system

Abstract

The system according to this embodiment aims to enable parents to understand the learning progress of each child. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a distribution unit. The collection unit collects timetables, transcripts of teacher speeches, test results, homework submission status, and general announcements. The analysis unit analyzes the data collected by the collection unit and extracts information about each child. The generation unit generates different content for each child based on the information extracted by the analysis unit. The distribution unit distributes the content generated by the generation unit to the parents.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the communication from school is limited to content for all, and parents have few opportunities to grasp the learning situation of individual children.

[0005] The system according to the embodiment aims to enable parents to grasp the learning situation of individual children.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a distribution unit. The collection unit collects timetables, transcripts of teacher speeches, test results, homework submission status, and general announcements. The analysis unit analyzes the data collected by the collection unit and extracts information about each child. The generation unit generates different content for each child based on the information extracted by the analysis unit. The distribution unit distributes the content generated by the generation unit to the parents. [Effects of the Invention]

[0007] The system according to this embodiment allows parents to understand the learning progress of each child. [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 receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that uses AI to personalize school newsletters distributed by schools and automatically generates review content for children and communication content for parents. This system solves the problem that conventional school newsletters only contain information for all students and are limited to being a communication tool. It also solves the problem that parents have few opportunities to know what their children are learning and that it takes time and effort for teachers to understand the characteristics of each child. For example, this system first collects data such as timetables, transcripts of teacher speeches, test results, homework submission status, and general announcements. Next, the AI ​​analyzes the collected data and automatically generates different content for each child. The generated content is delivered to parents daily. Specifically, the content of the lessons is summarized by subject. For example, in mathematics, the summary may be "We learned two-digit addition," and in Japanese language, it may be "We learned 10 new kanji characters." Next, the test results are analyzed. For example, in mathematics, the analysis may be "It seems that the child is not good at adding 7," and in science, it may be "The child answered all the questions about temperature correctly." The messages also include positive comments, such as, "You actively participated in the social studies class." Furthermore, information regarding homework is provided, such as, "You have a math drill assignment." Finally, other announcements are also included, such as, "We will be holding a class observation during the fourth period on [date]." This reduces the burden on teachers, increases opportunities for parent-child conversations, and improves parents' awareness of their child's learning progress. It is also expected to have effects such as creating topics of conversation between parents and children, preventing forgotten items and homework, promoting understanding of students, reducing paper materials, preventing missed communications with parents, encouraging reflection habits, reducing teachers' routine tasks, and building relationships with each family.

[0029] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a distribution unit. The collection unit collects timetables, transcripts of teacher speeches, test results, homework submission status, and general announcements. For example, the collection unit obtains timetable information from the school's database. The collection unit can also transcribe teacher speeches using speech recognition technology. Furthermore, the collection unit can collect test results in digital format and obtain homework submission status from an online platform. For example, the collection unit records teacher speeches and converts them into text data using speech recognition technology. The collection unit obtains test results from the school's grade management system and stores them in digital format. The collection unit obtains homework submission status from an online learning platform and stores it in a database. The analysis unit analyzes the data collected by the collection unit and extracts information about each child. For example, the analysis unit uses AI to analyze the collected data and understand each child's learning situation and characteristics. The analysis unit can also analyze the content of teacher speeches and evaluate each child's level of understanding and interest. Furthermore, the analysis unit can analyze test results and identify each child's strengths and weaknesses. For example, the analysis unit uses AI to analyze the emotional tone of the teacher's remarks and evaluate each child's response. The analysis unit uses statistical methods to analyze test results and evaluate each child's academic ability. The analysis unit analyzes homework submission status and evaluates each child's study habits. The generation unit generates different content for each child based on the information extracted by the analysis unit. For example, the generation unit uses AI to generate content optimized for each child. The generation unit can summarize lesson content by subject, analyze test results, and generate content that includes praise. Furthermore, the generation unit can also generate content that includes information about homework and other announcements. For example, the generation unit uses AI to summarize lesson content and provide learning content tailored to each child. The generation unit analyzes test results and generates content that highlights each child's strengths and weaknesses. The generation unit generates content that includes praise to improve each child's motivation. The distribution unit distributes the content generated by the generation unit to parents.The distribution unit can deliver content using, for example, email or messaging apps. The distribution unit can also deliver content at times when parents are more likely to receive it. Furthermore, the distribution unit can monitor the content delivery status and evaluate the effectiveness of the delivery. For example, the distribution unit delivers generated content to parents using email. The distribution unit delivers content in real time using messaging apps. The distribution unit monitors the delivery status and confirms whether parents have received the content. As a result, the system according to this embodiment can reduce the burden on teachers, provide opportunities for parent-child conversations, and improve parents' awareness of their child's learning progress.

[0030] The data collection unit collects timetables, transcripts of teacher speeches, test results, homework submission status, and general announcements. For example, the unit retrieves timetable information from the school's database. Specifically, it accesses the school's management system and regularly updates timetable information for each class. The unit can also transcribe teacher speeches using speech recognition technology. Teacher speeches are recorded during class and converted into text data in real time by the speech recognition engine. Furthermore, the unit can collect test results digitally and retrieve homework submission status from online platforms. For example, the unit retrieves test results from the school's grade management system and stores them digitally. This allows for centralized management of each student's academic performance data. Regarding homework submission status, data is retrieved from online learning platforms, allowing for real-time tracking of submission deadlines and status. The unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit and extracts information about each child. For example, the analysis unit uses AI to analyze the collected data and understand each child's learning situation and characteristics. Specifically, it can analyze the content of teacher statements and evaluate each child's understanding and interest. The AI ​​uses natural language processing technology to analyze teacher statements and evaluate each child's reactions and understanding. It can also analyze test results to identify each child's strengths and weaknesses. For example, the analysis unit uses AI to analyze the emotional tone of teacher statements and evaluate each child's reactions. Furthermore, it analyzes test results using statistical methods to evaluate each child's academic ability. The analysis unit analyzes homework submission status to evaluate each child's study habits. This allows the analysis unit to quickly and accurately analyze collected data and understand each child's learning situation and characteristics. Furthermore, the analysis unit can utilize past data and statistical information to conduct long-term learning trend analysis. For example, it can evaluate progress in specific subjects or skills based on past test results and formulate future learning plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term learning management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The generation unit generates personalized content for each child based on the information extracted by the analysis unit. For example, the generation unit uses AI to generate content optimized for each child. Specifically, it can summarize lesson content by subject, analyze test results, and generate content that includes praise. The AI ​​uses natural language generation technology to concisely summarize lesson content and provide learning materials tailored to each child. It also analyzes test results and generates content that highlights each child's strengths and weaknesses. For example, the generation unit uses AI to summarize lesson content and provide learning materials tailored to each child. Furthermore, the generation unit generates content that includes praise to improve each child's motivation. For example, it can generate a message praising a child who scores highly on a particular test, increasing their motivation to learn. The generation unit can also generate content that includes information about homework and other important announcements. For example, it can generate reminders containing information about homework deadlines and submission methods, notifying children and parents. This allows the generation unit to provide optimal learning content for each child, maximizing learning effectiveness. Furthermore, the generation unit can continuously evaluate and improve the quality of the generated content. For example, it can revise the content and format based on feedback from parents and teachers to provide more effective learning support. This allows the generation unit to always provide high-quality content based on the latest information, supporting children's learning.

