system

Abstract

The system according to this embodiment aims to analyze a child's educational data and provide the child with the most suitable educational materials. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects children's educational data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates educational materials based on the analysis results obtained by the analysis unit. The provision unit provides the educational materials generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to find teaching materials optimal for a child's interests and learning speed, which takes time and effort.

[0005] The system according to the embodiment aims to analyze a child's educational data and provide teaching materials optimal for that child.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects a child's educational data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates teaching materials based on the analysis results obtained by the analysis unit. The provision unit provides the teaching materials generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze a child's educational data and provide the child with the most suitable educational materials. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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). [[ID=!15]]

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the present invention provides an educational support system in which AI analyzes a child's educational data and generates optimal learning materials based on the child's learning speed and interests. The educational support system collects the child's educational data, and the AI ​​analyzes this data to determine the child's learning patterns and interests. Next, the AI ​​generates the identified learning materials and provides them to the child. The AI ​​monitors the child's learning progress and updates the materials as needed. For example, the educational support system collects data from the child's tablet or smartphone, and the AI ​​analyzes this data to generate learning materials. The generated materials include text, videos, and interactive content. The AI ​​also monitors the child's learning progress and updates the materials as needed. This allows for maximizing each child's potential during early childhood, from ages 4 to 6. A personalized learning experience allows children to learn using materials best suited to them, enabling a smooth transition to primary education. Thus, the educational support system can maximize the child's learning effectiveness and support a smooth transition to primary education.

[0029] The educational support system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects educational data of children. The collection unit can collect data from, for example, tablets or smartphones used by children. The collection unit can collect educational data such as learning progress, test results, and behavioral data. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using, for example, AI, to identify children's learning patterns and interests. The analysis unit can analyze the data based on, for example, the algorithm used and the purpose of the analysis. The generation unit generates educational materials based on the analysis results obtained by the analysis unit. The generation unit can generate educational materials that include, for example, text, videos, and interactive content. The generation unit can generate educational materials in formats such as HTML5 and MP4. The provision unit provides the educational materials generated by the generation unit. The provision unit can provide educational materials by methods such as online distribution or distribution of printed materials. The provision unit can provide the generated educational materials to children and support their learning progress. As a result, the educational support system according to this embodiment can efficiently collect, analyze, generate, and provide educational materials based on children's educational data.

[0030] The data collection unit collects educational data from children. For example, it can collect data from tablets and smartphones used by children. Specifically, it collects educational data such as children's learning progress, test results, and behavioral data in real time through educational applications installed on tablets and smartphones. Learning progress data includes study time, achievement level, and comprehension level for each subject, while test result data includes scores, correct answer rates, and error trends for each test. Behavioral data includes application usage frequency, concentration level during study, and length of breaks. This data is centrally managed by the data collection unit and transmitted to a cloud server. Furthermore, the data collection unit can also collect data from sensors and cameras installed in the child's learning environment. For example, a camera installed on a study desk can monitor the child's posture and facial expressions, providing data to evaluate their concentration level and fatigue level. Sensors can collect environmental data such as room temperature, humidity, and illuminance, which can be used to optimize the learning environment. In this way, the data collection unit can collect a wide range of data from various devices and sensors, allowing for a detailed understanding of the child's learning situation.

[0031] The Analysis Department analyzes the data collected by the Data Collection Department. For example, the Analysis Department can use AI to analyze the collected data and identify children's learning patterns and interests. Specifically, it can use machine learning algorithms to cluster children's learning data and identify groups with common learning patterns. For example, it can identify children who show a high level of interest in a particular subject or children who concentrate best at a specific time of day. Furthermore, it can use natural language processing technology to analyze the content of essays and answers written by children and evaluate their language ability and comprehension. Based on these analysis results, the Analysis Department can identify children's strengths and weaknesses and propose individualized learning plans. For example, it can provide more advanced problems to children who are good at mathematics and provide learning materials that allow them to learn from the basics in subjects they struggle with. In addition, the Analysis Department can evaluate children's learning progress and growth by comparing them with past data and monitor the achievement of long-term learning goals. In this way, the Analysis Department can analyze the collected data from multiple angles, gain a detailed understanding of children's learning situations, and build a foundation for providing optimal learning support.

[0032] The generation unit generates learning materials based on the analysis results obtained by the analysis unit. The generation unit can generate materials including text, videos, and interactive content. Specifically, based on the analysis results, it creates customized learning materials tailored to each child's learning style and interests. For example, for children who prefer visual learning, it provides video materials with extensive illustrations and animations, and for children who want to improve their reading comprehension, it provides materials with abundant text and practice problems. The generation unit generates materials in formats such as HTML5 and MP4, enabling use on various devices. Furthermore, by generating interactive content, the generation unit encourages children to engage in learning proactively. For example, it can help children practically understand the learning content through quiz-style questions and simulation games. The generation unit can also use AI to automatically adjust the difficulty level of the materials according to the child's learning progress. This ensures that children always work with materials of appropriate difficulty, maximizing the effectiveness of their learning. Through these functions, the generation unit can provide an optimal learning experience for each individual child.

