system

Abstract

The system according to this embodiment aims to enable parents to understand their child's learning progress and challenges and provide appropriate support. [Solution] The system according to the embodiment comprises a reporting unit, a communication unit, an advice unit, a notification unit, and a translation unit. The reporting unit reports the child's learning progress and challenges in real time. The communication unit smoothly supports communication and consultation with teachers based on the information reported by the reporting unit. The advice unit proposes methods of home support tailored to the child's characteristics based on the information supported by the communication unit. The notification unit notifies of school events and meeting schedules based on the information proposed by the advice unit. The translation unit also supports foreign national parents based on the information notified by the notification unit, overcoming language barriers.

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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for guardians to grasp the learning progress and problems of children and provide appropriate support.

[0005] The system according to the embodiment aims to enable guardians to grasp the learning progress and problems of children and provide appropriate support.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reporting unit, a communication unit, an advice unit, a notification unit, and a translation unit. The reporting unit reports the child's learning progress and challenges in real time. The communication unit facilitates communication and consultation with teachers based on the information reported by the reporting unit. The advice unit proposes methods of home support tailored to the child's characteristics based on the information provided by the communication unit. The notification unit notifies parents of school events and meeting schedules based on the information proposed by the advice unit. The translation unit also addresses the needs of foreign parents based on the information notified by the notification unit, overcoming language barriers. [Effects of the Invention]

[0007] The system according to this embodiment allows parents to understand their child's learning progress and challenges, and to provide appropriate support. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[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) An AI agent system according to an embodiment of the present invention is a system that supports parents in actively participating in their children's learning and growth. This AI agent system can strengthen cooperation between home and school and enhance student autonomy and learning effectiveness. Even busy parents can appropriately participate in their children's education. For example, the AI ​​agent system reports the child's learning progress and assignments in real time. For example, the AI ​​collects the results of assignments and tests that the child has done at school and notifies the parents. This makes it easier for parents to understand their child's learning situation. Next, the AI ​​agent system smoothly supports communication and consultation with teachers. For example, if a parent wants to ask a question or consult with a teacher, the AI ​​organizes the content and conveys it to the teacher at the appropriate time. This makes communication between parents and teachers smoother. Furthermore, the AI ​​agent system suggests ways to support the child at home that are tailored to the child's characteristics. For example, the AI ​​analyzes the child's learning style and interests and suggests learning methods and activities at home based on that. This allows parents to provide support that is tailored to the child's characteristics. In addition, the AI ​​agent system notifies parents of school events and meeting schedules. For example, the AI ​​collects information from the school and notifies parents. This makes it easier for parents to keep track of school event and parent-teacher conference schedules. Furthermore, the AI ​​agent system also supports parents of foreign nationality, eliminating language barriers. For example, the AI ​​is multilingual, allowing parents to ask questions and seek advice in their native language. This enables parents of foreign nationality to actively participate in their children's education. In this way, the AI ​​agent system can support parents in actively participating in their children's learning and growth, strengthen collaboration between home and school, and enhance student autonomy and learning effectiveness.

[0029] The AI ​​agent system according to this embodiment comprises a reporting unit, a communication unit, an advice unit, a notification unit, and a translation unit. The reporting unit reports the child's learning progress and assignments in real time. For example, the reporting unit collects assignments and test results that the child has done at school and notifies the parents. For example, the reporting unit can use AI to analyze the child's learning situation and report it to the parents in real time. The communication unit smoothly supports communication and consultation with teachers. For example, when parents ask questions or seek advice from teachers, the communication unit organizes the content and conveys it to the teacher at an appropriate time. For example, the communication unit can use AI to analyze the content of parents' questions and consultations and convey it to the teacher at an appropriate time. The advice unit proposes methods of home support tailored to the child's characteristics. For example, the advice unit can use AI to analyze the child's learning style and interests and propose home learning methods and activities based on that. For example, the advice unit can use AI to propose methods of support tailored to the child's characteristics. The notification unit notifies parents of school events and meeting schedules. The notification unit, for example, uses AI to collect announcements from schools and notify parents. The notification unit can, for example, use AI to notify parents of school events and meeting schedules. The translation unit also supports parents of foreign nationality and eliminates language barriers. The translation unit, for example, uses AI that supports multiple languages, allowing parents to ask questions and seek advice in their native language. The translation unit can, for example, use AI to translate parents' questions and consultations and provide them in their native language. As a result, the AI ​​agent system according to this embodiment can support parents in actively participating in their children's learning and growth, strengthen cooperation between home and school, and enhance student autonomy and learning effectiveness.

[0030] The reporting department reports on children's learning progress and challenges in real time. Specifically, it collects the results of assignments and tests that children complete at school and notifies parents. For example, the AI ​​automatically collects and analyzes the results of quizzes taken during class, the submission status of homework, and the child's participation and participation in class. Based on this data, the AI ​​evaluates the child's learning situation and generates a detailed report for parents. The report includes the child's strengths and weaknesses in subjects, learning progress, and areas that need improvement. Furthermore, the AI ​​visualizes the child's growth and changes by comparing them with past data, allowing parents to understand their child's learning situation at a glance. The reporting department provides an application compatible with devices such as smartphones, tablets, and PCs so that parents can check their child's learning situation anytime, anywhere. This allows parents to understand their child's learning situation in real time, even while at work or away from home, and provide appropriate support as needed. Because the reporting department uses AI to automatically collect, analyze, and report data, it reduces the burden on parents and can efficiently support children's learning.

[0031] The liaison department facilitates smooth communication and consultation between parents and teachers. Specifically, when parents have questions or concerns with teachers, the department organizes the content and conveys it to the teacher at the appropriate time. For example, if a parent wants to ask a question about their child's learning progress, the AI ​​analyzes the question and organizes the information needed to provide an appropriate answer. Based on past interactions and the child's learning data, the AI ​​provides teachers with specific questions and background information, supporting teachers in responding quickly and accurately. Furthermore, the liaison department provides chat and video call functions to streamline communication between parents and teachers. This allows parents to interact directly with teachers and ask questions and seek advice in real time. The AI ​​records these interactions, making it possible to refer to past interactions as needed. The liaison department can streamline communication between parents and teachers and strengthen support for children's learning.

[0032] The advice department proposes methods of home support tailored to each child's characteristics. Specifically, the AI ​​analyzes the child's learning style and interests, and based on that, suggests learning methods and activities at home. For example, if a child prefers visual learning, the AI ​​will suggest learning methods using visual materials and videos. Also, if a child is interested in a particular subject, it will suggest activities and projects related to that subject to pique the child's interest. Furthermore, the AI ​​will support the creation of home learning plans based on the child's learning progress and achievement of tasks. For example, it will suggest weekly learning goals and daily learning schedules, enabling parents to effectively support their child's learning. The advice department can provide individualized support tailored to each child's characteristics, thereby enhancing the effectiveness of home learning.

[0033] The notification system notifies parents of school events and parent-teacher conference schedules. Specifically, AI collects information from the school and notifies parents. For example, the AI ​​automatically collects and notifies parents of school event schedules and changes, conference schedules, and emergency contact information. Notifications are sent via smartphone apps, email, SMS, etc., to ensure that parents do not miss important information. Furthermore, the notification system provides a reminder function to help parents take necessary actions after receiving a notification. For example, it can send a reminder the day before a conference to ensure that parents do not forget to attend. The notification system enables parents to communicate smoothly with the school and provides timely information to support their child's learning and school life.

[0034] The translation department also caters to parents of foreign nationality, eliminating language barriers. Specifically, the AI ​​is multilingual, allowing parents to ask questions and seek advice in their native language. For example, if a parent enters a question in their native language, the AI ​​translates it and conveys it to the teacher. Conversely, the teacher's response is also translated into the parent's native language and provided to the parent. This enables smooth communication that transcends language barriers. Furthermore, the translation department provides school announcements and notifications in multiple languages. For example, announcements about school events and meeting schedules are sent in the parent's native language, allowing parents of foreign nationality to communicate with the school with confidence. By eliminating language barriers, the translation department can support parents of foreign nationality in actively participating in their child's learning and school life.

[0035] The reporting unit can collect the results of assignments and tests that children have completed at school and notify parents. For example, the reporting unit can collect the results of assignments and tests that children have completed at school and notify parents. The reporting unit can, for example, use AI to analyze the child's learning progress and report it to parents in real time. This makes it easier for parents to understand their child's learning progress. Some or all of the above processes in the reporting unit may be performed using AI, or not using AI. For example, the reporting unit can use AI to analyze data and generate notification content in order to collect the results of assignments and tests that children have completed at school and notify parents.

