System

Abstract

An object of a system according to an embodiment is to analyze data of an educational site, provide teaching materials and evaluation criteria according to the needs of the educational site, and respond to individual needs of learners.SOLUTION: A system includes a collection part, an analysis part, and an individualization part. The collection unit collects data of an education site. The analysis part analyzes the data collected by the collection part and provides teaching materials and evaluation standards corresponding to the needs of the education field. The individualizing section deals with the learner's individual needs based on the learning material and the evaluation criteria provided by the analyzing 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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

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

[0004] Conventional technologies have had the problem of not being able to effectively utilize data from educational settings to provide teaching materials and assessment criteria, and to adequately address the individual needs of learners.

[0005] The system according to the embodiment aims to analyze data from educational sites, provide teaching materials and assessment criteria that meet the needs of educational sites, and respond to the individual needs of learners. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and an individualization unit. The collection unit collects data from educational sites. The analysis unit analyzes the data collected by the collection unit and provides teaching materials and assessment criteria according to the needs of the educational site. The individualization unit responds to the individual needs of learners based on the teaching materials and assessment criteria provided by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze data from educational sites, provide teaching materials and assessment criteria that meet the needs of educational sites, and respond to the individual needs of learners. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An educational support system according to an embodiment of the present invention uses a generative AI to support tests and other school administrative tasks at educational institutions. The educational support system collects data from educational settings, analyzes it using a generative AI, and provides effective teaching materials and assessment criteria tailored to the needs of the educational settings. The educational support system also flexibly responds to the individual needs of learners and creates personalized learning plans. For example, the educational support system collects detailed data such as test results, attendance records, and learning progress, and analyzes this data using a generative AI. Next, the educational support system uses a generative AI to evaluate the learner's level of understanding of specific questions and provide effective teaching materials and assessment criteria. Furthermore, the educational support system uses a generative AI to analyze the learner's learning progress and understanding and create personalized learning plans. This reduces the burden on teachers and improves the quality of education. This allows the educational support system to automatically analyze data from educational settings and provide effective teaching materials and assessment criteria tailored to the needs of the educational settings. For example, the generative AI automatically scores tests, eliminating the need for teachers to manually grade them. Furthermore, the generative AI evaluates the learner's level of understanding and provides personalized feedback, thereby improving the learner's learning effectiveness.

[0029] An educational support system according to an embodiment includes a collection unit, an analysis unit, and an individualization unit. The collection unit collects data from educational sites. The educational site data includes, but is not limited to, test results, attendance data, and learning progress. For example, the collection unit collects test results digitally. The collection unit can also automatically record attendance data. Furthermore, the collection unit can monitor learning progress in real time. For example, the collection unit collects test results digitally, automatically records attendance data, and monitors learning progress in real time. The analysis unit uses a generative AI to analyze the data collected by the collection unit and provide teaching materials and assessment criteria tailored to the needs of the educational site. The analysis is performed using, for example, data mining, statistical analysis, and other methods, but is not limited to, these examples. For example, the analysis unit uses data mining techniques to extract educational needs from the collected data. The analysis unit can also analyze data trends using statistical analysis techniques. Furthermore, the analysis unit can use a generative AI to evaluate a learner's understanding of a specific problem. For example, the analysis unit extracts needs in educational settings using data mining technology, analyzes data trends using statistical analysis technology, and evaluates a learner's level of understanding of a specific problem using a generation AI. The individualization unit addresses the individual needs of learners based on the data collected by the collection unit and the teaching materials and evaluation criteria provided by the analysis unit. The individualization unit, for example, analyzes a learner's learning progress and level of understanding and creates an individualized learning plan. The individualization unit can provide effective learning support tailored to each learner using a generation AI. For example, the individualization unit analyzes a learner's learning progress and level of understanding and creates an individualized learning plan. As a result, the education support system according to the embodiment can improve the quality of education by collecting and analyzing data from educational settings and addressing individual needs.

[0030] The collection unit can collect test results, attendance data, and learning progress data. The collection unit, for example, collects test results in digital format. For example, the collection unit automatically collects online test results. The collection unit can also automatically record attendance data. For example, the collection unit records attendance data in real time using an attendance management system. The collection unit can also monitor learning progress in real time. For example, the collection unit tracks learners' progress in real time using a learning management system. This allows for more accurate analysis by collecting detailed data from educational settings. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input online test results into a generation AI, which can analyze the data.

[0031] The analysis unit can evaluate a learner's level of understanding of a specific question based on the collected data. The analysis unit can evaluate a learner's level of understanding of a specific question based on, for example, collected test results. For example, the analysis unit can analyze the correct answer rate on a test to evaluate the learner's level of understanding. The analysis unit can also evaluate the relationship between a learner's attendance status and their level of understanding based on collected attendance data. For example, the analysis unit can compare their attendance rate with their correct answer rate on a test to evaluate the impact of their attendance status on their level of understanding. The analysis unit can also evaluate the relationship between a learner's progress and their level of understanding based on collected learning progress data. For example, the analysis unit can compare their learning progress with their correct answer rate on a test to evaluate the impact of their progress on their level of understanding. This allows for the evaluation of a learner's level of understanding, thereby making it possible to provide effective teaching materials and evaluation criteria. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected data into a generation AI, which can then evaluate the learner's level of understanding.

[0032] The individualization unit can analyze the learner's learning progress and level of understanding and create an individualized learning plan. The individualization unit, for example, analyzes the learner's learning progress and creates an individualized learning plan. For example, the individualization unit can analyze the learner's progress using a learning management system and create an individualized learning plan. The individualization unit can also analyze the learner's level of understanding and create an individualized learning plan. For example, the individualization unit can analyze test results and create a learning plan based on the learner's level of understanding. The individualization unit can also analyze the learner's interests and create an individualized learning plan. For example, the individualization unit customizes the learning plan based on the learner's interests. This enables effective learning support tailored to each learner. Some or all of the above-mentioned processing in the individualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the individualization unit can input the learner's data into a generation AI, which then creates an individualized learning plan.

[0033] The analysis unit can update the teaching materials and evaluation criteria in response to new educational policies and changes in the curriculum. The analysis unit, for example, updates the teaching materials and evaluation criteria based on a new educational policy. For example, the analysis unit updates the teaching materials and evaluation criteria based on the content of educational reform. The analysis unit can also update the teaching materials and evaluation criteria in response to changes in the curriculum. For example, the analysis unit updates the teaching materials in response to the addition of a new subject and updates the evaluation criteria in response to changes in class times. This allows for flexible response to changes in the educational environment. Some 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 related to a new educational policy or changes in the curriculum into the generation AI, which can then update the teaching materials and evaluation criteria.

[0034] The individualization unit can automatically score tests using a generation AI and provide personalized feedback. The individualization unit can, for example, automatically score tests using a generation AI. For example, the individualization unit inputs test results into the generation AI, which then automatically scores the tests. The individualization unit can also provide personalized feedback using the generation AI. For example, the individualization unit has the generation AI evaluate the learner's level of understanding and provide personalized feedback. This reduces the burden on teachers and enables the learner to receive prompt and appropriate feedback. Some or all of the above-described processing in the individualization unit can be performed using, or without, a generation AI. For example, the individualization unit can input test results into the generation AI, which then automatically scores the tests and provides personalized feedback.

