system

Abstract

The system according to this embodiment aims to provide an appropriate learning format that is tailored to the cognitive characteristics of children. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a conversion unit, and a provision unit. The data collection unit measures the cognitive characteristics of children. The analysis unit analyzes the data collected by the data collection unit and derives an appropriate learning format. The conversion unit converts the learning materials based on the learning format derived by the analysis unit. The provision unit provides the learning materials converted by the conversion unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, an appropriate learning format according to the cognitive characteristics of children has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an appropriate learning format according to the cognitive characteristics of children.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a conversion unit, and a provision unit. The data collection unit measures the cognitive characteristics of children. The analysis unit analyzes the data collected by the data collection unit and derives an appropriate learning format. The conversion unit converts the learning materials based on the learning format derived by the analysis unit. The provision unit provides the learning materials converted by the conversion unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an appropriate learning format that is tailored to the cognitive characteristics of children. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The learning support system according to an embodiment of the present invention is a system that uses an AI agent to provide learning support tailored to the cognitive characteristics of children. This learning support system measures the cognitive characteristics of children and derives the optimal learning format (proportion of text, diagrams, audio, video, etc.). Next, it enhances learning efficiency by converting the learning materials used by the child into a format that suits their cognitive characteristics. First, the learning support system measures the cognitive characteristics of children. At this time, it grasps what kind of learning style the children are good at through questionnaires and simple tests. For example, it evaluates children with ADHD who excel in kinesthetic cognition and interactive learning but have difficulty understanding written text, children with ASD who excel in visual cognition and are good at processing visual information but have difficulty with verbal communication, and children with LD who have difficulty reading and writing but can be supported by multimedia materials that utilize audio and video. Next, the learning support system derives the optimal learning format based on the measurement results. For example, for visually dominant learners, it converts the materials to supplement important concepts in the text with diagrams and illustrations, so that even complex information can be intuitively understood with visual support. Furthermore, for learners with a mixed auditory and visual learning style, the text content is converted into video content, using animation and narration to make it easier to understand. For learners who prefer experiential learning, the materials are converted into interactive simulations and games, allowing them to learn while actually experiencing problem-solving. For learners who are primarily auditory learners, the materials are converted into audiobooks, enabling efficient learning during commutes, while doing housework, or other activities. In this way, the learning support system provides learning materials tailored to each child's cognitive characteristics, increasing learning efficiency. It also monitors learning progress and adjusts materials and plans as needed to provide continuous learning support. For example, for children who have difficulty reading long texts, the text is divided into key points and converted into a learning comic strip with short dialogues and illustrations connecting scenes. It also proposes a learning plan that takes into account the cognitive load and suggests a timer to maintain motivation. This system provides optimal learning for all children and realizes learning support that supports developmental disabilities.For example, providing interactive learning materials for children with ADHD and visual learning materials for children with ASD allows for learning support tailored to their specific characteristics. Furthermore, providing multimedia learning materials utilizing audio and video for children with learning disabilities (LD) supports their comprehension and memory. This improves children's learning efficiency and increases their motivation to learn. In this way, the learning support system can provide learning support that is aligned with each child's cognitive characteristics.

[0029] The learning support system according to the embodiment comprises a data collection unit, an analysis unit, a conversion unit, and a provision unit. The data collection unit measures the child's cognitive characteristics. The data collection unit measures the child's cognitive characteristics, for example, through questionnaires or simple tests. The data collection unit conducts questionnaires, for example, to understand what learning styles the child is good at. The questionnaires include, for example, questions about the child's preferred learning style and learning environment. The data collection unit measures the child's cognitive characteristics, for example, through simple tests. Simple tests include, for example, questions that evaluate characteristics such as visual cognition, auditory cognition, and kinesthetic cognition. The analysis unit analyzes the data collected by the data collection unit and derives the optimal learning style. The analysis unit derives, for example, the optimal learning style for the child's cognitive characteristics based on the collected data. The analysis unit proposes, for example, teaching materials that supplement important concepts in the text with diagrams and illustrations for visually dominant learners. The analysis unit proposes, for example, converting the text content into video content for auditory-visual mixed learners. The analysis unit proposes, for example, converting learning materials into interactive simulations or game formats for learners who prefer experiential learning. The conversion unit converts the learning materials based on the learning format derived by the analysis unit. The conversion unit converts the materials into supplementary materials with diagrams and illustrations for visually dominant learners. The conversion unit converts the content of the text into video content for auditory-visual mixed learners. The conversion unit converts the learning materials into interactive simulations or game formats for learners who prefer experiential learning. The provision unit provides the learning materials converted by the conversion unit. The provision unit provides the converted learning materials online, for example. The provision unit provides the converted learning materials in downloadable format, for example. The provision unit provides the converted learning materials in printed form, for example. This enables the learning support system according to the embodiment to provide learning support tailored to the cognitive characteristics of children.

[0030] The data collection unit measures children's cognitive characteristics. This is done, for example, through questionnaires and simple tests. Specifically, questionnaires include questions about children's preferred learning styles and environments. For example, questions to determine whether a child prefers visual, auditory, or physical learning materials. Questions about the learning environment might include whether a quiet environment is preferable, whether learning with music is effective, or whether group learning is suitable. Furthermore, simple tests include questions that assess characteristics such as visual cognition, auditory cognition, and kinesthetic cognition. For example, visual cognition might be assessed through shape and color recognition tests, auditory cognition through tests that require distinguishing sound patterns, and kinesthetic cognition through tests measuring simple movements and manual dexterity. This data is centrally managed by the data collection unit and transmitted to the analysis unit. The data collection unit can also utilize online questionnaires and digital tests to efficiently collect this data. This allows the data collection unit to gain a detailed understanding of children's cognitive characteristics and provide foundational data for offering optimal learning support to each individual child.

[0031] The analysis unit analyzes the data collected by the collection unit to determine the optimal learning format. For example, based on the collected data, the analysis unit determines the learning format best suited to each child's cognitive characteristics. Specifically, for visually dominant learners, it proposes teaching materials that supplement important concepts in the text with diagrams and illustrations. Since visually dominant learners tend to deepen their understanding through visual information, using diagrams and illustrations can convey the learning content more effectively. For auditory-visual mixed learners, it proposes converting the text content into video content. Video content stimulates both sight and hearing, allowing learners to understand the learning content through multiple senses. Furthermore, for learners who prefer experiential learning, it proposes converting the teaching materials into interactive simulations or games. Since experiential learners tend to deeply understand the learning content through actual experience, simulation and game-style teaching materials are effective in enhancing learning effectiveness. The analysis unit uses AI to analyze the collected data and quickly and accurately determine the optimal learning format for each learner. Based on past data and statistical information, the AI ​​analyzes each learner's characteristics and proposes the optimal learning format. Furthermore, the analysis unit can continuously monitor the collected data and revise the learning format as needed in response to the learner's progress and changes. This allows the analysis unit to provide optimal learning support to individual learners and maximize learning effectiveness.

[0032] The conversion unit transforms learning materials based on the learning format derived by the analysis unit. For example, for visually-oriented learners, the conversion unit transforms the materials into supplementary materials that use diagrams and illustrations to highlight important concepts in the text. Specifically, to make the text content visually easier to understand, important concepts and keywords are represented with diagrams and illustrations, allowing learners to grasp them intuitively. For auditory-visual mixed learners, the conversion unit transforms the text content into video content. Video content stimulates both sight and hearing, allowing learners to absorb more information efficiently. By combining narration, music, and animation in videos, the learning content can be conveyed in a more engaging way. Furthermore, for learners who prefer experiential learning, the materials are transformed into interactive simulations or games. Simulation materials reproduce real-world situations, allowing learners to deeply understand the learning content by acting on their own judgments. Game-style materials allow learners to learn while having fun, thus increasing their motivation. The conversion unit can utilize specialized software and tools to efficiently perform these transformation tasks. This allows the conversion unit to provide learning materials in the most optimal format for each learner, maximizing learning effectiveness.