[0033] The provision department provides educational materials generated by the generation department. The provision department can provide materials through methods such as online distribution or printed distribution. Specifically, generated materials can be uploaded to the cloud, allowing children to access them via tablets and smartphones. Online distribution provides materials in real time via the internet, creating an environment where children can learn anytime, anywhere. Printed distribution provides paper-based materials intended for use at home and school. By combining these methods, the provision department can provide flexible educational materials tailored to each child's learning style and environment. Furthermore, the provision department can monitor children's learning progress and provide additional materials and support as needed. For example, children struggling with a particular unit can be provided with supplementary materials and explanatory videos to help them deepen their understanding. The provision department can also collaborate with parents and teachers to share information on children's learning progress, thereby strengthening learning support at home and school. In this way, the provision department can comprehensively support children's learning and realize effective educational support.

[0034] The data collection unit can collect data from tablets and smartphones used by children. For example, the data collection unit can collect data from tablets and smartphones used by children. The data collection unit can also collect data considering specific device types and specifications, such as iOS devices or Android devices. This allows for more accurate educational data to be obtained by collecting data from devices used by children. Some or all of the above-described processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data collected from tablets and smartphones used by children into an AI and have the AI ​​perform data analysis.

[0035] The generation unit can generate educational materials that include text, videos, and interactive content. For example, the generation unit can generate educational materials that include text, videos, and interactive content. The generation unit can generate educational materials in formats such as HTML5 and MP4. For example, the generation unit can generate educational materials in diverse formats to maximize the learning effectiveness for children. By generating educational materials in diverse formats, the learning effectiveness for children can be maximized. 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 children's learning data into AI to determine the format and content of the educational materials that the AI ​​will generate.

[0036] The distribution unit can provide the generated learning materials to children. The distribution unit can, for example, provide the generated learning materials to children. The distribution unit can provide the learning materials by methods such as online distribution or distribution of printed materials. The distribution unit can, for example, provide the generated learning materials to children and support their learning progress. By providing the generated learning materials to children, the distribution unit can support their 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 the generated learning materials into AI, and the AI ​​can determine the optimal method of provision.

[0037] The service provider can monitor the child's learning progress and update the learning materials as needed. For example, the service provider can monitor the child's learning progress and update the learning materials as needed. For example, the service provider can monitor learning progress such as test results and assignment submission status and update the learning materials as needed. This maximizes learning effectiveness by updating the learning materials according to the child's learning progress. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input the child's learning progress data into the AI, which can then determine the optimal method for updating the learning materials.

[0038] The data collection unit can analyze a child's past learning history and select the optimal data collection method. For example, the data collection unit can analyze a child's past learning history and select the optimal data collection method. For example, the data collection unit can collect data during times when a child has shown high levels of concentration in the past. The data collection unit can also collect data from devices and applications that a child has previously preferred to use. Furthermore, the data collection unit can collect data by referring to methods in which a child has learned effectively in the past. This enables efficient data collection by selecting the optimal data collection method based on past learning history. 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 the child's past learning history data into AI, which can then select the optimal data collection method.

[0039] The data collection unit can filter data based on the child's current learning status and areas of interest during data collection. For example, the data collection unit can filter data based on the child's current learning status and areas of interest during data collection. For example, the data collection unit can collect only data related to the subject the child is currently studying. The data collection unit can also prioritize the collection of data related to topics the child is interested in. Furthermore, the data collection unit can collect only the necessary data according to the child's learning progress. This allows for the collection of only the necessary data by filtering the data based on the child's current learning status and areas of interest. 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 data on the child's current learning status and areas of interest into an AI, which can then determine the optimal data filtering method.

[0040] The data collection unit can prioritize the collection of highly relevant data based on the child's geographical location during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the child's geographical location during data collection. For example, if the child is in a specific region, the data collection unit can prioritize the collection of data related to that region. Furthermore, if the child is traveling, the data collection unit can also collect educational content related to the travel destination. In addition, if the child is at school, the data collection unit can prioritize the collection of data related to the school curriculum. This enables more appropriate data collection by collecting highly relevant data based on geographical location. 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 the child's geographical location into AI, which can then determine the optimal data collection method.

[0041] The data collection unit can analyze a child's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze a child's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to topics that a child shows interest in on social media. The data collection unit can also collect data from educational accounts that a child follows. Furthermore, the data collection unit can analyze the activities of online communities that a child participates in and collect relevant data. This makes it possible to collect data based on a child's interests by analyzing their social media activity. 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 the child's social media activity data into AI, which can then determine the optimal data collection method.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a concise analysis on less important data. The analysis unit can also perform an in-depth analysis on data that significantly impacts a child's learning progress. Furthermore, the analysis unit can perform a detailed analysis on data related to a child's interests. By adjusting the level of detail of the analysis based on the importance of the data, efficient data analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can determine the optimal level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a time series analysis algorithm to data related to learning speed. It can also apply a clustering algorithm to data related to interests. Furthermore, it can apply a regression analysis algorithm to data related to learning progress. By applying the appropriate analysis algorithm according to the data category, highly accurate analysis becomes possible. 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 the data category into the AI, and the AI ​​can determine the optimal analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis based on the data collection timing during analysis. The analysis unit can, for example, prioritize the analysis of the latest data to grasp the learning status in real time. The analysis unit can also analyze the current learning status while referring to past data. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period to evaluate the learning effect during that period. In this way, by determining the priority of analysis based on the data collection timing, the learning status in real time can be grasped. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the data collection timing into the AI, and the AI ​​can determine the optimal analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to maximize learning effectiveness. Alternatively, the analysis unit can postpone the analysis of less relevant data and focus on analyzing important data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This maximizes learning effectiveness by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the data into the AI, which can then determine the optimal analysis order.

