system

Abstract

The system according to this embodiment aims to identify a child's potential talents and areas of expertise and provide professional coaching based on them. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data on children. The analysis unit analyzes the data collected by the collection unit and identifies the children's interests and areas of expertise. The generation unit automatically generates relevant educational materials based on the interests and areas of expertise identified by the analysis unit. The provision unit provides professional coaching based on the educational materials generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] <00​​​​​​​​​​​The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data on children. The analysis unit analyzes the data collected by the collection unit to identify the children's interests and areas of expertise. The generation unit automatically generates relevant educational materials based on the interests and areas of expertise identified by the analysis unit. The provision unit provides professional coaching based on the educational materials generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify a child's potential talents and areas of expertise, and provide professional coaching based on them. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The learning support system according to an embodiment of the present invention is a system that uses generative AI to comprehensively analyze children's data and identify their potential talents and areas of expertise. This learning support system collects children's data, which is then analyzed by the generative AI to identify the children's interests and areas of expertise. Furthermore, the generative AI automatically generates relevant learning materials based on the identified interests and areas of expertise. Finally, the generative AI provides expert coaching. This mechanism solves the problem of a lack of learning materials that motivate and engage children. By analyzing children's interests and automatically generating relevant learning materials, the generative AI enhances children's motivation to learn and supports effective learning. For example, if a child is interested in a particular field, generating and providing learning materials related to that field can increase the child's motivation to learn. In addition, the generative AI provides expert coaching to maximize children's talents and realize a learning experience tailored to individual needs. Thus, the learning support system can comprehensively analyze children's data, identify their potential talents and areas of expertise, and provide expert coaching to realize a learning experience tailored to individual needs.

[0029] The learning support system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data on children. This data includes, but is not limited to, learning history, interests, and behavioral patterns. For example, the collection unit records past test results and study time to collect learning history. The collection unit can also analyze survey results and behavioral data to collect interests. Furthermore, the collection unit can record the frequency and timing of learning to collect behavioral patterns. The analysis unit analyzes the data collected by the collection unit to identify children's interests and areas of expertise. For example, the analysis unit extracts patterns from the collected data using data mining techniques. The analysis unit can also analyze data trends using statistical analysis. Furthermore, the analysis unit can identify children's interests and areas of expertise using machine learning algorithms. The generation unit automatically generates relevant learning materials based on the interests and areas of expertise identified by the analysis unit. For example, the generation unit generates learning materials related to children's interests using text generation AI. Furthermore, the generation unit can use video generation AI to generate visually appealing educational materials. Additionally, the generation unit can use interactive content generation AI to generate educational materials that children can participate in. The delivery unit provides expert coaching based on the educational materials generated by the generation unit. For example, the delivery unit can provide individual instruction. The delivery unit can also conduct group sessions. Furthermore, the delivery unit can provide online coaching. As a result, the learning support system according to this embodiment can comprehensively analyze children's data, identify their potential talents and areas of expertise, and provide expert coaching to realize a learning experience tailored to individual needs.

[0030] The data collection unit collects data on children. This data includes, but is not limited to, learning history, interests, and behavioral patterns. For example, to collect learning history, the unit records past test results and study time. Specifically, it collects school report cards and log data from online learning platforms to understand how well children perform in different subjects. The unit can also analyze survey results and behavioral data to collect information on interests. For example, it records what books children read, what websites they visit, and what apps they use, and uses this data to identify their interests. Furthermore, the unit can record the frequency and timing of learning to collect behavioral patterns. For example, it records when children study and how long they spend studying, and uses this data to analyze behavioral patterns. This allows the unit to collect diverse data on children's learning and gain a detailed understanding of their individual learning styles and interests. In addition, the unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit to identify children's interests and areas of expertise. For example, the analysis unit extracts patterns from the collected data using data mining techniques. Specifically, it uses clustering algorithms to group children's learning history and behavioral patterns, identifying groups with common characteristics. The analysis unit can also analyze data trends using statistical analysis. For example, it uses regression analysis to clarify the relationship between children's learning outcomes and study time, and proposes optimal study time. Furthermore, the analysis unit can identify children's interests and areas of expertise using machine learning algorithms. For example, it uses supervised learning to build models that predict children's interests and areas of expertise from past data, and then makes predictions based on new data. This allows the analysis unit to quickly and accurately analyze collected data and identify children's interests and areas of expertise. Additionally, the analysis unit can utilize past data and statistical information to predict long-term learning outcomes and perform trend analysis. For example, it can predict growth trends in specific subjects or skills based on past learning data and formulate future learning plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to predict long-term learning outcomes and detect anomalies, thereby improving the reliability and effectiveness of the entire system.

[0032] The generation unit automatically generates relevant learning materials based on the interests and areas of expertise identified by the analysis unit. For example, the generation unit uses text generation AI to generate materials related to children's interests. Specifically, it uses natural language processing technology to generate articles and workbooks on topics that children are interested in. The generation unit can also use video generation AI to generate visually engaging materials. For example, it uses animation and infographics to provide information in a way that is easy for children to understand. Furthermore, the generation unit can use interactive content generation AI to generate materials that children can participate in. For example, it can generate quizzes and simulation games to provide an environment where children can learn while having fun. In this way, the generation unit can automatically generate a variety of learning materials tailored to children's interests and areas of expertise, and respond to their individual learning needs. In addition, the generation unit can evaluate the quality of the generated materials and make corrections and improvements as needed. For example, it can evaluate the content and difficulty level of the generated materials and adjust them according to the child's level of understanding and learning progress. The generation unit can also continuously update the generated materials to reflect the latest information and topics. In this way, the generation unit can always provide high-quality learning materials based on the latest information, thereby increasing children's motivation to learn.

[0033] The service provider offers expert coaching based on the materials generated by the production unit. For example, the service provider provides individualized instruction. Specifically, they create individualized instruction plans tailored to each child's learning progress and understanding, with expert coaches providing one-on-one instruction. The service provider can also conduct group sessions. For example, they can divide children with similar interests and strengths into groups, allowing them to learn and discuss collaboratively, stimulating each other's learning. Furthermore, the service provider can provide online coaching. For example, they can provide expert instruction to children in remote locations via video calls or chat. This allows the service provider to quickly provide appropriate instructions to each user, minimizing the risk of disaster. Additionally, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of their instruction. For example, they can revise teaching methods and materials based on feedback from children who have received instruction. The service provider can also reliably transmit information using multiple communication methods. For example, they can ensure important information is delivered reliably using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to quickly and reliably provide users with action instructions, minimizing the risk of disaster.

[0034] The data collection unit can collect data on learning history, interests, and behavioral patterns. For example, to collect learning history, the data collection unit can record past test results and study time. For example, the data collection unit can save past test results to a database and record study time using a time tracking tool. The data collection unit can also analyze survey results and behavioral data to collect interests. For example, the data collection unit can aggregate survey results and analyze behavioral data using a log analysis tool. The data collection unit can also record the frequency and timing of learning to collect behavioral patterns. For example, the data collection unit can record the frequency of learning using a calendar app and detect the timing of learning using sensors. This allows the data collection unit to collect diverse data, enabling more detailed analysis. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input learning history data into AI, which can automate data collection.