[0033] The provisioning department provides the learning materials converted by the conversion department. For example, the provisioning department provides the converted learning materials online. Specifically, it provides a platform that learners can access via the internet, making the converted learning materials available anytime, anywhere. The provisioning department can also provide the converted learning materials in downloadable format. This allows learners to use the materials offline. Furthermore, the provisioning department can provide the converted learning materials in printed form. Printed materials are useful for learners who do not have digital devices or who prefer learning on paper. By combining these provisioning methods, the provisioning department can meet the diverse needs of learners. In addition, the provisioning department can collect learner usage data and feedback to continuously improve the quality of the materials provided. For example, it can monitor access history and learning progress on the online platform to understand which materials learners are using and to what extent. Based on learner feedback, it can also review the content and delivery methods of the materials to provide more effective learning support. In this way, the provisioning department can provide learners with the most suitable learning materials and maximize learning effectiveness.

[0034] The data collection unit can measure a child's cognitive characteristics through questionnaires or simple tests. For example, the data collection unit measures a child's cognitive characteristics through questionnaires. For example, the data collection unit measures a child's cognitive characteristics through simple tests. For example, the data collection unit conducts questionnaires to understand what learning styles a child excels at. Questionnaires may include questions about the child's preferred learning styles and learning environments. The data collection unit measures a child's cognitive characteristics through simple tests. Simple tests may include questions that assess characteristics such as visual cognition, auditory cognition, and kinesthetic cognition. This allows the data collection unit to accurately measure a child's cognitive characteristics. 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 questionnaire response data into a generating AI and have the generating AI perform analysis of the response data.

[0035] The conversion unit can convert text into learning materials that supplement important concepts in the text with diagrams and illustrations for visually-oriented learners. For example, the conversion unit converts text into learning materials that supplement important concepts in the text with diagrams and illustrations. For example, the conversion unit supplements important concepts in the text with diagrams and illustrations for visually-oriented learners. For example, the conversion unit makes complex information intuitively understandable with visual support. For example, by supplementing important concepts in the text with diagrams and illustrations, the conversion unit provides learning materials that are intuitively easy for visually-oriented learners to understand. Thus, the conversion unit can provide learning materials that are intuitively easy for visually-oriented learners to understand. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input text data into a generating AI and have the generating AI generate diagrams and illustrations.

[0036] The conversion unit can convert text content into video content for learners with a mixed auditory and visual learning style. The conversion unit converts text content into video content, for example. The conversion unit converts text content into video content for learners with a mixed auditory and visual learning style, for example. The conversion unit converts text content into video content using animation and narration, for example. By converting text content into video content, the conversion unit provides learning materials that are easy for learners with a mixed auditory and visual learning style to understand. In this way, the conversion unit can provide learning materials that are easy for learners with a mixed auditory and visual learning style to understand. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input text data into a generating AI and have the generating AI generate video content.

[0037] The conversion unit can convert learning materials into interactive simulations or game formats for learners who prefer experiential learning. The conversion unit can, for example, convert learning materials into interactive simulations or game formats. The conversion unit can, for example, convert learning materials into interactive simulations or game formats for learners who prefer experiential learning. The conversion unit can, for example, convert learning materials into interactive simulations or game formats so that learners can learn while actually experiencing problem-solving. The conversion unit, for example, by converting learning materials into interactive simulations or game formats, enables learners who prefer experiential learning to learn while actually experiencing problem-solving. In this way, the conversion unit enables learners who prefer experiential learning to learn while actually experiencing problem-solving. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input text data into a generating AI and have the generating AI execute the generation of interactive simulations or game formats.

[0038] The conversion unit can convert learning materials into audiobooks for learners who are auditorily dominant. The conversion unit converts learning materials into audiobooks, for example. The conversion unit converts learning materials into audiobooks for learners who are auditorily dominant. The conversion unit converts learning materials into audiobooks so that learners can study efficiently, for example, while commuting to school or work or while doing housework. The conversion unit enables learners who are auditorily dominant to study efficiently by converting learning materials into audiobooks. In this way, the conversion unit enables learners who are auditorily dominant to study efficiently. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without using AI. For example, the conversion unit can input text data into a generating AI and have the generating AI perform the generation of an audiobook.

[0039] The provider can provide the converted teaching materials. For example, the provider can provide the converted teaching materials online. For example, the provider can provide the converted teaching materials in downloadable format. For example, the provider can provide the converted teaching materials in printed form. For example, the provider can provide the converted teaching materials through a web application or a mobile application. For example, the provider can send the converted teaching materials by email. In this way, the provider can appropriately provide the converted teaching materials. Some or all of the above processing in the provider may be performed using AI, for example, or not using AI. For example, the provider can input the converted teaching materials into a generating AI and have the generating AI select the method of provision.

[0040] The service provider can monitor learning progress and adjust materials and plans as needed. For example, the service provider monitors learning progress. For example, the service provider adjusts materials and plans according to learning progress. For example, the service provider monitors learning progress in real time and provides materials according to progress. For example, if learning progress is behind, the service provider provides supplementary materials. For example, if learning progress is on track, the service provider provides materials to move on to the next step. This allows the service provider to flexibly adjust materials and plans according to learning progress. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input learning progress data into a generating AI and have the generating AI perform adjustments to materials and plans.

[0041] The data collection unit can analyze a child's past learning history and select the optimal measurement method. For example, the data collection unit analyzes a child's past learning history. For example, the data collection unit selects the optimal measurement method. For example, the data collection unit selects a measurement method based on learning tools and materials that the child has preferred to use in the past. For example, the data collection unit analyzes a child's past learning outcomes and prioritizes selecting measurement methods that were effective. For example, the data collection unit selects a measurement method suitable for a specific learning style from a child's past learning history. In this way, the data collection unit can select the optimal measurement 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 past learning history data into a generating AI and have the generating AI perform the selection of a measurement method.

[0042] The data collection unit can filter data based on the child's current learning status and areas of interest when measuring cognitive characteristics. For example, the data collection unit can grasp the child's current learning status when measuring cognitive characteristics. For example, the data collection unit can customize measurement items based on the child's areas of interest. For example, the data collection unit can adjust measurement items according to the child's learning progress. For example, the data collection unit can grasp the child's current learning status and select appropriate measurement items. For example, the data collection unit can customize measurement items based on the child's areas of interest. For example, the data collection unit can adjust measurement items according to the child's learning progress. This allows the data collection unit to select appropriate measurement items 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 without AI. For example, the data collection unit can input current learning status data into a generating AI and have the generating AI select measurement items.

