system

Abstract

The system according to this embodiment aims to effectively provide online learning support and mental health care to students who are absent from school. [Solution] The system according to the embodiment comprises a selection unit, a generation unit, a problem generation unit, a check-in unit, and a dashboard unit. The selection unit selects topics that interest the students. The generation unit automatically searches for and provides videos and articles related to the topics selected by the selection unit. The problem generation unit generates problems according to the students' level of understanding based on the content provided by the generation unit. The check-in unit checks the students' mental health status through a periodic check-in function. The dashboard unit provides a dashboard for parents.

Description

Technical Field

[0006] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] The system according to this embodiment can effectively provide online learning support and mental health care to students who are absent from school. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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 school absenteeism support AI agent according to an embodiment of the present invention is a system that uses AI to provide online learning support and mental health care to students who are absent from school. This system uses a generative AI based on a large-scale language model (LLM) to provide customized learning content based on topics that interest the student. Specifically, it automatically searches for videos and articles related to the theme selected by the student and generates questions according to their level of understanding. In addition, it checks the student's mental health status through a regular check-in function and encourages consultation with a specialist as needed. Furthermore, a dashboard for parents is provided so that they can understand their child's progress and mental health status in real time. For example, the student selects a topic that interests them. For example, they can choose from themes such as history, science, and literature. Next, the generative AI automatically searches for videos and articles related to the selected theme and provides them to the student. This allows the student to proceed with learning based on their interests. Furthermore, the generative AI generates questions according to the student's level of understanding. For example, a student who has chosen the theme of history will be provided with history quizzes and essay assignments. This allows the student to proceed with learning at their own pace. In addition, the student's mental health status is checked through a regular check-in function. The generating AI analyzes students' responses and behavioral patterns to detect signs of stress and anxiety. It sends notifications prompting consultation with a professional as needed. A dashboard for parents allows them to monitor their child's progress and mental health in real time. For example, they can see what topics their child is interested in, their level of understanding, and their mental health status. This enables parents to effectively support their child's learning and mental health. Thus, the school absenteeism support AI agent offers unique value by providing personalized learning experiences and mental healthcare, addressing learning delays caused by school absenteeism and providing emotional support. This allows the school absenteeism support AI agent to provide learning content and mental healthcare based on the student's interests.

[0029] The truancy support AI agent according to this embodiment comprises a selection unit, a generation unit, a problem generation unit, a check-in unit, and a dashboard unit. The selection unit selects topics of interest to the student. Topics of interest to the student include, but are not limited to, academic fields, hobbies, and specific themes. The selection unit provides, for example, an interface for the student to select topics of interest. The selection unit can also conduct a survey to help the student select topics of interest. Furthermore, the selection unit can refer to the student's past learning history to help them select topics of interest. For example, the selection unit prioritizes recommending topics in which the student has previously received high marks. The generation unit uses a generation AI to automatically search for and provide videos and articles related to the topics selected by the selection unit. The generation unit can also use a generation AI to automatically search for and provide videos and articles related to the selected topics. Furthermore, the generation unit can build a system to automatically search for and provide videos and articles related to the selected topics using a generation AI. For example, the generation unit uses a generation AI to automatically search for videos and articles related to selected topics and provide them to students. The question generation unit uses the generation AI to generate questions based on the content provided by the generation unit, tailored to the student's level of understanding. The question generation unit can also use the generation AI to generate questions based on the content provided, tailored to the student's level of understanding. Furthermore, the question generation unit can build a system using the generation AI to generate questions based on the content provided, tailored to the student's level of understanding. For example, the question generation unit uses the generation AI to generate questions based on the content provided, tailored to the student's level of understanding. The check-in unit checks the students' mental health status through a regular check-in function. The check-in unit checks the students' mental health status through a regular check-in function.Furthermore, the check-in unit can also build a system for checking students' mental health status through a regular check-in function. For example, the check-in unit can check students' mental health status through a regular check-in function. The dashboard unit provides a dashboard for parents. For example, the dashboard unit provides a dashboard for parents. Furthermore, the dashboard unit can also build a system for providing a dashboard for parents. For example, the dashboard unit provides a dashboard for parents. This enables the truancy support AI agent according to the embodiment to provide learning content based on students' interests and mental health care.

[0030] The selection function selects topics that students are interested in. These topics may include, but are not limited to, academic fields, hobbies, or specific themes. The selection function provides an interface for students to select topics of interest. Specifically, it provides an interface with a text box where students can enter their interests and a dropdown menu with multiple options. The selection function can also conduct surveys to help students select topics of interest. These surveys include questions designed to elicit student interests and concerns, such as "What are you interested in lately?" or "What topics would you like to learn more about?" Furthermore, the selection function can refer to students' past learning history to help them select topics of interest. For example, it can prioritize recommending topics that students have previously rated highly. This allows for the provision of more appropriate learning content based on topics students have shown interest in or achieved high learning outcomes in the past. The selection function incorporates an algorithm that comprehensively analyzes this information to suggest the most suitable topics for each student. The algorithm prioritizes topics of interest based on student responses and past data, and selects the most appropriate topic. This allows the selection process to effectively choose learning topics based on students' interests and concerns, thereby increasing their motivation to learn.

[0031] The generation unit uses a generation AI to automatically search for and provide videos and articles related to the topic selected by the selection unit. For example, the generation unit automatically searches for videos and articles related to the topic selected by the generation AI and provides them to students. Specifically, the generation AI extracts relevant content from a vast database on the internet and presents it to students in an appropriate format. The generation AI uses natural language processing technology to extract keywords related to the selected topic and performs searches based on them. For example, if the selected topic is "space," the generation AI searches for relevant videos and articles using keywords such as "space," "astronomy," and "space exploration." The generation unit can also use the generation AI to automatically search for and provide videos and articles related to the selected topic. The generation AI evaluates the search results and prioritizes providing the most relevant content. Furthermore, the generation unit can build a system that uses the generation AI to automatically search for and provide videos and articles related to the selected topic. For example, the generation unit automatically searches for videos and articles related to the topic selected by the generation AI and provides them to students. The generation unit filters the search results and prioritizes providing content from reliable sources. Furthermore, the generation unit can select content of appropriate difficulty based on the student's learning history and level of understanding. This allows the generation unit to provide optimal learning content tailored to the student's interests and level of understanding, maximizing learning effectiveness.

[0032] The question generation unit uses a generation AI to generate questions tailored to the student's level of understanding based on the content provided by the unit. Specifically, the generation AI analyzes the content of the provided video or article and extracts important points and keywords. Based on this, it generates questions in various formats, such as multiple-choice and written response questions. The generation AI uses natural language processing technology to understand the content and create appropriate questions. For example, if the video is about "space exploration," it will generate questions such as "What is the purpose of space exploration?" or "What technologies are used in space exploration?" Furthermore, the question generation unit can also use the generation AI to generate questions tailored to the student's level of understanding based on the provided content. The generation AI considers the student's past answer history and level of understanding to generate questions of appropriate difficulty. Additionally, the question generation unit can build a system using the generation AI to generate questions tailored to the student's level of understanding based on the provided content. For example, the generation AI can adjust the difficulty of the questions and provide feedback tailored to the student's level of understanding. This allows the problem generation unit to effectively assess students' understanding and provide appropriate learning support.