[0035] The analysis unit can analyze the collected data and identify a child's potential talents and areas of expertise. For example, the analysis unit can extract patterns from the collected data using data mining techniques. For instance, it can use data mining algorithms to find common patterns in learning history. The analysis unit can also analyze data trends using statistical analysis. For example, it can use statistical software to analyze interest data and understand trends. Furthermore, the analysis unit can use machine learning algorithms to identify a child's interests and areas of expertise. For example, it can use machine learning models to predict a child's areas of expertise from behavioral patterns. This allows the analysis unit to identify a child's talents and areas of expertise, enabling the provision of learning experiences tailored to individual needs. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the collected data into a generative AI, which can then analyze the data.

[0036] The generation unit can automatically generate relevant educational materials based on identified interests and areas of expertise. For example, the generation unit can use a text generation AI to generate educational materials related to a child's interests. For instance, it can use a text generation AI to generate science materials for a child interested in science. The generation unit can also use a video generation AI to generate visually engaging educational materials. For example, it can use a video generation AI to generate history video materials for a child interested in history. The generation unit can also use an interactive content generation AI to generate educational materials that children can participate in. For example, it can use an interactive content generation AI to generate interactive mathematics materials for a child interested in mathematics. This allows the generation unit to automatically generate educational materials, thereby stimulating children's interest and increasing their motivation to learn. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may not. For example, the generation unit can input data on identified interests and areas of expertise into a generation AI, which can then generate educational materials.

[0037] The service provider can offer professional coaching based on the generated materials. For example, the service provider can provide individual instruction. For instance, it can create individualized instruction plans based on a child's strengths and provide individualized instruction. The service provider can also conduct group sessions. For example, it can divide children with similar interests into groups and conduct group sessions. Furthermore, the service provider can provide online coaching. For example, it can provide coaching to children through an online platform. This allows the service provider to maximize children's potential through professional coaching. Some or all of the processes described above in the service provider may be performed using AI, or not. For example, the service provider can input the generated materials into an AI, which can then automate the coaching process.

[0038] The data collection unit can analyze a child's past learning history and select an appropriate data collection method. For example, the data collection unit can identify the time of day when a child can learn most effectively based on their past learning history and collect data during that time. For example, the data collection unit can analyze past test results and study times to identify the optimal study time. The data collection unit can also prioritize the collection of data related to topics that children are likely to be interested in, based on their past learning history. For example, the data collection unit can analyze past survey results and behavioral data to identify topics that children are likely to be interested in. The data collection unit can also analyze past learning history and focus on collecting data in areas where children struggle. For example, the data collection unit can analyze past test results and study times to identify areas where children struggle. This allows for effective data collection by selecting the optimal data collection method based on past learning history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past learning history data into a generating AI, which can then select a data collection method.

[0039] The data collection unit can filter data based on the child's current learning status and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to ongoing tasks based on the child's current learning status. For example, the data collection unit can monitor the child's current learning progress and collect data related to ongoing tasks. The data collection unit can also collect data on relevant learning materials and resources based on the child's areas of interest. For example, the data collection unit can identify the child's areas of interest and collect data related to those areas. The data collection unit can also filter and collect necessary data in real time according to the child's learning progress. For example, the data collection unit can monitor learning progress in real time and filter and collect necessary data. This allows for the collection of highly relevant data by filtering data based on the child's current learning status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the child's current learning status and areas of interest into a generating AI, which can then filter the data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the child's geographical location information during data collection. For example, if the child is at school, the data collection unit can collect data related to the school curriculum. For example, if the child is at school, the data collection unit can collect data related to the school curriculum. For example, if the child is at home, the data collection unit can collect data suitable for home learning. For example, if the child is at the library, the data collection unit can collect data related to library resources. For example, if the child is at the library, the data collection unit can collect data related to library resources. By considering geographical location information, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's geographical location information into a generating AI, and the generating AI can collect the data.

[0041] The data collection unit can analyze a child's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to topics the child has shown interest in on social media. The data collection unit can also analyze the activity of accounts the child follows on social media and collect relevant data. The data collection unit can also collect relevant data based on content the child has shared on social media. For example, the data collection unit can analyze content the child has shared on social media and collect relevant data. This allows data related to the child's interests to be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the child's social media activity into a generating AI, which can then collect the data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data. For example, the analysis unit can perform a detailed statistical analysis on highly important data. The analysis unit can also perform a simplified analysis on less important data. For example, the analysis unit can perform a simplified data mining on less important data. The analysis unit can also perform an analysis with an appropriate level of detail on data of moderate importance. For example, the analysis unit can apply a machine learning algorithm with an appropriate level of detail to data of moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a pattern recognition algorithm to learning history data. For instance, the analysis unit uses a pattern recognition algorithm to find common patterns in the learning history data. The analysis unit can also apply a clustering algorithm to interest data. For example, the analysis unit uses a clustering algorithm to group the data. The analysis unit can also apply a time series analysis algorithm to behavior pattern data. For example, the analysis unit uses a time series analysis algorithm to analyze the fluctuations in the behavior pattern data. By applying an appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into a generative AI, and the generative AI can apply an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may prioritize the analysis of the most recent data to grasp the latest trends. The analysis unit can also emphasize the most recent data while referring to past data. For example, the analysis unit may emphasize the most recent data while referring to past data. The analysis unit can also focus on analyzing data collected during a specific period. For example, the analysis unit may focus on analyzing data collected during a specific period to grasp the trends during that period. This makes it possible to perform analysis that emphasizes the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the data collection period into the generative AI, and the generative AI can determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may prioritize the analysis of data with high relevance based on the correlation between the data. The analysis unit may also analyze data with moderate relevance next. For example, the analysis unit may analyze data with moderate relevance next based on co-occurrence frequency. The analysis unit may also analyze data with low relevance last. For example, the analysis unit may analyze data with low relevance last based on relevance score. By adjusting the order of analysis based on the relevance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI, and the generative AI can adjust the order of analysis.

[0046] The generation unit can adjust the level of detail of the materials based on identified interests and areas of expertise when generating the materials. For example, the generation unit can generate detailed materials for areas of high interest to children. For example, the generation unit can generate materials that include detailed text and videos for areas of high interest to children. The generation unit can also generate materials that include applied content for areas of expertise to children. For example, the generation unit can generate materials that include applied problems and tasks for areas of expertise to children. The generation unit can also generate materials that include basic content for areas of low interest to children. For example, the generation unit can generate materials that include basic concepts and examples for areas of low interest to children. In this way, by adjusting the level of detail of the materials based on interests and areas of expertise, more appropriate materials can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data on identified interests and areas of expertise into the generation AI, and the generation AI can adjust the level of detail of the materials.