[0035] The collection unit can analyze the learner's past learning history and select an appropriate data collection method. The collection unit, for example, analyzes the learner's past learning history. For example, the collection unit analyzes past test results and learning progress data to understand the learner's learning history. The collection unit can also analyze the learner's past learning patterns and select an optimal data collection method. For example, the collection unit collects data by preferentially using learning tools that the learner has used in the past. The collection unit can also collect data by focusing on specific subjects or topics based on the learner's past grades. For example, the collection unit preferentially collects data related to specific subjects based on the learner's past grades. This allows data to be collected in an optimal manner based on the learner's past history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the learner's past learning history into a generation AI, which can select an optimal data collection method.

[0036] The collection unit can filter data based on the learner's current learning situation and areas of interest when collecting data. The collection unit, for example, analyzes the learner's current learning situation. For example, the collection unit uses a learning management system to grasp the learner's current learning progress. The collection unit can also analyze the learner's areas of interest and filter the data when collecting it. For example, the collection unit prioritizes collecting relevant data based on the learner's areas of interest. The collection unit can also collect only necessary data according to the learner's current learning progress. For example, the collection unit prioritizes collecting data related to the task the learner is currently working on. This allows efficient collection of data according to the learner's current situation. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the learner's current learning situation and areas of interest to the generation AI, which then filters the data.

[0037] When collecting data, the collection unit can select an appropriate collection means depending on the learner's input method. The collection unit, for example, analyzes the learner's input method. For example, the collection unit analyzes input methods such as voice input, text input, and image input. The collection unit can also select an appropriate collection means depending on the learner's input method. For example, if the learner prefers voice input, the collection unit can preferentially collect voice data. Also, if the learner prefers text input, the collection unit can preferentially collect text data. Furthermore, if the learner prefers image input, the collection unit can preferentially collect image data. For example, if the learner prefers voice input, the collection unit collects voice data using voice recognition technology. This enables optimal data collection depending on the learner's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the learner's input method to a generation AI, which can select an appropriate collection means.

[0038] When collecting data, the collection unit can prioritize collecting appropriate data by taking into account the learner's geographical location information. The collection unit, for example, considers the learner's geographical location information. For example, the collection unit identifies the learner's current location using GPS data. The collection unit can also prioritize collecting highly relevant data based on the learner's geographical location information. For example, if the learner is in a specific area, the collection unit prioritizes collecting data related to that area. Also, if the learner is moving, the collection unit can collect data related to the learner's current location in real time. Furthermore, if the learner is in a specific location, the collection unit can prioritize collecting data related to that location. For example, if the learner is in a specific area, the collection unit prioritizes collecting data related to that area. This makes it possible to collect highly relevant data based on the learner's geographical location information. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the learner's geographical location information to a generation AI, which can then prioritize collecting highly relevant data.

[0039] The collection unit can analyze the learner's social media activities and collect related data when collecting data. The collection unit, for example, analyzes the learner's social media activities. For example, the collection unit analyzes information shared by the learner on social media. The collection unit can also analyze the learner's social media activities and collect related data. For example, the collection unit analyzes the content of the learner's social media posts and collect data related to topics of interest. The collection unit can also collect related data by referring to the activities of the learner's friends on social media. For example, the collection unit collects related data by referring to the activities of the learner's friends on social media. In this way, related data based on the learner's social media activities can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the learner's social media activities into a generation AI, which can collect related data.

[0040] When collecting data, the collection unit can customize the collection method by reflecting the learner's past feedback. The collection unit, for example, analyzes the learner's past feedback. For example, the collection unit analyzes feedback provided by the learner in the past. The collection unit can also customize the collection method by reflecting the learner's past feedback. For example, the collection unit adjusts the collection method based on feedback provided by the learner in the past. The collection unit can also select an optimal collection means from the learner's past feedback. Furthermore, the collection unit can adjust the collection timing by reflecting the learner's past feedback. For example, the collection unit adjusts the collection timing based on feedback provided by the learner in the past. This enables optimal data collection based on the learner's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the learner's past feedback to a generation AI, which can customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit, for example, evaluates the importance of the data. For example, the analysis unit evaluates the importance based on the impact and relevance of the data. The analysis unit can also adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. For example, the analysis unit prioritizes analysis of data with high importance. This enables optimal analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the data to the generation AI, which can then adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. The analysis unit, for example, classifies data categories. For example, the analysis unit may classify data into categories such as text data, numerical data, and image data. The analysis unit can also apply different analysis algorithms depending on the data category. For example, the analysis unit may apply a specific analysis algorithm to test result data. The analysis unit may also apply a different analysis algorithm to attendance data. The analysis unit can also apply yet another analysis algorithm to learning progress data. For example, the analysis unit may apply a specific analysis algorithm to test result data, a different analysis algorithm to attendance data, and yet another analysis algorithm to learning progress data. This enables optimal analysis depending on the data category. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the data category into a generation AI, which then applies a different analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the learner's past analysis results. The analysis unit, for example, analyzes the learner's past analysis results. For example, the analysis unit analyzes past test results and learning progress data to grasp the learner's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the learner's past analysis results. For example, the analysis unit corrects the current analysis results based on the past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the past analysis results. The analysis unit can also improve the accuracy of the analysis by using the past analysis results. For example, the analysis unit corrects the current analysis results based on the past analysis results and adjusts the analysis algorithm to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the learner's past analysis results into the generation AI, which can improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the data submission time. The analysis unit, for example, evaluates the data submission time. For example, the analysis unit evaluates the submission time based on the submission deadline and the submission frequency. The analysis unit can also determine the analysis priority based on the data submission time. For example, the analysis unit prioritizes analysis of data with an upcoming submission deadline. The analysis unit can also postpone analysis of data whose submission time has passed. The analysis unit can also adjust the analysis schedule based on the submission time. For example, the analysis unit prioritizes analysis of data with an upcoming submission deadline and postpones analysis of data whose submission time has passed, thereby adjusting the analysis schedule based on the submission time. This enables optimal analysis based on the data submission time. Some 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 the data submission time to the generation AI, and the generation AI can determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, evaluates the relevance of the data. For example, the analysis unit evaluates the relevance based on correlation or causality of the data. The analysis unit can also adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data and postpones analysis of less relevant data, thereby determining the order of analysis based on the relevance of the data. This enables optimal analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of the data into the generation AI, which can then adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the learner's level of expertise. The analysis unit, for example, evaluates the learner's level of expertise. For example, the analysis unit evaluates the learner's level of expertise based on test results or self-assessment. The analysis unit can also adjust the use of technical terms in the analysis according to the learner's level of expertise. For example, if the learner's level of expertise is high, the analysis unit provides analysis results that use a lot of technical terms. Also, if the learner's level of expertise is low, the analysis unit can provide analysis results that are explained in simple terms. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the learner's level of expertise. For example, if the learner's level of expertise is high, the analysis unit provides analysis results that use a lot of technical terms, and if the learner's level of expertise is low, the analysis unit provides analysis results that are explained in simple terms, thereby adjusting the way the analysis results are presented according to the learner's level of expertise. This makes it possible to provide optimal analysis results according to the learner's level of expertise. Some 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 the learner's level of expertise into the generation AI, which can then adjust the use of technical terminology in the analysis.

