System

Abstract

An object of the system according to the embodiment is to provide learning support customized in accordance with the interest and ability of each child.SOLUTION: A system includes a collection unit, an analysis unit, a provision unit, a learning support unit, and a feedback unit. The collection unit collects action data of the child. The analysis unit analyzes the data collected by the collection unit and grasps a specific interest or ability of the child. The provision unit provides a topic or content customized on the basis of the analysis result obtained by the analysis unit. The learning support unit performs learning support based on the content provided by the providing unit. The feedback unit feeds back the progress of learning obtained by the learning support unit to the parent.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not adequately provide learning support tailored to each child's interests and abilities, and there is room for improvement.

[0005] The system according to the embodiment aims to provide learning support customized to the interests and abilities of each child. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a learning support unit, and a feedback unit. The collection unit collects behavioral data of the child. The analysis unit analyzes the data collected by the collection unit to identify the child's specific interests and abilities. The provision unit provides customized topics and content based on the analysis results obtained by the analysis unit. The learning support unit provides learning support based on the content provided by the provision unit. The feedback unit provides feedback to the parent about the learning progress obtained by the learning support unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide customized learning support tailored to the interests and abilities of each child. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention is an advanced AI partner for all children. This system customizes topics and content to match each child's interests and abilities, allowing them to learn at their own pace while emphasizing safety and privacy. It also incorporates gamification elements to enhance learning while having fun. For example, the system collects children's behavioral data and uses AI to analyze it. It then provides customized topics and content based on the analysis results. It also incorporates a reward system that allows children to earn points based on their learning progress and feel a sense of accomplishment. This allows children to deepen their learning while having fun. The system is multilingual and provides content based on local cultures and learning curricula. For example, it provides English content for children who want to learn English, while incorporating topics based on local cultures, allowing children to learn from more familiar themes and environments. Furthermore, a parental feedback function allows parents to track their children's learning progress and provide necessary support. For example, parents can support their children's learning by providing feedback to them about the topics their children are interested in and the pace at which they are progressing. Furthermore, a rich learning experience utilizing AR / VR technology allows children to gain a deeper understanding of the content they are learning. For example, by experiencing science experiments in virtual reality, children can learn by actually doing. This stimulates their curiosity and imagination, increasing their motivation to learn. This makes the system an AI partner that focuses on children's growth and future potential. This allows the system to support children's learning and growth. For example, children can learn at their own pace and deepen their learning while having fun. Parents can also keep track of their children's learning progress and provide any support they need. Furthermore, rich learning experiences utilizing AR / VR technology allow children to gain a deeper understanding of the content they are learning.

[0029] A learning support system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a learning support unit, and a feedback unit. The collection unit collects behavioral data of a child. The behavioral data of a child includes, but is not limited to, learning behavior, play behavior, and daily life behavior. The collection unit collects the behavioral data of a child using, for example, a sensor. The collection unit can also collect data from devices used by the child. The collection unit can also collect input data from parents or teachers. For example, the collection unit collects data from a tablet or smartphone used by the child. The sensor detects the child's movements and records them as behavioral data. The parent or teacher observes the child's behavior and inputs the data into the collection unit. The analysis unit analyzes the data collected by the collection unit to understand the child's interests and abilities. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods, but is not limited to these examples. For example, the analysis unit can extract patterns from the child's behavioral data using data mining techniques. The analysis unit can also evaluate the child's interests and abilities using statistical analysis. The analysis unit may also use a machine learning algorithm to analyze the child's behavioral data. For example, the analysis unit may use data mining technology to identify topics of interest from the child's behavioral data. Statistical analysis may be used to evaluate the child's learning outcomes and understand their abilities. The machine learning algorithm may study large amounts of data and predict the child's behavioral patterns. The provision unit may provide customized topics and content based on the analysis results obtained by the analysis unit. The topics and content provided may include, but are not limited to, learning materials, interactive learning tools, entertainment content, etc. For example, the provision unit may provide science-related topics based on the child's interests. The provision unit may also provide content with a difficulty level appropriate to the child's ability. The provision unit may also provide entertainment content based on the child's interests and abilities. For example, the provision unit may provide science-related topics to enable the child to learn through experiments. The provision unit may provide beginner, intermediate, and advanced content appropriate to the child's ability.The entertainment content is designed to allow children to learn while having fun. The learning support unit provides learning support based on the content provided by the providing unit. The learning support is provided by, for example, but not limited to, methods such as online learning materials, interactive learning tools, and private tutors. For example, the learning support unit provides online learning materials to allow children to learn at their own pace. The learning support unit can also provide interactive learning tools to allow children to learn while having fun. The learning support unit can also dispatch private tutors to support children's learning. For example, the learning support unit provides online learning materials to allow children to learn at their own pace. The interactive learning tools are designed to allow children to learn in a game-like manner. The private tutors provide individual support for children's learning and improve their understanding. The feedback unit feeds back the learning progress obtained by the learning support unit to parents. The feedback is provided by, for example, but not limited to, methods such as progress reports, advice, and suggestions for next steps. For example, the feedback unit provides a report on the child's learning progress to the parent. The feedback unit can also provide advice to the parent and suggest ways to support the child's learning. The feedback unit can also suggest what kind of learning should be done as the next step. For example, the feedback unit provides a report of the child's learning progress to the parent. The advice shows specific ways for the parent to support the child's learning. The next step suggestion shows what the child should learn next and clarifies the direction of the learning. In this way, the learning support system according to the embodiment can support the child's learning and growth.

[0030] The collection unit can analyze the child's past behavioral data and select the optimal collection method. For example, the collection unit prioritizes collecting data from devices that the child has previously used. For example, the collection unit collects data from tablets and smartphones that the child has previously used. The collection unit can also prioritize collecting data related to topics that the child has previously shown interest in. For example, the collection unit collects data related to science that the child has previously shown interest in. The collection unit can also set the optimal collection timing based on the child's past behavioral patterns. For example, the collection unit collects data during times when the child previously studied. This enables efficient data collection by selecting the optimal collection method based on the past behavioral data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past behavioral data into a generation AI and have the generation AI select the optimal collection method.

[0031] When collecting behavioral data, the collection unit can filter the data based on the child's current interests. For example, the collection unit prioritizes collecting data related to topics in which the child is currently interested. For example, the collection unit collects data related to science in which the child is currently interested. The collection unit can also filter the data based on keywords recently searched by the child. For example, the collection unit collects data based on science-related keywords recently searched by the child. The collection unit can also collect data related to events recently attended by the child. For example, the collection unit collects data related to science events attended by the child. This allows for highly relevant data to be collected by filtering the data based on the child's current interests. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data related to the child's current interests to the generation AI and have the generation AI perform the filtering.

[0032] When collecting behavioral data, the collection unit can select an appropriate collection means depending on the child's input method. For example, if the child prefers voice input, the collection unit preferentially collects voice data. For example, the collection unit collects behavioral data using voice input. Also, if the child prefers text input, the collection unit can preferentially collect text data. For example, the collection unit collects behavioral data using text input. Also, if the child prefers image input, the collection unit can preferentially collect image data. For example, the collection unit collects behavioral data using image input. This enables efficient data collection by selecting the optimal collection means depending on the input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data regarding the child's input method into the generation AI and cause the generation AI to select the optimal collection means.

[0033] When collecting behavioral data, the collection unit can prioritize collecting highly relevant data by taking into account the child's geographical location information. The collection unit, for example, prioritizes collecting learning content related to the child's current location. For example, the collection unit collects historical data related to the child's current location. The collection unit can also collect data related to places the child has visited in the past. For example, the collection unit collects data related to museums the child has visited in the past. The collection unit can also collect data related to places the child plans to visit in the future. For example, the collection unit collects data related to scientific facilities the child plans to visit in the future. In this way, highly relevant data can be collected by taking the geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the child's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0034] When collecting behavioral data, the collection unit can analyze the child's social media activities and collect related data. For example, the collection unit collects data related to content shared by the child on social media. For example, the collection unit collects science-related content shared by the child on social media. The collection unit can also collect data based on the activities of the child's friends on social media. For example, the collection unit collects science-related content shared by the child's friends. The collection unit can also collect data related to topics in which the child has shown interest on social media. For example, the collection unit collects science-related topics in which the child has shown interest on social media. This allows related data to be collected by analyzing social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the child's social media activity data into the generation AI and cause the generation AI to collect related data.

