System

Abstract

To provide a system that grasps the characteristics and learning depth of a child and provides a learning program.SOLUTION: A system includes a data collection section, an analysis section, a generation section, and a providing section. The data collection section collects data about the characteristics and learning depth of a child. The analysis section analyzes data collected by the data collection section and grasps the characteristics and learning depth of the child. The generation section generates a learning program individualized on the basis of information grasped by the analysis section. The providing section provides the learning program generated by the generation section.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have the drawback that it is difficult to provide individualized learning programs for all children, including those with developmental disabilities.

[0005] The system according to the embodiment aims to grasp a child's characteristics and learning depth and provide an individualized learning program. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a generation unit, and a provision unit. The data collection unit collects data related to the characteristics and learning depth of the child. The analysis unit analyzes the data collected by the data collection unit to grasp the characteristics and learning depth of the child. The generation unit generates an individualized learning program based on the information grasped by the analysis unit. The provision unit provides the learning program generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can grasp a child's characteristics and learning depth and provide an individualized learning program. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The education support system according to the embodiment of the present invention is a system that uses AI to grasp the characteristics and learning depth of children with developmental disabilities and provides an individualized learning program. This education support system collects data on the characteristics and learning depth of children, analyzes them with AI, and generates and provides an individualized learning program. For example, the education support system collects data such as the results of behavioral observations of children, learning history, and test results. Next, the AI ​​analyzes the collected data to grasp the characteristics and learning depth of children. For example, if a child shows a high interest in a specific learning method, a learning program is constructed centered on that learning method. In addition, if a child is not good at a specific task, support is provided to overcome that task. Finally, an individualized learning program is generated and provided based on the information grasped by the AI. For example, a program containing a lot of visual teaching materials is provided to a child who is good at visual learning. In addition, a program that emphasizes repetitive learning is provided to a child who needs repetitive learning. In this way, the education support system realizes effective educational support by providing an individualized learning program tailored to the characteristics and learning depth of each child with developmental disabilities. This enables the educational support system to provide effective educational support by providing individualized learning programs tailored to the characteristics and learning depth of each child with developmental disabilities.

[0029] The education support system according to the embodiment includes a data collection unit, an analysis unit, a generation unit, and a provision unit. The data collection unit collects data on the characteristics and learning depth of the child. The characteristics of the child include, but are not limited to, for example, learning style, interests, and cognitive ability. The data collection unit collects data such as, for example, the child's behavioral observation results, learning history, and test results. For example, the data collection unit can collect data using a sensor or device. The sensors and devices include, but are not limited to, for example, a heart rate sensor, a camera, and a microphone. The analysis unit uses an AI algorithm to analyze the data collected by the data collection unit and grasp the characteristics and learning depth of the child. The AI ​​algorithm includes, but is not limited to, for example, a neural network, a decision tree, and a support vector machine. The generation unit generates an individualized learning program based on the information grasped by the analysis unit. The generation unit can generate, for example, a program that emphasizes visual teaching materials and repetitive learning. The visual teaching materials include, but are not limited to, for example, videos, charts, and animations. Repetitive learning includes, for example, repetition of the same content, review at intervals, and the like, but is not limited to such examples. The providing unit provides the learning program generated by the generating unit. The providing unit can provide the learning program, for example, in a manner that matches the characteristics of the child. The manner that matches the characteristics of the child includes, for example, selection of teaching materials according to the learning style, adjustment of the progress speed, and the like, but is not limited to such examples. As a result, the education support system according to the embodiment can realize effective education support by providing an individualized learning program based on the characteristics and learning depth of the child.

[0030] The data collection unit collects data on the characteristics and learning depth of the child. The characteristics of the child include, but are not limited to, for example, learning style, interests, and cognitive ability. The data collection unit collects data such as, for example, the results of behavioral observation of the child, learning history, and test results. Specifically, the data collection unit uses a camera and a microphone installed in the classroom to record the child's facial expressions, remarks, and behavior, and analyzes these data to grasp the child's interests and learning style. In addition, a heart rate sensor can be used to measure the child's stress level and concentration, and a break can be suggested at an appropriate time according to the progress of the learning. Furthermore, by collecting the learning history and test results, the learning progress and understanding of the child can be evaluated, and basic data for creating an individual learning plan can be provided. As a result, the data collection unit can provide important information for grasping the characteristics and learning situation of each child in detail and providing individualized educational support.

[0031] The analysis unit uses an AI algorithm to analyze the data collected by the data collection unit and grasp the characteristics and learning depth of the child. Examples of AI algorithms include, but are not limited to, neural networks, decision trees, and support vector machines. Specifically, a neural network can be used to analyze the child's facial expressions and behavioral data, and the emotional state and concentration level can be evaluated in real time. A decision tree can be used to analyze the child's learning history and test results, and the level of understanding and learning progress can be evaluated. Furthermore, a support vector machine can be used to classify the child's interests and learning style, and an optimal learning program can be proposed. This allows the analysis unit to analyze the collected data from multiple angles and grasp the child's characteristics and learning depth in detail. Furthermore, the analysis unit can utilize past data and statistical information to analyze long-term learning effects and trends, and use them to improve future learning plans. For example, based on past learning data, it is possible to evaluate how effective a particular learning style or teaching material was, and reflect it in future learning plans. This allows the analysis unit to not only grasp the situation in real time, but also evaluate and improve long-term learning effects, thereby improving the reliability and effectiveness of the entire system.

[0032] The generating unit generates an individualized learning program based on the information grasped by the analyzing unit. The generating unit can generate a program that emphasizes visual teaching materials and repetitive learning, for example. Specifically, videos, charts, animations, etc. can be used as visual teaching materials to attract children's interest and deepen their understanding. For example, in science classes, the process of an experiment can be shown in animation, allowing children to deepen their understanding before actually performing the experiment. In addition, in a program that emphasizes repetitive learning, the same content can be repeatedly learned to solidify memory. For example, calculation skills can be improved by repeatedly solving basic mathematical calculation problems. Furthermore, the generating unit can adjust the content and progress speed of the learning program according to the child's learning progress and level of understanding. For example, more advanced content can be provided to children who have advanced understanding in a particular field, and basic content can be repeatedly learned by children who have insufficient understanding. As a result, the generating unit can provide an optimal learning program according to the characteristics and learning situation of each child, thereby achieving effective educational support.

[0033] The providing unit provides the learning program generated by the generating unit. The providing unit can provide the learning program in a manner that matches the characteristics of the child, for example. Specifically, the unit selects teaching materials according to the learning style and adjusts the progress speed. For example, for a child who prefers visual learning, teaching materials that use a lot of videos and charts are provided, and for a child who prefers auditory learning, teaching materials in the form of audio commentary or podcasts are provided. In addition, in adjusting the progress speed, the learning content can be quickly advanced for a child who understands quickly, while a child who understands slowly can proceed with learning at a slow pace. Furthermore, the providing unit can increase the child's motivation to learn by diversifying the method of providing the learning program. For example, learning can be advanced while having fun using game-style teaching materials. In addition, an online platform can be utilized to enable a child to continue learning at home. In this way, the providing unit can provide an optimal learning program according to the characteristics and learning situation of each child, thereby realizing effective educational support. Furthermore, the providing unit can continuously evaluate the effectiveness of the learning program and improve the content and provision method of the program as necessary. For example, by regularly evaluating a child's learning progress and comprehension, and adjusting the content and progress speed of the program, more effective learning support can be provided. This allows the provider to always provide highly accurate learning programs based on the latest information, maximizing the learning effect of children.

[0034] The data collection unit can collect data using sensors and devices. For example, the data collection unit can measure the child's heart rate using a heart rate sensor and collect data. The data collection unit can also capture the child's facial expression using a camera and collect data. Furthermore, the data collection unit can record the child's voice using a microphone and collect data. For example, the data collection unit can monitor the child's heart rate in real time using a heart rate sensor and collect data. The data collection unit can also record changes in the child's facial expression using a camera and collect data. Furthermore, the data collection unit can analyze the tone and speed of the child's voice using a microphone and collect data. This makes it possible to collect data more accurately by using sensors and devices. Part or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input data acquired by the heart rate sensor to AI and cause AI to perform heart rate analysis.

