system

Abstract

The system according to this embodiment aims to safely and educationally manage children's smartphone use. [Solution] The system according to the embodiment comprises a filtering unit, a monitoring unit, a control unit, a recommendation unit, and a maintenance unit. The filtering unit filters and blocks educationally inappropriate content. The monitoring unit allows parents to monitor their children's smartphone usage. The control unit remotely controls the system based on the information monitored by the monitoring unit. The recommendation unit analyzes the children's smartphone usage history and recommends educational content. The maintenance unit performs security updates and maintenance.

Description

Technical Field

[0006] , , ,

[0005] , ,

[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 prior art, there is a problem that it is difficult to effectively filter educationally inappropriate content in children's smartphone use and for parents to appropriately monitor and control it.

[0005] The system according to the embodiment aims to manage children's smartphone use safely and educationally.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a filtering unit, a monitoring unit, a control unit, a recommendation unit, and a maintenance unit. The filtering unit filters and blocks educationally inappropriate content. The monitoring unit allows parents to monitor their children's smartphone usage. The control unit remotely controls the system based on the information monitored by the monitoring unit. The recommendation unit analyzes the children's smartphone usage history and recommends educational content. The maintenance unit performs security updates and maintenance. [Effects of the Invention]

[0007] The system according to this embodiment can safely and educationally manage children's smartphone use. [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 signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the 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) The system according to an embodiment of the present invention is a system that uses generative AI to safely and educationally support children's smartphone use. This system provides a function to filter and block educationally inappropriate websites and content. Furthermore, it provides a tool that allows parents to monitor and remotely control their children's smartphone use. In addition, the generative AI analyzes the child's smartphone usage history and recommends educational content based on their personality and interests. Furthermore, it provides security updates and maintenance to always offer the latest security measures. In this way, the system can safely and educationally support children's smartphone use.

[0029] The system according to the embodiment comprises a filtering unit, a monitoring unit, a control unit, a recommendation unit, and a maintenance unit. The filtering unit filters and blocks educationally inappropriate content. For example, the filtering unit automatically blocks sites containing violent content or inappropriate advertisements. The filtering unit can analyze internet content in real time using a generative AI to detect and block inappropriate content. For example, the filtering unit inputs a prompt to the generative AI such as "detect and block violent content," and the generative AI analyzes and blocks the content based on that instruction. The monitoring unit provides a tool for parents to monitor their children's smartphone use. For example, the monitoring unit allows parents to check their children's smartphone usage history. The monitoring unit can analyze a child's smartphone usage history using a generative AI and report the usage status to the parents. For example, the monitoring unit inputs a prompt to the generative AI such as "analyze and report the child's smartphone usage history," and the generative AI analyzes and reports the usage history based on that instruction. The control unit remotely controls the system based on the information monitored by the monitoring unit. For example, the control unit can limit the usage time of specific applications. The control unit can use a generating AI to execute instructions that allow parents to remotely control their child's smartphone. For example, the control unit can input a prompt to the generating AI such as "limit the usage time of a specific app," and the generating AI will limit the app's usage time based on that instruction. The recommendation unit analyzes the child's smartphone usage history and recommends educational content. For example, the recommendation unit can recommend learning materials that match the child's interests. The recommendation unit can use a generating AI to analyze the child's smartphone usage history and recommend educational content based on their personality and interests. For example, the recommendation unit can input a prompt to the generating AI such as "recommend learning materials that match the child's interests," and the generating AI will recommend learning materials based on that instruction. The maintenance unit performs security updates and maintenance. For example, the maintenance unit can always provide the latest security measures.The maintenance unit can use a generating AI to automatically perform security updates and maintenance on the system. For example, the maintenance unit can input a prompt to the generating AI such as "Perform security updates and maintenance on the system," and the generating AI will perform the security updates and maintenance based on that instruction. As a result, the system according to this embodiment can safely and educationally support children's smartphone use.

[0030] The filtering unit filters and blocks educationally inappropriate content. For example, the filtering unit automatically blocks sites containing violent content or inappropriate advertisements. Specifically, the filtering unit uses generative AI to analyze internet content in real time, detecting and blocking inappropriate content. The generative AI utilizes natural language processing and image recognition technologies to analyze the content of text, images, and videos in detail. For example, if the generative AI is prompted with "detect and block violent content," it will analyze the content of web pages and videos based on that instruction and determine whether they contain violent elements. Specifically, it will detect and block violent expressions or words in text, or violent scenes in images and videos. Furthermore, the generative AI can continuously learn and improve the accuracy of its filtering. For example, it can learn newly discovered patterns of inappropriate content and reflect them in subsequent filtering. As a result, the filtering unit can always block inappropriate content based on the latest information, providing a safe environment for children to use the internet. Furthermore, the filtering function provides parents and educators with customizable filtering settings, allowing them to add specific content categories and keywords to block lists. This enables flexible filtering tailored to the needs of each household and educational institution.

[0031] The monitoring unit provides tools for parents to monitor their children's smartphone usage. For example, the monitoring unit allows parents to view their child's smartphone usage history. Specifically, it uses a generative AI to analyze a child's smartphone usage history and report the usage status to the parent. The generative AI analyzes smartphone app usage history, web browsing history, call history, message history, etc., to understand in detail what apps the child uses, for how long, and what websites they visit. For example, the monitoring unit can input a prompt to the generative AI such as "analyze and report the child's smartphone usage history," and the generative AI will analyze the usage history based on that instruction and generate a visual report for the parent. The report includes graphs of usage time, lists of the most frequently used apps and websites, and usage patterns during specific time periods. The monitoring unit also has a function to send alerts to parents if it detects abnormal usage patterns or access to inappropriate content. This allows parents to understand their child's smartphone usage in real time and take appropriate measures as needed. Furthermore, the monitoring unit can automatically manage a child's smartphone usage based on rules and restrictions set by the parent. For example, it can restrict smartphone use during specific time periods or prohibit the use of certain apps. This allows the monitoring unit to safely and appropriately manage children's smartphone use, enabling parents to confidently support their children's digital lives.

[0032] The control unit remotely controls the device based on information monitored by the monitoring unit. For example, the control unit can limit the usage time of specific apps. Specifically, the control unit can use a generating AI to execute instructions for parents to remotely control their child's smartphone. The generating AI receives instructions from the parent and modifies the smartphone settings accordingly. For example, the control unit can input a prompt to the generating AI such as "limit the usage time of a specific app," and the generating AI will limit the app's usage time based on that instruction. Specifically, the generating AI modifies the smartphone settings to restrict the use of a specific app beyond a certain period of time. The control unit also allows parents to remotely unlock the smartphone or install specific apps. This allows parents to flexibly manage their child's smartphone use and impose appropriate restrictions as needed. Furthermore, the control unit provides an interface for centrally managing rules and restrictions regarding children's smartphone use. Parents can easily set and modify rules and restrictions through this interface. For example, they can set rules to prohibit smartphone use during specific time periods or to restrict the use of specific apps. This allows the control unit to effectively manage children's smartphone use and provide an environment where children can use smartphones safely and appropriately.

[0033] The recommendation team analyzes children's smartphone usage history and recommends educational content. Specifically, the recommendation team uses generative AI to analyze children's smartphone usage history and recommend educational content based on their personality and interests. The generative AI extracts interests from the child's usage history and recommends appropriate learning materials and educational apps based on that. For example, the recommendation team can input a prompt to the generative AI such as "recommend learning materials that match the child's interests," and the generative AI will recommend learning materials based on that instruction. Specifically, the generative AI analyzes the categories of apps and websites that the child frequently uses and suggests educational content related to them. For example, if the child is interested in science, the generative AI will recommend science-related learning apps, videos, and articles. The recommendation team can also provide content of appropriate difficulty level, taking into account the child's learning progress and understanding. This allows children to learn at their own pace and deepen their knowledge effectively. Furthermore, the recommendation team provides an interface for parents and educators to review and approve the recommended content. This allows parents and educators to confirm that appropriate content is being provided for their children and to use it with peace of mind. The recommendation department can enhance children's motivation to learn and support effective learning by continuously analyzing children's usage history and recommending the most suitable content based on the latest information.

