System

Abstract

An object of a system according to an embodiment is to provide optimal education based on a learning history and interests of a child.SOLUTION: A system includes a collection part, an evaluation part, a provision part, a feedback analysis part, a support part, a progress management part, and a cooperation part. The collection unit collects data such as the child's learning history, test results, and interests. The evaluation unit evaluates the ability of the child on the basis of the data collected by the collection unit. The providing unit provides the individual curriculum based on a result evaluated by the evaluation unit. The feedback analysis unit analyzes the feedback of the child and the parent based on the curriculum provided by the providing unit. The support unit supports self-learning based on the feedback analyzed by the feedback analysis unit. The progress management unit manages the progress of the learning supported by the support unit. The cooperation part cooperates with the education service on the basis of the progress managed by the progress management part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide optimal education based on a child's learning history and interests, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal education based on a child's learning history and interests. [Means for solving the problem]

[0006] The system according to the embodiment comprises a collection unit, an evaluation unit, a provision unit, a feedback analysis unit, a support unit, a progress management unit, and a collaboration unit. The collection unit collects data such as a child's learning history, test results, and interests. The evaluation unit evaluates the child's ability based on the data collected by the collection unit. The provision unit provides an individual curriculum based on the results of the evaluation by the evaluation unit. The feedback analysis unit analyzes feedback from the child and parent based on the curriculum provided by the provision unit. The support unit supports self-study based on the feedback analyzed by the feedback analysis unit. The progress management unit manages the progress of learning supported by the support unit. The collaboration unit collaborates with educational services based on the progress managed by the progress management unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal education based on a child's learning history and interests. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An educational support system according to an embodiment of the present invention utilizes a generative AI to determine a child's suitability and provide methods, learning environments, and services for receiving a high-quality education. The educational support system collects data such as the child's learning history, test results, and interests, and the generative AI analyzes the data to evaluate the child's abilities. The generative AI then provides an individualized curriculum and incorporates feedback from the child and their parents. The generative AI then supports self-study, visualizes learning, and manages progress. Finally, the system partners with leading educational services to provide high-quality education. For example, the educational support system collects data such as the child's learning history, test results, and interests. The generative AI then analyzes this data to identify the child's strengths and weaknesses. The generative AI then creates an individualized curriculum and provides the child with an optimal learning plan. The generative AI then collects feedback from the child and their parents, analyzes it, and adjusts the learning plan. The generative AI then supports self-study and provides appropriate advice and tips. Finally, the generative AI visualizes learning progress and manages progress. This allows the educational support system to determine a child's suitability and provide methods, learning environments, and services for receiving a high-quality education. This allows the educational support system to evaluate a child's abilities, provide an individualized curriculum, support self-study, and manage learning progress. For example, data such as a child's learning history, test results, and interests is collected and analyzed by the generative AI to identify the child's strong and weak subjects. The generative AI then creates an individualized curriculum and provides the child with the optimal learning plan. Furthermore, feedback from the child and parents is collected, analyzed, and the generative AI adjusts the learning plan. The generative AI then supports self-study and provides appropriate advice and hints. Finally, the generative AI visualizes learning progress and manages it. This allows the educational support system to determine what is suitable for a child and provide ways to receive a high-quality education, as well as learning environments and services.

[0029] An education support system according to an embodiment includes a collection unit, an evaluation unit, a provision unit, a feedback analysis unit, a support unit, a progress management unit, and a collaboration unit. The collection unit collects data such as a child's learning history, test results, and interests. For example, the collection unit obtains the results of tests the child has taken in the past from a database. The collection unit can also collect the child's learning history on an online learning platform. The collection unit can also collect the results of the child's responses to questionnaires. The evaluation unit evaluates the child's ability based on the data collected by the collection unit. For example, the evaluation unit analyzes test results to identify the child's strong and weak subjects. The evaluation unit can also analyze the learning history to understand the child's learning patterns. The evaluation unit can also analyze interest data to identify the child's areas of interest. The provision unit provides an individualized curriculum based on the results of the evaluation by the evaluation unit. For example, the provision unit provides teaching materials to further improve the child's strong subjects. The provision unit can also provide practice questions to help the child overcome their weak subjects. The provision unit can also provide teaching materials that will attract the child's interest. The feedback analysis unit analyzes feedback from the child and parent based on the curriculum provided by the provision unit. For example, the feedback analysis unit collects feedback on what the child has learned. The feedback analysis unit can also collect feedback provided by parents. The feedback analysis unit can also analyze the collected feedback and adjust the learning plan. The support unit supports self-study based on the feedback analyzed by the feedback analysis unit. For example, the support unit provides appropriate advice when the child progresses with their own learning. The support unit can also provide hints on how to solve difficult problems when the child is faced with them. The support unit can also provide support to increase motivation when the child gets stuck in their studies. The progress management unit manages the progress of learning supported by the support unit. For example, the progress management unit visualizes the child's learning situation and manages progress.The progress management unit can also display the learning achievement level and progress status in graphs and charts. The progress management unit can also set learning goals and create progress reports. The collaboration unit collaborates with excellent educational services based on the progress managed by the progress management unit. For example, the collaboration unit utilizes teaching materials and programs provided by partner educational services. The collaboration unit can also use online classes and educational apps. The collaboration unit can also collaborate with partner educational institutions to provide an optimal learning environment for children. As a result, the education support system according to the embodiment can evaluate a child's abilities, provide an individualized curriculum, support self-study, and manage learning progress. For example, the collection unit collects data such as a child's learning history, test results, and interests, and the evaluation unit evaluates the child's abilities based on the collected data. The provision unit provides an individualized curriculum based on the evaluation results, and the feedback analysis unit analyzes feedback from the child and their guardian. The support unit supports self-study, and the progress management unit manages learning progress. The collaboration unit collaborates with excellent educational services. This allows the educational support system to determine what is suitable for each child and provide ways to receive a high-quality education, as well as learning opportunities and services.

[0030] The collection unit can analyze the child's past learning history and select the optimal data collection method. The collection unit, for example, analyzes the learning materials and learning methods used by the child in the past and collects data in a similar manner. The collection unit can also select an effective data collection method for a specific time period from the child's learning history. The collection unit can also select the most effective data collection means (audio, text, image, etc.) based on the child's learning history. This makes it possible to select the optimal data collection method based on the child's past learning history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the child's past learning history data into the generation AI and have the generation AI select the optimal data collection method.

[0031] When collecting data, the collection unit can filter the data based on the child's current learning situation and interests. For example, the collection unit collects only data related to the subject the child is currently studying. The collection unit can also prioritize the collection of highly relevant data based on the child's interests. The collection unit can also filter and collect only necessary data according to the child's learning progress. This makes it possible to collect highly relevant data based on the child's current learning situation and interests. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the child's current learning situation data into the generation AI and have the generation AI perform the filtering.

[0032] When collecting data, the collection unit can select an appropriate collection means depending on the child's input method. For example, if the child uses voice input, the collection unit can prioritize collecting voice data. Furthermore, if the child uses text input, the collection unit can also prioritize collecting text data. Furthermore, if the child uses image input, the collection unit can also prioritize collecting image data. This makes it possible to select the optimal data collection means depending on the child's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's input data into a generation AI and cause the generation AI to select the optimal collection means.

[0033] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the child's geographical location information. For example, if the child is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, if the child is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, if the child is at school, the collection unit can prioritize collecting data related to the school curriculum. This makes it possible to collect highly relevant data based on geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's geographical location information data into the generation AI and cause the generation AI to collect highly relevant data.

[0034] During data collection, the collection unit can analyze the child's online activities and collect related data. For example, the collection unit collects data based on the interests the child has shared on social media. The collection unit can also collect data useful for learning from the child's social media activities. The collection unit can also collect related data by referring to the activities of the child's friends on social media. This makes it possible to collect related data based on social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the child's online activity data into the generation AI and cause the generation AI to collect related data.

[0035] The collection unit can customize the collection method by reflecting the child's past feedback when collecting data. For example, the collection unit selects the optimal data collection method based on feedback provided by the child in the past. The collection unit can also customize an effective data collection method based on the child's past feedback. The collection unit can also adjust the timing and means of data collection by reflecting the child's feedback. This makes it possible to customize the optimal data collection method based on the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0036] During evaluation, the evaluation unit can adjust the level of detail of the evaluation based on the child's learning history. For example, the evaluation unit can provide a detailed evaluation for subjects in which the child is good based on the child's learning history. The evaluation unit can also provide a simple evaluation for subjects in which the child is weak based on the child's learning history. The evaluation unit can also adjust the level of detail of the evaluation to an appropriate level based on the child's learning history. This makes it possible to adjust the level of detail of the evaluation based on the learning history. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the child's learning history data into the generation AI and have the generation AI adjust the level of detail of the evaluation.

[0037] During evaluation, the evaluation unit can apply different evaluation algorithms depending on the child's interests. For example, the evaluation unit can apply a detailed evaluation algorithm to subjects in which the child is interested. The evaluation unit can also apply a simple evaluation algorithm to subjects in which the child is not interested. The evaluation unit can also select the optimal evaluation algorithm based on the child's interests. This makes it possible to apply the optimal evaluation algorithm depending on the interests. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the child's interest data into the generation AI and have the generation AI select the evaluation algorithm.