[0035] The analysis unit can analyze the data using an AI algorithm. The analysis unit can analyze the data using, for example, a neural network. A neural network is an algorithm that processes data using multiple artificial neurons and performs pattern recognition and prediction. The analysis unit can also analyze the data using a decision tree. A decision tree is an algorithm that branches data based on conditions and performs classification and regression. The analysis unit can also analyze the data using a support vector machine. A support vector machine is an algorithm that maps data to a high-dimensional space and performs classification and regression. For example, the analysis unit can analyze a child's learning history data using a neural network and grasp the learning pattern. The analysis unit can also analyze a child's behavioral observation data using a decision tree and grasp the behavior pattern. The analysis unit can also analyze a child's test result data using a support vector machine and evaluate the learning outcome. As a result, the accuracy of data analysis is improved by using an AI algorithm. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data to a generation AI using a neural network and output the analysis result to the generation AI.

[0036] The generating unit can generate a program that emphasizes visual teaching materials and repetitive learning. The generating unit can generate a program that includes a lot of visual teaching materials, for example. Visual teaching materials include, but are not limited to, for example, videos, charts, animations, etc. The generating unit can also generate a program that emphasizes repetitive learning. Repetitive learning includes, but is not limited to, for example, repetition of the same content, review at intervals, etc. For example, the generating unit can provide a program that includes a lot of visual teaching materials to a child who is good at visual learning. The generating unit can also provide a program that emphasizes repetitive learning to a child who needs repetitive learning. Furthermore, the generating unit can generate a program that combines visual teaching materials and repetitive learning. For example, the generating unit can provide a program that explains basic concepts using visual teaching materials and then deepens understanding through repetitive learning. As a result, by emphasizing visual teaching materials and repetitive learning, the learning effect of the child is improved. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using a generating AI, or may be performed without using a generating AI. For example, the generation unit can cause a generation AI to generate visual teaching materials.

[0037] The providing unit can provide a learning program in a manner that matches the characteristics of the child. The providing unit can, for example, select and provide teaching materials according to the child's learning style. The learning style includes, for example, visual, auditory, experiential, and the like, but is not limited to such examples. The providing unit can also adjust and provide the learning program in accordance with the child's progress speed. For example, the providing unit can monitor the child's learning progress and adjust the progress speed. Furthermore, the providing unit can customize and provide the learning program based on the child's interests. For example, the providing unit can provide teaching materials related to topics in which the child is interested. As a result, the learning program is provided in a manner that matches the child's characteristics, thereby improving the learning effect. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generating AI, or may be performed without using a generating AI. For example, the providing unit can cause the generating AI to select teaching materials according to the child's learning style.

[0038] The data collection unit can analyze the child's past learning history and select the optimal data collection method. The data collection unit can, for example, prioritize learning methods that were effective in the past to collect data. The data collection unit can also collect data that is effective in a specific time period from the past learning history. Furthermore, the data collection unit can also collect data by focusing on teaching materials and tasks that interest the child based on the past learning history. For example, the data collection unit can collect data by prioritizing learning methods that were effective in the past. The data collection unit can also collect data that is effective in a specific time period from the past learning history. Furthermore, the data collection unit can collect data by focusing on teaching materials and tasks that interest the child based on the past learning history. This makes it possible to effectively collect data by selecting the optimal data collection method based on the past learning history. Part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input past learning history data to the generation AI and cause the generation AI to select the optimal data collection method.

[0039] The data collection unit can perform filtering based on the child's current learning environment and field of interest when collecting data. The data collection unit can, for example, preferentially collect data related to the learning field in which the child is interested. In addition, the data collection unit can also collect data that requires concentration when the child's learning environment is quiet. In addition, the data collection unit can also collect data that is easy or relaxing when the child's learning environment is noisy. For example, the data collection unit can preferentially collect data related to the learning field in which the child is interested. In addition, the data collection unit can also collect data that requires concentration when the child's learning environment is quiet. In addition, the data collection unit can also collect data that is easy or relaxing when the child's learning environment is noisy. In this way, by filtering the data based on the child's learning environment and field of interest, more relevant data can be collected. Part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input the child's learning environment data to the generation AI and cause the generation AI to perform filtering.

[0040] The data collection unit can preferentially collect highly relevant data in consideration of the child's geographical location information when collecting data. For example, when the child is at school, the data collection unit can preferentially collect data related to learning. In addition, when the child is at home, the data collection unit can preferentially collect data related to home learning. Furthermore, when the child is out, the data collection unit can preferentially collect data that can be learned while moving. For example, when the child is at school, the data collection unit can preferentially collect data related to learning. In addition, when the child is at home, the data collection unit can preferentially collect data that can be learned while moving. In this way, by considering the geographical location information, more relevant data can be collected. Part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input the child's geographical location information to the generation AI and cause the generation AI to execute the priority order of highly relevant data.

[0041] The data collection unit can analyze the child's social media activity and collect related data at the time of data collection. The data collection unit can collect data related to topics in which the child is interested on social media, for example. The data collection unit can also collect related data based on learning outcomes shared by the child on social media. Furthermore, the data collection unit can collect data related to educational content that the child follows on social media. For example, the data collection unit can collect data related to topics in which the child is interested on social media. The data collection unit can also collect related data based on learning outcomes shared by the child on social media. Furthermore, the data collection unit can collect data related to educational content that the child follows on social media. This makes it possible to collect data based on the child's interests by analyzing social media activities. Some or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input the child's social media activity data to the generation AI and cause the generation AI to collect related data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. The analysis unit can perform detailed analysis on data with high importance, for example. The analysis unit can also perform concise analysis on data with low importance. Furthermore, the analysis unit can perform analysis with a moderate level of detail on data with medium importance. For example, the analysis unit can perform detailed analysis on data with high importance. The analysis unit can also perform concise analysis on data with low importance. Furthermore, the analysis unit can perform analysis with a moderate level of detail on data with medium importance. This enables efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that evaluates the importance of the data.

[0043] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply a behavioral analysis algorithm to behavioral observation data. The analysis unit can also apply a learning pattern analysis algorithm to learning history data. Furthermore, the analysis unit can apply a grade analysis algorithm to test result data. For example, the analysis unit can apply a behavioral analysis algorithm to behavioral observation data. The analysis unit can also apply a learning pattern analysis algorithm to learning history data. Furthermore, the analysis unit can apply a grade analysis algorithm to test result data. Thereby, by applying an appropriate analysis algorithm depending on the category of data, the analysis accuracy is improved. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that selects an analysis algorithm according to the category of data.

[0044] The analysis unit can determine the priority of the analysis based on the collection time of the data during the analysis. The analysis unit can, for example, preferentially analyze the latest data. The analysis unit can also prioritize the latest data while referring to past data. Furthermore, the analysis unit can also preferentially analyze data collected during a specific period. For example, the analysis unit can preferentially analyze the latest data. The analysis unit can also prioritize the latest data while referring to past data. Furthermore, the analysis unit can also preferentially analyze data collected during a specific period. As a result, by determining the priority of the analysis based on the collection time of the data, it is possible to perform analysis that prioritizes the latest data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can determine the priority of the analysis using an AI model that evaluates the collection time of the data.

[0045] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit can, for example, preferentially analyze data with high relevance. The analysis unit can also analyze data with medium relevance next. Furthermore, the analysis unit can analyze data with low relevance last. For example, the analysis unit can preferentially analyze data with high relevance. The analysis unit can also analyze data with medium relevance next. Furthermore, the analysis unit can analyze data with low relevance last. This enables efficient data analysis by adjusting the order of analysis based on the relevance of data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can adjust the order of analysis using an AI model that evaluates the relevance of data.

