system

Abstract

The system according to this embodiment aims to provide an environment in which children can learn efficiently while safely using the internet. [Solution] The system according to the embodiment comprises a filtering unit, a curriculum generation unit, a progress analysis unit, and a safety management unit. The filtering unit organizes the search results. The curriculum generation unit generates an optimal curriculum based on the search results organized by the filtering unit. The progress analysis unit analyzes learning progress in real time based on the curriculum generated by the curriculum generation unit. The safety management unit safely manages online activities based on the progress status analyzed by the progress analysis unit.

Description

Technical Field

[0006] ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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, an environment for children to learn efficiently while using the Internet safely has not been fully provided, and there is room for improvement.Related to the system according to the embodiment aims to provide an environment for children to learn efficiently while using the Internet safely.

[0005] The system according to the embodiment aims to provide an environment for children to learn efficiently while using the Internet safely.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a filtering unit, a curriculum generation unit, a progress analysis unit, and a safety management unit. The filtering unit organizes the search results. The curriculum generation unit generates an optimal curriculum based on the search results organized by the filtering unit. The progress analysis unit analyzes learning progress in real time based on the curriculum generated by the curriculum generation unit. The safety management unit safely manages online activities based on the progress status analyzed by the progress analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an environment in which children can learn efficiently while safely using the internet. [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 numbered 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 Internet support system for children according to an embodiment of the present invention is a mechanism that utilizes generative AI to provide an environment in which children can safely surf the internet and an environment in which they can concentrate and efficiently engage in learning. The Internet support system for children uses generative AI to create an environment in which children can safely collect information via the internet. Next, it uses generative AI to provide an optimal curriculum for each child. Furthermore, it uses generative AI to analyze the child's learning progress in real time and provide appropriate advice. It also uses generative AI to safely manage the child's online activities. This mechanism allows children to use the internet with peace of mind and engage in learning efficiently. Furthermore, it enables them to balance study time and private time and avoid taking in harmful information. For example, a filtering unit is provided that uses generative AI to organize search results. The filtering unit displays appropriate educational content based on the keywords searched by the child and eliminates inappropriate information. Next, a curriculum generation unit is provided that provides an optimal curriculum for each child. The curriculum generation unit analyzes the child's learning data and automatically generates individually optimized curricula and teaching materials. Furthermore, a progress analysis unit is provided that analyzes the child's learning progress in real time and provides appropriate advice. The progress analysis department analyzes students' learning data in real time and provides advice tailored to their progress. Finally, a safety management department is established to securely manage students' online activities. The safety management department monitors the websites and applications students access and prevents access to inappropriate content. As a result, the internet support system for children can provide an environment where students can use the internet with peace of mind and engage in learning efficiently.

[0029] The Internet support system for children according to this embodiment comprises a filtering unit, a curriculum generation unit, a progress analysis unit, and a safety management unit. The filtering unit organizes search results. The filtering unit, for example, uses a generating AI to display appropriate educational content based on keywords searched by children and excludes inappropriate information. The filtering unit, for example, uses a generating AI to analyze search results and prioritizes the display of content with high educational value. The filtering unit can also automatically detect and exclude inappropriate information using a generating AI. For example, the filtering unit uses a generating AI to detect violent content and adult content and exclude them from search results. The curriculum generation unit generates an optimal curriculum based on the search results organized by the filtering unit. The curriculum generation unit, for example, uses a generating AI to analyze children's learning data and automatically generates individually optimized curricula and teaching materials. The curriculum generation unit, for example, uses a generating AI to analyze children's learning styles and progress and provides an optimal learning plan. The curriculum generation unit can also use a generating AI to generate curricula that correspond to children's learning goals. For example, the curriculum generation unit uses a generating AI to analyze students' learning objectives and select the most appropriate teaching materials based on that analysis. The progress analysis unit analyzes learning progress in real time based on the curriculum generated by the curriculum generation unit. The progress analysis unit, for example, uses a generating AI to analyze students' learning data in real time and provides advice tailored to their progress. The progress analysis unit, for example, uses a generating AI to analyze students' learning data and provides advice to maximize the effectiveness of their learning. The progress analysis unit can also use a generating AI to visualize students' learning progress and provide feedback to teachers and parents. For example, the progress analysis unit uses a generating AI to analyze students' learning data and display it as graphs and charts. The safety management unit securely manages online activities based on the progress analyzed by the progress analysis unit. The safety management unit, for example, uses a generating AI to monitor websites and applications that students access and prevent access to inappropriate content.The safety management department, for example, uses a generating AI to monitor children's online activities and automatically block inappropriate content. The safety management department can also use the generating AI to record children's online activities and report them to parents. For instance, the safety management department can use the generating AI to analyze children's online activities and report them to parents via email or an app. This allows the internet usage support system for children according to this embodiment to provide an environment where children can use the internet safely and engage in learning efficiently.

[0030] The filtering unit organizes search results. For example, it uses generative AI to display appropriate educational content based on keywords searched by children and eliminates inappropriate information. Specifically, the generative AI uses natural language processing technology to analyze search keywords and understand their meaning. Next, the generative AI extracts relevant educational content from a vast database on the internet and prioritizes displaying content with high educational value. For example, if a child searches for "planets of the solar system," the generative AI displays reliable websites and videos containing scientific information, eliminating advertisements and inappropriate information. The generative AI also uses machine learning algorithms to learn from past search history and user feedback, improving the accuracy of search results. Furthermore, the filtering unit can also automatically detect and eliminate inappropriate information using generative AI. For example, the generative AI analyzes the text and images of content to detect violent or adult content. This provides a safe environment for children to use the internet. Through these functions, the filtering unit supports children in learning with peace of mind.

[0031] The curriculum generation unit generates an optimal curriculum based on the search results compiled by the filtering unit. For example, the curriculum generation unit uses a generation AI to analyze students' learning data and automatically generate individually optimized curricula and teaching materials. Specifically, the generation AI analyzes students' past learning history and current learning status to provide an optimal learning plan tailored to their learning style and progress. For example, the generation AI identifies subjects that students excel at and subjects they struggle with, and adjusts the learning content accordingly. The generation AI also analyzes students' learning goals and selects the most appropriate teaching materials based on those goals. For example, if a student's goal is to "strengthen their math fundamentals," the generation AI will prioritize providing basic math teaching materials. Furthermore, the curriculum generation unit can use the generation AI to monitor students' learning progress in real time and adjust the curriculum as needed. This allows the curriculum generation unit to provide an optimal environment for students to learn efficiently.

[0032] The progress analysis unit analyzes learning progress in real time based on the curriculum generated by the curriculum generation unit. For example, the progress analysis unit uses a generating AI to analyze students' learning data in real time and provide advice tailored to their progress. Specifically, the generating AI collects students' learning data and provides advice to maximize the effectiveness of their learning. For example, the generating AI analyzes how much time students spend on specific problems and suggests efficient learning methods. The generating AI can also visualize students' learning progress and provide feedback to teachers and parents. For example, the generating AI analyzes students' learning data and displays it as graphs and charts, allowing them to grasp their learning progress at a glance. Furthermore, the progress analysis unit can also use the generating AI to analyze students' learning data over the long term and grasp learning trends and patterns. This allows the progress analysis unit to provide support to help students learn effectively.

[0033] The Safety Management Department securely manages online activities based on progress analyzed by the Progress Analysis Department. For example, the Safety Management Department uses generative AI to monitor websites and applications accessed by children and prevent access to inappropriate content. Specifically, the generative AI analyzes the content of websites and applications and automatically blocks violent or adult content. The generative AI can also record children's online activities and report them to parents. For example, the generative AI analyzes children's online activities and reports them to parents via email or app. Furthermore, the Safety Management Department can use the generative AI to monitor children's online activities in real time and issue immediate warnings if abnormal behavior is detected. This allows the Safety Management Department to provide a safe environment for children to use the internet. Through these functions, the Safety Management Department supports children in learning with peace of mind.

[0034] The filtering unit can display appropriate educational content and eliminate inappropriate information based on keywords searched by children. For example, the filtering unit can analyze search results using generative AI and prioritize displaying content of high educational value. The filtering unit can also automatically detect and eliminate inappropriate information using generative AI. For example, the filtering unit can use generative AI to detect violent content and adult content and exclude it from search results. This allows children to use the internet safely. Appropriate educational content includes, for example, educational value and age appropriateness. Inappropriate information includes, for example, violent content and adult content. Some or all of the above processing in the filtering unit may be performed using generative AI or without generative AI. For example, the filtering unit can use generative AI to analyze search results and prioritize displaying content of high educational value.

[0035] The curriculum generation unit can analyze students' learning data and automatically generate individually optimized curricula and teaching materials. For example, the curriculum generation unit uses a generation AI to analyze students' learning data and automatically generate individually optimized curricula and teaching materials. For example, the generation AI analyzes students' learning styles and progress to provide an optimal learning plan. The curriculum generation unit can also use the generation AI to generate curricula tailored to students' learning objectives. For example, the generation AI analyzes students' learning objectives and selects the most suitable teaching materials based on them. This allows for the provision of an optimal curriculum for each individual student. Individually optimized curricula include, for example, learning style and progress. Automatic generation of teaching materials includes, for example, the type of teaching material and the generation algorithm. Some or all of the above-described processes in the curriculum generation unit may be performed using a generation AI or without one. For example, the curriculum generation unit uses a generation AI to analyze students' learning data and automatically generate individually optimized curricula and teaching materials.