[0036] The liaison department can organize the content of questions and consultations that parents have with teachers and convey them to teachers at the appropriate time. For example, when parents have questions or consultations with teachers, the liaison department can organize the content and convey it to teachers at the appropriate time. For example, the liaison department can use AI to analyze the content of parents' questions and consultations and convey it to teachers at the appropriate time. This makes communication between parents and teachers smoother. Some or all of the above processing in the liaison department may be performed using AI, or not. For example, when parents have questions or consultations with teachers, the liaison department can use AI to analyze the data and generate communication content in order to organize the content and convey it to teachers at the appropriate time.

[0037] The advice unit can analyze a child's learning style and interests and, based on that, suggest learning methods and activities for home study. For example, the advice unit can use AI to analyze a child's learning style and interests and, based on that, suggest learning methods and activities for home study. For example, the advice unit can use AI to suggest support methods tailored to the child's characteristics. This allows parents to provide support tailored to their child's characteristics. Some or all of the above processing in the advice unit may be performed using AI, or not. For example, the advice unit can use AI to analyze data and generate advice in order to analyze a child's learning style and interests and, based on that, suggest learning methods and activities for home study.

[0038] The notification unit can collect information from schools and notify parents. For example, the notification unit can use AI to collect information from schools and notify parents. For example, the notification unit can use AI to notify parents of school event and meeting schedules. This makes it easier for parents to keep track of school event and meeting schedules. Some or all of the above processes in the notification unit may be performed using AI, or not using AI. For example, the notification unit can use AI to analyze data and generate notification content in order to collect information from schools and notify parents.

[0039] The translation department is multilingual, allowing parents to ask questions and seek advice in their native language. For example, the AI ​​in the translation department is multilingual, enabling parents to ask questions and seek advice in their native language. The AI ​​in the translation department can translate parents' questions and seek advice and provide it in their native language. This allows parents of foreign nationality to actively participate in their children's education. Some or all of the above-described processes in the translation department may be performed using AI, or not. For example, the translation department can use AI to analyze data and generate translations in order to translate parents' questions and seek advice and provide them in their native language.

[0040] The reporting unit can highlight changes in a child's learning progress by comparing it with past learning data when reporting the child's learning progress in real time. For example, the reporting unit can visually display changes in progress in a graph by comparing it with learning data from the past month. For example, the reporting unit can highlight improvements in grades or areas for improvement by comparing past test results with current test results. For example, the reporting unit can report changes in learning habits by comparing past study time with current study time. This allows for a visual understanding of changes in progress by comparing it with past learning data. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, when reporting a child's learning progress in real time, the reporting unit can use AI to analyze the data and generate the report content in order to highlight changes in progress by comparing it with past learning data.

[0041] The reporting unit can adjust the frequency of reports on a child's learning progress according to the difficulty level and achievement level of the learning. For example, if a child completes a difficult task, the reporting unit will immediately report it and inform the parents. If a child's learning achievement level is low, the reporting unit will report frequently to encourage parents to provide support. If a child's learning achievement level is high, the reporting unit will limit reports to regular updates to reassure parents. This allows for the provision of an appropriate reporting frequency according to the difficulty level and achievement level of the learning. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not. For example, when reporting a child's learning progress, the reporting unit can use AI to analyze data and generate report content in order to adjust the frequency of reports according to the difficulty level and achievement level of the learning.

[0042] The reporting system can customize the content of reports on a child's learning progress based on the parents' areas of interest. For example, if a parent is interested in mathematics, the reporting system will focus on reporting the progress and details of assignments in mathematics. If a parent is interested in English, the reporting system will report in detail on English grades and learning content. If a parent is interested in the overall learning situation, the reporting system will report on the progress of each subject in a balanced manner. This allows for the provision of appropriate reports tailored to the parents' areas of interest. Some or all of the above processing in the reporting system may be performed using AI, for example, or not. For example, the reporting system can use AI to analyze data and generate reports in order to customize the content of reports on a child's learning progress based on the parents' areas of interest.

[0043] The reporting department can adjust the timing of reports on children's learning progress to match the parents' schedules. For example, the reporting department can report at night or on weekends, avoiding busy times for parents. For example, if a parent is in a meeting, the reporting department can report after the meeting ends. For example, if a parent is traveling, the reporting department can report all at once after the trip. This allows for appropriate reporting timing according to the parents' schedules. Some or all of the above processes in the reporting department may be performed using AI, for example, or not. For example, when reporting on children's learning progress, the reporting department can use AI to analyze data and generate report content in order to adjust the timing of reports to match the parents' schedules.

[0044] The liaison department can provide appropriate advice when parents ask questions or seek advice from teachers by referring to past communication history. For example, the liaison department can refer to similar questions or consultations from past communication history and provide appropriate advice. For example, the liaison department can provide additional information regarding current questions or consultations based on advice parents have received in the past. For example, the liaison department can analyze past communication history and provide advice tailored to the parents' interests and needs. This allows for the provision of appropriate advice by referring to past communication history. Some or all of the above processes in the liaison department may be performed using AI, for example, or not. For example, the liaison department can use AI to analyze data and generate advice in order to provide appropriate advice when parents ask questions or seek advice from teachers by referring to past communication history.

[0045] The liaison department can prioritize inquiries and consultations from parents to teachers based on their urgency. For example, the liaison department can immediately relay urgent questions and consultations to teachers. For example, the liaison department can relay less urgent questions and consultations to teachers at an appropriate time. The liaison department can set priorities for inquiries based on their urgency and notify parents accordingly. This allows for the provision of appropriate priorities based on the urgency of the inquiries. Some or all of the above processes in the liaison department may be performed using AI, or not. For example, the liaison department can use AI to analyze data and set priorities based on the urgency of inquiries when parents ask questions or consult with teachers.

[0046] The liaison department can customize the content of communications based on the parents' areas of interest when parents ask questions or seek advice from teachers. For example, if a parent is interested in mathematics, the liaison department will focus on questions and consultations related to mathematics. If a parent is interested in English, the liaison department will focus on questions and consultations related to English. If a parent is interested in overall learning progress, the liaison department will provide a balanced mix of questions and consultations across all subjects. This allows for the provision of appropriate communications tailored to the parents' areas of interest. Some or all of the above processing in the liaison department may be performed using AI, for example, or not. For example, the liaison department can use AI to analyze data and generate communications in order to customize the content of communications based on the parents' areas of interest when parents ask questions or seek advice from teachers.

[0047] The liaison department can adjust the timing of communication with parents to match their schedules when parents have questions or concerns with teachers. For example, the liaison department can contact parents in the evenings or on weekends, avoiding busy times. For example, if a parent is in a meeting, the liaison department can contact them after the meeting ends. For example, if a parent is traveling, the liaison department can contact them all at once after their trip. This allows for appropriate communication timing according to parents' schedules. Some or all of the above processes in the liaison department may be performed using AI, for example, or not. For example, the liaison department can use AI to analyze data and generate communication content in order to adjust the timing of communication with parents to match their schedules when parents have questions or concerns with teachers.

[0048] The advice unit can provide optimal advice by referring to past learning data when analyzing a child's learning style and interests. For example, the advice unit can identify subjects a child excels at or areas of interest from past learning data and provide advice based on that. For example, the advice unit can analyze past learning data and provide specific advice for subjects or tasks a child struggles with. For example, the advice unit can refer to past learning data and provide advice based on the child's learning habits and patterns. In this way, by referring to past learning data, it can provide optimal advice. Some or all of the above processes in the advice unit may be performed using AI, for example, or not. For example, the advice unit can use AI to analyze data and generate advice in order to provide optimal advice by referring to past learning data when analyzing a child's learning style and interests.

[0049] The advice unit can analyze a child's learning style and interests, and adjust the level of detail of the advice according to the difficulty and achievement level of the learning. For example, if the learning difficulty is high, the advice unit will provide specific and detailed advice. For example, if the learning achievement level is low, the advice unit will provide more frequent advice and strengthen support. For example, if the learning achievement level is high, the advice unit will limit itself to periodic advice to give parents peace of mind. This allows for the provision of appropriate advice according to the difficulty and achievement level of the learning. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, when analyzing a child's learning style and interests, the advice unit can use AI to analyze data and generate advice to adjust the level of detail of the advice according to the difficulty and achievement level of the learning.

[0050] The advice unit can customize its advice based on the parents' areas of interest when analyzing a child's learning style and interests. For example, if a parent is interested in mathematics, the advice unit will focus on providing advice related to mathematics. If a parent is interested in English, the advice unit will focus on providing advice related to English. If a parent is interested in the child's overall learning progress, the advice unit will provide balanced advice across all subjects. This allows the advice unit to provide appropriate advice tailored to the parents' areas of interest. Some or all of the above processing in the advice unit may be performed using AI, for example, or not. For example, when analyzing a child's learning style and interests, the advice unit can use AI to analyze data and generate advice in order to customize the advice based on the parents' areas of interest.