[0046] The generating unit can adjust the level of detail of the learning program based on the characteristics of the child at the time of generation. For example, the generating unit can provide a program that includes a lot of visual teaching materials to a child who is good at visual learning. The generating unit can also provide a program that emphasizes repetitive learning to a child who needs repetitive learning. Furthermore, the generating unit can provide a program that includes a lot of audio teaching materials to a child who is good at auditory learning. For example, the generating unit can provide a program that includes a lot of visual teaching materials to a child who is good at visual learning. The generating unit can also provide a program that emphasizes repetitive learning to a child who needs repetitive learning. Furthermore, the generating unit can provide a program that includes a lot of audio teaching materials to a child who is good at auditory learning. This allows for more individualized learning support by adjusting the level of detail of the learning program based on the characteristics of the child. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the child's characteristic data into the generating AI to adjust the level of detail of the learning program.

[0047] The generating unit can apply different learning programs according to the learning style of the child at the time of generation. For example, the generating unit can provide a program centered on visual teaching materials to a child with a visual learning style. The generating unit can also provide a program centered on audio teaching materials to a child with an auditory learning style. Furthermore, the generating unit can provide a program including practical activities to a child with an experiential learning style. For example, the generating unit can provide a program centered on visual teaching materials to a child with a visual learning style. The generating unit can also provide a program centered on audio teaching materials to a child with an auditory learning style. Furthermore, the generating unit can provide a program including practical activities to a child with an experiential learning style. This enables more effective learning support by applying the learning program according to the child's learning style. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI or may be performed without using AI. For example, the generating unit can input the child's learning style data into the generating AI to generate an appropriate learning program.

[0048] The generating unit can determine the priority of the learning program based on the child's learning history at the time of generation. The generating unit can, for example, preferentially incorporate into the program a learning method that was effective in the past. The generating unit can also provide a program that is effective in a specific time period based on the past learning history. Furthermore, the generating unit can preferentially incorporate into the program teaching materials and assignments that interest the child based on the past learning history. For example, the generating unit can preferentially incorporate into the program a learning method that was effective in the past. The generating unit can also provide a program that is effective in a specific time period based on the past learning history. Furthermore, the generating unit can preferentially incorporate into the program teaching materials and assignments that interest the child based on the past learning history. As a result, by determining the priority of the learning program based on the child's learning history, more effective learning support is possible. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the child's learning history data into the generating AI to determine the priority of the learning program.

[0049] The generating unit can adjust the order of the learning program based on the child's interests at the time of generation. The generating unit can, for example, place a topic in which the child is interested first. The generating unit can also postpone tasks that the child is not good at and start with content that interests the child. Furthermore, the generating unit can configure the program centered on content that interests the child. For example, the generating unit can place a topic in which the child is interested first. The generating unit can also postpone tasks that the child is not good at and start with content that interests the child. Furthermore, the generating unit can configure the program centered on content that interests the child. This allows for more effective learning support by adjusting the order of the learning program based on the child's interests. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the child's interest data into the generating AI and adjust the order of the learning program.

[0050] The providing unit can select the optimal providing method by referring to the child's past learning history at the time of providing. The providing unit can, for example, give priority to a providing method that was effective in the past. The providing unit can also select a providing method that is effective in a specific time period from the past learning history. Furthermore, the providing unit can also give priority to providing teaching materials and assignments that the child is interested in based on the past learning history. For example, the providing unit can give priority to a providing method that was effective in the past. The providing unit can also select a providing method that is effective in a specific time period from the past learning history. Furthermore, the providing unit can also give priority to providing teaching materials and assignments that the child is interested in based on the past learning history. As a result, more effective learning support is possible by selecting the optimal providing method based on the past learning history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the child's learning history data into the generating AI and select the optimal providing method.

[0051] The providing unit can customize the providing means based on the child's current learning environment at the time of providing. For example, when the child is at home, the providing unit can provide online teaching materials. Also, when the child is at school, the providing unit can provide teaching materials in cooperation with a teacher. Furthermore, when the child is out, the providing unit can provide teaching materials through a mobile device. For example, when the child is at home, the providing unit can provide online teaching materials. Also, when the child is at school, the providing unit can provide teaching materials in cooperation with a teacher. Furthermore, when the child is out, the providing unit can provide teaching materials through a mobile device. This enables more effective learning support by customizing the providing means based on the child's learning environment. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the child's learning environment data into the generating AI to customize the providing means.

[0052] The providing unit can select the optimal providing method in consideration of the child's geographical location information at the time of providing. For example, when the child is at school, the providing unit can provide learning materials preferentially. In addition, when the child is at home, the providing unit can also provide learning materials related to home learning preferentially. Furthermore, when the child is out, the providing unit can also provide learning materials that can be used while traveling preferentially. For example, when the child is at school, the providing unit can provide learning materials preferentially. In addition, when the child is at home, the providing unit can also provide learning materials that can be used while traveling preferentially. This makes it possible to provide more effective learning support by considering the geographical location information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the child's geographical location information into the generating AI and select the optimal providing method.

[0053] The providing unit can analyze the child's social media activity at the time of providing and suggest a means of providing. The providing unit can provide teaching materials related to a topic in which the child is interested on social media, for example. The providing unit can also provide related teaching materials based on the learning outcomes shared by the child on social media. Furthermore, the providing unit can provide teaching materials related to educational content that the child follows on social media. For example, the providing unit can provide teaching materials related to a topic in which the child is interested on social media. The providing unit can also provide related teaching materials based on the learning outcomes shared by the child on social media. Furthermore, the providing unit can provide teaching materials related to educational content that the child follows on social media. Thereby, by analyzing social media activity, it is possible to provide learning support based on the child's interests. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the child's social media activity data into a generating AI and suggest a means of providing.

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

[0055] The education support system may further include a feedback unit. The feedback unit may provide feedback in real time based on the child's learning progress and results. For example, the feedback unit may display a message of praise or encouragement when the child completes a particular task. The feedback unit may also provide appropriate advice or hints when the child is working on a task that is difficult for the child. Furthermore, the feedback unit may present the child with the next step or goal according to the child's learning progress. This allows the child to grasp his / her learning situation in real time and proceed with the learning while maintaining motivation.

[0056] The data collection unit can further collect data on the child's learning environment using an environmental sensor. For example, the environmental sensor can measure the brightness and temperature of classroom lighting, noise levels, and the like, and collect data. The environmental sensor can also detect the child's sitting position and posture, and collect data. Furthermore, the environmental sensor can issue an alert if the child's learning environment is not appropriate. This makes it possible to optimize the child's learning environment and support more effective learning.

[0057] The generating unit can further generate a learning program incorporating game elements. For example, the generating unit can enable the child to earn points each time he or she clears a specific task. The generating unit can also award a badge or title each time the child earns a certain number of points. Furthermore, the generating unit can introduce a ranking system that enables the child to compete with other children. This allows the child to proceed with learning in a game-like manner, thereby increasing motivation for learning.

[0058] The providing unit can further automatically adjust the difficulty of the learning program according to the child's learning progress. For example, if the child completes a particular task smoothly, the providing unit can increase the difficulty of the next task. Also, if the child is struggling with a particular task, the providing unit can lower the difficulty of the next task. Furthermore, the providing unit can provide review tasks at an appropriate time according to the child's learning progress. This allows the child to progress with the learning at his or her own pace and receive effective learning support.

[0059] The data collection unit can further analyze the child's past learning history and select the optimal data collection method. For example, the data collection unit can collect data by prioritizing learning methods that were effective in the past. The data collection unit can also collect effective data at specific times based on the past learning history. Furthermore, the data collection unit can collect data by focusing on teaching materials and tasks that interest the child based on the past learning history. This makes it possible to collect effective data by selecting the optimal data collection method based on the past learning history.