[0034] The maintenance department performs security updates and maintenance. Specifically, the maintenance department can use a generating AI to automatically perform security updates and maintenance on the system. The generating AI collects the latest security threat and vulnerability information and updates the system's security measures based on that information. For example, the maintenance department can input a prompt to the generating AI such as "Perform system security updates and maintenance," and the generating AI will perform security updates and maintenance based on that instruction. Specifically, the generating AI periodically checks the system's security status and automatically applies necessary updates. The generating AI also monitors the system's performance and operational status and responds quickly if any anomalies are detected. For example, if the system's resource utilization is abnormally high or if an unauthorized access attempt is made, the generating AI will automatically take countermeasures. This allows the maintenance department to maintain the system's security and stability and keep it always up-to-date. Furthermore, the maintenance department also provides system backup and recovery functions to support data protection and recovery. For example, it can create system backups regularly and quickly recover data in the event of a failure. This allows the maintenance department to ensure the reliability and safety of the system and provide users with an environment where they can use the system with peace of mind.

[0035] The filtering unit can automatically block websites containing violent content or inappropriate advertisements. For example, it can detect and block videos and games containing violent content. It can also detect and block websites containing inappropriate advertisements. Furthermore, the filtering unit can use generative AI to analyze internet content in real time and detect and block inappropriate content. For example, the filtering unit can input a prompt to the generative AI such as "detect and block violent content," and the generative AI will analyze the content based on that instruction and block it. This prevents children from accessing dangerous sites and provides a safe online environment.

[0036] The monitoring unit can provide a tool for parents to check their child's smartphone usage history. For example, the monitoring unit can allow parents to check their child's smartphone usage history. The monitoring unit can use a generating AI to analyze the child's smartphone usage history and report the usage status to the parents. For example, the monitoring unit can input a prompt to the generating AI such as "Analyze and report the child's smartphone usage history," and the generating AI will analyze the usage history based on that instruction and report it. This allows parents to check their child's smartphone usage history.

[0037] The control unit can limit the usage time of specific apps. For example, the control unit can limit the usage time of a particular app. The control unit can use generative AI to issue instructions that allow parents to remotely control their child's smartphone. For example, the control unit can input a prompt to the generative AI such as "limit the usage time of a specific app," and the generative AI will limit the app's usage time based on that instruction. This allows parents to manage their child's smartphone usage by limiting the usage time of specific apps.

[0038] The control unit can prohibit the use of a smartphone during specific time periods. For example, the control unit can prohibit the use of a smartphone during specific time periods. The control unit can use a generative AI to issue instructions that allow parents to remotely control their child's smartphone. For example, the control unit can input a prompt to the generative AI such as "prohibit smartphone use during specific time periods," and the generative AI will prohibit smartphone use based on that instruction. This allows parents to manage their child's smartphone use by prohibiting its use during specific time periods.

[0039] The recommendation system can recommend learning materials that match a child's interests. For example, the recommendation system can recommend learning materials that match a child's interests. Using generative AI, the recommendation system can analyze a child's smartphone usage history and recommend educational content based on their personality and interests. For example, the recommendation system can input a prompt to the generative AI such as "recommend learning materials that match the child's interests," and the generative AI will recommend learning materials based on that instruction. This allows children to learn while enjoying learning materials that match their interests.

[0040] The maintenance department can always provide the latest security measures. For example, the maintenance department can always provide the latest security measures. The maintenance department can use generative AI to automatically perform security updates and maintenance on the system. For example, the maintenance department can input a prompt to the generative AI such as "Perform system security updates and maintenance," and the generative AI will perform security updates and maintenance based on that instruction. This ensures that children's smartphones are protected from the latest threats and can be used safely.

[0041] The filtering unit can customize filtering criteria according to the child's age and grade level during the filtering process. For example, for preschoolers, the filtering unit prioritizes displaying educational and visually appealing content. The filtering unit can use generative AI to customize filtering criteria according to the child's age and grade level. For example, the filtering unit can input a prompt to the generative AI such as "Customize filtering criteria according to the child's age and grade level," and the generative AI will customize the filtering criteria based on that instruction. This allows for the provision of appropriate content according to the child's age and grade level.

[0042] The filtering unit can improve the accuracy of filtering by referring to the child's past browsing history. For example, the filtering unit adjusts the filtering criteria based on safe websites the child has visited in the past. The filtering unit can also improve the accuracy of filtering by analyzing the child's past browsing history using a generative AI. For example, the filtering unit can input a prompt to the generative AI such as "Improve filtering accuracy by referring to the child's past browsing history," and the generative AI will analyze the browsing history based on that instruction and adjust the filtering criteria. As a result, the accuracy of filtering is improved by referring to past browsing history.

[0043] The filtering unit can block region-specific inappropriate content based on the child's geographical location information during filtering. For example, if the child is in a specific region, the filtering unit will block region-specific inappropriate content. The filtering unit can use a generative AI to analyze the child's geographical location information and block region-specific inappropriate content based on that information. For example, the filtering unit can input a prompt to the generative AI such as "Block region-specific inappropriate content based on the child's geographical location information," and the generative AI will analyze the geographical location information based on that instruction and block the content. This allows for a safer online environment by blocking region-specific inappropriate content.

[0044] The filtering unit can analyze a child's social media activity during filtering and block relevant inappropriate content. For example, if a child is viewing inappropriate content on a particular social media platform, the filtering unit will block that content. The filtering unit can use generative AI to analyze a child's social media activity and block relevant inappropriate content based on that information. For example, the filtering unit can input a prompt to the generative AI such as "Analyze the child's social media activity and block relevant inappropriate content," and the generative AI will analyze the social media activity based on that instruction and block the content. In this way, relevant inappropriate content can be blocked by analyzing social media activity.

[0045] The monitoring unit can analyze a child's smartphone usage history in real time during monitoring and detect abnormal usage patterns. For example, if a child is using their smartphone at an unusual time of day, the monitoring unit will detect this as an abnormal usage pattern. The monitoring unit can use a generation AI to analyze a child's smartphone usage history in real time and detect abnormal usage patterns. For example, the monitoring unit can input a prompt to the generation AI such as "Analyze the child's smartphone usage history in real time and detect abnormal usage patterns," and the generation AI will analyze the usage history based on that instruction and detect abnormal usage patterns. This ensures the safety of children by detecting abnormal usage patterns in real time.

[0046] The monitoring unit can change its monitoring focus by referring to the child's learning progress during monitoring. For example, if the child's learning progress is good, the monitoring unit can reduce the monitoring focus and increase free time. The monitoring unit can use generative AI to analyze the child's learning progress and change the monitoring focus based on that information. For example, the monitoring unit can input a prompt to the generative AI such as "change the monitoring focus by referring to the child's learning progress," and the generative AI will analyze the learning progress based on that instruction and change the monitoring focus. This makes it possible to perform more appropriate monitoring by changing the monitoring focus according to the learning progress.

[0047] The monitoring unit can send notifications to the parent's smartphone during monitoring, reporting the child's usage in real time. For example, if the child is using a specific app, the monitoring unit will send a notification to the parent's smartphone. The monitoring unit can use a generation AI to analyze the child's smartphone usage in real time and notify the parent's smartphone of that information. For example, the monitoring unit can input a prompt to the generation AI such as "Report the child's usage in real time and send a notification to the parent's smartphone," and the generation AI will analyze the usage based on that instruction and send a notification. This allows parents to understand their child's smartphone usage by reporting on it in real time.