[0051] The advice unit can analyze a child's learning style and interests, and adjust the timing of advice to match the parents' schedules. For example, the advice unit can provide advice at night or on weekends, avoiding busy times for parents. For example, if a parent is in a meeting, the advice unit can provide advice after the meeting ends. For example, if a parent is traveling, the advice unit can provide advice all at once after the trip. This allows for appropriate advice timing according to the parents' schedules. Some or all of the above processes in the advice unit may be performed using AI, for example, or not. For example, when analyzing a child's learning style and interests, the advice unit can use AI to analyze data and generate advice to adjust the timing of advice to match the parents' schedules.

[0052] The notification unit can select the most suitable notification method by referring to past notification history when notifying parents of school events or meeting schedules. For example, the notification unit can select a notification method preferred by parents (email, SMS, etc.) from past notification history. For example, the notification unit can analyze past notification history and send notifications at the time when parents are most likely to respond. For example, the notification unit can refer to past notification history to select a notification method that parents are less likely to miss. In this way, the notification unit can provide the most suitable notification method by referring to past notification history. Some or all of the above processes in the notification unit may be performed using AI, for example, or not using AI. For example, when notifying parents of school events or meeting schedules, the notification unit can use AI to analyze data and generate notification content in order to select the most suitable notification method by referring to past notification history.

[0053] The notification unit can adjust the level of detail in notifications regarding school events and meeting schedules according to the importance and urgency of the event. For example, the notification unit provides detailed notifications for high-priority events and meetings to ensure that parents receive the information. For example, the notification unit provides immediate notifications for high-urgency events and meetings to quickly inform parents. For example, the notification unit provides concise notifications for events and meetings of low importance or urgency, conveying only the necessary information to parents. This ensures that appropriate notification content is provided according to the importance and urgency of the event. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, when notifying about school events and meeting schedules, the notification unit can use AI to analyze data and generate notification content in order to adjust the level of detail in notifications according to the importance and urgency of the event.

[0054] The notification unit can customize notification content based on parents' areas of interest when notifying them of school events and meeting schedules. For example, if a parent is interested in school presentations, the notification unit will focus on providing notifications related to school presentations. For example, if a parent is interested in sports events, the notification unit will focus on providing notifications related to sports events. For example, if a parent is interested in school events in general, the notification unit will provide notifications for each event in a balanced manner. This allows for the provision of appropriate notification content tailored to parents' areas of interest. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, when notifying parents of school events and meeting schedules, the notification unit can use AI to analyze data and generate notification content in order to customize the notification content based on parents' areas of interest.

[0055] The notification unit can adjust the timing of notifications to match parents' schedules when notifying them of school events and meeting schedules. For example, the notification unit can send notifications at night or on weekends, avoiding busy times for parents. For example, if a parent is in a meeting, the notification unit can send a notification after the meeting ends. For example, if a parent is traveling, the notification unit can send all notifications at once after the trip. This allows for appropriate notification timing according to parents' schedules. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, when notifying parents of school events and meeting schedules, the notification unit can use AI to analyze data and generate notification content in order to adjust the timing of notifications to match parents' schedules.

[0056] The translation department can select the optimal translation method by referring to past translation history when providing multilingual support. For example, the translation department can select a translation method preferred by parents (literal translation, paraphrasing, etc.) from past translation history. For example, the translation department can analyze past translation history and select the translation method that is easiest for parents to understand. For example, the translation department can refer to past translation history to select a translation method that parents are less likely to overlook. In this way, the optimal translation method can be provided by referring to past translation history. Some or all of the above processes in the translation department may be performed using AI, for example, or not using AI. For example, when providing multilingual support, the translation department can use AI to analyze data and generate translation content in order to select the optimal translation method by referring to past translation history.

[0057] The translation department can adjust the level of detail in translations according to the difficulty and frequency of use of each language when providing multilingual support. For example, for difficult languages, the translation department can provide detailed translations to ensure that the information is conveyed to parents. For example, for frequently used languages, the translation department can provide concise translations to quickly convey the information to parents. For example, for languages ​​that are not difficult or frequently used, the translation department can provide concise translations to convey only the information necessary to parents. This ensures that appropriate translations are provided according to the difficulty and frequency of use of each language. Some or all of the above processing in the translation department may be performed using AI, for example, or not. For example, when providing multilingual support, the translation department can use AI to analyze data and generate translations in order to adjust the level of detail in translations according to the difficulty and frequency of use of each language.

[0058] The translation department can customize translations based on parents' areas of interest when providing multilingual support. For example, if a parent is interested in a school presentation, the translation department will focus on providing translations related to the presentation. If a parent is interested in a sports event, the translation department will focus on providing translations related to the sports event. If a parent is interested in school events in general, the translation department will provide translations for each event in a balanced manner. This ensures that appropriate translations are provided according to the parents' areas of interest. Some or all of the above processing in the translation department may be performed using AI, for example, or not. For example, when providing multilingual support, the translation department can use AI to analyze data and generate translations in order to customize the translation based on parents' areas of interest.

[0059] The translation department can adjust the timing of translations to match the parents' schedules when providing multilingual support. For example, the translation department can provide translations at night or on weekends, avoiding busy times for parents. For example, if a parent is in a meeting, the translation department can provide the translation after the meeting ends. For example, if a parent is traveling, the translation department can provide all the translations at once after the trip. This allows for appropriate translation timing according to the parents' schedules. Some or all of the above processes in the translation department may be performed using AI, for example, or not. For example, when providing multilingual support, the translation department can use AI to analyze data and generate translation content in order to adjust the timing of translations to match the parents' schedules.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reporting unit can incorporate gamification elements to enhance learning motivation when reporting on a child's learning progress. For example, the reporting unit can award badges or points when a child completes a specific task and notify parents of the achievement. For example, if a child continues to study for several consecutive days, the reporting unit can record the number of consecutive study days and report it to the parents. For example, when a child achieves a goal, the reporting unit can generate special rewards or praise messages and notify parents. This can increase the child's motivation to learn. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can use AI to analyze data and generate report content in order to incorporate gamification elements to enhance learning motivation when reporting on a child's learning progress.

[0062] The advice unit can analyze a child's learning style and interests, and adjust the level of detail of the advice according to the difficulty and achievement level of the learning. For example, if the learning difficulty is high, it will provide specific and detailed advice. If the learning achievement level is low, for example, the advice unit will provide more frequent advice and strengthen support. If the learning achievement level is high, for example, the advice unit will limit itself to periodic advice to give parents peace of mind. This allows for the provision of appropriate advice according to the difficulty and achievement level of the learning. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, when analyzing a child's learning style and interests, the advice unit can use AI to analyze data and generate advice to adjust the level of detail of the advice according to the difficulty and achievement level of the learning.

[0063] The reporting unit can highlight changes in a child's learning progress by comparing it with past learning data when reporting on the child's progress. For example, it can visually display changes in progress in a graph by comparing it with learning data from the past month. The reporting unit can highlight improvements in grades or areas for improvement by comparing past test results with current test results. The reporting unit can report changes in learning habits by comparing past and current study time. This allows for a visual understanding of changes in progress by comparing it with past learning data. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not. For example, when reporting a child's learning progress in real time, the reporting unit can use AI to analyze the data and generate the report content in order to highlight changes in progress by comparing it with past learning data.

[0064] The liaison department can provide appropriate advice when parents ask questions or seek advice from teachers by referring to past communication history. For example, it can refer to similar questions or consultations from past communication history and provide appropriate advice. For example, the liaison department can provide additional information regarding current questions or consultations based on advice parents have received in the past. For example, the liaison department can analyze past communication history and provide advice tailored to the parents' interests and needs. This allows for the provision of appropriate advice by referring to past communication history. Some or all of the above processes in the liaison department may be performed using AI, for example, or not using AI. For example, the liaison department can use AI to analyze data and generate advice in order to provide appropriate advice when parents ask questions or seek advice from teachers by referring to past communication history.

[0065] The advice unit can customize its advice based on the parents' areas of interest when analyzing a child's learning style and interests. For example, if a parent is interested in mathematics, it will focus on providing advice related to mathematics. If a parent is interested in English, it will focus on providing advice related to English. If a parent is interested in the child's overall learning progress, it will provide advice across all subjects in a balanced manner. This allows the advice unit to provide appropriate advice tailored to the parents' areas of interest. Some or all of the above processing in the advice unit may be performed using AI, for example, or not. For example, when analyzing a child's learning style and interests, the advice unit can use AI to analyze data and generate advice in order to customize the advice based on the parents' areas of interest.

[0066] The notification unit can adjust the level of detail in notifications regarding school events and meeting schedules according to the importance and urgency of the event. For example, for highly important events and meetings, detailed notifications are provided to ensure that parents are informed. For urgent events and meetings, the notification unit provides immediate notifications to quickly inform parents. For events and meetings of low importance or urgency, the notification unit provides concise notifications, conveying only the necessary information to parents. This ensures that appropriate notifications are provided according to the importance and urgency of the event. Some or all of the above processing in the notification unit may be performed using AI, for example, or not. For example, when notifying about school events and meeting schedules, the notification unit can use AI to analyze data and generate notification content in order to adjust the level of detail in notifications according to the importance and urgency of the event.