[0033] The check-in department monitors students' mental health status through a regular check-in function. Specifically, the check-in department provides an interface that periodically asks students questions about their mental health. These questions check on the student's mood, stress levels, sleep patterns, etc., and include questions such as, "How have you been feeling lately?" or "Are you feeling stressed?" The check-in department can also build a system to monitor students' mental health status through a regular check-in function. The system has the function to record student responses and track changes in mental health. Furthermore, the check-in department can build a system to monitor students' mental health status through a regular check-in function. For example, the check-in department can monitor students' mental health status through a regular check-in function. Based on student responses, the check-in department can provide counseling and support as needed. This allows the check-in department to continuously monitor students' mental health, detect problems early, and provide appropriate support.

[0034] The Dashboard section provides a dashboard for parents. Specifically, the Dashboard section provides an interface that visually displays students' learning progress and mental health status. Parents can check changes in their child's learning status and mental health in real time through the dashboard. The Dashboard section can also build a system to provide a dashboard for parents. This system has the functionality to collect student learning and mental health data and display it clearly for parents. Furthermore, the Dashboard section can build a system to provide a dashboard for parents. For example, the Dashboard section provides a dashboard for parents. The dashboard visually displays students' learning progress and mental health status using graphs and charts. The dashboard also provides information to enable parents to provide advice and support tailored to the child's situation. In this way, the Dashboard section can provide parents with tools to effectively support their child's learning and mental health, thereby supporting the child's growth.

[0035] The selection unit can analyze a student's past learning history and recommend the most suitable topics. For example, the selection unit may prioritize recommending topics in which the student has previously received high marks. The selection unit may also recommend new topics related to topics the student has previously shown interest in. The selection unit may also recommend topics to complement topics in which the student has previously struggled. This enhances learning effectiveness by recommending the most suitable topics based on past learning history. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the student's past learning history data into a generating AI and have the generating AI recommend the most suitable topics.

[0036] The selection unit can filter topics based on the student's current learning progress and areas of interest. For example, the selection unit can present topics of appropriate difficulty according to the student's current learning progress. The selection unit can also prioritize presenting highly relevant topics based on the student's areas of interest. The selection unit can also filter and present topics necessary to achieve the student's learning goals. This allows for the provision of appropriate learning content by filtering topics based on the student's current learning progress and areas of interest. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the student's current learning progress data into a generating AI and have the generating AI perform topic filtering.

[0037] The selection unit can prioritize presenting highly relevant topics by considering the student's geographical location when selecting a topic. For example, the selection unit can prioritize presenting topics related to the student's region based on their geographical location. The selection unit can also prioritize presenting topics related to the region's culture or history based on the student's geographical location. The selection unit can also prioritize presenting topics related to local events or news based on the student's geographical location. This makes it easier to engage students' interest in learning by presenting highly relevant topics based on their geographical location. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the student's geographical location data into a generating AI and have the generating AI perform the task of presenting highly relevant topics.

[0038] The selection unit can analyze students' social media activity when they select a topic and present relevant topics. For example, the selection unit can analyze topics that students are interested in from their social media activity and present relevant topics. For example, the selection unit can also present topics related to accounts and groups that students follow from their social media activity. For example, the selection unit can also present topics related to recent posts from their social media activity. This makes it easier to attract students' interest by presenting relevant topics based on their social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input students' social media activity data into a generating AI and have the generating AI perform the task of presenting relevant topics.

[0039] The generation unit can adjust the level of detail of the content it provides based on the importance of the topic during content generation. For example, the generation unit can generate content with detailed explanations for high-importance topics. For example, the generation unit can also generate content with concise explanations for low-importance topics. The generation unit can also adjust the length and level of detail of the content according to its importance. This improves learning efficiency by adjusting the level of detail of the content based on the importance of the topic. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input topic importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the content.

[0040] The generation unit can apply different generation algorithms depending on the topic category when generating content. For example, for a history topic, the generation unit can apply an algorithm that generates content organized chronologically. For a science topic, the generation unit can also apply an algorithm that generates content emphasizing experiments and observations. For a literature topic, the generation unit can also apply an algorithm that generates content emphasizing narrative structure and themes. By applying a generation algorithm according to the topic category, appropriate content can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input topic category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0041] The generation unit can adjust the order of content provided based on topic relevance during content generation. For example, the generation unit can prioritize providing content with high topic relevance. The generation unit can also adjust the order of content according to topic relevance. For example, the generation unit can provide important information first based on topic relevance. This improves learning efficiency by adjusting the order of content based on topic relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input topic relevance data into a generation AI and have the generation AI perform the adjustment of the content order.

[0042] The generation unit can determine the priority of content to provide based on the submission dates of topics when generating content. For example, the generation unit can prioritize providing content to topics with upcoming submission dates. The generation unit can also adjust the content priority according to the submission dates. For example, the generation unit can postpone providing content to topics with later submission dates. This improves learning efficiency by prioritizing content based on submission dates. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input topic submission date data into a generation AI and have the generation AI perform the content priority determination.

[0043] The problem generation unit can adjust the level of detail of the questions based on the level of understanding of the content when generating questions. For example, if the level of understanding is high, the problem generation unit can provide detailed questions. For example, if the level of understanding is low, the problem generation unit can also provide concise questions. The problem generation unit can also adjust the level of detail of the questions according to the level of understanding. This enhances the learning effect by adjusting the level of detail of the questions based on the level of understanding of the content. Some or all of the above processing in the problem generation unit may be performed using AI, for example, or without using AI. For example, the problem generation unit can input content understanding data into a generation AI and have the generation AI perform the adjustment of the level of detail of the questions.

[0044] The problem generation unit can apply different problem generation algorithms depending on the topic category when generating problems. For example, for a history topic, the problem generation unit can apply an algorithm that generates problems organized chronologically. For a science topic, the problem generation unit can also apply an algorithm that generates problems emphasizing experiments and observations. For a literature topic, the problem generation unit can also apply an algorithm that generates problems emphasizing narrative structure and themes. By applying a problem generation algorithm according to the topic category, appropriate problems can be provided. Some or all of the above processing in the problem generation unit may be performed using AI, for example, or without AI. For example, the problem generation unit can input topic category data into a generation AI and have the generation AI execute the application of the problem generation algorithm.

[0045] The problem generation unit can adjust the order of problems based on the relevance of the content when generating problems. For example, the problem generation unit can prioritize providing problems that are highly relevant to a topic. The problem generation unit can also adjust the order of problems according to the relevance of topics. For example, the problem generation unit can provide important problems first based on the relevance of topics. This improves learning efficiency by adjusting the order of problems based on the relevance of the content. Some or all of the above processing in the problem generation unit may be performed using AI, for example, or without AI. For example, the problem generation unit can input content relevance data into a generation AI and have the generation AI perform the adjustment of the order of problems.