[0043] The data collection unit can prioritize the collection of highly relevant data when measuring cognitive characteristics, taking into account the child's geographical location. For example, the data collection unit prioritizes the collection of highly relevant data by considering the child's geographical location. For example, if the child is at school, the data collection unit prioritizes the collection of data related to their learning situation at school. For example, if the child is at home, the data collection unit prioritizes the collection of data related to their learning environment at home. For example, if the child is at the library, the data collection unit prioritizes the collection of data related to their learning activities at the library. This allows the data collection unit to prioritize the collection of highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0044] The data collection unit can analyze a child's social media activity and collect relevant data when measuring cognitive characteristics. For example, the data collection unit analyzes a child's social media activity. For example, the data collection unit collects relevant data. For example, the data collection unit analyzes the content of a child's social media posts and collects data related to their interests. For example, the data collection unit analyzes a child's social media friendships and identifies factors that influence learning. For example, the data collection unit analyzes the time a child spends on social media and determines the optimal measurement timing. This allows the data collection unit to collect relevant data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI collect relevant data.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the cognitive characteristics during the analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the cognitive characteristics. For example, the analysis unit performs a detailed analysis for important cognitive characteristics. For example, the analysis unit performs a concise analysis for less important cognitive characteristics. For example, the analysis unit determines the priority of the analysis according to the importance of the cognitive characteristics. In this way, the analysis unit can adjust the level of detail of the analysis according to the importance of the cognitive characteristics. 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 cognitive characteristic data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the category of cognitive characteristics during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of cognitive characteristics. For example, the analysis unit can apply an image analysis algorithm to visual cognitive characteristics. For example, the analysis unit can apply a speech analysis algorithm to auditory cognitive characteristics. For example, the analysis unit can apply a motion analysis algorithm to kinesthetic cognitive characteristics. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of cognitive characteristics. 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 cognitive characteristic data into a generating AI and have the generating AI perform the application of the analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the timing of cognitive characteristic data collection during the analysis. For example, the analysis unit may prioritize the analysis based on the timing of cognitive characteristic data collection. For example, the analysis unit may prioritize the analysis of recently collected cognitive characteristics. For example, the analysis unit may postpone the analysis of cognitive characteristics collected in the past. The analysis unit may adjust the analysis schedule based on the collection timing. This allows the analysis unit to determine the priority of analysis based on the collection timing. 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 may input cognitive characteristic data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0048] The analysis unit can adjust the order of analysis based on the relationships between cognitive characteristics during the analysis. For example, the analysis unit adjusts the order of analysis based on the relationships between cognitive characteristics. For example, the analysis unit prioritizes the analysis of highly relevant cognitive characteristics. For example, the analysis unit postpones the analysis of less relevant cognitive characteristics. For example, the analysis unit adjusts the analysis schedule based on the relationships between cognitive characteristics. In this way, the analysis unit can adjust the order of analysis based on the relationships between cognitive characteristics. 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 cognitive characteristic data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0049] The conversion unit can adjust the level of detail of the conversion based on the importance of the teaching materials during the conversion process. For example, the conversion unit adjusts the level of detail of the conversion based on the importance of the teaching materials. For example, the conversion unit performs a detailed conversion for important teaching materials. For example, the conversion unit performs a concise conversion for less important teaching materials. For example, the conversion unit determines the priority of the conversion according to the importance of the teaching materials. This allows the conversion unit to adjust the level of detail of the conversion according to the importance of the teaching materials. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input teaching material data into a generating AI and have the generating AI perform the adjustment of the level of detail of the conversion.

[0050] The conversion unit can apply different conversion algorithms depending on the category of the teaching material during conversion. For example, the conversion unit applies different conversion algorithms depending on the category of the teaching material. For example, the conversion unit applies an image conversion algorithm to visual teaching materials. For example, the conversion unit applies an audio conversion algorithm to auditory teaching materials. For example, the conversion unit applies a motion conversion algorithm to physical movement teaching materials. In this way, the conversion unit can apply an appropriate conversion algorithm depending on the category of the teaching material. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input teaching material data into a generating AI and have the generating AI perform the application of the conversion algorithm.

[0051] The conversion unit can determine the conversion priority based on the submission dates of the teaching materials during the conversion process. For example, the conversion unit determines the conversion priority based on the submission dates of the teaching materials. For example, the conversion unit prioritizes the conversion of teaching materials with approaching submission deadlines. For example, the conversion unit postpones the conversion of teaching materials with distant submission deadlines. For example, the conversion unit adjusts the conversion schedule based on the submission dates. This allows the conversion unit to determine the conversion priority based on the submission dates. Some or all of the above-described processes in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input teaching material data into a generating AI and have the generating AI determine the conversion priority.

[0052] The conversion unit can adjust the order of conversion based on the relevance of the materials during the conversion process. For example, the conversion unit adjusts the order of conversion based on the relevance of the materials. For example, the conversion unit prioritizes conversion of highly relevant materials. For example, the conversion unit postpones conversion of less relevant materials. For example, the conversion unit adjusts the conversion schedule based on the relevance of the materials. This allows the conversion unit to adjust the order of conversion based on the relevance of the materials. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input material data into a generating AI and have the generating AI perform the adjustment of the conversion order.

[0053] The delivery unit can select the optimal delivery method based on the child's past learning history at the time of delivery. For example, the delivery unit refers to the child's past learning history. For example, the delivery unit selects the optimal delivery method. For example, the delivery unit selects a delivery method based on the learning tools and materials the child has preferred to use in the past. For example, the delivery unit analyzes the child's past learning outcomes and prioritizes the delivery methods that were effective. For example, the delivery unit selects a delivery method suitable for a specific learning style from the child's past learning history. In this way, the delivery unit can select the optimal delivery method based on past learning history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input past learning history data into a generating AI and have the generating AI perform the selection of the delivery method.

[0054] The delivery unit can select the optimal delivery method based on the child's device information at the time of delivery. For example, the delivery unit selects the optimal delivery method considering the child's device information. For example, if the child is using a smartphone, the delivery unit selects a delivery method that matches the screen size. For example, if the child is using a tablet, the delivery unit selects a delivery method optimized for a large screen. For example, if the child is using a personal computer, the delivery unit selects a delivery method for desktops. In this way, the delivery unit can select the optimal delivery method based on device information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input device information data into a generating AI and have the generating AI perform the selection of the delivery method.

[0055] The service provider can monitor the child's learning progress at the time of delivery and adjust the materials and plans as needed. For example, the service provider can monitor the child's learning progress. For example, the service provider can adjust the materials and plans according to the learning progress. For example, the service provider can monitor the child's learning progress in real time and provide materials according to the progress. For example, if the child's learning progress is behind, the service provider can provide supplementary materials. For example, if the child's learning progress is on track, the service provider can provide materials to move to the next step. This allows the service provider to flexibly adjust the materials and plans according to the learning progress. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input learning progress data into a generating AI and have the generating AI perform adjustments to the materials and plans.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The learning support system can also optimize the learning environment based on the child's cognitive characteristics. For example, the data collection unit collects data about the child's learning environment, and the analysis unit proposes an optimal learning environment based on that data. The conversion unit adjusts the placement of teaching materials and the tools used based on the proposed learning environment. The delivery unit provides the optimized learning environment to the child. In this way, the learning support system can provide a learning environment tailored to the child's cognitive characteristics and further enhance learning efficiency.

[0058] The learning support system can also provide real-time feedback on learning progress based on the child's cognitive characteristics. For example, the data collection unit collects data on the child's learning progress, and the analysis unit evaluates the progress based on that data. The conversion unit adjusts the feedback content based on the evaluation results. The provision unit provides feedback to the child in real time. As a result, the learning support system can provide appropriate feedback on the child's learning progress and maintain their motivation to learn.

[0059] The learning support system can also assist in setting learning goals based on a child's cognitive characteristics. For example, the data collection unit collects data on the child's learning goals, and the analysis unit proposes optimal learning goals based on that data. The conversion unit adjusts the learning plan based on the proposed learning goals. The delivery unit provides the child with the optimized learning goals and plan. In this way, the learning support system can set learning goals that are tailored to the child's cognitive characteristics and clarify the direction of learning.