[0036] The progress analysis unit can analyze students' learning data in real time and provide advice tailored to their progress. For example, the progress analysis unit can use a generative AI to analyze students' learning data in real time and provide advice tailored to their progress. For example, the progress analysis unit can use a generative AI to analyze students' learning data and provide advice to maximize learning effectiveness. Furthermore, the progress analysis unit can use a generative AI to visualize students' learning progress and provide feedback to teachers and parents. For example, the progress analysis unit can use a generative AI to analyze students' learning data and display it as graphs or charts. This allows for the provision of appropriate advice tailored to students' learning progress. Advice tailored to progress includes, for example, the content of the advice and the timing of its provision. Some or all of the above-described processes in the progress analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the progress analysis unit can use a generative AI to analyze students' learning data and provide advice tailored to their progress.

[0037] The Security Management Department can monitor websites and applications accessed by children and prevent access to inappropriate content. For example, the Security Management Department may use generative AI to monitor websites and applications accessed by children and prevent access to inappropriate content. For example, the Security Management Department may use generative AI to monitor children's online activities and automatically block inappropriate content. The Security Management Department may also use generative AI to record children's online activities and report them to parents. For example, the Security Management Department may use generative AI to analyze children's online activities and report them to parents via email or app. This allows for the secure management of children's online activities. Inappropriate content includes, for example, filtering criteria and monitoring methods. Some or all of the above processes in the Security Management Department may be performed using generative AI or not. For example, the Security Management Department may use generative AI to monitor children's online activities and automatically block inappropriate content.

[0038] The filtering unit can analyze a child's past search history and select the optimal filtering method. For example, the filtering unit may use generative AI to analyze a child's past search history and select the optimal filtering method. For example, the filtering unit may prioritize displaying highly relevant content based on keywords the child has frequently searched in the past. The filtering unit can also eliminate content the child has avoided in the past and suggest new content that might interest them. Furthermore, the filtering unit can suggest content suitable for a specific time of day based on the child's past search history. This allows the system to provide optimal content based on the child's past search history. The optimal filtering method includes, for example, a filtering algorithm and selection criteria. Some or all of the above processing in the filtering unit may be performed using generative AI or not. For example, the filtering unit may use generative AI to analyze a child's past search history and select the optimal filtering method.

[0039] The filtering unit can filter search results based on the child's current learning status and areas of interest. For example, the filtering unit can use a generative AI to filter search results based on the child's current learning status and areas of interest. For example, the filtering unit can prioritize displaying content related to the subject the child is currently studying. The filtering unit can also suggest content that might interest the child based on their areas of interest. Furthermore, the filtering unit can display content that includes what the child should learn next, according to their learning progress. This allows the system to provide search results that are tailored to the child's learning status and areas of interest. Current learning status includes, for example, learning progress and level of understanding. Areas of interest include, for example, past search history and topics of interest. Some or all of the above processing in the filtering unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the filtering unit may use a generative AI to filter based on the child's current learning status and areas of interest.

[0040] The filtering unit can prioritize displaying highly relevant information based on the child's geographical location when filtering search results. For example, the filtering unit uses a generative AI to prioritize displaying highly relevant information based on the child's geographical location when filtering search results. For example, the filtering unit prioritizes displaying educational content related to the region based on the child's current location. The filtering unit can also display information related to local events and activities based on the child's geographical location. Furthermore, the filtering unit can suggest content related to local culture and history, taking into account the child's geographical location. This allows for the provision of highly relevant information based on the child's geographical location. Geographical location information includes, for example, GPS data and location services. Some or all of the above processing in the filtering unit may be performed using a generative AI or without a generative AI. For example, the filtering unit may use a generative AI to prioritize displaying highly relevant information based on the child's geographical location.

[0041] The filtering unit can analyze a child's social media activity and display relevant information when filtering search results. For example, the filtering unit can use generative AI to analyze a child's social media activity and display relevant information when filtering search results. For example, the filtering unit can display content related to topics that the child has shown interest in on social media. The filtering unit can also suggest new content that might interest the child based on their social media activity. Furthermore, the filtering unit can consider the child's social media friendships and display content shared by their friends. This allows the system to provide relevant information based on the child's social media activity. Social media activity includes, for example, posts and follower information. Some or all of the above processing in the filtering unit may be performed using generative AI or not. For example, the filtering unit may use generative AI to analyze a child's social media activity and display relevant information.

[0042] The curriculum generation unit can adjust the difficulty level of the curriculum based on the students' learning progress during curriculum generation. For example, the curriculum generation unit uses a generation AI to adjust the difficulty level of the curriculum based on the students' learning progress during curriculum generation. For example, if a student's learning progress is fast, the curriculum generation unit can provide a curriculum that includes challenging tasks. If a student's learning progress is slow, the curriculum generation unit can also provide a curriculum that reinforces basic content. Furthermore, the curriculum generation unit can provide a curriculum that includes tasks of appropriate difficulty level according to the student's learning progress. This allows for the provision of a curriculum with a difficulty level that matches the student's learning progress. The difficulty level of the curriculum includes, for example, the complexity of the learning content and the student's progress. Some or all of the above-described processes in the curriculum generation unit may be performed using a generation AI or not. For example, the curriculum generation unit may use a generation AI to analyze the student's learning progress and adjust the difficulty level of the curriculum based on that analysis.

[0043] The curriculum generation unit can apply different curriculum generation algorithms depending on the interests and concerns of the children when generating the curriculum. For example, the curriculum generation unit can use a generation AI to apply different curriculum generation algorithms depending on the interests and concerns of the children when generating the curriculum. For example, if a child is interested in science, the curriculum generation unit can provide a curriculum that includes science-related tasks. Also, if a child is interested in history, the curriculum generation unit can provide a curriculum that includes history-related tasks. Furthermore, the curriculum generation unit can apply an appropriate curriculum generation algorithm depending on the interests and concerns of the children and provide an individually optimized curriculum. This makes it possible to provide a curriculum that is tailored to the interests and concerns of the children. Curriculum generation algorithms include, for example, machine learning algorithms and rule-based algorithms. Some or all of the above-described processes in the curriculum generation unit may be performed using a generation AI or not. For example, the curriculum generation unit may use a generation AI to analyze the children's interests and concerns and generate a curriculum based on that.

[0044] The curriculum generation unit can determine curriculum priorities based on the student's learning history when generating the curriculum. For example, the curriculum generation unit uses a generation AI to determine curriculum priorities based on the student's learning history when generating the curriculum. For example, the curriculum generation unit may prioritize subjects that the student has struggled with in the past. The curriculum generation unit can also postpone subjects that the student excels at and prioritize subjects that the student struggles with. Furthermore, the curriculum generation unit can determine the most effective learning order based on the student's learning history. This allows for the provision of an optimal curriculum based on the student's learning history. Curriculum priorities include, for example, learning objectives and progress. Some or all of the above-described processes in the curriculum generation unit may be performed using a generation AI or not. For example, the curriculum generation unit may use a generation AI to analyze the student's learning history and determine curriculum priorities based on that analysis.

[0045] The curriculum generation unit can adjust the curriculum content by referring to the student's relevant learning resources during curriculum generation. For example, the curriculum generation unit can use a generation AI to adjust the curriculum content by referring to the student's relevant learning resources during curriculum generation. For example, the curriculum generation unit can incorporate relevant new materials into the curriculum based on materials the student has used in the past. The curriculum generation unit can also incorporate relevant assignments into the curriculum by referring to online courses the student has previously participated in. Furthermore, the curriculum generation unit can refer to the student's learning resources and incorporate the most suitable materials and assignments into the curriculum. This allows for the provision of an optimal curriculum based on the student's learning resources. Relevant learning resources include, for example, reference books and online materials. Some or all of the above processing in the curriculum generation unit may be performed using a generation AI or not. For example, the curriculum generation unit may use a generation AI to refer to the student's relevant learning resources and adjust the curriculum content based on that.

[0046] The progress analysis unit can improve the accuracy of its analysis by considering the interrelationships of students' learning data during progress analysis. For example, the progress analysis unit can use a generative AI to improve the accuracy of its analysis by considering the interrelationships of students' learning data during progress analysis. For example, the progress analysis unit can correlate students' learning data to perform a more accurate progress analysis. The progress analysis unit can also perform an analysis to maximize the effectiveness of learning by considering the interrelationships of students' learning data. Furthermore, the progress analysis unit can integrate students' learning data and analyze overall learning progress. This makes it possible to perform a more accurate progress analysis by considering the interrelationships of students' learning data. Interrelationships of learning data include, for example, data relationships and correlation analysis. Some or all of the above processing in the progress analysis unit may be performed using a generative AI or not. For example, the progress analysis unit may use a generative AI to analyze the interrelationships of students' learning data and improve the accuracy of the progress analysis based on that.