[0046] The problem generation unit can determine the priority of problems based on the submission deadlines of topics when generating problems. For example, the problem generation unit can prioritize providing problems for topics with upcoming submission deadlines. The problem generation unit can also adjust the priority of problems according to the submission deadlines. For example, the problem generation unit can postpone providing problems for topics with later submission deadlines. This improves learning efficiency by prioritizing problems based on submission deadlines. Some or all of the above processing in the problem generation unit may be performed using AI, for example, or without AI. For example, the problem generation unit can input topic submission deadline data into a generation AI and have the generation AI perform the problem priority determination.

[0047] The check-in unit can customize the check-in process based on the student's current living situation. For example, if a student is busy, the check-in unit can provide a quick check-in method. If a student is relaxed, the check-in unit can also provide a more detailed check-in method. If a student is out, the check-in unit can also provide a check-in method using a mobile device. This enhances the effectiveness of mental health care by customizing the check-in process based on the student's current living situation. Some or all of the above processing in the check-in unit may be performed using AI, for example, or without AI. For example, the check-in unit can input data on the student's current living situation into a generating AI and have the generating AI customize the check-in process.

[0048] The check-in unit can select the optimal check-in method at the time of check-in, taking into account the student's geographical location. For example, if the student is at home, the check-in unit can provide a relaxing check-in method. If the student is at school, the check-in unit can also provide a quick check-in method. If the student is out, the check-in unit can also provide a check-in method using a mobile device. This enhances the effectiveness of mental health care by selecting the optimal check-in method based on geographical location. Some or all of the above processing in the check-in unit may be performed using AI, for example, or without AI. For example, the check-in unit can input the student's geographical location data into a generating AI and have the generating AI select the optimal check-in method.

[0049] The check-in unit can analyze a student's social media activity during check-in and suggest a method for checking in. For example, the check-in unit can use the student's social media activity to conduct a relaxing check-in. For example, the check-in unit can use the student's social media activity to conduct a reassuring check-in. For example, the check-in unit can use the student's social media activity to conduct a check-in in an encouraging or uplifting way. This enhances the effectiveness of mental health care by suggesting a method for checking in based on social media activity. Some or all of the above processing in the check-in unit may be performed using AI, for example, or without AI. For example, the check-in unit can input the student's social media activity data into a generating AI and have the generating AI suggest a method for checking in.

[0050] The dashboard unit can select the optimal display method by referring to the parent's past operation history when displaying the dashboard. For example, the dashboard unit can prioritize display methods that the parent has frequently used in the past. For example, the dashboard unit can also suggest the optimal display method based on the parent's past operation history. For example, the dashboard unit can analyze the parent's past operation history and provide the most efficient display method. This improves the visibility of information by selecting the optimal display method based on past operation history. Some or all of the above processing in the dashboard unit may be performed using AI, for example, or without AI. For example, the dashboard unit can input the parent's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0051] The dashboard can customize the displayed content based on the parent's current interests when the dashboard is displayed. For example, the dashboard can prioritize displaying relevant information based on the parent's current interests. The dashboard can also customize the displayed content based on the parent's current interests. For example, the dashboard can display important information first based on the parent's current interests. This enhances the visibility of information by customizing the displayed content based on current interests. Some or all of the above processing in the dashboard may be performed using AI, for example, or without AI. For example, the dashboard can input data on the parent's current interests into a generating AI and have the generating AI perform the customization of the displayed content.

[0052] The dashboard section can select the optimal display method when displaying the dashboard, taking into account the parent's device information. For example, if the parent is using a smartphone, the dashboard section can provide a display method that matches the screen size. For example, if the parent is using a tablet, the dashboard section can also provide a display method optimized for a larger screen. For example, if the parent is using a smartwatch, the dashboard section can also provide a concise and highly visible display method. This enhances the visibility of information by selecting the optimal display method based on device information. Some or all of the above processing in the dashboard section may be performed using AI, for example, or without AI. For example, the dashboard section can input the parent's device information data into a generating AI and have the generating AI select the optimal display method.

[0053] The dashboard section can analyze parents' social media activity and suggest content to display when the dashboard is shown. For example, the dashboard section can prioritize displaying information that parents are interested in based on their social media activity. For example, the dashboard section can also display information related to accounts and groups that parents follow based on their social media activity. For example, the dashboard section can also display information related to recent posts based on their social media activity. This improves the visibility of information by suggesting content based on social media activity. Some or all of the above processing in the dashboard section may be performed using AI, for example, or without AI. For example, the dashboard section can input parents' social media activity data into a generating AI and have the generating AI suggest content to display.

[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 selection unit can analyze students' learning styles and recommend the most suitable topics. For example, if a student is a visual learner, the selection unit will prioritize recommending topics that contain a lot of visual content. If a student is an auditory learner, the selection unit can also recommend topics that include audio or music. If a student is an experiential learner, the selection unit can also recommend topics that include hands-on activities. This enhances learning effectiveness by recommending topics that match the student's learning style. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input student learning style data into a generating AI and have the generating AI recommend the most suitable topics.

[0056] The generation unit can monitor students' learning progress in real time and adjust content according to their progress. For example, if a student is ahead of schedule, the generation unit can provide more advanced content. If a student is behind schedule, the generation unit can also provide basic content again. If a student is struggling with a particular topic, the generation unit can provide supplementary content related to that topic. This enhances learning effectiveness by adjusting content according to learning progress. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning progress data into a generation AI and have the generation AI perform content adjustments.

[0057] The problem generation unit can analyze students' learning history and adjust the difficulty level of problems based on their past performance. For example, the problem generation unit can provide more difficult problems on topics in which students have previously scored highly. For example, the problem generation unit can also provide basic problems on topics in which students have previously scored low. For example, the problem generation unit can also provide supplementary problems on topics in which students have previously struggled. This enhances learning effectiveness by adjusting the difficulty level of problems based on past performance. Some or all of the above processing in the problem generation unit may be performed using AI, for example, or without AI. For example, the problem generation unit can input student learning history data into a generation AI and have the generation AI perform the adjustment of problem difficulty.

[0058] The check-in unit can monitor students' learning performance and adjust the content of the check-in according to their performance. For example, if a student's learning performance is high, the check-in unit can provide a check-in that praises the student. If a student's learning performance is low, the check-in unit can also provide a check-in that includes encouragement and advice. If a student's learning performance is stable, the check-in unit can also provide guidance for moving on to the next step. This enhances the effectiveness of mental health care by adjusting the content of the check-in according to learning performance. Some or all of the above processing in the check-in unit may be performed using AI, for example, or not using AI. For example, the check-in unit can input student learning performance data into a generating AI and have the generating AI adjust the content of the check-in.

[0059] The dashboard unit can collect parental feedback and improve its functionality based on that feedback. For example, the dashboard unit can collect feedback from parents and make improvements to enhance usability. For example, the dashboard unit can collect feedback from parents and make improvements to customize the displayed content. For example, the dashboard unit can collect feedback from parents and make improvements to add new features. This improves the visibility and usability of information by improving the dashboard functionality based on parental feedback. Some or all of the above processes in the dashboard unit may be performed using AI, for example, or not using AI. For example, the dashboard unit can input parental feedback data into a generating AI and have the generating AI perform improvements to the dashboard functionality.