[0048] The monitoring unit can select the optimal monitoring method based on the child's device information during monitoring. For example, if the child is using a smartphone, the monitoring unit will provide the optimal monitoring method for the smartphone. The monitoring unit can analyze the child's device information using a generative AI and select the optimal monitoring method based on that information. For example, the monitoring unit can input a prompt to the generative AI such as "Select the optimal monitoring method based on the child's device information," and the generative AI will analyze the device information based on that instruction and select a monitoring method. This allows for more effective monitoring by selecting the optimal monitoring method based on device information.

[0049] The control unit can set restrictions on smartphone use based on the child's study schedule during operation. For example, the control unit can restrict smartphone use during specific time periods based on the child's study schedule. The control unit can use a generative AI to analyze the child's study schedule and set smartphone usage restrictions based on that information. For example, the control unit can input a prompt to the generative AI such as "Set smartphone usage restrictions based on the child's study schedule," and the generative AI will analyze the study schedule based on that instruction and set the usage restrictions. This allows for an environment where children can concentrate on their studies by setting smartphone usage restrictions based on their study schedule.

[0050] The control unit can select the optimal control method by referring to the child's past usage history during control. For example, the control unit selects the optimal control method based on the child's past usage history. The control unit can use a generating AI to analyze the child's past usage history and select the optimal control method based on that information. For example, the control unit inputs a prompt to the generating AI, "Select the optimal control method by referring to the child's past usage history," and the generating AI analyzes the usage history based on that instruction and selects a control method. In this way, the optimal control method can be selected by referring to past usage history.

[0051] The control unit allows for remote control from the parent's smartphone during operation, enabling real-time changes to settings. For example, the control unit can allow the parent to remotely control the device from their smartphone and limit the usage time of a specific app. The control unit can use a generative AI to execute instructions for the parent to remotely control the child's smartphone. For example, the control unit can input a prompt to the generative AI such as "Control remotely from the parent's smartphone and change settings in real time," and the generative AI will then control the device and change the settings based on that instruction. This allows the parent to remotely control the device and change settings in real time.

[0052] The control unit can select the optimal control method based on the child's device information during control. For example, if the child is using a smartphone, the control unit will provide the optimal control method for the smartphone. The control unit can analyze the child's device information using a generative AI and select the optimal control method based on that information. For example, the control unit can input a prompt to the generative AI such as "Select the optimal control method based on the child's device information," and the generative AI will analyze the device information based on that instruction and select a control method. This allows for more effective control by selecting the optimal control method based on the device information.

[0053] The recommendation system can recommend the most suitable learning materials by referring to the child's learning progress. For example, if the child's learning progress is good, the recommendation system will recommend learning materials to move on to the next step. The recommendation system can use generative AI to analyze the child's learning progress and recommend the most suitable learning materials based on that information. For example, the recommendation system can input a prompt to the generative AI such as "refer to the child's learning progress and recommend the most suitable learning materials," and the generative AI will analyze the learning progress based on that instruction and recommend learning materials. This allows for more effective learning by recommending the most suitable learning materials according to the child's learning progress.

[0054] The recommendation system can improve the accuracy of recommendations by analyzing a child's past learning history. For example, the recommendation system recommends the most suitable learning materials based on a child's past learning history. The recommendation system can use generative AI to analyze a child's past learning history and improve the accuracy of recommendations based on that information. For example, the recommendation system can input a prompt to the generative AI such as "Analyze the child's past learning history to improve the accuracy of recommendations," and the generative AI will analyze the learning history based on that instruction to improve the accuracy of recommendations. In this way, the accuracy of recommendations is improved by analyzing past learning history.

[0055] The recommendation department can suggest new hobbies and activities based on a child's interests during the recommendation process. For example, if a child is interested in science, the recommendation department might suggest a science experiment kit. The recommendation department can use generative AI to analyze a child's interests and suggest new hobbies and activities based on that information. For example, the recommendation department can input the prompt "Suggest new hobbies and activities based on the child's interests" into the generative AI, which then analyzes the child's interests and suggests hobbies and activities based on that instruction. In this way, the recommendation department can support a child's growth by suggesting new hobbies and activities based on their interests.

[0056] The recommendation system can recommend region-specific learning materials based on the child's geographical location. For example, if a child is in a specific region, the recommendation system will recommend region-specific learning materials. The recommendation system can use generative AI to analyze the child's geographical location and recommend region-specific learning materials based on that information. For example, the recommendation system can input a prompt to the generative AI such as "Recommend region-specific learning materials based on the child's geographical location," and the generative AI will analyze the geographical location based on that instruction and recommend learning materials. This allows for more appropriate learning by recommending region-specific learning materials based on geographical location.

[0057] The maintenance department can optimize its maintenance algorithms by referring to past security incidents during maintenance. For example, the maintenance department optimizes its maintenance algorithms based on past security incidents. The maintenance department can use generative AI to analyze past security incidents and optimize its maintenance algorithms based on that information. For example, the maintenance department can input a prompt to the generative AI such as "Optimize the maintenance algorithm by referring to past security incidents," and the generative AI will analyze security incidents based on that instruction and optimize the maintenance algorithm. In this way, the maintenance algorithm can be optimized by referring to past security incidents.

[0058] The maintenance unit can select the optimal maintenance method based on the child's device information during maintenance. For example, if the child is using a smartphone, the maintenance unit will provide the optimal maintenance method for the smartphone. The maintenance unit can use a generative AI to analyze the child's device information and select the optimal maintenance method based on that information. For example, the maintenance unit can input a prompt to the generative AI such as "Select the optimal maintenance method based on the child's device information," and the generative AI will analyze the device information based on that instruction and select a maintenance method. This allows for more effective maintenance by selecting the optimal maintenance method based on device information.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The filtering unit can assess the safety of content in real time while a child is using their smartphone and immediately block dangerous content. For example, the filtering unit uses generated AI to instantly analyze the content of websites and apps that a child is trying to access and immediately block them if they contain dangerous elements. The filtering unit can also pre-evaluate the safety of apps that a child is trying to install and prevent the installation of dangerous apps. Furthermore, the filtering unit can analyze the content of messages and emails that a child receives and display a warning if they contain inappropriate content. This prevents children from being exposed to dangerous content and provides a safe online environment.

[0061] The monitoring unit can notify parents in real time about their child's smartphone usage. For example, if the monitoring unit detects that the child is using a specific app, it will notify the parent's smartphone in real time. It can also notify parents if the child accesses a specific website. Furthermore, the monitoring unit can report to parents in real time the time of day and frequency of smartphone use. This allows parents to understand their child's smartphone usage in real time and take appropriate action.

[0062] The control unit can adjust usage restrictions in real time when a child is using a smartphone. For example, if a child is using a particular app for an extended period, the control unit can limit the usage time of that app. It can also restrict smartphone use during specific time periods if a child is using the smartphone during those times. Furthermore, if a child is in a specific location, the control unit can restrict smartphone use in that location. This allows for proper management of children's smartphone use and promotes healthy usage habits.

[0063] The recommendation system can recommend educational content in real time as children use their smartphones. For example, if a child is using a specific app, the recommendation system will recommend educational content related to that app. It can also recommend educational content related to a specific website if the child is accessing that website. Furthermore, if a child is using their smartphone at a particular time of day, the recommendation system can recommend educational content appropriate for that time. This ensures that children are always exposed to educational content when using their smartphones.