[0060] The data collection unit can further perform filtering based on the child's current learning environment and areas of interest. For example, the data collection unit can preferentially collect data related to areas of learning in which the child is interested. In addition, if the child's learning environment is quiet, the data collection unit can collect data that requires concentration. In addition, if the child's learning environment is noisy, the data collection unit can collect data on easy tasks or relaxing activities. This allows more relevant data to be collected by filtering data based on the child's learning environment and areas of interest.

[0061] The data collection unit can further take into account the geographical location information of the child to preferentially collect highly relevant data. For example, when the child is at school, the data collection unit can preferentially collect data related to learning. Also, when the child is at home, the data collection unit can preferentially collect data related to home learning. Furthermore, when the child is out, the data collection unit can preferentially collect data that can be learned while moving. In this way, more relevant data can be collected by taking into account the geographical location information.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The data collection unit collects data on the child's characteristics and learning depth. The child's characteristics include learning style, interests, cognitive ability, etc. The data collection unit collects data such as the child's behavioral observation results, learning history, test results, etc. Data can be collected using sensors and devices, including heart rate sensors, cameras, microphones, etc. Step 2: The analysis unit uses AI algorithms to analyze the data collected by the data collection unit and understand the child's characteristics and learning depth. AI algorithms include neural networks, decision trees, support vector machines, etc. Step 3: The generator generates an individualized learning program based on the information obtained by the analyzer. The generator can generate a program that emphasizes visual learning materials and repetitive learning. Visual learning materials include videos, charts, animations, etc., and repetitive learning includes repetition of the same content and spaced review. Step 4: The providing unit provides the learning program generated by the generating unit. The providing unit can provide the learning program in a manner that matches the characteristics of the child. The manner that matches the characteristics of the child includes the selection of learning materials according to the learning style, adjustment of the progress speed, etc.

[0064] (Example 2) The education support system according to the embodiment of the present invention is a system that uses AI to grasp the characteristics and learning depth of children with developmental disabilities and provides an individualized learning program. This education support system collects data on the characteristics and learning depth of children, analyzes them with AI, and generates and provides an individualized learning program. For example, the education support system collects data such as the results of behavioral observations of children, learning history, and test results. Next, the AI ​​analyzes the collected data to grasp the characteristics and learning depth of children. For example, if a child shows a high interest in a specific learning method, a learning program is constructed centered on that learning method. In addition, if a child is not good at a specific task, support is provided to overcome that task. Finally, an individualized learning program is generated and provided based on the information grasped by the AI. For example, a program containing a lot of visual teaching materials is provided to a child who is good at visual learning. In addition, a program that emphasizes repetitive learning is provided to a child who needs repetitive learning. In this way, the education support system realizes effective educational support by providing an individualized learning program tailored to the characteristics and learning depth of each child with developmental disabilities. This enables the educational support system to provide effective educational support by providing individualized learning programs tailored to the characteristics and learning depth of each child with developmental disabilities.

[0065] The education support system according to the embodiment includes a data collection unit, an analysis unit, a generation unit, and a provision unit. The data collection unit collects data on the characteristics and learning depth of the child. The characteristics of the child include, but are not limited to, for example, learning style, interests, and cognitive ability. The data collection unit collects data such as, for example, the child's behavioral observation results, learning history, and test results. For example, the data collection unit can collect data using a sensor or device. The sensors and devices include, but are not limited to, for example, a heart rate sensor, a camera, and a microphone. The analysis unit uses an AI algorithm to analyze the data collected by the data collection unit and grasp the characteristics and learning depth of the child. The AI ​​algorithm includes, but is not limited to, for example, a neural network, a decision tree, and a support vector machine. The generation unit generates an individualized learning program based on the information grasped by the analysis unit. The generation unit can generate, for example, a program that emphasizes visual teaching materials and repetitive learning. The visual teaching materials include, but are not limited to, for example, videos, charts, and animations. Repetitive learning includes, for example, repetition of the same content, review at intervals, and the like, but is not limited to such examples. The providing unit provides the learning program generated by the generating unit. The providing unit can provide the learning program, for example, in a manner that matches the characteristics of the child. The manner that matches the characteristics of the child includes, for example, selection of teaching materials according to the learning style, adjustment of the progress speed, and the like, but is not limited to such examples. As a result, the education support system according to the embodiment can realize effective education support by providing an individualized learning program based on the characteristics and learning depth of the child.

[0066] The data collection unit collects data on the characteristics and learning depth of the child. The characteristics of the child include, but are not limited to, for example, learning style, interests, and cognitive ability. The data collection unit collects data such as, for example, the results of behavioral observation of the child, learning history, and test results. Specifically, the data collection unit uses a camera and a microphone installed in the classroom to record the child's facial expressions, remarks, and behavior, and analyzes these data to grasp the child's interests and learning style. In addition, a heart rate sensor can be used to measure the child's stress level and concentration, and a break can be suggested at an appropriate time according to the progress of the learning. Furthermore, by collecting the learning history and test results, the learning progress and understanding of the child can be evaluated, and basic data for creating an individual learning plan can be provided. As a result, the data collection unit can provide important information for grasping the characteristics and learning situation of each child in detail and providing individualized educational support.

[0067] The analysis unit uses an AI algorithm to analyze the data collected by the data collection unit and grasp the characteristics and learning depth of the child. Examples of AI algorithms include, but are not limited to, neural networks, decision trees, and support vector machines. Specifically, a neural network can be used to analyze the child's facial expressions and behavioral data, and the emotional state and concentration level can be evaluated in real time. A decision tree can be used to analyze the child's learning history and test results, and the level of understanding and learning progress can be evaluated. Furthermore, a support vector machine can be used to classify the child's interests and learning style, and an optimal learning program can be proposed. This allows the analysis unit to analyze the collected data from multiple angles and grasp the child's characteristics and learning depth in detail. Furthermore, the analysis unit can utilize past data and statistical information to analyze long-term learning effects and trends, and use them to improve future learning plans. For example, based on past learning data, it is possible to evaluate how effective a particular learning style or teaching material was, and reflect it in future learning plans. This allows the analysis unit to not only grasp the situation in real time, but also evaluate and improve long-term learning effects, thereby improving the reliability and effectiveness of the entire system.

[0068] The generating unit generates an individualized learning program based on the information grasped by the analyzing unit. The generating unit can generate a program that emphasizes visual teaching materials and repetitive learning, for example. Specifically, videos, charts, animations, etc. can be used as visual teaching materials to attract children's interest and deepen their understanding. For example, in science classes, the process of an experiment can be shown in animation, allowing children to deepen their understanding before actually performing the experiment. In addition, in a program that emphasizes repetitive learning, the same content can be repeatedly learned to solidify memory. For example, calculation skills can be improved by repeatedly solving basic mathematical calculation problems. Furthermore, the generating unit can adjust the content and progress speed of the learning program according to the child's learning progress and level of understanding. For example, more advanced content can be provided to children who have advanced understanding in a particular field, and basic content can be repeatedly learned by children who have insufficient understanding. As a result, the generating unit can provide an optimal learning program according to the characteristics and learning situation of each child, thereby achieving effective educational support.

[0069] The providing unit provides the learning program generated by the generating unit. The providing unit can provide the learning program in a manner that matches the characteristics of the child, for example. Specifically, the unit selects teaching materials according to the learning style and adjusts the progress speed. For example, for a child who prefers visual learning, teaching materials that use a lot of videos and charts are provided, and for a child who prefers auditory learning, teaching materials in the form of audio commentary or podcasts are provided. In addition, in adjusting the progress speed, the learning content can be quickly advanced for a child who understands quickly, while a child who understands slowly can proceed with learning at a slow pace. Furthermore, the providing unit can increase the child's motivation to learn by diversifying the method of providing the learning program. For example, learning can be advanced while having fun using game-style teaching materials. In addition, an online platform can be utilized to enable a child to continue learning at home. In this way, the providing unit can provide an optimal learning program according to the characteristics and learning situation of each child, thereby realizing effective educational support. Furthermore, the providing unit can continuously evaluate the effectiveness of the learning program and improve the content and provision method of the program as necessary. For example, by regularly evaluating a child's learning progress and comprehension, and adjusting the content and progress speed of the program, more effective learning support can be provided. This allows the provider to always provide highly accurate learning programs based on the latest information, maximizing the learning effect of children.