[0067] The translation department can customize the translation content based on the parents' areas of interest when providing multilingual support. For example, if a parent is interested in a school presentation, the translation department will focus on providing translations related to the presentation. If a parent is interested in a sports event, the translation department will focus on providing translations related to the sports event. If a parent is interested in school events in general, the translation department will provide translations for each event in a balanced manner. This ensures that appropriate translations are provided according to the parents' areas of interest. Some or all of the above processing in the translation department may be performed using AI, for example, or not. For example, when providing multilingual support, the translation department can use AI to analyze data and generate translations in order to customize the translation content based on the parents' areas of interest.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reporting unit reports on the child's learning progress and challenges in real time. For example, it collects the results of assignments and tests the child has completed at school and notifies the parents. Furthermore, AI can analyze the child's learning situation and report it to the parents in real time. Step 2: The liaison department facilitates smooth communication and consultation with teachers based on the information reported by the reporting department. For example, when parents ask questions or seek advice from teachers, the department organizes the content and conveys it to the teacher at the appropriate time. Furthermore, AI can analyze the content of parents' questions and consultations and convey it to teachers at the appropriate time. Step 3: The Advice Department proposes home support methods tailored to the child's characteristics, based on the support provided by the Liaison Department. For example, the AI ​​analyzes the child's learning style and interests and proposes home learning methods and activities based on that. Furthermore, the AI ​​can propose support methods tailored to the child's characteristics. Step 4: The notification unit notifies parents of school events and meeting schedules based on the suggestions made by the advice unit. For example, the AI ​​collects announcements from the school and notifies parents. Furthermore, the AI ​​can notify parents of school event and meeting schedules. Step 5: The translation department will address the needs of foreign national guardians based on the information provided by the notification department, thereby overcoming language barriers. For example, the AI ​​is multilingual, allowing guardians to ask questions and seek advice in their native language. Furthermore, the AI ​​can translate the guardians' questions and concerns and provide them in their native language.

[0070] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that supports parents in actively participating in their children's learning and growth. This AI agent system can strengthen cooperation between home and school and enhance student autonomy and learning effectiveness. Even busy parents can appropriately participate in their children's education. For example, the AI ​​agent system reports the child's learning progress and assignments in real time. For example, the AI ​​collects the results of assignments and tests that the child has done at school and notifies the parents. This makes it easier for parents to understand their child's learning situation. Next, the AI ​​agent system smoothly supports communication and consultation with teachers. For example, if a parent wants to ask a question or consult with a teacher, the AI ​​organizes the content and conveys it to the teacher at the appropriate time. This makes communication between parents and teachers smoother. Furthermore, the AI ​​agent system suggests ways to support the child at home that are tailored to the child's characteristics. For example, the AI ​​analyzes the child's learning style and interests and suggests learning methods and activities at home based on that. This allows parents to provide support that is tailored to the child's characteristics. In addition, the AI ​​agent system notifies parents of school events and meeting schedules. For example, the AI ​​collects information from the school and notifies parents. This makes it easier for parents to keep track of school event and parent-teacher conference schedules. Furthermore, the AI ​​agent system also supports parents of foreign nationality, eliminating language barriers. For example, the AI ​​is multilingual, allowing parents to ask questions and seek advice in their native language. This enables parents of foreign nationality to actively participate in their children's education. In this way, the AI ​​agent system can support parents in actively participating in their children's learning and growth, strengthen collaboration between home and school, and enhance student autonomy and learning effectiveness.

[0071] The AI ​​agent system according to this embodiment comprises a reporting unit, a communication unit, an advice unit, a notification unit, and a translation unit. The reporting unit reports the child's learning progress and assignments in real time. For example, the reporting unit collects assignments and test results that the child has done at school and notifies the parents. For example, the reporting unit can use AI to analyze the child's learning situation and report it to the parents in real time. The communication unit smoothly supports communication and consultation with teachers. For example, when parents ask questions or seek advice from teachers, the communication unit organizes the content and conveys it to the teacher at an appropriate time. For example, the communication unit can use AI to analyze the content of parents' questions and consultations and convey it to the teacher at an appropriate time. The advice unit proposes methods of home support tailored to the child's characteristics. For example, the advice unit can use AI to analyze the child's learning style and interests and propose home learning methods and activities based on that. For example, the advice unit can use AI to propose methods of support tailored to the child's characteristics. The notification unit notifies parents of school events and meeting schedules. The notification unit, for example, uses AI to collect announcements from schools and notify parents. The notification unit can, for example, use AI to notify parents of school events and meeting schedules. The translation unit also supports parents of foreign nationality and eliminates language barriers. The translation unit, for example, uses AI that supports multiple languages, allowing parents to ask questions and seek advice in their native language. The translation unit can, for example, use AI to translate parents' questions and consultations and provide them in their native language. As a result, the AI ​​agent system according to this embodiment can support parents in actively participating in their children's learning and growth, strengthen cooperation between home and school, and enhance student autonomy and learning effectiveness.

[0072] The reporting department reports on children's learning progress and challenges in real time. Specifically, it collects the results of assignments and tests that children complete at school and notifies parents. For example, the AI ​​automatically collects and analyzes the results of quizzes taken during class, the submission status of homework, and the child's participation and participation in class. Based on this data, the AI ​​evaluates the child's learning situation and generates a detailed report for parents. The report includes the child's strengths and weaknesses in subjects, learning progress, and areas that need improvement. Furthermore, the AI ​​visualizes the child's growth and changes by comparing them with past data, allowing parents to understand their child's learning situation at a glance. The reporting department provides an application compatible with devices such as smartphones, tablets, and PCs so that parents can check their child's learning situation anytime, anywhere. This allows parents to understand their child's learning situation in real time, even while at work or away from home, and provide appropriate support as needed. Because the reporting department uses AI to automatically collect, analyze, and report data, it reduces the burden on parents and can efficiently support children's learning.

[0073] The liaison department facilitates smooth communication and consultation between parents and teachers. Specifically, when parents have questions or concerns with teachers, the department organizes the content and conveys it to the teacher at the appropriate time. For example, if a parent wants to ask a question about their child's learning progress, the AI ​​analyzes the question and organizes the information needed to provide an appropriate answer. Based on past interactions and the child's learning data, the AI ​​provides teachers with specific questions and background information, supporting teachers in responding quickly and accurately. Furthermore, the liaison department provides chat and video call functions to streamline communication between parents and teachers. This allows parents to interact directly with teachers and ask questions and seek advice in real time. The AI ​​records these interactions, making it possible to refer to past interactions as needed. The liaison department can streamline communication between parents and teachers and strengthen support for children's learning.

[0074] The advice department proposes methods of home support tailored to each child's characteristics. Specifically, the AI ​​analyzes the child's learning style and interests, and based on that, suggests learning methods and activities at home. For example, if a child prefers visual learning, the AI ​​will suggest learning methods using visual materials and videos. Also, if a child is interested in a particular subject, it will suggest activities and projects related to that subject to pique the child's interest. Furthermore, the AI ​​will support the creation of home learning plans based on the child's learning progress and achievement of tasks. For example, it will suggest weekly learning goals and daily learning schedules, enabling parents to effectively support their child's learning. The advice department can provide individualized support tailored to each child's characteristics, thereby enhancing the effectiveness of home learning.

[0075] The notification system notifies parents of school events and parent-teacher conference schedules. Specifically, AI collects information from the school and notifies parents. For example, the AI ​​automatically collects and notifies parents of school event schedules and changes, conference schedules, and emergency contact information. Notifications are sent via smartphone apps, email, SMS, etc., to ensure that parents do not miss important information. Furthermore, the notification system provides a reminder function to help parents take necessary actions after receiving a notification. For example, it can send a reminder the day before a conference to ensure that parents do not forget to attend. The notification system enables parents to communicate smoothly with the school and provides timely information to support their child's learning and school life.

[0076] The translation department also caters to parents of foreign nationality, eliminating language barriers. Specifically, the AI ​​is multilingual, allowing parents to ask questions and seek advice in their native language. For example, if a parent enters a question in their native language, the AI ​​translates it and conveys it to the teacher. Conversely, the teacher's response is also translated into the parent's native language and provided to the parent. This enables smooth communication that transcends language barriers. Furthermore, the translation department provides school announcements and notifications in multiple languages. For example, announcements about school events and meeting schedules are sent in the parent's native language, allowing parents of foreign nationality to communicate with the school with confidence. By eliminating language barriers, the translation department can support parents of foreign nationality in actively participating in their child's learning and school life.