[0047] The progress analysis unit can perform progress analysis while considering the child's learning history. For example, the progress analysis unit can use a generative AI to perform progress analysis while considering the child's learning history. For example, the progress analysis unit can analyze the child's current progress based on the child's past learning history. The progress analysis unit can also perform analysis to maximize the effectiveness of learning by considering the child's learning history. Furthermore, the progress analysis unit can perform analysis to propose the optimal learning method based on the child's learning history. This makes it possible to perform more effective progress analysis by considering the child's learning history. Learning history includes, for example, past learning data and progress records. Some or all of the above processing in the progress analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the progress analysis unit may have a generative AI analyze the child's learning history and analyze the progress based on that.

[0048] The progress analysis unit can perform progress analysis while considering the geographical distribution of students. For example, the progress analysis unit can use a generative AI to perform progress analysis while considering the geographical distribution of students. For example, the progress analysis unit can analyze learning progress by region based on the geographical distribution of students. The progress analysis unit can also perform analysis to maximize the learning effect in each region, taking into account the geographical distribution of students. Furthermore, the progress analysis unit can perform analysis to propose learning methods for each region based on the geographical distribution of students. This makes it possible to perform progress analysis based on the geographical distribution of students. Geographical distribution includes, for example, regional data and geographic information systems. Some or all of the above processing in the progress analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the progress analysis unit may have a generative AI analyze the geographical distribution of students and analyze progress based on that.

[0049] The progress analysis unit can improve the accuracy of its analysis by referring to relevant literature for the child during the progress analysis. For example, the progress analysis unit can improve the accuracy of its analysis by referring to relevant literature for the child during the progress analysis using a generative AI. For example, the progress analysis unit can improve the accuracy of its progress analysis by referring to literature related to the child's learning content. The progress analysis unit can also improve the accuracy of its progress analysis by referring to the latest research results related to the child's learning content. Furthermore, the progress analysis unit can improve the accuracy of its progress analysis by referring to past research results related to the child's learning content. In this way, the accuracy of the progress analysis is improved by referring to relevant literature. Relevant literature includes, for example, academic papers and reference books. Some or all of the above processing in the progress analysis unit may be performed using a generative AI or not. For example, the progress analysis unit may have a generative AI refer to relevant literature for the child and analyze the progress based on that.

[0050] The Safety Management Department can improve the accuracy of its safety management by considering the interrelationships of children's online activities. For example, the Safety Management Department can use generative AI to improve the accuracy of its safety management by considering the interrelationships of children's online activities. For example, the Safety Management Department can correlate children's online activities to perform more accurate safety management. The Safety Management Department can also prevent access to inappropriate content by considering the interrelationships of children's online activities. Furthermore, the Safety Management Department can integrate children's online activities to perform overall safety management. This enables more accurate safety management by considering the interrelationships of children's online activities. The interrelationships of online activities include, for example, activity logs and correlation analysis. Some or all of the above processes in the Safety Management Department may be performed using generative AI or not. For example, the Safety Management Department may use generative AI to analyze the interrelationships of children's online activities and improve the accuracy of safety management based on that.

[0051] The Security Management Department can manage security while considering the child's access history. For example, the Security Management Department can use generative AI to manage security while considering the child's access history. For example, the Security Management Department can prevent access to inappropriate content based on the child's past access history. The Security Management Department can also provide a safe online environment while considering the child's access history. Furthermore, the Security Management Department can propose the optimal security management method based on the child's access history. This makes more effective security management possible by considering the child's access history. Access history includes, for example, website visit history and application usage history. Some or all of the above processes in the Security Management Department may be performed using generative AI or not. For example, the Security Management Department may use generative AI to analyze the child's access history and perform security management based on that.

[0052] The Safety Management Department can manage safety while considering the geographical distribution of children. For example, the Safety Management Department can use generative AI to manage safety while considering the geographical distribution of children. For example, the Safety Management Department can perform safety management for each region based on the geographical distribution of children. The Safety Management Department can also prevent access to inappropriate content in each region, taking into account the geographical distribution of children. Furthermore, the Safety Management Department can propose the optimal safety management method for each region based on the geographical distribution of children. This makes safety management based on the geographical distribution of children possible. Geographical distribution includes, for example, regional data and geographic information systems. Some or all of the above processing in the Safety Management Department may be performed using generative AI or not. For example, the Safety Management Department may use generative AI to analyze the geographical distribution of children and perform safety management based on that.

[0053] The Safety Management Department can improve the accuracy of safety management by referring to relevant literature on children during safety management. For example, the Safety Management Department can use generative AI to improve the accuracy of safety management by referring to relevant literature on children during safety management. For example, the Safety Management Department can improve the accuracy of safety management by referring to literature related to children's online activities. Furthermore, the Safety Management Department can also improve the accuracy of safety management by referring to the latest research findings related to children's online activities. In addition, the Safety Management Department can improve the accuracy of safety management by referring to past research findings related to children's online activities. Thus, the accuracy of safety management is improved by referring to relevant literature. Relevant literature includes, for example, academic papers and reference books. Some or all of the above processing in the Safety Management Department may be performed using generative AI, or without generative AI. For example, the Safety Management Department may have a generative AI refer to relevant literature on children and perform safety management based on that.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The internet usage support system for children may further include a motivational unit to enhance children's motivation to learn. The motivational unit, for example, uses generative AI to analyze children's learning data and provides a reward system to increase their motivation. For example, the motivational unit may award badges or points when children achieve specific learning goals. The motivational unit can also provide appropriate rewards according to the children's learning progress. Furthermore, the motivational unit may incorporate game elements related to the learning content to enhance children's motivation. This can increase children's motivation and sustain their interest in learning. The reward system may include, for example, badges, points, or perks. Some or all of the above-described processes in the motivational unit may be performed using generative AI or without it. For example, the motivational unit may use generative AI to analyze children's learning data and provide a reward system based on that analysis.

[0056] The internet usage support system for children may further include an environment adjustment unit to optimize the child's learning environment. The environment adjustment unit, for example, uses generative AI to analyze the child's learning environment and provide an optimal learning environment. The environment adjustment unit may, for example, suggest appropriate lighting and music based on the child's learning data. Furthermore, the environment adjustment unit can customize the learning environment according to the child's learning style. In addition, the environment adjustment unit can adjust the temperature and humidity of the learning environment to improve the child's learning efficiency. This optimizes the child's learning environment and improves learning efficiency. Adjustments to the learning environment include, for example, lighting, music, temperature, and humidity. Some or all of the above-described processes in the environment adjustment unit may be performed using generative AI, or without generative AI. For example, the environment adjustment unit may use generative AI to analyze the child's learning environment and provide an optimal learning environment based on that analysis.

[0057] The internet usage support system for children may further include an evaluation unit for evaluating children's learning outcomes. The evaluation unit may, for example, use a generative AI to analyze children's learning data and evaluate their learning outcomes. The evaluation unit may, for example, make appropriate evaluations based on children's learning progress and level of understanding. The evaluation unit may also visualize children's learning outcomes and display them as graphs or charts. Furthermore, the evaluation unit may set the next learning goals based on children's learning outcomes. This allows for appropriate evaluation of children's learning outcomes and clarifies the next learning steps. Evaluation of learning outcomes may include, for example, progress, level of understanding, and learning goals. Some or all of the above-described processes in the evaluation unit may be performed using a generative AI or not. For example, the evaluation unit may have a generative AI analyze children's learning data and evaluate learning outcomes based on that analysis.

[0058] The internet usage support system for children may further include a habit formation unit for shaping children's learning habits. The habit formation unit, for example, uses a generative AI to analyze children's learning data and provides advice for forming learning habits. The habit formation unit can, for example, send reminders to children to study for a certain amount of time each day. It can also suggest an appropriate learning schedule based on the child's learning progress. Furthermore, the habit formation unit can visualize children's learning habits and display their achievement levels as graphs or charts. This helps to form children's learning habits and promote sustained learning. Habit formation includes, for example, reminders, learning schedules, and visualization of achievement levels. Some or all of the above-described processes in the habit formation unit may be performed using a generative AI or without one. For example, the habit formation unit may use a generative AI to analyze children's learning data and provide advice for forming learning habits based on that analysis.