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

[0061] Step 1: The selection section selects topics that students are interested in. These topics can include academic fields, hobbies, or specific themes. The selection section provides an interface for students to select topics they are interested in and can also conduct surveys. It can also refer to past learning history and prioritize recommending topics that students have previously performed well on. Step 2: The generation unit uses generation AI to automatically search for and provide videos and articles related to the topic selected by the selection unit. The generation unit can also build a system to automatically search for videos and articles related to the selected topic and provide them to students. Step 3: The problem generation unit uses a generation AI to generate problems tailored to the level of understanding based on the content provided by the generation unit. The problem generation unit can also build a system to generate problems tailored to the level of understanding based on the provided content. Step 4: The check-in department will monitor students' mental health status through a regular check-in function. The check-in department can also build a system to monitor students' mental health status through a regular check-in function. Step 5: The dashboard section provides a dashboard for parents. The dashboard section can also build a system to provide a dashboard for parents.

[0062] (Example of form 2) The school absenteeism support AI agent according to an embodiment of the present invention is a system that uses AI to provide online learning support and mental health care to students who are absent from school. This system uses a generative AI based on a large-scale language model (LLM) to provide customized learning content based on topics that interest the student. Specifically, it automatically searches for videos and articles related to the theme selected by the student and generates questions according to their level of understanding. In addition, it checks the student's mental health status through a regular check-in function and encourages consultation with a specialist as needed. Furthermore, a dashboard for parents is provided so that they can understand their child's progress and mental health status in real time. For example, the student selects a topic that interests them. For example, they can choose from themes such as history, science, and literature. Next, the generative AI automatically searches for videos and articles related to the selected theme and provides them to the student. This allows the student to proceed with learning based on their interests. Furthermore, the generative AI generates questions according to the student's level of understanding. For example, a student who has chosen the theme of history will be provided with history quizzes and essay assignments. This allows the student to proceed with learning at their own pace. In addition, the student's mental health status is checked through a regular check-in function. The generating AI analyzes students' responses and behavioral patterns to detect signs of stress and anxiety. It sends notifications prompting consultation with a professional as needed. A dashboard for parents allows them to monitor their child's progress and mental health in real time. For example, they can see what topics their child is interested in, their level of understanding, and their mental health status. This enables parents to effectively support their child's learning and mental health. Thus, the school absenteeism support AI agent offers unique value by providing personalized learning experiences and mental healthcare, addressing learning delays caused by school absenteeism and providing emotional support. This allows the school absenteeism support AI agent to provide learning content and mental healthcare based on the student's interests.

[0063] The truancy support AI agent according to this embodiment comprises a selection unit, a generation unit, a problem generation unit, a check-in unit, and a dashboard unit. The selection unit selects topics of interest to the student. Topics of interest to the student include, but are not limited to, academic fields, hobbies, and specific themes. The selection unit provides, for example, an interface for the student to select topics of interest. The selection unit can also conduct a survey to help the student select topics of interest. Furthermore, the selection unit can refer to the student's past learning history to help them select topics of interest. For example, the selection unit prioritizes recommending topics in which the student has previously received high marks. The generation unit uses a generation AI to automatically search for and provide videos and articles related to the topics selected by the selection unit. The generation unit can also use a generation AI to automatically search for and provide videos and articles related to the selected topics. Furthermore, the generation unit can build a system to automatically search for and provide videos and articles related to the selected topics using a generation AI. For example, the generation unit uses a generation AI to automatically search for videos and articles related to selected topics and provide them to students. The question generation unit uses the generation AI to generate questions based on the content provided by the generation unit, tailored to the student's level of understanding. The question generation unit can also use the generation AI to generate questions based on the content provided, tailored to the student's level of understanding. Furthermore, the question generation unit can build a system using the generation AI to generate questions based on the content provided, tailored to the student's level of understanding. For example, the question generation unit uses the generation AI to generate questions based on the content provided, tailored to the student's level of understanding. The check-in unit checks the students' mental health status through a regular check-in function. The check-in unit checks the students' mental health status through a regular check-in function.Furthermore, the check-in unit can also build a system for checking students' mental health status through a regular check-in function. For example, the check-in unit can check students' mental health status through a regular check-in function. The dashboard unit provides a dashboard for parents. For example, the dashboard unit provides a dashboard for parents. Furthermore, the dashboard unit can also build a system for providing a dashboard for parents. For example, the dashboard unit provides a dashboard for parents. This enables the truancy support AI agent according to the embodiment to provide learning content based on students' interests and mental health care.

[0064] The selection function selects topics that students are interested in. These topics may include, but are not limited to, academic fields, hobbies, or specific themes. The selection function provides an interface for students to select topics of interest. Specifically, it provides an interface with a text box where students can enter their interests and a dropdown menu with multiple options. The selection function can also conduct surveys to help students select topics of interest. These surveys include questions designed to elicit student interests and concerns, such as "What are you interested in lately?" or "What topics would you like to learn more about?" Furthermore, the selection function can refer to students' past learning history to help them select topics of interest. For example, it can prioritize recommending topics that students have previously rated highly. This allows for the provision of more appropriate learning content based on topics students have shown interest in or achieved high learning outcomes in the past. The selection function incorporates an algorithm that comprehensively analyzes this information to suggest the most suitable topics for each student. The algorithm prioritizes topics of interest based on student responses and past data, and selects the most appropriate topic. This allows the selection process to effectively choose learning topics based on students' interests and concerns, thereby increasing their motivation to learn.

[0065] The generation unit uses a generation AI to automatically search for and provide videos and articles related to the topic selected by the selection unit. For example, the generation unit automatically searches for videos and articles related to the topic selected by the generation AI and provides them to students. Specifically, the generation AI extracts relevant content from a vast database on the internet and presents it to students in an appropriate format. The generation AI uses natural language processing technology to extract keywords related to the selected topic and performs searches based on them. For example, if the selected topic is "space," the generation AI searches for relevant videos and articles using keywords such as "space," "astronomy," and "space exploration." The generation unit can also use the generation AI to automatically search for and provide videos and articles related to the selected topic. The generation AI evaluates the search results and prioritizes providing the most relevant content. Furthermore, the generation unit can build a system that uses the generation AI to automatically search for and provide videos and articles related to the selected topic. For example, the generation unit automatically searches for videos and articles related to the topic selected by the generation AI and provides them to students. The generation unit filters the search results and prioritizes providing content from reliable sources. Furthermore, the generation unit can select content of appropriate difficulty based on the student's learning history and level of understanding. This allows the generation unit to provide optimal learning content tailored to the student's interests and level of understanding, maximizing learning effectiveness.

[0066] The question generation unit uses a generation AI to generate questions tailored to the student's level of understanding based on the content provided by the unit. Specifically, the generation AI analyzes the content of the provided video or article and extracts important points and keywords. Based on this, it generates questions in various formats, such as multiple-choice and written response questions. The generation AI uses natural language processing technology to understand the content and create appropriate questions. For example, if the video is about "space exploration," it will generate questions such as "What is the purpose of space exploration?" or "What technologies are used in space exploration?" Furthermore, the question generation unit can also use the generation AI to generate questions tailored to the student's level of understanding based on the provided content. The generation AI considers the student's past answer history and level of understanding to generate questions of appropriate difficulty. Additionally, the question generation unit can build a system using the generation AI to generate questions tailored to the student's level of understanding based on the provided content. For example, the generation AI can adjust the difficulty of the questions and provide feedback tailored to the student's level of understanding. This allows the problem generation unit to effectively assess students' understanding and provide appropriate learning support.