[0046] The generation unit can adjust the level of detail in the learning materials based on the child's learning progress when generating them. For example, the generation unit can adjust the level of detail in the learning materials based on the child's learning progress when generating them. For example, if the child has a high level of understanding, the generation unit can generate learning materials that omit detailed explanations. Conversely, if the child has a low level of understanding, the generation unit can generate learning materials that include detailed explanations. Furthermore, the generation unit can generate learning materials of an appropriate difficulty level according to the child's learning progress. In this way, by adjusting the level of detail in the learning materials based on learning progress, learning materials of an appropriate difficulty level can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's learning progress data into the AI, and the AI ​​can determine the optimal level of detail in the learning materials.

[0047] The generation unit can apply different material generation algorithms depending on the child's interests when generating materials. For example, the generation unit can generate materials related to topics that the child is interested in. The generation unit can also generate customized materials based on the child's interests. Furthermore, the generation unit can generate materials in different formats (text, video, interactive content) depending on the child's interests. This allows for increased motivation to learn by applying material generation algorithms tailored to the 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 child interest data into AI, which can then determine the optimal material generation algorithm.

[0048] The generation unit can determine the priority of learning materials based on the child's learning history when generating them. For example, the generation unit can determine the priority of learning materials based on the child's learning history when generating them. For example, the generation unit can generate learning materials that the child should learn next based on what the child has learned in the past. The generation unit can also prioritize the generation of learning materials related to specific topics based on the child's learning history. Furthermore, the generation unit can analyze the child's learning history and prioritize the generation of the most effective learning materials. This enables efficient learning by determining the priority of learning materials based on the learning history. 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 the child's learning history data into AI, and the AI ​​can determine the optimal priority of learning materials.

[0049] The generation unit can adjust the order of learning materials based on the child's relevant data when generating them. For example, the generation unit can prioritize generating materials related to topics the child is interested in. The generation unit can also generate materials in an appropriate order according to the child's learning progress. Furthermore, the generation unit can generate materials in the optimal order by referring to the child's learning history. This maximizes learning effectiveness by adjusting the order of materials based on relevant data. 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 the child's relevant data into AI, which can then determine the optimal order of learning materials.

[0050] The delivery unit can select the optimal delivery method by referring to the child's past learning history when providing learning materials. For example, the delivery unit can select the optimal delivery method by referring to the child's past learning history when providing learning materials. The delivery unit can, for example, provide learning materials using devices or applications that the child has previously preferred to use. The delivery unit can also select the most effective delivery method from the child's learning history. Furthermore, the delivery unit can provide learning materials at the appropriate time by referring to the child's learning history. This allows the delivery unit to select the optimal delivery method by referring to past learning history. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the child's learning history data into AI, and the AI ​​can determine the optimal method for providing learning materials.

[0051] The service provider can customize the delivery method based on the child's current learning situation when providing learning materials. For example, the service provider can customize the delivery method based on the child's current learning situation when providing learning materials. For example, the service provider can provide learning materials related to the subject the child is currently studying. The service provider can also provide learning materials of an appropriate difficulty level according to the child's learning progress. Furthermore, the service provider can provide customized learning materials based on the child's interests. By customizing the delivery method based on the current learning situation, more effective learning becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the child's current learning situation into AI, and the AI ​​can determine the optimal delivery method.

[0052] The service provider can select the optimal delivery method based on the child's geographical location when providing educational materials. For example, the service provider can select the optimal delivery method based on the child's geographical location when providing educational materials. For example, if the child is in a specific region, the service provider can prioritize providing educational materials related to that region. Furthermore, if the child is traveling, the service provider can provide educational content related to the travel destination. In addition, if the child is at school, the service provider can provide educational materials related to the school curriculum. By selecting the optimal delivery method based on geographical location, more effective learning becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the child's geographical location into AI, and the AI ​​can determine the optimal delivery method.

[0053] The service provider can analyze a child's social media activity and suggest a method of providing educational materials. For example, the service provider can provide educational materials related to topics the child has shown interest in on social media. The service provider can also provide educational materials from educational accounts the child follows. Furthermore, the service provider can analyze the activities of online communities the child participates in and provide relevant educational materials. This makes it possible to provide educational materials based on the child's interests by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the child's social media activity data into AI, which can then determine the optimal method of provision.

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

[0055] The educational support system can also include a feedback unit. This unit provides feedback on what the child has learned. For example, it can provide immediate feedback indicating whether a problem the child has solved is correct or incorrect. It can also suggest what the child should learn next based on their learning progress. Furthermore, it can evaluate the child's learning attitude and effort, and provide positive feedback to boost their motivation. Thus, including a feedback unit can further enhance the effectiveness of the child's learning.

[0056] The data collection unit can gather physiological data from children to determine the optimal timing for learning. For example, it can collect physiological data such as a child's heart rate, body temperature, and sleep patterns. This allows the system to identify the time of day when a child is most focused and encourage learning during that time. The data collection unit can also monitor a child's fatigue level and suggest appropriate breaks. Furthermore, it can measure a child's stress level and provide relaxing content. In this way, by utilizing physiological data, the learning effectiveness of children can be maximized.