[0059] The internet usage support system for children may further include a content diversification unit to diversify the learning content of children. The content diversification unit may, for example, use a generative AI to analyze children's learning data and provide diverse learning content. The content diversification unit may, for example, suggest different formats of learning content based on children's interests and concerns. The content diversification unit may also provide diverse content such as videos, audio, and text according to children's learning styles. Furthermore, the content diversification unit may suggest new learning content according to children's learning progress. This diversifies the learning content of children and helps maintain their interest in learning. Diversification of learning content includes, for example, videos, audio, text, and interactive content. Some or all of the above processing in the content diversification unit may be performed using a generative AI or not. For example, the content diversification unit may use a generative AI to analyze children's learning data and provide diverse learning content based on that analysis.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The filtering unit organizes the search results. For example, it uses a generative AI to display appropriate educational content based on the keywords searched by the child and eliminates inappropriate information. The generative AI analyzes the search results, prioritizing the display of content with high educational value and detecting and excluding violent or adult content. Step 2: The curriculum generation unit generates the optimal curriculum based on the search results compiled by the filtering unit. For example, it uses a generation AI to analyze students' learning data and automatically generates individually optimized curricula and teaching materials. The generation AI analyzes students' learning styles and progress, provides the optimal learning plan, and generates a curriculum that matches their learning objectives. Step 3: The progress analysis unit analyzes learning progress in real time based on the curriculum generated by the curriculum generation unit. For example, it uses a generation AI to analyze students' learning data in real time and provides advice according to their progress. The generation AI analyzes students' learning data, provides advice to maximize learning effectiveness, visualizes learning progress, and provides feedback to teachers and parents. Step 4: The Safety Management Department securely manages online activities based on the progress analyzed by the Progress Analysis Department. For example, it uses generative AI to monitor websites and applications accessed by children and prevent access to inappropriate content. The generative AI monitors children's online activities, automatically blocks inappropriate content, records online activities, and reports them to parents.

[0062] (Example of form 2) The Internet support system for children according to an embodiment of the present invention is a mechanism that utilizes generative AI to provide an environment in which children can safely surf the internet and an environment in which they can concentrate and efficiently engage in learning. The Internet support system for children uses generative AI to create an environment in which children can safely collect information via the internet. Next, it uses generative AI to provide an optimal curriculum for each child. Furthermore, it uses generative AI to analyze the child's learning progress in real time and provide appropriate advice. It also uses generative AI to safely manage the child's online activities. This mechanism allows children to use the internet with peace of mind and engage in learning efficiently. Furthermore, it enables them to balance study time and private time and avoid taking in harmful information. For example, a filtering unit is provided that uses generative AI to organize search results. The filtering unit displays appropriate educational content based on the keywords searched by the child and eliminates inappropriate information. Next, a curriculum generation unit is provided that provides an optimal curriculum for each child. The curriculum generation unit analyzes the child's learning data and automatically generates individually optimized curricula and teaching materials. Furthermore, a progress analysis unit is provided that analyzes the child's learning progress in real time and provides appropriate advice. The progress analysis department analyzes students' learning data in real time and provides advice tailored to their progress. Finally, a safety management department is established to securely manage students' online activities. The safety management department monitors the websites and applications students access and prevents access to inappropriate content. As a result, the internet support system for children can provide an environment where students can use the internet with peace of mind and engage in learning efficiently.

[0063] The Internet support system for children according to this embodiment comprises a filtering unit, a curriculum generation unit, a progress analysis unit, and a safety management unit. The filtering unit organizes search results. The filtering unit, for example, uses a generating AI to display appropriate educational content based on keywords searched by children and excludes inappropriate information. The filtering unit, for example, uses a generating AI to analyze search results and prioritizes the display of content with high educational value. The filtering unit can also automatically detect and exclude inappropriate information using a generating AI. For example, the filtering unit uses a generating AI to detect violent content and adult content and exclude them from search results. The curriculum generation unit generates an optimal curriculum based on the search results organized by the filtering unit. The curriculum generation unit, for example, uses a generating AI to analyze children's learning data and automatically generates individually optimized curricula and teaching materials. The curriculum generation unit, for example, uses a generating AI to analyze children's learning styles and progress and provides an optimal learning plan. The curriculum generation unit can also use a generating AI to generate curricula that correspond to children's learning goals. For example, the curriculum generation unit uses a generating AI to analyze students' learning objectives and select the most appropriate teaching materials based on that analysis. The progress analysis unit analyzes learning progress in real time based on the curriculum generated by the curriculum generation unit. The progress analysis unit, for example, uses a generating AI to analyze students' learning data in real time and provides advice tailored to their progress. The progress analysis unit, for example, uses a generating AI to analyze students' learning data and provides advice to maximize the effectiveness of their learning. The progress analysis unit can also use a generating AI to visualize students' learning progress and provide feedback to teachers and parents. For example, the progress analysis unit uses a generating AI to analyze students' learning data and display it as graphs and charts. The safety management unit securely manages online activities based on the progress analyzed by the progress analysis unit. The safety management unit, for example, uses a generating AI to monitor websites and applications that students access and prevent access to inappropriate content.The safety management department, for example, uses a generating AI to monitor children's online activities and automatically block inappropriate content. The safety management department can also use the generating AI to record children's online activities and report them to parents. For instance, the safety management department can use the generating AI to analyze children's online activities and report them to parents via email or an app. This allows the internet usage support system for children according to this embodiment to provide an environment where children can use the internet safely and engage in learning efficiently.

[0064] The filtering unit organizes search results. For example, it uses generative AI to display appropriate educational content based on keywords searched by children and eliminates inappropriate information. Specifically, the generative AI uses natural language processing technology to analyze search keywords and understand their meaning. Next, the generative AI extracts relevant educational content from a vast database on the internet and prioritizes displaying content with high educational value. For example, if a child searches for "planets of the solar system," the generative AI displays reliable websites and videos containing scientific information, eliminating advertisements and inappropriate information. The generative AI also uses machine learning algorithms to learn from past search history and user feedback, improving the accuracy of search results. Furthermore, the filtering unit can also automatically detect and eliminate inappropriate information using generative AI. For example, the generative AI analyzes the text and images of content to detect violent or adult content. This provides a safe environment for children to use the internet. Through these functions, the filtering unit supports children in learning with peace of mind.

[0065] The curriculum generation unit generates an optimal curriculum based on the search results compiled by the filtering unit. For example, the curriculum generation unit uses a generation AI to analyze students' learning data and automatically generate individually optimized curricula and teaching materials. Specifically, the generation AI analyzes students' past learning history and current learning status to provide an optimal learning plan tailored to their learning style and progress. For example, the generation AI identifies subjects that students excel at and subjects they struggle with, and adjusts the learning content accordingly. The generation AI also analyzes students' learning goals and selects the most appropriate teaching materials based on those goals. For example, if a student's goal is to "strengthen their math fundamentals," the generation AI will prioritize providing basic math teaching materials. Furthermore, the curriculum generation unit can use the generation AI to monitor students' learning progress in real time and adjust the curriculum as needed. This allows the curriculum generation unit to provide an optimal environment for students to learn efficiently.

[0066] The progress analysis unit analyzes learning progress in real time based on the curriculum generated by the curriculum generation unit. For example, the progress analysis unit uses a generating AI to analyze students' learning data in real time and provide advice tailored to their progress. Specifically, the generating AI collects students' learning data and provides advice to maximize the effectiveness of their learning. For example, the generating AI analyzes how much time students spend on specific problems and suggests efficient learning methods. The generating AI can also visualize students' learning progress and provide feedback to teachers and parents. For example, the generating AI analyzes students' learning data and displays it as graphs and charts, allowing them to grasp their learning progress at a glance. Furthermore, the progress analysis unit can also use the generating AI to analyze students' learning data over the long term and grasp learning trends and patterns. This allows the progress analysis unit to provide support to help students learn effectively.

[0067] The Safety Management Department securely manages online activities based on progress analyzed by the Progress Analysis Department. For example, the Safety Management Department uses generative AI to monitor websites and applications accessed by children and prevent access to inappropriate content. Specifically, the generative AI analyzes the content of websites and applications and automatically blocks violent or adult content. The generative AI can also record children's online activities and report them to parents. For example, the generative AI analyzes children's online activities and reports them to parents via email or app. Furthermore, the Safety Management Department can use the generative AI to monitor children's online activities in real time and issue immediate warnings if abnormal behavior is detected. This allows the Safety Management Department to provide a safe environment for children to use the internet. Through these functions, the Safety Management Department supports children in learning with peace of mind.

[0068] The filtering unit can display appropriate educational content and eliminate inappropriate information based on keywords searched by children. For example, the filtering unit can analyze search results using generative AI and prioritize displaying content of high educational value. The filtering unit can also automatically detect and eliminate inappropriate information using generative AI. For example, the filtering unit can use generative AI to detect violent content and adult content and exclude it from search results. This allows children to use the internet safely. Appropriate educational content includes, for example, educational value and age appropriateness. Inappropriate information includes, for example, violent content and adult content. Some or all of the above processing in the filtering unit may be performed using generative AI or without generative AI. For example, the filtering unit can use generative AI to analyze search results and prioritize displaying content of high educational value.