[0067] The check-in department monitors students' mental health status through a regular check-in function. Specifically, the check-in department provides an interface that periodically asks students questions about their mental health. These questions check on the student's mood, stress levels, sleep patterns, etc., and include questions such as, "How have you been feeling lately?" or "Are you feeling stressed?" The check-in department can also build a system to monitor students' mental health status through a regular check-in function. The system has the function to record student responses and track changes in mental health. Furthermore, the check-in department can build a system to monitor students' mental health status through a regular check-in function. For example, the check-in department can monitor students' mental health status through a regular check-in function. Based on student responses, the check-in department can provide counseling and support as needed. This allows the check-in department to continuously monitor students' mental health, detect problems early, and provide appropriate support.

[0068] The Dashboard section provides a dashboard for parents. Specifically, the Dashboard section provides an interface that visually displays students' learning progress and mental health status. Parents can check changes in their child's learning status and mental health in real time through the dashboard. The Dashboard section can also build a system to provide a dashboard for parents. This system has the functionality to collect student learning and mental health data and display it clearly for parents. Furthermore, the Dashboard section can build a system to provide a dashboard for parents. For example, the Dashboard section provides a dashboard for parents. The dashboard visually displays students' learning progress and mental health status using graphs and charts. The dashboard also provides information to enable parents to provide advice and support tailored to the child's situation. In this way, the Dashboard section can provide parents with tools to effectively support their child's learning and mental health, thereby supporting the child's growth.

[0069] The selection unit can estimate a student's emotions and present topic options based on those emotions. For example, if a student is stressed, the selection unit may prioritize presenting relaxing topics. If a student is excited, the selection unit may also present engaging and active topics. If a student is depressed, the selection unit may also present encouraging and uplifting topics. This allows for a more appropriate learning experience by presenting topic options that match the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input student emotion data into a generative AI and have the generative AI present topic options.

[0070] The selection unit can analyze a student's past learning history and recommend the most suitable topics. For example, the selection unit may prioritize recommending topics in which the student has previously received high marks. The selection unit may also recommend new topics related to topics the student has previously shown interest in. The selection unit may also recommend topics to complement topics in which the student has previously struggled. This enhances learning effectiveness by recommending the most suitable topics based on past learning history. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the student's past learning history data into a generating AI and have the generating AI recommend the most suitable topics.

[0071] The selection unit can filter topics based on the student's current learning progress and areas of interest. For example, the selection unit can present topics of appropriate difficulty according to the student's current learning progress. The selection unit can also prioritize presenting highly relevant topics based on the student's areas of interest. The selection unit can also filter and present topics necessary to achieve the student's learning goals. This allows for the provision of appropriate learning content by filtering topics based on the student's current learning progress and areas of interest. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the student's current learning progress data into a generating AI and have the generating AI perform topic filtering.

[0072] The selection unit can estimate a student's emotions and prioritize topics based on those emotions. For example, if a student is stressed, the selection unit may prioritize relaxing topics. If a student is excited, the selection unit may prioritize engaging and active topics. If a student is depressed, the selection unit may prioritize encouraging and uplifting topics. This enhances learning effectiveness by prioritizing topics according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit can input student emotion data into a generative AI and have the generative AI determine the topic priorities.

[0073] The selection unit can prioritize presenting highly relevant topics by considering the student's geographical location when selecting a topic. For example, the selection unit can prioritize presenting topics related to the student's region based on their geographical location. The selection unit can also prioritize presenting topics related to the region's culture or history based on the student's geographical location. The selection unit can also prioritize presenting topics related to local events or news based on the student's geographical location. This makes it easier to engage students' interest in learning by presenting highly relevant topics based on their geographical location. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the student's geographical location data into a generating AI and have the generating AI perform the task of presenting highly relevant topics.

[0074] The selection unit can analyze students' social media activity when they select a topic and present relevant topics. For example, the selection unit can analyze topics that students are interested in from their social media activity and present relevant topics. For example, the selection unit can also present topics related to accounts and groups that students follow from their social media activity. For example, the selection unit can also present topics related to recent posts from their social media activity. This makes it easier to attract students' interest by presenting relevant topics based on their social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input students' social media activity data into a generating AI and have the generating AI perform the task of presenting relevant topics.

[0075] The generation unit can estimate the student's emotions and adjust the way the content is presented based on the estimated emotions. For example, if the student is relaxed, the generation unit can generate a video that progresses at a relaxed pace. If the student is in a hurry, the generation unit can also generate a video that emphasizes the shortest route. If the student is excited, the generation unit can also generate a video with visually stimulating effects. This enhances learning effectiveness by adjusting the way the content is presented according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input student emotion data into the generative AI and have the generative AI adjust the way the content is presented.

[0076] The generation unit can adjust the level of detail of the content it provides based on the importance of the topic during content generation. For example, the generation unit can generate content with detailed explanations for high-importance topics. For example, the generation unit can also generate content with concise explanations for low-importance topics. The generation unit can also adjust the length and level of detail of the content according to its importance. This improves learning efficiency by adjusting the level of detail of the content based on the importance of the topic. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input topic importance data into a generation AI and have the generation AI perform the adjustment of the level of detail of the content.

[0077] The generation unit can apply different generation algorithms depending on the topic category when generating content. For example, for a history topic, the generation unit can apply an algorithm that generates content organized chronologically. For a science topic, the generation unit can also apply an algorithm that generates content emphasizing experiments and observations. For a literature topic, the generation unit can also apply an algorithm that generates content emphasizing narrative structure and themes. By applying a generation algorithm according to the topic category, appropriate content can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input topic category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0078] The generation unit can estimate a student's emotions and adjust the length of the content provided based on the estimated emotions. For example, if a student is in a hurry, the generation unit can generate short, concise content. If a student is relaxed, the generation unit can also generate longer content with detailed explanations. If a student is excited, the generation unit can also generate content with visually stimulating effects. This enhances learning effectiveness by adjusting the length of content according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input student emotion data into a generative AI and have the generative AI adjust the length of the content.

[0079] The generation unit can adjust the order of content provided based on topic relevance during content generation. For example, the generation unit can prioritize providing content with high topic relevance. The generation unit can also adjust the order of content according to topic relevance. For example, the generation unit can provide important information first based on topic relevance. This improves learning efficiency by adjusting the order of content based on topic relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input topic relevance data into a generation AI and have the generation AI perform the adjustment of the content order.

[0080] The generation unit can determine the priority of content to provide based on the submission dates of topics when generating content. For example, the generation unit can prioritize providing content to topics with upcoming submission dates. The generation unit can also adjust the content priority according to the submission dates. For example, the generation unit can postpone providing content to topics with later submission dates. This improves learning efficiency by prioritizing content based on submission dates. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input topic submission date data into a generation AI and have the generation AI perform the content priority determination.