[0060] A learning support system can also implement a reward system to enhance learning motivation based on a child's cognitive characteristics. For example, a data collection unit collects data on a child's learning motivation, and an analysis unit proposes an optimal reward system based on that data. A conversion unit adjusts the reward content based on the proposed reward system. A provisioning unit provides the reward system to the child. In this way, the learning support system can implement a reward system tailored to a child's cognitive characteristics and enhance their motivation to learn.

[0061] A learning support system can also provide relaxation functions to reduce learning stress based on a child's cognitive characteristics. For example, a data collection unit collects data on a child's stress level, and an analysis unit proposes the most suitable relaxation method based on that data. A conversion unit adjusts the relaxation content based on the proposed relaxation method. A delivery unit provides the relaxation content to the child. In this way, the learning support system can provide relaxation functions tailored to a child's cognitive characteristics and reduce learning stress.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit measures the child's cognitive characteristics. The data collection unit measures the child's cognitive characteristics, for example, through questionnaires or simple tests. Questionnaires include questions about the child's preferred learning style and learning environment, while simple tests include questions that assess characteristics such as visual cognition, auditory cognition, and kinesthetic cognition. Step 2: The analysis unit analyzes the data collected by the collection unit and derives the optimal learning format. For example, the analysis unit suggests teaching materials that supplement important concepts in the text with diagrams and illustrations for visually dominant learners, suggests converting the text content into video content for auditory-visual mixed learners, and suggests converting the teaching materials into interactive simulations or games for learners who prefer experiential learning. Step 3: The conversion unit converts the learning materials based on the learning format derived by the analysis unit. For example, the conversion unit converts the materials to supplement important concepts in the text with diagrams and illustrations for visually-oriented learners, converts the text content into video content for auditory-visual mixed learners, and converts the materials into interactive simulations or games for learners who prefer experiential learning. Step 4: The provider provides the materials converted by the conversion unit. The provider may, for example, provide the converted materials online, provide them in downloadable format, or provide them in printable form.

[0064] (Example of form 2) The learning support system according to an embodiment of the present invention is a system that uses an AI agent to provide learning support tailored to the cognitive characteristics of children. This learning support system measures the cognitive characteristics of children and derives the optimal learning format (proportion of text, diagrams, audio, video, etc.). Next, it enhances learning efficiency by converting the learning materials used by the child into a format that suits their cognitive characteristics. First, the learning support system measures the cognitive characteristics of children. At this time, it grasps what kind of learning style the children are good at through questionnaires and simple tests. For example, it evaluates children with ADHD who excel in kinesthetic cognition and interactive learning but have difficulty understanding written text, children with ASD who excel in visual cognition and are good at processing visual information but have difficulty with verbal communication, and children with LD who have difficulty reading and writing but can be supported by multimedia materials that utilize audio and video. Next, the learning support system derives the optimal learning format based on the measurement results. For example, for visually dominant learners, it converts the materials to supplement important concepts in the text with diagrams and illustrations, so that even complex information can be intuitively understood with visual support. Furthermore, for learners with a mixed auditory and visual learning style, the text content is converted into video content, using animation and narration to make it easier to understand. For learners who prefer experiential learning, the materials are converted into interactive simulations and games, allowing them to learn while actually experiencing problem-solving. For learners who are primarily auditory learners, the materials are converted into audiobooks, enabling efficient learning during commutes, while doing housework, or other activities. In this way, the learning support system provides learning materials tailored to each child's cognitive characteristics, increasing learning efficiency. It also monitors learning progress and adjusts materials and plans as needed to provide continuous learning support. For example, for children who have difficulty reading long texts, the text is divided into key points and converted into a learning comic strip with short dialogues and illustrations connecting scenes. It also proposes a learning plan that takes into account the cognitive load and suggests a timer to maintain motivation. This system provides optimal learning for all children and realizes learning support that supports developmental disabilities.For example, providing interactive learning materials for children with ADHD and visual learning materials for children with ASD allows for learning support tailored to their specific characteristics. Furthermore, providing multimedia learning materials utilizing audio and video for children with learning disabilities (LD) supports their comprehension and memory. This improves children's learning efficiency and increases their motivation to learn. In this way, the learning support system can provide learning support that is aligned with each child's cognitive characteristics.

[0065] The learning support system according to the embodiment comprises a data collection unit, an analysis unit, a conversion unit, and a provision unit. The data collection unit measures the child's cognitive characteristics. The data collection unit measures the child's cognitive characteristics, for example, through questionnaires or simple tests. The data collection unit conducts questionnaires, for example, to understand what learning styles the child is good at. The questionnaires include, for example, questions about the child's preferred learning style and learning environment. The data collection unit measures the child's cognitive characteristics, for example, through simple tests. Simple tests include, for example, questions that evaluate characteristics such as visual cognition, auditory cognition, and kinesthetic cognition. The analysis unit analyzes the data collected by the data collection unit and derives the optimal learning style. The analysis unit derives, for example, the optimal learning style for the child's cognitive characteristics based on the collected data. The analysis unit proposes, for example, teaching materials that supplement important concepts in the text with diagrams and illustrations for visually dominant learners. The analysis unit proposes, for example, converting the text content into video content for auditory-visual mixed learners. The analysis unit proposes, for example, converting learning materials into interactive simulations or game formats for learners who prefer experiential learning. The conversion unit converts the learning materials based on the learning format derived by the analysis unit. The conversion unit converts the materials into supplementary materials with diagrams and illustrations for visually dominant learners. The conversion unit converts the content of the text into video content for auditory-visual mixed learners. The conversion unit converts the learning materials into interactive simulations or game formats for learners who prefer experiential learning. The provision unit provides the learning materials converted by the conversion unit. The provision unit provides the converted learning materials online, for example. The provision unit provides the converted learning materials in downloadable format, for example. The provision unit provides the converted learning materials in printed form, for example. This enables the learning support system according to the embodiment to provide learning support tailored to the cognitive characteristics of children.

[0066] The data collection unit measures children's cognitive characteristics. This is done, for example, through questionnaires and simple tests. Specifically, questionnaires include questions about children's preferred learning styles and environments. For example, questions to determine whether a child prefers visual, auditory, or physical learning materials. Questions about the learning environment might include whether a quiet environment is preferable, whether learning with music is effective, or whether group learning is suitable. Furthermore, simple tests include questions that assess characteristics such as visual cognition, auditory cognition, and kinesthetic cognition. For example, visual cognition might be assessed through shape and color recognition tests, auditory cognition through tests that require distinguishing sound patterns, and kinesthetic cognition through tests measuring simple movements and manual dexterity. This data is centrally managed by the data collection unit and transmitted to the analysis unit. The data collection unit can also utilize online questionnaires and digital tests to efficiently collect this data. This allows the data collection unit to gain a detailed understanding of children's cognitive characteristics and provide foundational data for offering optimal learning support to each individual child.

[0067] The analysis unit analyzes the data collected by the collection unit to determine the optimal learning format. For example, based on the collected data, the analysis unit determines the learning format best suited to each child's cognitive characteristics. Specifically, for visually dominant learners, it proposes teaching materials that supplement important concepts in the text with diagrams and illustrations. Since visually dominant learners tend to deepen their understanding through visual information, using diagrams and illustrations can convey the learning content more effectively. For auditory-visual mixed learners, it proposes converting the text content into video content. Video content stimulates both sight and hearing, allowing learners to understand the learning content through multiple senses. Furthermore, for learners who prefer experiential learning, it proposes converting the teaching materials into interactive simulations or games. Since experiential learners tend to deeply understand the learning content through actual experience, simulation and game-style teaching materials are effective in enhancing learning effectiveness. The analysis unit uses AI to analyze the collected data and quickly and accurately determine the optimal learning format for each learner. Based on past data and statistical information, the AI ​​analyzes each learner's characteristics and proposes the optimal learning format. Furthermore, the analysis unit can continuously monitor the collected data and revise the learning format as needed in response to the learner's progress and changes. This allows the analysis unit to provide optimal learning support to individual learners and maximize learning effectiveness.