[0038] During evaluation, the evaluation unit can improve the accuracy of the evaluation by referring to the child's past evaluation results. The evaluation unit improves the accuracy of the evaluation, for example, based on the child's past evaluation results. The evaluation unit can also eliminate evaluation bias from the child's past evaluation results. The evaluation unit can also optimize the evaluation algorithm by referring to the child's past evaluation results. This makes it possible to improve the accuracy of the evaluation based on the past evaluation results. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the child's past evaluation result data into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0039] During evaluation, the evaluation unit can determine the priority of evaluation based on the time of submission of the child's learning history. For example, the evaluation unit prioritizes evaluation of the child's most recently submitted learning history. The evaluation unit can also determine the priority of evaluation based on the time of submission of the child's learning history. The evaluation unit can also adjust the order of evaluation taking into account the time of submission of the child's learning history. This makes it possible to determine the priority of evaluation based on the time of submission of the learning history. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the child's learning history data into the generation AI and have the generation AI determine the priority of evaluation.

[0040] The evaluation unit can adjust the order of evaluation based on the relationships between children during evaluation. The evaluation unit can adjust the order of evaluation based on, for example, the relevance of the child's learning history. The evaluation unit can also adjust the order of evaluation based on the child's interests. The evaluation unit can also adjust the order of evaluation based on the child's learning progress. This makes it possible to adjust the order of evaluation based on relevance. Some or all of the above-mentioned processing in the evaluation unit can be performed using AI, for example, or without using AI. For example, the evaluation unit can input the child's learning history data into the generation AI and cause the generation AI to adjust the order of evaluation.

[0041] During the evaluation, the evaluation unit can adjust the use of technical terminology in the evaluation according to the child's level of expertise. The evaluation unit can, for example, adjust the technical terminology in the evaluation according to the child's level of expertise. The evaluation unit can also simplify the technical terminology so that it is easier for the child to understand. The evaluation unit can also use appropriate technical terminology based on the child's level of expertise. This makes it possible to adjust the use of technical terminology in the evaluation according to the level of expertise. Some or all of the above-mentioned processing in the evaluation unit can be performed using AI, for example, or can be performed without using AI. For example, the evaluation unit can input the child's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0042] When providing the curriculum, the provision unit can adjust the level of detail of the curriculum based on the child's learning history. For example, the provision unit can provide a detailed curriculum for subjects in which the child is good based on the child's learning history. The provision unit can also provide a simple curriculum for subjects in which the child is weak based on the child's learning history. The provision unit can also adjust the level of detail of the curriculum to an appropriate level based on the child's learning history. This makes it possible to adjust the level of detail of the curriculum based on the learning history. Some or all of the above-mentioned processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input the child's learning history data into a generation AI and cause the generation AI to adjust the level of detail of the curriculum.

[0043] When providing a curriculum, the provision unit can apply different curriculum algorithms depending on the child's interests. For example, the provision unit can apply a detailed curriculum algorithm to subjects in which the child is interested. The provision unit can also apply a simple curriculum algorithm to subjects in which the child is not interested. The provision unit can also select an optimal curriculum algorithm based on the child's interests. This makes it possible to apply an optimal curriculum algorithm depending on the interests. Some or all of the above-mentioned processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input the child's interest data into the generation AI and cause the generation AI to select a curriculum algorithm.

[0044] When providing a curriculum, the providing unit can improve the accuracy of the curriculum by referring to the child's past curriculum results. The providing unit improves the accuracy of the curriculum, for example, based on the child's past curriculum results. The providing unit can also eliminate curriculum bias from the child's past curriculum results. The providing unit can also optimize the curriculum algorithm by referring to the child's past curriculum results. This makes it possible to improve the accuracy of the curriculum based on the past curriculum results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the child's past curriculum result data into the generating AI and cause the generating AI to improve the accuracy of the curriculum.

[0045] When providing a curriculum, the providing unit can determine the priority of the curriculum based on the time of submission of the child's learning history. For example, the providing unit preferentially reflects the child's most recently submitted learning history in the curriculum. The providing unit can also determine the priority of the curriculum based on the time of submission of the child's learning history. The providing unit can also adjust the order of the curricula taking into account the time of submission of the child's learning history. This makes it possible to determine the priority of the curriculum based on the time of submission of the learning history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the child's learning history data into a generating AI and have the generating AI determine the priority of the curriculum.

[0046] When providing the curriculum, the providing unit can adjust the order of the curriculum based on the relationships between children. The providing unit can adjust the order of the curriculum based on, for example, the relevance of the child's learning history. The providing unit can also adjust the order of the curriculum based on the child's interests. The providing unit can also adjust the order of the curriculum based on the child's learning progress. This makes it possible to adjust the order of the curriculum based on the relevance. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without using AI. For example, the providing unit can input the child's learning history data into the generating AI and cause the generating AI to adjust the order of the curriculum.

[0047] When providing the curriculum, the providing unit can adjust the use of technical terminology in the curriculum according to the child's level of expertise. The providing unit, for example, adjusts the technical terminology in the curriculum according to the child's level of expertise. The providing unit can also simplify technical terminology so that it is easier for the child to understand. The providing unit can also use appropriate technical terminology based on the child's level of expertise. This makes it possible to adjust the use of technical terminology in the curriculum according to the level of expertise. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the child's level of expertise data into the generating AI and cause the generating AI to use technical terminology.

[0048] During feedback analysis, the feedback analysis unit can optimize the analysis algorithm by referring to past feedback data. The feedback analysis unit, for example, optimizes the analysis algorithm based on past feedback data. The feedback analysis unit can also eliminate feedback bias from the past feedback data. The feedback analysis unit can also improve the analysis algorithm by referring to the past feedback data. This makes it possible to optimize the analysis algorithm based on the past feedback data. Some or all of the above-described processing in the feedback analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback analysis unit can input past feedback data into a generation AI and cause the generation AI to optimize the analysis algorithm.

[0049] The feedback analysis unit can perform feedback analysis while taking into account attribute information of the child and guardian. The feedback analysis unit performs feedback analysis based on, for example, the child's age and grade. The feedback analysis unit can also perform feedback analysis based on the guardian's occupation and educational background. The feedback analysis unit can also perform optimal feedback analysis by taking into account attribute information of the child and guardian. This allows optimal feedback analysis to be performed based on the attribute information. Some or all of the above-described processing in the feedback analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback analysis unit can input attribute information data of the child and guardian into the generation AI and cause the generation AI to perform feedback analysis.

[0050] The feedback analysis unit can adjust the level of detail of the analysis based on the child's learning progress during feedback analysis. For example, if the child's learning progress is good, the feedback analysis unit performs a detailed feedback analysis. Furthermore, if the child's learning progress is lagging behind, the feedback analysis unit can also perform a simple feedback analysis. Furthermore, the feedback analysis unit can adjust the level of detail of the feedback analysis based on the child's learning progress. This makes it possible to adjust the level of detail of the feedback analysis based on the learning progress. Some or all of the above-described processing in the feedback analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback analysis unit can input the child's learning progress data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0051] The feedback analysis unit can weight the analysis based on the time of submission of the feedback when analyzing the feedback. For example, the feedback analysis unit prioritizes analysis of the most recently submitted feedback. The feedback analysis unit can also weight the analysis based on the time of submission of the feedback. The feedback analysis unit can also adjust the order of analysis taking into account the time of submission of the feedback. This makes it possible to weight the analysis of the feedback based on the time of submission. Some or all of the above-described processing in the feedback analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback analysis unit can input feedback submission time data to the generation AI and cause the generation AI to perform analysis weighting.

[0052] During feedback analysis, the feedback analysis unit can integrate information from multiple data sources to expand the analysis data. For example, the feedback analysis unit integrates and analyzes a child's learning history and parental feedback. The feedback analysis unit can also integrate and analyze school grade data and parental feedback. The feedback analysis unit can also integrate and analyze a child's social media activity and parental feedback. This makes it possible to integrate information from different data sources and expand the analysis data. Some or all of the above-described processing in the feedback analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback analysis unit can input information from multiple data sources into a generation AI and have the generation AI integrate the analysis data.

[0053] The feedback analysis unit can adjust the level of detail of the analysis based on the child's learning environment during feedback analysis. For example, the feedback analysis unit performs a detailed feedback analysis when the child's learning environment is in good condition. The feedback analysis unit can also perform a simple feedback analysis when the child's learning environment is insufficient. The feedback analysis unit can also adjust the level of detail of the feedback analysis based on the child's learning environment. This makes it possible to adjust the level of detail of the feedback analysis based on the learning environment. Some or all of the above-described processing in the feedback analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback analysis unit can input data about the child's learning environment to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0054] When providing self-study support, the support unit can provide optimal support by referring to the child's past learning history. The support unit can provide optimal support based on, for example, the child's past learning history. The support unit can also select an effective support method from the child's past learning history. The support unit can also customize the support content by referring to the child's past learning history. This makes it possible to provide optimal support based on the past learning history. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or can be performed without using AI. For example, the support unit can input the child's past learning history data into the generation AI and cause the generation AI to provide optimal support.

[0055] The support unit can adjust the support content based on the child's current learning situation when providing self-study support. The support unit can provide optimal support based on the child's current learning situation, for example. The support unit can also select an effective support method based on the child's current learning situation. The support unit can also customize the support content by referring to the child's current learning situation. This allows the support content to be customized based on the current learning situation. Some or all of the above-described processing in the support unit can be performed using AI, for example, or without AI. For example, the support unit can input data on the child's current learning situation into the generation AI and have the generation AI adjust the support content.