[0057] The generation unit can generate learning materials customized to each child's learning style. For example, it can generate materials that heavily utilize graphics and videos for visual learners. It can also generate materials that include audio explanations for auditory learners. Furthermore, it can generate materials that include interactive content for tactile learners. This allows for the provision of learning materials tailored to each child's learning style, thereby enhancing learning effectiveness.

[0058] The distribution department can adjust the method of providing learning materials according to the child's learning environment. For example, if the child is studying at home, the distribution department can provide materials online. If the child is studying at school, the materials can be provided as printed materials. Furthermore, if the child is on the go, the distribution department can provide materials optimized for mobile devices. This ensures continuity of learning by selecting the method of providing materials that suits the learning environment.

[0059] The system can set learning goals based on a child's learning progress and monitor their achievement. For example, it can display the child's progress toward their set learning goals in real time. It can also provide advice and reminders to help the child achieve their goals. Furthermore, it can motivate children by offering rewards and praise upon goal achievement. In this way, setting learning goals and monitoring their achievement can improve children's motivation to learn.

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

[0061] Step 1: The data collection unit collects the child's educational data. The data collection unit can collect data from, for example, tablets or smartphones used by the child. The data collection unit can collect educational data such as learning progress, test results, and behavioral data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, use AI to analyze the collected data and identify children's learning patterns and interests. The analysis unit can, for example, analyze the data based on the algorithm used and the purpose of the analysis. Step 3: The generation unit generates educational materials based on the analysis results obtained by the analysis unit. The generation unit can generate educational materials that include, for example, text, videos, and interactive content. The generation unit can generate educational materials in formats such as HTML5 and MP4. Step 4: The provider provides the educational materials generated by the generator. The provider can provide the materials by methods such as online distribution or printed distribution. The provider can, for example, provide the generated materials to children and support their learning progress.

[0062] (Example of form 2) An embodiment of the present invention provides an educational support system in which AI analyzes a child's educational data and generates optimal learning materials based on the child's learning speed and interests. The educational support system collects the child's educational data, and the AI ​​analyzes this data to determine the child's learning patterns and interests. Next, the AI ​​generates the identified learning materials and provides them to the child. The AI ​​monitors the child's learning progress and updates the materials as needed. For example, the educational support system collects data from the child's tablet or smartphone, and the AI ​​analyzes this data to generate learning materials. The generated materials include text, videos, and interactive content. The AI ​​also monitors the child's learning progress and updates the materials as needed. This allows for maximizing each child's potential during early childhood, from ages 4 to 6. A personalized learning experience allows children to learn using materials best suited to them, enabling a smooth transition to primary education. Thus, the educational support system can maximize the child's learning effectiveness and support a smooth transition to primary education.

[0063] The educational support system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects educational data of children. The collection unit can collect data from, for example, tablets or smartphones used by children. The collection unit can collect educational data such as learning progress, test results, and behavioral data. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using, for example, AI, to identify children's learning patterns and interests. The analysis unit can analyze the data based on, for example, the algorithm used and the purpose of the analysis. The generation unit generates educational materials based on the analysis results obtained by the analysis unit. The generation unit can generate educational materials that include, for example, text, videos, and interactive content. The generation unit can generate educational materials in formats such as HTML5 and MP4. The provision unit provides the educational materials generated by the generation unit. The provision unit can provide educational materials by methods such as online distribution or distribution of printed materials. The provision unit can provide the generated educational materials to children and support their learning progress. As a result, the educational support system according to this embodiment can efficiently collect, analyze, generate, and provide educational materials based on children's educational data.

[0064] The data collection unit collects educational data from children. For example, it can collect data from tablets and smartphones used by children. Specifically, it collects educational data such as children's learning progress, test results, and behavioral data in real time through educational applications installed on tablets and smartphones. Learning progress data includes study time, achievement level, and comprehension level for each subject, while test result data includes scores, correct answer rates, and error trends for each test. Behavioral data includes application usage frequency, concentration level during study, and length of breaks. This data is centrally managed by the data collection unit and transmitted to a cloud server. Furthermore, the data collection unit can also collect data from sensors and cameras installed in the child's learning environment. For example, a camera installed on a study desk can monitor the child's posture and facial expressions, providing data to evaluate their concentration level and fatigue level. Sensors can collect environmental data such as room temperature, humidity, and illuminance, which can be used to optimize the learning environment. In this way, the data collection unit can collect a wide range of data from various devices and sensors, allowing for a detailed understanding of the child's learning situation.

[0065] The Analysis Department analyzes the data collected by the Data Collection Department. For example, the Analysis Department can use AI to analyze the collected data and identify children's learning patterns and interests. Specifically, it can use machine learning algorithms to cluster children's learning data and identify groups with common learning patterns. For example, it can identify children who show a high level of interest in a particular subject or children who concentrate best at a specific time of day. Furthermore, it can use natural language processing technology to analyze the content of essays and answers written by children and evaluate their language ability and comprehension. Based on these analysis results, the Analysis Department can identify children's strengths and weaknesses and propose individualized learning plans. For example, it can provide more advanced problems to children who are good at mathematics and provide learning materials that allow them to learn from the basics in subjects they struggle with. In addition, the Analysis Department can evaluate children's learning progress and growth by comparing them with past data and monitor the achievement of long-term learning goals. In this way, the Analysis Department can analyze the collected data from multiple angles, gain a detailed understanding of children's learning situations, and build a foundation for providing optimal learning support.