[0070] The data collection unit can collect data using sensors and devices. For example, the data collection unit can measure the child's heart rate using a heart rate sensor and collect data. The data collection unit can also capture the child's facial expression using a camera and collect data. Furthermore, the data collection unit can record the child's voice using a microphone and collect data. For example, the data collection unit can monitor the child's heart rate in real time using a heart rate sensor and collect data. The data collection unit can also record changes in the child's facial expression using a camera and collect data. Furthermore, the data collection unit can analyze the tone and speed of the child's voice using a microphone and collect data. This makes it possible to collect data more accurately by using sensors and devices. Part or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can input data acquired by the heart rate sensor to AI and cause AI to perform heart rate analysis.

[0071] The analysis unit can analyze the data using an AI algorithm. The analysis unit can analyze the data using, for example, a neural network. A neural network is an algorithm that processes data using multiple artificial neurons and performs pattern recognition and prediction. The analysis unit can also analyze the data using a decision tree. A decision tree is an algorithm that branches data based on conditions and performs classification and regression. The analysis unit can also analyze the data using a support vector machine. A support vector machine is an algorithm that maps data to a high-dimensional space and performs classification and regression. For example, the analysis unit can analyze a child's learning history data using a neural network and grasp the learning pattern. The analysis unit can also analyze a child's behavioral observation data using a decision tree and grasp the behavior pattern. The analysis unit can also analyze a child's test result data using a support vector machine and evaluate the learning outcome. As a result, the accuracy of data analysis is improved by using an AI algorithm. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input data to a generation AI using a neural network and output the analysis result to the generation AI.

[0072] The generating unit can generate a program that emphasizes visual teaching materials and repetitive learning. The generating unit can generate a program that includes a lot of visual teaching materials, for example. Visual teaching materials include, but are not limited to, for example, videos, charts, animations, etc. The generating unit can also generate a program that emphasizes repetitive learning. Repetitive learning includes, but is not limited to, for example, repetition of the same content, review at intervals, etc. For example, the generating unit can provide a program that includes a lot of visual teaching materials to a child who is good at visual learning. The generating unit can also provide a program that emphasizes repetitive learning to a child who needs repetitive learning. Furthermore, the generating unit can generate a program that combines visual teaching materials and repetitive learning. For example, the generating unit can provide a program that explains basic concepts using visual teaching materials and then deepens understanding through repetitive learning. As a result, by emphasizing visual teaching materials and repetitive learning, the learning effect of the child is improved. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using a generating AI, or may be performed without using a generating AI. For example, the generation unit can cause a generation AI to generate visual teaching materials.

[0073] The providing unit can provide a learning program in a manner that matches the characteristics of the child. The providing unit can, for example, select and provide teaching materials according to the child's learning style. The learning style includes, for example, visual, auditory, experiential, and the like, but is not limited to such examples. The providing unit can also adjust and provide the learning program in accordance with the child's progress speed. For example, the providing unit can monitor the child's learning progress and adjust the progress speed. Furthermore, the providing unit can customize and provide the learning program based on the child's interests. For example, the providing unit can provide teaching materials related to topics in which the child is interested. As a result, the learning program is provided in a manner that matches the child's characteristics, thereby improving the learning effect. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using a generating AI, or may be performed without using a generating AI. For example, the providing unit can cause the generating AI to select teaching materials according to the child's learning style.

[0074] The data collection unit can estimate the child's emotions and adjust the timing of data collection based on the estimated child's emotions. The data collection unit can collect, for example, a learning history or behavioral observation results when the child is relaxed. The data collection unit can also collect test results or interests in learning methods when the child is concentrating. Furthermore, the data collection unit can temporarily suspend data collection when the child is tired and resume it after a break. For example, the data collection unit can collect learning history or behavioral observation results when the child is relaxed. The data collection unit can also collect test results or interests in learning methods when the child is concentrating. Furthermore, the data collection unit can temporarily suspend data collection when the child is tired and resume it after a break. This allows more appropriate data collection by adjusting the timing of data collection according to the child's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit may input image data of a child taken by a camera to the generation AI, and cause the generation AI to estimate the child's emotions.

[0075] The data collection unit can analyze the child's past learning history and select the optimal data collection method. The data collection unit can, for example, prioritize learning methods that were effective in the past to collect data. The data collection unit can also collect data that is effective in a specific time period from the past learning history. Furthermore, the data collection unit can also collect data by focusing on teaching materials and tasks that interest the child based on the past learning history. For example, the data collection unit can collect data by prioritizing learning methods that were effective in the past. The data collection unit can also collect data that is effective in a specific time period from the past learning history. Furthermore, the data collection unit can collect data by focusing on teaching materials and tasks that interest the child based on the past learning history. This makes it possible to effectively collect data by selecting the optimal data collection method based on the past learning history. Part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input past learning history data to the generation AI and cause the generation AI to select the optimal data collection method.

[0076] The data collection unit can perform filtering based on the child's current learning environment and field of interest when collecting data. The data collection unit can, for example, preferentially collect data related to the learning field in which the child is interested. In addition, the data collection unit can also collect data that requires concentration when the child's learning environment is quiet. In addition, the data collection unit can also collect data that is easy or relaxing when the child's learning environment is noisy. For example, the data collection unit can preferentially collect data related to the learning field in which the child is interested. In addition, the data collection unit can also collect data that requires concentration when the child's learning environment is quiet. In addition, the data collection unit can also collect data that is easy or relaxing when the child's learning environment is noisy. In this way, by filtering the data based on the child's learning environment and field of interest, more relevant data can be collected. Part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input the child's learning environment data to the generation AI and cause the generation AI to perform filtering.

[0077] The data collection unit can estimate the child's emotion and determine the priority of data to be collected based on the estimated emotion of the child. For example, when the child is excited, the data collection unit can preferentially collect data that attracts the child's interest. Furthermore, when the child is calm, the data collection unit can preferentially collect data that allows the child to concentrate on learning. Furthermore, when the child is tired, the data collection unit can preferentially collect data that allows the child to relax. For example, when the child is excited, the data collection unit can preferentially collect data that attracts the child's interest. Furthermore, when the child is calm, the data collection unit can preferentially collect data that allows the child to concentrate on learning. Furthermore, when the child is tired, the data collection unit can preferentially collect data that allows the child to relax. This enables more effective data collection by determining the priority of data according to the child's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the data collection unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the data collection unit can input image data of a child taken with a camera into the generation AI and have the generation AI estimate the child's emotions.

[0078] The data collection unit can preferentially collect highly relevant data in consideration of the child's geographical location information when collecting data. For example, when the child is at school, the data collection unit can preferentially collect data related to learning. In addition, when the child is at home, the data collection unit can preferentially collect data related to home learning. Furthermore, when the child is out, the data collection unit can preferentially collect data that can be learned while moving. For example, when the child is at school, the data collection unit can preferentially collect data related to learning. In addition, when the child is at home, the data collection unit can preferentially collect data that can be learned while moving. In this way, by considering the geographical location information, more relevant data can be collected. Part or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input the child's geographical location information to the generation AI and cause the generation AI to execute the priority order of highly relevant data.

[0079] The data collection unit can analyze the child's social media activity and collect related data at the time of data collection. The data collection unit can collect data related to topics in which the child is interested on social media, for example. The data collection unit can also collect related data based on learning outcomes shared by the child on social media. Furthermore, the data collection unit can collect data related to educational content that the child follows on social media. For example, the data collection unit can collect data related to topics in which the child is interested on social media. The data collection unit can also collect related data based on learning outcomes shared by the child on social media. Furthermore, the data collection unit can collect data related to educational content that the child follows on social media. This makes it possible to collect data based on the child's interests by analyzing social media activities. Some or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit can input the child's social media activity data to the generation AI and cause the generation AI to collect related data.