[0033] The distribution unit delivers content generated by the generation unit to parents. The distribution unit can deliver content using, for example, email or messaging apps. Specifically, it can deliver content at times when parents are likely to receive it. For example, it can deliver content around the time parents return home from work or when children return home from school. The distribution unit can also monitor the content delivery status and evaluate the effectiveness of the delivery. For example, the distribution unit can deliver content generated using email to parents. The distribution unit can deliver content in real time using messaging apps. Furthermore, the distribution unit monitors the delivery status and confirms whether parents have received the content. This allows the distribution unit to quickly provide appropriate action instructions to each user and minimize the risk of disaster. In addition, the distribution unit can collect user feedback and continuously improve the accuracy and effectiveness of the instructions. For example, based on feedback from users who received evacuation instructions, it can revise evacuation routes and improve the content of the instructions. The distribution unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email in combination. This allows the distribution department to provide users with quick and reliable instructions, minimizing the risk of disaster.

[0034] The data collection unit can filter data based on each child's learning progress during collection. For example, the data collection unit can prioritize collecting data from children who are behind in their learning progress and perform detailed analysis. The data collection unit can also collect data from children who are progressing well in their learning progress with normal priority. Furthermore, the data collection unit can prioritize data from other children, delaying the collection of data from children who are progressing very quickly. For example, the data collection unit can monitor learning progress data in real time and filter the data according to the progress. The data collection unit can prioritize collecting data from children who are falling behind and perform detailed analysis. The data collection unit can collect data from children who are progressing well in their learning progress with normal priority and delay the collection of data from children who are progressing very quickly. This allows for the efficient collection of necessary information by filtering data based on learning progress. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input learning progress data into a generating AI and have the generating AI perform data filtering.

[0035] The data collection unit can adjust its data collection method while considering each child's home environment information. For example, the data collection unit can collect data from children with stable home environments using standard methods. It can also carefully collect data from children with unstable home environments, respecting their privacy. Furthermore, the data collection unit can collect data from children with special home environments using special methods and take appropriate action. For example, the data collection unit can monitor home environment information in real time and adjust the collection method. The data collection unit collects data from children with stable home environments using standard methods. The data collection unit carefully collects data from children with unstable home environments, respecting their privacy. The data collection unit collects data from children with special home environments using special methods and takes appropriate action. This allows for appropriate data collection by adjusting the data collection method while considering home environment information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input home environment information into a generating AI and have the generating AI adjust the collection method.

[0036] The data collection unit can analyze each child's social media activity and collect relevant data during the collection process. For example, the data collection unit can collect learning-related posts from a child's social media activity. The data collection unit can also collect data on friendships from a child's social media activity. Furthermore, the data collection unit can collect data on interests from a child's social media activity. For example, the data collection unit can monitor social media activity in real time and collect learning-related posts. The data collection unit can collect data on friendships to understand a child's social connections. The data collection unit can collect data on interests to understand a child's interests. This allows for the efficient collection of learning-related data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into a generating AI and have the generating AI collect relevant data.

[0037] The analysis unit can improve the accuracy of its analysis by referring to each child's past learning data during the analysis process. For example, the analysis unit can refer to each child's past test results to analyze their current learning situation. It can also refer to each child's past homework submission status to analyze their learning habits. Furthermore, it can refer to each child's past class participation status to analyze their motivation to learn. For example, the analysis unit retrieves past test results from a database and analyzes the current learning situation. The analysis unit refers to past homework submission status to evaluate learning habits. The analysis unit refers to past class participation status to evaluate motivation to learn. This improves the accuracy of the analysis by referring to past learning data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past learning data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0038] The analysis unit can apply different analysis methods to each child's learning style during analysis. For example, the analysis unit can apply an analysis method that emphasizes visual data to visual learners. It can also apply an analysis method that emphasizes audio data to auditory learners. Furthermore, it can apply an analysis method that emphasizes practical data to experiential learners. For example, the analysis unit can apply an analysis method that emphasizes visual data to visual learners. The analysis unit can apply an analysis method that emphasizes audio data to auditory learners. The analysis unit can apply an analysis method that emphasizes practical data to experiential learners. This allows for more appropriate analysis by applying an analysis method that matches the learning style. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input learning style data into a generating AI and have the generating AI perform the application of the analysis method.

[0039] The analysis unit can perform analysis while considering the geographical background of each child. For example, when analyzing data of children living in urban areas, the analysis unit can consider the learning environment specific to urban areas. Similarly, when analyzing data of children living in rural areas, the analysis unit can consider the learning environment specific to rural areas. Furthermore, when analyzing data of children living overseas, the analysis unit can consider the local education system. For example, when analyzing data of children living in urban areas, the analysis unit can consider the learning environment specific to urban areas. When analyzing data of children living in rural areas, the analysis unit can consider the learning environment specific to rural areas. When analyzing data of children living overseas, the analysis unit can consider the local education system. This allows for more appropriate analysis by considering geographical background. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical background data into a generating AI and have the generating AI perform adjustments to the analysis.

[0040] The analysis unit can improve the accuracy of its analysis by referring to relevant academic literature during the analysis process. For example, the analysis unit can improve its analysis algorithm by referring to the latest educational research. It can also improve the reliability of its analysis results by referring to past academic literature. Furthermore, the analysis unit can optimize its analysis methods by referring to research results from other educational institutions. For example, the analysis unit improves its analysis algorithm by referring to the latest educational research. The analysis unit improves the reliability of its analysis results by referring to past academic literature. The analysis unit optimizes its analysis methods by referring to research results from other educational institutions. This improves the accuracy of the analysis by referring to academic literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input academic literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0041] The generation unit can adjust the level of detail of the content based on each child's learning progress during generation. For example, the generation unit can generate content with detailed explanations for children who are behind in their learning progress. It can also generate content with normal detail for children who are progressing well. Furthermore, it can generate concise content that gets straight to the point for children who are progressing very quickly. For example, the generation unit monitors learning progress data in real time and adjusts the level of detail of the content according to the progress. The generation unit generates content with detailed explanations for children who are falling behind. The generation unit generates content with normal detail for children who are progressing well. The generation unit generates concise content that gets straight to the point for children who are progressing very quickly. This allows for the provision of more appropriate information by adjusting the level of detail of the content based on learning progress. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input learning progress data into a generation AI and have the generation AI perform the adjustment of the level of detail of the content.

[0042] The generation unit can apply different generation algorithms to each child's interests during generation. For example, for a child interested in science, the generation unit can generate content that emphasizes scientific content. It can also generate content that emphasizes artistic content for a child interested in art. Furthermore, it can generate content that emphasizes sports-related content for a child interested in sports. For example, the generation unit monitors interest data in real time and applies a generation algorithm according to the child's interests. For a child interested in science, the generation unit generates content that emphasizes scientific content. For a child interested in art, the generation unit generates content that emphasizes artistic content. For a child interested in sports, the generation unit generates content that emphasizes sports-related content. This allows for the provision of more appropriate information by applying a generation algorithm tailored to each child's interests. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input interest data into a generation AI and have the generation AI apply the generation algorithm.