[0066] The generation unit generates learning materials based on the analysis results obtained by the analysis unit. The generation unit can generate materials including text, videos, and interactive content. Specifically, based on the analysis results, it creates customized learning materials tailored to each child's learning style and interests. For example, for children who prefer visual learning, it provides video materials with extensive illustrations and animations, and for children who want to improve their reading comprehension, it provides materials with abundant text and practice problems. The generation unit generates materials in formats such as HTML5 and MP4, enabling use on various devices. Furthermore, by generating interactive content, the generation unit encourages children to engage in learning proactively. For example, it can help children practically understand the learning content through quiz-style questions and simulation games. The generation unit can also use AI to automatically adjust the difficulty level of the materials according to the child's learning progress. This ensures that children always work with materials of appropriate difficulty, maximizing the effectiveness of their learning. Through these functions, the generation unit can provide an optimal learning experience for each individual child.

[0067] The provision department provides educational materials generated by the generation department. The provision department can provide materials through methods such as online distribution or printed distribution. Specifically, generated materials can be uploaded to the cloud, allowing children to access them via tablets and smartphones. Online distribution provides materials in real time via the internet, creating an environment where children can learn anytime, anywhere. Printed distribution provides paper-based materials intended for use at home and school. By combining these methods, the provision department can provide flexible educational materials tailored to each child's learning style and environment. Furthermore, the provision department can monitor children's learning progress and provide additional materials and support as needed. For example, children struggling with a particular unit can be provided with supplementary materials and explanatory videos to help them deepen their understanding. The provision department can also collaborate with parents and teachers to share information on children's learning progress, thereby strengthening learning support at home and school. In this way, the provision department can comprehensively support children's learning and realize effective educational support.

[0068] The data collection unit can collect data from tablets and smartphones used by children. For example, the data collection unit can collect data from tablets and smartphones used by children. The data collection unit can also collect data considering specific device types and specifications, such as iOS devices or Android devices. This allows for more accurate educational data to be obtained by collecting data from devices used by children. Some or all of the above-described processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input data collected from tablets and smartphones used by children into an AI and have the AI ​​perform data analysis.

[0069] The generation unit can generate educational materials that include text, videos, and interactive content. For example, the generation unit can generate educational materials that include text, videos, and interactive content. The generation unit can generate educational materials in formats such as HTML5 and MP4. For example, the generation unit can generate educational materials in diverse formats to maximize the learning effectiveness for children. By generating educational materials in diverse formats, the learning effectiveness for children can be maximized. 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 children's learning data into AI to determine the format and content of the educational materials that the AI ​​will generate.

[0070] The distribution unit can provide the generated learning materials to children. The distribution unit can, for example, provide the generated learning materials to children. The distribution unit can provide the learning materials by methods such as online distribution or distribution of printed materials. The distribution unit can, for example, provide the generated learning materials to children and support their learning progress. By providing the generated learning materials to children, the distribution unit can support their 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 the generated learning materials into AI, and the AI ​​can determine the optimal method of provision.

[0071] The service provider can monitor the child's learning progress and update the learning materials as needed. For example, the service provider can monitor the child's learning progress and update the learning materials as needed. For example, the service provider can monitor learning progress such as test results and assignment submission status and update the learning materials as needed. This maximizes learning effectiveness by updating the learning materials according to the child's learning progress. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input the child's learning progress data into the AI, which can then determine the optimal method for updating the learning materials.

[0072] The data collection unit can estimate a child's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can estimate a child's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can collect data when the child is concentrating, minimizing interruptions to learning. The data collection unit can also refrain from collecting data when the child is tired and collect data during breaks. Furthermore, when the child is excited, the data collection unit can collect data while providing engaging content. This minimizes interruptions to learning by adjusting the timing of data collection according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input child emotion data into an AI, which can determine the optimal timing for data collection.

[0073] The data collection unit can analyze a child's past learning history and select the optimal data collection method. For example, the data collection unit can analyze a child's past learning history and select the optimal data collection method. For example, the data collection unit can collect data during times when a child has shown high levels of concentration in the past. The data collection unit can also collect data from devices and applications that a child has previously preferred to use. Furthermore, the data collection unit can collect data by referring to methods in which a child has learned effectively in the past. This enables efficient data collection by selecting the optimal data collection method based on past learning history. 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 the child's past learning history data into AI, which can then select the optimal data collection method.

[0074] The data collection unit can filter data based on the child's current learning status and areas of interest during data collection. For example, the data collection unit can filter data based on the child's current learning status and areas of interest during data collection. For example, the data collection unit can collect only data related to the subject the child is currently studying. The data collection unit can also prioritize the collection of data related to topics the child is interested in. Furthermore, the data collection unit can collect only the necessary data according to the child's learning progress. This allows for the collection of only the necessary data by filtering the data based on the child's current learning status and areas of interest. 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 data on the child's current learning status and areas of interest into an AI, which can then determine the optimal data filtering method.

[0075] The data collection unit can estimate a child's emotions and determine the priority of data to collect based on the estimated emotions. For example, if a child is excited, the data collection unit can prioritize collecting data that will interest them. If a child is tired, the data collection unit can prioritize collecting data related to relaxing content. Furthermore, if a child is focused, the data collection unit can prioritize collecting data that will help with learning. This allows for more effective data collection by prioritizing data according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input child emotion data into an AI, which can determine the optimal data priority.