[0064] The maintenance department can perform security updates in real time while a child is using their smartphone. For example, if a new security threat is detected while the child is using the smartphone, the maintenance department will immediately perform a security update. Furthermore, if the child is using a specific app, the maintenance department can monitor the security status of that app in real time and perform updates as needed. In addition, if the child is accessing a specific website, the maintenance department can monitor the security status of that website in real time and block access if necessary. This ensures that the child's smartphone is always protected with the latest security measures and can be used safely.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The filtering unit filters and blocks educationally inappropriate content. For example, it automatically blocks sites containing violent content or inappropriate advertisements. The filtering unit can analyze internet content in real time using generative AI to detect and block inappropriate content. Step 2: The monitoring unit provides tools for parents to monitor their children's smartphone usage. For example, parents can check their child's smartphone usage history. The monitoring unit can analyze the child's smartphone usage history using generated AI and report the usage status to the parents. Step 3: The control unit remotely controls the device based on information monitored by the monitoring unit. For example, it can limit the usage time of specific apps. The control unit can use generated AI to issue instructions to allow parents to remotely control their child's smartphone. Step 4: The recommendation team analyzes the child's smartphone usage history and recommends educational content. For example, it can recommend learning materials that match the child's interests. The recommendation team uses generative AI to analyze the child's smartphone usage history and recommend educational content based on their personality and interests. Step 5: The maintenance department performs security updates and maintenance. For example, they can always provide the latest security measures. The maintenance department can use generative AI to automatically perform system security updates and maintenance.

[0067] (Example of form 2) The system according to an embodiment of the present invention is a system that uses generative AI to safely and educationally support children's smartphone use. This system provides a function to filter and block educationally inappropriate websites and content. Furthermore, it provides a tool that allows parents to monitor and remotely control their children's smartphone use. In addition, the generative AI analyzes the child's smartphone usage history and recommends educational content based on their personality and interests. Furthermore, it provides security updates and maintenance to always offer the latest security measures. In this way, the system can safely and educationally support children's smartphone use.

[0068] The system according to the embodiment comprises a filtering unit, a monitoring unit, a control unit, a recommendation unit, and a maintenance unit. The filtering unit filters and blocks educationally inappropriate content. For example, the filtering unit automatically blocks sites containing violent content or inappropriate advertisements. The filtering unit can analyze internet content in real time using a generative AI to detect and block inappropriate content. For example, the filtering unit inputs a prompt to the generative AI such as "detect and block violent content," and the generative AI analyzes and blocks the content based on that instruction. The monitoring unit provides a tool for parents to monitor their children's smartphone use. For example, the monitoring unit allows parents to check their children's smartphone usage history. The monitoring unit can analyze a child's smartphone usage history using a generative AI and report the usage status to the parents. For example, the monitoring unit inputs a prompt to the generative AI such as "analyze and report the child's smartphone usage history," and the generative AI analyzes and reports the usage history based on that instruction. The control unit remotely controls the system based on the information monitored by the monitoring unit. For example, the control unit can limit the usage time of specific applications. The control unit can use a generating AI to execute instructions that allow parents to remotely control their child's smartphone. For example, the control unit can input a prompt to the generating AI such as "limit the usage time of a specific app," and the generating AI will limit the app's usage time based on that instruction. The recommendation unit analyzes the child's smartphone usage history and recommends educational content. For example, the recommendation unit can recommend learning materials that match the child's interests. The recommendation unit can use a generating AI to analyze the child's smartphone usage history and recommend educational content based on their personality and interests. For example, the recommendation unit can input a prompt to the generating AI such as "recommend learning materials that match the child's interests," and the generating AI will recommend learning materials based on that instruction. The maintenance unit performs security updates and maintenance. For example, the maintenance unit can always provide the latest security measures.The maintenance unit can use a generating AI to automatically perform security updates and maintenance on the system. For example, the maintenance unit can input a prompt to the generating AI such as "Perform security updates and maintenance on the system," and the generating AI will perform the security updates and maintenance based on that instruction. As a result, the system according to this embodiment can safely and educationally support children's smartphone use.

[0069] The filtering unit filters and blocks educationally inappropriate content. For example, the filtering unit automatically blocks sites containing violent content or inappropriate advertisements. Specifically, the filtering unit uses generative AI to analyze internet content in real time, detecting and blocking inappropriate content. The generative AI utilizes natural language processing and image recognition technologies to analyze the content of text, images, and videos in detail. For example, if the generative AI is prompted with "detect and block violent content," it will analyze the content of web pages and videos based on that instruction and determine whether they contain violent elements. Specifically, it will detect and block violent expressions or words in text, or violent scenes in images and videos. Furthermore, the generative AI can continuously learn and improve the accuracy of its filtering. For example, it can learn newly discovered patterns of inappropriate content and reflect them in subsequent filtering. As a result, the filtering unit can always block inappropriate content based on the latest information, providing a safe environment for children to use the internet. Furthermore, the filtering function provides parents and educators with customizable filtering settings, allowing them to add specific content categories and keywords to block lists. This enables flexible filtering tailored to the needs of each household and educational institution.

[0070] The monitoring unit provides tools for parents to monitor their children's smartphone usage. For example, the monitoring unit allows parents to view their child's smartphone usage history. Specifically, it uses a generative AI to analyze a child's smartphone usage history and report the usage status to the parent. The generative AI analyzes smartphone app usage history, web browsing history, call history, message history, etc., to understand in detail what apps the child uses, for how long, and what websites they visit. For example, the monitoring unit can input a prompt to the generative AI such as "analyze and report the child's smartphone usage history," and the generative AI will analyze the usage history based on that instruction and generate a visual report for the parent. The report includes graphs of usage time, lists of the most frequently used apps and websites, and usage patterns during specific time periods. The monitoring unit also has a function to send alerts to parents if it detects abnormal usage patterns or access to inappropriate content. This allows parents to understand their child's smartphone usage in real time and take appropriate measures as needed. Furthermore, the monitoring unit can automatically manage a child's smartphone usage based on rules and restrictions set by the parent. For example, it can restrict smartphone use during specific time periods or prohibit the use of certain apps. This allows the monitoring unit to safely and appropriately manage children's smartphone use, enabling parents to confidently support their children's digital lives.

[0071] The control unit remotely controls the device based on information monitored by the monitoring unit. For example, the control unit can limit the usage time of specific apps. Specifically, the control unit can use a generating AI to execute instructions for parents to remotely control their child's smartphone. The generating AI receives instructions from the parent and modifies the smartphone settings accordingly. For example, the control unit can input a prompt to the generating AI such as "limit the usage time of a specific app," and the generating AI will limit the app's usage time based on that instruction. Specifically, the generating AI modifies the smartphone settings to restrict the use of a specific app beyond a certain period of time. The control unit also allows parents to remotely unlock the smartphone or install specific apps. This allows parents to flexibly manage their child's smartphone use and impose appropriate restrictions as needed. Furthermore, the control unit provides an interface for centrally managing rules and restrictions regarding children's smartphone use. Parents can easily set and modify rules and restrictions through this interface. For example, they can set rules to prohibit smartphone use during specific time periods or to restrict the use of specific apps. This allows the control unit to effectively manage children's smartphone use and provide an environment where children can use smartphones safely and appropriately.

[0072] The recommendation team analyzes children's smartphone usage history and recommends educational content. Specifically, the recommendation team uses generative AI to analyze children's smartphone usage history and recommend educational content based on their personality and interests. The generative AI extracts interests from the child's usage history and recommends appropriate learning materials and educational apps based on that. For example, the recommendation team can input a prompt to the generative AI such as "recommend learning materials that match the child's interests," and the generative AI will recommend learning materials based on that instruction. Specifically, the generative AI analyzes the categories of apps and websites that the child frequently uses and suggests educational content related to them. For example, if the child is interested in science, the generative AI will recommend science-related learning apps, videos, and articles. The recommendation team can also provide content of appropriate difficulty level, taking into account the child's learning progress and understanding. This allows children to learn at their own pace and deepen their knowledge effectively. Furthermore, the recommendation team provides an interface for parents and educators to review and approve the recommended content. This allows parents and educators to confirm that appropriate content is being provided for their children and to use it with peace of mind. The recommendation department can enhance children's motivation to learn and support effective learning by continuously analyzing children's usage history and recommending the most suitable content based on the latest information.

