effective education for children.

[0134] The service provider can diversify the types of content they offer. For example, they can provide not only educational content but also entertainment and relaxation content. The service provider can use AI to analyze children's interests and needs and provide the most suitable content. For example, they can use AI to analyze children's usage history and recommend content based on their interests. Furthermore, the service provider can use AI to adjust the types of content offered in real time. This allows them to provide children with a variety of content and keep them interested.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The analysis department analyzes children's smartphone usage in real time. For example, it monitors smartphone usage history and application usage, collecting data in real time. Using AI, it analyzes the collected data to identify patterns in children's smartphone usage. Step 2: The warning unit warns of inappropriate content and suspicious account access based on the results analyzed by the analysis unit. For example, if a child attempts to access inappropriate content, a warning message will be displayed. Using AI, messages from suspicious accounts are automatically analyzed, and the risk is assessed and notified. Step 3: The Ministry of Education will teach digital literacy based on the warnings issued by the Warning Department. For example, they will incorporate quiz formats and gamification elements into the teaching of digital literacy. They will use AI to monitor children's learning progress in real time and provide appropriate educational content. Step 4: The providing department will provide learning content provided by the Ministry of Education. For example, they will provide video materials and interactive quizzes. They will also use AI to provide learning content tailored to the child's interests and age. Step 5: The response team provides initial support when problems occur online. For example, if bullying or harassment occurs online, it will notify parents or schools. Using AI, it analyzes the nature of the problem and suggests appropriate response methods. Step 6: The visualization section visualizes children's smartphone usage and learning progress. For example, it provides a dashboard so that parents can understand their children's smartphone usage and learning progress. AI is used to visualize children's smartphone usage and learning progress in real time.

[0137] 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.

[0138] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0139] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the analysis unit, warning unit, education unit, provision unit, response unit, and visualization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14, which monitors the usage history and application usage of the smart device 14 and is analyzed by the specific processing unit 290 of the data processing unit 12. The warning unit is implemented by the control unit 46A of the smart device 14, which displays a warning message. The education unit is implemented by the control unit 46A of the smart device 14, which teaches digital literacy by incorporating quiz formats and gamification elements. The provision unit is implemented by the control unit 46A of the smart device 14, which provides video teaching materials and interactive quizzes. The response unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the nature of the problem and suggests appropriate countermeasures. The visualization unit is implemented by the control unit 46A of the smart device 14, which provides a dashboard so that parents can understand their child's smartphone usage and learning progress. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0144] 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.

[0145] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, 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.

[0146] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] 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.

[0148] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the analysis unit, warning unit, education unit, provision unit, response unit, and visualization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214, which monitors the usage history of the smart glasses 214 and the usage status of applications, and is analyzed by the identification processing unit 290 of the data processing unit 12. The warning unit is implemented by the control unit 46A of the smart glasses 214, which displays a warning message. The education unit is implemented by the control unit 46A of the smart glasses 214, which teaches digital literacy by incorporating quiz formats and gamification elements. The provision unit is implemented by the control unit 46A of the smart glasses 214, which provides video teaching materials and interactive quizzes. The response unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the content of the trouble and suggests an appropriate response method. The visualization unit is implemented, for example, by the control unit 46A of the smart glasses 214, providing a dashboard that allows parents to monitor their child's smartphone usage and learning progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0160] 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.

[0161] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, 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.

[0162] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] 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.

[0164] 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.

[0165] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the analysis unit, warning unit, education unit, provision unit, response unit, and visualization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314, which monitors the usage history and application usage of the headset terminal 314 and analyzes it in the specific processing unit 290 of the data processing unit 12. The warning unit is implemented by the control unit 46A of the headset terminal 314, which displays a warning message. The education unit is implemented by the control unit 46A of the headset terminal 314, which teaches digital literacy by incorporating quiz formats and gamification elements. The provision unit is implemented by the control unit 46A of the headset terminal 314, which provides video teaching materials and interactive quizzes. The response unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the nature of the trouble and suggests appropriate response methods. The visualization unit is implemented, for example, by the control unit 46A of the headset-type terminal 314, providing a dashboard that allows parents to understand their child's smartphone usage and learning progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0176] 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.

[0177] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, 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.

[0178] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0179] 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.

[0180] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0181] 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.

[0182] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0183] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0184] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0185] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0186] 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.

[0187] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0189] Each of the multiple elements described above, including the analysis unit, warning unit, education unit, provision unit, response unit, and visualization unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414, which monitors the robot 414's usage history and application usage, and is analyzed by the specific processing unit 290 of the data processing unit 12. The warning unit is implemented by the control unit 46A of the robot 414, which displays a warning message. The education unit is implemented by the control unit 46A of the robot 414, which teaches digital literacy by incorporating quiz formats and gamification elements. The provision unit is implemented by the control unit 46A of the robot 414, which provides video teaching materials and interactive quizzes. The response unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the nature of the problem and suggests appropriate response methods. The visualization unit is implemented by the control unit 46A of the robot 414, which provides a dashboard so that parents can understand their child's smartphone usage and learning progress. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0190] 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.

[0191] Figure 9 shows the 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.

[0192] 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.

[0193] 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.

[0194] 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, and motorcycles, 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 based, for example, 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.

[0195] 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."