[0043] The generation unit can prioritize content based on each child's submission timing during generation. For example, the generation unit can prioritize generating content related to homework with an approaching deadline. It can also postpone generating content related to homework with a distant deadline. Furthermore, the generation unit can prioritize generating content related to homework that has passed its deadline and provide a warning. For example, the generation unit monitors submission timing data in real time and determines content priority according to the submission deadline. The generation unit prioritizes generating content related to homework with an approaching deadline. The generation unit postpones generating content related to homework with a distant deadline. The generation unit prioritizes generating content related to homework that has passed its deadline and provides a warning. This allows important information to be provided preferentially by prioritizing content based on submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input submission timing data into a generation AI and have the generation AI perform the content priority determination.

[0044] The generation unit can adjust the order of content based on the relevance of each child during generation. For example, the generation unit can display content related to learning progress first. It can also display content related to interests next. Furthermore, it can display other announcements last. For example, the generation unit monitors relevance data in real time and adjusts the order of content according to relevance. The generation unit displays content related to learning progress first. The generation unit displays content related to interests next. The generation unit displays other announcements last. This allows for more appropriate information to be provided by adjusting the order of content based on relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input relevance data into a generation AI and have the generation AI perform the adjustment of the content order.

[0045] The distribution unit can customize the distribution method at the time of distribution, taking into account each child's home environment. For example, if the home environment is stable, the distribution unit will use the standard distribution method. If the home environment is unstable, the distribution unit can also use a privacy-conscious distribution method. Furthermore, if the home environment is unusual, the distribution unit can use a special distribution method to provide appropriate support. For example, the distribution unit monitors home environment information in real time and customizes the distribution method. The distribution unit uses the standard distribution method for children with stable home environments. The distribution unit uses a privacy-conscious distribution method for children with unstable home environments. The distribution unit uses a special distribution method to provide appropriate support for children with unusual home environments. This makes it possible to provide appropriate information by customizing the distribution method according to the home environment. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input home environment information into a generating AI and have the generating AI perform the customization of the distribution method.

[0046] The distribution unit can adjust the content of the distributed materials based on each child's learning progress at the time of distribution. For example, the distribution unit can provide children who are falling behind in their learning progress with content that includes detailed explanations. The distribution unit can also provide children who are progressing well with their learning progress with their usual content. Furthermore, the distribution unit can provide children who are progressing very quickly with concise content that focuses on the essentials. For example, the distribution unit monitors learning progress data in real time and adjusts the content of the distributed materials according to the progress. The distribution unit provides children who are falling behind with content that includes detailed explanations. The distribution unit provides children who are progressing well with their usual content. The distribution unit provides children who are progressing very quickly with concise content that focuses on the essentials. This makes it possible to provide appropriate information by adjusting the content of the distributed materials based on learning progress. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input learning progress data into a generating AI and have the generating AI perform the adjustment of the distributed materials.

[0047] The distribution unit can select a distribution method that takes into account each child's geographical background at the time of distribution. For example, the distribution unit can use a distribution method specific to urban areas for children living in urban areas. It can also use a distribution method specific to rural areas for children living in rural areas. Furthermore, it can use a distribution method adapted to the local education system for children living overseas. For example, the distribution unit monitors geographical background data in real time and selects a distribution method according to the geographical background. The distribution unit uses a distribution method specific to urban areas for children living in urban areas. The distribution unit uses a distribution method specific to rural areas for children living in rural areas. The distribution unit uses a distribution method adapted to the local education system for children living overseas. This enables the provision of appropriate information by selecting a distribution method according to the geographical background. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input geographical background data into a generating AI and have the generating AI select the distribution method.

[0048] The distribution unit can analyze each child's social media activity and adjust the content delivered during distribution. For example, the distribution unit can prioritize the distribution of learning-related posts from a child's social media activity. The distribution unit can also incorporate data on friendships from a child's social media activity into the content delivered. Furthermore, the distribution unit can incorporate data on interests from a child's social media activity into the content delivered. For example, the distribution unit monitors social media activity in real time and prioritizes the distribution of learning-related posts. The distribution unit incorporates data on friendships into the content delivered. The distribution unit incorporates data on interests into the content delivered. This allows for the efficient provision of learning-related information by analyzing social media activity. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input social media data into a generating AI and have the generating AI adjust the content delivered.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection unit can adjust the data collection method based on each child's learning style. For example, a visual learner can be assigned a data collection method that emphasizes visual data. Similarly, an auditory learner can be assigned a data collection method that emphasizes audio data. Furthermore, an experiential learner can be assigned a data collection method that emphasizes practical data. The data collection unit monitors learning style data in real time and adjusts the data collection method according to the learning style. This enables data collection tailored to the learning style, and is expected to provide more appropriate information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input learning style data into a generating AI and have the generating AI perform the adjustment of the data collection method.

[0051] The generation unit can adjust the content generation method based on each child's learning objectives during the generation process. For example, it can generate content that shows specific steps for children with short-term learning objectives. It can also generate content that shows the overall picture for children with long-term learning objectives. Furthermore, it can generate content specialized for children who want to improve a particular skill. The generation unit monitors the learning objective data in real time and adjusts the generation method according to the objectives. This enables the generation of content that is tailored to the learning objectives, and more appropriate information can be expected to be provided. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input learning objective data into a generation AI and have the generation AI perform the adjustment of the generation method.

[0052] The data collection unit can adjust the data collection method based on each child's learning environment during collection. For example, it can prioritize collecting data from children in online learning environments and perform detailed analysis. It can also collect data from children in offline learning environments with the usual priority. Furthermore, it can collect data from children in hybrid learning environments using special methods and take appropriate action. The data collection unit monitors learning environment data in real time and adjusts the data collection method according to the environment. This enables data collection tailored to the learning environment, and is expected to provide more appropriate information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input learning environment data into a generating AI and have the generating AI perform the adjustment of the collection method.

[0053] The generation unit can adjust the content generation method based on each child's learning history during generation. For example, it can focus on generating content related to subjects the child has struggled with in the past. It can also generate content related to subjects the child excels at with the usual level of detail. Furthermore, it can generate content related to new subjects in a special way and provide appropriate responses. The generation unit monitors learning history data in real time and adjusts the generation method according to the history. This enables content generation tailored to the learning history, and more appropriate information can be expected to be provided. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input learning history data into a generation AI and have the generation AI perform the adjustment of the generation method.

[0054] The data collection unit can adjust the data collection method based on each child's motivation to learn during the collection process. For example, it can prioritize the collection of data from children with high motivation and perform detailed analysis. It can also collect data from children with low motivation using the usual priority order. Furthermore, it can collect data from children whose motivation fluctuates using a special method and take appropriate action. The data collection unit monitors motivation data in real time and adjusts the data collection method according to the motivation. This enables data collection tailored to motivation, and is expected to provide more appropriate information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input motivation data into a generating AI and have the generating AI adjust the collection method.