[0035] When collecting behavioral data, the collection unit can customize the collection method by reflecting the child's past feedback. For example, the collection unit prioritizes using a collection method that the child previously preferred. For example, the collection unit prioritizes using voice input that the child previously preferred. The collection unit can also adjust the collection timing based on feedback provided by the child in the past. For example, the collection unit adjusts the collection timing based on feedback provided by the child in the past. The collection unit can also select a collection method by referring to the child's past feedback. For example, the collection unit selects an optimal collection method based on feedback provided by the child in the past. This allows the collection method to be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the child's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a detailed analysis on learning data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs a simplified analysis on play data with low importance. The analysis unit can also perform an analysis with a moderate level of detail on data with medium importance. For example, the analysis unit performs an analysis with a moderate level of detail on lifestyle data with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the behavioral data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of behavioral data. For example, the analysis unit applies an analysis algorithm dedicated to learning to training data. For example, the analysis unit applies a clustering algorithm to training data. The analysis unit can also apply an emotion analysis algorithm to emotion data. For example, the analysis unit applies an emotion classification algorithm to emotion data. The analysis unit can also apply a behavior analysis algorithm to behavioral data. For example, the analysis unit applies a behavior pattern analysis algorithm to behavioral data. This enables highly accurate analysis by applying an analysis algorithm depending on the category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of behavioral data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the child's past analysis results. The analysis unit, for example, corrects the current analysis result based on the child's past analysis results. For example, the analysis unit corrects the analysis result of the current learning data based on the past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the child's past analysis results. For example, the analysis unit adjusts the analysis algorithm of the current emotion data based on the past analysis results. The analysis unit can also improve the accuracy of the analysis by using the child's past analysis results. For example, the analysis unit improves the analysis accuracy of the current behavioral data based on the past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the time when the behavioral data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit prioritizes analysis of the most recent learning data. The analysis unit can also analyze current data by referring to past data. For example, the analysis unit analyzes current learning data by referring to past learning data. The analysis unit can also lower the priority of data collected earlier by analyzing it. For example, the analysis unit lowers the priority of play data collected earlier by analyzing it. This enables efficient analysis by determining the priority based on the time of collection. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the behavioral data was collected into the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the behavioral data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant learning data. The analysis unit can also postpone analysis of low-relevance data. For example, the analysis unit postpones analysis of low-relevance play data. The analysis unit can also analyze data of medium relevance in an appropriate order. For example, the analysis unit analyzes life data of medium relevance in an appropriate order. This enables efficient analysis by adjusting the order of analysis based on relevance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the behavioral data to the generation AI and cause the generation AI to adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the child's level of expertise. For example, if the child's level of expertise is high, the analysis unit provides analysis results that use a lot of technical terminology. For example, the analysis unit provides analysis results of the training data that use a lot of technical terminology to a child with a high level of expertise. The analysis unit can also provide analysis results in simple language if the child's level of expertise is low. For example, the analysis unit provides analysis results of the training data in simple language to a child with a low level of expertise. The analysis unit can also provide analysis results that use appropriate technical terminology according to the child's level of expertise. For example, the analysis unit provides analysis results of the training data that use appropriate technical terminology according to the level of expertise. In this way, by adjusting the use of technical terminology according to the level of expertise, it is possible to provide analysis results that are suitable for the child. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the child's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0042] The providing unit can adjust the level of detail of the content provided based on the importance of the topic or content when providing the content. For example, the providing unit provides detailed content for a topic with high importance. For example, the providing unit provides detailed content for a learning topic with high importance. The providing unit can also provide simplified content for a topic with low importance. For example, the providing unit provides simplified content for an entertainment topic with low importance. The providing unit can also provide content with an appropriate level of detail for a topic with medium importance. For example, the providing unit provides content with an appropriate level of detail for a lifestyle topic with medium importance. This enables efficient content provision by adjusting the level of detail based on the importance. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the importance of the topic or content to a generation AI and cause the generation AI to adjust the level of detail of the content provided.

[0043] The providing unit can apply different providing algorithms depending on the topic or content category when providing content. For example, the providing unit applies a providing algorithm dedicated to learning to learning content. For example, the providing unit applies a recommendation algorithm to learning content. The providing unit can also apply a providing algorithm dedicated to entertainment to entertainment content. For example, the providing unit applies a personalized filtering algorithm to entertainment content. The providing unit can also apply a providing algorithm dedicated to emotions to emotional content. For example, the providing unit applies an emotion analysis algorithm to emotional content. This enables content to be provided with high accuracy by applying a providing algorithm depending on the category. Some or all of the above-mentioned processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the topic or content category to the generation AI and cause the generation AI to apply an appropriate providing algorithm.

[0044] The providing unit can improve the accuracy of the content provision by referring to the child's past provision results. The providing unit, for example, corrects the current provision result based on the child's past provision results. For example, the providing unit corrects the provision result of the current learning content based on the past provision results. The providing unit can also adjust the provision algorithm by referring to the child's past provision results. For example, the providing unit adjusts the provision algorithm of the current entertainment content based on the past provision results. The providing unit can also improve the accuracy of the content provision by using the child's past provision results. For example, the providing unit improves the accuracy of the current emotional content provision based on the past provision results. In this way, the accuracy of the content provision is improved by referring to the past provision results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the past provision results into the generation AI and cause the generation AI to improve the accuracy of the content provision.

[0045] The providing unit can determine the priority of provision based on the time when the topics and content were collected when providing the content. For example, the providing unit prioritizes providing the latest topics and content. For example, the providing unit prioritizes providing the latest learning topics and content. The providing unit can also provide current topics and content by referring to past topics and content. For example, the providing unit provides current learning topics and content by referring to past learning topics and content. The providing unit can also provide topics and content that were collected earlier by lowering the priority. For example, the providing unit provides entertainment topics and content that were collected earlier by lowering the priority. This enables efficient content provision by determining the priority based on the time of collection. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the time when the topics and content were collected to the generation AI and cause the generation AI to determine the priority of provision.

[0046] The providing unit can adjust the order of provision based on the relevance of the topics and content when providing them. For example, the providing unit prioritizes providing highly relevant topics and content. For example, the providing unit prioritizes providing highly relevant learning topics and content. The providing unit can also postpone providing less relevant topics and content. For example, the providing unit postpones providing less relevant entertainment topics and content. The providing unit can also provide topics and content with a moderate degree of relevance in a moderate order. For example, the providing unit provides lifestyle topics and content with a moderate degree of relevance in a moderate order. This enables efficient content provision by adjusting the order of provision based on relevance. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the relevance of topics and content to a generation AI and cause the generation AI to adjust the order of provision.

[0047] The providing unit can adjust the use of technical terminology in the content provided according to the child's level of expertise. For example, if the child's level of expertise is high, the providing unit provides content that uses a lot of technical terminology. For example, the providing unit provides learning content that uses a lot of technical terminology to a child with a high level of expertise. The providing unit can also provide content in simple language if the child's level of expertise is low. For example, the providing unit provides learning content in simple language to a child with a low level of expertise. The providing unit can also provide content that uses appropriate technical terminology according to the child's level of expertise. For example, the providing unit provides learning content that uses appropriate technical terminology according to the level of expertise. This makes it possible to provide content that is suitable for children by adjusting the use of technical terminology according to the level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the child's level of expertise to the generating AI and cause the generating AI to adjust the use of technical terminology.

[0048] When providing learning support, the learning support unit can select an optimal learning support method by analyzing the child's past learning behavior. For example, the learning support unit prioritizes using a learning method that the child previously preferred. For example, the learning support unit prioritizes using online learning materials that the child previously preferred. The learning support unit can also select an optimal learning support method based on the child's past learning behavior. For example, the learning support unit selects individual instruction based on the child's past learning behavior. The learning support unit can also customize the learning support method by referring to the child's past learning behavior. For example, the learning support unit customizes the learning support method based on the child's past learning behavior. This enables efficient learning support by selecting an optimal learning support method based on the child's past learning behavior. Some or all of the above-described processing in the learning support unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning support unit can input data on the child's past learning behavior into a generation AI and cause the generation AI to select an optimal learning support method.

[0049] The learning support unit can customize the learning support means based on the child's current living situation when providing learning support. The learning support unit, for example, adjusts the learning support means according to the child's current living situation. For example, the learning support unit provides online learning materials according to the child's home environment. The learning support unit can also provide optimal learning support means taking the child's living situation into consideration. For example, the learning support unit dispatches a tutor according to the child's school situation. The learning support unit can also customize the learning support means based on the child's living situation. For example, the learning support unit provides a learning app that allows the child to relax according to the child's health condition. In this way, customizing the learning support means based on the living situation enables learning support that is appropriate for the child. Some or all of the above-described processing in the learning support unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning support unit can input the child's living situation data into a generation AI and cause the generation AI to customize the learning support means.

[0050] The learning support unit can improve the learning support method by reflecting the child's feedback during learning support. The learning support unit, for example, improves the learning support method based on the child's feedback. For example, the learning support unit improves the content of online learning materials based on the feedback provided by the child. The learning support unit can also adjust the learning support method by referring to the child's feedback. For example, the learning support unit adjusts the tutor's teaching method based on the feedback provided by the child. The learning support unit can also optimize the learning support method by using the child's feedback. For example, the learning support unit optimizes the functions of a learning app based on the feedback provided by the child. In this way, the learning support method can be optimized by reflecting the feedback. Some or all of the above-mentioned processing in the learning support unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning support unit can input the child's feedback data into a generation AI and cause the generation AI to improve the learning support method.