[0047] The generation unit can apply different generation algorithms to the category of interest or area of ​​expertise when generating educational materials. For example, for the science field, the generation unit can generate educational materials that include experimental videos. For example, for the science field, the generation unit can generate educational materials that include experimental videos and experimental procedures. The generation unit can also generate educational materials that include reading comprehension questions for the literature field. For example, for the literature field, the generation unit can generate educational materials that include reading comprehension questions and explanations of literary works. The generation unit can also generate educational materials that include practice problems for the mathematics field. For example, for the mathematics field, the generation unit can generate educational materials that include practice problems and solutions and explanations. This improves the quality of the educational materials by applying an appropriate generation algorithm according to the category of interest or area of ​​expertise. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the category of interest or area of ​​expertise into the generation AI, and the generation AI can apply an appropriate generation algorithm.

[0048] The generation unit can determine the priority of educational materials based on the timing of collection of interests and areas of expertise when generating educational materials. For example, the generation unit can generate educational materials based on the most recent interests and areas of expertise. For example, the generation unit can generate educational materials that include the latest information based on the most recent interests and areas of expertise. The generation unit can also prioritize the latest information while referring to past interests and areas of expertise. For example, the generation unit can generate educational materials that prioritize the latest information while referring to past interests and areas of expertise. The generation unit can also generate educational materials based on interests and areas of expertise collected during a specific period. For example, the generation unit can generate educational materials that include information from a specific period based on interests and areas of expertise collected during that period. This allows for the provision of educational materials that prioritize the latest information by determining the priority of educational materials based on the collection timing. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the timing of collection of interests and areas of expertise into the generation AI, and the generation AI can determine the priority of educational materials.

[0049] The generation unit can adjust the order of learning materials based on the relevance of interests and areas of expertise during material generation. For example, the generation unit can generate learning materials based on highly relevant interests and areas of expertise. For example, the generation unit can generate learning materials containing relevant information based on highly relevant interests and areas of expertise. The generation unit can also generate learning materials based on moderately relevant interests and areas of expertise. For example, the generation unit can generate learning materials containing relevant information based on moderately relevant interests and areas of expertise. The generation unit can also generate learning materials based on lowly relevant interests and areas of expertise. For example, the generation unit can generate learning materials containing relevant information based on lowly relevant interests and areas of expertise. This allows for a smoother learning flow by adjusting the order of learning materials based on relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of interests and areas of expertise into the generation AI, which can then adjust the order of the learning materials.

[0050] The service provider can select the optimal coaching method by referring to the child's past learning history when providing coaching. For example, the service provider can identify the most effective learning method for the child from their past learning history and provide coaching using that method. For example, the service provider can analyze past test results and study time to identify the optimal learning method. The service provider can also provide coaching related to topics that the child is likely to be interested in, based on their past learning history. For example, the service provider can analyze past survey results and behavioral data to identify topics that the child is likely to be interested in. The service provider can also analyze past learning history and provide focused coaching in areas where the child struggles. For example, the service provider can analyze past test results and study time to identify areas where the child struggles. This allows for effective coaching by selecting the optimal coaching method based on past learning history. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input data on past learning history into a generating AI, which can then select the optimal coaching method.

[0051] The service provider can customize the coaching methods based on the child's current learning situation when providing coaching. For example, the service provider can provide coaching related to ongoing tasks based on the child's current learning situation. For example, the service provider can monitor the child's current learning progress and provide coaching related to ongoing tasks. The service provider can also provide necessary support in real time according to the child's learning progress. For example, the service provider can monitor the child's learning progress in real time and provide necessary support. The service provider can also provide coaching of appropriate difficulty based on the child's current level of understanding. For example, the service provider can assess the child's level of understanding and provide coaching of appropriate difficulty. This allows for more appropriate support to be provided by customizing the coaching methods based on the child's current learning situation. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the child's current learning situation into a generating AI, which can then customize the coaching methods.

[0052] The service provider can select the most appropriate coaching method when providing coaching, taking into account the child's geographical location. For example, if the child is at school, the service provider can provide coaching related to the school curriculum. For example, if the child is at school, the service provider can provide coaching related to the school curriculum. The service provider can also provide coaching suitable for home learning if the child is at home. For example, if the child is at the library, the service provider can provide coaching related to library resources. For example, if the child is at the library, the service provider can provide coaching related to library resources. By considering geographical location, more relevant coaching can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the child's geographical location into a generating AI, which can then select the most appropriate coaching method.

[0053] The service provider can analyze a child's social media activity and propose coaching methods when providing coaching. For example, the service provider can provide coaching related to topics the child has shown interest in on social media. The service provider can also analyze the activity of accounts the child follows on social media and provide relevant coaching. The service provider can also provide relevant coaching based on content the child has shared on social media. For example, the service provider can analyze content the child has shared on social media and provide relevant coaching. In this way, by analyzing social media activity, the service provider can provide coaching related to the child's interests. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the child's social media activity into a generating AI, which can then propose coaching methods.

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

[0055] The data collection unit can adjust its data collection methods based on the child's learning environment. For example, if the child is at school, it can collect data related to the school curriculum. For example, the data collection unit can collect data related to the school curriculum when the child is at school. It can also collect data suitable for home learning when the child is at home. For example, if the child is at the library, it can collect data related to library resources. For example, the data collection unit can collect data related to library resources when the child is at the library. By adjusting the data collection methods based on the learning environment, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data on the child's learning environment into a generating AI, which can then adjust the data collection methods.

[0056] The analysis unit can adjust its analysis method based on the data source. For example, for data collected from schools, it can perform analysis based on the school curriculum. For example, for data collected from homes, it can perform analysis suitable for home study. For example, for data collected from libraries, it can perform analysis based on library resources. By adjusting the analysis method based on the data source, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information about the data source into a generating AI, which can then adjust the analysis method.

[0057] The generation unit can adjust the method of generating learning materials based on the child's learning progress. For example, if the child's learning progress is fast, it can generate learning materials that include applied content. For example, when the child's learning progress is fast, the generation unit can generate learning materials that include applied problems and tasks. Also, if the child's learning progress is slow, it can generate learning materials that include basic content. For example, when the child's learning progress is slow, the generation unit can generate learning materials that include basic concepts and examples. Also, if the child's learning progress is average, it can generate learning materials that include standard content. For example, when the child's learning progress is average, the generation unit can generate learning materials that include standard problems and tasks. By adjusting the method of generating learning materials based on learning progress, more effective learning becomes possible. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the child's learning progress into a generation AI, which can then adjust the method of generating learning materials.

[0058] The service provider can adjust the content of coaching based on the child's learning history. For example, it can identify the most effective learning method for the child from their past learning history and provide coaching in that way. For example, the service provider can analyze past test results and study time to identify the optimal learning method. It can also provide coaching related to topics that the child is likely to be interested in, based on their past learning history. For example, the service provider can analyze past survey results and behavioral data to identify topics that the child is likely to be interested in. It can also analyze past learning history and provide focused coaching in areas where the child struggles. For example, the service provider can analyze past test results and study time to identify areas where the child struggles. This allows for effective coaching by selecting the optimal coaching method based on past learning history. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input data on past learning history into a generating AI, which can then select the optimal coaching method.