[0055] The analysis unit can adjust its analysis algorithm based on each child's learning pace during the analysis. For example, it can perform a detailed analysis for children with a fast learning pace, and a simplified analysis for children with a slow learning pace. Furthermore, it can apply an appropriate analysis method to children whose learning pace fluctuates. The analysis unit monitors the learning pace data in real time and adjusts the analysis algorithm according to the pace. This enables analysis tailored to the learning pace, and is expected to provide more appropriate information. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input learning pace data into a generating AI and have the generating AI perform the adjustment of the analysis algorithm.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects timetables, transcripts of teacher speeches, test results, homework submission status, and general announcements. For example, the data collection unit retrieves timetable information from the school database and transcribes teacher speeches using speech recognition technology. Furthermore, it collects test results in digital format and retrieves homework submission status from online platforms. Step 2: The analysis unit analyzes the data collected by the collection unit and extracts information about each child. For example, the analysis unit uses AI to analyze the collected data and understand each child's learning situation and characteristics. Furthermore, it analyzes the content of the teacher's remarks to evaluate each child's level of understanding and interest, and analyzes test results to identify areas of strength and weakness. Step 3: The generation unit generates personalized content for each child based on the information extracted by the analysis unit. For example, the generation unit uses AI to generate content optimized for each child, summarizing lesson content by subject, analyzing test results, and generating content that includes praise. It also generates content that includes information about homework and other announcements. Step 4: The distribution unit distributes the content generated by the generation unit to the parent. For example, the distribution unit distributes the content using email or messaging apps, and distributes it at a time when the parent is likely to receive it. Furthermore, the distribution unit monitors the content distribution status and evaluates the effectiveness of the distribution.

[0058] (Example of form 2) The system according to an embodiment of the present invention is a system that uses AI to personalize school newsletters distributed by schools and automatically generates review content for children and communication content for parents. This system solves the problem that conventional school newsletters only contain information for all students and are limited to being a communication tool. It also solves the problem that parents have few opportunities to know what their children are learning and that it takes time and effort for teachers to understand the characteristics of each child. For example, this system first collects data such as timetables, transcripts of teacher speeches, test results, homework submission status, and general announcements. Next, the AI ​​analyzes the collected data and automatically generates different content for each child. The generated content is delivered to parents daily. Specifically, the content of the lessons is summarized by subject. For example, in mathematics, the summary may be "We learned two-digit addition," and in Japanese language, it may be "We learned 10 new kanji characters." Next, the test results are analyzed. For example, in mathematics, the analysis may be "It seems that the child is not good at adding 7," and in science, it may be "The child answered all the questions about temperature correctly." The messages also include positive comments, such as, "You actively participated in the social studies class." Furthermore, information regarding homework is provided, such as, "You have a math drill assignment." Finally, other announcements are also included, such as, "We will be holding a class observation during the fourth period on [date]." This reduces the burden on teachers, increases opportunities for parent-child conversations, and improves parents' awareness of their child's learning progress. It is also expected to have effects such as creating topics of conversation between parents and children, preventing forgotten items and homework, promoting understanding of students, reducing paper materials, preventing missed communications with parents, encouraging reflection habits, reducing teachers' routine tasks, and building relationships with each family.

[0059] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a distribution unit. The collection unit collects timetables, transcripts of teacher speeches, test results, homework submission status, and general announcements. For example, the collection unit obtains timetable information from the school's database. The collection unit can also transcribe teacher speeches using speech recognition technology. Furthermore, the collection unit can collect test results in digital format and obtain homework submission status from an online platform. For example, the collection unit records teacher speeches and converts them into text data using speech recognition technology. The collection unit obtains test results from the school's grade management system and stores them in digital format. The collection unit obtains homework submission status from an online learning platform and stores it in a database. The analysis unit analyzes the data collected by the collection unit and extracts information about each child. For example, the analysis unit uses AI to analyze the collected data and understand each child's learning situation and characteristics. The analysis unit can also analyze the content of teacher speeches and evaluate each child's level of understanding and interest. Furthermore, the analysis unit can analyze test results and identify each child's strengths and weaknesses. For example, the analysis unit uses AI to analyze the emotional tone of the teacher's remarks and evaluate each child's response. The analysis unit uses statistical methods to analyze test results and evaluate each child's academic ability. The analysis unit analyzes homework submission status and evaluates each child's study habits. The generation unit generates different content for each child based on the information extracted by the analysis unit. For example, the generation unit uses AI to generate content optimized for each child. The generation unit can summarize lesson content by subject, analyze test results, and generate content that includes praise. Furthermore, the generation unit can also generate content that includes information about homework and other announcements. For example, the generation unit uses AI to summarize lesson content and provide learning content tailored to each child. The generation unit analyzes test results and generates content that highlights each child's strengths and weaknesses. The generation unit generates content that includes praise to improve each child's motivation. The distribution unit distributes the content generated by the generation unit to parents.The distribution unit can deliver content using, for example, email or messaging apps. The distribution unit can also deliver content at times when parents are more likely to receive it. Furthermore, the distribution unit can monitor the content delivery status and evaluate the effectiveness of the delivery. For example, the distribution unit delivers generated content to parents using email. The distribution unit delivers content in real time using messaging apps. The distribution unit monitors the delivery status and confirms whether parents have received the content. As a result, the system according to this embodiment can reduce the burden on teachers, provide opportunities for parent-child conversations, and improve parents' awareness of their child's learning progress.

[0060] The data collection unit collects timetables, transcripts of teacher speeches, test results, homework submission status, and general announcements. For example, the unit retrieves timetable information from the school's database. Specifically, it accesses the school's management system and regularly updates timetable information for each class. The unit can also transcribe teacher speeches using speech recognition technology. Teacher speeches are recorded during class and converted into text data in real time by the speech recognition engine. Furthermore, the unit can collect test results digitally and retrieve homework submission status from online platforms. For example, the unit retrieves test results from the school's grade management system and stores them digitally. This allows for centralized management of each student's academic performance data. Regarding homework submission status, data is retrieved from online learning platforms, allowing for real-time tracking of submission deadlines and status. The unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server, making it accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0061] The analysis unit analyzes the data collected by the collection unit and extracts information about each child. For example, the analysis unit uses AI to analyze the collected data and understand each child's learning situation and characteristics. Specifically, it can analyze the content of teacher statements and evaluate each child's understanding and interest. The AI ​​uses natural language processing technology to analyze teacher statements and evaluate each child's reactions and understanding. It can also analyze test results to identify each child's strengths and weaknesses. For example, the analysis unit uses AI to analyze the emotional tone of teacher statements and evaluate each child's reactions. Furthermore, it analyzes test results using statistical methods to evaluate each child's academic ability. The analysis unit analyzes homework submission status to evaluate each child's study habits. This allows the analysis unit to quickly and accurately analyze collected data and understand each child's learning situation and characteristics. Furthermore, the analysis unit can utilize past data and statistical information to conduct long-term learning trend analysis. For example, it can evaluate progress in specific subjects or skills based on past test results and formulate future learning plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term learning management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0062] The generation unit generates personalized content for each child based on the information extracted by the analysis unit. For example, the generation unit uses AI to generate content optimized for each child. Specifically, it can summarize lesson content by subject, analyze test results, and generate content that includes praise. The AI ​​uses natural language generation technology to concisely summarize lesson content and provide learning materials tailored to each child. It also analyzes test results and generates content that highlights each child's strengths and weaknesses. For example, the generation unit uses AI to summarize lesson content and provide learning materials tailored to each child. Furthermore, the generation unit generates content that includes praise to improve each child's motivation. For example, it can generate a message praising a child who scores highly on a particular test, increasing their motivation to learn. The generation unit can also generate content that includes information about homework and other important announcements. For example, it can generate reminders containing information about homework deadlines and submission methods, notifying children and parents. This allows the generation unit to provide optimal learning content for each child, maximizing learning effectiveness. Furthermore, the generation unit can continuously evaluate and improve the quality of the generated content. For example, it can revise the content and format based on feedback from parents and teachers to provide more effective learning support. This allows the generation unit to always provide high-quality content based on the latest information, supporting children's learning.