[0069] The curriculum generation unit can analyze students' learning data and automatically generate individually optimized curricula and teaching materials. For example, the curriculum generation unit uses a generation AI to analyze students' learning data and automatically generate individually optimized curricula and teaching materials. For example, the generation AI analyzes students' learning styles and progress to provide an optimal learning plan. The curriculum generation unit can also use the generation AI to generate curricula tailored to students' learning objectives. For example, the generation AI analyzes students' learning objectives and selects the most suitable teaching materials based on them. This allows for the provision of an optimal curriculum for each individual student. Individually optimized curricula include, for example, learning style and progress. Automatic generation of teaching materials includes, for example, the type of teaching material and the generation algorithm. Some or all of the above-described processes in the curriculum generation unit may be performed using a generation AI or without one. For example, the curriculum generation unit uses a generation AI to analyze students' learning data and automatically generate individually optimized curricula and teaching materials.

[0070] The progress analysis unit can analyze students' learning data in real time and provide advice tailored to their progress. For example, the progress analysis unit can use a generative AI to analyze students' learning data in real time and provide advice tailored to their progress. For example, the progress analysis unit can use a generative AI to analyze students' learning data and provide advice to maximize learning effectiveness. Furthermore, the progress analysis unit can use a generative AI to visualize students' learning progress and provide feedback to teachers and parents. For example, the progress analysis unit can use a generative AI to analyze students' learning data and display it as graphs or charts. This allows for the provision of appropriate advice tailored to students' learning progress. Advice tailored to progress includes, for example, the content of the advice and the timing of its provision. Some or all of the above-described processes in the progress analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the progress analysis unit can use a generative AI to analyze students' learning data and provide advice tailored to their progress.

[0071] The Security Management Department can monitor websites and applications accessed by children and prevent access to inappropriate content. For example, the Security Management Department may use generative AI to monitor websites and applications accessed by children and prevent access to inappropriate content. For example, the Security Management Department may use generative AI to monitor children's online activities and automatically block inappropriate content. The Security Management Department may also use generative AI to record children's online activities and report them to parents. For example, the Security Management Department may use generative AI to analyze children's online activities and report them to parents via email or app. This allows for the secure management of children's online activities. Inappropriate content includes, for example, filtering criteria and monitoring methods. Some or all of the above processes in the Security Management Department may be performed using generative AI or not. For example, the Security Management Department may use generative AI to monitor children's online activities and automatically block inappropriate content.

[0072] The filtering unit can estimate a child's emotions and adjust the display order of search results based on the estimated emotions. For example, the filtering unit might use generative AI to estimate a child's emotions and adjust the display order of search results based on the estimated emotions. For example, if a child is stressed, the filtering unit might prioritize displaying relaxing content. It could also prioritize displaying educational content to improve concentration if the child is excited. Furthermore, if the child is tired, the filtering unit might prioritize displaying simple and easy-to-understand content. This allows for the provision of search results tailored to the child's emotions. Estimating a child's emotions might involve, for example, an emotion recognition algorithm and data sources. Adjusting the display order of search results might involve, for example, display order criteria and adjustment algorithms. Some or all of the above processing in the filtering unit may be performed using generative AI or without it. For example, the filtering unit might use generative AI to estimate a child's emotions and adjust the display order of search results based on the estimated emotions.

[0073] The filtering unit can analyze a child's past search history and select the optimal filtering method. For example, the filtering unit may use generative AI to analyze a child's past search history and select the optimal filtering method. For example, the filtering unit may prioritize displaying highly relevant content based on keywords the child has frequently searched in the past. The filtering unit can also eliminate content the child has avoided in the past and suggest new content that might interest them. Furthermore, the filtering unit can suggest content suitable for a specific time of day based on the child's past search history. This allows the system to provide optimal content based on the child's past search history. The optimal filtering method includes, for example, a filtering algorithm and selection criteria. Some or all of the above processing in the filtering unit may be performed using generative AI or not. For example, the filtering unit may use generative AI to analyze a child's past search history and select the optimal filtering method.

[0074] The filtering unit can filter search results based on the child's current learning status and areas of interest. For example, the filtering unit can use a generative AI to filter search results based on the child's current learning status and areas of interest. For example, the filtering unit can prioritize displaying content related to the subject the child is currently studying. The filtering unit can also suggest content that might interest the child based on their areas of interest. Furthermore, the filtering unit can display content that includes what the child should learn next, according to their learning progress. This allows the system to provide search results that are tailored to the child's learning status and areas of interest. Current learning status includes, for example, learning progress and level of understanding. Areas of interest include, for example, past search history and topics of interest. Some or all of the above processing in the filtering unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the filtering unit may use a generative AI to filter based on the child's current learning status and areas of interest.

[0075] The filtering unit can estimate a child's emotions and adjust the filtering strictness based on the estimated emotions. For example, the filtering unit might use generative AI to estimate a child's emotions and adjust the filtering strictness based on the estimated emotions. For example, if a child is relaxed, the filtering unit might loosen the filtering strictness and display a variety of content. Conversely, if a child is stressed, the filtering unit might increase the filtering strictness and display only reassuring content. Furthermore, if a child is excited, the filtering unit might display carefully selected educational content to enhance their concentration. This allows for the provision of more appropriate content by filtering according to the child's emotions. Filtering strictness includes, for example, the filtering level and the adjustment algorithm. Some or all of the above processing in the filtering unit may be performed using generative AI or not. For example, the filtering unit might use generative AI to estimate a child's emotions and adjust the filtering strictness based on the estimated emotions.

[0076] The filtering unit can prioritize displaying highly relevant information based on the child's geographical location when filtering search results. For example, the filtering unit uses a generative AI to prioritize displaying highly relevant information based on the child's geographical location when filtering search results. For example, the filtering unit prioritizes displaying educational content related to the region based on the child's current location. The filtering unit can also display information related to local events and activities based on the child's geographical location. Furthermore, the filtering unit can suggest content related to local culture and history, taking into account the child's geographical location. This allows for the provision of highly relevant information based on the child's geographical location. Geographical location information includes, for example, GPS data and location services. Some or all of the above processing in the filtering unit may be performed using a generative AI or without a generative AI. For example, the filtering unit may use a generative AI to prioritize displaying highly relevant information based on the child's geographical location.

[0077] The filtering unit can analyze a child's social media activity and display relevant information when filtering search results. For example, the filtering unit can use generative AI to analyze a child's social media activity and display relevant information when filtering search results. For example, the filtering unit can display content related to topics that the child has shown interest in on social media. The filtering unit can also suggest new content that might interest the child based on their social media activity. Furthermore, the filtering unit can consider the child's social media friendships and display content shared by their friends. This allows the system to provide relevant information based on the child's social media activity. Social media activity includes, for example, posts and follower information. Some or all of the above processing in the filtering unit may be performed using generative AI or not. For example, the filtering unit may use generative AI to analyze a child's social media activity and display relevant information.

[0078] The curriculum generation unit can estimate a child's emotions and adjust the curriculum content based on those emotions. For example, the curriculum generation unit might use a generating AI to estimate a child's emotions and adjust the curriculum content based on those emotions. For example, if a child is relaxed, the curriculum generation unit might provide a curriculum that includes challenging tasks. If a child is stressed, the curriculum generation unit might also provide a curriculum that includes easy tasks that provide a sense of accomplishment. Furthermore, if a child is excited, the curriculum generation unit might provide a curriculum that includes topics that pique their interest. This allows for the provision of a curriculum that responds to a child's emotions. Adjusting the curriculum content might include, for example, learning objectives and progress. Some or all of the above-described processes in the curriculum generation unit may be performed using a generating AI or not. For example, the curriculum generation unit might use a generating AI to estimate a child's emotions and adjust the curriculum content based on those emotions.

[0079] The curriculum generation unit can adjust the difficulty level of the curriculum based on the students' learning progress during curriculum generation. For example, the curriculum generation unit uses a generation AI to adjust the difficulty level of the curriculum based on the students' learning progress during curriculum generation. For example, if a student's learning progress is fast, the curriculum generation unit can provide a curriculum that includes challenging tasks. If a student's learning progress is slow, the curriculum generation unit can also provide a curriculum that reinforces basic content. Furthermore, the curriculum generation unit can provide a curriculum that includes tasks of appropriate difficulty level according to the student's learning progress. This allows for the provision of a curriculum with a difficulty level that matches the student's learning progress. The difficulty level of the curriculum includes, for example, the complexity of the learning content and the student's progress. Some or all of the above-described processes in the curriculum generation unit may be performed using a generation AI or not. For example, the curriculum generation unit may use a generation AI to analyze the student's learning progress and adjust the difficulty level of the curriculum based on that analysis.

[0080] The curriculum generation unit can apply different curriculum generation algorithms depending on the interests and concerns of the children when generating the curriculum. For example, the curriculum generation unit can use a generation AI to apply different curriculum generation algorithms depending on the interests and concerns of the children when generating the curriculum. For example, if a child is interested in science, the curriculum generation unit can provide a curriculum that includes science-related tasks. Also, if a child is interested in history, the curriculum generation unit can provide a curriculum that includes history-related tasks. Furthermore, the curriculum generation unit can apply an appropriate curriculum generation algorithm depending on the interests and concerns of the children and provide an individually optimized curriculum. This makes it possible to provide a curriculum that is tailored to the interests and concerns of the children. Curriculum generation algorithms include, for example, machine learning algorithms and rule-based algorithms. Some or all of the above-described processes in the curriculum generation unit may be performed using a generation AI or not. For example, the curriculum generation unit may use a generation AI to analyze the children's interests and concerns and generate a curriculum based on that.