[0056] The support unit can adjust the support method according to the child's learning style when supporting self-study. For example, if the child has a visual learning style, the support unit can provide visual support. Also, if the child has an auditory learning style, the support unit can provide auditory support. The support unit can also select the optimal support method based on the child's learning style. This makes it possible to provide the optimal support method according to the learning style. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the child's learning style data into the generation AI and have the generation AI adjust the support method.

[0057] When supporting self-study, the support unit can select the optimal support method taking into account the child's geographical location information. For example, if the child is in a specific area, the support unit can support learning content related to that area. Furthermore, if the child is traveling, the support unit can also support learning content related to the travel destination. Furthermore, if the child is at school, the support unit can also support learning content related to the school curriculum. This makes it possible to provide the optimal support method based on the geographical location information. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the child's geographical location information data into the generation AI and cause the generation AI to select the optimal support method.

[0058] The support unit can analyze the child's online activities and suggest support content when supporting self-study. For example, the support unit can suggest support content based on the interests the child has shared on social media. The support unit can also suggest content useful for learning based on the child's social media activities. The support unit can also suggest related learning content based on the activities of the child's friends on social media. This makes it possible to suggest optimal support content based on social media activities. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the child's online activity data into a generation AI and have the generation AI suggest support content.

[0059] The support unit can customize the support method by reflecting the child's past feedback when supporting self-study. For example, the support unit selects the optimal support method based on feedback provided by the child in the past. The support unit can also customize an effective support method based on the child's past feedback. The support unit can also adjust the timing and means of support by reflecting the child's feedback. This makes it possible to customize the optimal support method based on the past feedback. Some or all of the above-described processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the child's past feedback data into the generation AI and cause the generation AI to customize the support method.

[0060] During progress management, the progress management unit can select the optimal progress management method by referring to the child's past learning history. The progress management unit selects the optimal progress management method based on, for example, the child's past learning history. The progress management unit can also select an effective progress management method from the child's past learning history. The progress management unit can also customize the progress management method by referring to the child's past learning history. This makes it possible to select the optimal progress management method based on the past learning history. Some or all of the above-described processing in the progress management unit may be performed using, for example, AI, or may be performed without using AI. For example, the progress management unit can input the child's past learning history data into the generation AI and cause the generation AI to select the optimal progress management method.

[0061] During progress management, the progress management unit can adjust the progress management means based on the child's current learning situation. The progress management unit, for example, selects the optimal progress management means based on the child's current learning situation. The progress management unit can also select an effective progress management method based on the child's current learning situation. The progress management unit can also customize the progress management means by referring to the child's current learning situation. This makes it possible to customize the progress management means based on the current learning situation. Some or all of the above-described processing in the progress management unit may be performed using AI, for example, or may be performed without using AI. For example, the progress management unit can input data on the child's current learning situation into the generation AI and cause the generation AI to adjust the progress management means.

[0062] The progress management unit can adjust the progress management method according to the child's learning style when managing progress. For example, if the child has a visual learning style, the progress management unit can perform visual progress management. Furthermore, if the child has an auditory learning style, the progress management unit can also perform auditory progress management. The progress management unit can also select the optimal progress management method based on the child's learning style. This makes it possible to provide the optimal progress management method according to the learning style. Some or all of the above-described processing in the progress management unit may be performed using AI, for example, or may be performed without using AI. For example, the progress management unit can input the child's learning style data into the generation AI and have the generation AI adjust the progress management method.

[0063] The progress management unit can select the optimal progress management method by taking into account the child's geographical location information when managing progress. For example, if the child is in a specific area, the progress management unit can manage the progress of learning content related to that area. Furthermore, if the child is traveling, the progress management unit can also manage the progress of learning content related to the travel destination. Furthermore, if the child is at school, the progress management unit can also manage the progress of learning content related to the school curriculum. This makes it possible to provide an optimal progress management method based on the geographical location information. Some or all of the above-mentioned processing in the progress management unit may be performed using, for example, AI, or may be performed without using AI. For example, the progress management unit can input the child's geographical location information data into the generation AI and cause the generation AI to select the optimal progress management method.

[0064] During progress management, the progress management unit can analyze the child's online activities and suggest progress management methods. The progress management unit can suggest progress management methods based on, for example, the interests the child has shared on social media. The progress management unit can also suggest progress management methods that are useful for learning based on the child's social media activities. The progress management unit can also suggest related progress management methods based on the activities of the child's friends on social media. This makes it possible to suggest optimal progress management methods based on social media activities. Some or all of the above-described processing in the progress management unit may be performed using, for example, AI, or may be performed without using AI. For example, the progress management unit can input the child's online activity data into a generation AI and have the generation AI suggest progress management methods.

[0065] The progress management unit can customize the progress management method by reflecting the child's past feedback during progress management. The progress management unit selects the optimal progress management method based on, for example, feedback provided by the child in the past. The progress management unit can also customize an effective progress management method based on the child's past feedback. The progress management unit can also adjust the timing and means of progress management by reflecting the child's feedback. This makes it possible to customize the optimal progress management method based on the past feedback. Some or all of the above-described processing in the progress management unit may be performed using, for example, AI, or may be performed without using AI. For example, the progress management unit can input the child's past feedback data into the generation AI and have the generation AI customize the progress management method.

[0066] The alliance unit can optimize the alliance algorithm by referring to past alliance data when alliances are made. The alliance unit, for example, optimizes the alliance algorithm based on past alliance data. The alliance unit can also eliminate alliance bias from the past alliance data. The alliance unit can also improve the alliance algorithm by referring to the past alliance data. This makes it possible to optimize the alliance algorithm based on the past alliance data. Some or all of the above-mentioned processing in the alliance unit may be performed using AI, for example, or may be performed without using AI. For example, the alliance unit can input past alliance data into the generation AI and cause the generation AI to optimize the alliance algorithm.

[0067] When forming an alliance, the alliance unit can form an alliance based on the child's attribute information. The alliance unit can form an alliance based on the child's age or grade, for example. The alliance unit can also form an alliance based on the child's interests. The alliance unit can also form an alliance based on the child's learning progress. This makes it possible to form an optimal alliance based on the attribute information. Some or all of the above-mentioned processing in the alliance unit can be performed using AI, for example, or can be performed without using AI. For example, the alliance unit can input the child's attribute information data into the generation AI and cause the generation AI to execute the alliance.

[0068] The association unit can adjust the level of detail of the association based on the child's learning environment during association. For example, if the child's learning environment is well-equipped, the association unit can perform detailed association. Furthermore, if the child's learning environment is insufficient, the association unit can perform simple association. Furthermore, the association unit can adjust the level of detail of the association based on the child's learning environment. This makes it possible to adjust the level of detail of the association based on the learning environment. Some or all of the above-described processing in the association unit may be performed using AI, for example, or may be performed without using AI. For example, the association unit can input data about the child's learning environment into the generation AI and cause the generation AI to adjust the level of detail of the association.

[0069] At the time of alliance, the alliance unit can weight the alliance data based on the time of submission of the alliance. For example, the alliance unit prioritizes analysis of recently submitted alliance data. The alliance unit can also weight the alliance data based on the time of submission of the alliance. The alliance unit can also adjust the order of the alliance data taking into account the time of submission of the alliance. This allows the alliance data to be weighted based on the time of submission. Some or all of the above-mentioned processing in the alliance unit may be performed using AI, for example, or may be performed without using AI. For example, the alliance unit can input data on the time of submission of the alliance to the generation AI and cause the generation AI to weight the alliance data.

[0070] The affiliation unit can integrate information from multiple data sources to expand the affiliation data during affiliation. For example, the affiliation unit can integrate a child's learning history with partner educational service data to form an affiliation. The affiliation unit can also integrate school grade data with partner educational service data to form an affiliation. The affiliation unit can also integrate a child's social media activity with partner educational service data to form an affiliation. This makes it possible to integrate information from different data sources and expand the affiliation data. Some or all of the above-mentioned processing in the affiliation unit may be performed, for example, using AI or without AI. For example, the affiliation unit can input information from multiple data sources into the generation AI and cause the generation AI to integrate the affiliation data.

[0071] The association unit can adjust the level of detail of the association based on the child's learning environment during association. For example, if the child's learning environment is well-equipped, the association unit can perform detailed association. Furthermore, if the child's learning environment is insufficient, the association unit can perform simple association. Furthermore, the association unit can adjust the level of detail of the association based on the child's learning environment. This makes it possible to adjust the level of detail of the association based on the learning environment. Some or all of the above-described processing in the association unit may be performed using AI, for example, or may be performed without using AI. For example, the association unit can input data about the child's learning environment into the generation AI and cause the generation AI to adjust the level of detail of the association.

[0072]

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The education support system may further include an environment monitoring unit that monitors the child's learning environment in real time. The environment monitoring unit may monitor, for example, the temperature, humidity, and lighting conditions of the child's learning environment and adjust them to provide an optimal learning environment. The environment monitoring unit may also provide a noise canceling function if the child's learning environment is noisy. Furthermore, the environment monitoring unit may provide appropriate lighting if the child's learning environment is dark. This allows the child to study in an optimal learning environment.

[0075] The education support system may further include a style adaptation unit that provides learning content according to a child's learning style. For example, the style adaptation unit may provide visual content if the child has a visual learning style. The style adaptation unit may also provide audio content if the child has an auditory learning style. The style adaptation unit may also provide interactive content if the child has a tactile learning style. This allows for the provision of content optimal for the child's learning style and supports effective learning.