[0063] The distribution unit delivers content generated by the generation unit to parents. The distribution unit can deliver content using, for example, email or messaging apps. Specifically, it can deliver content at times when parents are likely to receive it. For example, it can deliver content around the time parents return home from work or when children return home from school. The distribution unit can also monitor the content delivery status and evaluate the effectiveness of the delivery. For example, the distribution unit can deliver content generated using email to parents. The distribution unit can deliver content in real time using messaging apps. Furthermore, the distribution unit monitors the delivery status and confirms whether parents have received the content. This allows the distribution unit to quickly provide appropriate action instructions to each user and minimize the risk of disaster. In addition, the distribution unit can collect user feedback and continuously improve the accuracy and effectiveness of the instructions. For example, based on feedback from users who received evacuation instructions, it can revise evacuation routes and improve the content of the instructions. The distribution unit can also reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email in combination. This allows the distribution department to provide users with quick and reliable instructions, minimizing the risk of disaster.

[0064] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to collect data when the user is relaxed. Alternatively, if the user is relaxed, the data collection unit can collect data immediately and begin processing quickly. Furthermore, if the user is busy, the data collection unit can adjust the collection timing to match the user's schedule. For example, the data collection unit can monitor the user's emotions in real time and adjust the collection timing according to changes in emotions. The data collection unit can identify times when the user is relaxed and collect data during those times. The data collection unit can consider the user's schedule and collect data at the optimal time. This allows for more appropriate data collection by adjusting the data collection timing according to the user's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the collection timing.

[0065] The data collection unit can analyze the emotional tone of each teacher's statements and determine the priority of the data to be collected. For example, if a teacher's statement has a positive tone, the data collection unit will prioritize collecting that statement. Conversely, if a teacher's statement has a negative tone, the data collection unit may postpone its collection. Furthermore, if a teacher's statement has a neutral tone, the data collection unit may collect it with the same priority as other data. For example, the data collection unit can use speech analysis technology to analyze the emotional tone of teachers' statements in real time and determine the priority of the data to be collected. The data collection unit will prioritize collecting statements with a positive tone and postpone collecting statements with a negative tone. The data collection unit will collect statements with a neutral tone with the same priority as other data. This allows for the priority collection of important information by prioritizing data based on the emotional tone of teachers' statements. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input teacher speech data into a generating AI, which can then perform emotional tone analysis and determine data prioritization.

[0066] The data collection unit can filter data based on each child's learning progress during collection. For example, the data collection unit can prioritize collecting data from children who are behind in their learning progress and perform detailed analysis. The data collection unit can also collect data from children who are progressing well in their learning progress with normal priority. Furthermore, the data collection unit can prioritize data from other children, delaying the collection of data from children who are progressing very quickly. For example, the data collection unit can monitor learning progress data in real time and filter the data according to the progress. The data collection unit can prioritize collecting data from children who are falling behind and perform detailed analysis. The data collection unit can collect data from children who are progressing well in their learning progress with normal priority and delay the collection of data from children who are progressing very quickly. This allows for the efficient collection of necessary information by filtering data based on learning progress. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input learning progress data into a generating AI and have the generating AI perform data filtering.

[0067] The data collection unit can estimate the user's emotions and select the types of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect only essential data to reduce the burden. If the user is relaxed, the data collection unit can also collect detailed data and perform a comprehensive analysis. Furthermore, if the user is busy, the data collection unit can collect only the minimum necessary data and process it quickly. For example, the data collection unit monitors the user's emotions in real time and selects the types of data to collect according to changes in emotions. If the user is stressed, the data collection unit collects only essential data to reduce the burden. If the user is relaxed, the data collection unit collects detailed data and performs a comprehensive analysis. If the user is busy, the data collection unit collects only the minimum necessary data and processes it quickly. This reduces the user's burden by selecting the types of data to collect according to their emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI select the types of data to collect.

[0068] The data collection unit can adjust its data collection method while considering each child's home environment information. For example, the data collection unit can collect data from children with stable home environments using standard methods. It can also carefully collect data from children with unstable home environments, respecting their privacy. Furthermore, the data collection unit can collect data from children with special home environments using special methods and take appropriate action. For example, the data collection unit can monitor home environment information in real time and adjust the collection method. The data collection unit collects data from children with stable home environments using standard methods. The data collection unit carefully collects data from children with unstable home environments, respecting their privacy. The data collection unit collects data from children with special home environments using special methods and takes appropriate action. This allows for appropriate data collection by adjusting the data collection method while considering home environment information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input home environment information into a generating AI and have the generating AI adjust the collection method.

[0069] The data collection unit can analyze each child's social media activity and collect relevant data during the collection process. For example, the data collection unit can collect learning-related posts from a child's social media activity. The data collection unit can also collect data on friendships from a child's social media activity. Furthermore, the data collection unit can collect data on interests from a child's social media activity. For example, the data collection unit can monitor social media activity in real time and collect learning-related posts. The data collection unit can collect data on friendships to understand a child's social connections. The data collection unit can collect data on interests to understand a child's interests. This allows for the efficient collection of learning-related data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media data into a generating AI and have the generating AI collect relevant data.

[0070] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit can use a simplified algorithm for analysis. The analysis unit can also use a detailed algorithm for analysis if the user is relaxed. Furthermore, if the user is busy, the analysis unit can use an algorithm for rapid analysis. For example, the analysis unit monitors the user's emotions in real time and adjusts the analysis algorithm according to changes in emotions. The analysis unit uses a simplified algorithm for users who are stressed. The analysis unit uses a detailed algorithm for users who are relaxed. The analysis unit uses an algorithm for rapid analysis for busy users. This allows for more appropriate analysis by adjusting the analysis algorithm according to the user's emotions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust the analysis algorithm.