[0047] During personalization, the personalization unit can analyze the learner's past learning behavior and select the optimal personalization method. The personalization unit, for example, analyzes the learner's past learning behavior. For example, the personalization unit analyzes past test results and learning progress data to understand the learner's past learning behavior. The personalization unit can also analyze the learner's past learning patterns and select the optimal personalization method. For example, the personalization unit can perform personalization by preferentially using learning tools that the learner has used in the past. The personalization unit can also perform personalization by focusing on specific subjects or topics based on the learner's past grades. For example, the personalization unit provides an individualization plan related to a specific subject based on the learner's past grades. This enables optimal personalization based on the learner's past behavior. Some or all of the above-mentioned processing in the personalization unit may be performed using, for example, AI, or may be performed without using AI. For example, the personalization unit can input the learner's past learning behavior into a generation AI, which can select the optimal personalization method.

[0048] During personalization, the personalization unit can customize the personalization means based on the learner's current learning situation. The personalization unit, for example, analyzes the learner's current learning situation. For example, the personalization unit grasps the learner's current learning progress using a learning management system. The personalization unit can also customize the personalization means based on the learner's current learning situation. For example, the personalization unit provides an individualization plan related to the task the learner is currently working on. The personalization unit can also provide necessary personalization means according to the learner's current learning progress. Furthermore, the personalization unit can select an optimal personalization method based on the learner's current learning situation. For example, the personalization unit provides an individualization plan related to the task the learner is currently working on, provides necessary personalization means according to the learner's current learning progress, and selects an optimal personalization method based on the learner's current learning situation. This enables optimal personalization according to the learner's current situation. Some or all of the above-mentioned processing in the personalization unit may be performed using, for example, AI, or may be performed without using AI. For example, the personalization unit can input the learner's current learning situation into the generation AI, which can then customize the means of personalization.

[0049] During individualization, the individualization unit can improve the individualization method by reflecting the learner's feedback. The individualization unit, for example, analyzes the learner's feedback. For example, the individualization unit analyzes feedback provided by the learner. The individualization unit can also improve the individualization method by reflecting the learner's feedback. For example, the individualization unit adjusts the individualization plan based on the feedback provided by the learner. The individualization unit can also select an optimal individualization method from the learner's feedback. The individualization unit can also improve the individualization method by reflecting the learner's feedback. For example, the individualization unit adjusts the individualization plan, selects an optimal individualization method, and improves the individualization method based on the feedback provided by the learner. This enables optimal individualization based on the learner's feedback. Some or all of the above-mentioned processing in the individualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the individualization unit can input the learner's feedback to a generation AI, which can improve the individualization method.

[0050] During personalization, the personalization unit can select an optimal personalization method by taking into account the learner's geographical location information. The personalization unit, for example, considers the learner's geographical location information. For example, the personalization unit identifies the learner's current location using GPS data. The personalization unit can also select an optimal personalization method based on the learner's geographical location information. For example, if the learner is in a specific area, the personalization unit provides an individualization plan related to that area. Also, if the learner is on the move, the personalization unit can provide an individualization plan related to the learner's current location in real time. Furthermore, if the learner is in a specific location, the personalization unit can provide an individualization plan related to that location. For example, if the learner is in a specific area, the personalization unit provides an individualization plan related to that area. This enables optimal personalization based on the learner's geographical location information. Some or all of the above-described processing in the personalization unit may be performed using, for example, AI, or may be performed without using AI. For example, the personalization unit can input the learner's geographical location information to a generation AI, which can select an optimal personalization method.

[0051] During personalization, the personalization unit can analyze the learner's social media activity and suggest personalization measures. The personalization unit, for example, analyzes the learner's social media activity. For example, the personalization unit analyzes information shared by the learner on social media. The personalization unit can also analyze the learner's social media activity and provide a relevant personalization plan. For example, the personalization unit can analyze the content of the learner's social media posts and provide a personalization plan related to topics of interest. The personalization unit can also provide a relevant personalization plan by referring to the activities of the learner's friends on social media. For example, the personalization unit can provide a relevant personalization plan by referring to the activities of the learner's friends on social media. This enables optimal personalization based on the learner's social media activity. Some or all of the above-mentioned processing in the personalization unit may be performed using, for example, AI, or may be performed without using AI. For example, the personalization unit can input the learner's social media activity into a generation AI, which can then suggest personalization measures.

[0052] During individualization, the individualization unit can customize the individualization method by reflecting the learner's past feedback. The individualization unit, for example, analyzes the learner's past feedback. For example, the individualization unit analyzes feedback provided by the learner in the past. The individualization unit can also customize the individualization method by reflecting the learner's past feedback. For example, the individualization unit adjusts the individualization plan based on feedback provided by the learner in the past. The individualization unit can also select an optimal individualization method from the learner's past feedback. The individualization unit can also customize the individualization method by reflecting the learner's past feedback. For example, the individualization unit adjusts the individualization plan, selects an optimal individualization method, and customizes the individualization method based on feedback provided by the learner in the past. This enables optimal individualization based on the learner's past feedback. Some or all of the above-described processing in the individualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the individualization unit can input the learner's past feedback into a generation AI, which can customize the individualization method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The education support system can also collect health data of the learner, which the analysis unit analyzes to provide a study plan based on the learner's health condition. For example, the collection unit collects sleep data and exercise data of the learner, which the analysis unit analyzes to create a study plan based on the learner's health condition. The collection unit can also collect dietary data of the learner, which the analysis unit analyzes to provide a study plan based on nutritional status. Furthermore, the collection unit can monitor the learner's stress level, which the analysis unit analyzes to provide a study plan that helps reduce stress. This makes it possible to provide optimal study support based on the learner's health condition.

[0055] The collection unit collects device usage data from learners, and the analysis unit analyzes this data to provide a study plan based on the learner's device usage patterns. For example, the collection unit records the amount of time the learner spends using their smartphone or tablet, and the analysis unit analyzes this data to create a study plan based on the amount of time the learner spends using the device. The collection unit also collects app usage data from learners, and the analysis unit analyzes this data to provide a study plan based on the use of specific apps. Furthermore, the collection unit collects internet browsing history from learners, and the analysis unit analyzes this data to provide a study plan based on their interests. This makes it possible to provide optimal study support based on the learner's device usage patterns.

[0056] The personalization unit can also analyze the learner's learning style and suggest the optimal learning method. For example, if the learner prefers visual learning, the personalization unit can provide a learning plan that makes heavy use of visual materials. If the learner prefers auditory learning, the personalization unit can also provide a learning plan that makes heavy use of audio materials. Furthermore, if the learner prefers practical learning, the personalization unit can also provide a learning plan that includes experiments and practical training. This makes it possible to provide optimal learning support according to the learner's learning style.