[0051] When providing learning support, the learning support unit can select the optimal learning support method by taking into account the child's geographical location information. The learning support unit, for example, prioritizes providing learning content related to the child's current location. For example, the learning support unit provides historical data related to the child's current location. The learning support unit can also provide learning support related to places the child has visited in the past. For example, the learning support unit provides learning support related to museums the child has visited in the past. The learning support unit can also provide learning support related to places the child plans to visit in the future. For example, the learning support unit provides learning support related to scientific facilities the child plans to visit in the future. This enables highly relevant learning support by taking the geographical location information into consideration. Some or all of the above-described processing in the learning support unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning support unit can input the child's geographical location information into the generation AI and cause the generation AI to select the optimal learning support method.

[0052] During learning support, the learning support unit can analyze the child's social media activity and suggest learning support methods. The learning support unit, for example, provides learning support related to content shared by the child on social media. For example, the learning support unit provides learning support related to science-related content shared by the child on social media. The learning support unit can also provide learning support based on the activities of the child's friends on social media. For example, the learning support unit provides learning support related to science-related content shared by the child's friends. The learning support unit can also provide learning support related to topics in which the child has shown interest on social media. For example, the learning support unit provides learning support related to science-related topics in which the child has shown interest on social media. This enables highly relevant learning support to be provided by analyzing social media activity. Some or all of the above-described processing in the learning support unit may be performed using, for example, AI, or may be performed without AI. For example, the learning support unit can input the child's social media activity data into a generation AI and cause the generation AI to suggest learning support methods.

[0053] The learning support unit can customize the learning support method by reflecting the child's past feedback when providing learning support. For example, the learning support unit prioritizes the use of learning support methods that the child previously preferred. For example, the learning support unit prioritizes the use of online learning materials that the child previously preferred. The learning support unit can also adjust the learning support method based on the child's past feedback. For example, the learning support unit adjusts the tutor's teaching method based on feedback provided by the child in the past. The learning support unit can also customize the learning support method by referring to the child's past feedback. For example, the learning support unit customizes the functions of a learning app based on feedback provided by the child in the past. This allows the learning support method to be optimized by reflecting the past feedback. Some or all of the above-described processing in the learning support unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning support unit can input the child's past feedback data into a generation AI and cause the generation AI to customize the learning support method.

[0054] When providing feedback, the feedback unit can analyze the child's past learning progress and select the optimal feedback method. The feedback unit, for example, corrects the current feedback based on the child's past learning progress. For example, the feedback unit corrects the current learning feedback based on the child's past learning progress. The feedback unit can also adjust the feedback method by referring to the child's past learning progress. For example, the feedback unit adjusts the tutor's feedback method based on the child's past learning progress. The feedback unit can also improve the accuracy of the feedback by using the child's past learning progress. For example, the feedback unit improves the feedback accuracy of the learning app based on the child's past learning progress. This enables efficient feedback by selecting the optimal feedback method based on the child's past learning progress. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the child's past learning progress data into the generation AI and cause the generation AI to select the optimal feedback method.

[0055] The feedback unit can customize the feedback means based on the child's current learning situation when providing feedback. The feedback unit, for example, adjusts the feedback means according to the child's current learning situation. For example, the feedback unit provides online feedback according to the child's current learning situation. The feedback unit can also provide the optimal feedback means taking the child's learning situation into consideration. For example, the feedback unit provides face-to-face feedback according to the child's school situation. The feedback unit can also customize the feedback means based on the child's learning situation. For example, the feedback unit provides email feedback according to the child's level of understanding. This enables feedback that is appropriate for the child by customizing the feedback means based on the learning situation. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the child's learning situation data into the generation AI and cause the generation AI to customize the feedback means.

[0056] The feedback unit can improve the feedback method by reflecting the child's feedback when providing feedback. The feedback unit, for example, improves the feedback method based on the child's feedback. For example, the feedback unit improves the content of online feedback based on feedback provided by the child. The feedback unit can also adjust the feedback method by referring to the child's feedback. For example, the feedback unit adjusts the face-to-face feedback method based on feedback provided by the child. The feedback unit can also optimize the feedback method by using the child's feedback. For example, the feedback unit optimizes the content of email feedback based on feedback provided by the child. In this way, the feedback method can be optimized by reflecting the feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the child's feedback data into a generation AI and cause the generation AI to improve the feedback method.

[0057] The feedback unit can select the optimal feedback method by taking into account the child's geographical location information when providing feedback. For example, the feedback unit prioritizes providing feedback related to the child's current location. For example, the feedback unit provides historical feedback related to the child's current location. The feedback unit can also provide feedback related to places the child has visited in the past. For example, the feedback unit provides feedback related to museums the child has visited in the past. The feedback unit can also provide feedback related to places the child plans to visit in the future. For example, the feedback unit provides feedback related to scientific facilities the child plans to visit in the future. This enables highly relevant feedback by taking the geographical location information into consideration. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input the child's geographical location information to the generation AI and cause the generation AI to select the optimal feedback method.

[0058] When providing feedback, the feedback unit may analyze the child's social media activity and suggest a means of providing feedback. The feedback unit may, for example, provide feedback related to content shared by the child on social media. For example, the feedback unit may provide feedback related to science-related content shared by the child on social media. The feedback unit may also provide feedback based on the activities of the child's friends on social media. For example, the feedback unit may provide feedback related to science-related content shared by the child's friends. The feedback unit may also provide feedback related to topics in which the child has shown interest on social media. For example, the feedback unit may provide feedback related to science-related topics in which the child has shown interest on social media. This enables highly relevant feedback to be provided by analyzing social media activity. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input the child's social media activity data into a generation AI and cause the generation AI to suggest a means of providing feedback.

[0059] When providing feedback, the feedback unit can customize the feedback method by reflecting the child's past feedback. For example, the feedback unit prioritizes the use of feedback methods that the child previously preferred. For example, the feedback unit prioritizes the use of online feedback that the child previously preferred. The feedback unit can also adjust the feedback method based on the child's past feedback. For example, the feedback unit adjusts the face-to-face feedback method based on feedback provided by the child in the past. The feedback unit can also customize the feedback method by referring to the child's past feedback. For example, the feedback unit customizes the content of email feedback based on feedback provided by the child in the past. This allows the feedback method to be optimized by reflecting past feedback. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the child's past feedback data into the generation AI and cause the generation AI to customize the feedback method.

[0060] The provision unit supports multiple languages ​​and can provide content based on the culture and learning curriculum of each region. For example, the provision unit provides English content to children who want to learn English. For example, the provision unit provides English learning materials. Furthermore, by incorporating topics based on the culture of the region, children can learn from more familiar themes and environments. For example, the provision unit provides topics related to the history and culture of the region. This allows for multilingual support and the provision of content based on the culture and learning curriculum of each region. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input data related to the culture and learning curriculum of each region into the generation AI and cause the generation AI to provide appropriate content.

[0061] The learning support unit can provide a rich learning experience using AR / VR technology. For example, the learning support unit allows children to learn by actually using their hands by experiencing a science experiment in virtual reality. For example, the learning support unit allows children to experience a science experiment using a virtual reality headset. The learning support unit can also allow children to experience an interactive simulation using an augmented reality application. For example, the learning support unit can use an augmented reality application to allow children to learn while manipulating a 3D model. The learning support unit can also allow children to experience historical events using AR / VR technology. For example, the learning support unit can recreate historical events in virtual reality, allowing children to learn as if they were actually there. This makes it possible to provide a rich learning experience by utilizing AR / VR technology. Some or all of the above-mentioned processing in the learning support unit may be performed using AI, for example, or may be performed without AI. For example, the learning support unit can have a generative AI execute the design of a learning experience using AR / VR technology.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The provision unit can analyze the child's learning history and provide learning methods that have been effective in the past with priority. For example, video learning materials can be provided with priority to a child for whom video learning materials have been effective in the past. Game-style learning materials can also be provided with priority to a child for whom game-style learning materials have been effective in the past. Furthermore, text-style learning materials can also be provided with priority to a child for whom text-style learning materials have been effective in the past. This makes it possible to maximize learning effectiveness by providing the optimal learning method based on the child's past learning history.

[0064] The learning support unit can set a child's learning goals and customize the content of learning support based on those goals. For example, a child aiming to achieve high scores in a specific subject can be provided with learning support specialized for that subject. A child wanting to master a specific skill can also be provided with learning support related to that skill. Furthermore, a child aiming to improve their overall academic ability can be provided with balanced learning support. This makes it possible to support goal achievement by providing support according to the child's learning goals.