[0081] The curriculum generation unit can estimate a child's emotions and adjust the curriculum length based on the estimated emotions. For example, the curriculum generation unit can use a generating AI to estimate a child's emotions and adjust the curriculum length based on the estimated emotions. For example, if a child is relaxed, the curriculum generation unit can provide a longer curriculum. It can also provide a shorter curriculum if a child is stressed. Furthermore, if a child is excited, the curriculum generation unit can provide a curriculum of an appropriate length that will maintain their concentration. This allows for the provision of a curriculum length that is appropriate for the child's emotions. Curriculum length includes, for example, learning time and session length. Some or all of the above processing in the curriculum generation unit may be performed using a generating AI or not. For example, the curriculum generation unit may use a generating AI to estimate a child's emotions and adjust the curriculum length based on that.

[0082] The curriculum generation unit can determine curriculum priorities based on the student's learning history when generating the curriculum. For example, the curriculum generation unit uses a generation AI to determine curriculum priorities based on the student's learning history when generating the curriculum. For example, the curriculum generation unit may prioritize subjects that the student has struggled with in the past. The curriculum generation unit can also postpone subjects that the student excels at and prioritize subjects that the student struggles with. Furthermore, the curriculum generation unit can determine the most effective learning order based on the student's learning history. This allows for the provision of an optimal curriculum based on the student's learning history. Curriculum priorities include, for example, learning objectives and progress. Some or all of the above-described processes in the curriculum generation unit may be performed using a generation AI or not. For example, the curriculum generation unit may use a generation AI to analyze the student's learning history and determine curriculum priorities based on that analysis.

[0083] The curriculum generation unit can adjust the curriculum content by referring to the student's relevant learning resources during curriculum generation. For example, the curriculum generation unit can use a generation AI to adjust the curriculum content by referring to the student's relevant learning resources during curriculum generation. For example, the curriculum generation unit can incorporate relevant new materials into the curriculum based on materials the student has used in the past. The curriculum generation unit can also incorporate relevant assignments into the curriculum by referring to online courses the student has previously participated in. Furthermore, the curriculum generation unit can refer to the student's learning resources and incorporate the most suitable materials and assignments into the curriculum. This allows for the provision of an optimal curriculum based on the student's learning resources. Relevant learning resources include, for example, reference books and online materials. Some or all of the above processing in the curriculum generation unit may be performed using a generation AI or not. For example, the curriculum generation unit may use a generation AI to refer to the student's relevant learning resources and adjust the curriculum content based on that.

[0084] The progress analysis unit can estimate a child's emotions and adjust the progress analysis criteria based on the estimated emotions. For example, the progress analysis unit may use generative AI to estimate a child's emotions and adjust the progress analysis criteria based on the estimated emotions. For example, if a child is relaxed, the progress analysis unit may analyze progress using strict criteria. Conversely, if a child is stressed, the progress analysis unit may analyze progress using lenient criteria. Furthermore, if a child is excited, the progress analysis unit may analyze progress using criteria designed to enhance concentration. This allows for more appropriate advice to be provided by performing progress analysis in accordance with the child's emotions. The progress analysis criteria include, for example, evaluation criteria and analysis algorithms. Some or all of the above-described processes in the progress analysis unit may be performed using generative AI or not. For example, the progress analysis unit may use generative AI to estimate a child's emotions and adjust the progress analysis criteria based on that.

[0085] The progress analysis unit can improve the accuracy of its analysis by considering the interrelationships of students' learning data during progress analysis. For example, the progress analysis unit can use a generative AI to improve the accuracy of its analysis by considering the interrelationships of students' learning data during progress analysis. For example, the progress analysis unit can correlate students' learning data to perform a more accurate progress analysis. The progress analysis unit can also perform an analysis to maximize the effectiveness of learning by considering the interrelationships of students' learning data. Furthermore, the progress analysis unit can integrate students' learning data and analyze overall learning progress. This makes it possible to perform a more accurate progress analysis by considering the interrelationships of students' learning data. Interrelationships of learning data include, for example, data relationships and correlation analysis. Some or all of the above processing in the progress analysis unit may be performed using a generative AI or not. For example, the progress analysis unit may use a generative AI to analyze the interrelationships of students' learning data and improve the accuracy of the progress analysis based on that.

[0086] The progress analysis unit can perform progress analysis while considering the child's learning history. For example, the progress analysis unit can use a generative AI to perform progress analysis while considering the child's learning history. For example, the progress analysis unit can analyze the child's current progress based on the child's past learning history. The progress analysis unit can also perform analysis to maximize the effectiveness of learning by considering the child's learning history. Furthermore, the progress analysis unit can perform analysis to propose the optimal learning method based on the child's learning history. This makes it possible to perform more effective progress analysis by considering the child's learning history. Learning history includes, for example, past learning data and progress records. Some or all of the above processing in the progress analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the progress analysis unit may have a generative AI analyze the child's learning history and analyze the progress based on that.

[0087] The progress analysis unit can estimate the child's emotions and adjust the order in which the progress analysis results are displayed based on the estimated emotions. For example, the progress analysis unit can use a generative AI to estimate the child's emotions and adjust the order in which the progress analysis results are displayed based on the estimated emotions. For example, if the child is relaxed, the progress analysis unit can prioritize displaying detailed progress analysis results. It can also prioritize displaying concise progress analysis results if the child is stressed. Furthermore, if the child is excited, the progress analysis unit can prioritize displaying interesting progress analysis results. This allows for more appropriate feedback by providing progress analysis results that correspond to the child's emotions. The order in which the progress analysis results are displayed includes, for example, criteria for display order and adjustment algorithms. Some or all of the above processing in the progress analysis unit may be performed using a generative AI or not. For example, the progress analysis unit may use a generative AI to estimate the child's emotions and adjust the order in which the progress analysis results are displayed based on that.

[0088] The progress analysis unit can perform progress analysis while considering the geographical distribution of students. For example, the progress analysis unit can use a generative AI to perform progress analysis while considering the geographical distribution of students. For example, the progress analysis unit can analyze learning progress by region based on the geographical distribution of students. The progress analysis unit can also perform analysis to maximize the learning effect in each region, taking into account the geographical distribution of students. Furthermore, the progress analysis unit can perform analysis to propose learning methods for each region based on the geographical distribution of students. This makes it possible to perform progress analysis based on the geographical distribution of students. Geographical distribution includes, for example, regional data and geographic information systems. Some or all of the above processing in the progress analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the progress analysis unit may have a generative AI analyze the geographical distribution of students and analyze progress based on that.

[0089] The progress analysis unit can improve the accuracy of its analysis by referring to relevant literature for the child during the progress analysis. For example, the progress analysis unit can improve the accuracy of its analysis by referring to relevant literature for the child during the progress analysis using a generative AI. For example, the progress analysis unit can improve the accuracy of its progress analysis by referring to literature related to the child's learning content. The progress analysis unit can also improve the accuracy of its progress analysis by referring to the latest research results related to the child's learning content. Furthermore, the progress analysis unit can improve the accuracy of its progress analysis by referring to past research results related to the child's learning content. In this way, the accuracy of the progress analysis is improved by referring to relevant literature. Relevant literature includes, for example, academic papers and reference books. Some or all of the above processing in the progress analysis unit may be performed using a generative AI or not. For example, the progress analysis unit may have a generative AI refer to relevant literature for the child and analyze the progress based on that.

[0090] The Safety Management Department can estimate a child's emotions and adjust safety management standards based on those estimates. For example, the Safety Management Department might use generative AI to estimate a child's emotions and adjust safety management standards based on those estimates. For instance, if a child is relaxed, the Safety Management Department might apply normal safety management standards. Conversely, if a child is stressed, the Safety Management Department might apply stricter safety management standards. Furthermore, if a child is agitated, the Safety Management Department might apply safety management standards designed to enhance concentration. This allows for a more appropriate online environment by providing safety management standards tailored to the child's emotions. Safety management standards include, for example, security policies and access control standards. Some or all of the above processes in the Safety Management Department may be performed using generative AI or without it. For example, the Safety Management Department might use generative AI to estimate a child's emotions and adjust safety management standards based on that.

[0091] The Safety Management Department can improve the accuracy of its safety management by considering the interrelationships of children's online activities. For example, the Safety Management Department can use generative AI to improve the accuracy of its safety management by considering the interrelationships of children's online activities. For example, the Safety Management Department can correlate children's online activities to perform more accurate safety management. The Safety Management Department can also prevent access to inappropriate content by considering the interrelationships of children's online activities. Furthermore, the Safety Management Department can integrate children's online activities to perform overall safety management. This enables more accurate safety management by considering the interrelationships of children's online activities. The interrelationships of online activities include, for example, activity logs and correlation analysis. Some or all of the above processes in the Safety Management Department may be performed using generative AI or not. For example, the Safety Management Department may use generative AI to analyze the interrelationships of children's online activities and improve the accuracy of safety management based on that.