[0076] The data collection unit can prioritize the collection of highly relevant data based on the child's geographical location during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the child's geographical location during data collection. For example, if the child is in a specific region, the data collection unit can prioritize the collection of data related to that region. Furthermore, if the child is traveling, the data collection unit can also collect educational content related to the travel destination. In addition, if the child is at school, the data collection unit can prioritize the collection of data related to the school curriculum. This enables more appropriate data collection by collecting highly relevant data based on geographical location. 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 the child's geographical location into AI, which can then determine the optimal data collection method.

[0077] The data collection unit can analyze a child's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze a child's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to topics that a child shows interest in on social media. The data collection unit can also collect data from educational accounts that a child follows. Furthermore, the data collection unit can analyze the activities of online communities that a child participates in and collect relevant data. This makes it possible to collect data based on a child's interests by analyzing their social media activity. 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 the child's social media activity data into AI, which can then determine the optimal data collection method.

[0078] The analysis unit can estimate a child's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can estimate a child's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the child is relaxed, the analysis unit can provide detailed analysis results. If the child is tense, the analysis unit can provide concise and to-the-point analysis results. Furthermore, if the child is excited, the analysis unit can provide analysis results using visually appealing graphics. By adjusting the presentation of the analysis according to the child's emotions, more easily understandable analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input child emotion data into AI, and the AI ​​can determine the optimal presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data and a concise analysis on less important data. The analysis unit can also perform an in-depth analysis on data that significantly impacts a child's learning progress. Furthermore, the analysis unit can perform a detailed analysis on data related to a child's interests. By adjusting the level of detail of the analysis based on the importance of the data, efficient data analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can determine the optimal level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a time series analysis algorithm to data related to learning speed. It can also apply a clustering algorithm to data related to interests. Furthermore, it can apply a regression analysis algorithm to data related to learning progress. By applying the appropriate analysis algorithm according to the data category, highly accurate analysis becomes possible. 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 the data category into the AI, and the AI ​​can determine the optimal analysis algorithm.

[0081] The analysis unit can estimate a child's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the child is in a hurry, the analysis unit can provide a short, concise analysis. If the child is relaxed, the analysis unit can provide a longer analysis with more detailed explanations. Furthermore, if the child is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis according to the child's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input child emotion data into an AI, which can determine the optimal analysis length.

[0082] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis based on the data collection timing during analysis. The analysis unit can, for example, prioritize the analysis of the latest data to grasp the learning status in real time. The analysis unit can also analyze the current learning status while referring to past data. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period to evaluate the learning effect during that period. In this way, by determining the priority of analysis based on the data collection timing, the learning status in real time can be grasped. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the data collection timing into the AI, and the AI ​​can determine the optimal analysis priority.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to maximize learning effectiveness. Alternatively, the analysis unit can postpone the analysis of less relevant data and focus on analyzing important data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This maximizes learning effectiveness by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the data into the AI, which can then determine the optimal analysis order.

[0084] The generation unit can estimate a child's emotions and adjust the presentation of the generated learning materials based on the estimated emotions. For example, if a child is relaxed, the generation unit can generate learning materials that proceed at a relaxed pace. If a child is in a hurry, the generation unit can also generate learning materials that emphasize the shortest route. Furthermore, if a child is excited, the generation unit can generate learning materials with visually stimulating effects. By adjusting the presentation of the learning materials according to the child's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input child emotion data into an AI, which can determine the optimal presentation of the learning materials.

[0085] The generation unit can adjust the level of detail in the learning materials based on the child's learning progress when generating them. For example, the generation unit can adjust the level of detail in the learning materials based on the child's learning progress when generating them. For example, if the child has a high level of understanding, the generation unit can generate learning materials that omit detailed explanations. Conversely, if the child has a low level of understanding, the generation unit can generate learning materials that include detailed explanations. Furthermore, the generation unit can generate learning materials of an appropriate difficulty level according to the child's learning progress. In this way, by adjusting the level of detail in the learning materials based on learning progress, learning materials of an appropriate difficulty level can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's learning progress data into the AI, and the AI ​​can determine the optimal level of detail in the learning materials.

[0086] The generation unit can apply different material generation algorithms depending on the child's interests when generating materials. For example, the generation unit can generate materials related to topics that the child is interested in. The generation unit can also generate customized materials based on the child's interests. Furthermore, the generation unit can generate materials in different formats (text, video, interactive content) depending on the child's interests. This allows for increased motivation to learn by applying material generation algorithms tailored to the 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 child interest data into AI, which can then determine the optimal material generation algorithm.

[0087] The generation unit can estimate a child's emotions and adjust the length of the generated learning materials based on the estimated emotions. For example, if a child is in a hurry, the generation unit can generate short, concise materials. If a child is relaxed, the generation unit can generate longer materials with detailed explanations. Furthermore, if a child is excited, the generation unit can generate materials with visually stimulating effects. By adjusting the length of the materials according to the child's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input child emotion data into an AI, which can determine the optimal length of the learning materials.

[0088] The generation unit can determine the priority of learning materials based on the child's learning history when generating them. For example, the generation unit can determine the priority of learning materials based on the child's learning history when generating them. For example, the generation unit can generate learning materials that the child should learn next based on what the child has learned in the past. The generation unit can also prioritize the generation of learning materials related to specific topics based on the child's learning history. Furthermore, the generation unit can analyze the child's learning history and prioritize the generation of the most effective learning materials. This enables efficient learning by determining the priority of learning materials based on the learning history. 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 the child's learning history data into AI, and the AI ​​can determine the optimal priority of learning materials.