[0068] The conversion unit transforms learning materials based on the learning format derived by the analysis unit. For example, for visually-oriented learners, the conversion unit transforms the materials into supplementary materials that use diagrams and illustrations to highlight important concepts in the text. Specifically, to make the text content visually easier to understand, important concepts and keywords are represented with diagrams and illustrations, allowing learners to grasp them intuitively. For auditory-visual mixed learners, the conversion unit transforms the text content into video content. Video content stimulates both sight and hearing, allowing learners to absorb more information efficiently. By combining narration, music, and animation in videos, the learning content can be conveyed in a more engaging way. Furthermore, for learners who prefer experiential learning, the materials are transformed into interactive simulations or games. Simulation materials reproduce real-world situations, allowing learners to deeply understand the learning content by acting on their own judgments. Game-style materials allow learners to learn while having fun, thus increasing their motivation. The conversion unit can utilize specialized software and tools to efficiently perform these transformation tasks. This allows the conversion unit to provide learning materials in the most optimal format for each learner, maximizing learning effectiveness.

[0069] The provisioning department provides the learning materials converted by the conversion department. For example, the provisioning department provides the converted learning materials online. Specifically, it provides a platform that learners can access via the internet, making the converted learning materials available anytime, anywhere. The provisioning department can also provide the converted learning materials in downloadable format. This allows learners to use the materials offline. Furthermore, the provisioning department can provide the converted learning materials in printed form. Printed materials are useful for learners who do not have digital devices or who prefer learning on paper. By combining these provisioning methods, the provisioning department can meet the diverse needs of learners. In addition, the provisioning department can collect learner usage data and feedback to continuously improve the quality of the materials provided. For example, it can monitor access history and learning progress on the online platform to understand which materials learners are using and to what extent. Based on learner feedback, it can also review the content and delivery methods of the materials to provide more effective learning support. In this way, the provisioning department can provide learners with the most suitable learning materials and maximize learning effectiveness.

[0070] The data collection unit can measure a child's cognitive characteristics through questionnaires or simple tests. For example, the data collection unit measures a child's cognitive characteristics through questionnaires. For example, the data collection unit measures a child's cognitive characteristics through simple tests. For example, the data collection unit conducts questionnaires to understand what learning styles a child excels at. Questionnaires may include questions about the child's preferred learning styles and learning environments. The data collection unit measures a child's cognitive characteristics through simple tests. Simple tests may include questions that assess characteristics such as visual cognition, auditory cognition, and kinesthetic cognition. This allows the data collection unit to accurately measure a child's cognitive characteristics. 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 questionnaire response data into a generating AI and have the generating AI perform analysis of the response data.

[0071] The conversion unit can convert text into learning materials that supplement important concepts in the text with diagrams and illustrations for visually-oriented learners. For example, the conversion unit converts text into learning materials that supplement important concepts in the text with diagrams and illustrations. For example, the conversion unit supplements important concepts in the text with diagrams and illustrations for visually-oriented learners. For example, the conversion unit makes complex information intuitively understandable with visual support. For example, by supplementing important concepts in the text with diagrams and illustrations, the conversion unit provides learning materials that are intuitively easy for visually-oriented learners to understand. Thus, the conversion unit can provide learning materials that are intuitively easy for visually-oriented learners to understand. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input text data into a generating AI and have the generating AI generate diagrams and illustrations.

[0072] The conversion unit can convert text content into video content for learners with a mixed auditory and visual learning style. The conversion unit converts text content into video content, for example. The conversion unit converts text content into video content for learners with a mixed auditory and visual learning style, for example. The conversion unit converts text content into video content using animation and narration, for example. By converting text content into video content, the conversion unit provides learning materials that are easy for learners with a mixed auditory and visual learning style to understand. In this way, the conversion unit can provide learning materials that are easy for learners with a mixed auditory and visual learning style to understand. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input text data into a generating AI and have the generating AI generate video content.

[0073] The conversion unit can convert learning materials into interactive simulations or game formats for learners who prefer experiential learning. The conversion unit can, for example, convert learning materials into interactive simulations or game formats. The conversion unit can, for example, convert learning materials into interactive simulations or game formats for learners who prefer experiential learning. The conversion unit can, for example, convert learning materials into interactive simulations or game formats so that learners can learn while actually experiencing problem-solving. The conversion unit, for example, by converting learning materials into interactive simulations or game formats, enables learners who prefer experiential learning to learn while actually experiencing problem-solving. In this way, the conversion unit enables learners who prefer experiential learning to learn while actually experiencing problem-solving. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input text data into a generating AI and have the generating AI execute the generation of interactive simulations or game formats.

[0074] The conversion unit can convert learning materials into audiobooks for learners who are auditorily dominant. The conversion unit converts learning materials into audiobooks, for example. The conversion unit converts learning materials into audiobooks for learners who are auditorily dominant. The conversion unit converts learning materials into audiobooks so that learners can study efficiently, for example, while commuting to school or work or while doing housework. The conversion unit enables learners who are auditorily dominant to study efficiently by converting learning materials into audiobooks. In this way, the conversion unit enables learners who are auditorily dominant to study efficiently. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without using AI. For example, the conversion unit can input text data into a generating AI and have the generating AI perform the generation of an audiobook.

[0075] The provider can provide the converted teaching materials. For example, the provider can provide the converted teaching materials online. For example, the provider can provide the converted teaching materials in downloadable format. For example, the provider can provide the converted teaching materials in printed form. For example, the provider can provide the converted teaching materials through a web application or a mobile application. For example, the provider can send the converted teaching materials by email. In this way, the provider can appropriately provide the converted teaching materials. Some or all of the above processing in the provider may be performed using AI, for example, or not using AI. For example, the provider can input the converted teaching materials into a generating AI and have the generating AI select the method of provision.

[0076] The service provider can monitor learning progress and adjust materials and plans as needed. For example, the service provider monitors learning progress. For example, the service provider adjusts materials and plans according to learning progress. For example, the service provider monitors learning progress in real time and provides materials according to progress. For example, if learning progress is behind, the service provider provides supplementary materials. For example, if learning progress is on track, the service provider provides materials to move on to the next step. This allows the service provider to flexibly adjust materials and plans according to learning progress. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input learning progress data into a generating AI and have the generating AI perform adjustments to materials and plans.

[0077] The data collection unit can estimate the child's emotions and adjust the timing of cognitive characteristic measurements based on the estimated emotions. The data collection unit, for example, estimates the child's emotions. The data collection unit, for example, adjusts the timing of cognitive characteristic measurements based on the estimated emotions of the child. The data collection unit collects accurate data by, for example, measuring cognitive characteristics when the child is relaxed. The data collection unit, for example, temporarily suspends measurements if the child is stressed, and resumes them after creating a relaxing environment. The data collection unit, for example, measures cognitive characteristics during times when the child is focused. This allows the data collection unit to adjust the timing of cognitive characteristic measurements according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The 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 without AI. For example, the data collection unit can input children's emotional data into a generating AI, which can then perform emotion estimation.