[0057] The collection unit collects social network data of the learner, and the analysis unit analyzes the data to provide a learning plan based on the learner's social relationships. For example, the collection unit collects data on the learner's friendships and group activities, and the analysis unit analyzes the data to create a plan to promote collaborative learning. The collection unit also collects activity data on the learner in online communities, and the analysis unit analyzes the data to provide a plan to improve the effectiveness of online learning. Furthermore, the collection unit collects data on the learner's family relationships, and the analysis unit analyzes the data to provide a plan to support home learning. This makes it possible to provide optimal learning support according to the learner's social relationships.

[0058] The individualization unit can also analyze the learner's hobbies and interests and provide a learning plan based on these. For example, if the learner is interested in sports, the individualization unit can provide a learning plan that makes extensive use of sports-related materials. If the learner is interested in music, the individualization unit can also provide a learning plan that makes extensive use of music-related materials. Furthermore, if the learner is interested in art, the individualization unit can also provide a learning plan that makes extensive use of art-related materials. This makes it possible to provide optimal learning support according to the learner's hobbies and interests.

[0059] The personalization unit can also analyze the learner's learning environment and suggest the optimal learning environment. For example, if the learner prefers a quiet environment, the personalization unit can suggest studying in a quiet place. Also, if the learner prefers listening to music while studying, the personalization unit can suggest appropriate music. Furthermore, if the learner prefers studying in a group, the personalization unit can suggest group study opportunities. This makes it possible to provide optimal learning support according to the learner's learning environment.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects data from the educational field. The data from the educational field includes, for example, test results, attendance data, and learning progress. The collection unit collects test results digitally, automatically records attendance data, and monitors learning progress in real time. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and provide teaching materials and evaluation criteria that meet the needs of the educational field. The analysis is carried out using methods such as data mining and statistical analysis. For example, the analysis unit uses data mining technology to extract the needs of the educational field, statistical analysis technology to analyze data trends, and generation AI to evaluate the learner's level of understanding of specific problems. Step 3: The Personalization Department responds to the individual needs of learners based on the data collected by the Collection Department and the teaching materials and assessment criteria provided by the Analysis Department. The Personalization Department analyzes the learner's learning progress and level of understanding and creates an individualized learning plan. Using generative AI, it provides effective learning support tailored to each learner.

[0062] (Example 2) An educational support system according to an embodiment of the present invention uses a generative AI to support tests and other school administrative tasks at educational institutions. The educational support system collects data from educational settings, analyzes it using a generative AI, and provides effective teaching materials and assessment criteria tailored to the needs of the educational settings. The educational support system also flexibly responds to the individual needs of learners and creates personalized learning plans. For example, the educational support system collects detailed data such as test results, attendance records, and learning progress, and analyzes this data using a generative AI. Next, the educational support system uses a generative AI to evaluate the learner's level of understanding of specific questions and provide effective teaching materials and assessment criteria. Furthermore, the educational support system uses a generative AI to analyze the learner's learning progress and understanding and create personalized learning plans. This reduces the burden on teachers and improves the quality of education. This allows the educational support system to automatically analyze data from educational settings and provide effective teaching materials and assessment criteria tailored to the needs of the educational settings. For example, the generative AI automatically scores tests, eliminating the need for teachers to manually grade them. Furthermore, the generative AI evaluates the learner's level of understanding and provides personalized feedback, thereby improving the learner's learning effectiveness.

[0063] An educational support system according to an embodiment includes a collection unit, an analysis unit, and an individualization unit. The collection unit collects data from educational sites. The educational site data includes, but is not limited to, test results, attendance data, and learning progress. For example, the collection unit collects test results digitally. The collection unit can also automatically record attendance data. Furthermore, the collection unit can monitor learning progress in real time. For example, the collection unit collects test results digitally, automatically records attendance data, and monitors learning progress in real time. The analysis unit uses a generative AI to analyze the data collected by the collection unit and provide teaching materials and assessment criteria tailored to the needs of the educational site. The analysis is performed using, for example, data mining, statistical analysis, and other methods, but is not limited to, these examples. For example, the analysis unit uses data mining techniques to extract educational needs from the collected data. The analysis unit can also analyze data trends using statistical analysis techniques. Furthermore, the analysis unit can use a generative AI to evaluate a learner's understanding of a specific problem. For example, the analysis unit extracts needs in educational settings using data mining technology, analyzes data trends using statistical analysis technology, and evaluates a learner's level of understanding of a specific problem using a generation AI. The individualization unit addresses the individual needs of learners based on the data collected by the collection unit and the teaching materials and evaluation criteria provided by the analysis unit. The individualization unit, for example, analyzes a learner's learning progress and level of understanding and creates an individualized learning plan. The individualization unit can provide effective learning support tailored to each learner using a generation AI. For example, the individualization unit analyzes a learner's learning progress and level of understanding and creates an individualized learning plan. As a result, the education support system according to the embodiment can improve the quality of education by collecting and analyzing data from educational settings and addressing individual needs.

[0064] The collection unit can collect test results, attendance data, and learning progress data. The collection unit, for example, collects test results in digital format. For example, the collection unit automatically collects online test results. The collection unit can also automatically record attendance data. For example, the collection unit records attendance data in real time using an attendance management system. The collection unit can also monitor learning progress in real time. For example, the collection unit tracks learners' progress in real time using a learning management system. This allows for more accurate analysis by collecting detailed data from educational settings. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input online test results into a generation AI, which can analyze the data.

[0065] The analysis unit can evaluate a learner's level of understanding of a specific question based on the collected data. The analysis unit can evaluate a learner's level of understanding of a specific question based on, for example, collected test results. For example, the analysis unit can analyze the correct answer rate on a test to evaluate the learner's level of understanding. The analysis unit can also evaluate the relationship between a learner's attendance status and their level of understanding based on collected attendance data. For example, the analysis unit can compare their attendance rate with their correct answer rate on a test to evaluate the impact of their attendance status on their level of understanding. The analysis unit can also evaluate the relationship between a learner's progress and their level of understanding based on collected learning progress data. For example, the analysis unit can compare their learning progress with their correct answer rate on a test to evaluate the impact of their progress on their level of understanding. This allows for the evaluation of a learner's level of understanding, thereby making it possible to provide effective teaching materials and evaluation criteria. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected data into a generation AI, which can then evaluate the learner's level of understanding.

[0066] The individualization unit can analyze the learner's learning progress and level of understanding and create an individualized learning plan. The individualization unit, for example, analyzes the learner's learning progress and creates an individualized learning plan. For example, the individualization unit can analyze the learner's progress using a learning management system and create an individualized learning plan. The individualization unit can also analyze the learner's level of understanding and create an individualized learning plan. For example, the individualization unit can analyze test results and create a learning plan based on the learner's level of understanding. The individualization unit can also analyze the learner's interests and create an individualized learning plan. For example, the individualization unit customizes the learning plan based on the learner's interests. This enables effective learning support tailored to each learner. Some or all of the above-mentioned processing in the individualization unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the individualization unit can input the learner's data into a generation AI, which then creates an individualized learning plan.