[0077] The reporting unit can collect the results of assignments and tests that children have completed at school and notify parents. For example, the reporting unit can collect the results of assignments and tests that children have completed at school and notify parents. The reporting unit can, for example, use AI to analyze the child's learning progress and report it to parents in real time. This makes it easier for parents to understand their child's learning progress. Some or all of the above processes in the reporting unit may be performed using AI, or not using AI. For example, the reporting unit can use AI to analyze data and generate notification content in order to collect the results of assignments and tests that children have completed at school and notify parents.

[0078] The liaison department can organize the content of questions and consultations that parents have with teachers and convey them to teachers at the appropriate time. For example, when parents have questions or consultations with teachers, the liaison department can organize the content and convey it to teachers at the appropriate time. For example, the liaison department can use AI to analyze the content of parents' questions and consultations and convey it to teachers at the appropriate time. This makes communication between parents and teachers smoother. Some or all of the above processing in the liaison department may be performed using AI, or not. For example, when parents have questions or consultations with teachers, the liaison department can use AI to analyze the data and generate communication content in order to organize the content and convey it to teachers at the appropriate time.

[0079] The advice unit can analyze a child's learning style and interests and, based on that, suggest learning methods and activities for home study. For example, the advice unit can use AI to analyze a child's learning style and interests and, based on that, suggest learning methods and activities for home study. For example, the advice unit can use AI to suggest support methods tailored to the child's characteristics. This allows parents to provide support tailored to their child's characteristics. Some or all of the above processing in the advice unit may be performed using AI, or not. For example, the advice unit can use AI to analyze data and generate advice in order to analyze a child's learning style and interests and, based on that, suggest learning methods and activities for home study.

[0080] The notification unit can collect information from schools and notify parents. For example, the notification unit can use AI to collect information from schools and notify parents. For example, the notification unit can use AI to notify parents of school event and meeting schedules. This makes it easier for parents to keep track of school event and meeting schedules. Some or all of the above processes in the notification unit may be performed using AI, or not using AI. For example, the notification unit can use AI to analyze data and generate notification content in order to collect information from schools and notify parents.

[0081] The translation department is multilingual, allowing parents to ask questions and seek advice in their native language. For example, the AI ​​in the translation department is multilingual, enabling parents to ask questions and seek advice in their native language. The AI ​​in the translation department can translate parents' questions and seek advice and provide it in their native language. This allows parents of foreign nationality to actively participate in their children's education. Some or all of the above-described processes in the translation department may be performed using AI, or not. For example, the translation department can use AI to analyze data and generate translations in order to translate parents' questions and seek advice and provide them in their native language.

[0082] The reporting unit can estimate a child's emotions and adjust the level of detail in the report based on the estimated emotions. For example, if a child is stressed, the reporting unit can provide a concise report, allowing for later review of more detailed information. If a child is relaxed, the reporting unit can provide a detailed report, including details of learning progress and tasks. If a child is excited, the reporting unit can provide a report that emphasizes positive feedback and motivates them. This allows for the provision of appropriate reports tailored to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the reporting unit may be performed using AI or not. For example, the reporting unit can use AI to analyze data and generate reports in order to estimate a child's emotions and adjust the level of detail in the report based on the estimated emotions.

[0083] The reporting unit can highlight changes in a child's learning progress by comparing it with past learning data when reporting the child's learning progress in real time. For example, the reporting unit can visually display changes in progress in a graph by comparing it with learning data from the past month. For example, the reporting unit can highlight improvements in grades or areas for improvement by comparing past test results with current test results. For example, the reporting unit can report changes in learning habits by comparing past study time with current study time. This allows for a visual understanding of changes in progress by comparing it with past learning data. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not using AI. For example, when reporting a child's learning progress in real time, the reporting unit can use AI to analyze the data and generate the report content in order to highlight changes in progress by comparing it with past learning data.

[0084] The reporting unit can adjust the frequency of reports on a child's learning progress according to the difficulty level and achievement level of the learning. For example, if a child completes a difficult task, the reporting unit will immediately report it and inform the parents. If a child's learning achievement level is low, the reporting unit will report frequently to encourage parents to provide support. If a child's learning achievement level is high, the reporting unit will limit reports to regular updates to reassure parents. This allows for the provision of an appropriate reporting frequency according to the difficulty level and achievement level of the learning. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not. For example, when reporting a child's learning progress, the reporting unit can use AI to analyze data and generate report content in order to adjust the frequency of reports according to the difficulty level and achievement level of the learning.

[0085] The reporting unit can estimate the child's emotions and adjust the timing of reports based on the estimated emotions. For example, if the child is tired, the reporting unit may postpone the report to the next day and prioritize rest. For example, if the child is excited, the reporting unit may report immediately and provide positive feedback. For example, if the child is stressed, the reporting unit may refrain from reporting and provide more detailed information later. This allows for appropriate reporting timing according to the child'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 reporting unit may be performed using AI or not. For example, the reporting unit may use AI to analyze data and generate report content in order to estimate the child's emotions and adjust the timing of reports based on the estimated emotions.

[0086] The reporting system can customize the content of reports on a child's learning progress based on the parents' areas of interest. For example, if a parent is interested in mathematics, the reporting system will focus on reporting the progress and details of assignments in mathematics. If a parent is interested in English, the reporting system will report in detail on English grades and learning content. If a parent is interested in the overall learning situation, the reporting system will report on the progress of each subject in a balanced manner. This allows for the provision of appropriate reports tailored to the parents' areas of interest. Some or all of the above processing in the reporting system may be performed using AI, for example, or not. For example, the reporting system can use AI to analyze data and generate reports in order to customize the content of reports on a child's learning progress based on the parents' areas of interest.

[0087] The reporting department can adjust the timing of reports on children's learning progress to match the parents' schedules. For example, the reporting department can report at night or on weekends, avoiding busy times for parents. For example, if a parent is in a meeting, the reporting department can report after the meeting ends. For example, if a parent is traveling, the reporting department can report all at once after the trip. This allows for appropriate reporting timing according to the parents' schedules. Some or all of the above processes in the reporting department may be performed using AI, for example, or not. For example, when reporting on children's learning progress, the reporting department can use AI to analyze data and generate report content in order to adjust the timing of reports to match the parents' schedules.

[0088] The communication unit can estimate the parent's emotions and adjust the way the message is expressed based on the estimated emotions. For example, if the parent is stressed, the communication unit will provide a concise and clear message. If the parent is relaxed, the communication unit will provide a detailed message including additional information. If the parent is agitated, the communication unit will provide a message that emphasizes positive feedback and provides reassurance. This allows for the provision of appropriate messages tailored to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the communication unit may be performed using AI or not. For example, the communication unit can use AI to analyze data and generate message content in order to estimate the parent's emotions and adjust the way the message is expressed based on the estimated emotions.

[0089] The liaison department can provide appropriate advice when parents ask questions or seek advice from teachers by referring to past communication history. For example, the liaison department can refer to similar questions or consultations from past communication history and provide appropriate advice. For example, the liaison department can provide additional information regarding current questions or consultations based on advice parents have received in the past. For example, the liaison department can analyze past communication history and provide advice tailored to the parents' interests and needs. This allows for the provision of appropriate advice by referring to past communication history. Some or all of the above processes in the liaison department may be performed using AI, for example, or not. For example, the liaison department can use AI to analyze data and generate advice in order to provide appropriate advice when parents ask questions or seek advice from teachers by referring to past communication history.

[0090] The liaison department can prioritize inquiries and consultations from parents to teachers based on their urgency. For example, the liaison department can immediately relay urgent questions and consultations to teachers. For example, the liaison department can relay less urgent questions and consultations to teachers at an appropriate time. The liaison department can set priorities for inquiries based on their urgency and notify parents accordingly. This allows for the provision of appropriate priorities based on the urgency of the inquiries. Some or all of the above processes in the liaison department may be performed using AI, or not. For example, the liaison department can use AI to analyze data and set priorities based on the urgency of inquiries when parents ask questions or consult with teachers.

[0091] The communication unit can estimate the parent's emotions and adjust the timing of communication based on the estimated emotions. For example, if the parent is busy, the communication unit may postpone communication and contact them at an appropriate time. For example, if the parent is relaxed, the communication unit may contact them immediately and provide detailed information. For example, if the parent is stressed, the communication unit may refrain from contacting them and provide detailed information later. This allows for appropriate communication timing 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 communication unit may be performed using AI or not. For example, the communication unit may use AI to analyze data and generate communication content in order to estimate the parent's emotions and adjust the timing of communication based on the estimated emotions.