[0081] The problem generation unit can estimate a student's emotions and adjust the difficulty of the problems based on the estimated emotions. For example, if a student is feeling stressed, the problem generation unit can provide easy problems. For example, if a student is relaxed, the problem generation unit can provide problems of moderate difficulty. For example, if a student is excited, the problem generation unit can provide challenging problems. This enhances learning effectiveness by adjusting the difficulty of problems according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the problem generation unit may be performed using AI, or not using AI. For example, the problem generation unit can input student emotion data into the generative AI and have the generative AI adjust the difficulty of the problems.

[0082] The problem generation unit can adjust the level of detail of the questions based on the level of understanding of the content when generating questions. For example, if the level of understanding is high, the problem generation unit can provide detailed questions. For example, if the level of understanding is low, the problem generation unit can also provide concise questions. The problem generation unit can also adjust the level of detail of the questions according to the level of understanding. This enhances the learning effect by adjusting the level of detail of the questions based on the level of understanding of the content. Some or all of the above processing in the problem generation unit may be performed using AI, for example, or without using AI. For example, the problem generation unit can input content understanding data into a generation AI and have the generation AI perform the adjustment of the level of detail of the questions.

[0083] The problem generation unit can apply different problem generation algorithms depending on the topic category when generating problems. For example, for a history topic, the problem generation unit can apply an algorithm that generates problems organized chronologically. For a science topic, the problem generation unit can also apply an algorithm that generates problems emphasizing experiments and observations. For a literature topic, the problem generation unit can also apply an algorithm that generates problems emphasizing narrative structure and themes. By applying a problem generation algorithm according to the topic category, appropriate problems can be provided. Some or all of the above processing in the problem generation unit may be performed using AI, for example, or without AI. For example, the problem generation unit can input topic category data into a generation AI and have the generation AI execute the application of the problem generation algorithm.

[0084] The question generation unit can estimate a student's emotions and adjust the question format based on the estimated emotions. For example, if a student is stressed, the question generation unit may provide multiple-choice questions. If a student is relaxed, the question generation unit may also provide open-ended questions. If a student is excited, the question generation unit may also provide creative questions. This enhances learning effectiveness by adjusting the question format according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the question generation unit may be performed using AI, or not using AI. For example, the question generation unit can input student emotion data into a generative AI and have the generative AI adjust the question format.

[0085] The problem generation unit can adjust the order of problems based on the relevance of the content when generating problems. For example, the problem generation unit can prioritize providing problems that are highly relevant to a topic. The problem generation unit can also adjust the order of problems according to the relevance of topics. For example, the problem generation unit can provide important problems first based on the relevance of topics. This improves learning efficiency by adjusting the order of problems based on the relevance of the content. Some or all of the above processing in the problem generation unit may be performed using AI, for example, or without AI. For example, the problem generation unit can input content relevance data into a generation AI and have the generation AI perform the adjustment of the order of problems.

[0086] The problem generation unit can determine the priority of problems based on the submission deadlines of topics when generating problems. For example, the problem generation unit can prioritize providing problems for topics with upcoming submission deadlines. The problem generation unit can also adjust the priority of problems according to the submission deadlines. For example, the problem generation unit can postpone providing problems for topics with later submission deadlines. This improves learning efficiency by prioritizing problems based on submission deadlines. Some or all of the above processing in the problem generation unit may be performed using AI, for example, or without AI. For example, the problem generation unit can input topic submission deadline data into a generation AI and have the generation AI perform the problem priority determination.

[0087] The check-in unit can estimate a student's emotions and adjust the frequency of check-ins based on the estimated emotions. For example, if a student is stressed, the check-in unit will check in frequently. For example, if a student is relaxed, the check-in unit may check in at a moderate frequency. For example, if a student is excited, the check-in unit may check in as needed. This enhances the effectiveness of mental health care by adjusting the frequency of check-ins according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the check-in unit may be performed using AI, for example, or not using AI. For example, the check-in unit can input student emotion data into the generative AI and have the generative AI adjust the frequency of check-ins.

[0088] The check-in unit can select the optimal check-in method by referring to the student's past mental health status during check-in. For example, if the student has experienced stress in the past, the check-in unit may use a relaxing check-in method. If the student has experienced anxiety in the past, the check-in unit may use a reassuring check-in method. If the student has experienced depression in the past, the check-in unit may use an encouraging or uplifting check-in method. This enhances the effectiveness of mental healthcare by selecting the optimal check-in method based on the student's past mental health status. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the check-in unit may be performed using AI, or not using AI. For example, the check-in unit can input the student's past mental health status data into the generative AI and have the generative AI select the optimal check-in method.

[0089] The check-in unit can customize the check-in process based on the student's current living situation. For example, if a student is busy, the check-in unit can provide a quick check-in method. If a student is relaxed, the check-in unit can also provide a more detailed check-in method. If a student is out, the check-in unit can also provide a check-in method using a mobile device. This enhances the effectiveness of mental health care by customizing the check-in process based on the student's current living situation. Some or all of the above processing in the check-in unit may be performed using AI, for example, or without AI. For example, the check-in unit can input data on the student's current living situation into a generating AI and have the generating AI customize the check-in process.

[0090] The check-in unit can estimate a student's emotions and determine the priority of check-ins based on the estimated emotions. For example, the check-in unit prioritizes check-ins when a student is stressed. For example, the check-in unit can also check in students at a moderate frequency when they are relaxed. For example, the check-in unit can check in students as needed when they are excited. This enhances the effectiveness of mental health care by determining the priority of check-ins according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the check-in unit may be performed using AI or not. For example, the check-in unit can input student emotion data into a generative AI and have the generative AI determine the priority of check-ins.

[0091] The check-in unit can select the optimal check-in method at the time of check-in, taking into account the student's geographical location. For example, if the student is at home, the check-in unit can provide a relaxing check-in method. If the student is at school, the check-in unit can also provide a quick check-in method. If the student is out, the check-in unit can also provide a check-in method using a mobile device. This enhances the effectiveness of mental health care by selecting the optimal check-in method based on geographical location. Some or all of the above processing in the check-in unit may be performed using AI, for example, or without AI. For example, the check-in unit can input the student's geographical location data into a generating AI and have the generating AI select the optimal check-in method.

[0092] The check-in unit can analyze a student's social media activity during check-in and suggest a method for checking in. For example, the check-in unit can use the student's social media activity to conduct a relaxing check-in. For example, the check-in unit can use the student's social media activity to conduct a reassuring check-in. For example, the check-in unit can use the student's social media activity to conduct a check-in in an encouraging or uplifting way. This enhances the effectiveness of mental health care by suggesting a method for checking in based on social media activity. Some or all of the above processing in the check-in unit may be performed using AI, for example, or without AI. For example, the check-in unit can input the student's social media activity data into a generating AI and have the generating AI suggest a method for checking in.

[0093] The dashboard can estimate the parent's emotions and adjust the dashboard display based on the estimated emotions. For example, if the parent is stressed, the dashboard can provide a simple and highly visible display. If the parent is relaxed, the dashboard can also provide a display that includes detailed information. If the parent is in a hurry, the dashboard can also provide a concise display. This enhances the visibility of information by adjusting the dashboard display according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dashboard may be performed using AI or not. For example, the dashboard can input parent emotion data into a generative AI and have the generative AI adjust the dashboard display.