[0078] The data collection unit can analyze a child's past learning history and select the optimal measurement method. For example, the data collection unit analyzes a child's past learning history. For example, the data collection unit selects the optimal measurement method. For example, the data collection unit selects a measurement method based on learning tools and materials that the child has preferred to use in the past. For example, the data collection unit analyzes a child's past learning outcomes and prioritizes selecting measurement methods that were effective. For example, the data collection unit selects a measurement method suitable for a specific learning style from a child's past learning history. In this way, the data collection unit can select the optimal measurement 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 past learning history data into a generating AI and have the generating AI perform the selection of a measurement method.

[0079] The data collection unit can filter data based on the child's current learning status and areas of interest when measuring cognitive characteristics. For example, the data collection unit can grasp the child's current learning status when measuring cognitive characteristics. For example, the data collection unit can customize measurement items based on the child's areas of interest. For example, the data collection unit can adjust measurement items according to the child's learning progress. For example, the data collection unit can grasp the child's current learning status and select appropriate measurement items. For example, the data collection unit can customize measurement items based on the child's areas of interest. For example, the data collection unit can adjust measurement items according to the child's learning progress. This allows the data collection unit to select appropriate measurement items 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 without AI. For example, the data collection unit can input current learning status data into a generating AI and have the generating AI select measurement items.

[0080] The data collection unit can estimate a child's emotions and determine the priority of cognitive characteristics to measure based on the estimated emotions. For example, the data collection unit estimates the child's emotions. For example, the data collection unit determines the priority of cognitive characteristics to measure based on the estimated emotions of the child. For example, if the child is relaxed, the data collection unit prioritizes measuring complex cognitive characteristics. For example, if the child is tired, the data collection unit prioritizes measuring simple cognitive characteristics. For example, if the child is excited, the data collection unit prioritizes measuring interesting cognitive characteristics. This allows the data collection unit to determine the priority of cognitive characteristic measurement 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 without AI. For example, the data collection unit can input child emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The data collection unit can prioritize the collection of highly relevant data when measuring cognitive characteristics, taking into account the child's geographical location. For example, the data collection unit prioritizes the collection of highly relevant data by considering the child's geographical location. For example, if the child is at school, the data collection unit prioritizes the collection of data related to their learning situation at school. For example, if the child is at home, the data collection unit prioritizes the collection of data related to their learning environment at home. For example, if the child is at the library, the data collection unit prioritizes the collection of data related to their learning activities at the library. This allows the data collection unit to prioritize the collection of highly relevant data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0082] The data collection unit can analyze a child's social media activity and collect relevant data when measuring cognitive characteristics. For example, the data collection unit analyzes a child's social media activity. For example, the data collection unit collects relevant data. For example, the data collection unit analyzes the content of a child's social media posts and collects data related to their interests. For example, the data collection unit analyzes a child's social media friendships and identifies factors that influence learning. For example, the data collection unit analyzes the time a child spends on social media and determines the optimal measurement timing. This allows the data collection unit to collect relevant data based on social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity data into a generating AI and have the generating AI collect relevant data.

[0083] The analysis unit can estimate the child's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit estimates the child's emotions. For example, the analysis unit adjusts the presentation of the analysis based on the estimated emotions of the child. For example, if the child is relaxed, the analysis unit provides detailed analysis results. For example, if the child is stressed, the analysis unit provides concise and to-the-point analysis results. For example, if the child is excited, the analysis unit provides visually appealing analysis results. This allows the analysis unit to adjust the presentation of the analysis according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the cognitive characteristics during the analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the cognitive characteristics. For example, the analysis unit performs a detailed analysis for important cognitive characteristics. For example, the analysis unit performs a concise analysis for less important cognitive characteristics. For example, the analysis unit determines the priority of the analysis according to the importance of the cognitive characteristics. In this way, the analysis unit can adjust the level of detail of the analysis according to the importance of the cognitive characteristics. 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 cognitive characteristic data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0085] The analysis unit can apply different analysis algorithms depending on the category of cognitive characteristics during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of cognitive characteristics. For example, the analysis unit can apply an image analysis algorithm to visual cognitive characteristics. For example, the analysis unit can apply a speech analysis algorithm to auditory cognitive characteristics. For example, the analysis unit can apply a motion analysis algorithm to kinesthetic cognitive characteristics. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of cognitive characteristics. 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 cognitive characteristic data into a generating AI and have the generating AI perform the application of the analysis algorithm.

[0086] The analysis unit can estimate the child's emotions and adjust the length of the analysis based on the estimated emotions. The analysis unit, for example, estimates the child's emotions. The analysis unit, for example, adjusts the length of the analysis based on the estimated emotions. The analysis unit, for example, performs a detailed analysis if the child is relaxed. The analysis unit, for example, performs a concise analysis if the child is stressed. The analysis unit, for example, performs a visually appealing analysis if the child is excited. This allows the analysis unit to adjust the length of the analysis according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The analysis unit can determine the priority of analysis based on the timing of cognitive characteristic data collection during the analysis. For example, the analysis unit may prioritize the analysis based on the timing of cognitive characteristic data collection. For example, the analysis unit may prioritize the analysis of recently collected cognitive characteristics. For example, the analysis unit may postpone the analysis of cognitive characteristics collected in the past. The analysis unit may adjust the analysis schedule based on the collection timing. This allows the analysis unit to determine the priority of analysis based on the collection timing. 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 may input cognitive characteristic data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0088] The analysis unit can adjust the order of analysis based on the relationships between cognitive characteristics during the analysis. For example, the analysis unit adjusts the order of analysis based on the relationships between cognitive characteristics. For example, the analysis unit prioritizes the analysis of highly relevant cognitive characteristics. For example, the analysis unit postpones the analysis of less relevant cognitive characteristics. For example, the analysis unit adjusts the analysis schedule based on the relationships between cognitive characteristics. In this way, the analysis unit can adjust the order of analysis based on the relationships between cognitive characteristics. 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 cognitive characteristic data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0089] The transformation unit can estimate a child's emotions and adjust the transformation method of the teaching materials based on the estimated emotions. For example, the transformation unit estimates a child's emotions. For example, the transformation unit adjusts the transformation method of the teaching materials based on the estimated emotions of the child. For example, if the child is relaxed, the transformation unit provides detailed teaching materials. For example, if the child is stressed, the transformation unit provides concise and to-the-point teaching materials. For example, if the child is excited, the transformation unit provides visually appealing teaching materials. In this way, the transformation unit can adjust the transformation method of the teaching materials according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the transformation unit may be performed using AI, for example, or without AI. For example, the transformation unit can input child emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The conversion unit can adjust the level of detail of the conversion based on the importance of the teaching materials during the conversion process. For example, the conversion unit adjusts the level of detail of the conversion based on the importance of the teaching materials. For example, the conversion unit performs a detailed conversion for important teaching materials. For example, the conversion unit performs a concise conversion for less important teaching materials. For example, the conversion unit determines the priority of the conversion according to the importance of the teaching materials. This allows the conversion unit to adjust the level of detail of the conversion according to the importance of the teaching materials. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input teaching material data into a generating AI and have the generating AI perform the adjustment of the level of detail of the conversion.

[0091] The conversion unit can apply different conversion algorithms depending on the category of the teaching material during conversion. For example, the conversion unit applies different conversion algorithms depending on the category of the teaching material. For example, the conversion unit applies an image conversion algorithm to visual teaching materials. For example, the conversion unit applies an audio conversion algorithm to auditory teaching materials. For example, the conversion unit applies a motion conversion algorithm to physical movement teaching materials. In this way, the conversion unit can apply an appropriate conversion algorithm depending on the category of the teaching material. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input teaching material data into a generating AI and have the generating AI perform the application of the conversion algorithm.