[0065] The feedback unit can monitor a child's learning progress in real time and provide immediate feedback as needed. For example, it can provide feedback on whether the child is correct or incorrect immediately after solving a problem. It can also provide immediate advice if the child experiences difficulty while learning. Furthermore, it can provide immediate feedback praising the child when he or she achieves a goal. This makes it possible to increase a child's motivation to learn through real-time feedback.

[0066] The provision unit can analyze a child's learning environment and provide advice to provide the optimal learning environment. For example, for a child who learns effectively in a quiet environment, it can recommend that the child study in a quiet place. Also, for a child who learns effectively while listening to music, it can provide appropriate music. Furthermore, it can provide group learning opportunities for a child who learns effectively in a group. In this way, by optimizing a child's learning environment, it is possible to improve learning effectiveness.

[0067] The analysis unit can analyze a child's learning patterns and suggest an optimal learning schedule. For example, if a child is most effective at learning in the morning, it can recommend morning study. Also, if a child is most effective at learning in the evening, it can recommend evening study. Furthermore, it can suggest short study sessions for children who benefit from short, concentrated study sessions. In this way, it is possible to improve learning effectiveness by suggesting a schedule that suits a child's learning patterns.

[0068] The learning support department can analyze children's learning progress and introduce a reward system based on progress. For example, badges or points can be awarded to children who achieve certain goals. Special rewards can also be offered to children who continue studying continuously. Furthermore, team rewards can be offered to children who study collaboratively with friends. This can increase children's motivation to learn through the reward system.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The collection unit collects the child's behavioral data. The child's behavioral data includes learning behavior, play behavior, and daily life behavior. The collection unit uses sensors to collect the child's behavioral data, as well as input data from devices used by the child and from parents and teachers. For example, data is collected from tablets and smartphones, and sensors detect the child's movements and record them as behavioral data. Parents and teachers observe the child's behavior and enter the data into the collection unit. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the child's interests and abilities. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, data mining techniques are used to extract patterns from the behavioral data, and statistical analysis is used to evaluate the child's interests and abilities. Machine learning algorithms are used to analyze the behavioral data and identify topics of interest and behavioral patterns. Step 3: The provider provides customized topics and content based on the analysis results obtained by the analyzer. The topics and content provided include learning materials, interactive learning tools, entertainment content, etc. For example, science topics are provided based on the child's interests, and content of a difficulty level appropriate to the child's ability is provided. Entertainment content is also provided, allowing children to learn while having fun. Step 4: The learning support department provides learning support based on the content provided by the provision department. Learning support is provided through methods such as online learning materials, interactive learning tools, and tutors. For example, online learning materials are provided so that children can learn at their own pace. Interactive learning tools are designed to make learning feel like a game. Tutors provide individual support for children's learning and improve their understanding. Step 5: The feedback unit provides the parent with the learning progress obtained by the learning support unit. The feedback is given in the form of a progress report, advice, a proposal for the next step, etc. For example, the learning progress may be reported to the parent and advice may be given to the parent. As the next step, a proposal may be made as to what kind of learning should be done.

[0071] (Example 2) A system according to an embodiment of the present invention is an advanced AI partner for all children. This system customizes topics and content to match each child's interests and abilities, allowing them to learn at their own pace while emphasizing safety and privacy. It also incorporates gamification elements to enhance learning while having fun. For example, the system collects children's behavioral data and uses AI to analyze it. It then provides customized topics and content based on the analysis results. It also incorporates a reward system that allows children to earn points based on their learning progress and feel a sense of accomplishment. This allows children to deepen their learning while having fun. The system is multilingual and provides content based on local cultures and learning curricula. For example, it provides English content for children who want to learn English, while incorporating topics based on local cultures, allowing children to learn from more familiar themes and environments. Furthermore, a parental feedback function allows parents to track their children's learning progress and provide necessary support. For example, parents can support their children's learning by providing feedback to them about the topics their children are interested in and the pace at which they are progressing. Furthermore, a rich learning experience utilizing AR / VR technology allows children to gain a deeper understanding of the content they are learning. For example, by experiencing science experiments in virtual reality, children can learn by actually doing. This stimulates their curiosity and imagination, increasing their motivation to learn. This makes the system an AI partner that focuses on children's growth and future potential. This allows the system to support children's learning and growth. For example, children can learn at their own pace and deepen their learning while having fun. Parents can also keep track of their children's learning progress and provide any support they need. Furthermore, rich learning experiences utilizing AR / VR technology allow children to gain a deeper understanding of the content they are learning.

[0072] A learning support system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a learning support unit, and a feedback unit. The collection unit collects behavioral data of a child. The behavioral data of a child includes, but is not limited to, learning behavior, play behavior, and daily life behavior. The collection unit collects the behavioral data of a child using, for example, a sensor. The collection unit can also collect data from devices used by the child. The collection unit can also collect input data from parents or teachers. For example, the collection unit collects data from a tablet or smartphone used by the child. The sensor detects the child's movements and records them as behavioral data. The parent or teacher observes the child's behavior and inputs the data into the collection unit. The analysis unit analyzes the data collected by the collection unit to understand the child's interests and abilities. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods, but is not limited to these examples. For example, the analysis unit can extract patterns from the child's behavioral data using data mining techniques. The analysis unit can also evaluate the child's interests and abilities using statistical analysis. The analysis unit may also use a machine learning algorithm to analyze the child's behavioral data. For example, the analysis unit may use data mining technology to identify topics of interest from the child's behavioral data. Statistical analysis may be used to evaluate the child's learning outcomes and understand their abilities. The machine learning algorithm may study large amounts of data and predict the child's behavioral patterns. The provision unit may provide customized topics and content based on the analysis results obtained by the analysis unit. The topics and content provided may include, but are not limited to, learning materials, interactive learning tools, entertainment content, etc. For example, the provision unit may provide science-related topics based on the child's interests. The provision unit may also provide content with a difficulty level appropriate to the child's ability. The provision unit may also provide entertainment content based on the child's interests and abilities. For example, the provision unit may provide science-related topics to enable the child to learn through experiments. The provision unit may provide beginner, intermediate, and advanced content appropriate to the child's ability.The entertainment content is designed to allow children to learn while having fun. The learning support unit provides learning support based on the content provided by the providing unit. The learning support is provided by, for example, but not limited to, methods such as online learning materials, interactive learning tools, and private tutors. For example, the learning support unit provides online learning materials to allow children to learn at their own pace. The learning support unit can also provide interactive learning tools to allow children to learn while having fun. The learning support unit can also dispatch private tutors to support children's learning. For example, the learning support unit provides online learning materials to allow children to learn at their own pace. The interactive learning tools are designed to allow children to learn in a game-like manner. The private tutors provide individual support for children's learning and improve their understanding. The feedback unit feeds back the learning progress obtained by the learning support unit to parents. The feedback is provided by, for example, but not limited to, methods such as progress reports, advice, and suggestions for next steps. For example, the feedback unit provides a report on the child's learning progress to the parent. The feedback unit can also provide advice to the parent and suggest ways to support the child's learning. The feedback unit can also suggest what kind of learning should be done as the next step. For example, the feedback unit provides a report of the child's learning progress to the parent. The advice shows specific ways for the parent to support the child's learning. The next step suggestion shows what the child should learn next and clarifies the direction of the learning. In this way, the learning support system according to the embodiment can support the child's learning and growth.

[0073] The collection unit estimates the child's emotions and adjusts the timing of behavioral data collection based on the estimated emotions. For example, the collection unit captures the child's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on changes in facial expressions. The collection unit can also record the child's voice and estimate the emotions using voice analysis technology. For example, the collection unit analyzes the tone and speed of the voice and calculates an emotion score. The collection unit can also collect the child's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the collection unit calculates an emotion score based on heart rate fluctuations. This enables more appropriate data collection by adjusting the collection timing according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input image data of a child taken with a camera into the generation AI and have the generation AI estimate the child's emotions. Furthermore, the collection unit can analyze the child's emotional data in real time and immediately grasp changes in emotions. For example, the collection unit can monitor the child's facial expressions in real time and immediately detect changes in emotions. The collection unit can also accumulate the child's emotional data and analyze long-term emotional trends. For example, the collection unit can analyze the child's emotional data over time and identify patterns of emotional changes. This allows for a detailed understanding of the child's emotions and allows for adjustment of collection timing.

[0074] The collection unit can analyze the child's past behavioral data and select the optimal collection method. For example, the collection unit prioritizes collecting data from devices that the child has previously used. For example, the collection unit collects data from tablets and smartphones that the child has previously used. The collection unit can also prioritize collecting data related to topics that the child has previously shown interest in. For example, the collection unit collects data related to science that the child has previously shown interest in. The collection unit can also set the optimal collection timing based on the child's past behavioral patterns. For example, the collection unit collects data during times when the child previously studied. This enables efficient data collection by selecting the optimal collection method based on the past behavioral data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past behavioral data into a generation AI and have the generation AI select the optimal collection method.