[0094] The dashboard unit can select the optimal display method by referring to the parent's past operation history when displaying the dashboard. For example, the dashboard unit can prioritize display methods that the parent has frequently used in the past. For example, the dashboard unit can also suggest the optimal display method based on the parent's past operation history. For example, the dashboard unit can analyze the parent's past operation history and provide the most efficient display method. This improves the visibility of information by selecting the optimal display method based on past operation history. Some or all of the above processing in the dashboard unit may be performed using AI, for example, or without AI. For example, the dashboard unit can input the parent's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0095] The dashboard can customize the displayed content based on the parent's current interests when the dashboard is displayed. For example, the dashboard can prioritize displaying relevant information based on the parent's current interests. The dashboard can also customize the displayed content based on the parent's current interests. For example, the dashboard can display important information first based on the parent's current interests. This enhances the visibility of information by customizing the displayed content based on current interests. Some or all of the above processing in the dashboard may be performed using AI, for example, or without AI. For example, the dashboard can input data on the parent's current interests into a generating AI and have the generating AI perform the customization of the displayed content.

[0096] The dashboard can estimate the parent's emotions and adjust the dashboard's operating procedures based on the estimated emotions. For example, if the parent is stressed, the dashboard provides simple and easy-to-understand operating procedures. For example, if the parent is relaxed, the dashboard can also provide operating procedures that include detailed information. For example, if the parent is in a hurry, the dashboard can also provide concise operating procedures. This increases the efficiency of operation by adjusting the operating procedures according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dashboard may be performed using AI or not. For example, the dashboard can input parent emotion data into a generative AI and have the generative AI perform the adjustment of the operating procedures.

[0097] The dashboard section can select the optimal display method when displaying the dashboard, taking into account the parent's device information. For example, if the parent is using a smartphone, the dashboard section can provide a display method that matches the screen size. For example, if the parent is using a tablet, the dashboard section can also provide a display method optimized for a larger screen. For example, if the parent is using a smartwatch, the dashboard section can also provide a concise and highly visible display method. This enhances the visibility of information by selecting the optimal display method based on device information. Some or all of the above processing in the dashboard section may be performed using AI, for example, or without AI. For example, the dashboard section can input the parent's device information data into a generating AI and have the generating AI select the optimal display method.

[0098] The dashboard section can analyze parents' social media activity and suggest content to display when the dashboard is shown. For example, the dashboard section can prioritize displaying information that parents are interested in based on their social media activity. For example, the dashboard section can also display information related to accounts and groups that parents follow based on their social media activity. For example, the dashboard section can also display information related to recent posts based on their social media activity. This improves the visibility of information by suggesting content based on social media activity. Some or all of the above processing in the dashboard section may be performed using AI, for example, or without AI. For example, the dashboard section can input parents' social media activity data into a generating AI and have the generating AI suggest content to display.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The selection unit can analyze students' learning styles and recommend the most suitable topics. For example, if a student is a visual learner, the selection unit will prioritize recommending topics that contain a lot of visual content. If a student is an auditory learner, the selection unit can also recommend topics that include audio or music. If a student is an experiential learner, the selection unit can also recommend topics that include hands-on activities. This enhances learning effectiveness by recommending topics that match the student's learning style. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input student learning style data into a generating AI and have the generating AI recommend the most suitable topics.

[0101] The generation unit can monitor students' learning progress in real time and adjust content according to their progress. For example, if a student is ahead of schedule, the generation unit can provide more advanced content. If a student is behind schedule, the generation unit can also provide basic content again. If a student is struggling with a particular topic, the generation unit can provide supplementary content related to that topic. This enhances learning effectiveness by adjusting content according to learning progress. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning progress data into a generation AI and have the generation AI perform content adjustments.

[0102] The problem generation unit can analyze students' learning history and adjust the difficulty level of problems based on their past performance. For example, the problem generation unit can provide more difficult problems on topics in which students have previously scored highly. For example, the problem generation unit can also provide basic problems on topics in which students have previously scored low. For example, the problem generation unit can also provide supplementary problems on topics in which students have previously struggled. This enhances learning effectiveness by adjusting the difficulty level of problems based on past performance. Some or all of the above processing in the problem generation unit may be performed using AI, for example, or without AI. For example, the problem generation unit can input student learning history data into a generation AI and have the generation AI perform the adjustment of problem difficulty.

[0103] The check-in unit can monitor students' learning performance and adjust the content of the check-in according to their performance. For example, if a student's learning performance is high, the check-in unit can provide a check-in that praises the student. If a student's learning performance is low, the check-in unit can also provide a check-in that includes encouragement and advice. If a student's learning performance is stable, the check-in unit can also provide guidance for moving on to the next step. This enhances the effectiveness of mental health care by adjusting the content of the check-in according to learning performance. Some or all of the above processing in the check-in unit may be performed using AI, for example, or not using AI. For example, the check-in unit can input student learning performance data into a generating AI and have the generating AI adjust the content of the check-in.

[0104] The dashboard unit can collect parental feedback and improve its functionality based on that feedback. For example, the dashboard unit can collect feedback from parents and make improvements to enhance usability. For example, the dashboard unit can collect feedback from parents and make improvements to customize the displayed content. For example, the dashboard unit can collect feedback from parents and make improvements to add new features. This improves the visibility and usability of information by improving the dashboard functionality based on parental feedback. Some or all of the above processes in the dashboard unit may be performed using AI, for example, or not using AI. For example, the dashboard unit can input parental feedback data into a generating AI and have the generating AI perform improvements to the dashboard functionality.

[0105] The selection unit can estimate a student's emotions and adjust the learning environment based on the estimated emotions. For example, if a student is feeling stressed, the selection unit can provide a relaxing learning environment. For example, if a student is excited, the selection unit can also provide a learning environment that enhances concentration. For example, if a student is depressed, the selection unit can also provide an encouraging and uplifting learning environment. By adjusting the learning environment according to the student's emotions, the learning effect is enhanced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input student emotion data into a generative AI and have the generative AI perform the adjustment of the learning environment.

[0106] The generation unit can estimate a student's emotions and adjust the content format based on the estimated emotions. For example, if a student is relaxed, the generation unit can provide text-based content. If a student is excited, the generation unit can also provide interactive content. If a student is depressed, the generation unit can also provide visually engaging content. This enhances learning effectiveness by adjusting the content format according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input student emotion data into the generative AI and have the generative AI adjust the content format.

[0107] The problem generation unit can estimate a student's emotions and adjust the feedback on the problem based on the estimated emotions. For example, if a student is stressed, the problem generation unit can provide positive feedback. For example, if a student is relaxed, the problem generation unit can also provide detailed feedback. For example, if a student is excited, the problem generation unit can also provide challenging feedback. This enhances learning effectiveness by adjusting the feedback on the problem according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the problem generation unit may be performed using AI, or not using AI. For example, the problem generation unit can input student emotion data into a generative AI and have the generative AI adjust the feedback on the problem.

[0108] The check-in unit can estimate a student's emotions and adjust the content of the check-in based on the estimated emotions. For example, if a student is feeling stressed, the check-in unit can create a relaxing check-in. If a student is relaxed, the check-in unit can also create a more detailed check-in. If a student is excited, the check-in unit can also create a more challenging check-in. This enhances the effectiveness of mental health care by adjusting the content of the check-in according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the check-in unit may be performed using AI or not. For example, the check-in unit can input student emotion data into a generative AI and have the generative AI adjust the content of the check-in.