[0059] The service provider can customize coaching methods based on the child's current learning situation. For example, it can provide coaching related to ongoing tasks based on the child's current learning situation. For example, the service provider can monitor the child's current learning progress and provide coaching related to ongoing tasks. The service provider can also provide necessary support in real time according to the child's learning progress. For example, it can monitor the child's learning progress in real time and provide necessary support. The service provider can also provide coaching of appropriate difficulty based on the child's current level of understanding. For example, it can assess the child's level of understanding and provide coaching of appropriate difficulty. This allows for more appropriate support to be provided by customizing coaching methods based on the child's current learning situation. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the child's current learning situation into a generating AI, which can then customize the coaching methods.

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

[0061] Step 1: The data collection unit collects data on the children. This data includes learning history, interests, and behavioral patterns. For example, the data collection unit records past test results and study time, analyzes survey results and behavioral data, and records the frequency and timing of learning. Step 2: The analysis unit analyzes the data collected by the collection unit to identify children's interests and areas of expertise. The analysis unit uses data mining techniques, statistical analysis, and machine learning algorithms to extract patterns from the collected data and analyze data trends. Step 3: The generation unit automatically generates relevant educational materials based on the interests and areas of expertise identified by the analysis unit. The generation unit uses text generation AI, video generation AI, and interactive content generation AI to generate educational materials related to the child's interests. Step 4: The delivery department provides professional coaching based on the materials generated by the production department. The delivery department conducts individual lessons, group sessions, and online coaching.

[0062] (Example of form 2) The learning support system according to an embodiment of the present invention is a system that uses generative AI to comprehensively analyze children's data and identify their potential talents and areas of expertise. This learning support system collects children's data, which is then analyzed by the generative AI to identify the children's interests and areas of expertise. Furthermore, the generative AI automatically generates relevant learning materials based on the identified interests and areas of expertise. Finally, the generative AI provides expert coaching. This mechanism solves the problem of a lack of learning materials that motivate and engage children. By analyzing children's interests and automatically generating relevant learning materials, the generative AI enhances children's motivation to learn and supports effective learning. For example, if a child is interested in a particular field, generating and providing learning materials related to that field can increase the child's motivation to learn. In addition, the generative AI provides expert coaching to maximize children's talents and realize a learning experience tailored to individual needs. Thus, the learning support system can comprehensively analyze children's data, identify their potential talents and areas of expertise, and provide expert coaching to realize a learning experience tailored to individual needs.

[0063] The learning support system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data on children. This data includes, but is not limited to, learning history, interests, and behavioral patterns. For example, the collection unit records past test results and study time to collect learning history. The collection unit can also analyze survey results and behavioral data to collect interests. Furthermore, the collection unit can record the frequency and timing of learning to collect behavioral patterns. The analysis unit analyzes the data collected by the collection unit to identify children's interests and areas of expertise. For example, the analysis unit extracts patterns from the collected data using data mining techniques. The analysis unit can also analyze data trends using statistical analysis. Furthermore, the analysis unit can identify children's interests and areas of expertise using machine learning algorithms. The generation unit automatically generates relevant learning materials based on the interests and areas of expertise identified by the analysis unit. For example, the generation unit generates learning materials related to children's interests using text generation AI. Furthermore, the generation unit can use video generation AI to generate visually appealing educational materials. Additionally, the generation unit can use interactive content generation AI to generate educational materials that children can participate in. The delivery unit provides expert coaching based on the educational materials generated by the generation unit. For example, the delivery unit can provide individual instruction. The delivery unit can also conduct group sessions. Furthermore, the delivery unit can provide online coaching. As a result, the learning support system according to this embodiment can comprehensively analyze children's data, identify their potential talents and areas of expertise, and provide expert coaching to realize a learning experience tailored to individual needs.

[0064] The data collection unit collects data on children. This data includes, but is not limited to, learning history, interests, and behavioral patterns. For example, to collect learning history, the unit records past test results and study time. Specifically, it collects school report cards and log data from online learning platforms to understand how well children perform in different subjects. The unit can also analyze survey results and behavioral data to collect information on interests. For example, it records what books children read, what websites they visit, and what apps they use, and uses this data to identify their interests. Furthermore, the unit can record the frequency and timing of learning to collect behavioral patterns. For example, it records when children study and how long they spend studying, and uses this data to analyze behavioral patterns. This allows the unit to collect diverse data on children's learning and gain a detailed understanding of their individual learning styles and interests. In addition, the unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0065] The analysis unit analyzes the data collected by the data collection unit to identify children's interests and areas of expertise. For example, the analysis unit extracts patterns from the collected data using data mining techniques. Specifically, it uses clustering algorithms to group children's learning history and behavioral patterns, identifying groups with common characteristics. The analysis unit can also analyze data trends using statistical analysis. For example, it uses regression analysis to clarify the relationship between children's learning outcomes and study time, and proposes optimal study time. Furthermore, the analysis unit can identify children's interests and areas of expertise using machine learning algorithms. For example, it uses supervised learning to build models that predict children's interests and areas of expertise from past data, and then makes predictions based on new data. This allows the analysis unit to quickly and accurately analyze collected data and identify children's interests and areas of expertise. Additionally, the analysis unit can utilize past data and statistical information to predict long-term learning outcomes and perform trend analysis. For example, it can predict growth trends in specific subjects or skills based on past learning data and formulate future learning plans. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to predict long-term learning outcomes and detect anomalies, thereby improving the reliability and effectiveness of the entire system.

[0066] The generation unit automatically generates relevant learning materials based on the interests and areas of expertise identified by the analysis unit. For example, the generation unit uses text generation AI to generate materials related to children's interests. Specifically, it uses natural language processing technology to generate articles and workbooks on topics that children are interested in. The generation unit can also use video generation AI to generate visually engaging materials. For example, it uses animation and infographics to provide information in a way that is easy for children to understand. Furthermore, the generation unit can use interactive content generation AI to generate materials that children can participate in. For example, it can generate quizzes and simulation games to provide an environment where children can learn while having fun. In this way, the generation unit can automatically generate a variety of learning materials tailored to children's interests and areas of expertise, and respond to their individual learning needs. In addition, the generation unit can evaluate the quality of the generated materials and make corrections and improvements as needed. For example, it can evaluate the content and difficulty level of the generated materials and adjust them according to the child's level of understanding and learning progress. The generation unit can also continuously update the generated materials to reflect the latest information and topics. In this way, the generation unit can always provide high-quality learning materials based on the latest information, thereby increasing children's motivation to learn.

[0067] The service provider offers expert coaching based on the materials generated by the production unit. For example, the service provider provides individualized instruction. Specifically, they create individualized instruction plans tailored to each child's learning progress and understanding, with expert coaches providing one-on-one instruction. The service provider can also conduct group sessions. For example, they can divide children with similar interests and strengths into groups, allowing them to learn and discuss collaboratively, stimulating each other's learning. Furthermore, the service provider can provide online coaching. For example, they can provide expert instruction to children in remote locations via video calls or chat. This allows the service provider to quickly provide appropriate instructions to each user, minimizing the risk of disaster. Additionally, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of their instruction. For example, they can revise teaching methods and materials based on feedback from children who have received instruction. The service provider can also reliably transmit information using multiple communication methods. For example, they can ensure important information is delivered reliably using not only smartphone notifications but also voice calls, SMS, and email. This allows the service provider to quickly and reliably provide users with action instructions, minimizing the risk of disaster.