[0075] When collecting behavioral data, the collection unit can filter the data based on the child's current interests. For example, the collection unit prioritizes collecting data related to topics in which the child is currently interested. For example, the collection unit collects data related to science in which the child is currently interested. The collection unit can also filter the data based on keywords recently searched by the child. For example, the collection unit collects data based on science-related keywords recently searched by the child. The collection unit can also collect data related to events recently attended by the child. For example, the collection unit collects data related to science events attended by the child. This allows for highly relevant data to be collected by filtering the data based on the child's current interests. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input data related to the child's current interests to the generation AI and have the generation AI perform the filtering.

[0076] When collecting behavioral data, the collection unit can select an appropriate collection means depending on the child's input method. For example, if the child prefers voice input, the collection unit preferentially collects voice data. For example, the collection unit collects behavioral data using voice input. Also, if the child prefers text input, the collection unit can preferentially collect text data. For example, the collection unit collects behavioral data using text input. Also, if the child prefers image input, the collection unit can preferentially collect image data. For example, the collection unit collects behavioral data using image input. This enables efficient data collection by selecting the optimal collection means depending on the input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data regarding the child's input method into the generation AI and cause the generation AI to select the optimal collection means.

[0077] The collection unit can estimate the child's emotions and determine the priority of data to be collected based on the estimated emotions. For example, when the child is excited, the collection unit prioritizes collecting data related to topics the child is interested in. For example, when the child is excited, the collection unit prioritizes collecting data related to science. Also, when the child is tired, the collection unit can prioritize collecting data related to relaxing content. For example, when the child is tired, the collection unit collects data related to relaxing music. Also, when the child is relaxed, the collection unit can collect data with normal priority. For example, when the child is relaxed, the collection unit collects normal learning data. In this way, by prioritizing data based on emotions, important data can be preferentially collected. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input children's emotional data into the generation AI and have the generation AI determine the priority of the data.

[0078] When collecting behavioral data, the collection unit can prioritize collecting highly relevant data by taking into account the child's geographical location information. The collection unit, for example, prioritizes collecting learning content related to the child's current location. For example, the collection unit collects historical data related to the child's current location. The collection unit can also collect data related to places the child has visited in the past. For example, the collection unit collects data related to museums the child has visited in the past. The collection unit can also collect data related to places the child plans to visit in the future. For example, the collection unit collects data related to scientific facilities the child plans to visit in the future. In this way, highly relevant data can be collected by taking the geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the child's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0079] When collecting behavioral data, the collection unit can analyze the child's social media activities and collect related data. For example, the collection unit collects data related to content shared by the child on social media. For example, the collection unit collects science-related content shared by the child on social media. The collection unit can also collect data based on the activities of the child's friends on social media. For example, the collection unit collects science-related content shared by the child's friends. The collection unit can also collect data related to topics in which the child has shown interest on social media. For example, the collection unit collects science-related topics in which the child has shown interest on social media. This allows related data to be collected by analyzing social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the child's social media activity data into the generation AI and cause the generation AI to collect related data.

[0080] When collecting behavioral data, the collection unit can customize the collection method by reflecting the child's past feedback. For example, the collection unit prioritizes using a collection method that the child previously preferred. For example, the collection unit prioritizes using voice input that the child previously preferred. The collection unit can also adjust the collection timing based on feedback provided by the child in the past. For example, the collection unit adjusts the collection timing based on feedback provided by the child in the past. The collection unit can also select a collection method by referring to the child's past feedback. For example, the collection unit selects an optimal collection method based on feedback provided by the child in the past. This allows the collection method to be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the child's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0081] The analysis unit can estimate the child's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the child is excited, the analysis unit provides visually stimulating analysis results. For example, if the child is excited, the analysis unit provides analysis results using colorful graphs and animations. Furthermore, if the child is tired, the analysis unit can provide simple, highly visible analysis results. For example, if the child is tired, the analysis unit provides analysis results using simple text and icons. Furthermore, if the child is relaxed, the analysis unit can provide regular analysis results. For example, if the child is relaxed, the analysis unit provides analysis results using regular graphs and text. This allows the analysis results to be tailored to the child by adjusting the way the analysis is presented based on the emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input a child's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0082] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a detailed analysis on learning data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit performs a simplified analysis on play data with low importance. The analysis unit can also perform an analysis with a moderate level of detail on data with medium importance. For example, the analysis unit performs an analysis with a moderate level of detail on lifestyle data with medium importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the behavioral data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the category of behavioral data. For example, the analysis unit applies an analysis algorithm dedicated to learning to training data. For example, the analysis unit applies a clustering algorithm to training data. The analysis unit can also apply an emotion analysis algorithm to emotion data. For example, the analysis unit applies an emotion classification algorithm to emotion data. The analysis unit can also apply a behavior analysis algorithm to behavioral data. For example, the analysis unit applies a behavior pattern analysis algorithm to behavioral data. This enables highly accurate analysis by applying an analysis algorithm depending on the category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of behavioral data to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0084] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the child's past analysis results. The analysis unit, for example, corrects the current analysis result based on the child's past analysis results. For example, the analysis unit corrects the analysis result of the current learning data based on the past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the child's past analysis results. For example, the analysis unit adjusts the analysis algorithm of the current emotion data based on the past analysis results. The analysis unit can also improve the accuracy of the analysis by using the child's past analysis results. For example, the analysis unit improves the analysis accuracy of the current behavioral data based on the past analysis results. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0085] The analysis unit can estimate the child's emotions and adjust the length of the analysis based on the estimated child's emotions. For example, when the child is excited, the analysis unit provides a short and concise analysis result. For example, when the child is excited, the analysis unit provides a short summary. Also, when the child is tired, the analysis unit can provide a concise and easy-to-read analysis result. For example, when the child is tired, the analysis unit provides a concise report. Also, when the child is relaxed, the analysis unit can provide a detailed analysis result. For example, when the child is relaxed, the analysis unit provides a detailed report. In this way, by adjusting the length of the analysis based on the emotion, it is possible to provide an analysis result suitable for the child. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing 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 the generation AI and cause the generation AI to adjust the length of the analysis.

[0086] During analysis, the analysis unit can determine the priority of analysis based on the time when the behavioral data was collected. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit prioritizes analysis of the most recent learning data. The analysis unit can also analyze current data by referring to past data. For example, the analysis unit analyzes current learning data by referring to past learning data. The analysis unit can also lower the priority of data collected earlier by analyzing it. For example, the analysis unit lowers the priority of play data collected earlier by analyzing it. This enables efficient analysis by determining the priority based on the time of collection. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the behavioral data was collected into the generation AI and have the generation AI determine the analysis priority.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the behavioral data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant learning data. The analysis unit can also postpone analysis of low-relevance data. For example, the analysis unit postpones analysis of low-relevance play data. The analysis unit can also analyze data of medium relevance in an appropriate order. For example, the analysis unit analyzes life data of medium relevance in an appropriate order. This enables efficient analysis by adjusting the order of analysis based on relevance. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the behavioral data to the generation AI and cause the generation AI to adjust the order of analysis.

[0088] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the child's level of expertise. For example, if the child's level of expertise is high, the analysis unit provides analysis results that use a lot of technical terminology. For example, the analysis unit provides analysis results of the training data that use a lot of technical terminology to a child with a high level of expertise. The analysis unit can also provide analysis results in simple language if the child's level of expertise is low. For example, the analysis unit provides analysis results of the training data in simple language to a child with a low level of expertise. The analysis unit can also provide analysis results that use appropriate technical terminology according to the child's level of expertise. For example, the analysis unit provides analysis results of the training data that use appropriate technical terminology according to the level of expertise. In this way, by adjusting the use of technical terminology according to the level of expertise, it is possible to provide analysis results that are suitable for the child. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the child's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terminology.

[0089] The providing unit can estimate the child's emotions and adjust the topic and content presentation method to be provided based on the estimated child's emotions. For example, when the child is excited, the providing unit provides visually stimulating content. For example, when the child is excited, the providing unit provides content using colorful graphics and animations. Furthermore, when the child is tired, the providing unit can also provide simple, highly visible content. For example, when the child is tired, the providing unit provides content using simple text and icons. Furthermore, when the child is relaxed, the providing unit can also provide regular content. For example, when the child is relaxed, the providing unit provides content using regular graphics and text. In this way, by adjusting the presentation method based on the emotions, content suitable for the child can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input a child's emotional data into the generating AI and have the generating AI adjust the way the content is expressed.