[0080] The analysis unit can estimate the child's emotion and adjust the expression method of the analysis based on the estimated emotion of the child. For example, the analysis unit can provide a detailed analysis result when the child is relaxed. Also, the analysis unit can provide a concise and to the point analysis result when the child is nervous. Furthermore, the analysis unit can provide a visually stimulating analysis result when the child is excited. For example, the analysis unit can provide a detailed analysis result when the child is relaxed. Also, the analysis unit can provide a concise and to the point analysis result when the child is nervous. Furthermore, the analysis unit can provide a visually stimulating analysis result when the child is excited. This makes it possible to provide an analysis result that is easier to understand by adjusting the expression method of the analysis according to the child's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input image data of a child captured by a camera into the generation AI and have the generation AI estimate the child's emotions.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. The analysis unit can perform detailed analysis on data with high importance, for example. The analysis unit can also perform concise analysis on data with low importance. Furthermore, the analysis unit can perform analysis with a moderate level of detail on data with medium importance. For example, the analysis unit can perform detailed analysis on data with high importance. The analysis unit can also perform concise analysis on data with low importance. Furthermore, the analysis unit can perform analysis with a moderate level of detail on data with medium importance. This enables efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can adjust the level of detail of the analysis using an AI model that evaluates the importance of the data.

[0082] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply a behavioral analysis algorithm to behavioral observation data. The analysis unit can also apply a learning pattern analysis algorithm to learning history data. Furthermore, the analysis unit can apply a grade analysis algorithm to test result data. For example, the analysis unit can apply a behavioral analysis algorithm to behavioral observation data. The analysis unit can also apply a learning pattern analysis algorithm to learning history data. Furthermore, the analysis unit can apply a grade analysis algorithm to test result data. Thereby, by applying an appropriate analysis algorithm depending on the category of data, the analysis accuracy is improved. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that selects an analysis algorithm according to the category of data.

[0083] The analysis unit can estimate the child's emotion and adjust the length of the analysis based on the estimated child's emotion. The analysis unit can perform a detailed analysis when the child is concentrating, for example. The analysis unit can also perform a brief analysis when the child is tired. Furthermore, the analysis unit can perform a visually stimulating analysis when the child is excited. For example, the analysis unit can perform a detailed analysis when the child is concentrating. The analysis unit can also perform a brief analysis when the child is tired. Furthermore, the analysis unit can perform a visually stimulating analysis when the child is excited. This allows the length of the analysis to be adjusted according to the child's emotion, thereby providing a more appropriate analysis result. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input image data of a child taken by a camera to the generation AI and cause the generation AI to execute an estimation of the child's emotion.

[0084] The analysis unit can determine the priority of the analysis based on the collection time of the data during the analysis. The analysis unit can, for example, preferentially analyze the latest data. The analysis unit can also prioritize the latest data while referring to past data. Furthermore, the analysis unit can also preferentially analyze data collected during a specific period. For example, the analysis unit can preferentially analyze the latest data. The analysis unit can also prioritize the latest data while referring to past data. Furthermore, the analysis unit can also preferentially analyze data collected during a specific period. As a result, by determining the priority of the analysis based on the collection time of the data, it is possible to perform analysis that prioritizes the latest data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can determine the priority of the analysis using an AI model that evaluates the collection time of the data.

[0085] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit can, for example, preferentially analyze data with high relevance. The analysis unit can also analyze data with medium relevance next. Furthermore, the analysis unit can analyze data with low relevance last. For example, the analysis unit can preferentially analyze data with high relevance. The analysis unit can also analyze data with medium relevance next. Furthermore, the analysis unit can analyze data with low relevance last. This enables efficient data analysis by adjusting the order of analysis based on the relevance of data. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can adjust the order of analysis using an AI model that evaluates the relevance of data.

[0086] The generation unit can estimate the child's emotion and adjust the expression method of the learning program to be generated based on the estimated emotion of the child. For example, when the child is relaxed, the generation unit can provide visually rich teaching materials. Furthermore, when the child is nervous, the generation unit can also provide simple and easy-to-understand teaching materials. Furthermore, when the child is excited, the generation unit can also provide interactive teaching materials. For example, when the child is relaxed, the generation unit can provide visually rich teaching materials. Furthermore, when the child is nervous, the generation unit can also provide simple and easy-to-understand teaching materials. Furthermore, when the child is excited, the generation unit can also provide interactive teaching materials. This enables more effective learning support by adjusting the expression method of the learning program according to the child's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input image data of a child captured by a camera into the generation AI and have the generation AI estimate the child's emotions.

[0087] The generating unit can adjust the level of detail of the learning program based on the characteristics of the child at the time of generation. For example, the generating unit can provide a program that includes a lot of visual teaching materials to a child who is good at visual learning. The generating unit can also provide a program that emphasizes repetitive learning to a child who needs repetitive learning. Furthermore, the generating unit can provide a program that includes a lot of audio teaching materials to a child who is good at auditory learning. For example, the generating unit can provide a program that includes a lot of visual teaching materials to a child who is good at visual learning. The generating unit can also provide a program that emphasizes repetitive learning to a child who needs repetitive learning. Furthermore, the generating unit can provide a program that includes a lot of audio teaching materials to a child who is good at auditory learning. This allows for more individualized learning support by adjusting the level of detail of the learning program based on the characteristics of the child. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the child's characteristic data into the generating AI to adjust the level of detail of the learning program.

[0088] The generating unit can apply different learning programs according to the learning style of the child at the time of generation. For example, the generating unit can provide a program centered on visual teaching materials to a child with a visual learning style. The generating unit can also provide a program centered on audio teaching materials to a child with an auditory learning style. Furthermore, the generating unit can provide a program including practical activities to a child with an experiential learning style. For example, the generating unit can provide a program centered on visual teaching materials to a child with a visual learning style. The generating unit can also provide a program centered on audio teaching materials to a child with an auditory learning style. Furthermore, the generating unit can provide a program including practical activities to a child with an experiential learning style. This enables more effective learning support by applying the learning program according to the child's learning style. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI or may be performed without using AI. For example, the generating unit can input the child's learning style data into the generating AI to generate an appropriate learning program.

[0089] The generation unit can estimate the child's emotion and adjust the length of the learning program to be generated based on the estimated emotion of the child. For example, the generation unit can provide a longer learning program when the child is concentrating. Also, the generation unit can provide a shorter learning program when the child is tired. Furthermore, the generation unit can provide a learning program including interactive elements when the child is excited. For example, the generation unit can provide a longer learning program when the child is concentrating. Also, the generation unit can provide a shorter learning program when the child is tired. Furthermore, the generation unit can provide a learning program including interactive elements when the child is excited. This allows for more appropriate learning support by adjusting the length of the learning program according to the child's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input image data of a child captured by a camera into the generation AI and have the generation AI estimate the child's emotions.

[0090] The generating unit can determine the priority of the learning program based on the child's learning history at the time of generation. The generating unit can, for example, preferentially incorporate into the program a learning method that was effective in the past. The generating unit can also provide a program that is effective in a specific time period based on the past learning history. Furthermore, the generating unit can preferentially incorporate into the program teaching materials and assignments that interest the child based on the past learning history. For example, the generating unit can preferentially incorporate into the program a learning method that was effective in the past. The generating unit can also provide a program that is effective in a specific time period based on the past learning history. Furthermore, the generating unit can preferentially incorporate into the program teaching materials and assignments that interest the child based on the past learning history. As a result, by determining the priority of the learning program based on the child's learning history, more effective learning support is possible. Part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the child's learning history data into the generating AI to determine the priority of the learning program.