[0073] The maintenance department performs security updates and maintenance. Specifically, the maintenance department can use a generating AI to automatically perform security updates and maintenance on the system. The generating AI collects the latest security threat and vulnerability information and updates the system's security measures based on that information. For example, the maintenance department can input a prompt to the generating AI such as "Perform system security updates and maintenance," and the generating AI will perform security updates and maintenance based on that instruction. Specifically, the generating AI periodically checks the system's security status and automatically applies necessary updates. The generating AI also monitors the system's performance and operational status and responds quickly if any anomalies are detected. For example, if the system's resource utilization is abnormally high or if an unauthorized access attempt is made, the generating AI will automatically take countermeasures. This allows the maintenance department to maintain the system's security and stability and keep it always up-to-date. Furthermore, the maintenance department also provides system backup and recovery functions to support data protection and recovery. For example, it can create system backups regularly and quickly recover data in the event of a failure. This allows the maintenance department to ensure the reliability and safety of the system and provide users with an environment where they can use the system with peace of mind.

[0074] The filtering unit can automatically block websites containing violent content or inappropriate advertisements. For example, it can detect and block videos and games containing violent content. It can also detect and block websites containing inappropriate advertisements. Furthermore, the filtering unit can use generative AI to analyze internet content in real time and detect and block inappropriate content. For example, the filtering unit can input a prompt to the generative AI such as "detect and block violent content," and the generative AI will analyze the content based on that instruction and block it. This prevents children from accessing dangerous sites and provides a safe online environment.

[0075] The monitoring unit can provide a tool for parents to check their child's smartphone usage history. For example, the monitoring unit can allow parents to check their child's smartphone usage history. The monitoring unit can use a generating AI to analyze the child's smartphone usage history and report the usage status to the parents. For example, the monitoring unit can input a prompt to the generating AI such as "Analyze and report the child's smartphone usage history," and the generating AI will analyze the usage history based on that instruction and report it. This allows parents to check their child's smartphone usage history.

[0076] The control unit can limit the usage time of specific apps. For example, the control unit can limit the usage time of a particular app. The control unit can use generative AI to issue instructions that allow parents to remotely control their child's smartphone. For example, the control unit can input a prompt to the generative AI such as "limit the usage time of a specific app," and the generative AI will limit the app's usage time based on that instruction. This allows parents to manage their child's smartphone usage by limiting the usage time of specific apps.

[0077] The control unit can prohibit the use of a smartphone during specific time periods. For example, the control unit can prohibit the use of a smartphone during specific time periods. The control unit can use a generative AI to issue instructions that allow parents to remotely control their child's smartphone. For example, the control unit can input a prompt to the generative AI such as "prohibit smartphone use during specific time periods," and the generative AI will prohibit smartphone use based on that instruction. This allows parents to manage their child's smartphone use by prohibiting its use during specific time periods.

[0078] The recommendation system can recommend learning materials that match a child's interests. For example, the recommendation system can recommend learning materials that match a child's interests. Using generative AI, the recommendation system can analyze a child's smartphone usage history and recommend educational content based on their personality and interests. For example, the recommendation system can input a prompt to the generative AI such as "recommend learning materials that match the child's interests," and the generative AI will recommend learning materials based on that instruction. This allows children to learn while enjoying learning materials that match their interests.

[0079] The maintenance department can always provide the latest security measures. For example, the maintenance department can always provide the latest security measures. The maintenance department can use generative AI to automatically perform security updates and maintenance on the system. For example, the maintenance department can input a prompt to the generative AI such as "Perform system security updates and maintenance," and the generative AI will perform security updates and maintenance based on that instruction. This ensures that children's smartphones are protected from the latest threats and can be used safely.

[0080] The filtering unit can estimate a child's emotions and adjust the filtering strictness based on those emotions. For example, if a child is stressed, the filtering unit will reduce the filtering strictness and prioritize displaying relaxing content. The filtering unit can use generative AI to estimate a child's emotions and adjust the filtering strictness based on those emotions. For example, the filtering unit can input a prompt to the generative AI such as "Estimate the child's emotions and adjust the filtering strictness," and the generative AI will estimate the emotions based on that instruction and adjust the filtering strictness accordingly. This allows for the provision of more appropriate content by adjusting the filtering strictness according to the child's emotions.

[0081] The filtering unit can customize filtering criteria according to the child's age and grade level during the filtering process. For example, for preschoolers, the filtering unit prioritizes displaying educational and visually appealing content. The filtering unit can use generative AI to customize filtering criteria according to the child's age and grade level. For example, the filtering unit can input a prompt to the generative AI such as "Customize filtering criteria according to the child's age and grade level," and the generative AI will customize the filtering criteria based on that instruction. This allows for the provision of appropriate content according to the child's age and grade level.

[0082] The filtering unit can improve the accuracy of filtering by referring to the child's past browsing history. For example, the filtering unit adjusts the filtering criteria based on safe websites the child has visited in the past. The filtering unit can also improve the accuracy of filtering by analyzing the child's past browsing history using a generative AI. For example, the filtering unit can input a prompt to the generative AI such as "Improve filtering accuracy by referring to the child's past browsing history," and the generative AI will analyze the browsing history based on that instruction and adjust the filtering criteria. As a result, the accuracy of filtering is improved by referring to past browsing history.

[0083] The filtering unit can estimate a child's emotions and adjust the display method of the filtering results based on the estimated emotions. For example, if a child is stressed, the filtering unit provides a simple and highly visible display method. The filtering unit can use generative AI to estimate a child's emotions and adjust the display method of the filtering results based on those emotions. For example, the filtering unit can input a prompt to the generative AI such as "Estimate the child's emotions and adjust the display method of the filtering results," and the generative AI will estimate the emotions and adjust the display method based on that instruction. This allows for a more appropriate display by adjusting the display method of the filtering results according to the child's emotions.

[0084] The filtering unit can block region-specific inappropriate content based on the child's geographical location information during filtering. For example, if the child is in a specific region, the filtering unit will block region-specific inappropriate content. The filtering unit can use a generative AI to analyze the child's geographical location information and block region-specific inappropriate content based on that information. For example, the filtering unit can input a prompt to the generative AI such as "Block region-specific inappropriate content based on the child's geographical location information," and the generative AI will analyze the geographical location information based on that instruction and block the content. This allows for a safer online environment by blocking region-specific inappropriate content.

[0085] The filtering unit can analyze a child's social media activity during filtering and block relevant inappropriate content. For example, if a child is viewing inappropriate content on a particular social media platform, the filtering unit will block that content. The filtering unit can use generative AI to analyze a child's social media activity and block relevant inappropriate content based on that information. For example, the filtering unit can input a prompt to the generative AI such as "Analyze the child's social media activity and block relevant inappropriate content," and the generative AI will analyze the social media activity based on that instruction and block the content. In this way, relevant inappropriate content can be blocked by analyzing social media activity.

[0086] The monitoring unit can estimate the child's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the child is stressed, the monitoring unit can reduce the monitoring frequency to provide a more relaxing environment. The monitoring unit can use generative AI to estimate the child's emotions and adjust the monitoring frequency based on those emotions. For example, the monitoring unit can input a prompt to the generative AI such as "Estimate the child's emotions and adjust the monitoring frequency," and the generative AI will estimate the emotions and adjust the monitoring frequency based on that instruction. This allows for more appropriate monitoring by adjusting the monitoring frequency according to the child's emotions.