[0090] The providing unit can adjust the level of detail of the content provided based on the importance of the topic or content when providing the content. For example, the providing unit provides detailed content for a topic with high importance. For example, the providing unit provides detailed content for a learning topic with high importance. The providing unit can also provide simplified content for a topic with low importance. For example, the providing unit provides simplified content for an entertainment topic with low importance. The providing unit can also provide content with an appropriate level of detail for a topic with medium importance. For example, the providing unit provides content with an appropriate level of detail for a lifestyle topic with medium importance. This enables efficient content provision by adjusting the level of detail based on the importance. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the importance of the topic or content to a generation AI and cause the generation AI to adjust the level of detail of the content provided.

[0091] The providing unit can apply different providing algorithms depending on the topic or content category when providing content. For example, the providing unit applies a providing algorithm dedicated to learning to learning content. For example, the providing unit applies a recommendation algorithm to learning content. The providing unit can also apply a providing algorithm dedicated to entertainment to entertainment content. For example, the providing unit applies a personalized filtering algorithm to entertainment content. The providing unit can also apply a providing algorithm dedicated to emotions to emotional content. For example, the providing unit applies an emotion analysis algorithm to emotional content. This enables content to be provided with high accuracy by applying a providing algorithm depending on the category. Some or all of the above-mentioned processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the topic or content category to the generation AI and cause the generation AI to apply an appropriate providing algorithm.

[0092] The providing unit can improve the accuracy of the content provision by referring to the child's past provision results. The providing unit, for example, corrects the current provision result based on the child's past provision results. For example, the providing unit corrects the provision result of the current learning content based on the past provision results. The providing unit can also adjust the provision algorithm by referring to the child's past provision results. For example, the providing unit adjusts the provision algorithm of the current entertainment content based on the past provision results. The providing unit can also improve the accuracy of the content provision by using the child's past provision results. For example, the providing unit improves the accuracy of the current emotional content provision based on the past provision results. In this way, the accuracy of the content provision is improved by referring to the past provision results. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the past provision results into the generation AI and cause the generation AI to improve the accuracy of the content provision.

[0093] The providing unit can estimate the child's emotions and adjust the topic and length of the content to be provided based on the estimated child's emotions. For example, when the child is excited, the providing unit can provide short, to-the-point content. For example, when the child is excited, the providing unit can provide a short summary. When the child is tired, the providing unit can also provide concise, highly readable content. For example, when the child is tired, the providing unit can provide a concise report. When the child is relaxed, the providing unit can also provide detailed content. For example, when the child is relaxed, the providing unit can provide a detailed report. This allows content suitable for children to be provided by adjusting the length of the content based on the emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the length of the content.

[0094] The providing unit can determine the priority of provision based on the time when the topics and content were collected when providing the content. For example, the providing unit prioritizes providing the latest topics and content. For example, the providing unit prioritizes providing the latest learning topics and content. The providing unit can also provide current topics and content by referring to past topics and content. For example, the providing unit provides current learning topics and content by referring to past learning topics and content. The providing unit can also provide topics and content that were collected earlier by lowering the priority. For example, the providing unit provides entertainment topics and content that were collected earlier by lowering the priority. This enables efficient content provision by determining the priority based on the time of collection. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the time when the topics and content were collected to the generation AI and cause the generation AI to determine the priority of provision.

[0095] The providing unit can adjust the order of provision based on the relevance of the topics and content when providing them. For example, the providing unit prioritizes providing highly relevant topics and content. For example, the providing unit prioritizes providing highly relevant learning topics and content. The providing unit can also postpone providing less relevant topics and content. For example, the providing unit postpones providing less relevant entertainment topics and content. The providing unit can also provide topics and content with a moderate degree of relevance in a moderate order. For example, the providing unit provides lifestyle topics and content with a moderate degree of relevance in a moderate order. This enables efficient content provision by adjusting the order of provision based on relevance. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the relevance of topics and content to a generation AI and cause the generation AI to adjust the order of provision.

[0096] The providing unit can adjust the use of technical terminology in the content provided according to the child's level of expertise. For example, if the child's level of expertise is high, the providing unit provides content that uses a lot of technical terminology. For example, the providing unit provides learning content that uses a lot of technical terminology to a child with a high level of expertise. The providing unit can also provide content in simple language if the child's level of expertise is low. For example, the providing unit provides learning content in simple language to a child with a low level of expertise. The providing unit can also provide content that uses appropriate technical terminology according to the child's level of expertise. For example, the providing unit provides learning content that uses appropriate technical terminology according to the level of expertise. This makes it possible to provide content that is suitable for children by adjusting the use of technical terminology according to the level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the child's level of expertise to the generating AI and cause the generating AI to adjust the use of technical terminology.

[0097] The learning support unit can estimate the child's emotions and adjust the learning support method based on the estimated child's emotions. For example, when the child is excited, the learning support unit provides visually stimulating learning support. For example, when the child is excited, the learning support unit provides learning support using colorful graphics and animations. Furthermore, when the child is tired, the learning support unit can provide simple, highly visible learning support. For example, when the child is tired, the learning support unit provides learning support using simple text and icons. Furthermore, when the child is relaxed, the learning support unit can provide regular learning support. For example, when the child is relaxed, the learning support unit provides learning support using regular graphics and text. This allows the learning support method to be adjusted based on the child's emotions, thereby enabling learning support that is appropriate for the child. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the learning support unit may be performed using, for example, AI, or without AI. For example, the learning support unit can input the child's emotional data into the generation AI and have the generation AI adjust the learning support method.

[0098] When providing learning support, the learning support unit can select an optimal learning support method by analyzing the child's past learning behavior. For example, the learning support unit prioritizes using a learning method that the child previously preferred. For example, the learning support unit prioritizes using online learning materials that the child previously preferred. The learning support unit can also select an optimal learning support method based on the child's past learning behavior. For example, the learning support unit selects individual instruction based on the child's past learning behavior. The learning support unit can also customize the learning support method by referring to the child's past learning behavior. For example, the learning support unit customizes the learning support method based on the child's past learning behavior. This enables efficient learning support by selecting an optimal learning support method based on the child's past learning behavior. Some or all of the above-described processing in the learning support unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning support unit can input data on the child's past learning behavior into a generation AI and cause the generation AI to select an optimal learning support method.

[0099] The learning support unit can customize the learning support means based on the child's current living situation when providing learning support. The learning support unit, for example, adjusts the learning support means according to the child's current living situation. For example, the learning support unit provides online learning materials according to the child's home environment. The learning support unit can also provide optimal learning support means taking the child's living situation into consideration. For example, the learning support unit dispatches a tutor according to the child's school situation. The learning support unit can also customize the learning support means based on the child's living situation. For example, the learning support unit provides a learning app that allows the child to relax according to the child's health condition. In this way, customizing the learning support means based on the living situation enables learning support that is appropriate for the child. Some or all of the above-described processing in the learning support unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning support unit can input the child's living situation data into a generation AI and cause the generation AI to customize the learning support means.

[0100] The learning support unit can improve the learning support method by reflecting the child's feedback during learning support. The learning support unit, for example, improves the learning support method based on the child's feedback. For example, the learning support unit improves the content of online learning materials based on the feedback provided by the child. The learning support unit can also adjust the learning support method by referring to the child's feedback. For example, the learning support unit adjusts the tutor's teaching method based on the feedback provided by the child. The learning support unit can also optimize the learning support method by using the child's feedback. For example, the learning support unit optimizes the functions of a learning app based on the feedback provided by the child. In this way, the learning support method can be optimized by reflecting the feedback. Some or all of the above-mentioned processing in the learning support unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning support unit can input the child's feedback data into a generation AI and cause the generation AI to improve the learning support method.

[0101] The learning support unit can estimate a child's emotions and determine the priority of learning support based on the estimated emotions. For example, when a child is excited, the learning support unit prioritizes learning support for topics that the child is interested in. For example, when a child is excited, the learning support unit prioritizes learning support for science topics. Also, when a child is tired, the learning support unit can prioritize learning support for relaxing topics. For example, when a child is tired, the learning support unit prioritizes learning support for relaxing music topics. Also, when a child is relaxed, the learning support unit can provide learning support with normal priority. For example, when a child is relaxed, the learning support unit prioritizes learning support for normal learning topics. In this way, by determining priorities based on emotions, important learning support can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning support unit can be performed, for example, using AI or without AI. For example, the learning support unit can input a child's emotional data into the generation AI and have the generation AI determine the priorities of learning support.

[0102] When providing learning support, the learning support unit can select the optimal learning support method by taking into account the child's geographical location information. The learning support unit, for example, prioritizes providing learning content related to the child's current location. For example, the learning support unit provides historical data related to the child's current location. The learning support unit can also provide learning support related to places the child has visited in the past. For example, the learning support unit provides learning support related to museums the child has visited in the past. The learning support unit can also provide learning support related to places the child plans to visit in the future. For example, the learning support unit provides learning support related to scientific facilities the child plans to visit in the future. This enables highly relevant learning support by taking the geographical location information into consideration. Some or all of the above-described processing in the learning support unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning support unit can input the child's geographical location information into the generation AI and cause the generation AI to select the optimal learning support method.