[0196] 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.

[0197] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0206] 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 other things 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.

[0207] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0208] (Note 1) The analysis department analyzes children's smartphone usage in real time, Based on the results of the analysis performed by the aforementioned analysis unit, a warning unit warns of access to inappropriate content or suspicious accounts, The Education Department, which teaches digital literacy based on the content of the warning issued by the aforementioned warning unit, The Education Department provides the learning content, A support department that provides initial assistance when problems occur online, It includes a visualization unit that visualizes children's smartphone usage and learning progress. A system characterized by the following features. (Note 2) The aforementioned warning unit is Automatically analyzes messages from suspicious accounts, assesses their risk level, and notifies users. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned Ministry of Education, Teaching digital literacy through quizzes and gamification elements. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We provide learning content on digital skills and safety knowledge tailored to age and interests. The system described in Appendix 1, characterized by the features described herein. (Note 5) The corresponding part is, When problems occur online, parents and schools will be notified, and contact information for specialized organizations and procedures for action will be provided. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned visualization unit, Provides a dashboard that shares children's smartphone usage and learning progress with parents. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is We estimate children's emotions and adjust the analysis method for smartphone use based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is During the analysis, the optimal analysis algorithm is selected by referring to the child's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is During the analysis, filtering is performed based on the child's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is The system estimates the child's emotions and prioritizes the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is During analysis, the data is prioritized for analysis based on its relevance, taking into account the children's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During the analysis, we will analyze children's social media activity and obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned warning unit is The system estimates the child's emotions and adjusts the way warnings are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned warning unit is When issuing a warning, adjust the level of detail based on the importance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned warning unit is When issuing a warning, different warning algorithms are applied depending on the content category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned warning unit is It estimates the child's emotions and adjusts the length of the warning based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned warning unit is When issuing a warning, the priority of the warning is determined based on when the content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned warning unit is When issuing a warning, adjust the order of warnings based on the relevance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned Ministry of Education, We estimate children's emotions and adjust the teaching methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned Ministry of Education, During education, adjust the level of detail in the lesson based on the importance of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned Ministry of Education, During education, different teaching algorithms are applied depending on the category of learning content. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned Ministry of Education, The system estimates the child's emotions and adjusts the length of the education based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned Ministry of Education, Prioritizing education based on the submission deadlines for learning materials. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned Ministry of Education, During education, the order of lessons is adjusted based on the relevance of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the child's emotions and prioritizes the content provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing content, we improve the accuracy of the delivery by considering the interrelationships between the content. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing content, the attribute information of the content submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the child's emotions and adjusts how content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing content, we will take into account its geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing content, we refer to related literature to improve the accuracy of the provision. The system described in Appendix 1, characterized by the features described herein. (Note 31) The corresponding part is, Estimate the child's emotions and adjust the response method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The corresponding part is, When responding to an issue, refer to past response history to select the most appropriate response method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The corresponding part is, When responding, customize the response method based on the current situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The corresponding part is, The system estimates the child's emotions and determines the priority of responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The corresponding part is, When responding, the optimal response method will be selected considering geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The corresponding part is, When responding, we analyze social media activity and propose appropriate response methods. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned visualization unit, We estimate the child's emotions and adjust the visualization method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned visualization unit, When visualizing data, the optimal visualization method is selected by referring to past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned visualization unit, When visualizing, customize the visualization method based on the current situation. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned visualization unit, The system estimates the child's emotions and determines the priority of visualizations based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned visualization unit, When visualizing data, the optimal visualization method is selected, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned visualization unit, When visualizing, we analyze social media activity and propose methods for visualization. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes children's smartphone usage in real time, Based on the results of the analysis performed by the aforementioned analysis unit, a warning unit warns of access to inappropriate content or suspicious accounts, The Education Department, which teaches digital literacy based on the content of the warning issued by the aforementioned warning unit, The Education Department provides the learning content, A support department that provides initial assistance when problems occur online, It includes a visualization unit that visualizes children's smartphone usage and learning progress. A system characterized by the following features.

2. The aforementioned warning unit is Automatically analyzes messages from suspicious accounts, assesses their risk level, and provides notifications. The system according to feature 1.

3. The aforementioned Ministry of Education, Teaching digital literacy through quizzes and gamification elements. The system according to feature 1.

4. The aforementioned supply unit is, We provide learning content on digital skills and safety knowledge tailored to age and interests. The system according to feature 1.

5. The corresponding part is, When problems occur online, parents and schools will be notified, and contact information for specialized organizations and procedures for action will be provided. The system according to feature 1.

6. The aforementioned visualization unit, Provides a dashboard that shares children's smartphone usage and learning progress with parents. The system according to feature 1.

7. The aforementioned analysis unit is We estimate children's emotions and adjust the analysis method for smartphone use based on the estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit is During the analysis, the optimal analysis algorithm is selected by referring to the child's past usage history. The system according to feature 1.

9. The aforementioned analysis unit is During the analysis, filtering is performed based on the child's current living situation and areas of interest. The system according to feature 1.

10. The aforementioned analysis unit is The system estimates the child's emotions and prioritizes the analysis results based on the estimated emotions. The system according to feature 1.

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