[0087] The monitoring unit can analyze a child's smartphone usage history in real time during monitoring and detect abnormal usage patterns. For example, if a child is using their smartphone at an unusual time of day, the monitoring unit will detect this as an abnormal usage pattern. The monitoring unit can use a generation AI to analyze a child's smartphone usage history in real time and detect abnormal usage patterns. For example, the monitoring unit can input a prompt to the generation AI such as "Analyze the child's smartphone usage history in real time and detect abnormal usage patterns," and the generation AI will analyze the usage history based on that instruction and detect abnormal usage patterns. This ensures the safety of children by detecting abnormal usage patterns in real time.

[0088] The monitoring unit can change its monitoring focus by referring to the child's learning progress during monitoring. For example, if the child's learning progress is good, the monitoring unit can reduce the monitoring focus and increase free time. The monitoring unit can use generative AI to analyze the child's learning progress and change the monitoring focus based on that information. For example, the monitoring unit can input a prompt to the generative AI such as "change the monitoring focus by referring to the child's learning progress," and the generative AI will analyze the learning progress based on that instruction and change the monitoring focus. This makes it possible to perform more appropriate monitoring by changing the monitoring focus according to the learning progress.

[0089] The monitoring unit can estimate a child's emotions and adjust the display method of the monitoring results based on the estimated emotions. For example, if a child is stressed, the monitoring unit provides a simple and highly visible display method. The monitoring unit can use generative AI to estimate a child's emotions and adjust the display method of the monitoring results based on those emotions. For example, the monitoring unit can input a prompt to the generative AI such as "Estimate the child's emotions and adjust the display method of the monitoring results," and the generative AI will estimate the emotions and adjust the display method based on that instruction. This allows for a more appropriate display by adjusting the display method of the monitoring results according to the child's emotions.

[0090] The monitoring unit can send notifications to the parent's smartphone during monitoring, reporting the child's usage in real time. For example, if the child is using a specific app, the monitoring unit will send a notification to the parent's smartphone. The monitoring unit can use a generation AI to analyze the child's smartphone usage in real time and notify the parent's smartphone of that information. For example, the monitoring unit can input a prompt to the generation AI such as "Report the child's usage in real time and send a notification to the parent's smartphone," and the generation AI will analyze the usage based on that instruction and send a notification. This allows parents to understand their child's smartphone usage by reporting on it in real time.

[0091] The monitoring unit can select the optimal monitoring method based on the child's device information during monitoring. For example, if the child is using a smartphone, the monitoring unit will provide the optimal monitoring method for the smartphone. The monitoring unit can analyze the child's device information using a generative AI and select the optimal monitoring method based on that information. For example, the monitoring unit can input a prompt to the generative AI such as "Select the optimal monitoring method based on the child's device information," and the generative AI will analyze the device information based on that instruction and select a monitoring method. This allows for more effective monitoring by selecting the optimal monitoring method based on device information.

[0092] The control unit can estimate the child's emotions and adjust the strictness of control based on those emotions. For example, if the child is stressed, the control unit can reduce the strictness of control and provide a relaxing environment. The control unit can use generative AI to estimate the child's emotions and adjust the strictness of control based on those emotions. For example, the control unit can input a prompt to the generative AI such as "Estimate the child's emotions and adjust the strictness of control," and the generative AI will estimate the emotions and adjust the strictness of control based on that instruction. This allows for a more appropriate environment to be provided by adjusting the strictness of control according to the child's emotions.

[0093] The control unit can set restrictions on smartphone use based on the child's study schedule during operation. For example, the control unit can restrict smartphone use during specific time periods based on the child's study schedule. The control unit can use a generative AI to analyze the child's study schedule and set smartphone usage restrictions based on that information. For example, the control unit can input a prompt to the generative AI such as "Set smartphone usage restrictions based on the child's study schedule," and the generative AI will analyze the study schedule based on that instruction and set the usage restrictions. This allows for an environment where children can concentrate on their studies by setting smartphone usage restrictions based on their study schedule.

[0094] The control unit can select the optimal control method by referring to the child's past usage history during control. For example, the control unit selects the optimal control method based on the child's past usage history. The control unit can use a generating AI to analyze the child's past usage history and select the optimal control method based on that information. For example, the control unit inputs a prompt to the generating AI, "Select the optimal control method by referring to the child's past usage history," and the generating AI analyzes the usage history based on that instruction and selects a control method. In this way, the optimal control method can be selected by referring to past usage history.

[0095] The control unit can estimate the child's emotions and determine control priorities based on those estimated emotions. For example, if the child is stressed, the control unit will prioritize providing a relaxing environment. The control unit can use generative AI to estimate the child's emotions and determine control priorities based on those emotions. For example, the control unit can input a prompt to the generative AI, "Estimate the child's emotions and determine control priorities," and the generative AI will estimate the emotions and determine priorities based on that instruction. This allows for the provision of a more appropriate environment by determining control priorities according to the child's emotions.

[0096] The control unit allows for remote control from the parent's smartphone during operation, enabling real-time changes to settings. For example, the control unit can allow the parent to remotely control the device from their smartphone and limit the usage time of a specific app. The control unit can use a generative AI to execute instructions for the parent to remotely control the child's smartphone. For example, the control unit can input a prompt to the generative AI such as "Control remotely from the parent's smartphone and change settings in real time," and the generative AI will then control the device and change the settings based on that instruction. This allows the parent to remotely control the device and change settings in real time.

[0097] The control unit can select the optimal control method based on the child's device information during control. For example, if the child is using a smartphone, the control unit will provide the optimal control method for the smartphone. The control unit can analyze the child's device information using a generative AI and select the optimal control method based on that information. For example, the control unit can input a prompt to the generative AI such as "Select the optimal control method based on the child's device information," and the generative AI will analyze the device information based on that instruction and select a control method. This allows for more effective control by selecting the optimal control method based on the device information.

[0098] The recommendation system can estimate a child's emotions and adjust the types of content it recommends based on those emotions. For example, if a child is stressed, the recommendation system will prioritize recommending relaxing content. The recommendation system can use generative AI to estimate a child's emotions and adjust the types of content it recommends based on those emotions. For example, the recommendation system can input a prompt to the generative AI such as "Estimate the child's emotions and adjust the types of content to recommend," and the generative AI will estimate the emotions and adjust the types of content based on that instruction. This allows for the provision of more appropriate content by adjusting the types of content recommended according to the child's emotions.

[0099] The recommendation system can recommend the most suitable learning materials by referring to the child's learning progress. For example, if the child's learning progress is good, the recommendation system will recommend learning materials to move on to the next step. The recommendation system can use generative AI to analyze the child's learning progress and recommend the most suitable learning materials based on that information. For example, the recommendation system can input a prompt to the generative AI such as "refer to the child's learning progress and recommend the most suitable learning materials," and the generative AI will analyze the learning progress based on that instruction and recommend learning materials. This allows for more effective learning by recommending the most suitable learning materials according to the child's learning progress.

[0100] The recommendation system can improve the accuracy of recommendations by analyzing a child's past learning history. For example, the recommendation system recommends the most suitable learning materials based on a child's past learning history. The recommendation system can use generative AI to analyze a child's past learning history and improve the accuracy of recommendations based on that information. For example, the recommendation system can input a prompt to the generative AI such as "Analyze the child's past learning history to improve the accuracy of recommendations," and the generative AI will analyze the learning history based on that instruction to improve the accuracy of recommendations. In this way, the accuracy of recommendations is improved by analyzing past learning history.

[0101] The recommendation system can estimate a child's emotions and adjust how recommended content is displayed based on those emotions. For example, if a child is stressed, the recommendation system can provide a simple and highly visible display method. The recommendation system can use generative AI to estimate a child's emotions and adjust how recommended content is displayed based on those emotions. For example, the recommendation system can input a prompt to the generative AI such as "Estimate the child's emotions and adjust how recommended content is displayed," and the generative AI will estimate the emotions and adjust the display method based on that instruction. This allows for more appropriate display by adjusting how recommended content is displayed according to the child's emotions.