[0092] The liaison department can customize the content of communications based on the parents' areas of interest when parents ask questions or seek advice from teachers. For example, if a parent is interested in mathematics, the liaison department will focus on questions and consultations related to mathematics. If a parent is interested in English, the liaison department will focus on questions and consultations related to English. If a parent is interested in overall learning progress, the liaison department will provide a balanced mix of questions and consultations across all subjects. This allows for the provision of appropriate communications tailored to the parents' areas of interest. Some or all of the above processing in the liaison department may be performed using AI, for example, or not. For example, the liaison department can use AI to analyze data and generate communications in order to customize the content of communications based on the parents' areas of interest when parents ask questions or seek advice from teachers.

[0093] The liaison department can adjust the timing of communication with parents to match their schedules when parents have questions or concerns with teachers. For example, the liaison department can contact parents in the evenings or on weekends, avoiding busy times. For example, if a parent is in a meeting, the liaison department can contact them after the meeting ends. For example, if a parent is traveling, the liaison department can contact them all at once after their trip. This allows for appropriate communication timing according to parents' schedules. Some or all of the above processes in the liaison department may be performed using AI, for example, or not. For example, the liaison department can use AI to analyze data and generate communication content in order to adjust the timing of communication with parents to match their schedules when parents have questions or concerns with teachers.

[0094] The advice unit can estimate a child's emotions and adjust the way it expresses advice based on the estimated emotions. For example, if a child is stressed, the advice unit provides concise and clear advice. For example, if a child is relaxed, the advice unit provides detailed advice and includes additional information. For example, if a child is excited, the advice unit emphasizes positive feedback and provides motivational advice. This allows for the provision of appropriate advice tailored to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the advice unit may be performed using AI or not. For example, the advice unit can use AI to analyze data and generate advice in order to estimate a child's emotions and adjust the way it expresses advice based on the estimated emotions.

[0095] The advice unit can provide optimal advice by referring to past learning data when analyzing a child's learning style and interests. For example, the advice unit can identify subjects a child excels at or areas of interest from past learning data and provide advice based on that. For example, the advice unit can analyze past learning data and provide specific advice for subjects or tasks a child struggles with. For example, the advice unit can refer to past learning data and provide advice based on the child's learning habits and patterns. In this way, by referring to past learning data, it can provide optimal advice. Some or all of the above processes in the advice unit may be performed using AI, for example, or not. For example, the advice unit can use AI to analyze data and generate advice in order to provide optimal advice by referring to past learning data when analyzing a child's learning style and interests.

[0096] The advice unit can analyze a child's learning style and interests, and adjust the level of detail of the advice according to the difficulty and achievement level of the learning. For example, if the learning difficulty is high, the advice unit will provide specific and detailed advice. For example, if the learning achievement level is low, the advice unit will provide more frequent advice and strengthen support. For example, if the learning achievement level is high, the advice unit will limit itself to periodic advice to give parents peace of mind. This allows for the provision of appropriate advice according to the difficulty and achievement level of the learning. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, when analyzing a child's learning style and interests, the advice unit can use AI to analyze data and generate advice to adjust the level of detail of the advice according to the difficulty and achievement level of the learning.

[0097] The advice unit can estimate a child's emotions and adjust the timing of advice based on the estimated emotions. For example, if a child is tired, the advice unit may postpone advice until the next day and prioritize rest. For example, if a child is agitated, the advice unit may provide advice immediately and offer positive feedback. For example, if a child is stressed, the advice unit may refrain from giving advice and provide more detailed information later. This allows for appropriate timing of advice according to the child'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 advice unit may be performed using AI or not. For example, the advice unit may use AI to analyze data and generate advice in order to estimate a child's emotions and adjust the timing of advice based on the estimated emotions.

[0098] The advice unit can customize its advice based on the parents' areas of interest when analyzing a child's learning style and interests. For example, if a parent is interested in mathematics, the advice unit will focus on providing advice related to mathematics. If a parent is interested in English, the advice unit will focus on providing advice related to English. If a parent is interested in the child's overall learning progress, the advice unit will provide balanced advice across all subjects. This allows the advice unit to provide appropriate advice tailored to the parents' areas of interest. Some or all of the above processing in the advice unit may be performed using AI, for example, or not. For example, when analyzing a child's learning style and interests, the advice unit can use AI to analyze data and generate advice in order to customize the advice based on the parents' areas of interest.

[0099] The advice unit can analyze a child's learning style and interests, and adjust the timing of advice to match the parents' schedules. For example, the advice unit can provide advice at night or on weekends, avoiding busy times for parents. For example, if a parent is in a meeting, the advice unit can provide advice after the meeting ends. For example, if a parent is traveling, the advice unit can provide advice all at once after the trip. This allows for appropriate advice timing according to the parents' schedules. Some or all of the above processes in the advice unit may be performed using AI, for example, or not. For example, when analyzing a child's learning style and interests, the advice unit can use AI to analyze data and generate advice to adjust the timing of advice to match the parents' schedules.

[0100] The notification unit can estimate the parent's emotions and adjust the way the notification content is expressed based on the estimated emotions. For example, if the parent is stressed, the notification unit provides a concise and clear notification. For example, if the parent is relaxed, the notification unit provides a detailed notification and includes additional information. For example, if the parent is agitated, the notification unit provides a reassuring notification that emphasizes positive feedback. This allows for the provision of appropriate notification content 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 processing described above in the notification unit may be performed using AI or not. For example, the notification unit can use AI to analyze data and generate notification content in order to estimate the parent's emotions and adjust the way the notification content is expressed based on the estimated emotions.

[0101] The notification unit can select the most suitable notification method by referring to past notification history when notifying parents of school events or meeting schedules. For example, the notification unit can select a notification method preferred by parents (email, SMS, etc.) from past notification history. For example, the notification unit can analyze past notification history and send notifications at the time when parents are most likely to respond. For example, the notification unit can refer to past notification history to select a notification method that parents are less likely to miss. In this way, the notification unit can provide the most suitable notification method by referring to past notification history. Some or all of the above processes in the notification unit may be performed using AI, for example, or not using AI. For example, when notifying parents of school events or meeting schedules, the notification unit can use AI to analyze data and generate notification content in order to select the most suitable notification method by referring to past notification history.

[0102] The notification unit can adjust the level of detail in notifications regarding school events and meeting schedules according to the importance and urgency of the event. For example, the notification unit provides detailed notifications for high-priority events and meetings to ensure that parents receive the information. For example, the notification unit provides immediate notifications for high-urgency events and meetings to quickly inform parents. For example, the notification unit provides concise notifications for events and meetings of low importance or urgency, conveying only the necessary information to parents. This ensures that appropriate notification content is provided according to the importance and urgency of the event. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, when notifying about school events and meeting schedules, the notification unit can use AI to analyze data and generate notification content in order to adjust the level of detail in notifications according to the importance and urgency of the event.

[0103] The notification unit can estimate the parent's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the parent is busy, the notification unit may postpone the notification and send it at an appropriate time. For example, if the parent is relaxed, the notification unit may send an immediate notification and provide detailed information. For example, if the parent is stressed, the notification unit may refrain from sending a notification and provide detailed information later. This allows for appropriate notification timing 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 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 notification unit may be performed using AI or not. For example, the notification unit may use AI to analyze data and generate notification content in order to estimate the parent's emotions and adjust the timing of notifications based on the estimated emotions.

[0104] The notification unit can customize notification content based on parents' areas of interest when notifying them of school events and meeting schedules. For example, if a parent is interested in school presentations, the notification unit will focus on providing notifications related to school presentations. For example, if a parent is interested in sports events, the notification unit will focus on providing notifications related to sports events. For example, if a parent is interested in school events in general, the notification unit will provide notifications for each event in a balanced manner. This allows for the provision of appropriate notification content tailored to parents' areas of interest. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, when notifying parents of school events and meeting schedules, the notification unit can use AI to analyze data and generate notification content in order to customize the notification content based on parents' areas of interest.

[0105] The notification unit can adjust the timing of notifications to match parents' schedules when notifying them of school events and meeting schedules. For example, the notification unit can send notifications at night or on weekends, avoiding busy times for parents. For example, if a parent is in a meeting, the notification unit can send a notification after the meeting ends. For example, if a parent is traveling, the notification unit can send all notifications at once after the trip. This allows for appropriate notification timing according to parents' schedules. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, when notifying parents of school events and meeting schedules, the notification unit can use AI to analyze data and generate notification content in order to adjust the timing of notifications to match parents' schedules.

[0106] The translation unit can estimate the parent's emotions and adjust the expression of the translation based on the estimated emotions. For example, if the parent is stressed, the translation unit will provide a concise and clear translation. If the parent is relaxed, the translation unit will provide a detailed translation and include additional information. If the parent is agitated, the translation unit will emphasize positive feedback and provide reassuring translation. This allows for the provision of appropriate translations that correspond to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit can use AI to analyze data and generate translations in order to estimate the parent's emotions and adjust the expression of the translation based on the estimated emotions.