[0068] The data collection unit can collect data on learning history, interests, and behavioral patterns. For example, to collect learning history, the data collection unit can record past test results and study time. For example, the data collection unit can save past test results to a database and record study time using a time tracking tool. The data collection unit can also analyze survey results and behavioral data to collect interests. For example, the data collection unit can aggregate survey results and analyze behavioral data using a log analysis tool. The data collection unit can also record the frequency and timing of learning to collect behavioral patterns. For example, the data collection unit can record the frequency of learning using a calendar app and detect the timing of learning using sensors. This allows the data collection unit to collect diverse data, enabling more detailed analysis. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input learning history data into AI, which can automate data collection.

[0069] The analysis unit can analyze the collected data and identify a child's potential talents and areas of expertise. For example, the analysis unit can extract patterns from the collected data using data mining techniques. For instance, it can use data mining algorithms to find common patterns in learning history. The analysis unit can also analyze data trends using statistical analysis. For example, it can use statistical software to analyze interest data and understand trends. Furthermore, the analysis unit can use machine learning algorithms to identify a child's interests and areas of expertise. For example, it can use machine learning models to predict a child's areas of expertise from behavioral patterns. This allows the analysis unit to identify a child's talents and areas of expertise, enabling the provision of learning experiences tailored to individual needs. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the collected data into a generative AI, which can then analyze the data.

[0070] The generation unit can automatically generate relevant educational materials based on identified interests and areas of expertise. For example, the generation unit can use a text generation AI to generate educational materials related to a child's interests. For instance, it can use a text generation AI to generate science materials for a child interested in science. The generation unit can also use a video generation AI to generate visually engaging educational materials. For example, it can use a video generation AI to generate history video materials for a child interested in history. The generation unit can also use an interactive content generation AI to generate educational materials that children can participate in. For example, it can use an interactive content generation AI to generate interactive mathematics materials for a child interested in mathematics. This allows the generation unit to automatically generate educational materials, thereby stimulating children's interest and increasing their motivation to learn. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may not. For example, the generation unit can input data on identified interests and areas of expertise into a generation AI, which can then generate educational materials.

[0071] The service provider can offer professional coaching based on the generated materials. For example, the service provider can provide individual instruction. For instance, it can create individualized instruction plans based on a child's strengths and provide individualized instruction. The service provider can also conduct group sessions. For example, it can divide children with similar interests into groups and conduct group sessions. Furthermore, the service provider can provide online coaching. For example, it can provide coaching to children through an online platform. This allows the service provider to maximize children's potential through professional coaching. Some or all of the processes described above in the service provider may be performed using AI, or not. For example, the service provider can input the generated materials into an AI, which can then automate the coaching process.

[0072] The data collection unit can estimate a child's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can collect learning history and interest data when the child is relaxed. For example, the data collection unit can record past test results and study time when the child is relaxed. The data collection unit can also collect behavioral pattern data when the child is focused. For example, the data collection unit can record the frequency and timing of learning when the child is focused. The data collection unit can also temporarily stop data collection when the child is stressed. For example, the data collection unit can temporarily stop data collection when the child is stressed and wait until the child is relaxed. This allows for the collection of more appropriate data by adjusting the timing of data collection according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs children's emotional data into a generating AI, which can then adjust the timing of data collection.

[0073] The data collection unit can analyze a child's past learning history and select an appropriate data collection method. For example, the data collection unit can identify the time of day when a child can learn most effectively based on their past learning history and collect data during that time. For example, the data collection unit can analyze past test results and study times to identify the optimal study time. The data collection unit can also prioritize the collection of data related to topics that children are likely to be interested in, based on their past learning history. For example, the data collection unit can analyze past survey results and behavioral data to identify topics that children are likely to be interested in. The data collection unit can also analyze past learning history and focus on collecting data in areas where children struggle. For example, the data collection unit can analyze past test results and study times to identify areas where children struggle. This allows for effective data collection by selecting the optimal data collection method based on past learning history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past learning history data into a generating AI, which can then select a data collection method.

[0074] The data collection unit can filter data based on the child's current learning status and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to ongoing tasks based on the child's current learning status. For example, the data collection unit can monitor the child's current learning progress and collect data related to ongoing tasks. The data collection unit can also collect data on relevant learning materials and resources based on the child's areas of interest. For example, the data collection unit can identify the child's areas of interest and collect data related to those areas. The data collection unit can also filter and collect necessary data in real time according to the child's learning progress. For example, the data collection unit can monitor learning progress in real time and filter and collect necessary data. This allows for the collection of highly relevant data by filtering data based on the child's current learning status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the child's current learning status and areas of interest into a generating AI, which can then filter the data.

[0075] The data collection unit can estimate a child's emotions and determine the priority of data to collect based on the estimated emotions. For example, if a child is excited, the data collection unit will prioritize collecting data that is of interest to the child. For example, if a child is excited, the data collection unit will collect data related to topics that are of interest to the child. The data collection unit can also prioritize collecting data that is relaxing if a child is tired. For example, if a child is tired, the data collection unit will collect data that is relaxing if a child is tired. The data collection unit can also prioritize collecting data that is helpful for learning if a child is focused. For example, if a child is focused, the data collection unit will collect data that is helpful for learning if a child is focused. This allows for more effective data collection by prioritizing data according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input children's emotional data into a generating AI, which can then determine the priority of the data.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the child's geographical location information during data collection. For example, if the child is at school, the data collection unit can collect data related to the school curriculum. For example, if the child is at school, the data collection unit can collect data related to the school curriculum. For example, if the child is at home, the data collection unit can collect data suitable for home learning. For example, if the child is at the library, the data collection unit can collect data related to library resources. For example, if the child is at the library, the data collection unit can collect data related to library resources. By considering geographical location information, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's geographical location information into a generating AI, and the generating AI can collect the data.

[0077] The data collection unit can analyze a child's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to topics the child has shown interest in on social media. The data collection unit can also analyze the activity of accounts the child follows on social media and collect relevant data. The data collection unit can also collect relevant data based on content the child has shared on social media. For example, the data collection unit can analyze content the child has shared on social media and collect relevant data. This allows data related to the child's interests to be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the child's social media activity into a generating AI, which can then collect the data.

[0078] The analysis unit can estimate a child's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the child is relaxed, the analysis unit can provide detailed analysis results. For example, when the child is relaxed, the analysis unit can provide analysis results using detailed graphs and charts. The analysis unit can also provide concise analysis results that get straight to the point if the child is in a hurry. For example, when the child is in a hurry, the analysis unit can provide a summary text report. The analysis unit can also provide visually appealing analysis results if the child is excited. For example, when the child is excited, the analysis unit can provide analysis results using an interactive dashboard. This allows for more easily understood analysis results by adjusting the presentation of the analysis according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input child emotional data into a generating AI, which can then adjust the way the analysis is presented.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data. For example, the analysis unit can perform a detailed statistical analysis on highly important data. The analysis unit can also perform a simplified analysis on less important data. For example, the analysis unit can perform a simplified data mining on less important data. The analysis unit can also perform an analysis with an appropriate level of detail on data of moderate importance. For example, the analysis unit can apply a machine learning algorithm with an appropriate level of detail to data of moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a pattern recognition algorithm to learning history data. For instance, the analysis unit uses a pattern recognition algorithm to find common patterns in the learning history data. The analysis unit can also apply a clustering algorithm to interest data. For example, the analysis unit uses a clustering algorithm to group the data. The analysis unit can also apply a time series analysis algorithm to behavior pattern data. For example, the analysis unit uses a time series analysis algorithm to analyze the fluctuations in the behavior pattern data. By applying an appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into a generative AI, and the generative AI can apply an appropriate analysis algorithm.