[0076] The education support system may further include a notification unit that notifies parents and teachers of a child's learning progress in real time. For example, the notification unit may send a notification to parents and teachers when a child achieves a learning goal. The notification unit may also send an alert when a child falls behind in their studies. Furthermore, the notification unit may provide parents and teachers with regular reports on the child's learning progress, allowing them to understand the child's learning situation and provide appropriate support.

[0077] The education support system may further include a data sharing unit that anonymizes children's learning data and provides it to research institutions. For example, the data sharing unit anonymizes children's learning data and provides it to research institutions for educational research. The data sharing unit may also analyze children's learning data and use it to improve education. Furthermore, the data sharing unit may utilize children's learning data to develop new educational programs. This may contribute to research and development to improve the quality of education.

[0078] The education support system may further include a posture monitoring unit that monitors a child's posture while studying. The posture monitoring unit, for example, monitors whether the child is studying in the correct posture and sends an alert if the child's posture is poor. The posture monitoring unit may also encourage the child to take a break if the child has been studying in the same posture for a long period of time. Furthermore, the posture monitoring unit may analyze the child's posture data and provide advice on how to provide a healthy learning environment. This allows the child to continue studying in a healthy posture.

[0079] The processing flow of the first embodiment will be briefly explained below.

[0080] Step 1: The collection unit collects data such as the child's learning history, test results, and interests. For example, the collection unit retrieves the child's past test results from a database. The collection unit may also collect the child's learning history on an online learning platform. The collection unit may also collect the results of the child's responses to a questionnaire. Step 2: The evaluation unit evaluates the child's ability based on the data collected by the collection unit. For example, the evaluation unit analyzes test results to identify the child's strong and weak subjects. The evaluation unit can also analyze learning history to understand the child's learning patterns. The evaluation unit can also analyze interest data to identify the areas in which the child is interested. Step 3: The provider provides an individual curriculum based on the results of the evaluation by the evaluation department. For example, the provider provides teaching materials to further develop the child's strong subjects. The provider can also provide practice problems to help the child overcome weak subjects. The provider can also provide teaching materials that will attract the child's interest. Step 4: The feedback analysis unit analyzes feedback from the child and the parent based on the curriculum provided by the provision unit. For example, the feedback analysis unit collects feedback from the child about what he or she has learned. The feedback analysis unit can also collect feedback provided by the parent. The feedback analysis unit can also analyze the collected feedback and adjust the learning plan. Step 5: The support unit supports self-study based on the feedback analyzed by the feedback analysis unit. For example, the support unit provides appropriate advice when the child is studying on their own. The support unit can also provide hints on how to solve difficult problems when the child is faced with them. The support unit can also provide support to increase the child's motivation when they get stuck in their studies. Step 6: The progress management unit manages the progress of the learning supported by the support unit. For example, the progress management unit visualizes the child's learning situation and manages the progress. The progress management unit can also display the learning achievement level and progress status in graphs and charts. The progress management unit can also set learning goals and create progress reports. Step 7: The Partnering Department partners with excellent educational services based on the progress managed by the Progress Management Department. For example, the Partnering Department utilizes teaching materials and programs provided by the partner educational services. The Partnering Department can also use online classes and educational apps. The Partnering Department can also work with partner educational institutions to provide the best learning environment for children.

[0081] (Example 2) An educational support system according to an embodiment of the present invention utilizes a generative AI to determine a child's suitability and provide methods, learning environments, and services for receiving a high-quality education. The educational support system collects data such as the child's learning history, test results, and interests, and the generative AI analyzes the data to evaluate the child's abilities. The generative AI then provides an individualized curriculum and incorporates feedback from the child and their parents. The generative AI then supports self-study, visualizes learning, and manages progress. Finally, the system partners with leading educational services to provide high-quality education. For example, the educational support system collects data such as the child's learning history, test results, and interests. The generative AI then analyzes this data to identify the child's strengths and weaknesses. The generative AI then creates an individualized curriculum and provides the child with an optimal learning plan. The generative AI then collects feedback from the child and their parents, analyzes it, and adjusts the learning plan. The generative AI then supports self-study and provides appropriate advice and tips. Finally, the generative AI visualizes learning progress and manages progress. This allows the educational support system to determine a child's suitability and provide methods, learning environments, and services for receiving a high-quality education. This allows the educational support system to evaluate a child's abilities, provide an individualized curriculum, support self-study, and manage learning progress. For example, data such as a child's learning history, test results, and interests is collected and analyzed by the generative AI to identify the child's strong and weak subjects. The generative AI then creates an individualized curriculum and provides the child with the optimal learning plan. Furthermore, feedback from the child and parents is collected, analyzed, and the generative AI adjusts the learning plan. The generative AI then supports self-study and provides appropriate advice and hints. Finally, the generative AI visualizes learning progress and manages it. This allows the educational support system to determine what is suitable for a child and provide ways to receive a high-quality education, as well as learning environments and services.

[0082] An education support system according to an embodiment includes a collection unit, an evaluation unit, a provision unit, a feedback analysis unit, a support unit, a progress management unit, and a collaboration unit. The collection unit collects data such as a child's learning history, test results, and interests. For example, the collection unit obtains the results of tests the child has taken in the past from a database. The collection unit can also collect the child's learning history on an online learning platform. The collection unit can also collect the results of the child's responses to questionnaires. The evaluation unit evaluates the child's ability based on the data collected by the collection unit. For example, the evaluation unit analyzes test results to identify the child's strong and weak subjects. The evaluation unit can also analyze the learning history to understand the child's learning patterns. The evaluation unit can also analyze interest data to identify the child's areas of interest. The provision unit provides an individualized curriculum based on the results of the evaluation by the evaluation unit. For example, the provision unit provides teaching materials to further improve the child's strong subjects. The provision unit can also provide practice questions to help the child overcome their weak subjects. The provision unit can also provide teaching materials that will attract the child's interest. The feedback analysis unit analyzes feedback from the child and parent based on the curriculum provided by the provision unit. For example, the feedback analysis unit collects feedback on what the child has learned. The feedback analysis unit can also collect feedback provided by parents. The feedback analysis unit can also analyze the collected feedback and adjust the learning plan. The support unit supports self-study based on the feedback analyzed by the feedback analysis unit. For example, the support unit provides appropriate advice when the child progresses with their own learning. The support unit can also provide hints on how to solve difficult problems when the child is faced with them. The support unit can also provide support to increase motivation when the child gets stuck in their studies. The progress management unit manages the progress of learning supported by the support unit. For example, the progress management unit visualizes the child's learning situation and manages progress.The progress management unit can also display the learning achievement level and progress status in graphs and charts. The progress management unit can also set learning goals and create progress reports. The collaboration unit collaborates with excellent educational services based on the progress managed by the progress management unit. For example, the collaboration unit utilizes teaching materials and programs provided by partner educational services. The collaboration unit can also use online classes and educational apps. The collaboration unit can also collaborate with partner educational institutions to provide an optimal learning environment for children. As a result, the education support system according to the embodiment can evaluate a child's abilities, provide an individualized curriculum, support self-study, and manage learning progress. For example, the collection unit collects data such as a child's learning history, test results, and interests, and the evaluation unit evaluates the child's abilities based on the collected data. The provision unit provides an individualized curriculum based on the evaluation results, and the feedback analysis unit analyzes feedback from the child and their guardian. The support unit supports self-study, and the progress management unit manages learning progress. The collaboration unit collaborates with excellent educational services. This allows the educational support system to determine what is suitable for each child and provide ways to receive a high-quality education, as well as learning opportunities and services.

[0083] The collection unit can estimate the child's emotions and adjust the timing of data collection based on the estimated child's emotions. For example, if the child is relaxed, the collection unit can collect data while the child is studying, maintaining a natural learning environment. Furthermore, if the child is feeling stressed, the collection unit can collect data during breaks to avoid interrupting the child's study. Furthermore, if the child is concentrating, the collection unit can collect data in accordance with the child's study progress, so as not to disrupt the flow of the study. This allows data to be collected at the optimal timing according to the child's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input image data of a child captured by a camera into the generation AI and cause the generation AI to estimate the child's emotions.

[0084] The collection unit can analyze the child's past learning history and select the optimal data collection method. The collection unit, for example, analyzes the learning materials and learning methods used by the child in the past and collects data in a similar manner. The collection unit can also select an effective data collection method for a specific time period from the child's learning history. The collection unit can also select the most effective data collection means (audio, text, image, etc.) based on the child's learning history. This makes it possible to select the optimal data collection method based on the child's past learning history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the child's past learning history data into the generation AI and have the generation AI select the optimal data collection method.

[0085] When collecting data, the collection unit can filter the data based on the child's current learning situation and interests. For example, the collection unit collects only data related to the subject the child is currently studying. The collection unit can also prioritize the collection of highly relevant data based on the child's interests. The collection unit can also filter and collect only necessary data according to the child's learning progress. This makes it possible to collect highly relevant data based on the child's current learning situation and interests. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the child's current learning situation data into the generation AI and have the generation AI perform the filtering.

[0086] When collecting data, the collection unit can select an appropriate collection means depending on the child's input method. For example, if the child uses voice input, the collection unit can prioritize collecting voice data. Furthermore, if the child uses text input, the collection unit can also prioritize collecting text data. Furthermore, if the child uses image input, the collection unit can also prioritize collecting image data. This makes it possible to select the optimal data collection means depending on the child's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's input data into a generation AI and cause the generation AI to select the optimal collection means.