[0102] The recommendation department can suggest new hobbies and activities based on a child's interests during the recommendation process. For example, if a child is interested in science, the recommendation department might suggest a science experiment kit. The recommendation department can use generative AI to analyze a child's interests and suggest new hobbies and activities based on that information. For example, the recommendation department can input the prompt "Suggest new hobbies and activities based on the child's interests" into the generative AI, which then analyzes the child's interests and suggests hobbies and activities based on that instruction. In this way, the recommendation department can support a child's growth by suggesting new hobbies and activities based on their interests.

[0103] The recommendation system can recommend region-specific learning materials based on the child's geographical location. For example, if a child is in a specific region, the recommendation system will recommend region-specific learning materials. The recommendation system can use generative AI to analyze the child's geographical location and recommend region-specific learning materials based on that information. For example, the recommendation system can input a prompt to the generative AI such as "Recommend region-specific learning materials based on the child's geographical location," and the generative AI will analyze the geographical location based on that instruction and recommend learning materials. This allows for more appropriate learning by recommending region-specific learning materials based on geographical location.

[0104] The maintenance unit can estimate the child's emotions and adjust the frequency of maintenance based on those estimates. For example, if the child is stressed, the maintenance unit will reduce the frequency of maintenance to provide a more relaxing environment. The maintenance unit can use generative AI to estimate the child's emotions and adjust the frequency of maintenance based on those emotions. For example, the maintenance unit can input a prompt to the generative AI such as "Estimate the child's emotions and adjust the frequency of maintenance," and the generative AI will estimate the emotions and adjust the frequency of maintenance based on that instruction. This allows for a more appropriate environment to be provided by adjusting the frequency of maintenance according to the child's emotions.

[0105] The maintenance department can optimize its maintenance algorithms by referring to past security incidents during maintenance. For example, the maintenance department optimizes its maintenance algorithms based on past security incidents. The maintenance department can use generative AI to analyze past security incidents and optimize its maintenance algorithms based on that information. For example, the maintenance department can input a prompt to the generative AI such as "Optimize the maintenance algorithm by referring to past security incidents," and the generative AI will analyze security incidents based on that instruction and optimize the maintenance algorithm. In this way, the maintenance algorithm can be optimized by referring to past security incidents.

[0106] The maintenance unit can estimate a child's emotions and determine maintenance priorities based on those estimates. For example, if a child is stressed, the maintenance unit will prioritize providing a relaxing environment. The maintenance unit can use generative AI to estimate a child's emotions and determine maintenance priorities based on those emotions. For example, the maintenance unit can input a prompt to the generative AI such as "Estimate the child's emotions and determine maintenance priorities," and the generative AI will estimate the emotions and determine priorities based on that instruction. This allows for the provision of a more appropriate environment by determining maintenance priorities according to the child's emotions.

[0107] The maintenance unit can select the optimal maintenance method based on the child's device information during maintenance. For example, if the child is using a smartphone, the maintenance unit will provide the optimal maintenance method for the smartphone. The maintenance unit can use a generative AI to analyze the child's device information and select the optimal maintenance method based on that information. For example, the maintenance unit can input a prompt to the generative AI such as "Select the optimal maintenance method based on the child's device information," and the generative AI will analyze the device information based on that instruction and select a maintenance method. This allows for more effective maintenance by selecting the optimal maintenance method based on device information.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The filtering unit can assess the safety of content in real time while a child is using their smartphone and immediately block dangerous content. For example, the filtering unit uses generated AI to instantly analyze the content of websites and apps that a child is trying to access and immediately block them if they contain dangerous elements. The filtering unit can also pre-evaluate the safety of apps that a child is trying to install and prevent the installation of dangerous apps. Furthermore, the filtering unit can analyze the content of messages and emails that a child receives and display a warning if they contain inappropriate content. This prevents children from being exposed to dangerous content and provides a safe online environment.

[0110] The monitoring unit can notify parents in real time about their child's smartphone usage. For example, if the monitoring unit detects that the child is using a specific app, it will notify the parent's smartphone in real time. It can also notify parents if the child accesses a specific website. Furthermore, the monitoring unit can report to parents in real time the time of day and frequency of smartphone use. This allows parents to understand their child's smartphone usage in real time and take appropriate action.

[0111] The control unit can adjust usage restrictions in real time when a child is using a smartphone. For example, if a child is using a particular app for an extended period, the control unit can limit the usage time of that app. It can also restrict smartphone use during specific time periods if a child is using the smartphone during those times. Furthermore, if a child is in a specific location, the control unit can restrict smartphone use in that location. This allows for proper management of children's smartphone use and promotes healthy usage habits.

[0112] The recommendation system can recommend educational content in real time as children use their smartphones. For example, if a child is using a specific app, the recommendation system will recommend educational content related to that app. It can also recommend educational content related to a specific website if the child is accessing that website. Furthermore, if a child is using their smartphone at a particular time of day, the recommendation system can recommend educational content appropriate for that time. This ensures that children are always exposed to educational content when using their smartphones.

[0113] The maintenance department can perform security updates in real time while a child is using their smartphone. For example, if a new security threat is detected while the child is using the smartphone, the maintenance department will immediately perform a security update. Furthermore, if the child is using a specific app, the maintenance department can monitor the security status of that app in real time and perform updates as needed. In addition, if the child is accessing a specific website, the maintenance department can monitor the security status of that website in real time and block access if necessary. This ensures that the child's smartphone is always protected with the latest security measures and can be used safely.

[0114] The filtering unit can estimate a child's emotions and adjust the filtering strictness based on those estimates. For example, if a child is stressed, the filtering strictness can be reduced, prioritizing the display of relaxing content. Conversely, if a child is agitated, the filtering strictness can be increased, prioritizing the display of calming content. Furthermore, if a child is tired, the filtering strictness can be adjusted to display relaxing content. This allows for the provision of more appropriate content by adjusting the filtering strictness according to the child's emotions.

[0115] The monitoring unit can estimate the child's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the child is stressed, the monitoring frequency can be reduced to provide a relaxing environment. Conversely, if the child is excited, the monitoring frequency can be increased to maintain appropriate usage. Furthermore, if the child is tired, the monitoring frequency can be adjusted to provide a relaxing environment. This allows for more appropriate monitoring by adjusting the monitoring frequency according to the child's emotions.

[0116] The control unit can estimate the child's emotions and adjust the strictness of control based on those emotions. For example, if the child is stressed, the strictness of control can be reduced to provide a relaxing environment. Conversely, if the child is agitated, the strictness of control can be increased to provide a calm environment. Furthermore, if the child is tired, the strictness of control can be adjusted to provide a relaxing environment. In this way, a more appropriate environment can be provided by adjusting the strictness of control according to the child's emotions.

[0117] The recommendation system can estimate a child's emotions and adjust the types of content recommended based on those estimates. For example, if a child is stressed, it can prioritize recommending relaxing content. Similarly, if a child is excited, it can prioritize recommending calming content. Furthermore, if a child is tired, it can recommend relaxing content. This allows for the provision of more appropriate content by adjusting recommendations according to the child's emotions.