[0092] The Security Management Department can manage security while considering the child's access history. For example, the Security Management Department can use generative AI to manage security while considering the child's access history. For example, the Security Management Department can prevent access to inappropriate content based on the child's past access history. The Security Management Department can also provide a safe online environment while considering the child's access history. Furthermore, the Security Management Department can propose the optimal security management method based on the child's access history. This makes more effective security management possible by considering the child's access history. Access history includes, for example, website visit history and application usage history. Some or all of the above processes in the Security Management Department may be performed using generative AI or not. For example, the Security Management Department may use generative AI to analyze the child's access history and perform security management based on that.

[0093] The safety management unit can estimate a child's emotions and adjust the order in which safety management results are displayed based on the estimated emotions. For example, the safety management unit may use generative AI to estimate a child's emotions and adjust the order in which safety management results are displayed based on the estimated emotions. For example, if a child is relaxed, the safety management unit may prioritize displaying detailed safety management results. It may also prioritize displaying concise safety management results if a child is stressed. Furthermore, if a child is excited, the safety management unit may prioritize displaying safety management results that are of interest to the child. This enables more appropriate feedback by providing safety management results that are tailored to the child's emotions. The order in which safety management results are displayed includes, for example, criteria for display order and adjustment algorithms. Some or all of the above processing in the safety management unit may be performed using generative AI or not. For example, the safety management unit may use generative AI to estimate a child's emotions and adjust the order in which safety management results are displayed based on that.

[0094] The Safety Management Department can manage safety while considering the geographical distribution of children. For example, the Safety Management Department can use generative AI to manage safety while considering the geographical distribution of children. For example, the Safety Management Department can perform safety management for each region based on the geographical distribution of children. The Safety Management Department can also prevent access to inappropriate content in each region, taking into account the geographical distribution of children. Furthermore, the Safety Management Department can propose the optimal safety management method for each region based on the geographical distribution of children. This makes safety management based on the geographical distribution of children possible. Geographical distribution includes, for example, regional data and geographic information systems. Some or all of the above processing in the Safety Management Department may be performed using generative AI or not. For example, the Safety Management Department may use generative AI to analyze the geographical distribution of children and perform safety management based on that.

[0095] The Safety Management Department can improve the accuracy of safety management by referring to relevant literature on children during safety management. For example, the Safety Management Department can use generative AI to improve the accuracy of safety management by referring to relevant literature on children during safety management. For example, the Safety Management Department can improve the accuracy of safety management by referring to literature related to children's online activities. Furthermore, the Safety Management Department can also improve the accuracy of safety management by referring to the latest research findings related to children's online activities. In addition, the Safety Management Department can improve the accuracy of safety management by referring to past research findings related to children's online activities. Thus, the accuracy of safety management is improved by referring to relevant literature. Relevant literature includes, for example, academic papers and reference books. Some or all of the above processing in the Safety Management Department may be performed using generative AI, or without generative AI. For example, the Safety Management Department may have a generative AI refer to relevant literature on children and perform safety management based on that.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The internet usage support system for children may further include a motivational unit to enhance children's motivation to learn. The motivational unit, for example, uses generative AI to analyze children's learning data and provides a reward system to increase their motivation. For example, the motivational unit may award badges or points when children achieve specific learning goals. The motivational unit can also provide appropriate rewards according to the children's learning progress. Furthermore, the motivational unit may incorporate game elements related to the learning content to enhance children's motivation. This can increase children's motivation and sustain their interest in learning. The reward system may include, for example, badges, points, or perks. Some or all of the above-described processes in the motivational unit may be performed using generative AI or without it. For example, the motivational unit may use generative AI to analyze children's learning data and provide a reward system based on that analysis.

[0098] The internet usage support system for children may further include an environment adjustment unit to optimize the child's learning environment. The environment adjustment unit, for example, uses generative AI to analyze the child's learning environment and provide an optimal learning environment. The environment adjustment unit may, for example, suggest appropriate lighting and music based on the child's learning data. Furthermore, the environment adjustment unit can customize the learning environment according to the child's learning style. In addition, the environment adjustment unit can adjust the temperature and humidity of the learning environment to improve the child's learning efficiency. This optimizes the child's learning environment and improves learning efficiency. Adjustments to the learning environment include, for example, lighting, music, temperature, and humidity. Some or all of the above-described processes in the environment adjustment unit may be performed using generative AI, or without generative AI. For example, the environment adjustment unit may use generative AI to analyze the child's learning environment and provide an optimal learning environment based on that analysis.

[0099] The internet usage support system for children may further include an evaluation unit for evaluating children's learning outcomes. The evaluation unit may, for example, use a generative AI to analyze children's learning data and evaluate their learning outcomes. The evaluation unit may, for example, make appropriate evaluations based on children's learning progress and level of understanding. The evaluation unit may also visualize children's learning outcomes and display them as graphs or charts. Furthermore, the evaluation unit may set the next learning goals based on children's learning outcomes. This allows for appropriate evaluation of children's learning outcomes and clarifies the next learning steps. Evaluation of learning outcomes may include, for example, progress, level of understanding, and learning goals. Some or all of the above-described processes in the evaluation unit may be performed using a generative AI or not. For example, the evaluation unit may have a generative AI analyze children's learning data and evaluate learning outcomes based on that analysis.

[0100] The internet usage support system for children may further include a habit formation unit for shaping children's learning habits. The habit formation unit, for example, uses a generative AI to analyze children's learning data and provides advice for forming learning habits. The habit formation unit can, for example, send reminders to children to study for a certain amount of time each day. It can also suggest an appropriate learning schedule based on the child's learning progress. Furthermore, the habit formation unit can visualize children's learning habits and display their achievement levels as graphs or charts. This helps to form children's learning habits and promote sustained learning. Habit formation includes, for example, reminders, learning schedules, and visualization of achievement levels. Some or all of the above-described processes in the habit formation unit may be performed using a generative AI or without one. For example, the habit formation unit may use a generative AI to analyze children's learning data and provide advice for forming learning habits based on that analysis.

[0101] The internet usage support system for children may further include a content diversification unit to diversify the learning content of children. The content diversification unit may, for example, use a generative AI to analyze children's learning data and provide diverse learning content. The content diversification unit may, for example, suggest different formats of learning content based on children's interests and concerns. The content diversification unit may also provide diverse content such as videos, audio, and text according to children's learning styles. Furthermore, the content diversification unit may suggest new learning content according to children's learning progress. This diversifies the learning content of children and helps maintain their interest in learning. Diversification of learning content includes, for example, videos, audio, text, and interactive content. Some or all of the above processing in the content diversification unit may be performed using a generative AI or not. For example, the content diversification unit may use a generative AI to analyze children's learning data and provide diverse learning content based on that analysis.

[0102] The filtering unit can estimate a child's emotions and adjust the display order of search results based on the estimated emotions. For example, the filtering unit might use generative AI to estimate a child's emotions and adjust the display order of search results based on the estimated emotions. For example, if a child is stressed, the filtering unit might prioritize displaying relaxing content. It could also prioritize displaying educational content to improve concentration if the child is excited. Furthermore, if the child is tired, the filtering unit might prioritize displaying simple and easy-to-understand content. This allows for the provision of search results tailored to the child's emotions. Estimating a child's emotions might involve, for example, an emotion recognition algorithm and data sources. Adjusting the display order of search results might involve, for example, display order criteria and adjustment algorithms. Some or all of the above processing in the filtering unit may be performed using generative AI or without it. For example, the filtering unit might use generative AI to estimate a child's emotions and adjust the display order of search results based on the estimated emotions.

[0103] The curriculum generation unit can estimate a child's emotions and adjust the curriculum content based on those emotions. For example, the curriculum generation unit might use a generating AI to estimate a child's emotions and adjust the curriculum content based on those emotions. For example, if a child is relaxed, the curriculum generation unit might provide a curriculum that includes challenging tasks. If a child is stressed, the curriculum generation unit might also provide a curriculum that includes easy tasks that provide a sense of accomplishment. Furthermore, if a child is excited, the curriculum generation unit might provide a curriculum that includes topics that pique their interest. This allows for the provision of a curriculum that responds to a child's emotions. Adjusting the curriculum content might include, for example, learning objectives and progress. Some or all of the above-described processes in the curriculum generation unit may be performed using a generating AI or not. For example, the curriculum generation unit might use a generating AI to estimate a child's emotions and adjust the curriculum content based on those emotions.

[0104] The progress analysis unit can estimate a child's emotions and adjust the progress analysis criteria based on the estimated emotions. For example, the progress analysis unit may use generative AI to estimate a child's emotions and adjust the progress analysis criteria based on the estimated emotions. For example, if a child is relaxed, the progress analysis unit may analyze progress using strict criteria. Conversely, if a child is stressed, the progress analysis unit may analyze progress using lenient criteria. Furthermore, if a child is excited, the progress analysis unit may analyze progress using criteria designed to enhance concentration. This allows for more appropriate advice to be provided by performing progress analysis in accordance with the child's emotions. The progress analysis criteria include, for example, evaluation criteria and analysis algorithms. Some or all of the above-described processes in the progress analysis unit may be performed using generative AI or not. For example, the progress analysis unit may use generative AI to estimate a child's emotions and adjust the progress analysis criteria based on that.