[0107] The translation department can select the optimal translation method by referring to past translation history when providing multilingual support. For example, the translation department can select a translation method preferred by parents (literal translation, paraphrasing, etc.) from past translation history. For example, the translation department can analyze past translation history and select the translation method that is easiest for parents to understand. For example, the translation department can refer to past translation history to select a translation method that parents are less likely to overlook. In this way, the optimal translation method can be provided by referring to past translation history. Some or all of the above processes in the translation department may be performed using AI, for example, or not using AI. For example, when providing multilingual support, the translation department can use AI to analyze data and generate translation content in order to select the optimal translation method by referring to past translation history.

[0108] The translation department can adjust the level of detail in translations according to the difficulty and frequency of use of each language when providing multilingual support. For example, for difficult languages, the translation department can provide detailed translations to ensure that the information is conveyed to parents. For example, for frequently used languages, the translation department can provide concise translations to quickly convey the information to parents. For example, for languages ​​that are not difficult or frequently used, the translation department can provide concise translations to convey only the information necessary to parents. This ensures that appropriate translations are provided according to the difficulty and frequency of use of each language. Some or all of the above processing in the translation department may be performed using AI, for example, or not. For example, when providing multilingual support, the translation department can use AI to analyze data and generate translations in order to adjust the level of detail in translations according to the difficulty and frequency of use of each language.

[0109] The translation unit can estimate the parent's emotions and adjust the timing of the translation based on the estimated emotions. For example, if the parent is busy, the translation unit will postpone the translation and perform it at an appropriate time. For example, if the parent is relaxed, the translation unit will perform the translation immediately and provide detailed information. For example, if the parent is stressed, the translation unit will refrain from translating and provide detailed information later. This allows for appropriate translation timing 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 translation unit may be performed using AI or not. For example, the translation unit can use AI to analyze data and generate translation content in order to estimate the parent's emotions and adjust the timing of the translation based on the estimated emotions.

[0110] The translation department can customize translations based on parents' areas of interest when providing multilingual support. For example, if a parent is interested in a school presentation, the translation department will focus on providing translations related to the presentation. If a parent is interested in a sports event, the translation department will focus on providing translations related to the sports event. If a parent is interested in school events in general, the translation department will provide translations for each event in a balanced manner. This ensures that appropriate translations are provided according to the parents' areas of interest. Some or all of the above processing in the translation department may be performed using AI, for example, or not. For example, when providing multilingual support, the translation department can use AI to analyze data and generate translations in order to customize the translation based on parents' areas of interest.

[0111] The translation department can adjust the timing of translations to match the parents' schedules when providing multilingual support. For example, the translation department can provide translations at night or on weekends, avoiding busy times for parents. For example, if a parent is in a meeting, the translation department can provide the translation after the meeting ends. For example, if a parent is traveling, the translation department can provide all the translations at once after the trip. This allows for appropriate translation timing according to the parents' schedules. Some or all of the above processes in the translation department may be performed using AI, for example, or not. For example, when providing multilingual support, the translation department can use AI to analyze data and generate translation content in order to adjust the timing of translations to match the parents' schedules.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The reporting unit can incorporate gamification elements to enhance learning motivation when reporting on a child's learning progress. For example, the reporting unit can award badges or points when a child completes a specific task and notify parents of the achievement. For example, if a child continues to study for several consecutive days, the reporting unit can record the number of consecutive study days and report it to the parents. For example, when a child achieves a goal, the reporting unit can generate special rewards or praise messages and notify parents. This can increase the child's motivation to learn. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can use AI to analyze data and generate report content in order to incorporate gamification elements to enhance learning motivation when reporting on a child's learning progress.

[0114] The liaison department can estimate the emotions of parents when they ask questions or seek advice from teachers, and adjust the wording of the message based on the estimated emotions. For example, if a parent is stressed, the liaison department can provide a concise and clear message. If a parent is relaxed, the liaison department can provide a detailed message including additional information. If a parent is agitated, the liaison department can provide a message that emphasizes positive feedback and provides reassurance. This allows for the provision of appropriate messages tailored to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the liaison department may be performed using AI or not. For example, the liaison department can use AI to analyze data and generate messages in order to estimate the emotions of parents and adjust the wording of the message based on the estimated emotions.

[0115] The advice unit can analyze a child's learning style and interests, and adjust the level of detail of the advice according to the difficulty and achievement level of the learning. For example, if the learning difficulty is high, it will provide specific and detailed advice. If the learning achievement level is low, for example, the advice unit will provide more frequent advice and strengthen support. If the learning achievement level is high, for example, the advice unit will limit itself to periodic advice to give parents peace of mind. This allows for the provision of appropriate advice according to the difficulty and achievement level of the learning. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, when analyzing a child's learning style and interests, the advice unit can use AI to analyze data and generate advice to adjust the level of detail of the advice according to the difficulty and achievement level of the learning.

[0116] The notification unit can estimate the parent's emotions and adjust the way the notification is expressed based on the estimated emotions. For example, if the parent is stressed, it provides a concise and clear notification. If the parent is relaxed, it provides a detailed notification with additional information. If the parent is agitated, it provides a reassuring notification that emphasizes positive feedback. This allows for the provision of appropriate notification content tailored to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the notification unit may be performed using AI or not. For example, the notification unit can use AI to analyze data and generate notification content in order to estimate the parent's emotions and adjust the way the notification is expressed based on the estimated emotions.

[0117] The translation unit can estimate the parent's emotions and adjust the expression of the translation based on the estimated emotions. For example, if the parent is stressed, it will provide a concise and clear translation. If the parent is relaxed, it will provide a detailed translation and include additional information. If the parent is excited, it will emphasize positive feedback and provide a reassuring translation. This allows for the provision of appropriate translations that correspond to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit can use AI to analyze data and generate translations in order to estimate the parent's emotions and adjust the expression of the translation based on the estimated emotions.

[0118] The reporting unit can highlight changes in a child's learning progress by comparing it with past learning data when reporting on the child's progress. For example, it can visually display changes in progress in a graph by comparing it with learning data from the past month. The reporting unit can highlight improvements in grades or areas for improvement by comparing past test results with current test results. The reporting unit can report changes in learning habits by comparing past and current study time. This allows for a visual understanding of changes in progress by comparing it with past learning data. Some or all of the above processing in the reporting unit may be performed using AI, for example, or not. For example, when reporting a child's learning progress in real time, the reporting unit can use AI to analyze the data and generate the report content in order to highlight changes in progress by comparing it with past learning data.

[0119] The liaison department can provide appropriate advice when parents ask questions or seek advice from teachers by referring to past communication history. For example, it can refer to similar questions or consultations from past communication history and provide appropriate advice. For example, the liaison department can provide additional information regarding current questions or consultations based on advice parents have received in the past. For example, the liaison department can analyze past communication history and provide advice tailored to the parents' interests and needs. This allows for the provision of appropriate advice by referring to past communication history. Some or all of the above processes in the liaison department may be performed using AI, for example, or not using AI. For example, the liaison department can use AI to analyze data and generate advice in order to provide appropriate advice when parents ask questions or seek advice from teachers by referring to past communication history.

[0120] The advice unit can customize its advice based on the parents' areas of interest when analyzing a child's learning style and interests. For example, if a parent is interested in mathematics, it will focus on providing advice related to mathematics. If a parent is interested in English, it will focus on providing advice related to English. If a parent is interested in the child's overall learning progress, it will provide advice across all subjects in a balanced manner. This allows the advice unit to provide appropriate advice tailored to the parents' areas of interest. Some or all of the above processing in the advice unit may be performed using AI, for example, or not. For example, when analyzing a child's learning style and interests, the advice unit can use AI to analyze data and generate advice in order to customize the advice based on the parents' areas of interest.

[0121] The notification unit can adjust the level of detail in notifications regarding school events and meeting schedules according to the importance and urgency of the event. For example, for highly important events and meetings, detailed notifications are provided to ensure that parents are informed. For urgent events and meetings, the notification unit provides immediate notifications to quickly inform parents. For events and meetings of low importance or urgency, the notification unit provides concise notifications, conveying only the necessary information to parents. This ensures that appropriate notifications are provided according to the importance and urgency of the event. Some or all of the above processing in the notification unit may be performed using AI, for example, or not. For example, when notifying about school events and meeting schedules, the notification unit can use AI to analyze data and generate notification content in order to adjust the level of detail in notifications according to the importance and urgency of the event.