[0067] The analysis unit can update the teaching materials and evaluation criteria in response to new educational policies and changes in the curriculum. The analysis unit, for example, updates the teaching materials and evaluation criteria based on a new educational policy. For example, the analysis unit updates the teaching materials and evaluation criteria based on the content of educational reform. The analysis unit can also update the teaching materials and evaluation criteria in response to changes in the curriculum. For example, the analysis unit updates the teaching materials in response to the addition of a new subject and updates the evaluation criteria in response to changes in class times. This allows for flexible response to changes in the educational environment. Some 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 related to a new educational policy or changes in the curriculum into the generation AI, which can then update the teaching materials and evaluation criteria.

[0068] The individualization unit can automatically score tests using a generation AI and provide personalized feedback. The individualization unit can, for example, automatically score tests using a generation AI. For example, the individualization unit inputs test results into the generation AI, which then automatically scores the tests. The individualization unit can also provide personalized feedback using the generation AI. For example, the individualization unit has the generation AI evaluate the learner's level of understanding and provide personalized feedback. This reduces the burden on teachers and enables the learner to receive prompt and appropriate feedback. Some or all of the above-described processing in the individualization unit can be performed using, or without, a generation AI. For example, the individualization unit can input test results into the generation AI, which then automatically scores the tests and provides personalized feedback.

[0069] The collection unit can analyze the learner's emotions and adjust the timing of data collection based on the analyzed learner's emotions. The collection unit, for example, analyzes the learner's emotions. For example, the collection unit can analyze the learner's emotions using facial expression analysis technology. The collection unit can also analyze the learner's emotions using voice analysis technology. For example, the collection unit can analyze voice data and estimate the learner's emotions. The collection unit can also adjust the timing of data collection based on the learner's emotions. For example, if the learner is feeling stressed, the collection unit delays the timing of collection and collects data when the learner is relaxed. If the learner is concentrating, the collection unit can collect data at that timing to obtain more accurate information. If the learner is tired, the collection unit can collect data after a break to obtain data that takes the learner's state into consideration. This allows data to be collected at the optimal timing depending on the learner's state. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the learner's emotional data into the generation AI, and the generation AI may adjust the timing of data collection.

[0070] The collection unit can analyze the learner's past learning history and select an appropriate data collection method. The collection unit, for example, analyzes the learner's past learning history. For example, the collection unit analyzes past test results and learning progress data to understand the learner's learning history. The collection unit can also analyze the learner's past learning patterns and select an optimal data collection method. For example, the collection unit collects data by preferentially using learning tools that the learner has used in the past. The collection unit can also collect data by focusing on specific subjects or topics based on the learner's past grades. For example, the collection unit preferentially collects data related to specific subjects based on the learner's past grades. This allows data to be collected in an optimal manner based on the learner's past history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the learner's past learning history into a generation AI, which can select an optimal data collection method.

[0071] The collection unit can filter data based on the learner's current learning situation and areas of interest when collecting data. The collection unit, for example, analyzes the learner's current learning situation. For example, the collection unit uses a learning management system to grasp the learner's current learning progress. The collection unit can also analyze the learner's areas of interest and filter the data when collecting it. For example, the collection unit prioritizes collecting relevant data based on the learner's areas of interest. The collection unit can also collect only necessary data according to the learner's current learning progress. For example, the collection unit prioritizes collecting data related to the task the learner is currently working on. This allows efficient collection of data according to the learner's current situation. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the learner's current learning situation and areas of interest to the generation AI, which then filters the data.

[0072] When collecting data, the collection unit can select an appropriate collection means depending on the learner's input method. The collection unit, for example, analyzes the learner's input method. For example, the collection unit analyzes input methods such as voice input, text input, and image input. The collection unit can also select an appropriate collection means depending on the learner's input method. For example, if the learner prefers voice input, the collection unit can preferentially collect voice data. Also, if the learner prefers text input, the collection unit can preferentially collect text data. Furthermore, if the learner prefers image input, the collection unit can preferentially collect image data. For example, if the learner prefers voice input, the collection unit collects voice data using voice recognition technology. This enables optimal data collection depending on the learner's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the learner's input method to a generation AI, which can select an appropriate collection means.

[0073] The collection unit can estimate the learner's emotions and determine the priority of data to be collected based on the estimated learner's emotions. The collection unit, for example, estimates the learner's emotions. For example, the collection unit can estimate the learner's emotions using facial expression analysis technology. The collection unit can also estimate the learner's emotions using voice analysis technology. For example, the collection unit can analyze voice data to estimate the learner's emotions. The collection unit can also determine the priority of data to be collected based on the estimated learner's emotions. For example, if the learner is excited, the collection unit can prioritize collecting data related to topics of interest. Also, if the learner is relaxed, the collection unit can prioritize collecting general learning data. Furthermore, if the learner is stressed, the collection unit can prioritize collecting data that is useful for stress reduction. For example, if the learner is excited, the collection unit prioritizes collecting data related to topics of interest. This enables more effective data collection by prioritizing data according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input learner emotion data into the generation AI, and the generation AI may determine the priority of the data.

[0074] When collecting data, the collection unit can prioritize collecting appropriate data by taking into account the learner's geographical location information. The collection unit, for example, considers the learner's geographical location information. For example, the collection unit identifies the learner's current location using GPS data. The collection unit can also prioritize collecting highly relevant data based on the learner's geographical location information. For example, if the learner is in a specific area, the collection unit prioritizes collecting data related to that area. Also, if the learner is moving, the collection unit can collect data related to the learner's current location in real time. Furthermore, if the learner is in a specific location, the collection unit can prioritize collecting data related to that location. For example, if the learner is in a specific area, the collection unit prioritizes collecting data related to that area. This makes it possible to collect highly relevant data based on the learner's geographical location information. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the learner's geographical location information to a generation AI, which can then prioritize collecting highly relevant data.

[0075] The collection unit can analyze the learner's social media activities and collect related data when collecting data. The collection unit, for example, analyzes the learner's social media activities. For example, the collection unit analyzes information shared by the learner on social media. The collection unit can also analyze the learner's social media activities and collect related data. For example, the collection unit analyzes the content of the learner's social media posts and collect data related to topics of interest. The collection unit can also collect related data by referring to the activities of the learner's friends on social media. For example, the collection unit collects related data by referring to the activities of the learner's friends on social media. In this way, related data based on the learner's social media activities can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the learner's social media activities into a generation AI, which can collect related data.

[0076] When collecting data, the collection unit can customize the collection method by reflecting the learner's past feedback. The collection unit, for example, analyzes the learner's past feedback. For example, the collection unit analyzes feedback provided by the learner in the past. The collection unit can also customize the collection method by reflecting the learner's past feedback. For example, the collection unit adjusts the collection method based on feedback provided by the learner in the past. The collection unit can also select an optimal collection means from the learner's past feedback. Furthermore, the collection unit can adjust the collection timing by reflecting the learner's past feedback. For example, the collection unit adjusts the collection timing based on feedback provided by the learner in the past. This enables optimal data collection based on the learner's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the learner's past feedback to a generation AI, which can customize the collection method.