[0071] The analysis unit can improve the accuracy of its analysis by referring to each child's past learning data during the analysis process. For example, the analysis unit can refer to each child's past test results to analyze their current learning situation. It can also refer to each child's past homework submission status to analyze their learning habits. Furthermore, it can refer to each child's past class participation status to analyze their motivation to learn. For example, the analysis unit retrieves past test results from a database and analyzes the current learning situation. The analysis unit refers to past homework submission status to evaluate learning habits. The analysis unit refers to past class participation status to evaluate motivation to learn. This improves the accuracy of the analysis by referring to past learning data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past learning data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0072] The analysis unit can apply different analysis methods to each child's learning style during analysis. For example, the analysis unit can apply an analysis method that emphasizes visual data to visual learners. It can also apply an analysis method that emphasizes audio data to auditory learners. Furthermore, it can apply an analysis method that emphasizes practical data to experiential learners. For example, the analysis unit can apply an analysis method that emphasizes visual data to visual learners. The analysis unit can apply an analysis method that emphasizes audio data to auditory learners. The analysis unit can apply an analysis method that emphasizes practical data to experiential learners. This allows for more appropriate analysis by applying an analysis method that matches the learning style. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input learning style data into a generating AI and have the generating AI perform the application of the analysis method.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, the analysis unit can provide a concise display method if the user is busy. For example, the analysis unit monitors the user's emotions in real time and adjusts the display method according to changes in emotions. The analysis unit provides a simple and highly visible display method for stressed users. The analysis unit provides a display method that includes detailed information for relaxed users. The analysis unit provides a concise display method for busy users. This allows for the provision of more appropriate information by adjusting the display method of the analysis results according to the user's emotions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI adjust the display method.

[0074] The analysis unit can perform analysis while considering the geographical background of each child. For example, when analyzing data of children living in urban areas, the analysis unit can consider the learning environment specific to urban areas. Similarly, when analyzing data of children living in rural areas, the analysis unit can consider the learning environment specific to rural areas. Furthermore, when analyzing data of children living overseas, the analysis unit can consider the local education system. For example, when analyzing data of children living in urban areas, the analysis unit can consider the learning environment specific to urban areas. When analyzing data of children living in rural areas, the analysis unit can consider the learning environment specific to rural areas. When analyzing data of children living overseas, the analysis unit can consider the local education system. This allows for more appropriate analysis by considering geographical background. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical background data into a generating AI and have the generating AI perform adjustments to the analysis.

[0075] The analysis unit can improve the accuracy of its analysis by referring to relevant academic literature during the analysis process. For example, the analysis unit can improve its analysis algorithm by referring to the latest educational research. It can also improve the reliability of its analysis results by referring to past academic literature. Furthermore, the analysis unit can optimize its analysis methods by referring to research results from other educational institutions. For example, the analysis unit improves its analysis algorithm by referring to the latest educational research. The analysis unit improves the reliability of its analysis results by referring to past academic literature. The analysis unit optimizes its analysis methods by referring to research results from other educational institutions. This improves the accuracy of the analysis by referring to academic literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input academic literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0076] The generation unit can estimate the user's emotions and adjust the presentation of the generated content based on the estimated emotions. For example, if the user is stressed, the generation unit can generate simple and highly visual content. It can also generate content with detailed information if the user is relaxed. Furthermore, if the user is busy, the generation unit can generate concise content. For example, the generation unit monitors the user's emotions in real time and adjusts the presentation of the content according to changes in emotions. The generation unit generates simple and highly visual content for stressed users. The generation unit generates content with detailed information for relaxed users. The generation unit generates concise content for busy users. This allows for more appropriate information to be provided by adjusting the presentation of content according to the user's emotions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the presentation of the content.

[0077] The generation unit can adjust the level of detail of the content based on each child's learning progress during generation. For example, the generation unit can generate content with detailed explanations for children who are behind in their learning progress. It can also generate content with normal detail for children who are progressing well. Furthermore, it can generate concise content that gets straight to the point for children who are progressing very quickly. For example, the generation unit monitors learning progress data in real time and adjusts the level of detail of the content according to the progress. The generation unit generates content with detailed explanations for children who are falling behind. The generation unit generates content with normal detail for children who are progressing well. The generation unit generates concise content that gets straight to the point for children who are progressing very quickly. This allows for the provision of more appropriate information by adjusting the level of detail of the content based on learning progress. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input learning progress data into a generation AI and have the generation AI perform the adjustment of the level of detail of the content.

[0078] The generation unit can apply different generation algorithms to each child's interests during generation. For example, for a child interested in science, the generation unit can generate content that emphasizes scientific content. It can also generate content that emphasizes artistic content for a child interested in art. Furthermore, it can generate content that emphasizes sports-related content for a child interested in sports. For example, the generation unit monitors interest data in real time and applies a generation algorithm according to the child's interests. For a child interested in science, the generation unit generates content that emphasizes scientific content. For a child interested in art, the generation unit generates content that emphasizes artistic content. For a child interested in sports, the generation unit generates content that emphasizes sports-related content. This allows for the provision of more appropriate information by applying a generation algorithm tailored to each child's interests. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input interest data into a generation AI and have the generation AI apply the generation algorithm.

[0079] The generation unit can estimate the user's emotions and adjust the length of the content it generates based on those emotions. For example, if the user is stressed, the generation unit can generate short, concise content. If the user is relaxed, the generation unit can also generate longer content with detailed explanations. Furthermore, if the user is busy, the generation unit can generate short, easily understandable content. For example, the generation unit monitors the user's emotions in real time and adjusts the content length according to changes in those emotions. For stressed users, the generation unit generates short, concise content. For relaxed users, the generation unit generates longer content with detailed explanations. For busy users, the generation unit generates short, easily understandable content. This allows for more appropriate information to be provided by adjusting the content length according to the user's emotions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI adjust the content length.

[0080] The generation unit can prioritize content based on each child's submission timing during generation. For example, the generation unit can prioritize generating content related to homework with an approaching deadline. It can also postpone generating content related to homework with a distant deadline. Furthermore, the generation unit can prioritize generating content related to homework that has passed its deadline and provide a warning. For example, the generation unit monitors submission timing data in real time and determines content priority according to the submission deadline. The generation unit prioritizes generating content related to homework with an approaching deadline. The generation unit postpones generating content related to homework with a distant deadline. The generation unit prioritizes generating content related to homework that has passed its deadline and provides a warning. This allows important information to be provided preferentially by prioritizing content based on submission timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input submission timing data into a generation AI and have the generation AI perform the content priority determination.

[0081] The generation unit can adjust the order of content based on the relevance of each child during generation. For example, the generation unit can display content related to learning progress first. It can also display content related to interests next. Furthermore, it can display other announcements last. For example, the generation unit monitors relevance data in real time and adjusts the order of content according to relevance. The generation unit displays content related to learning progress first. The generation unit displays content related to interests next. The generation unit displays other announcements last. This allows for more appropriate information to be provided by adjusting the order of content based on relevance. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input relevance data into a generation AI and have the generation AI perform the adjustment of the content order.