[0087] The collection unit can estimate the child's emotions and determine the priority of data to be collected based on the estimated child's emotions. For example, if the child is excited, the collection unit can prioritize collecting data that interests the child. Furthermore, if the child is tired, the collection unit can prioritize collecting data that helps the child relax. Furthermore, if the child is concentrating, the collection unit can prioritize collecting data that is useful for learning. This allows the priority of data to be collected to be determined according to the child's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input image data of a child captured by a camera into the generation AI and cause the generation AI to estimate the child's emotions.

[0088] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the child's geographical location information. For example, if the child is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, if the child is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, if the child is at school, the collection unit can prioritize collecting data related to the school curriculum. This makes it possible to collect highly relevant data based on geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's geographical location information data into the generation AI and cause the generation AI to collect highly relevant data.

[0089] During data collection, the collection unit can analyze the child's online activities and collect related data. For example, the collection unit collects data based on the interests the child has shared on social media. The collection unit can also collect data useful for learning from the child's social media activities. The collection unit can also collect related data by referring to the activities of the child's friends on social media. This makes it possible to collect related data based on social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the child's online activity data into the generation AI and cause the generation AI to collect related data.

[0090] The collection unit can customize the collection method by reflecting the child's past feedback when collecting data. For example, the collection unit selects the optimal data collection method based on feedback provided by the child in the past. The collection unit can also customize an effective data collection method based on the child's past feedback. The collection unit can also adjust the timing and means of data collection by reflecting the child's feedback. This makes it possible to customize the optimal data collection method based on the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0091] The evaluation unit can estimate the child's emotions and adjust the way the evaluation is expressed based on the estimated child's emotions. For example, if the child is relaxed, the evaluation unit can provide a detailed evaluation. If the child is nervous, the evaluation unit can provide a simple and easy-to-understand evaluation. If the child is excited, the evaluation unit can provide a visually appealing evaluation. This allows the way the evaluation is expressed to be adjusted according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using AI, for example, or without AI. For example, the evaluation unit can input image data of a child captured with a camera into the generation AI and cause the generation AI to estimate the child's emotions.

[0092] During evaluation, the evaluation unit can adjust the level of detail of the evaluation based on the child's learning history. For example, the evaluation unit can provide a detailed evaluation for subjects in which the child is good based on the child's learning history. The evaluation unit can also provide a simple evaluation for subjects in which the child is weak based on the child's learning history. The evaluation unit can also adjust the level of detail of the evaluation to an appropriate level based on the child's learning history. This makes it possible to adjust the level of detail of the evaluation based on the learning history. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the child's learning history data into the generation AI and have the generation AI adjust the level of detail of the evaluation.

[0093] During evaluation, the evaluation unit can apply different evaluation algorithms depending on the child's interests. For example, the evaluation unit can apply a detailed evaluation algorithm to subjects in which the child is interested. The evaluation unit can also apply a simple evaluation algorithm to subjects in which the child is not interested. The evaluation unit can also select the optimal evaluation algorithm based on the child's interests. This makes it possible to apply the optimal evaluation algorithm depending on the interests. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the child's interest data into the generation AI and have the generation AI select the evaluation algorithm.

[0094] During evaluation, the evaluation unit can improve the accuracy of the evaluation by referring to the child's past evaluation results. The evaluation unit improves the accuracy of the evaluation, for example, based on the child's past evaluation results. The evaluation unit can also eliminate evaluation bias from the child's past evaluation results. The evaluation unit can also optimize the evaluation algorithm by referring to the child's past evaluation results. This makes it possible to improve the accuracy of the evaluation based on the past evaluation results. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the child's past evaluation result data into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0095] The evaluation unit can estimate the child's emotions and adjust the duration of the evaluation based on the estimated child's emotions. For example, if the child is relaxed, the evaluation unit can provide a detailed evaluation. If the child is nervous, the evaluation unit can provide a short and to-the-point evaluation. If the child is excited, the evaluation unit can provide a visually appealing evaluation. This allows the duration of the evaluation to be adjusted according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using AI, for example, or without AI. For example, the evaluation unit can input image data of the child captured by a camera into the generation AI and cause the generation AI to estimate the child's emotions.

[0096] During evaluation, the evaluation unit can determine the priority of evaluation based on the time of submission of the child's learning history. For example, the evaluation unit prioritizes evaluation of the child's most recently submitted learning history. The evaluation unit can also determine the priority of evaluation based on the time of submission of the child's learning history. The evaluation unit can also adjust the order of evaluation taking into account the time of submission of the child's learning history. This makes it possible to determine the priority of evaluation based on the time of submission of the learning history. Some or all of the above-mentioned processing in the evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the evaluation unit can input the child's learning history data into the generation AI and have the generation AI determine the priority of evaluation.

[0097] The evaluation unit can adjust the order of evaluation based on the relationships between children during evaluation. The evaluation unit can adjust the order of evaluation based on, for example, the relevance of the child's learning history. The evaluation unit can also adjust the order of evaluation based on the child's interests. The evaluation unit can also adjust the order of evaluation based on the child's learning progress. This makes it possible to adjust the order of evaluation based on relevance. Some or all of the above-mentioned processing in the evaluation unit can be performed using AI, for example, or without using AI. For example, the evaluation unit can input the child's learning history data into the generation AI and cause the generation AI to adjust the order of evaluation.

[0098] During the evaluation, the evaluation unit can adjust the use of technical terminology in the evaluation according to the child's level of expertise. The evaluation unit can, for example, adjust the technical terminology in the evaluation according to the child's level of expertise. The evaluation unit can also simplify the technical terminology so that it is easier for the child to understand. The evaluation unit can also use appropriate technical terminology based on the child's level of expertise. This makes it possible to adjust the use of technical terminology in the evaluation according to the level of expertise. Some or all of the above-mentioned processing in the evaluation unit can be performed using AI, for example, or can be performed without using AI. For example, the evaluation unit can input the child's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0099] The providing unit can estimate the child's emotions and adjust the way the curriculum is presented based on the estimated child's emotions. For example, if the child is relaxed, the providing unit can provide a detailed curriculum. Furthermore, if the child is nervous, the providing unit can provide a simple and easy-to-understand curriculum. Furthermore, if the child is excited, the providing unit can provide a visually appealing curriculum. This allows the way the curriculum is presented to be adjusted according to the child's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input image data of a child captured by a camera into the generation AI and cause the generation AI to estimate the child's emotions.

[0100] When providing the curriculum, the provision unit can adjust the level of detail of the curriculum based on the child's learning history. For example, the provision unit can provide a detailed curriculum for subjects in which the child is good based on the child's learning history. The provision unit can also provide a simple curriculum for subjects in which the child is weak based on the child's learning history. The provision unit can also adjust the level of detail of the curriculum to an appropriate level based on the child's learning history. This makes it possible to adjust the level of detail of the curriculum based on the learning history. Some or all of the above-mentioned processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input the child's learning history data into a generation AI and cause the generation AI to adjust the level of detail of the curriculum.

[0101] When providing a curriculum, the provision unit can apply different curriculum algorithms depending on the child's interests. For example, the provision unit can apply a detailed curriculum algorithm to subjects in which the child is interested. The provision unit can also apply a simple curriculum algorithm to subjects in which the child is not interested. The provision unit can also select an optimal curriculum algorithm based on the child's interests. This makes it possible to apply an optimal curriculum algorithm depending on the interests. Some or all of the above-mentioned processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input the child's interest data into the generation AI and cause the generation AI to select a curriculum algorithm.

[0102] When providing a curriculum, the providing unit can improve the accuracy of the curriculum by referring to the child's past curriculum results. The providing unit improves the accuracy of the curriculum, for example, based on the child's past curriculum results. The providing unit can also eliminate curriculum bias from the child's past curriculum results. The providing unit can also optimize the curriculum algorithm by referring to the child's past curriculum results. This makes it possible to improve the accuracy of the curriculum based on the past curriculum results. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the child's past curriculum result data into the generating AI and cause the generating AI to improve the accuracy of the curriculum.

[0103] The providing unit can estimate the child's emotions and adjust the duration of the curriculum based on the estimated child's emotions. For example, if the child is relaxed, the providing unit can provide a detailed curriculum. If the child is nervous, the providing unit can provide a short, concise curriculum. If the child is excited, the providing unit can provide a visually appealing curriculum. This allows the duration of the curriculum to be adjusted according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input image data of a child captured by a camera into the generation AI and cause the generation AI to estimate the child's emotions.

[0104] When providing a curriculum, the providing unit can determine the priority of the curriculum based on the time of submission of the child's learning history. For example, the providing unit preferentially reflects the child's most recently submitted learning history in the curriculum. The providing unit can also determine the priority of the curriculum based on the time of submission of the child's learning history. The providing unit can also adjust the order of the curricula taking into account the time of submission of the child's learning history. This makes it possible to determine the priority of the curriculum based on the time of submission of the learning history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the child's learning history data into a generating AI and have the generating AI determine the priority of the curriculum.

[0105] When providing the curriculum, the providing unit can adjust the order of the curriculum based on the relationships between children. The providing unit can adjust the order of the curriculum based on, for example, the relevance of the child's learning history. The providing unit can also adjust the order of the curriculum based on the child's interests. The providing unit can also adjust the order of the curriculum based on the child's learning progress. This makes it possible to adjust the order of the curriculum based on the relevance. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without using AI. For example, the providing unit can input the child's learning history data into the generating AI and cause the generating AI to adjust the order of the curriculum.