[0109] The dashboard can estimate the parent's emotions and adjust the notification method based on the estimated emotions. For example, if the parent is stressed, the dashboard can provide a simple and highly visible notification method. If the parent is relaxed, the dashboard can also provide a notification method that includes detailed information. If the parent is in a hurry, the dashboard can also provide a notification method that gets straight to the point. This enhances the visibility of information by adjusting the notification method according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dashboard may be performed using AI or not. For example, the dashboard can input parent emotion data into a generative AI and have the generative AI adjust the notification method.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The selection section selects topics that students are interested in. These topics can include academic fields, hobbies, or specific themes. The selection section provides an interface for students to select topics they are interested in and can also conduct surveys. It can also refer to past learning history and prioritize recommending topics that students have previously performed well on. Step 2: The generation unit uses generation AI to automatically search for and provide videos and articles related to the topic selected by the selection unit. The generation unit can also build a system to automatically search for videos and articles related to the selected topic and provide them to students. Step 3: The problem generation unit uses a generation AI to generate problems tailored to the level of understanding based on the content provided by the generation unit. The problem generation unit can also build a system to generate problems tailored to the level of understanding based on the provided content. Step 4: The check-in department will monitor students' mental health status through a regular check-in function. The check-in department can also build a system to monitor students' mental health status through a regular check-in function. Step 5: The dashboard section provides a dashboard for parents. The dashboard section can also build a system to provide a dashboard for parents.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the selection unit, generation unit, problem generation unit, check-in unit, and dashboard unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart device 14 and provides an interface for students to select topics of interest. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically searches for and provides videos and articles related to the selected topic using generation AI. The problem generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates problems according to the level of understanding based on the provided content. The check-in unit is implemented by the control unit 46A of the smart device 14 and checks the mental health status of students through a periodic check-in function. The dashboard unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a dashboard for parents. 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.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the selection unit, generation unit, problem generation unit, check-in unit, and dashboard unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for students to select topics of interest. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically searches for and provides videos and articles related to the selected topic using generation AI. The problem generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates problems according to the level of understanding based on the provided content. The check-in unit is implemented by the control unit 46A of the smart glasses 214 and checks the student's mental health status through a periodic check-in function. The dashboard unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a dashboard for parents. 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.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the selection unit, generation unit, problem generation unit, check-in unit, and dashboard unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for students to select topics of interest. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically searches for and provides videos and articles related to the selected topic using a generation AI. The problem generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates problems according to the level of understanding based on the provided content. The check-in unit is implemented by the control unit 46A of the headset terminal 314 and checks the mental health status of students through a periodic check-in function. The dashboard unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a dashboard for parents. 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.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the selection unit, generation unit, problem generation unit, check-in unit, and dashboard unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the robot 414 and provides an interface for students to select topics of interest. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically searches for and provides videos and articles related to the selected topic using a generation AI. The problem generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates problems according to the level of understanding based on the provided content. The check-in unit is implemented by, for example, the control unit 46A of the robot 414 and checks the mental health status of students through a periodic check-in function. The dashboard unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides a dashboard for parents. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A selection section where students choose topics that interest them, A generation unit that automatically searches for and provides videos and articles related to the topic selected by the selection unit, A question generation unit generates questions according to the level of understanding based on the content provided by the generation unit, The check-in department monitors students' mental health status through a regular check-in function, It includes a dashboard section that provides a dashboard for parents. A system characterized by the following features. (Note 2) The aforementioned selection unit is The system estimates students' emotions and presents topic options based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned selection unit is Analyze students' past learning history and recommend the most suitable topics. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned selection unit is When selecting a topic, filtering is performed based on students' current learning progress and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned selection unit is Estimate students' emotions and prioritize topics based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned selection unit is When selecting a topic, the system prioritizes presenting topics that are highly relevant to the student's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned selection unit is When selecting a topic, analyze students' social media activity and suggest relevant topics. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is We estimate students' emotions and adjust the way we present content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating content, adjust the level of detail provided based on the importance of the topic. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating content, different generation algorithms are applied depending on the topic category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is The system estimates students' emotions and adjusts the length of the content provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating content, adjust the order of the content provided based on topic relevance. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating content, prioritize the content to be provided based on when the topic was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 14) The problem generation unit, The system estimates students' emotions and adjusts the difficulty level of the questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The problem generation unit, When generating questions, adjust the level of detail based on the level of understanding of the content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The problem generation unit, When generating a problem, different problem generation algorithms are applied depending on the topic category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The problem generation unit, The system estimates students' emotions and adjusts the question format based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The problem generation unit, When generating questions, the order of questions is adjusted based on the relevance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The problem generation unit, When generating a problem, the priority of the problem is determined based on when the topic was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned check-in section is, The system estimates students' emotions and adjusts the frequency of check-ins based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned check-in section is, During check-in, the most appropriate check-in method is selected by referring to the student's past mental health history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned check-in section is, At check-in, customize the check-in method based on the student's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned check-in section is, The system estimates students' emotions and prioritizes check-ins based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned check-in section is, During check-in, the most suitable check-in method will be selected, taking into account the student's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned check-in section is, During check-in, we analyze students' social media activity and suggest check-in methods. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned dashboard section is It estimates the parent's emotions and adjusts how the dashboard is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned dashboard section is When displaying the dashboard, the system will refer to the parent's past operation history to select the most suitable display method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned dashboard section is When the dashboard is displayed, the content shown will be customized based on the parent's current interests. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned dashboard section is It estimates the parent's emotions and adjusts the dashboard operation procedures based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned dashboard section is When displaying the dashboard, the system selects the optimal display method considering the parent's device information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned dashboard section is When displaying the dashboard, the system analyzes parents' social media activity and suggests content to show. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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 selection section where students choose topics that interest them, A generation unit that automatically searches for and provides videos and articles related to the topic selected by the selection unit, A question generation unit generates questions according to the level of understanding based on the content provided by the generation unit, The check-in department monitors students' mental health status through a regular check-in function, It includes a dashboard section that provides a dashboard for parents. A system characterized by the following features.

2. The aforementioned selection unit is The system estimates students' emotions and presents topic options based on those estimated emotions. The system according to feature 1.

3. The aforementioned selection unit is Analyze students' past learning history and recommend the most suitable topics. The system according to feature 1.

4. The aforementioned selection unit is When selecting a topic, filtering is performed based on students' current learning progress and areas of interest. The system according to feature 1.

5. The aforementioned selection unit is Estimate students' emotions and prioritize topics based on those estimated emotions. The system according to feature 1.

6. The aforementioned selection unit is When selecting a topic, the system prioritizes presenting topics that are highly relevant to the student's geographical location. The system according to feature 1.

7. The aforementioned selection unit is When selecting a topic, analyze students' social media activity and suggest relevant topics. The system according to feature 1.

8. The generating unit is We estimate students' emotions and adjust the way we present content based on those estimated emotions. The system according to feature 1.

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