[0082] The delivery unit can estimate the user's emotions and adjust the delivery timing based on the estimated emotions. For example, if the user is stressed, the delivery unit can delay the delivery timing to deliver when the user is relaxed. Alternatively, if the user is relaxed, the delivery unit can deliver immediately to provide information quickly. Furthermore, if the user is busy, the delivery unit can adjust the delivery timing to match the user's schedule. For example, the delivery unit monitors the user's emotions in real time and adjusts the delivery timing according to changes in emotions. For stressed users, the delivery unit delays the delivery timing to deliver when the user is relaxed. For relaxed users, the delivery unit delivers immediately to provide information quickly. For busy users, the delivery unit adjusts the delivery timing to match the user's schedule. This allows for more appropriate information delivery by adjusting the delivery timing according to the user's emotions. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input user emotion data into a generating AI and have the generating AI adjust the delivery timing.

[0083] The distribution unit can customize the distribution method at the time of distribution, taking into account each child's home environment. For example, if the home environment is stable, the distribution unit will use the standard distribution method. If the home environment is unstable, the distribution unit can also use a privacy-conscious distribution method. Furthermore, if the home environment is unusual, the distribution unit can use a special distribution method to provide appropriate support. For example, the distribution unit monitors home environment information in real time and customizes the distribution method. The distribution unit uses the standard distribution method for children with stable home environments. The distribution unit uses a privacy-conscious distribution method for children with unstable home environments. The distribution unit uses a special distribution method to provide appropriate support for children with unusual home environments. This makes it possible to provide appropriate information by customizing the distribution method according to the home environment. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input home environment information into a generating AI and have the generating AI perform the customization of the distribution method.

[0084] The distribution unit can adjust the content of the distributed materials based on each child's learning progress at the time of distribution. For example, the distribution unit can provide children who are falling behind in their learning progress with content that includes detailed explanations. The distribution unit can also provide children who are progressing well with their learning progress with their usual content. Furthermore, the distribution unit can provide children who are progressing very quickly with concise content that focuses on the essentials. For example, the distribution unit monitors learning progress data in real time and adjusts the content of the distributed materials according to the progress. The distribution unit provides children who are falling behind with content that includes detailed explanations. The distribution unit provides children who are progressing well with their usual content. The distribution unit provides children who are progressing very quickly with concise content that focuses on the essentials. This makes it possible to provide appropriate information by adjusting the content of the distributed materials based on learning progress. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input learning progress data into a generating AI and have the generating AI perform the adjustment of the distributed materials.

[0085] The distribution unit can estimate the user's emotions and determine the priority of content to deliver based on the estimated emotions. For example, if the user is stressed, the distribution unit will prioritize delivering important content. It can also prioritize delivering detailed content if the user is relaxed. Furthermore, if the user is busy, it can prioritize delivering concise content. For example, the distribution unit monitors the user's emotions in real time and determines the priority of content to deliver in response to changes in emotions. It prioritizes delivering important content to stressed users, detailed content to relaxed users, and concise content to busy users. This allows for the priority delivery of important information by prioritizing content according to the user's emotions. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input user emotion data into a generating AI and have the generating AI determine the content priority.

[0086] The distribution unit can select a distribution method that takes into account each child's geographical background at the time of distribution. For example, the distribution unit can use a distribution method specific to urban areas for children living in urban areas. It can also use a distribution method specific to rural areas for children living in rural areas. Furthermore, it can use a distribution method adapted to the local education system for children living overseas. For example, the distribution unit monitors geographical background data in real time and selects a distribution method according to the geographical background. The distribution unit uses a distribution method specific to urban areas for children living in urban areas. The distribution unit uses a distribution method specific to rural areas for children living in rural areas. The distribution unit uses a distribution method adapted to the local education system for children living overseas. This enables the provision of appropriate information by selecting a distribution method according to the geographical background. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input geographical background data into a generating AI and have the generating AI select the distribution method.

[0087] The distribution unit can analyze each child's social media activity and adjust the content delivered during distribution. For example, the distribution unit can prioritize the distribution of learning-related posts from a child's social media activity. The distribution unit can also incorporate data on friendships from a child's social media activity into the content delivered. Furthermore, the distribution unit can incorporate data on interests from a child's social media activity into the content delivered. For example, the distribution unit monitors social media activity in real time and prioritizes the distribution of learning-related posts. The distribution unit incorporates data on friendships into the content delivered. The distribution unit incorporates data on interests into the content delivered. This allows for the efficient provision of learning-related information by analyzing social media activity. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input social media data into a generating AI and have the generating AI adjust the content delivered.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The data collection unit can adjust the data collection method based on each child's learning style. For example, a visual learner can be assigned a data collection method that emphasizes visual data. Similarly, an auditory learner can be assigned a data collection method that emphasizes audio data. Furthermore, an experiential learner can be assigned a data collection method that emphasizes practical data. The data collection unit monitors learning style data in real time and adjusts the data collection method according to the learning style. This enables data collection tailored to the learning style, and is expected to provide more appropriate information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input learning style data into a generating AI and have the generating AI perform the adjustment of the data collection method.

[0090] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user emotions. For example, if the user is stressed, positive feedback will be prioritized. If the user is relaxed, detailed feedback can be provided. Furthermore, if the user is busy, concise feedback that gets straight to the point can be provided. The analysis unit monitors the user's emotions in real time and adjusts the feedback method according to changes in emotions. This enables feedback that is tailored to the user's emotions, and more appropriate information can be expected to be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform the adjustment of the feedback method.

[0091] The generation unit can adjust the content generation method based on each child's learning objectives during the generation process. For example, it can generate content that shows specific steps for children with short-term learning objectives. It can also generate content that shows the overall picture for children with long-term learning objectives. Furthermore, it can generate content specialized for children who want to improve a particular skill. The generation unit monitors the learning objective data in real time and adjusts the generation method according to the objectives. This enables the generation of content that is tailored to the learning objectives, and more appropriate information can be expected to be provided. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input learning objective data into a generation AI and have the generation AI perform the adjustment of the generation method.

[0092] The distribution unit can estimate the user's emotions and adjust the format of the content it delivers based on those emotions. For example, if the user is stressed, it can prioritize delivering text-based content. If the user is relaxed, it can deliver visual content. Furthermore, if the user is busy, it can deliver audio content. The distribution unit monitors the user's emotions in real time and adjusts the content format according to changes in those emotions. This makes it possible to provide content in a format that matches the user's emotions, leading to the expectation of more appropriate information delivery. Some or all of the above processing in the distribution unit may be performed using AI or not. For example, the distribution unit can input user emotion data into a generating AI and have the generating AI perform the content format adjustments.

[0093] The data collection unit can adjust the data collection method based on each child's learning environment during collection. For example, it can prioritize collecting data from children in online learning environments and perform detailed analysis. It can also collect data from children in offline learning environments with the usual priority. Furthermore, it can collect data from children in hybrid learning environments using special methods and take appropriate action. The data collection unit monitors learning environment data in real time and adjusts the data collection method according to the environment. This enables data collection tailored to the learning environment, and is expected to provide more appropriate information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input learning environment data into a generating AI and have the generating AI perform the adjustment of the collection method.