[0091] The generating unit can adjust the order of the learning program based on the child's interests at the time of generation. The generating unit can, for example, place a topic in which the child is interested first. The generating unit can also postpone tasks that the child is not good at and start with content that interests the child. Furthermore, the generating unit can configure the program centered on content that interests the child. For example, the generating unit can place a topic in which the child is interested first. The generating unit can also postpone tasks that the child is not good at and start with content that interests the child. Furthermore, the generating unit can configure the program centered on content that interests the child. This allows for more effective learning support by adjusting the order of the learning program based on the child's interests. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the child's interest data into the generating AI and adjust the order of the learning program.

[0092] The providing unit can estimate the child's emotion and adjust the method of providing the learning program based on the estimated emotion of the child. For example, when the child is relaxed, the providing unit can provide visually rich teaching materials. Furthermore, when the child is nervous, the providing unit can also provide simple and easy-to-understand teaching materials. Furthermore, when the child is excited, the providing unit can also provide interactive teaching materials. For example, when the child is relaxed, the providing unit can provide visually rich teaching materials. Furthermore, when the child is nervous, the providing unit can also provide simple and easy-to-understand teaching materials. Furthermore, when the child is excited, the providing unit can also provide interactive teaching materials. This enables more effective learning support by adjusting the method of providing the learning program according to the child's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using 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 generating AI and cause the generating AI to estimate the child's emotions.

[0093] The providing unit can select the optimal providing method by referring to the child's past learning history at the time of providing. The providing unit can, for example, give priority to a providing method that was effective in the past. The providing unit can also select a providing method that is effective in a specific time period from the past learning history. Furthermore, the providing unit can also give priority to providing teaching materials and assignments that the child is interested in based on the past learning history. For example, the providing unit can give priority to a providing method that was effective in the past. The providing unit can also select a providing method that is effective in a specific time period from the past learning history. Furthermore, the providing unit can also give priority to providing teaching materials and assignments that the child is interested in based on the past learning history. As a result, more effective learning support is possible by selecting the optimal providing method based on the past learning history. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the child's learning history data into the generating AI and select the optimal providing method.

[0094] The providing unit can customize the providing means based on the child's current learning environment at the time of providing. For example, when the child is at home, the providing unit can provide online teaching materials. Also, when the child is at school, the providing unit can provide teaching materials in cooperation with a teacher. Furthermore, when the child is out, the providing unit can provide teaching materials through a mobile device. For example, when the child is at home, the providing unit can provide online teaching materials. Also, when the child is at school, the providing unit can provide teaching materials in cooperation with a teacher. Furthermore, when the child is out, the providing unit can provide teaching materials through a mobile device. This enables more effective learning support by customizing the providing means based on the child's learning environment. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the child's learning environment data into the generating AI to customize the providing means.

[0095] The providing unit can estimate the child's emotion and determine the priority order of providing the learning program based on the estimated emotion of the child. For example, when the child is excited, the providing unit can provide educational materials that attract the child's interest preferentially. Furthermore, when the child is calm, the providing unit can provide educational materials that require concentration preferentially. Furthermore, when the child is tired, the providing unit can provide educational materials that allow the child to relax preferentially. For example, when the child is excited, the providing unit can provide educational materials that attract the child's interest preferentially. Furthermore, when the child is calm, the providing unit can provide educational materials that require concentration preferentially. Furthermore, when the child is tired, the providing unit can provide educational materials that allow the child to relax preferentially. This enables more effective learning support by determining the priority order of providing the learning program according to the child's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 generating AI and cause the generating AI to estimate the child's emotions.

[0096] The providing unit can select the optimal providing method in consideration of the child's geographical location information at the time of providing. For example, when the child is at school, the providing unit can provide learning materials preferentially. In addition, when the child is at home, the providing unit can also provide learning materials related to home learning preferentially. Furthermore, when the child is out, the providing unit can also provide learning materials that can be used while traveling preferentially. For example, when the child is at school, the providing unit can provide learning materials preferentially. In addition, when the child is at home, the providing unit can also provide learning materials that can be used while traveling preferentially. This makes it possible to provide more effective learning support by considering the geographical location information. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the child's geographical location information into the generating AI and select the optimal providing method.

[0097] The providing unit can analyze the child's social media activity at the time of providing and suggest a means of providing. The providing unit can provide teaching materials related to a topic in which the child is interested on social media, for example. The providing unit can also provide related teaching materials based on the learning outcomes shared by the child on social media. Furthermore, the providing unit can provide teaching materials related to educational content that the child follows on social media. For example, the providing unit can provide teaching materials related to a topic in which the child is interested on social media. The providing unit can also provide related teaching materials based on the learning outcomes shared by the child on social media. Furthermore, the providing unit can provide teaching materials related to educational content that the child follows on social media. Thereby, by analyzing social media activity, it is possible to provide learning support based on the child's interests. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the child's social media activity data into a generating AI and suggest a means of providing.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The education support system may further include a feedback unit. The feedback unit may provide feedback in real time based on the child's learning progress and results. For example, the feedback unit may display a message of praise or encouragement when the child completes a particular task. The feedback unit may also provide appropriate advice or hints when the child is working on a task that is difficult for the child. Furthermore, the feedback unit may present the child with the next step or goal according to the child's learning progress. This allows the child to grasp his / her learning situation in real time and proceed with the learning while maintaining motivation.

[0100] The data collection unit can further collect data on the child's learning environment using an environmental sensor. For example, the environmental sensor can measure the brightness and temperature of classroom lighting, noise levels, and the like, and collect data. The environmental sensor can also detect the child's sitting position and posture, and collect data. Furthermore, the environmental sensor can issue an alert if the child's learning environment is not appropriate. This makes it possible to optimize the child's learning environment and support more effective learning.

[0101] The analysis unit can further use the emotion estimation function to estimate the emotion of the child and adjust the analysis result based on the estimated emotion. For example, the analysis unit can provide a detailed analysis result when the child is relaxed. The analysis unit can also provide a concise and to the point analysis result when the child is nervous. The analysis unit can also provide a visually stimulating analysis result when the child is excited. In this way, by adjusting the analysis result according to the emotion of the child, it is possible to provide an analysis result that is easier to understand.

[0102] The generating unit can further generate a learning program incorporating game elements. For example, the generating unit can enable the child to earn points each time he or she clears a specific task. The generating unit can also award a badge or title each time the child earns a certain number of points. Furthermore, the generating unit can introduce a ranking system that enables the child to compete with other children. This allows the child to proceed with learning in a game-like manner, thereby increasing motivation for learning.

[0103] The providing unit can further automatically adjust the difficulty of the learning program according to the child's learning progress. For example, if the child completes a particular task smoothly, the providing unit can increase the difficulty of the next task. Also, if the child is struggling with a particular task, the providing unit can lower the difficulty of the next task. Furthermore, the providing unit can provide review tasks at an appropriate time according to the child's learning progress. This allows the child to progress with the learning at his or her own pace and receive effective learning support.

[0104] The data collection unit can further estimate the child's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can collect learning history and behavioral observation results when the child is relaxed. The data collection unit can also collect test results and interests in learning methods when the child is concentrating. Furthermore, the data collection unit can temporarily suspend data collection when the child is tired and resume it after a break. This allows for more appropriate data collection by adjusting the timing of data collection according to the child's emotions.

[0105] The data collection unit can further analyze the child's past learning history and select the optimal data collection method. For example, the data collection unit can collect data by prioritizing learning methods that were effective in the past. The data collection unit can also collect effective data at specific times based on the past learning history. Furthermore, the data collection unit can collect data by focusing on teaching materials and tasks that interest the child based on the past learning history. This makes it possible to collect effective data by selecting the optimal data collection method based on the past learning history.

[0106] The data collection unit can further perform filtering based on the child's current learning environment and areas of interest. For example, the data collection unit can preferentially collect data related to areas of learning in which the child is interested. In addition, if the child's learning environment is quiet, the data collection unit can collect data that requires concentration. In addition, if the child's learning environment is noisy, the data collection unit can collect data on easy tasks or relaxing activities. This allows more relevant data to be collected by filtering data based on the child's learning environment and areas of interest.