[0105] The Safety Management Department can estimate a child's emotions and adjust safety management standards based on those estimates. For example, the Safety Management Department might use generative AI to estimate a child's emotions and adjust safety management standards based on those estimates. For instance, if a child is relaxed, the Safety Management Department might apply normal safety management standards. Conversely, if a child is stressed, the Safety Management Department might apply stricter safety management standards. Furthermore, if a child is agitated, the Safety Management Department might apply safety management standards designed to enhance concentration. This allows for a more appropriate online environment by providing safety management standards tailored to the child's emotions. Safety management standards include, for example, security policies and access control standards. Some or all of the above processes in the Safety Management Department may be performed using generative AI or without it. For example, the Safety Management Department might use generative AI to estimate a child's emotions and adjust safety management standards based on that.

[0106] The safety management unit can estimate a child's emotions and adjust the order in which safety management results are displayed based on the estimated emotions. For example, the safety management unit may use generative AI to estimate a child's emotions and adjust the order in which safety management results are displayed based on the estimated emotions. For example, if a child is relaxed, the safety management unit may prioritize displaying detailed safety management results. It may also prioritize displaying concise safety management results if a child is stressed. Furthermore, if a child is excited, the safety management unit may prioritize displaying safety management results that are of interest to the child. This enables more appropriate feedback by providing safety management results that are tailored to the child's emotions. The order in which safety management results are displayed includes, for example, criteria for display order and adjustment algorithms. Some or all of the above processing in the safety management unit may be performed using generative AI or not. For example, the safety management unit may use generative AI to estimate a child's emotions and adjust the order in which safety management results are displayed based on that.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The filtering unit organizes the search results. For example, it uses a generative AI to display appropriate educational content based on the keywords searched by the child and eliminates inappropriate information. The generative AI analyzes the search results, prioritizing the display of content with high educational value and detecting and excluding violent or adult content. Step 2: The curriculum generation unit generates the optimal curriculum based on the search results compiled by the filtering unit. For example, it uses a generation AI to analyze students' learning data and automatically generates individually optimized curricula and teaching materials. The generation AI analyzes students' learning styles and progress, provides the optimal learning plan, and generates a curriculum that matches their learning objectives. Step 3: The progress analysis unit analyzes learning progress in real time based on the curriculum generated by the curriculum generation unit. For example, it uses a generation AI to analyze students' learning data in real time and provides advice according to their progress. The generation AI analyzes students' learning data, provides advice to maximize learning effectiveness, visualizes learning progress, and provides feedback to teachers and parents. Step 4: The Safety Management Department securely manages online activities based on the progress analyzed by the Progress Analysis Department. For example, it uses generative AI to monitor websites and applications accessed by children and prevent access to inappropriate content. The generative AI monitors children's online activities, automatically blocks inappropriate content, records online activities, and reports them to parents.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the filtering unit, curriculum generation unit, progress analysis unit, and safety management 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 and uses a generation AI to organize search results. The curriculum generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses a generation AI to analyze the child's learning data and generate an optimal curriculum. The progress analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses a generation AI to analyze learning progress in real time and provide appropriate advice. The safety management unit is implemented by the control unit 46A of the smart device 14 and uses a generation AI to monitor online activities and prevent access to inappropriate content. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the filtering unit, curriculum generation unit, progress analysis unit, and safety management unit, is implemented, for example, 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 and uses a generating AI to organize search results. The curriculum generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and uses a generating AI to analyze the child's learning data and generate an optimal curriculum. The progress analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and uses a generating AI to analyze learning progress in real time and provide appropriate advice. The safety management unit is implemented, for example, by the control unit 46A of the smart glasses 214 and uses a generating AI to monitor online activities and prevent access to inappropriate content. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the filtering unit, curriculum generation unit, progress analysis unit, and safety management 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 and uses a generation AI to organize search results. The curriculum generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses a generation AI to analyze the child's learning data and generate an optimal curriculum. The progress analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses a generation AI to analyze learning progress in real time and provide appropriate advice. The safety management unit is implemented by the control unit 46A of the headset terminal 314 and uses a generation AI to monitor online activities and prevent access to inappropriate content. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the filtering unit, curriculum generation unit, progress analysis unit, and safety management 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 and uses a generation AI to organize search results. The curriculum generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses a generation AI to analyze the child's learning data and generate an optimal curriculum. The progress analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses a generation AI to analyze learning progress in real time and provide appropriate advice. The safety management unit is implemented by the control unit 46A of the robot 414 and uses a generation AI to monitor online activities and prevent access to inappropriate content. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A filtering unit that organizes search results, A curriculum generation unit that generates an optimal curriculum based on the search results compiled by the filtering unit, A progress analysis unit analyzes learning progress in real time based on the curriculum generated by the curriculum generation unit, The system includes a safety management unit that securely manages online activities based on the progress status analyzed by the aforementioned progress analysis unit. A system characterized by the following features. (Note 2) The filtering unit is Display appropriate educational content and filter out inappropriate information based on keywords searched by children. The system described in Appendix 1, characterized by the features described herein. (Note 3) The curriculum generation unit, Analyzes students' learning data and automatically generates individually optimized curricula and teaching materials. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned progress analysis unit, The system analyzes children's learning data in real time and provides advice tailored to their progress. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned safety management department, Monitor the websites and applications that children access and prevent them from accessing inappropriate content. The system described in Appendix 1, characterized by the features described herein. (Note 6) The filtering unit is The system estimates the emotions of children and adjusts the display order of search results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The filtering unit is Analyze the child's past search history and select the optimal filtering method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The filtering unit is When filtering search results, filter based on the child's current learning situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The filtering unit is The system estimates the children's emotions and adjusts the filtering strictness based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 10) The filtering unit is When filtering search results, prioritize displaying highly relevant information based on the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The filtering unit is When filtering search results, we analyze children's social media activity and display relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The curriculum generation unit, We estimate the children's emotions and adjust the curriculum content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The curriculum generation unit, When generating the curriculum, adjust the difficulty level based on the students' learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 14) The curriculum generation unit, When generating the curriculum, different curriculum generation algorithms are applied depending on the students' interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 15) The curriculum generation unit, The system estimates the children's emotions and adjusts the length of the curriculum based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 16) The curriculum generation unit, When generating the curriculum, prioritize the curriculum based on the students' learning history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The curriculum generation unit, When generating the curriculum, adjust the curriculum content by referring to the students' relevant learning resources. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned progress analysis unit, We estimate the children's emotions and adjust the progress analysis criteria based on the estimated emotions of the children. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned progress analysis unit, When analyzing progress, consider the interrelationships between students' learning data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned progress analysis unit, When analyzing progress, the analysis will take into account the children's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned progress analysis unit, The system estimates the children's emotions and adjusts the order in which the progress analysis results are displayed based on the estimated emotions of the children. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned progress analysis unit, When analyzing progress, the geographical distribution of children should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned progress analysis unit, During progress analysis, we improve the accuracy of the analysis by referring to relevant literature on children. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned safety management department, We estimate the children's emotions and adjust safety management standards based on the estimated emotions of the children. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned safety management department, When managing safety, we improve the accuracy of management by considering the interrelationships of children's online activities. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned safety management department, When managing security, the access history of children should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned safety management department, The system estimates the children's emotions and adjusts the order in which safety management results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned safety management department, When managing safety, take into account the geographical distribution of children. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned safety management department, When managing safety, we refer to relevant literature on children to improve the accuracy of management. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 organizes search results, A curriculum generation unit that generates an optimal curriculum based on the search results compiled by the filtering unit, A progress analysis unit analyzes learning progress in real time based on the curriculum generated by the curriculum generation unit, The system includes a safety management unit that securely manages online activities based on the progress status analyzed by the aforementioned progress analysis unit. A system characterized by the following features.

2. The filtering unit is Display appropriate educational content and filter out inappropriate information based on keywords searched by children. The system according to feature 1.

3. The curriculum generation unit, Analyzes students' learning data and automatically generates individually optimized curricula and teaching materials. The system according to feature 1.

4. The aforementioned progress analysis unit, The system analyzes children's learning data in real time and provides advice tailored to their progress. The system according to feature 1.

5. The aforementioned safety management department, Monitor the websites and applications that children access and prevent them from accessing inappropriate content. The system according to feature 1.

6. The filtering unit is The system estimates the emotions of children and adjusts the display order of search results based on the estimated emotions. The system according to feature 1.

7. The filtering unit is Analyze the child's past search history and select the optimal filtering method. The system according to feature 1.

8. The filtering unit is When filtering search results, filter based on the child's current learning situation and areas of interest. The system according to feature 1.

Images