[0089] The generation unit can adjust the order of learning materials based on the child's relevant data when generating them. For example, the generation unit can prioritize generating materials related to topics the child is interested in. The generation unit can also generate materials in an appropriate order according to the child's learning progress. Furthermore, the generation unit can generate materials in the optimal order by referring to the child's learning history. This maximizes learning effectiveness by adjusting the order of materials based on relevant data. 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 the child's relevant data into AI, which can then determine the optimal order of learning materials.

[0090] The delivery unit can estimate a child's emotions and adjust the method of delivering the learning materials based on the estimated emotions. For example, if a child is relaxed, the delivery unit can provide materials that proceed at a relaxed pace. If a child is in a hurry, the delivery unit can provide materials that emphasize the shortest route. Furthermore, if a child is excited, the delivery unit can provide materials with visually stimulating effects. By adjusting the method of delivering the learning materials according to the child's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input child emotion data into AI, and the AI ​​can determine the optimal method of delivering the learning materials.

[0091] The delivery unit can select the optimal delivery method by referring to the child's past learning history when providing learning materials. For example, the delivery unit can select the optimal delivery method by referring to the child's past learning history when providing learning materials. The delivery unit can, for example, provide learning materials using devices or applications that the child has previously preferred to use. The delivery unit can also select the most effective delivery method from the child's learning history. Furthermore, the delivery unit can provide learning materials at the appropriate time by referring to the child's learning history. This allows the delivery unit to select the optimal delivery method by referring to past learning history. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the child's learning history data into AI, and the AI ​​can determine the optimal method for providing learning materials.

[0092] The service provider can customize the delivery method based on the child's current learning situation when providing learning materials. For example, the service provider can customize the delivery method based on the child's current learning situation when providing learning materials. For example, the service provider can provide learning materials related to the subject the child is currently studying. The service provider can also provide learning materials of an appropriate difficulty level according to the child's learning progress. Furthermore, the service provider can provide customized learning materials based on the child's interests. By customizing the delivery method based on the current learning situation, more effective learning becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the child's current learning situation into AI, and the AI ​​can determine the optimal delivery method.

[0093] The distribution unit can estimate a child's emotions and determine the order in which to provide learning materials based on the estimated emotions. For example, if a child is excited, the distribution unit can prioritize providing materials that will capture the child's interest. If a child is tired, the distribution unit can prioritize providing relaxing content. Furthermore, if a child is focused, the distribution unit can prioritize providing materials that will help with learning. By determining the order in which to provide materials according to the child's emotions, the learning effect can be maximized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input child emotion data into AI, and the AI ​​can determine the optimal order in which to provide learning materials.

[0094] The service provider can select the optimal delivery method based on the child's geographical location when providing educational materials. For example, the service provider can select the optimal delivery method based on the child's geographical location when providing educational materials. For example, if the child is in a specific region, the service provider can prioritize providing educational materials related to that region. Furthermore, if the child is traveling, the service provider can provide educational content related to the travel destination. In addition, if the child is at school, the service provider can provide educational materials related to the school curriculum. By selecting the optimal delivery method based on geographical location, more effective learning becomes possible. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the child's geographical location into AI, and the AI ​​can determine the optimal delivery method.

[0095] The service provider can analyze a child's social media activity and suggest a method of providing educational materials. For example, the service provider can provide educational materials related to topics the child has shown interest in on social media. The service provider can also provide educational materials from educational accounts the child follows. Furthermore, the service provider can analyze the activities of online communities the child participates in and provide relevant educational materials. This makes it possible to provide educational materials based on the child's interests by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the child's social media activity data into AI, which can then determine the optimal method of provision.

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

[0097] The educational support system can also include a feedback unit. This unit provides feedback on what the child has learned. For example, it can provide immediate feedback indicating whether a problem the child has solved is correct or incorrect. It can also suggest what the child should learn next based on their learning progress. Furthermore, it can evaluate the child's learning attitude and effort, and provide positive feedback to boost their motivation. Thus, including a feedback unit can further enhance the effectiveness of the child's learning.

[0098] The data collection unit can gather physiological data from children to determine the optimal timing for learning. For example, it can collect physiological data such as a child's heart rate, body temperature, and sleep patterns. This allows the system to identify the time of day when a child is most focused and encourage learning during that time. The data collection unit can also monitor a child's fatigue level and suggest appropriate breaks. Furthermore, it can measure a child's stress level and provide relaxing content. In this way, by utilizing physiological data, the learning effectiveness of children can be maximized.

[0099] The generation unit can generate learning materials customized to each child's learning style. For example, it can generate materials that heavily utilize graphics and videos for visual learners. It can also generate materials that include audio explanations for auditory learners. Furthermore, it can generate materials that include interactive content for tactile learners. This allows for the provision of learning materials tailored to each child's learning style, thereby enhancing learning effectiveness.

[0100] The distribution department can adjust the method of providing learning materials according to the child's learning environment. For example, if the child is studying at home, the distribution department can provide materials online. If the child is studying at school, the materials can be provided as printed materials. Furthermore, if the child is on the go, the distribution department can provide materials optimized for mobile devices. This ensures continuity of learning by selecting the method of providing materials that suits the learning environment.