[0106] When providing the curriculum, the providing unit can adjust the use of technical terminology in the curriculum according to the child's level of expertise. The providing unit, for example, adjusts the technical terminology in the curriculum according to the child's level of expertise. The providing unit can also simplify technical terminology so that it is easier for the child to understand. The providing unit can also use appropriate technical terminology based on the child's level of expertise. This makes it possible to adjust the use of technical terminology in the curriculum according to the level of expertise. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the child's level of expertise data into the generating AI and cause the generating AI to use technical terminology.

[0107] The feedback analysis unit can estimate the emotions of the child and guardian and adjust the feedback method based on the estimated emotions. For example, if the child is relaxed, the feedback analysis unit can perform a detailed feedback analysis. Furthermore, if the guardian is stressed, the feedback analysis unit can perform a simple and easy-to-understand feedback analysis. Furthermore, if the child and guardian are excited, the feedback analysis unit can perform a visually appealing feedback analysis. This allows the feedback analysis method to be adjusted according to the emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the feedback analysis unit can be performed using, for example, AI, or without AI. For example, the feedback analysis unit can input image data of the child and guardian taken with a camera into the generation AI and cause the generation AI to estimate emotions.

[0108] During feedback analysis, the feedback analysis unit can optimize the analysis algorithm by referring to past feedback data. The feedback analysis unit, for example, optimizes the analysis algorithm based on past feedback data. The feedback analysis unit can also eliminate feedback bias from the past feedback data. The feedback analysis unit can also improve the analysis algorithm by referring to the past feedback data. This makes it possible to optimize the analysis algorithm based on the past feedback data. Some or all of the above-described processing in the feedback analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback analysis unit can input past feedback data into a generation AI and cause the generation AI to optimize the analysis algorithm.

[0109] The feedback analysis unit can perform feedback analysis while taking into account attribute information of the child and guardian. The feedback analysis unit performs feedback analysis based on, for example, the child's age and grade. The feedback analysis unit can also perform feedback analysis based on the guardian's occupation and educational background. The feedback analysis unit can also perform optimal feedback analysis by taking into account attribute information of the child and guardian. This allows optimal feedback analysis to be performed based on the attribute information. Some or all of the above-described processing in the feedback analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback analysis unit can input attribute information data of the child and guardian into the generation AI and cause the generation AI to perform feedback analysis.

[0110] The feedback analysis unit can adjust the level of detail of the analysis based on the child's learning progress during feedback analysis. For example, if the child's learning progress is good, the feedback analysis unit performs a detailed feedback analysis. Furthermore, if the child's learning progress is lagging behind, the feedback analysis unit can also perform a simple feedback analysis. Furthermore, the feedback analysis unit can adjust the level of detail of the feedback analysis based on the child's learning progress. This makes it possible to adjust the level of detail of the feedback analysis based on the learning progress. Some or all of the above-described processing in the feedback analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback analysis unit can input the child's learning progress data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0111] The feedback analysis unit can estimate the emotions of the child and guardian and determine the order of feedback based on the estimated emotions. For example, if the child is excited, the feedback analysis unit can prioritize analyzing interesting feedback. Furthermore, if the guardian is stressed, the feedback analysis unit can prioritize analyzing relaxing feedback. Furthermore, if the child and guardian are concentrating, the feedback analysis unit can prioritize analyzing feedback that is useful for learning. This allows the priority of feedback to be determined according to emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the feedback analysis unit can be performed using, for example, AI, or without AI. For example, the feedback analysis unit can input image data of the child and guardian taken with a camera into the generation AI and cause the generation AI to estimate emotions.

[0112] The feedback analysis unit can weight the analysis based on the time of submission of the feedback when analyzing the feedback. For example, the feedback analysis unit prioritizes analysis of the most recently submitted feedback. The feedback analysis unit can also weight the analysis based on the time of submission of the feedback. The feedback analysis unit can also adjust the order of analysis taking into account the time of submission of the feedback. This makes it possible to weight the analysis of the feedback based on the time of submission. Some or all of the above-described processing in the feedback analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback analysis unit can input feedback submission time data to the generation AI and cause the generation AI to perform analysis weighting.

[0113] During feedback analysis, the feedback analysis unit can integrate information from multiple data sources to expand the analysis data. For example, the feedback analysis unit integrates and analyzes a child's learning history and parental feedback. The feedback analysis unit can also integrate and analyze school grade data and parental feedback. The feedback analysis unit can also integrate and analyze a child's social media activity and parental feedback. This makes it possible to integrate information from different data sources and expand the analysis data. Some or all of the above-described processing in the feedback analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback analysis unit can input information from multiple data sources into a generation AI and have the generation AI integrate the analysis data.

[0114] The feedback analysis unit can adjust the level of detail of the analysis based on the child's learning environment during feedback analysis. For example, the feedback analysis unit performs a detailed feedback analysis when the child's learning environment is in good condition. The feedback analysis unit can also perform a simple feedback analysis when the child's learning environment is insufficient. The feedback analysis unit can also adjust the level of detail of the feedback analysis based on the child's learning environment. This makes it possible to adjust the level of detail of the feedback analysis based on the learning environment. Some or all of the above-described processing in the feedback analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback analysis unit can input data about the child's learning environment to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0115] The support unit can estimate the child's emotions and adjust the self-learning method based on the estimated child's emotions. For example, the support unit can provide detailed support when the child is relaxed. The support unit can also provide simple and easy-to-understand support when the child is nervous. The support unit can also provide visually appealing support when the child is excited. This allows the self-learning support method to be adjusted according to the emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the support unit can be performed using AI, for example, or without AI. For example, the support unit can input image data of the child captured by a camera into the generation AI and cause the generation AI to estimate the child's emotions.

[0116] When providing self-study support, the support unit can provide optimal support by referring to the child's past learning history. The support unit can provide optimal support based on, for example, the child's past learning history. The support unit can also select an effective support method from the child's past learning history. The support unit can also customize the support content by referring to the child's past learning history. This makes it possible to provide optimal support based on the past learning history. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or can be performed without using AI. For example, the support unit can input the child's past learning history data into the generation AI and cause the generation AI to provide optimal support.

[0117] The support unit can adjust the support content based on the child's current learning situation when providing self-study support. The support unit can provide optimal support based on the child's current learning situation, for example. The support unit can also select an effective support method based on the child's current learning situation. The support unit can also customize the support content by referring to the child's current learning situation. This allows the support content to be customized based on the current learning situation. Some or all of the above-described processing in the support unit can be performed using AI, for example, or without AI. For example, the support unit can input data on the child's current learning situation into the generation AI and have the generation AI adjust the support content.

[0118] The support unit can adjust the support method according to the child's learning style when supporting self-study. For example, if the child has a visual learning style, the support unit can provide visual support. Also, if the child has an auditory learning style, the support unit can provide auditory support. The support unit can also select the optimal support method based on the child's learning style. This makes it possible to provide the optimal support method according to the learning style. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the child's learning style data into the generation AI and have the generation AI adjust the support method.

[0119] The support unit can estimate the child's emotions and determine the order of self-study based on the estimated emotions. For example, if the child is excited, the support unit can prioritize support of learning content that interests the child. Furthermore, if the child is tired, the support unit can prioritize support of learning content that helps the child relax. Furthermore, if the child is concentrating, the support unit can prioritize support of content that is useful for learning. This allows the priority of self-study to be determined according to emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the support unit can input image data of the child captured by a camera into the generation AI and cause the generation AI to estimate the child's emotions.

[0120] When supporting self-study, the support unit can select the optimal support method taking into account the child's geographical location information. For example, if the child is in a specific area, the support unit can support learning content related to that area. Furthermore, if the child is traveling, the support unit can also support learning content related to the travel destination. Furthermore, if the child is at school, the support unit can also support learning content related to the school curriculum. This makes it possible to provide the optimal support method based on the geographical location information. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the child's geographical location information data into the generation AI and cause the generation AI to select the optimal support method.

[0121] The support unit can analyze the child's online activities and suggest support content when supporting self-study. For example, the support unit can suggest support content based on the interests the child has shared on social media. The support unit can also suggest content useful for learning based on the child's social media activities. The support unit can also suggest related learning content based on the activities of the child's friends on social media. This makes it possible to suggest optimal support content based on social media activities. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the child's online activity data into a generation AI and have the generation AI suggest support content.

[0122] The support unit can customize the support method by reflecting the child's past feedback when supporting self-study. For example, the support unit selects the optimal support method based on feedback provided by the child in the past. The support unit can also customize an effective support method based on the child's past feedback. The support unit can also adjust the timing and means of support by reflecting the child's feedback. This makes it possible to customize the optimal support method based on the past feedback. Some or all of the above-described processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the child's past feedback data into the generation AI and cause the generation AI to customize the support method.

[0123] The progress management unit can estimate the child's emotions and adjust the progress management method based on the estimated child's emotions. For example, if the child is relaxed, the progress management unit can perform detailed progress management. If the child is nervous, the progress management unit can also perform simple and easy-to-understand progress management. If the child is excited, the progress management unit can also perform visually appealing progress management. This makes it possible to adjust the progress management method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the progress management unit can be performed using AI, for example, or without AI. For example, the progress management unit can input image data of the child taken with a camera into the generation AI and have the generation AI estimate the child's emotions.