[0077] The analysis unit can estimate the learner's emotions and adjust the analysis expression based on the estimated learner's emotions. The analysis unit, for example, estimates the learner's emotions. For example, the analysis unit can estimate the learner's emotions using facial expression analysis technology. The analysis unit can also estimate the learner's emotions using voice analysis technology. For example, the analysis unit can analyze voice data to estimate the learner's emotions. The analysis unit can also adjust the analysis expression based on the estimated learner's emotions. For example, the analysis unit can provide detailed analysis results when the learner is relaxed. The analysis unit can also provide simple, highly visible analysis results when the learner is nervous. Furthermore, the analysis unit can provide analysis results with visually stimulating effects when the learner is excited. For example, the analysis unit can provide detailed analysis results when the learner is relaxed. This makes it possible to provide optimal analysis results according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the 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 may input the learner's emotional data into the generation AI, and the generation AI may adjust the expression of the analysis.

[0078] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit, for example, evaluates the importance of the data. For example, the analysis unit evaluates the importance based on the impact and relevance of the data. The analysis unit can also adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. For example, the analysis unit prioritizes analysis of data with high importance. This enables optimal analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the importance of the data to the generation AI, which can then adjust the level of detail of the analysis.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. The analysis unit, for example, classifies data categories. For example, the analysis unit may classify data into categories such as text data, numerical data, and image data. The analysis unit can also apply different analysis algorithms depending on the data category. For example, the analysis unit may apply a specific analysis algorithm to test result data. The analysis unit may also apply a different analysis algorithm to attendance data. The analysis unit can also apply yet another analysis algorithm to learning progress data. For example, the analysis unit may apply a specific analysis algorithm to test result data, a different analysis algorithm to attendance data, and yet another analysis algorithm to learning progress data. This enables optimal analysis depending on the data category. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit may input the data category into a generation AI, which then applies a different analysis algorithm.

[0080] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the learner's past analysis results. The analysis unit, for example, analyzes the learner's past analysis results. For example, the analysis unit analyzes past test results and learning progress data to grasp the learner's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the learner's past analysis results. For example, the analysis unit corrects the current analysis results based on the past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the past analysis results. The analysis unit can also improve the accuracy of the analysis by using the past analysis results. For example, the analysis unit corrects the current analysis results based on the past analysis results and adjusts the analysis algorithm to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the learner's past analysis results into the generation AI, which can improve the accuracy of the analysis.

[0081] The analysis unit can estimate the learner's emotions and adjust the analysis time based on the estimated learner's emotions. The analysis unit, for example, estimates the learner's emotions. For example, the analysis unit can estimate the learner's emotions using facial expression analysis technology. The analysis unit can also estimate the learner's emotions using voice analysis technology. For example, the analysis unit can analyze voice data to estimate the learner's emotions. The analysis unit can also adjust the analysis time based on the estimated learner's emotions. For example, the analysis unit can provide a short and concise analysis result if the learner is in a hurry. The analysis unit can also provide a detailed analysis result if the learner is relaxed. Furthermore, the analysis unit can provide an analysis result with visually stimulating effects if the learner is excited. For example, the analysis unit can provide a short and concise analysis result if the learner is in a hurry. This makes it possible to provide optimal analysis results according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit may input the learner's emotional data into the generation AI, and the generation AI may adjust the analysis time.

[0082] During analysis, the analysis unit can determine the analysis priority based on the data submission time. The analysis unit, for example, evaluates the data submission time. For example, the analysis unit evaluates the submission time based on the submission deadline and the submission frequency. The analysis unit can also determine the analysis priority based on the data submission time. For example, the analysis unit prioritizes analysis of data with an upcoming submission deadline. The analysis unit can also postpone analysis of data whose submission time has passed. The analysis unit can also adjust the analysis schedule based on the submission time. For example, the analysis unit prioritizes analysis of data with an upcoming submission deadline and postpones analysis of data whose submission time has passed, thereby adjusting the analysis schedule based on the submission time. This enables optimal analysis based on the data submission time. Some 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 the data submission time to the generation AI, and the generation AI can determine the analysis priority.

[0083] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, evaluates the relevance of the data. For example, the analysis unit evaluates the relevance based on correlation or causality of the data. The analysis unit can also adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data and postpones analysis of less relevant data, thereby determining the order of analysis based on the relevance of the data. This enables optimal analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the relevance of the data into the generation AI, which can then adjust the order of analysis.

[0084] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the learner's level of expertise. The analysis unit, for example, evaluates the learner's level of expertise. For example, the analysis unit evaluates the learner's level of expertise based on test results or self-assessment. The analysis unit can also adjust the use of technical terms in the analysis according to the learner's level of expertise. For example, if the learner's level of expertise is high, the analysis unit provides analysis results that use a lot of technical terms. Also, if the learner's level of expertise is low, the analysis unit can provide analysis results that are explained in simple terms. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the learner's level of expertise. For example, if the learner's level of expertise is high, the analysis unit provides analysis results that use a lot of technical terms, and if the learner's level of expertise is low, the analysis unit provides analysis results that are explained in simple terms, thereby adjusting the way the analysis results are presented according to the learner's level of expertise. This makes it possible to provide optimal analysis results according to the learner's level of expertise. Some 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 the learner's level of expertise into the generation AI, which can then adjust the use of technical terminology in the analysis.

[0085] The personalization unit can estimate the learner's emotions and adjust the personalization measures based on the estimated learner's emotions. The personalization unit, for example, estimates the learner's emotions. For example, the personalization unit can estimate the learner's emotions using facial expression analysis technology. The personalization unit can also estimate the learner's emotions using voice analysis technology. For example, the personalization unit can analyze voice data to estimate the learner's emotions. The personalization unit can also adjust the personalization measures based on the estimated learner's emotions. For example, the personalization unit can provide a detailed personalization plan if the learner is relaxed. The personalization unit can also provide a simple, highly visible personalization plan if the learner is nervous. The personalization unit can also provide a personalization plan with visually stimulating effects if the learner is excited. For example, the personalization unit can provide a detailed personalization plan if the learner is relaxed. This makes it possible to provide an optimal personalization plan according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the individualization unit may be performed using AI, or may be performed without using AI. For example, the individualization unit may input the learner's emotional data into the generation AI, which may then adjust the individualization measures.

[0086] During personalization, the personalization unit can analyze the learner's past learning behavior and select the optimal personalization method. The personalization unit, for example, analyzes the learner's past learning behavior. For example, the personalization unit analyzes past test results and learning progress data to understand the learner's past learning behavior. The personalization unit can also analyze the learner's past learning patterns and select the optimal personalization method. For example, the personalization unit can perform personalization by preferentially using learning tools that the learner has used in the past. The personalization unit can also perform personalization by focusing on specific subjects or topics based on the learner's past grades. For example, the personalization unit provides an individualization plan related to a specific subject based on the learner's past grades. This enables optimal personalization based on the learner's past behavior. Some or all of the above-mentioned processing in the personalization unit may be performed using, for example, AI, or may be performed without using AI. For example, the personalization unit can input the learner's past learning behavior into a generation AI, which can select the optimal personalization method.