[0118] The maintenance unit can estimate the child's emotions and adjust the frequency of maintenance based on those estimates. For example, if the child is stressed, the frequency of maintenance can be reduced to provide a relaxing environment. Conversely, if the child is agitated, the frequency of maintenance can be increased to maintain appropriate usage. Furthermore, if the child is tired, the frequency of maintenance can be adjusted to provide a relaxing environment. In this way, a more appropriate environment can be provided by adjusting the frequency of maintenance according to the child's emotions.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The filtering unit filters and blocks educationally inappropriate content. For example, it automatically blocks sites containing violent content or inappropriate advertisements. The filtering unit can analyze internet content in real time using generative AI to detect and block inappropriate content. Step 2: The monitoring unit provides tools for parents to monitor their children's smartphone usage. For example, parents can check their child's smartphone usage history. The monitoring unit can analyze the child's smartphone usage history using generated AI and report the usage status to the parents. Step 3: The control unit remotely controls the device based on information monitored by the monitoring unit. For example, it can limit the usage time of specific apps. The control unit can use generated AI to issue instructions to allow parents to remotely control their child's smartphone. Step 4: The recommendation team analyzes the child's smartphone usage history and recommends educational content. For example, it can recommend learning materials that match the child's interests. The recommendation team uses generative AI to analyze the child's smartphone usage history and recommend educational content based on their personality and interests. Step 5: The maintenance department performs security updates and maintenance. For example, they can always provide the latest security measures. The maintenance department can use generative AI to automatically perform system security updates and maintenance.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the filtering unit, monitoring unit, control unit, recommendation unit, and maintenance unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the filtering unit is implemented by the control unit 46A of the smart device 14, which uses generated AI to analyze content on the internet in real time and detects and blocks inappropriate content. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12, which allows parents to check their child's smartphone usage history and report on usage. The control unit is implemented by the control unit 46A of the smart device 14, which executes instructions for parents to remotely control their child's smartphone. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the child's smartphone usage history and recommends educational content based on their personality and interests. The maintenance unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically performs security updates and maintenance of the system. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the filtering unit, monitoring unit, control unit, recommendation unit, and maintenance unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the filtering unit is implemented by the control unit 46A of the smart glasses 214, which uses generated AI to analyze internet content in real time and detect and block inappropriate content. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12, which allows parents to check their child's smartphone usage history and report on usage. The control unit is implemented by the control unit 46A of the smart glasses 214, which executes instructions for parents to remotely control their child's smartphone. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the child's smartphone usage history and recommends educational content based on their personality and interests. The maintenance unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically performs security updates and maintenance of the system. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[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 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.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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 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.

[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 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.

[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 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.

[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 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.

[0156] Each of the multiple elements described above, including the filtering unit, monitoring unit, control unit, recommendation unit, and maintenance unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the filtering unit is implemented by the control unit 46A of the headset terminal 314, which uses generated AI to analyze internet content in real time and detect and block inappropriate content. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12, which allows parents to check their child's smartphone usage history and report on usage. The control unit is implemented by the control unit 46A of the headset terminal 314, which executes instructions for parents to remotely control their child's smartphone. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the child's smartphone usage history and recommends educational content based on their personality and interests. The maintenance unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically performs security updates and maintenance of the system. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[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 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.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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 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).

[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] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the filtering unit, monitoring unit, control unit, recommendation unit, and maintenance unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the filtering unit is implemented by the control unit 46A of the robot 414, which uses generated AI to analyze internet content in real time and detect and block inappropriate content. The monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12, which allows parents to check their child's smartphone usage history and report on usage. The control unit is implemented by the control unit 46A of the robot 414, which executes instructions for parents to remotely control their child's smartphone. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the child's smartphone usage history and recommends educational content based on their personality and interests. The maintenance unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically performs security updates and maintenance of the system. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) A filtering unit that filters and blocks educationally inappropriate content, A monitoring unit where parents can monitor their children's smartphone usage, A control unit that remotely controls based on information monitored by the monitoring unit, A recommendation department analyzes children's smartphone usage history and recommends educational content, It includes a maintenance department that performs security updates and maintenance. A system characterized by the following features. (Note 2) The filtering unit is Automatically blocks sites containing violent content or inappropriate advertisements. The system described in Appendix 1, characterized by the features described herein. (Note 3) The monitoring unit, Provides a tool for parents to check their children's smartphone usage history. The system described in Appendix 1, characterized by the features described herein. (Note 4) The control unit, Limit the usage time of specific apps. The system described in Appendix 1, characterized by the features described herein. (Note 5) The control unit, Prohibit the use of smartphones during specific time periods. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned recommendation department, Recommend learning materials that match the child's interests. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned maintenance unit is We always provide the latest security measures. The system described in Appendix 1, characterized by the features described herein. (Note 8) The filtering unit is The system estimates the child's emotions and adjusts the filtering strictness based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The filtering unit is When filtering, customize the filtering criteria according to the child's age and grade level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The filtering unit is When filtering, we refer to the child's past browsing history to improve the accuracy of the filtering. The system described in Appendix 1, characterized by the features described herein. (Note 11) The filtering unit is It estimates the child's emotions and adjusts how the filtering results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The filtering unit is During filtering, region-specific inappropriate content is blocked based on the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The filtering unit is During filtering, the system analyzes children's social media activity and blocks relevant inappropriate content. The system described in Appendix 1, characterized by the features described herein. (Note 14) The monitoring unit, The system estimates the child's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The monitoring unit, During monitoring, the system analyzes the child's smartphone usage history in real time and detects abnormal usage patterns. The system described in Appendix 1, characterized by the features described herein. (Note 16) The monitoring unit, During monitoring, adjust the focus of monitoring based on the child's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 17) The monitoring unit, The system estimates the child's emotions and adjusts how the monitoring results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The monitoring unit, During monitoring, a notification is sent to the parent's smartphone, reporting the child's usage in real time. The system described in Appendix 1, characterized by the features described herein. (Note 19) The monitoring unit, During monitoring, the optimal monitoring method is selected based on the child's device information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The control unit, It estimates the child's emotions and adjusts the level of control based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The control unit, When controlling the device, set smartphone usage restrictions based on the child's learning schedule. The system described in Appendix 1, characterized by the features described herein. (Note 22) The control unit, During control, the optimal control method is selected by referring to the child's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The control unit, It estimates the child's emotions and determines the priority of control based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The control unit, During control, the parent can remotely control the device from their smartphone and change settings in real time. The system described in Appendix 1, characterized by the features described herein. (Note 25) The control unit, During control, the optimal control method is selected based on the child's device information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned recommendation department, It estimates the child's emotions and adjusts the types of content recommended based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned recommendation department, When making recommendations, the system will refer to the child's learning progress to suggest the most suitable learning materials. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned recommendation department, When making recommendations, we analyze the child's past learning history to improve the accuracy of the recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned recommendation department, It estimates a child's emotions and adjusts how recommended content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned recommendation department, When making a recommendation, suggest new hobbies and activities based on the child's interests. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned recommendation department, When making recommendations, the system will recommend learning materials specific to the child's region based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned maintenance unit is The system estimates the child's emotions and adjusts the frequency of maintenance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned maintenance unit is During maintenance, the maintenance algorithm is optimized by referring to past security incidents. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned maintenance unit is Estimate the child's emotions and determine the priority of conservatism based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned maintenance unit is During maintenance, the optimal maintenance method is selected based on the child's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 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. A filtering unit that filters and blocks educationally inappropriate content, A monitoring unit where parents can monitor their children's smartphone usage, A control unit that remotely controls based on information monitored by the monitoring unit, A recommendation department analyzes children's smartphone usage history and recommends educational content, It includes a maintenance department that performs security updates and maintenance. A system characterized by the following features.

2. The filtering unit is Automatically blocks sites containing violent content or inappropriate advertisements. The system according to feature 1.

3. The monitoring unit, Provides a tool for parents to check their children's smartphone usage history. The system according to feature 1.

4. The control unit, Limit the usage time of specific apps. The system according to feature 1.

5. The control unit, Prohibit the use of smartphones during specific time periods. The system according to feature 1.

6. The aforementioned recommendation department, Recommend learning materials that match the child's interests. The system according to feature 1.

7. The aforementioned maintenance unit is We always provide the latest security measures. The system according to feature 1.

8. The filtering unit is The system estimates the child's emotions and adjusts the filtering strictness based on the estimated emotions. The system according to feature 1.

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