[0124] During progress management, the progress management unit can select the optimal progress management method by referring to the child's past learning history. The progress management unit selects the optimal progress management method based on, for example, the child's past learning history. The progress management unit can also select an effective progress management method from the child's past learning history. The progress management unit can also customize the progress management method by referring to the child's past learning history. This makes it possible to select the optimal progress management method based on the past learning history. Some or all of the above-described processing in the progress management unit may be performed using, for example, AI, or may be performed without using AI. For example, the progress management unit can input the child's past learning history data into the generation AI and cause the generation AI to select the optimal progress management method.

[0125] During progress management, the progress management unit can adjust the progress management means based on the child's current learning situation. The progress management unit, for example, selects the optimal progress management means based on the child's current learning situation. The progress management unit can also select an effective progress management method based on the child's current learning situation. The progress management unit can also customize the progress management means by referring to the child's current learning situation. This makes it possible to customize the progress management means based on the current learning situation. Some or all of the above-described processing in the progress management unit may be performed using AI, for example, or may be performed without using AI. For example, the progress management unit can input data on the child's current learning situation into the generation AI and cause the generation AI to adjust the progress management means.

[0126] The progress management unit can adjust the progress management method according to the child's learning style when managing progress. For example, if the child has a visual learning style, the progress management unit can perform visual progress management. Furthermore, if the child has an auditory learning style, the progress management unit can also perform auditory progress management. The progress management unit can also select the optimal progress management method based on the child's learning style. This makes it possible to provide the optimal progress management method according to the learning style. Some or all of the above-described processing in the progress management unit may be performed using AI, for example, or may be performed without using AI. For example, the progress management unit can input the child's learning style data into the generation AI and have the generation AI adjust the progress management method.

[0127] The progress management unit can estimate the child's emotions and determine the order of progress management based on the estimated child's emotions. For example, if the child is excited, the progress management unit can prioritize managing the progress of learning content that interests the child. Furthermore, if the child is tired, the progress management unit can prioritize managing the progress of learning content that helps the child relax. Furthermore, if the child is concentrating, the progress management unit can prioritize managing the progress of content that is useful for learning. This allows the priority of progress management to be determined according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the progress management unit can be performed using AI, for example, or without AI. For example, the progress management unit can input image data of a child taken with a camera into the generation AI and have the generation AI estimate the child's emotions.

[0128] The progress management unit can select the optimal progress management method by taking into account the child's geographical location information when managing progress. For example, if the child is in a specific area, the progress management unit can manage the progress of learning content related to that area. Furthermore, if the child is traveling, the progress management unit can also manage the progress of learning content related to the travel destination. Furthermore, if the child is at school, the progress management unit can also manage the progress of learning content related to the school curriculum. This makes it possible to provide an optimal progress management method based on the geographical location information. Some or all of the above-mentioned processing in the progress management unit may be performed using, for example, AI, or may be performed without using AI. For example, the progress management unit can input the child's geographical location information data into the generation AI and cause the generation AI to select the optimal progress management method.

[0129] During progress management, the progress management unit can analyze the child's online activities and suggest progress management methods. The progress management unit can suggest progress management methods based on, for example, the interests the child has shared on social media. The progress management unit can also suggest progress management methods that are useful for learning based on the child's social media activities. The progress management unit can also suggest related progress management methods based on the activities of the child's friends on social media. This makes it possible to suggest optimal progress management methods based on social media activities. Some or all of the above-described processing in the progress management unit may be performed using, for example, AI, or may be performed without using AI. For example, the progress management unit can input the child's online activity data into a generation AI and have the generation AI suggest progress management methods.

[0130] The progress management unit can customize the progress management method by reflecting the child's past feedback during progress management. The progress management unit selects the optimal progress management method based on, for example, feedback provided by the child in the past. The progress management unit can also customize an effective progress management method based on the child's past feedback. The progress management unit can also adjust the timing and means of progress management by reflecting the child's feedback. This makes it possible to customize the optimal progress management method based on the past feedback. Some or all of the above-described processing in the progress management unit may be performed using, for example, AI, or may be performed without using AI. For example, the progress management unit can input the child's past feedback data into the generation AI and have the generation AI customize the progress management method.

[0131] The association unit can estimate the child's emotions and select an educational service to associate based on the estimated child's emotions. For example, if the child is relaxed, the association unit can associate a detailed educational service. If the child is nervous, the association unit can associate a simple and easy-to-understand educational service. If the child is excited, the association unit can associate a visually appealing educational service. This makes it possible to select the optimal educational service according to the emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the association unit may be performed using AI, for example, or without AI. For example, the association unit can input image data of a child taken with a camera into the generation AI and cause the generation AI to estimate the child's emotions.

[0132] The alliance unit can optimize the alliance algorithm by referring to past alliance data when alliances are made. The alliance unit, for example, optimizes the alliance algorithm based on past alliance data. The alliance unit can also eliminate alliance bias from the past alliance data. The alliance unit can also improve the alliance algorithm by referring to the past alliance data. This makes it possible to optimize the alliance algorithm based on the past alliance data. Some or all of the above-mentioned processing in the alliance unit may be performed using AI, for example, or may be performed without using AI. For example, the alliance unit can input past alliance data into the generation AI and cause the generation AI to optimize the alliance algorithm.

[0133] When forming an alliance, the alliance unit can form an alliance based on the child's attribute information. The alliance unit can form an alliance based on the child's age or grade, for example. The alliance unit can also form an alliance based on the child's interests. The alliance unit can also form an alliance based on the child's learning progress. This makes it possible to form an optimal alliance based on the attribute information. Some or all of the above-mentioned processing in the alliance unit can be performed using AI, for example, or can be performed without using AI. For example, the alliance unit can input the child's attribute information data into the generation AI and cause the generation AI to execute the alliance.

[0134] The association unit can adjust the level of detail of the association based on the child's learning environment during association. For example, if the child's learning environment is well-equipped, the association unit can perform detailed association. Furthermore, if the child's learning environment is insufficient, the association unit can perform simple association. Furthermore, the association unit can adjust the level of detail of the association based on the child's learning environment. This makes it possible to adjust the level of detail of the association based on the learning environment. Some or all of the above-described processing in the association unit may be performed using AI, for example, or may be performed without using AI. For example, the association unit can input data about the child's learning environment into the generation AI and cause the generation AI to adjust the level of detail of the association.

[0135] The association unit can estimate the child's emotions and determine the order of association based on the estimated child's emotions. For example, if the child is excited, the association unit can prioritize association with educational services that interest the child. Also, if the child is tired, the association unit can prioritize association with educational services that help the child relax. Also, if the child is concentrating, the association unit can prioritize association with educational services that are useful for learning. This makes it possible to determine the priority of associations according to emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the association unit may be performed using AI, for example, or without AI. For example, the association unit can input image data of a child taken with a camera into the generation AI and cause the generation AI to estimate the child's emotions.

[0136] At the time of alliance, the alliance unit can weight the alliance data based on the time of submission of the alliance. For example, the alliance unit prioritizes analysis of recently submitted alliance data. The alliance unit can also weight the alliance data based on the time of submission of the alliance. The alliance unit can also adjust the order of the alliance data taking into account the time of submission of the alliance. This allows the alliance data to be weighted based on the time of submission. Some or all of the above-mentioned processing in the alliance unit may be performed using AI, for example, or may be performed without using AI. For example, the alliance unit can input data on the time of submission of the alliance to the generation AI and cause the generation AI to weight the alliance data.

[0137] The affiliation unit can integrate information from multiple data sources to expand the affiliation data during affiliation. For example, the affiliation unit can integrate a child's learning history with partner educational service data to form an affiliation. The affiliation unit can also integrate school grade data with partner educational service data to form an affiliation. The affiliation unit can also integrate a child's social media activity with partner educational service data to form an affiliation. This makes it possible to integrate information from different data sources and expand the affiliation data. Some or all of the above-mentioned processing in the affiliation unit may be performed, for example, using AI or without AI. For example, the affiliation unit can input information from multiple data sources into the generation AI and cause the generation AI to integrate the affiliation data.

[0138] The association unit can adjust the level of detail of the association based on the child's learning environment during association. For example, if the child's learning environment is well-equipped, the association unit can perform detailed association. Furthermore, if the child's learning environment is insufficient, the association unit can perform simple association. Furthermore, the association unit can adjust the level of detail of the association based on the child's learning environment. This makes it possible to adjust the level of detail of the association based on the learning environment. Some or all of the above-described processing in the association unit may be performed using AI, for example, or may be performed without using AI. For example, the association unit can input data about the child's learning environment into the generation AI and cause the generation AI to adjust the level of detail of the association.