[0087] During personalization, the personalization unit can customize the personalization means based on the learner's current learning situation. The personalization unit, for example, analyzes the learner's current learning situation. For example, the personalization unit grasps the learner's current learning progress using a learning management system. The personalization unit can also customize the personalization means based on the learner's current learning situation. For example, the personalization unit provides an individualization plan related to the task the learner is currently working on. The personalization unit can also provide necessary personalization means according to the learner's current learning progress. Furthermore, the personalization unit can select an optimal personalization method based on the learner's current learning situation. For example, the personalization unit provides an individualization plan related to the task the learner is currently working on, provides necessary personalization means according to the learner's current learning progress, and selects an optimal personalization method based on the learner's current learning situation. This enables optimal personalization according to the learner's current situation. Some or all of the above-mentioned processing in the personalization unit may be performed using, for example, AI, or may be performed without using AI. For example, the personalization unit can input the learner's current learning situation into the generation AI, which can then customize the means of personalization.

[0088] During individualization, the individualization unit can improve the individualization method by reflecting the learner's feedback. The individualization unit, for example, analyzes the learner's feedback. For example, the individualization unit analyzes feedback provided by the learner. The individualization unit can also improve the individualization method by reflecting the learner's feedback. For example, the individualization unit adjusts the individualization plan based on the feedback provided by the learner. The individualization unit can also select an optimal individualization method from the learner's feedback. The individualization unit can also improve the individualization method by reflecting the learner's feedback. For example, the individualization unit adjusts the individualization plan, selects an optimal individualization method, and improves the individualization method based on the feedback provided by the learner. This enables optimal individualization based on the learner's feedback. Some or all of the above-mentioned processing in the individualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the individualization unit can input the learner's feedback to a generation AI, which can improve the individualization method.

[0089] The personalization unit can estimate the learner's emotions and determine the priorities of personalization based on the estimated learner's emotions. The personalization unit, for example, estimates the learner's emotions. For example, the personalization unit can estimate the learner's emotions using facial expression analysis technology. The personalization unit can also estimate the learner's emotions using voice analysis technology. For example, the personalization unit can analyze voice data to estimate the learner's emotions. The personalization unit can also determine the priorities of personalization based on the estimated learner's emotions. For example, if the learner is excited, the personalization unit can prioritize providing a personalized plan related to a topic of interest. If the learner is relaxed, the personalization unit can prioritize providing a general learning plan. Furthermore, if the learner is stressed, the personalization unit can prioritize providing a personalized plan that helps reduce stress. For example, if the learner is excited, the personalization unit can prioritize providing a personalized plan related to a topic of interest. This makes it possible to provide an optimal personalized plan according to the learner's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the individualization unit may be performed using AI, or may be performed without using AI. For example, the individualization unit may input the learner's emotional data into the generation AI, and the generation AI may determine the priority of the individualization.

[0090] During personalization, the personalization unit can select an optimal personalization method by taking into account the learner's geographical location information. The personalization unit, for example, considers the learner's geographical location information. For example, the personalization unit identifies the learner's current location using GPS data. The personalization unit can also select an optimal personalization method based on the learner's geographical location information. For example, if the learner is in a specific area, the personalization unit provides an individualization plan related to that area. Also, if the learner is on the move, the personalization unit can provide an individualization plan related to the learner's current location in real time. Furthermore, if the learner is in a specific location, the personalization unit can provide an individualization plan related to that location. For example, if the learner is in a specific area, the personalization unit provides an individualization plan related to that area. This enables optimal personalization based on the learner's geographical location information. Some or all of the above-described processing in the personalization unit may be performed using, for example, AI, or may be performed without using AI. For example, the personalization unit can input the learner's geographical location information to a generation AI, which can select an optimal personalization method.

[0091] During personalization, the personalization unit can analyze the learner's social media activity and suggest personalization measures. The personalization unit, for example, analyzes the learner's social media activity. For example, the personalization unit analyzes information shared by the learner on social media. The personalization unit can also analyze the learner's social media activity and provide a relevant personalization plan. For example, the personalization unit can analyze the content of the learner's social media posts and provide a personalization plan related to topics of interest. The personalization unit can also provide a relevant personalization plan by referring to the activities of the learner's friends on social media. For example, the personalization unit can provide a relevant personalization plan by referring to the activities of the learner's friends on social media. This enables optimal personalization based on the learner's social media activity. Some or all of the above-mentioned processing in the personalization unit may be performed using, for example, AI, or may be performed without using AI. For example, the personalization unit can input the learner's social media activity into a generation AI, which can then suggest personalization measures.

[0092] During individualization, the individualization unit can customize the individualization method by reflecting the learner's past feedback. The individualization unit, for example, analyzes the learner's past feedback. For example, the individualization unit analyzes feedback provided by the learner in the past. The individualization unit can also customize the individualization method by reflecting the learner's past feedback. For example, the individualization unit adjusts the individualization plan based on feedback provided by the learner in the past. The individualization unit can also select an optimal individualization method from the learner's past feedback. The individualization unit can also customize the individualization method by reflecting the learner's past feedback. For example, the individualization unit adjusts the individualization plan, selects an optimal individualization method, and customizes the individualization method based on feedback provided by the learner in the past. This enables optimal individualization based on the learner's past feedback. Some or all of the above-described processing in the individualization unit may be performed using, for example, AI, or may be performed without using AI. For example, the individualization unit can input the learner's past feedback into a generation AI, which can customize the individualization method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and individualization unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect data from educational settings using the camera 42 and microphone 38B of the smart device 14. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12, analyze the collected data, and provide teaching materials and evaluation criteria according to the needs of the educational settings. For example, the individualization unit can be realized by the control unit 46A of the smart device 14, and create a learning plan tailored to the individual needs of the learner. For example, the collection unit can analyze the learner's emotions using the camera 42 and microphone 38B of the smart device 14 and adjust the timing of data collection. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and individualization unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect data from educational settings using the camera 42 and microphone 238 of the smart glasses 214. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12, analyze the collected data, and provide teaching materials and assessment criteria according to the needs of the educational settings. For example, the individualization unit can be realized by the control unit 46A of the smart glasses 214, and create a learning plan tailored to the individual needs of the learner. For example, the collection unit can analyze the learner's emotions using the camera 42 and microphone 238 of the smart glasses 214 and adjust the timing of data collection. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and individualization unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect data from educational settings using the camera 42 and microphone 238 of the headset terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and provides teaching materials and assessment criteria according to the needs of the educational settings. For example, the individualization unit is realized by the control unit 46A of the headset terminal 314, and creates a learning plan according to the individual needs of the learner. For example, the collection unit can analyze the learner's emotions using the camera 42 and microphone 238 of the headset terminal 314 and adjust the timing of data collection. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and individualization unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect data from an educational setting using the camera 42 and microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and provides teaching materials and evaluation criteria according to the needs of the educational setting. For example, the individualization unit is realized by the control unit 46A of the robot 414, and creates a learning plan according to the individual needs of the learner. For example, the collection unit can analyze the learner's emotions using the camera 42 and microphone 238 of the robot 414 and adjust the timing of data collection.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The education support system can also collect health data of the learner, which the analysis unit analyzes to provide a study plan based on the learner's health condition. For example, the collection unit collects sleep data and exercise data of the learner, which the analysis unit analyzes to create a study plan based on the learner's health condition. The collection unit can also collect dietary data of the learner, which the analysis unit analyzes to provide a study plan based on nutritional status. Furthermore, the collection unit can monitor the learner's stress level, which the analysis unit analyzes to provide a study plan that helps reduce stress. This makes it possible to provide optimal study support based on the learner's health condition.