[0092] The transformation unit can estimate a child's emotions and adjust the order in which the teaching materials are transformed based on the estimated emotions. For example, the transformation unit estimates a child's emotions. For example, the transformation unit adjusts the order in which the teaching materials are transformed based on the estimated emotions of the child. For example, if the child is relaxed, the transformation unit prioritizes transforming detailed teaching materials. For example, if the child is stressed, the transformation unit prioritizes transforming concise teaching materials. For example, if the child is excited, the transformation unit prioritizes transforming visually appealing teaching materials. In this way, the transformation unit can adjust the order in which the teaching materials are transformed according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the transformation unit may be performed using AI, for example, or without AI. For example, the transformation unit can input child emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The conversion unit can determine the conversion priority based on the submission dates of the teaching materials during the conversion process. For example, the conversion unit determines the conversion priority based on the submission dates of the teaching materials. For example, the conversion unit prioritizes the conversion of teaching materials with approaching submission deadlines. For example, the conversion unit postpones the conversion of teaching materials with distant submission deadlines. For example, the conversion unit adjusts the conversion schedule based on the submission dates. This allows the conversion unit to determine the conversion priority based on the submission dates. Some or all of the above-described processes in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input teaching material data into a generating AI and have the generating AI determine the conversion priority.

[0094] The conversion unit can adjust the order of conversion based on the relevance of the materials during the conversion process. For example, the conversion unit adjusts the order of conversion based on the relevance of the materials. For example, the conversion unit prioritizes conversion of highly relevant materials. For example, the conversion unit postpones conversion of less relevant materials. For example, the conversion unit adjusts the conversion schedule based on the relevance of the materials. This allows the conversion unit to adjust the order of conversion based on the relevance of the materials. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input material data into a generating AI and have the generating AI perform the adjustment of the conversion order.

[0095] The service provider can estimate a child's emotions and adjust the method of providing educational materials based on the estimated emotions. For example, the service provider estimates a child's emotions. For example, the service provider adjusts the method of providing educational materials based on the estimated emotions. For example, if the child is relaxed, the service provider provides detailed materials. For example, if the child is stressed, the service provider provides concise and to-the-point materials. For example, if the child is excited, the service provider provides visually appealing materials. This allows the service provider to adjust the method of providing educational materials 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-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input child emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The delivery unit can select the optimal delivery method based on the child's past learning history at the time of delivery. For example, the delivery unit refers to the child's past learning history. For example, the delivery unit selects the optimal delivery method. For example, the delivery unit selects a delivery method based on the learning tools and materials the child has preferred to use in the past. For example, the delivery unit analyzes the child's past learning outcomes and prioritizes the delivery methods that were effective. For example, the delivery unit selects a delivery method suitable for a specific learning style from the child's past learning history. In this way, the delivery unit can select the optimal delivery method based on past learning history. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input past learning history data into a generating AI and have the generating AI perform the selection of the delivery method.

[0097] The service provider can estimate a child's emotions and adjust the order in which materials are provided based on the estimated emotions. For example, the service provider estimates a child's emotions. For example, the service provider adjusts the order in which materials are provided based on the estimated emotions. For example, if a child is relaxed, the service provider prioritizes providing detailed materials. For example, if a child is stressed, the service provider prioritizes providing concise materials. For example, if a child is excited, the service provider prioritizes providing visually appealing materials. In this way, the service provider can adjust the order in which materials are provided according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input child emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The delivery unit can select the optimal delivery method based on the child's device information at the time of delivery. For example, the delivery unit selects the optimal delivery method considering the child's device information. For example, if the child is using a smartphone, the delivery unit selects a delivery method that matches the screen size. For example, if the child is using a tablet, the delivery unit selects a delivery method optimized for a large screen. For example, if the child is using a personal computer, the delivery unit selects a delivery method for desktops. In this way, the delivery unit can select the optimal delivery method based on device information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input device information data into a generating AI and have the generating AI perform the selection of the delivery method.

[0099] The service provider can monitor the child's learning progress at the time of delivery and adjust the materials and plans as needed. For example, the service provider can monitor the child's learning progress. For example, the service provider can adjust the materials and plans according to the learning progress. For example, the service provider can monitor the child's learning progress in real time and provide materials according to the progress. For example, if the child's learning progress is behind, the service provider can provide supplementary materials. For example, if the child's learning progress is on track, the service provider can provide materials to move to the next step. This allows the service provider to flexibly adjust the materials and plans according to the learning progress. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input learning progress data into a generating AI and have the generating AI perform adjustments to the materials and plans.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The learning support system can also optimize the learning environment based on the child's cognitive characteristics. For example, the data collection unit collects data about the child's learning environment, and the analysis unit proposes an optimal learning environment based on that data. The conversion unit adjusts the placement of teaching materials and the tools used based on the proposed learning environment. The delivery unit provides the optimized learning environment to the child. In this way, the learning support system can provide a learning environment tailored to the child's cognitive characteristics and further enhance learning efficiency.

[0102] The learning support system can also provide real-time feedback on learning progress based on the child's cognitive characteristics. For example, the data collection unit collects data on the child's learning progress, and the analysis unit evaluates the progress based on that data. The conversion unit adjusts the feedback content based on the evaluation results. The provision unit provides feedback to the child in real time. As a result, the learning support system can provide appropriate feedback on the child's learning progress and maintain their motivation to learn.

[0103] The learning support system can also assist in setting learning goals based on a child's cognitive characteristics. For example, the data collection unit collects data on the child's learning goals, and the analysis unit proposes optimal learning goals based on that data. The conversion unit adjusts the learning plan based on the proposed learning goals. The delivery unit provides the child with the optimized learning goals and plan. In this way, the learning support system can set learning goals that are tailored to the child's cognitive characteristics and clarify the direction of learning.

[0104] A learning support system can also implement a reward system to enhance learning motivation based on a child's cognitive characteristics. For example, a data collection unit collects data on a child's learning motivation, and an analysis unit proposes an optimal reward system based on that data. A conversion unit adjusts the reward content based on the proposed reward system. A provisioning unit provides the reward system to the child. In this way, the learning support system can implement a reward system tailored to a child's cognitive characteristics and enhance their motivation to learn.

[0105] A learning support system can also provide relaxation functions to reduce learning stress based on a child's cognitive characteristics. For example, a data collection unit collects data on a child's stress level, and an analysis unit proposes the most suitable relaxation method based on that data. A conversion unit adjusts the relaxation content based on the proposed relaxation method. A delivery unit provides the relaxation content to the child. In this way, the learning support system can provide relaxation functions tailored to a child's cognitive characteristics and reduce learning stress.

[0106] The learning support system can also estimate a child's emotions and adjust the learning content based on those emotions. For example, the collection unit collects data on the child's emotions, and the analysis unit estimates the emotions based on that data. The conversion unit adjusts the learning content based on the estimated emotions. The provision unit provides the adjusted learning content to the child. In this way, the learning support system can provide learning content that matches the child's emotions and maintain their motivation to learn.

[0107] The learning support system can also estimate a child's emotions and adjust the timing of learning based on those emotions. For example, the data collection unit collects data on the child's emotions, and the analysis unit estimates those emotions based on that data. The conversion unit adjusts the timing of learning based on the estimated emotions. The provision unit provides the child with the adjusted learning timing. In this way, the learning support system can provide learning timing that matches the child's emotions, thereby improving learning efficiency.