[0081] The analysis unit can estimate the child's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the child is in a hurry, the analysis unit can provide a short, concise analysis. For example, if the child is in a hurry, the analysis unit can provide a short, summary report. The analysis unit can also provide a detailed analysis when the child is relaxed. For example, if the child is relaxed, the analysis unit can provide a detailed analysis using graphs and charts. The analysis unit can also provide a visually appealing analysis when the child is excited. For example, if the child is excited, the analysis unit can provide an interactive dashboard. By adjusting the length of the analysis according to the child's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit inputs the child's emotional data into a generating AI, which can then adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may prioritize the analysis of the most recent data to grasp the latest trends. The analysis unit can also emphasize the most recent data while referring to past data. For example, the analysis unit may emphasize the most recent data while referring to past data. The analysis unit can also focus on analyzing data collected during a specific period. For example, the analysis unit may focus on analyzing data collected during a specific period to grasp the trends during that period. This makes it possible to perform analysis that emphasizes the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the data collection period into the generative AI, and the generative AI can determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may prioritize the analysis of data with high relevance based on the correlation between the data. The analysis unit may also analyze data with moderate relevance next. For example, the analysis unit may analyze data with moderate relevance next based on co-occurrence frequency. The analysis unit may also analyze data with low relevance last. For example, the analysis unit may analyze data with low relevance last based on relevance score. By adjusting the order of analysis based on the relevance of the data, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI, and the generative AI can adjust the order of analysis.

[0084] The generation unit can estimate a child's emotions and adjust the method of generating educational materials based on the estimated emotions. For example, if the child is relaxed, the generation unit can generate detailed educational materials. For example, when the child is relaxed, the generation unit can generate educational materials that include detailed text and videos. The generation unit can also generate concise educational materials that get straight to the point if the child is in a hurry. For example, when the child is in a hurry, the generation unit can generate short, concise educational materials that summarize the key points. The generation unit can also generate visually engaging educational materials if the child is excited. For example, when the child is excited, the generation unit can generate educational materials that include interactive content. By adjusting the method of generating educational materials according to the child's emotions, more effective educational materials can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the generation unit may be performed using a generative AI, for example, or without a generative AI. For example, the generation unit can input children's emotional data into the generation AI, which can then adjust the method of generating the teaching materials.

[0085] The generation unit can adjust the level of detail of the materials based on identified interests and areas of expertise when generating the materials. For example, the generation unit can generate detailed materials for areas of high interest to children. For example, the generation unit can generate materials that include detailed text and videos for areas of high interest to children. The generation unit can also generate materials that include applied content for areas of expertise to children. For example, the generation unit can generate materials that include applied problems and tasks for areas of expertise to children. The generation unit can also generate materials that include basic content for areas of low interest to children. For example, the generation unit can generate materials that include basic concepts and examples for areas of low interest to children. In this way, by adjusting the level of detail of the materials based on interests and areas of expertise, more appropriate materials can be provided. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data on identified interests and areas of expertise into the generation AI, and the generation AI can adjust the level of detail of the materials.

[0086] The generation unit can apply different generation algorithms to the category of interest or area of ​​expertise when generating educational materials. For example, for the science field, the generation unit can generate educational materials that include experimental videos. For example, for the science field, the generation unit can generate educational materials that include experimental videos and experimental procedures. The generation unit can also generate educational materials that include reading comprehension questions for the literature field. For example, for the literature field, the generation unit can generate educational materials that include reading comprehension questions and explanations of literary works. The generation unit can also generate educational materials that include practice problems for the mathematics field. For example, for the mathematics field, the generation unit can generate educational materials that include practice problems and solutions and explanations. This improves the quality of the educational materials by applying an appropriate generation algorithm according to the category of interest or area of ​​expertise. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the category of interest or area of ​​expertise into the generation AI, and the generation AI can apply an appropriate generation algorithm.

[0087] The generation unit can estimate a child's emotions and adjust the length of the learning materials based on the estimated emotions. For example, if the child is in a hurry, the generation unit can generate short, concise learning materials. For example, if the child is in a hurry, the generation unit can generate short, concise learning materials. The generation unit can also generate longer learning materials with detailed explanations if the child is relaxed. For example, if the child is relaxed, the generation unit can generate longer learning materials with detailed text and videos. The generation unit can also generate learning materials with visually stimulating effects if the child is excited. For example, if the child is excited, the generation unit can generate learning materials with visually engaging interactive content. By adjusting the length of the learning materials according to the child's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, for example, or without a generative AI. For example, the generation unit inputs children's emotional data into the generating AI, which can then adjust the length of the teaching materials.

[0088] The generation unit can determine the priority of educational materials based on the timing of collection of interests and areas of expertise when generating educational materials. For example, the generation unit can generate educational materials based on the most recent interests and areas of expertise. For example, the generation unit can generate educational materials that include the latest information based on the most recent interests and areas of expertise. The generation unit can also prioritize the latest information while referring to past interests and areas of expertise. For example, the generation unit can generate educational materials that prioritize the latest information while referring to past interests and areas of expertise. The generation unit can also generate educational materials based on interests and areas of expertise collected during a specific period. For example, the generation unit can generate educational materials that include information from a specific period based on interests and areas of expertise collected during that period. This allows for the provision of educational materials that prioritize the latest information by determining the priority of educational materials based on the collection timing. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the timing of collection of interests and areas of expertise into the generation AI, and the generation AI can determine the priority of educational materials.

[0089] The generation unit can adjust the order of learning materials based on the relevance of interests and areas of expertise during material generation. For example, the generation unit can generate learning materials based on highly relevant interests and areas of expertise. For example, the generation unit can generate learning materials containing relevant information based on highly relevant interests and areas of expertise. The generation unit can also generate learning materials based on moderately relevant interests and areas of expertise. For example, the generation unit can generate learning materials containing relevant information based on moderately relevant interests and areas of expertise. The generation unit can also generate learning materials based on lowly relevant interests and areas of expertise. For example, the generation unit can generate learning materials containing relevant information based on lowly relevant interests and areas of expertise. This allows for a smoother learning flow by adjusting the order of learning materials based on relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of interests and areas of expertise into the generation AI, which can then adjust the order of the learning materials.

[0090] The service provider can estimate a child's emotions and adjust the coaching delivery method based on the estimated emotions. For example, if the child is relaxed, the service provider can provide detailed coaching. For example, when the child is relaxed, the service provider can create a detailed instruction plan and provide individualized instruction. The service provider can also provide concise, to-the-point coaching if the child is in a hurry. For example, when the child is in a hurry, the service provider can provide short, summarized coaching. The service provider can also provide visually engaging coaching if the child is excited. For example, when the child is excited, the service provider can provide coaching using interactive content. By adjusting the coaching delivery method according to the child's emotions, more effective coaching becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the child's emotional data into a generating AI, which can then adjust the method of providing coaching.