[0139] === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, evaluation unit, provision unit, feedback analysis unit, support unit, progress management unit, and collaboration unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data such as a child's learning history, test results, and interests using the camera 42 and microphone 38B of the smart device 14. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate the child's ability. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides an individual curriculum based on the evaluation results. The feedback analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes feedback from the child and their parent. The support unit is realized by the control unit 46A of the smart device 14 and supports self-study. The progress management unit is realized by the specific processing unit 290 of the data processing device 12 and manages learning progress. The collaboration unit is realized by the specific processing unit 290 of the data processing device 12 and collaborates with excellent educational services. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, evaluation unit, provision unit, feedback analysis unit, support unit, progress management unit, and collaboration unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect data such as a child's learning history, test results, and interests using the camera 42 and microphone 238 of the smart glasses 214. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate the child's ability. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides an individual curriculum based on the evaluation results. The feedback analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes feedback from the child and their guardian. The support unit is realized by the control unit 46A of the smart glasses 214 and supports self-study. The progress management unit is realized by the specific processing unit 290 of the data processing device 12 and manages learning progress. The collaboration unit is realized by the specific processing unit 290 of the data processing device 12 and collaborates with excellent educational services. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, evaluation unit, provision unit, feedback analysis unit, support unit, progress management unit, and collaboration unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect data such as a child's learning history, test results, and interests using the camera 42 and microphone 238 of the headset-type terminal 314. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate the child's ability. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides an individual curriculum based on the evaluation results. The feedback analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes feedback from the child and their guardian. The support unit is realized by the control unit 46A of the headset-type terminal 314 and supports self-study. The progress management unit is realized by the specific processing unit 290 of the data processing device 12 and manages learning progress. The affiliation unit is realized by the specific processing unit 290 of the data processing device 12, and affiliations with excellent educational services are established. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, evaluation unit, provision unit, feedback analysis unit, support unit, progress management unit, and collaboration unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data such as a child's learning history, test results, and interests using the camera 42 and microphone 238 of the robot 414. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate the child's ability. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides an individual curriculum based on the evaluation results. The feedback analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes feedback from the child and their guardian. The support unit is realized by the control unit 46A of the robot 414 and supports self-study. The progress management unit is realized by the specific processing unit 290 of the data processing device 12 and manages learning progress. The collaboration unit is realized by the specific processing unit 290 of the data processing device 12 and collaborates with excellent educational services.

[0140] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0141] The education support system may further include an environment monitoring unit that monitors the child's learning environment in real time. The environment monitoring unit may monitor, for example, the temperature, humidity, and lighting conditions of the child's learning environment and adjust them to provide an optimal learning environment. The environment monitoring unit may also provide a noise canceling function if the child's learning environment is noisy. Furthermore, the environment monitoring unit may provide appropriate lighting if the child's learning environment is dark. This allows the child to study in an optimal learning environment.

[0142] The education support system may further include a gamification unit to enhance children's motivation to learn. For example, the gamification unit may award points each time a child achieves a learning goal, and children may earn rewards by collecting points. The gamification unit may also provide a system that allows children to earn badges and titles as they progress with their studies. Furthermore, the gamification unit may provide a ranking function that allows children to compete with other children. This enhances children's motivation to learn and allows them to enjoy learning.

[0143] The education support system may further include a style adaptation unit that provides learning content according to a child's learning style. For example, the style adaptation unit may provide visual content if the child has a visual learning style. The style adaptation unit may also provide audio content if the child has an auditory learning style. The style adaptation unit may also provide interactive content if the child has a tactile learning style. This allows for the provision of content optimal for the child's learning style and supports effective learning.

[0144] The education support system may further include a notification unit that notifies parents and teachers of a child's learning progress in real time. For example, the notification unit may send a notification to parents and teachers when a child achieves a learning goal. The notification unit may also send an alert when a child falls behind in their studies. Furthermore, the notification unit may provide parents and teachers with regular reports on the child's learning progress, allowing them to understand the child's learning situation and provide appropriate support.

[0145] The education support system may further include a data sharing unit that anonymizes children's learning data and provides it to research institutions. For example, the data sharing unit anonymizes children's learning data and provides it to research institutions for educational research. The data sharing unit may also analyze children's learning data and use it to improve education. Furthermore, the data sharing unit may utilize children's learning data to develop new educational programs. This may contribute to research and development to improve the quality of education.

[0146] The education support system may further include a posture monitoring unit that monitors a child's posture while studying. The posture monitoring unit, for example, monitors whether the child is studying in the correct posture and sends an alert if the child's posture is poor. The posture monitoring unit may also encourage the child to take a break if the child has been studying in the same posture for a long period of time. Furthermore, the posture monitoring unit may analyze the child's posture data and provide advice on how to provide a healthy learning environment. This allows the child to continue studying in a healthy posture.

[0147] The education support system may further include a stress monitoring unit that monitors the child's stress level while studying. The stress monitoring unit may, for example, measure the child's heart rate or electrodermal activity to estimate the stress level. The stress monitoring unit may also provide advice on how to relax if the child is experiencing high stress. The stress monitoring unit may also analyze the child's stress data and propose a study plan to reduce stress. This allows the child to study without feeling stressed.

[0148] The education support system may further include a concentration improvement unit for improving a child's concentration while studying. The concentration improvement unit may, for example, provide music or environmental sounds to help the child improve their concentration. The concentration improvement unit may also provide a break timer to help the child maintain their concentration. Furthermore, the concentration improvement unit may analyze the child's concentration data and suggest an optimal learning environment. This allows the child to concentrate and progress with their studies.

[0149] The educational support system may further include an emotion analysis unit that analyzes a child's emotions in real time while learning. The emotion analysis unit may, for example, analyze a child's facial expressions and tone of voice to estimate their emotions. The emotion analysis unit may also provide appropriate feedback and advice based on the emotions the child feels while learning. The emotion analysis unit may also analyze the child's emotional data and adjust the learning plan. This may provide support according to the child's emotions and improve the learning effect.

[0150] The education support system may further include a fatigue monitoring unit that monitors the child's level of fatigue while studying. The fatigue monitoring unit may, for example, monitor the child's eye movements and posture to estimate the child's level of fatigue. The fatigue monitoring unit may also encourage the child to take a break if the child feels tired. The fatigue monitoring unit may also analyze the child's fatigue data and propose a study plan to reduce fatigue. This allows the child to continue studying without feeling tired.

[0151] The processing flow of the second embodiment will be briefly explained below.

[0152] Step 1: The collection unit collects data such as the child's learning history, test results, and interests. For example, the collection unit retrieves the child's past test results from a database. The collection unit may also collect the child's learning history on an online learning platform. The collection unit may also collect the results of the child's responses to a questionnaire. Step 2: The evaluation unit evaluates the child's ability based on the data collected by the collection unit. For example, the evaluation unit analyzes test results to identify the child's strong and weak subjects. The evaluation unit can also analyze learning history to understand the child's learning patterns. The evaluation unit can also analyze interest data to identify the areas in which the child is interested. Step 3: The provider provides an individual curriculum based on the results of the evaluation by the evaluation department. For example, the provider provides teaching materials to further develop the child's strong subjects. The provider can also provide practice problems to help the child overcome weak subjects. The provider can also provide teaching materials that will attract the child's interest. Step 4: The feedback analysis unit analyzes feedback from the child and the parent based on the curriculum provided by the provision unit. For example, the feedback analysis unit collects feedback from the child about what he or she has learned. The feedback analysis unit can also collect feedback provided by the parent. The feedback analysis unit can also analyze the collected feedback and adjust the learning plan. Step 5: The support unit supports self-study based on the feedback analyzed by the feedback analysis unit. For example, the support unit provides appropriate advice when the child is studying on their own. The support unit can also provide hints on how to solve difficult problems when the child is faced with them. The support unit can also provide support to increase the child's motivation when they get stuck in their studies. Step 6: The progress management unit manages the progress of the learning supported by the support unit. For example, the progress management unit visualizes the child's learning situation and manages the progress. The progress management unit can also display the learning achievement level and progress status in graphs and charts. The progress management unit can also set learning goals and create progress reports. Step 7: The Partnering Department partners with excellent educational services based on the progress managed by the Progress Management Department. For example, the Partnering Department utilizes teaching materials and programs provided by the partner educational services. The Partnering Department can also use online classes and educational apps. The Partnering Department can also work with partner educational institutions to provide the best learning environment for children.

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0158] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0174] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0175] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0180] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0181] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0182] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0183] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0184] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0185] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0186] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0187] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0188] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0189] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0190] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0191] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0192] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0193] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0194] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0195] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0196] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0197] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0198] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0199] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0200] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0201] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0202] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0203] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0204] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0205] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0206] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0207] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0208] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0209] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0210] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0211] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0212] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0213] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0214] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0215] 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.

[0216] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0217] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0218] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0219] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0220] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0221] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0222] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0223] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0224] [Explanation of symbols]

[0225] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The collection department collects data such as children's learning history, test results, and interests. an evaluation unit that evaluates the child's ability based on the data collected by the collection unit; a providing unit that provides an individual curriculum based on the results of the evaluation by the evaluation unit; a feedback analysis unit that analyzes feedback from children and parents based on the curriculum provided by the provision unit; a support unit that supports self-learning based on the feedback analyzed by the feedback analysis unit; a progress management unit that manages the progress of the learning supported by the support unit; and a cooperation unit that cooperates with educational services based on the progress managed by the progress management unit. A system characterized by:

2. The collecting unit Estimate the child's emotions and adjust the timing of data collection based on the estimated emotions.

2. The system of claim 1.

3. The collecting unit Analyze the child's past learning history and select the appropriate data collection method 2. The system of claim 1.

4. The collecting unit When collecting data, filtering is performed based on the child's current learning status and interests.

2. The system of claim 1.

5. The collecting unit When collecting data, choose appropriate collection methods depending on the child's input method.

2. The system of claim 1.

6. The collecting unit Estimate the child's emotions and prioritize the data to collect based on the estimated emotions 2. The system of claim 1.

7. The collecting unit Prioritize the collection of relevant data based on a child's geographic location during data collection 2. The system of claim 1.

8. The collecting unit At the time of data collection, analyze children's online activities and collect relevant data 2. The system of claim 1.

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