[0107] The data collection unit can further estimate the child's emotions and determine the priority of data to be collected based on the estimated emotions. For example, if the child is excited, the data collection unit can prioritize collecting data that attracts the child's interest. Also, if the child is calm, the data collection unit can prioritize collecting data that allows the child to concentrate on learning. Furthermore, if the child is tired, the data collection unit can prioritize collecting data that allows the child to relax. This allows more effective data collection by determining the priority of data according to the child's emotions.

[0108] The data collection unit can further take into account the geographical location information of the child to preferentially collect highly relevant data. For example, when the child is at school, the data collection unit can preferentially collect data related to learning. Also, when the child is at home, the data collection unit can preferentially collect data related to home learning. Furthermore, when the child is out, the data collection unit can preferentially collect data that can be learned while moving. In this way, more relevant data can be collected by taking into account the geographical location information.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The data collection unit collects data on the child's characteristics and learning depth. The child's characteristics include learning style, interests, cognitive ability, etc. The data collection unit collects data such as the child's behavioral observation results, learning history, test results, etc. Data can be collected using sensors and devices, including heart rate sensors, cameras, microphones, etc. Step 2: The analysis unit uses AI algorithms to analyze the data collected by the data collection unit and understand the child's characteristics and learning depth. AI algorithms include neural networks, decision trees, support vector machines, etc. Step 3: The generator generates an individualized learning program based on the information obtained by the analyzer. The generator can generate a program that emphasizes visual learning materials and repetitive learning. Visual learning materials include videos, charts, animations, etc., and repetitive learning includes repetition of the same content and spaced review. Step 4: The providing unit provides the learning program generated by the generating unit. The providing unit can provide the learning program in a manner that matches the characteristics of the child. The manner that matches the characteristics of the child includes the selection of learning materials according to the learning style, adjustment of the progress speed, etc.

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0112] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the data collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data collection unit collects the child's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14, and measures the heart rate using a heart rate sensor. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data with an AI algorithm. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an individualized learning program based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the generated learning program to the child. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0118] 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.

[0119] 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 the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] 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 performed in a secure state.

[0122] Fig. 4 shows an example of 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.

[0123] 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.

[0124] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0128] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the data collection unit, analysis unit, generation unit, and provision 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 data collection unit collects the child's facial expressions and voice using the camera 42 and microphone 238 of the smart glasses 214, and measures the heart rate using a heart rate sensor. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data with an AI algorithm. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an individualized learning program based on the analysis result. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the generated learning program to the child. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0134] 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.

[0135] 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 the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] 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 performed in a secure state.

[0138] 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.

[0139] 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.

[0140] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0142] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] 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 voice indicating a user input for 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.

[0144] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the data collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the data collection unit collects the child's facial expressions and voice using the camera 42 and microphone 238 of the headset type terminal 314, and measures the heart rate using a heart rate sensor. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data with an AI algorithm. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an individualized learning program based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the generated learning program to the child. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0150] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] 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 the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] 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 performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0155] 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.

[0156] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] 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 a voice indicating a user input for 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.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0163] Each of the multiple elements including the data collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data collection unit collects the child's facial expressions and voice using the camera 42 and microphone 238 of the robot 414, and measures the heart rate using a heart rate sensor. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data with an AI algorithm. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates an individualized learning program based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated learning program to the child. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0171] 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.

[0172] 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 Universal Serial Bus (USB) 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.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] 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.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0177] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) A data collection department that collects data on children's characteristics and learning depth; an analysis unit that analyzes the data collected by the data collection unit and grasps the characteristics and learning depth of the child; a generation unit that generates an individualized learning program based on the information grasped by the analysis unit; A provision unit that provides the learning program generated by the generation unit. A system characterized by: (Appendix 2) The data collection unit includes: Collect data using sensors and devices 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze data using AI algorithms 2. The system of claim 1, (Appendix 4) The generation unit is Generate programs that emphasize visual learning and repetitive learning 2. The system of claim 1, (Appendix 5) The providing unit is Providing learning programs that are tailored to the characteristics of each child 2. The system of claim 1, (Appendix 6) The data collection unit includes: Inferring a child's emotions and adjusting the timing of data collection based on the estimated child's emotions 2. The system of claim 1, (Appendix 7) The data collection unit includes: Analyze the child's past learning history and choose the most appropriate data collection method 2. The system of claim 1, (Appendix 8) The data collection unit includes: Filter data collection based on the child's current learning environment and interests 2. The system of claim 1, (Appendix 9) The data collection unit includes: Inferring a child's emotions and prioritizing data collection based on the inferred emotions 2. The system of claim 1, (Appendix 10) The data collection unit includes: When collecting data, take into account the child's geographic location and prioritize the collection of more relevant data 2. The system of claim 1, (Appendix 11) The data collection unit includes: During data collection, analyze children's social media activity and collect relevant data 2. The system of claim 1, (Appendix 12) The analysis unit is Infer the child's emotions and adjust the way the analysis is presented based on the estimated child's emotions 2. The system of claim 1, (Appendix 13) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 15) The analysis unit is Infer the child's emotions and adjust the length of the analysis based on the estimated child's emotions 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 18) The generation unit is Estimate the child's emotions and adjust the expression method of the generated learning program based on the estimated child's emotions. 2. The system of claim 1, (Appendix 19) The generation unit is At generation time, adjust the level of detail of the learning program based on the child's characteristics 2. The system of claim 1, (Appendix 20) The generation unit is During generation, different learning programs are applied depending on the child's learning style. 2. The system of claim 1, (Appendix 21) The generation unit is Estimating a child's emotions and adjusting the length of the learning program to be generated based on the estimated child's emotions 2. The system of claim 1, (Appendix 22) The generation unit is At the time of generation, learning programs are prioritized based on the child's learning history. 2. The system of claim 1, (Appendix 23) The generation unit is At generation time, the sequence of the learning program is adjusted based on the child's interests 2. The system of claim 1, (Appendix 24) The providing unit is Estimating a child's emotions and adjusting the delivery of a learning program based on the estimated child's emotions 2. The system of claim 1, (Appendix 25) The providing unit is When providing the service, the optimal method of providing the service will be selected based on the child's past learning history. 2. The system of claim 1, (Appendix 26) The providing unit is At the time of delivery, tailor delivery methods based on the child's current learning environment 2. The system of claim 1, (Appendix 27) The providing unit is Estimating a child's emotions and determining the priority of providing learning programs based on the estimated child's emotions 2. The system of claim 1, (Appendix 28) The providing unit is When providing information, the optimal method of providing the information is selected taking into account the child's geographic location information. 2. The system of claim 1, (Appendix 29) The providing unit is When providing content, analyze the child's social media activity to suggest a method of providing content 2. The system of claim 1, [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection department that collects data on children's characteristics and learning depth; an analysis unit that analyzes the data collected by the data collection unit and grasps the characteristics and learning depth of the child; a generation unit that generates an individualized learning program based on the information grasped by the analysis unit; A provision unit that provides the learning program generated by the generation unit. A system characterized by:

2. The data collection unit includes: Collect data using sensors and devices 2. The system of claim 1 .

3. The analysis unit is Analyze the data using AI algorithms 2. The system of claim 1 .

4. The generation unit is Generate programs that emphasize visual learning and repetitive learning 2. The system of claim 1 .

5. The providing unit is Providing learning programs that are tailored to the characteristics of each child 2. The system of claim 1 .

6. The data collection unit includes: Inferring a child's emotions and adjusting the timing of data collection based on the estimated child's emotions 2. The system of claim 1 .

7. The data collection unit includes: Analyze the child's past learning history and choose the most appropriate data collection method 2. The system of claim 1 .

8. The data collection unit includes: Filter data collection based on the child's current learning environment and interests 2. The system of claim 1 .

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