[0091] The service provider can select the optimal coaching method by referring to the child's past learning history when providing coaching. For example, the service provider can identify the most effective learning method for the child from their past learning history and provide coaching using that method. For example, the service provider can analyze past test results and study time to identify the optimal learning method. The service provider can also provide coaching related to topics that the child is likely to be interested in, based on their past learning history. For example, the service provider can analyze past survey results and behavioral data to identify topics that the child is likely to be interested in. The service provider can also analyze past learning history and provide focused coaching in areas where the child struggles. For example, the service provider can analyze past test results and study time to identify areas where the child struggles. This allows for effective coaching by selecting the optimal coaching method based on past learning history. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input data on past learning history into a generating AI, which can then select the optimal coaching method.

[0092] The service provider can customize the coaching methods based on the child's current learning situation when providing coaching. For example, the service provider can provide coaching related to ongoing tasks based on the child's current learning situation. For example, the service provider can monitor the child's current learning progress and provide coaching related to ongoing tasks. The service provider can also provide necessary support in real time according to the child's learning progress. For example, the service provider can monitor the child's learning progress in real time and provide necessary support. The service provider can also provide coaching of appropriate difficulty based on the child's current level of understanding. For example, the service provider can assess the child's level of understanding and provide coaching of appropriate difficulty. This allows for more appropriate support to be provided by customizing the coaching methods based on the child's current learning situation. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the child's current learning situation into a generating AI, which can then customize the coaching methods.

[0093] The service provider can estimate a child's emotions and determine coaching priorities based on those priorities. For example, if a child is excited, the service provider will prioritize coaching related to topics that interest them. For example, if a child is excited, the service provider will provide coaching related to topics that interest them. The service provider can also prioritize coaching that promotes relaxation if a child is tired. For example, if a child is tired, the service provider will provide coaching that promotes relaxation. The service provider can also prioritize coaching that helps with learning if a child is focused. For example, if a child is focused, the service provider will provide coaching that helps with learning. This allows for more effective coaching by prioritizing coaching according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input children's emotional data into a generating AI, which can then determine the priorities for coaching.

[0094] The service provider can select the most appropriate coaching method when providing coaching, taking into account the child's geographical location. For example, if the child is at school, the service provider can provide coaching related to the school curriculum. For example, if the child is at school, the service provider can provide coaching related to the school curriculum. The service provider can also provide coaching suitable for home learning if the child is at home. For example, if the child is at the library, the service provider can provide coaching related to library resources. For example, if the child is at the library, the service provider can provide coaching related to library resources. By considering geographical location, more relevant coaching can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the child's geographical location into a generating AI, which can then select the most appropriate coaching method.

[0095] The service provider can analyze a child's social media activity and propose coaching methods when providing coaching. For example, the service provider can provide coaching related to topics the child has shown interest in on social media. The service provider can also analyze the activity of accounts the child follows on social media and provide relevant coaching. The service provider can also provide relevant coaching based on content the child has shared on social media. For example, the service provider can analyze content the child has shared on social media and provide relevant coaching. In this way, by analyzing social media activity, the service provider can provide coaching related to the child's interests. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the child's social media activity into a generating AI, which can then propose coaching methods.

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

[0097] The analysis unit can estimate a child's emotions and adjust the analysis priority based on the estimated emotions. For example, if a child is excited, it can prioritize analyzing data that is of interest to the child. For example, when a child is excited, the analysis unit can prioritize analyzing data related to topics that are of interest to the child. Also, if a child is tired, it can prioritize analyzing data that is relaxing to the child. For example, when a child is tired, the analysis unit can prioritize analyzing data that is relaxing to the child. Also, if a child is focused, it can prioritize analyzing data that is helpful for learning. For example, when a child is focused, the analysis unit can prioritize analyzing data that is helpful for learning. By adjusting the analysis priority according to the child's emotions, more effective analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the child's emotion data into the generative AI, and the generative AI can adjust the analysis priority.

[0098] The generation unit can estimate a child's emotions and adjust the format of the learning materials based on the estimated emotions. For example, if the child is relaxed, it can generate learning materials that include detailed text and videos. For example, if the child is relaxed, it can generate learning materials that include detailed text and videos. If the child is in a hurry, it can also generate concise learning materials that get straight to the point. For example, if the child is in a hurry, it can generate short, concise learning materials that summarize the key points. If the child is excited, it can also generate visually engaging learning materials. For example, if the child is excited, it can generate learning materials that include interactive content. By adjusting the format of the learning materials according to the child's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input the child's emotion data into the generative AI, which can then adjust the format of the learning materials.

[0099] The service provider can estimate a child's emotions and adjust the coaching content based on the estimated emotions. For example, if a child is relaxed, it can provide detailed instruction. For example, when a child is relaxed, the service provider can create a detailed instruction plan and provide individualized instruction. If a child is in a hurry, it can also provide concise coaching that gets straight to the point. For example, when a child is in a hurry, the service provider can provide short, summarized coaching. If a child is excited, it can also provide visually engaging coaching. For example, when a child is excited, the service provider can provide coaching using interactive content. By adjusting the coaching content according to the child's emotions, more effective coaching becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the child's emotion data into the generative AI, and the generative AI can adjust the coaching content.

[0100] The data collection unit can estimate a child's emotions and adjust the data collection method based on the estimated emotions. For example, if the child is relaxed, it can collect detailed data. For example, when the child is relaxed, the data collection unit can collect detailed learning history and interest data. If the child is in a hurry, it can also collect concise data. For example, when the child is in a hurry, the data collection unit can collect concise data. If the child is excited, it can also collect visually appealing data. For example, when the child is excited, the data collection unit can collect visually appealing data. This allows for more effective data collection by adjusting the data collection method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the child's emotion data into the generative AI, which can then adjust the data collection method.

[0101] The analysis unit can estimate a child's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the child is relaxed, it can provide detailed analysis results. For example, when the child is relaxed, the analysis unit can provide analysis results using detailed graphs and charts. If the child is in a hurry, it can also provide concise analysis results that get straight to the point. For example, when the child is in a hurry, the analysis unit can provide a summary text report. If the child is excited, it can also provide visually appealing analysis results. For example, when the child is excited, the analysis unit can provide analysis results using an interactive dashboard. This allows for more easily understood analysis results by adjusting the presentation of the analysis according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input child emotional data into a generating AI, which can then adjust the way the analysis is presented.

[0102] The data collection unit can adjust its data collection methods based on the child's learning environment. For example, if the child is at school, it can collect data related to the school curriculum. For example, the data collection unit can collect data related to the school curriculum when the child is at school. It can also collect data suitable for home learning when the child is at home. For example, if the child is at the library, it can collect data related to library resources. For example, the data collection unit can collect data related to library resources when the child is at the library. By adjusting the data collection methods based on the learning environment, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input data on the child's learning environment into a generating AI, which can then adjust the data collection methods.