[0122] The translation department can customize the translation content based on the parents' areas of interest when providing multilingual support. For example, if a parent is interested in a school presentation, the translation department will focus on providing translations related to the presentation. If a parent is interested in a sports event, the translation department will focus on providing translations related to the sports event. If a parent is interested in school events in general, the translation department will provide translations for each event in a balanced manner. This ensures that appropriate translations are provided according to the parents' areas of interest. Some or all of the above processing in the translation department may be performed using AI, for example, or not. For example, when providing multilingual support, the translation department can use AI to analyze data and generate translations in order to customize the translation content based on the parents' areas of interest.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The reporting unit reports on the child's learning progress and challenges in real time. For example, it collects the results of assignments and tests the child has completed at school and notifies the parents. Furthermore, AI can analyze the child's learning situation and report it to the parents in real time. Step 2: The liaison department facilitates smooth communication and consultation with teachers based on the information reported by the reporting department. For example, when parents ask questions or seek advice from teachers, the department organizes the content and conveys it to the teacher at the appropriate time. Furthermore, AI can analyze the content of parents' questions and consultations and convey it to teachers at the appropriate time. Step 3: The Advice Department proposes home support methods tailored to the child's characteristics, based on the support provided by the Liaison Department. For example, the AI ​​analyzes the child's learning style and interests and proposes home learning methods and activities based on that. Furthermore, the AI ​​can propose support methods tailored to the child's characteristics. Step 4: The notification unit notifies parents of school events and meeting schedules based on the suggestions made by the advice unit. For example, the AI ​​collects announcements from the school and notifies parents. Furthermore, the AI ​​can notify parents of school event and meeting schedules. Step 5: The translation department will address the needs of foreign national guardians based on the information provided by the notification department, thereby overcoming language barriers. For example, the AI ​​is multilingual, allowing guardians to ask questions and seek advice in their native language. Furthermore, the AI ​​can translate the guardians' questions and concerns and provide them in their native language.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reporting unit, communication unit, advice unit, notification unit, and translation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reporting unit is implemented by the control unit 46A of the smart device 14 and reports the child's learning progress and challenges in real time. The communication unit is implemented by the specific processing unit 290 of the data processing unit 12 and smoothly supports communication and consultation between parents and teachers. The advice unit is implemented by the control unit 46A of the smart device 14 and proposes methods of home support tailored to the child's characteristics. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and notifies of school events and meeting schedules. The translation unit is implemented by the control unit 46A of the smart device 14 and supports parents of foreign nationality, eliminating language barriers. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 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.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the 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.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 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.

[0144] Each of the multiple elements described above, including the reporting unit, communication unit, advice unit, notification unit, and translation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reporting unit is implemented by the control unit 46A of the smart glasses 214 and reports the child's learning progress and challenges in real time. The communication unit is implemented by the specific processing unit 290 of the data processing unit 12 and smoothly supports communication and consultation between parents and teachers. The advice unit is implemented by the control unit 46A of the smart glasses 214 and proposes methods of home support tailored to the child's characteristics. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and notifies of school events and meeting schedules. The translation unit is implemented by the control unit 46A of the smart glasses 214 and supports parents of foreign nationality, eliminating language barriers. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the reporting unit, communication unit, advice unit, notification unit, and translation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reporting unit is implemented by the control unit 46A of the headset terminal 314 and reports the child's learning progress and challenges in real time. The communication unit is implemented by the specific processing unit 290 of the data processing unit 12 and smoothly supports communication and consultation between parents and teachers. The advice unit is implemented by the control unit 46A of the headset terminal 314 and proposes methods of home support tailored to the child's characteristics. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and notifies of school events and meeting schedules. The translation unit is implemented by the control unit 46A of the headset terminal 314 and supports parents of foreign nationality, eliminating language barriers. 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.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the reporting unit, communication unit, advice unit, notification unit, and translation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reporting unit is implemented by the control unit 46A of the robot 414 and reports the child's learning progress and challenges in real time. The communication unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and smoothly supports communication and consultation between parents and teachers. The advice unit is implemented by, for example, the control unit 46A of the robot 414 and proposes methods of home support tailored to the child's characteristics. The notification unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and notifies of school events and meeting schedules. The translation unit is implemented by, for example, the control unit 46A of the robot 414 and can accommodate parents of foreign nationality, eliminating language barriers. 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A reporting department that reports on children's learning progress and challenges in real time, Based on the information reported by the aforementioned reporting department, a liaison department will be established to smoothly support communication and consultation with teachers. Based on the support provided by the aforementioned Liaison Department, the Advice Department proposes methods of home support tailored to the child's characteristics. Based on the proposals made by the aforementioned advisory department, the notification department notifies the school of school events and interview schedules, The system includes a translation unit that responds to foreign national guardians based on the content notified by the aforementioned notification unit and overcomes language barriers. A system characterized by the following features. (Note 2) The aforementioned reporting department, Collect the results of assignments and tests that children have completed at school and notify their parents. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned liaison department, When parents ask questions or seek advice from teachers, they should organize the content of their questions and communicate it to the teacher at the appropriate time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned advice section, We analyze children's learning styles and interests and propose home learning methods and activities based on that analysis. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, Collect information from the school and notify parents. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned translation department, It offers multilingual support, allowing parents to ask questions and seek advice in their native language. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reporting department, The system estimates the child's emotions and adjusts the level of detail in the report based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reporting department, When reporting a child's learning progress in real time, highlight changes in progress by comparing it with past learning data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reporting department, When reporting on a child's learning progress, adjust the frequency of reports according to the difficulty level and achievement of the learning material. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reporting department, The system estimates the child's emotions and adjusts the timing of reports based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reporting department, When reporting on a child's learning progress, customize the report content based on the parents' areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reporting department, When reporting on a child's learning progress, adjust the timing of the report to fit the parents' schedules. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned liaison department, We estimate the parents' emotions and adjust the way we communicate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned liaison department, When parents ask questions or seek advice from teachers, we refer to past communication records to provide appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned liaison department, When parents have questions or concerns with teachers, they should prioritize them according to the urgency of the matter. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned liaison department, We estimate the parents' emotions and adjust the timing of contact based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned liaison department, When parents ask questions or seek advice from teachers, the content of the communication should be customized based on the parents' areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned liaison department, When parents have questions or concerns with teachers, the timing of contact will be adjusted to suit the parents' schedules. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advice section, We estimate the child's emotions and adjust the way we express advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advice section, When analyzing a child's learning style and interests, we refer to past learning data to provide optimal advice. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advice section, When analyzing a child's learning style and interests, adjust the level of detail in the advice based on the difficulty and achievement level of the learning. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advice section, The system estimates the child's emotions and adjusts the timing of advice based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advice section, When analyzing a child's learning style and interests, the advice is customized based on the parents' areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advice section, When analyzing a child's learning style and interests, we adjust the timing of advice to match the parents' schedules. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, We estimate the parents' emotions and adjust the wording of the notification based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, When notifying students of school events or meeting schedules, the system will refer to past notification history to select the most appropriate notification method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When notifying students of school events or parent-teacher conference schedules, adjust the level of detail in the notification according to the importance and urgency of the event. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, We estimate the parent's emotions and adjust the timing of notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When notifying parents about school events or parent-teacher conference schedules, customize the notification content based on their areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When notifying parents about school events or parent-teacher conferences, we adjust the timing of the notification to match their schedules. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned translation department, The system estimates the parents' emotions and adjusts the way the translated content is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned translation department, When providing multilingual support, the optimal translation method is selected by referring to past translation history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned translation department, When providing multilingual support, adjust the level of detail in the translation according to the difficulty and frequency of use of each language. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned translation department, The system estimates the parent's emotions and adjusts the timing of the translation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned translation department, When providing multilingual support, the translation content is customized based on the parents' areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned translation department, When providing multilingual support, we adjust the timing of translations to match the parents' schedules. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0197] 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 reporting department that reports on children's learning progress and challenges in real time, Based on the information reported by the aforementioned reporting department, a liaison department will be established to smoothly support communication and consultation with teachers. Based on the support provided by the aforementioned Liaison Department, the Advice Department proposes methods of home support tailored to the child's characteristics. Based on the proposals made by the aforementioned advisory department, the notification department notifies the school of school events and interview schedules, The system includes a translation unit that responds to foreign national guardians based on the content notified by the aforementioned notification unit and overcomes language barriers. A system characterized by the following features.

2. The aforementioned reporting department, Collect the results of assignments and tests that children have completed at school and notify their parents. The system according to feature 1.

3. The aforementioned liaison department, When parents ask questions or seek advice from teachers, they should organize the content of their questions and communicate it to the teacher at the appropriate time. The system according to feature 1.

4. The aforementioned advice section, We analyze children's learning styles and interests and propose home learning methods and activities based on that analysis. The system according to feature 1.

5. The aforementioned notification unit, Collect information from the school and notify parents. The system according to feature 1.

6. The aforementioned translation department, It offers multilingual support, allowing parents to ask questions and seek advice in their native language. The system according to feature 1.

7. The aforementioned reporting department, The system estimates the child's emotions and adjusts the level of detail in the report based on the estimated emotions. The system according to feature 1.

8. The aforementioned reporting department, When reporting a child's learning progress in real time, highlight changes in progress by comparing it with past learning data. The system according to feature 1.

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