[0103] During learning support, the learning support unit can analyze the child's social media activity and suggest learning support methods. The learning support unit, for example, provides learning support related to content shared by the child on social media. For example, the learning support unit provides learning support related to science-related content shared by the child on social media. The learning support unit can also provide learning support based on the activities of the child's friends on social media. For example, the learning support unit provides learning support related to science-related content shared by the child's friends. The learning support unit can also provide learning support related to topics in which the child has shown interest on social media. For example, the learning support unit provides learning support related to science-related topics in which the child has shown interest on social media. This enables highly relevant learning support to be provided by analyzing social media activity. Some or all of the above-described processing in the learning support unit may be performed using, for example, AI, or may be performed without AI. For example, the learning support unit can input the child's social media activity data into a generation AI and cause the generation AI to suggest learning support methods.

[0104] The learning support unit can customize the learning support method by reflecting the child's past feedback when providing learning support. For example, the learning support unit prioritizes the use of learning support methods that the child previously preferred. For example, the learning support unit prioritizes the use of online learning materials that the child previously preferred. The learning support unit can also adjust the learning support method based on the child's past feedback. For example, the learning support unit adjusts the tutor's teaching method based on feedback provided by the child in the past. The learning support unit can also customize the learning support method by referring to the child's past feedback. For example, the learning support unit customizes the functions of a learning app based on feedback provided by the child in the past. This allows the learning support method to be optimized by reflecting the past feedback. Some or all of the above-described processing in the learning support unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning support unit can input the child's past feedback data into a generation AI and cause the generation AI to customize the learning support method.

[0105] The feedback unit can estimate the child's emotions and adjust the feedback method based on the estimated child's emotions. For example, if the child is excited, the feedback unit provides visually stimulating feedback. For example, if the child is excited, the feedback unit provides feedback using colorful graphics or animations. Furthermore, if the child is tired, the feedback unit can provide simple, highly visible feedback. For example, if the child is tired, the feedback unit provides feedback using simple text or icons. Furthermore, if the child is relaxed, the feedback unit can provide regular feedback. For example, if the child is relaxed, the feedback unit provides feedback using regular graphics or text. This allows for feedback that is appropriate for the child by adjusting the feedback method based on the emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI. For example, the feedback unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the feedback method.

[0106] When providing feedback, the feedback unit can analyze the child's past learning progress and select the optimal feedback method. The feedback unit, for example, corrects the current feedback based on the child's past learning progress. For example, the feedback unit corrects the current learning feedback based on the child's past learning progress. The feedback unit can also adjust the feedback method by referring to the child's past learning progress. For example, the feedback unit adjusts the tutor's feedback method based on the child's past learning progress. The feedback unit can also improve the accuracy of the feedback by using the child's past learning progress. For example, the feedback unit improves the feedback accuracy of the learning app based on the child's past learning progress. This enables efficient feedback by selecting the optimal feedback method based on the child's past learning progress. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the child's past learning progress data into the generation AI and cause the generation AI to select the optimal feedback method.

[0107] The feedback unit can customize the feedback means based on the child's current learning situation when providing feedback. The feedback unit, for example, adjusts the feedback means according to the child's current learning situation. For example, the feedback unit provides online feedback according to the child's current learning situation. The feedback unit can also provide the optimal feedback means taking the child's learning situation into consideration. For example, the feedback unit provides face-to-face feedback according to the child's school situation. The feedback unit can also customize the feedback means based on the child's learning situation. For example, the feedback unit provides email feedback according to the child's level of understanding. This enables feedback that is appropriate for the child by customizing the feedback means based on the learning situation. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the child's learning situation data into the generation AI and cause the generation AI to customize the feedback means.

[0108] The feedback unit can improve the feedback method by reflecting the child's feedback when providing feedback. The feedback unit, for example, improves the feedback method based on the child's feedback. For example, the feedback unit improves the content of online feedback based on feedback provided by the child. The feedback unit can also adjust the feedback method by referring to the child's feedback. For example, the feedback unit adjusts the face-to-face feedback method based on feedback provided by the child. The feedback unit can also optimize the feedback method by using the child's feedback. For example, the feedback unit optimizes the content of email feedback based on feedback provided by the child. In this way, the feedback method can be optimized by reflecting the feedback. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the child's feedback data into a generation AI and cause the generation AI to improve the feedback method.

[0109] The feedback unit can estimate the child's emotions and determine the priority of feedback based on the estimated child's emotions. For example, if the child is excited, the feedback unit can prioritize providing feedback related to topics that the child is interested in. For example, the feedback unit can prioritize science feedback when the child is excited. Also, if the child is tired, the feedback unit can prioritize feedback related to relaxing topics. For example, the feedback unit can prioritize relaxing music feedback when the child is tired. Also, the feedback unit can provide feedback with a normal priority when the child is relaxed. For example, the feedback unit can provide normal learning feedback when the child is relaxed. In this way, by determining the priority based on emotions, important feedback can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI or without AI. For example, the feedback unit can input a child's emotional data into the generation AI and have the generation AI determine the priority of the feedback.

[0110] The feedback unit can select the optimal feedback method by taking into account the child's geographical location information when providing feedback. For example, the feedback unit prioritizes providing feedback related to the child's current location. For example, the feedback unit provides historical feedback related to the child's current location. The feedback unit can also provide feedback related to places the child has visited in the past. For example, the feedback unit provides feedback related to museums the child has visited in the past. The feedback unit can also provide feedback related to places the child plans to visit in the future. For example, the feedback unit provides feedback related to scientific facilities the child plans to visit in the future. This enables highly relevant feedback by taking the geographical location information into consideration. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input the child's geographical location information to the generation AI and cause the generation AI to select the optimal feedback method.

[0111] When providing feedback, the feedback unit may analyze the child's social media activity and suggest a means of providing feedback. The feedback unit may, for example, provide feedback related to content shared by the child on social media. For example, the feedback unit may provide feedback related to science-related content shared by the child on social media. The feedback unit may also provide feedback based on the activities of the child's friends on social media. For example, the feedback unit may provide feedback related to science-related content shared by the child's friends. The feedback unit may also provide feedback related to topics in which the child has shown interest on social media. For example, the feedback unit may provide feedback related to science-related topics in which the child has shown interest on social media. This enables highly relevant feedback to be provided by analyzing social media activity. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input the child's social media activity data into a generation AI and cause the generation AI to suggest a means of providing feedback.

[0112] When providing feedback, the feedback unit can customize the feedback method by reflecting the child's past feedback. For example, the feedback unit prioritizes the use of feedback methods that the child previously preferred. For example, the feedback unit prioritizes the use of online feedback that the child previously preferred. The feedback unit can also adjust the feedback method based on the child's past feedback. For example, the feedback unit adjusts the face-to-face feedback method based on feedback provided by the child in the past. The feedback unit can also customize the feedback method by referring to the child's past feedback. For example, the feedback unit customizes the content of email feedback based on feedback provided by the child in the past. This allows the feedback method to be optimized by reflecting past feedback. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the child's past feedback data into the generation AI and cause the generation AI to customize the feedback method.

[0113] The provision unit supports multiple languages ​​and can provide content based on the culture and learning curriculum of each region. For example, the provision unit provides English content to children who want to learn English. For example, the provision unit provides English learning materials. Furthermore, by incorporating topics based on the culture of the region, children can learn from more familiar themes and environments. For example, the provision unit provides topics related to the history and culture of the region. This allows for multilingual support and the provision of content based on the culture and learning curriculum of each region. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input data related to the culture and learning curriculum of each region into the generation AI and cause the generation AI to provide appropriate content.