[0094] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the notification can be kept to a minimum. If the user is relaxed, a detailed notification can be provided. Furthermore, if the user is busy, a concise notification that gets straight to the point can be provided. The analysis unit monitors the user's emotions in real time and adjusts the notification method according to changes in emotions. This enables notifications that are tailored to the user's emotions, and more appropriate information can be expected to be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform the adjustment of the notification method.

[0095] The generation unit can adjust the content generation method based on each child's learning history during generation. For example, it can focus on generating content related to subjects the child has struggled with in the past. It can also generate content related to subjects the child excels at with the usual level of detail. Furthermore, it can generate content related to new subjects in a special way and provide appropriate responses. The generation unit monitors learning history data in real time and adjusts the generation method according to the history. This enables content generation tailored to the learning history, and more appropriate information can be expected to be provided. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input learning history data into a generation AI and have the generation AI perform the adjustment of the generation method.

[0096] The delivery unit can estimate the user's emotions and adjust the order of content delivered based on those emotions. For example, if the user is stressed, important content can be delivered first. If the user is relaxed, detailed content can be delivered later. Furthermore, if the user is busy, concise content can be delivered first. The delivery unit monitors the user's emotions in real time and adjusts the order of content according to changes in emotions. This makes it possible to provide content in an order that matches the user's emotions, leading to the expectation of more appropriate information delivery. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user emotion data into a generating AI and have the generating AI perform the content order adjustment.

[0097] The data collection unit can adjust the data collection method based on each child's motivation to learn during the collection process. For example, it can prioritize the collection of data from children with high motivation and perform detailed analysis. It can also collect data from children with low motivation using the usual priority order. Furthermore, it can collect data from children whose motivation fluctuates using a special method and take appropriate action. The data collection unit monitors motivation data in real time and adjusts the data collection method according to the motivation. This enables data collection tailored to motivation, and is expected to provide more appropriate information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input motivation data into a generating AI and have the generating AI adjust the collection method.

[0098] The analysis unit can adjust its analysis algorithm based on each child's learning pace during the analysis. For example, it can perform a detailed analysis for children with a fast learning pace, and a simplified analysis for children with a slow learning pace. Furthermore, it can apply an appropriate analysis method to children whose learning pace fluctuates. The analysis unit monitors the learning pace data in real time and adjusts the analysis algorithm according to the pace. This enables analysis tailored to the learning pace, and is expected to provide more appropriate information. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input learning pace data into a generating AI and have the generating AI perform the adjustment of the analysis algorithm.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The data collection unit collects timetables, transcripts of teacher speeches, test results, homework submission status, and general announcements. For example, the data collection unit retrieves timetable information from the school database and transcribes teacher speeches using speech recognition technology. Furthermore, it collects test results in digital format and retrieves homework submission status from online platforms. Step 2: The analysis unit analyzes the data collected by the collection unit and extracts information about each child. For example, the analysis unit uses AI to analyze the collected data and understand each child's learning situation and characteristics. Furthermore, it analyzes the content of the teacher's remarks to evaluate each child's level of understanding and interest, and analyzes test results to identify areas of strength and weakness. Step 3: The generation unit generates personalized content for each child based on the information extracted by the analysis unit. For example, the generation unit uses AI to generate content optimized for each child, summarizing lesson content by subject, analyzing test results, and generating content that includes praise. It also generates content that includes information about homework and other announcements. Step 4: The distribution unit distributes the content generated by the generation unit to the parent. For example, the distribution unit distributes the content using email or messaging apps, and distributes it at a time when the parent is likely to receive it. Furthermore, the distribution unit monitors the content distribution status and evaluates the effectiveness of the distribution.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and distribution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the teacher's speech using the camera 42 and microphone 38B of the smart device 14 and transcribes it using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 to understand each child's learning progress. The generation unit generates content optimized for each child using the identification processing unit 290 of the data processing unit 12. The distribution unit distributes the content generated by the control unit 46A of the smart device 14 to the parents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and distribution unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the teacher's speech using the camera 42 and microphone 238 of the smart glasses 214 and transcribes it using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 to understand each child's learning progress. The generation unit generates content optimized for each child using the identification processing unit 290 of the data processing unit 12. The distribution unit distributes the content generated by the control unit 46A of the smart glasses 214 to the parents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and distribution unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the teacher's speech using the camera 42 and microphone 238 of the headset terminal 314 and transcribes it using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 to understand each child's learning progress. The generation unit generates content optimized for each child using the identification processing unit 290 of the data processing unit 12. The distribution unit distributes the content generated by the control unit 46A of the headset terminal 314 to the parents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and distribution unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the teacher's speech using the camera 42 and microphone 238 of the robot 414 and transcribes it using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 to understand each child's learning progress. The generation unit generates content optimized for each child using the identification processing unit 290 of the data processing unit 12. The distribution unit distributes the content generated by the control unit 46A of the robot 414 to the parent. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) The collection department is responsible for gathering timetables, transcripts of teacher speeches, test results, homework submission status, and general announcements. An analysis unit analyzes the data collected by the aforementioned collection unit and extracts information about each child, Based on the information extracted by the analysis unit, a generation unit generates different content for each child, The system includes a distribution unit that distributes the content generated by the generation unit to a parent. A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the emotional tone of each teacher's statements and prioritize the data to collect. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is During data collection, the data is filtered based on each child's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system estimates the user's emotions and selects the types of data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting data, we adjust the data collection method to take into account each child's home environment information. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is During data collection, we analyze each child's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by referring to each child's past learning data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During the analysis, different analytical methods are applied according to each child's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, the geographical background of each child will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, we refer to relevant academic literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates user emotions and adjusts how generated content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, adjust the level of detail of the content based on each child's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, different generation algorithms are applied according to each child's interests. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts the length of the generated content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, content prioritization is determined based on each child's submission timing. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the order of content is adjusted based on the relevance of each child. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned distribution unit, It estimates the user's emotions and adjusts the delivery timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned distribution unit, When streaming, customize the streaming method to take into account each child's home environment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned distribution unit, During distribution, the content will be adjusted based on each child's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned distribution unit, It estimates user sentiment and prioritizes the content delivered based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned distribution unit, When distributing content, the distribution method will be selected taking into account the geographical background of each child. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned distribution unit, During distribution, we analyze each child's social media activity and adjust the content accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department is responsible for gathering timetables, transcripts of teacher speeches, test results, homework submission status, and general announcements. An analysis unit analyzes the data collected by the aforementioned collection unit and extracts information about each child, Based on the information extracted by the analysis unit, a generation unit generates different content for each child, The system includes a distribution unit that distributes the content generated by the generation unit to a parent. A system characterized by the following features.

2. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze the emotional tone of each teacher's statements and prioritize the data to collect. The system according to feature 1.

4. The aforementioned collection unit is During data collection, the data is filtered based on each child's learning progress. The system according to feature 1.

5. The aforementioned collection unit is The system estimates the user's emotions and selects the types of data to collect based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting data, we adjust the data collection method to take into account each child's home environment information. The system according to feature 1.

7. The aforementioned collection unit is During data collection, we analyze each child's social media activity and collect relevant data. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system according to feature 1.

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