[0108] The learning support system can also estimate a child's emotions and provide feedback on learning progress based on those estimated emotions. For example, a data collection unit collects data on the child's emotions, and an analysis unit estimates those emotions based on that data. A conversion unit adjusts the feedback content based on the estimated emotions. A delivery unit provides the adjusted feedback to the child. In this way, the learning support system can provide feedback tailored to the child's emotions and maintain their motivation to learn.

[0109] The learning support system can also estimate a child's emotions and set learning goals based on those estimated emotions. For example, the data collection unit collects data on the child's emotions, and the analysis unit estimates those emotions based on that data. The conversion unit adjusts the learning goals based on the estimated emotions. The provision unit provides the adjusted learning goals to the child. This allows the learning support system to set learning goals that match the child's emotions and to clarify the direction of their learning.

[0110] The learning support system can also estimate a child's emotions and adjust the learning reward system based on those estimated emotions. For example, a data collection unit collects data on the child's emotions, and an analysis unit estimates those emotions based on that data. A conversion unit adjusts the reward system based on the estimated emotions. A provisioning unit provides the adjusted reward system to the child. In this way, the learning support system can provide a reward system that matches the child's emotions and increase their motivation to learn.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit measures the child's cognitive characteristics. The data collection unit measures the child's cognitive characteristics, for example, through questionnaires or simple tests. Questionnaires include questions about the child's preferred learning style and learning environment, while simple tests include questions that assess characteristics such as visual cognition, auditory cognition, and kinesthetic cognition. Step 2: The analysis unit analyzes the data collected by the collection unit and derives the optimal learning format. For example, the analysis unit suggests teaching materials that supplement important concepts in the text with diagrams and illustrations for visually dominant learners, suggests converting the text content into video content for auditory-visual mixed learners, and suggests converting the teaching materials into interactive simulations or games for learners who prefer experiential learning. Step 3: The conversion unit converts the learning materials based on the learning format derived by the analysis unit. For example, the conversion unit converts the materials to supplement important concepts in the text with diagrams and illustrations for visually-oriented learners, converts the text content into video content for auditory-visual mixed learners, and converts the materials into interactive simulations or games for learners who prefer experiential learning. Step 4: The provider provides the materials converted by the conversion unit. The provider may, for example, provide the converted materials online, provide them in downloadable format, or provide them in printable form.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, conversion 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 measures the child's cognitive characteristics using the camera 42 and microphone 38B of the smart device 14 and collects the results of questionnaires and tests using the control unit 46A. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and derives the optimal learning format. The conversion unit converts the teaching materials based on the analysis results using the specific processing unit 290 of the data processing unit 12. The provision unit provides the teaching materials converted by the control unit 46A of the smart device 14. 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.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, conversion unit, and provision unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit measures the child's cognitive characteristics using the camera 42 and microphone 238 of the smart glasses 214 and collects the results of questionnaires and tests using the control unit 46A. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 and derives the optimal learning format. The conversion unit converts the teaching materials based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides the teaching materials converted by, for example, the control unit 46A of the smart glasses 214. 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.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, conversion unit, and provision unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit measures the child's cognitive characteristics using the camera 42 and microphone 238 of the headset terminal 314 and collects the results of questionnaires and tests using the control unit 46A. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 and derives the optimal learning format. The conversion unit converts the teaching materials based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides the converted teaching materials using, for example, the control unit 46A of the headset terminal 314. 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.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, conversion unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit measures the child's cognitive characteristics using the camera 42 and microphone 238 of the robot 414 and collects the results of questionnaires and tests using the control unit 46A. The analysis unit analyzes the collected data using, for example, the specific processing unit 290 of the data processing unit 12 and derives the optimal learning format. The conversion unit converts the teaching materials based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The provision unit provides the teaching materials converted by, for example, the control unit 46A of the robot 414. 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A data collection unit that measures children's cognitive characteristics, An analysis unit analyzes the data collected by the aforementioned collection unit and derives an appropriate learning format, A conversion unit that converts teaching materials based on the learning format derived by the analysis unit, The system comprises a providing unit that provides the teaching materials converted by the conversion unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Measure children's cognitive characteristics through questionnaires or simple tests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The conversion unit is Convert the text into learning materials that supplement important concepts with diagrams and illustrations for visually-oriented learners. The system described in Appendix 1, characterized by the features described herein. (Note 4) The conversion unit is Converting text content into video content for learners with a mixed auditory and visual learning style. The system described in Appendix 1, characterized by the features described herein. (Note 5) The conversion unit is For learners who prefer experiential learning, we convert learning materials into interactive simulations and games. The system described in Appendix 1, characterized by the features described herein. (Note 6) The conversion unit is Convert learning materials into audiobooks for learners who primarily learn through listening. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Provide converted teaching materials The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, Monitor learning progress and adjust materials and plans as needed. 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 adjusts the timing of cognitive characteristic measurements based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the child's past learning history and select an appropriate measurement method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When measuring cognitive characteristics, 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 12) The aforementioned collection unit is The system estimates a child's emotions and prioritizes the cognitive traits to measure based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When measuring cognitive characteristics, prioritize 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 14) The aforementioned collection unit is When measuring cognitive characteristics, analyze children's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, We estimate the child's emotions and adjust the representation 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, During analysis, adjust the level of detail based on the importance of cognitive characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of cognitive characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, 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 19) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on the timing of cognitive characteristic data collection. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relationships between cognitive characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 21) The conversion unit is The system estimates the child's emotions and adjusts the method of transforming the teaching materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The conversion unit is During conversion, the level of detail in the conversion is adjusted based on the importance of the learning materials. The system described in Appendix 1, characterized by the features described herein. (Note 23) The conversion unit is During conversion, different conversion algorithms are applied depending on the category of the teaching material. The system described in Appendix 1, characterized by the features described herein. (Note 24) The conversion unit is The system estimates the child's emotions and adjusts the order in which the teaching materials are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The conversion unit is During the conversion process, the priority of conversions will be determined based on the submission deadlines for the learning materials. The system described in Appendix 1, characterized by the features described herein. (Note 26) The conversion unit is During conversion, the order of conversion is adjusted based on the relevance of the materials. 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 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 28) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected based on the child's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, The system estimates the child's emotions and adjusts 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 30) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected based on the child's device information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, During the provision of the program, we monitor the child's learning progress and adjust the materials and plan as needed. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 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 unit that measures children's cognitive characteristics, An analysis unit analyzes the data collected by the aforementioned collection unit and derives an appropriate learning format, A conversion unit that converts teaching materials based on the learning format derived by the analysis unit, The system comprises a providing unit that provides the teaching materials converted by the conversion unit. A system characterized by the following features.

2. The aforementioned collection unit is Measure children's cognitive characteristics through questionnaires or simple tests. The system according to feature 1.

3. The conversion unit is Convert the text into learning materials that supplement important concepts with diagrams and illustrations for visually-oriented learners. The system according to feature 1.

4. The conversion unit is Converting text content into video content for learners with a mixed auditory and visual learning style. The system according to feature 1.

5. The conversion unit is For learners who prefer experiential learning, we convert learning materials into interactive simulations and games. The system according to feature 1.

6. The conversion unit is Convert learning materials into audiobooks for learners who primarily learn through listening. The system according to feature 1.

7. The aforementioned supply unit is, Provide converted teaching materials The system according to feature 1.

8. The aforementioned supply unit is, Monitor learning progress and adjust materials and plans as needed. The system according to feature 1.

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