[0114] The learning support unit can provide a rich learning experience using AR / VR technology. For example, the learning support unit allows children to learn by actually using their hands by experiencing a science experiment in virtual reality. For example, the learning support unit allows children to experience a science experiment using a virtual reality headset. The learning support unit can also allow children to experience an interactive simulation using an augmented reality application. For example, the learning support unit can use an augmented reality application to allow children to learn while manipulating a 3D model. The learning support unit can also allow children to experience historical events using AR / VR technology. For example, the learning support unit can recreate historical events in virtual reality, allowing children to learn as if they were actually there. This makes it possible to provide a rich learning experience by utilizing AR / VR technology. Some or all of the above-mentioned processing in the learning support unit may be performed using AI, for example, or may be performed without AI. For example, the learning support unit can have a generative AI execute the design of a learning experience using AR / VR technology. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the child's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14, and executes an emotion estimation algorithm. For example, the collection unit calculates an emotion score using the specific processing unit 290 of the data processing device 12, and adjusts the collection timing. For example, the collection unit collects the child's biometric data using the sensor of the smart device 14, and executes an emotion estimation algorithm. For example, the collection unit analyzes the emotion data in real time using the specific processing unit 290 of the data processing device 12, and immediately grasps changes in emotion. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the child's facial expressions and voice using the camera 42 and microphone 238 of the smart glasses 214 and executes an emotion estimation algorithm. For example, the collection unit calculates an emotion score using the specific processing unit 290 of the data processing device 12 and adjusts the collection timing. For example, the collection unit collects the child's biometric data using the sensor of the smart glasses 214 and executes an emotion estimation algorithm. For example, the collection unit analyzes the emotion data in real time using the specific processing unit 290 of the data processing device 12 and immediately grasps changes in emotion. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the child's facial expressions and voice using the camera 42 and microphone 238 of the headset-type terminal 314 and executes an emotion estimation algorithm. For example, the collection unit calculates an emotion score using the specific processing unit 290 of the data processing device 12 and adjusts the collection timing. For example, the collection unit collects the child's biometric data using the sensor of the headset-type terminal 314 and executes an emotion estimation algorithm. For example, the collection unit analyzes the emotion data in real time using the specific processing unit 290 of the data processing device 12 and immediately grasps changes in emotion. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the child's facial expressions and voice using the camera 42 and microphone 238 of the robot 414 and executes an emotion estimation algorithm. For example, the collection unit calculates an emotion score using the specific processing unit 290 of the data processing device 12 and adjusts the collection timing. For example, the collection unit collects the child's biometric data using the sensor of the robot 414 and executes an emotion estimation algorithm. For example, the collection unit analyzes the emotion data in real time using the specific processing unit 290 of the data processing device 12 and immediately grasps changes in emotion.

[0115] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0116] The analysis unit can estimate a child's learning style and adjust the way in which analysis results are presented based on the estimated learning style. For example, analysis results that make extensive use of graphs and charts can be provided to a child with a visual learning style. Analysis results that include audio explanations can also be provided to a child with an auditory learning style. Furthermore, analysis results that include interactive elements can be provided to a child with a tactile learning style. In this way, by providing analysis results that are tailored to each child's learning style, comprehension can be improved.

[0117] The provision unit can analyze the child's learning history and provide learning methods that have been effective in the past with priority. For example, video learning materials can be provided with priority to a child for whom video learning materials have been effective in the past. Game-style learning materials can also be provided with priority to a child for whom game-style learning materials have been effective in the past. Furthermore, text-style learning materials can also be provided with priority to a child for whom text-style learning materials have been effective in the past. This makes it possible to maximize learning effectiveness by providing the optimal learning method based on the child's past learning history.

[0118] The learning support unit can set a child's learning goals and customize the content of learning support based on those goals. For example, a child aiming to achieve high scores in a specific subject can be provided with learning support specialized for that subject. A child wanting to master a specific skill can also be provided with learning support related to that skill. Furthermore, a child aiming to improve their overall academic ability can be provided with balanced learning support. This makes it possible to support goal achievement by providing support according to the child's learning goals.

[0119] The feedback unit can monitor a child's learning progress in real time and provide immediate feedback as needed. For example, it can provide feedback on whether the child is correct or incorrect immediately after solving a problem. It can also provide immediate advice if the child experiences difficulty while learning. Furthermore, it can provide immediate feedback praising the child when he or she achieves a goal. This makes it possible to increase a child's motivation to learn through real-time feedback.

[0120] The provision unit can analyze a child's learning environment and provide advice to provide the optimal learning environment. For example, for a child who learns effectively in a quiet environment, it can recommend that the child study in a quiet place. Also, for a child who learns effectively while listening to music, it can provide appropriate music. Furthermore, it can provide group learning opportunities for a child who learns effectively in a group. In this way, by optimizing a child's learning environment, it is possible to improve learning effectiveness.

[0121] The collection unit can estimate the child's emotions and adjust the difficulty of the learning content based on the estimated emotions. For example, if the child is excited, it can provide content with a high level of difficulty. If the child is tired, it can provide content with a low level of difficulty. Furthermore, if the child is relaxed, it can provide content with a normal level of difficulty. This makes it possible to maximize the effectiveness of learning by providing content with a level of difficulty that corresponds to the child's emotions.

[0122] The analysis unit can analyze a child's learning patterns and suggest an optimal learning schedule. For example, if a child is most effective at learning in the morning, it can recommend morning study. Also, if a child is most effective at learning in the evening, it can recommend evening study. Furthermore, it can suggest short study sessions for children who benefit from short, concentrated study sessions. In this way, it is possible to improve learning effectiveness by suggesting a schedule that suits a child's learning patterns.

[0123] The providing unit can estimate the child's emotions and adjust the format of the learning content based on the estimated emotions. For example, if the child is excited, interactive content can be provided. If the child is tired, relaxing video content can be provided. Furthermore, if the child is relaxed, regular text content can be provided. This maximizes the effectiveness of learning by providing content in a format that suits the child's emotions.

[0124] The learning support department can analyze children's learning progress and introduce a reward system based on progress. For example, badges or points can be awarded to children who achieve certain goals. Special rewards can also be offered to children who continue studying continuously. Furthermore, team rewards can be offered to children who study collaboratively with friends. This can increase children's motivation to learn through the reward system.

[0125] The feedback unit can estimate the child's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the child is excited, it can emphasize positive feedback. If the child is tired, it can provide encouraging feedback. Furthermore, if the child is relaxed, it can provide detailed feedback. In this way, by providing feedback according to the child's emotions, it is possible to maintain the child's motivation to learn.

[0126] The processing flow of the second embodiment will be briefly explained below.

[0127] Step 1: The collection unit collects the child's behavioral data. The child's behavioral data includes learning behavior, play behavior, and daily life behavior. The collection unit uses sensors to collect the child's behavioral data, as well as input data from devices used by the child and from parents and teachers. For example, data is collected from tablets and smartphones, and sensors detect the child's movements and record them as behavioral data. Parents and teachers observe the child's behavior and enter the data into the collection unit. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the child's interests and abilities. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, data mining techniques are used to extract patterns from the behavioral data, and statistical analysis is used to evaluate the child's interests and abilities. Machine learning algorithms are used to analyze the behavioral data and identify topics of interest and behavioral patterns. Step 3: The provider provides customized topics and content based on the analysis results obtained by the analyzer. The topics and content provided include learning materials, interactive learning tools, entertainment content, etc. For example, science topics are provided based on the child's interests, and content of a difficulty level appropriate to the child's ability is provided. Entertainment content is also provided, allowing children to learn while having fun. Step 4: The learning support department provides learning support based on the content provided by the provision department. Learning support is provided through methods such as online learning materials, interactive learning tools, and tutors. For example, online learning materials are provided so that children can learn at their own pace. Interactive learning tools are designed to make learning feel like a game. Tutors provide individual support for children's learning and improve their understanding. Step 5: The feedback unit provides the parent with the learning progress obtained by the learning support unit. The feedback is given in the form of a progress report, advice, a proposal for the next step, etc. For example, the learning progress may be reported to the parent and advice may be given to the parent. As the next step, a proposal may be made as to what kind of learning should be done.

[0128] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0129] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0146] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0149] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0150] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0155] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0158] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0162] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 7, a 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.

[0166] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0168] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0170] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0171] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0172] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0173] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0174] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0175] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0176] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0177] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0178] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0179] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0180] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0181] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0182] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0183] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0184] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0185] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0186] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0189] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0190] 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.

[0191] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0192] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0193] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0194] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0195] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0196] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0197] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0198] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0199] [Explanation of symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects behavioral data of children; an analysis unit that analyzes the data collected by the collection unit to identify the child's specific interests and abilities; a providing unit that provides customized topics and content based on the analysis results obtained by the analyzing unit; a learning support unit that provides learning support based on the content provided by the providing unit; a feedback unit that feeds back to the parent the progress of learning obtained by the learning support unit. A system characterized by:

2. The collecting unit Estimating a child's emotions in a specific way and adjusting the timing of behavioral data collection based on the estimated child's emotions The system of claim 1 .

3. The collecting unit Analyze children's past behavioral data and select appropriate collection methods The system of claim 1 .

4. The collecting unit As behavioral data is collected, it is filtered based on the child's current specific interests and concerns. The system of claim 1 .

5. The collecting unit When collecting behavioral data, choose the appropriate collection method based on the child's input method. The system of claim 1 .

6. The collecting unit Inferring a child's emotions in a specific way and prioritizing data collection based on the inferred emotions The system of claim 1 .

7. The collecting unit When collecting behavioral data, prioritize the collection of relevant data based on the child's geographic location. The system of claim 1 .

8. The collecting unit When collecting behavioral data, analyze children's social media activity in a specific way and collect relevant data. The system of claim 1 .

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