[0095] The collection unit collects device usage data from learners, and the analysis unit analyzes this data to provide a study plan based on the learner's device usage patterns. For example, the collection unit records the amount of time the learner spends using their smartphone or tablet, and the analysis unit analyzes this data to create a study plan based on the amount of time the learner spends using the device. The collection unit also collects app usage data from learners, and the analysis unit analyzes this data to provide a study plan based on the use of specific apps. Furthermore, the collection unit collects internet browsing history from learners, and the analysis unit analyzes this data to provide a study plan based on their interests. This makes it possible to provide optimal study support based on the learner's device usage patterns.

[0096] The analysis unit can also estimate the learner's emotions and adjust the difficulty of the study plan based on the estimated learner's emotions. For example, if the learner is feeling stressed, the analysis unit can provide a study plan with a lower difficulty level. If the learner is relaxed, the analysis unit can also provide a study plan with a higher difficulty level. Furthermore, if the learner is excited, the analysis unit can also provide a study plan including challenging tasks. In this way, it is possible to provide an optimal study plan according to the learner's emotions.

[0097] The personalization unit can also analyze the learner's learning style and suggest the optimal learning method. For example, if the learner prefers visual learning, the personalization unit can provide a learning plan that makes heavy use of visual materials. If the learner prefers auditory learning, the personalization unit can also provide a learning plan that makes heavy use of audio materials. Furthermore, if the learner prefers practical learning, the personalization unit can also provide a learning plan that includes experiments and practical training. This makes it possible to provide optimal learning support according to the learner's learning style.

[0098] The analysis unit can also estimate the learner's emotions and adjust the content of the feedback based on the estimated learner's emotions. For example, if the learner is feeling down, the analysis unit can provide feedback including encouraging words. If the learner is feeling confident, the analysis unit can also provide feedback that encourages the learner to try harder. Furthermore, if the learner is feeling anxious, the analysis unit can also provide feedback that gives the learner a sense of security. In this way, it is possible to provide optimal feedback according to the learner's emotions.

[0099] The collection unit collects social network data of the learner, and the analysis unit analyzes the data to provide a learning plan based on the learner's social relationships. For example, the collection unit collects data on the learner's friendships and group activities, and the analysis unit analyzes the data to create a plan to promote collaborative learning. The collection unit also collects activity data on the learner in online communities, and the analysis unit analyzes the data to provide a plan to improve the effectiveness of online learning. Furthermore, the collection unit collects data on the learner's family relationships, and the analysis unit analyzes the data to provide a plan to support home learning. This makes it possible to provide optimal learning support according to the learner's social relationships.

[0100] The analysis unit can also estimate the learner's emotions and adjust the learning progress based on the estimated learner's emotions. For example, if the learner is tired, the analysis unit can slow down the learning progress to encourage the learner to take a break. In addition, if the learner is concentrating, the analysis unit can speed up the learning progress. Furthermore, if the learner is excited, the analysis unit can adjust the learning progress to proceed at an appropriate pace. This makes it possible to provide an optimal learning progress according to the learner's emotions.

[0101] The individualization unit can also analyze the learner's hobbies and interests and provide a learning plan based on these. For example, if the learner is interested in sports, the individualization unit can provide a learning plan that makes extensive use of sports-related materials. If the learner is interested in music, the individualization unit can also provide a learning plan that makes extensive use of music-related materials. Furthermore, if the learner is interested in art, the individualization unit can also provide a learning plan that makes extensive use of art-related materials. This makes it possible to provide optimal learning support according to the learner's hobbies and interests.

[0102] The analysis unit can also estimate the learner's emotions and adjust the timing of learning based on the estimated learner's emotions. For example, the analysis unit can set the timing to start learning if the learner is relaxed. The analysis unit can also set the timing to temporarily suspend learning if the learner is feeling stressed. Furthermore, the analysis unit can also set the timing to continue learning if the learner is concentrating. This makes it possible to provide the optimal learning timing according to the learner's emotions.

[0103] The personalization unit can also analyze the learner's learning environment and suggest the optimal learning environment. For example, if the learner prefers a quiet environment, the personalization unit can suggest studying in a quiet place. Also, if the learner prefers listening to music while studying, the personalization unit can suggest appropriate music. Furthermore, if the learner prefers studying in a group, the personalization unit can suggest group study opportunities. This makes it possible to provide optimal learning support according to the learner's learning environment.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The collection unit collects data from the educational field. The data from the educational field includes, for example, test results, attendance data, and learning progress. The collection unit collects test results digitally, automatically records attendance data, and monitors learning progress in real time. Step 2: The analysis unit uses the generation AI to analyze the data collected by the collection unit and provide teaching materials and evaluation criteria that meet the needs of the educational field. The analysis is carried out using methods such as data mining and statistical analysis. For example, the analysis unit uses data mining technology to extract the needs of the educational field, statistical analysis technology to analyze data trends, and generation AI to evaluate the learner's level of understanding of specific problems. Step 3: The Personalization Department responds to the individual needs of learners based on the data collected by the Collection Department and the teaching materials and assessment criteria provided by the Analysis Department. The Personalization Department analyzes the learner's learning progress and level of understanding and creates an individualized learning plan. Using generative AI, it provides effective learning support tailored to each learner.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] 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.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] 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.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] 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.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0169] 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.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects data from educational sites; an analysis unit that analyzes the data collected by the collection unit and provides teaching materials and evaluation criteria according to the needs of educational sites; an individualization unit that responds to the individual needs of learners based on the learning materials and evaluation criteria provided by the analysis unit; A system characterized by:

2. The collecting unit Collect test results, attendance data and learning progress data The system of claim 1 .

3. The analysis unit Evaluate learners' understanding of specific questions based on collected data The system of claim 1 .

4. The individualization unit Analyze learners' progress and comprehension to create personalized learning plans The system of claim 1 .

5. The analysis unit Update teaching materials and assessment criteria in line with new educational policies and curriculum changes The system of claim 1 .

6. The individualization unit Use generative AI to automatically score tests and provide personalized feedback The system of claim 1 .

7. The collecting unit Analyze learners' emotions and adjust the timing of data collection based on the analyzed learners' emotions. The system of claim 1 .

8. The collecting unit Analyze learners' past learning history and select appropriate data collection methods The system of claim 1 .

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