[0103] The analysis unit can adjust its analysis method based on the data source. For example, for data collected from schools, it can perform analysis based on the school curriculum. For example, for data collected from homes, it can perform analysis suitable for home study. For example, for data collected from libraries, it can perform analysis based on library resources. By adjusting the analysis method based on the data source, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information about the data source into a generating AI, which can then adjust the analysis method.

[0104] The generation unit can adjust the method of generating learning materials based on the child's learning progress. For example, if the child's learning progress is fast, it can generate learning materials that include applied content. For example, when the child's learning progress is fast, the generation unit can generate learning materials that include applied problems and tasks. Also, if the child's learning progress is slow, it can generate learning materials that include basic content. For example, when the child's learning progress is slow, the generation unit can generate learning materials that include basic concepts and examples. Also, if the child's learning progress is average, it can generate learning materials that include standard content. For example, when the child's learning progress is average, the generation unit can generate learning materials that include standard problems and tasks. By adjusting the method of generating learning materials based on learning progress, more effective learning becomes possible. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the child's learning progress into a generation AI, which can then adjust the method of generating learning materials.

[0105] The service provider can adjust the content of coaching based on the child's learning history. For example, it can identify the most effective learning method for the child from their past learning history and provide coaching in that way. For example, the service provider can analyze past test results and study time to identify the optimal learning method. It can also provide coaching related to topics that the child is likely to be interested in, based on their past learning history. For example, the service provider can analyze past survey results and behavioral data to identify topics that the child is likely to be interested in. It can also analyze past learning history and provide focused coaching in areas where the child struggles. For example, the service provider can analyze past test results and study time to identify areas where the child struggles. This allows for effective coaching by selecting the optimal coaching method based on past learning history. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input data on past learning history into a generating AI, which can then select the optimal coaching method.

[0106] The service provider can customize coaching methods based on the child's current learning situation. For example, it can provide coaching related to ongoing tasks based on the child's current learning situation. For example, the service provider can monitor the child's current learning progress and provide coaching related to ongoing tasks. The service provider can also provide necessary support in real time according to the child's learning progress. For example, it can monitor the child's learning progress in real time and provide necessary support. The service provider can also provide coaching of appropriate difficulty based on the child's current level of understanding. For example, it can assess the child's level of understanding and provide coaching of appropriate difficulty. This allows for more appropriate support to be provided by customizing coaching methods based on the child's current learning situation. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the child's current learning situation into a generating AI, which can then customize the coaching methods.

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

[0108] Step 1: The data collection unit collects data on the children. This data includes learning history, interests, and behavioral patterns. For example, the data collection unit records past test results and study time, analyzes survey results and behavioral data, and records the frequency and timing of learning. Step 2: The analysis unit analyzes the data collected by the collection unit to identify children's interests and areas of expertise. The analysis unit uses data mining techniques, statistical analysis, and machine learning algorithms to extract patterns from the collected data and analyze data trends. Step 3: The generation unit automatically generates relevant educational materials based on the interests and areas of expertise identified by the analysis unit. The generation unit uses text generation AI, video generation AI, and interactive content generation AI to generate educational materials related to the child's interests. Step 4: The delivery department provides professional coaching based on the materials generated by the production department. The delivery department conducts individual lessons, group sessions, and online coaching.

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

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

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

[0112] For example, the data collection unit can collect data on children using the camera 42 and microphone 38B of the smart device 14. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the collected data and identifies the child's interests and areas of expertise. For example, the generation unit is implemented by the identification processing unit 290 of the data processing device 12, which automatically generates relevant educational materials based on the identified interests and areas of expertise. For example, the provision unit is implemented by the control unit 46A of the smart device 14, which provides professional coaching based on the generated educational materials. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0128] For example, the data collection unit can collect data on children using the camera 42 and microphone 238 of the smart glasses 214. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the collected data and identifies the child's interests and areas of expertise. For example, the generation unit is implemented by the identification processing unit 290 of the data processing device 12, which automatically generates relevant educational materials based on the identified interests and areas of expertise. For example, the provision unit is implemented by the control unit 46A of the smart glasses 214, which provides expert coaching based on the generated educational materials. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0144] For example, the data collection unit can collect data on children using the camera 42 and microphone 238 of the headset terminal 314. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the collected data and identifies the child's interests and areas of expertise. For example, the generation unit is implemented by the identification processing unit 290 of the data processing device 12, which automatically generates relevant educational materials based on the identified interests and areas of expertise. For example, the provision unit is implemented by the control unit 46A of the headset terminal 314, which provides professional coaching based on the generated educational materials. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0161] For example, the data collection unit can collect data on children using the camera 42 and microphone 238 of the robot 414. For example, the analysis unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the collected data and identifies the child's interests and areas of expertise. For example, the generation unit is implemented by the identification processing unit 290 of the data processing device 12, which automatically generates relevant educational materials based on the identified interests and areas of expertise. For example, the provision unit is implemented by the control unit 46A of the robot 414, which provides expert coaching based on the generated educational materials. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0180] (Note 1) A data collection unit that collects children's data, The data collected by the aforementioned collection unit is analyzed by an analysis unit to identify the child's interests and areas of expertise, A generation unit that automatically generates relevant teaching materials based on the interests and areas of expertise identified by the analysis unit, The system comprises a providing unit that provides professional coaching based on the teaching materials generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data on learning history, interests, and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to identify the child's potential talents and areas of expertise. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on identified interests and areas of expertise, relevant learning materials are automatically generated. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide professional coaching based on the generated materials. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the child's emotions and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the child's past learning history and select the appropriate data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the child's current learning situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the child's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking into account the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, analyze children's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, We estimate the child's emotions and adjust the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the child's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is The system estimates children's emotions and adjusts the method of generating educational materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating learning materials, adjust the level of detail based on identified interests and areas of expertise. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating educational materials, different generation algorithms are applied depending on the category of interests and areas of expertise. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is The system estimates the child's emotions and adjusts the length of the learning materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating learning materials, prioritize them based on when the student's interests and areas of expertise were gathered. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating learning materials, the order of the materials is adjusted based on the relevance of the user's interests and areas of expertise. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, We estimate the child's emotions and adjust the coaching method based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing coaching, we select the most suitable coaching method by referring to the child's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing coaching, the coaching methods are customized based on the child's current learning situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, Estimate the child's emotions and determine coaching priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing coaching, we select the most suitable coaching method by taking into account the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing coaching, we analyze the child's social media activity and propose coaching methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

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

Claims

1. A data collection unit that collects children's data, The data collected by the aforementioned collection unit is analyzed by an analysis unit to identify the child's interests and areas of expertise, A generation unit that automatically generates relevant teaching materials based on the interests and areas of expertise identified by the analysis unit, The system comprises a providing unit that provides professional coaching based on the teaching materials generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data on learning history, interests, and behavioral patterns. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to identify the child's potential talents and areas of expertise. The system according to feature 1.

4. The generating unit is Based on identified interests and areas of expertise, relevant learning materials are automatically generated. The system according to feature 1.

5. The aforementioned supply unit is, We provide professional coaching based on the generated materials. The system according to feature 1.

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

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

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

Images