[0101] The system can set learning goals based on a child's learning progress and monitor their achievement. For example, it can display the child's progress toward their set learning goals in real time. It can also provide advice and reminders to help the child achieve their goals. Furthermore, it can motivate children by offering rewards and praise upon goal achievement. In this way, setting learning goals and monitoring their achievement can improve children's motivation to learn.

[0102] The data collection unit can estimate a child's emotions and adjust the difficulty level of learning content based on those estimates. For example, if a child is stressed, the unit can provide content with a lower difficulty level. If the child is relaxed, it can provide content with a higher difficulty level. Furthermore, if the child is excited, it can provide challenging content. By adjusting the difficulty level according to the child's emotions, the learning effect can be maximized.

[0103] The analysis unit can estimate a child's emotions and adjust the pace of learning based on those emotions. For example, if a child is focused, the analysis unit can speed up the pace of learning. If a child is tired, it can slow down the pace of learning. Furthermore, if a child is excited, it can increase interactive elements to promote learning. By adjusting the pace of learning according to emotions, the effectiveness of a child's learning can be enhanced.

[0104] The generation unit can estimate a child's emotions and adjust the design of the learning materials based on those emotions. For example, if a child is relaxed, the generation unit can use a design with calming colors. If a child is excited, it can use a bright and colorful design. Furthermore, if a child is concentrating, it can use a simple and visually less burdensome design. By adjusting the design according to the child's emotions, it is possible to increase the child's motivation to learn.

[0105] The system can estimate a child's emotions and adjust the learning feedback method based on those estimates. For example, if a child is relaxed, the system can provide detailed feedback. If a child is tense, it can provide concise and to-the-point feedback. Furthermore, if a child is excited, it can provide visually engaging feedback. By selecting a feedback method that matches the child's emotions, the system can enhance the child's learning effectiveness.

[0106] The system can estimate a child's emotions and suggest appropriate breaks based on those emotions. For example, if a child is tired, the system can suggest an appropriate break. If a child is focused, it can encourage them to continue learning. Furthermore, if a child is excited, it can provide relaxing content to encourage a break. By suggesting break times that are appropriate to the child's emotions, the system can maximize the effectiveness of the child's learning.

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

[0108] Step 1: The data collection unit collects the child's educational data. The data collection unit can collect data from, for example, tablets or smartphones used by the child. The data collection unit can collect educational data such as learning progress, test results, and behavioral data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, use AI to analyze the collected data and identify children's learning patterns and interests. The analysis unit can, for example, analyze the data based on the algorithm used and the purpose of the analysis. Step 3: The generation unit generates educational materials based on the analysis results obtained by the analysis unit. The generation unit can generate educational materials that include, for example, text, videos, and interactive content. The generation unit can generate educational materials in formats such as HTML5 and MP4. Step 4: The provider provides the educational materials generated by the generator. The provider can provide the materials by methods such as online distribution or printed distribution. The provider can, for example, provide the generated materials to children and support their learning progress.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision 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 child's educational data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to identify the child's learning patterns and interests. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12 and generates educational materials based on the analysis results. The provision unit is implemented in the identification processing unit 46A of the smart device 14 and provides the generated educational materials to the child. 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.

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

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision 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 child's educational data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the child's learning patterns and interests. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates educational materials based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides the generated educational materials to the child. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

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

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the child's educational data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to identify the child's learning patterns and interests. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12 and generates educational materials based on the analysis results. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides the generated educational materials to the child. 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.

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

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

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

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects educational data of children using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to identify the child's learning patterns and interests. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which generates educational materials based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414, which provides the generated educational materials to the child. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A data collection department that collects children's educational data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates teaching materials based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides educational materials generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collecting data from tablets and smartphones used by children. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate educational materials that include text, videos, and interactive content. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide the generated educational materials to children. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Monitor children's learning progress and update learning materials as needed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the child's emotions and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the child's past learning history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the child's current learning situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the child's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, analyze children's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is We estimate the child's emotions and adjust the way the analysis is presented based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is The system estimates the child's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is We estimate children's emotions and adjust the way educational materials are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating learning materials, adjust the level of detail based on the child's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating educational materials, different material generation algorithms are applied according to the child's interests. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the child's emotions and adjusts the length of the generated learning materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating learning materials, the priority of the materials is determined based on the child's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating learning materials, adjust the order of the materials based on the child's relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The system estimates the child's emotions and adjusts the method of providing educational materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing learning materials, we will refer to the child's past learning history to select the most suitable method of delivery. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing learning materials, customize the delivery method based on the child's current learning situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the child's emotions and determines the order in which educational materials are provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing educational materials, the optimal delivery method will be selected based on the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing educational materials, we analyze children's social media activity and propose methods for providing them. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection department that collects children's education data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit generates teaching materials based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides educational materials generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collecting data from tablets and smartphones used by children. The system according to feature 1.

3. The generating unit is Generate educational materials that include text, videos, and interactive content. The system according to feature 1.

4. The aforementioned supply unit is, Provide the generated educational materials to children. The system according to feature 1.

5. The aforementioned supply unit is, Monitor children's learning progress and update learning materials as needed. The system according to feature 1.

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

7. The aforementioned collection unit is Analyze the child's past learning history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on the child's current learning situation and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is The system estimates the child's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on the child's geographical location. The system according to feature 1.

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