System

Abstract

An object of the system according to the embodiment is to provide a customized teaching material based on a learning style and a comprehension level of a learner.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, an identification unit, a provision unit, and an adjustment unit. The collection unit collects data of the learner. The analysis unit analyzes the data collected by the collection unit and analyzes the learning style and the comprehension level of the learner. The identification unit identifies a weak point of the learner based on the analysis result obtained by the analysis unit. The providing unit provides customized teaching materials or practice problems for strengthening the weak point specified by the specifying unit. The adjustment unit adjusts the degree of difficulty in accordance with the degree of progress of the teaching material or the exercise presented by the presentation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide learning materials that are suited to each learner's individual learning style and level of understanding, and there is room for improvement.

[0005] The system according to the embodiment aims to provide customized learning materials based on the learner's learning style and level of understanding. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an identification unit, a provision unit, and an adjustment unit. The collection unit collects data on learners. The analysis unit analyzes the data collected by the collection unit and analyzes the learner's learning style and level of understanding. The identification unit identifies weak points of the learner based on the analysis results obtained by the analysis unit. The provision unit provides customized learning materials and exercises to strengthen the weak points identified by the identification unit. The adjustment unit adjusts the difficulty level of the learning materials and exercises provided by the provision unit according to the progress of the learner. [Effects of the Invention]

[0007] The system according to the embodiment can provide customized learning materials based on the learner's learning style and level of understanding. [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 system according to an embodiment of the present invention analyzes the learning style and comprehension level of each individual student and dynamically adjusts the content of educational materials based on the analysis. This educational system collects, analyzes, identifies, provides, and adjusts learner data to provide an optimal learning experience for each individual learner. For example, the educational system collects data such as the learner's past learning history, test results, response time, and answer patterns, and analyzes it using AI. The AI ​​then identifies the learner's weaknesses and provides customized educational materials and practice problems to strengthen them. For example, if a learner has weaknesses in a specific area of ​​mathematics, the AI ​​provides practice problems specialized for that area. Furthermore, the difficulty level is adjusted according to the learner's progress, allowing the learner to progress at an optimal pace. This allows the learner to efficiently progress through learning at their own pace and deepen their understanding. This allows the educational system to provide an optimal learning experience for each individual learner.

[0029] The educational system according to the embodiment includes a collection unit, an analysis unit, an identification unit, a provision unit, and an adjustment unit. The collection unit collects data on the learner. The learner's data includes, but is not limited to, learning history, test results, response time, and answer patterns. For example, the collection unit records the learner's past learning activities, study time, and learning content to collect the learning history. The collection unit can also record the learner's test scores, correct answer rate, and tendency of incorrect answers to collect test results. Furthermore, the collection unit can also record the response time for each question and the overall response time to collect response time. The analysis unit analyzes the data collected by the collection unit to analyze the learner's learning style and level of understanding. The analysis is performed using, for example, data mining technology or statistical analysis method, but is not limited to, examples. For example, the analysis unit uses data mining technology to classify the learner's learning style into visual, auditory, experiential, etc. The analysis unit can also use statistical analysis techniques to evaluate the learner's level of understanding based on test scores, the rate of correct answers to questions, etc. The identification unit identifies the learner's weak points based on the analysis results obtained by the analysis unit. Weak points can be identified, for example, by the rate of incorrect answers to specific questions or a lack of understanding of a specific concept, but examples are not limited to these. For example, the identification unit can identify that the learner makes many incorrect answers in a specific field based on the analysis results. The identification unit can also identify that the learner does not understand a specific concept. The provision unit provides customized learning materials and practice problems to strengthen the weak points identified by the identification unit. The customized learning materials and practice problems can be provided by, for example, adjusting the content and difficulty level according to the learner's weak points, but examples are not limited to these. For example, if the learner has a weakness in a specific field, the provision unit can provide practice problems specialized for that field. The provision unit can also provide learning materials with adjusted difficulty level according to the learner's level of understanding. The adjustment unit adjusts the difficulty level according to the progress of the learning materials and practice problems provided by the provision unit. The difficulty level can be adjusted, for example, by the complexity of the questions and the time limit for answering, but examples are not limited to these. The adjustment unit increases the difficulty of the questions according to the learner's progress, for example.The adjustment unit can also set a time limit for each learner to answer questions depending on their progress. This allows the education system according to the embodiment to provide an optimal learning experience for each individual learner.

[0030] The collection unit can collect at least one of the following data: learning history, test results, response time, and answer patterns. For example, to collect learning history, the collection unit records the learner's past learning activities, study time, and learning content. To collect test results, the collection unit can also record the learner's test scores, correct answer rate, and tendency of incorrect answers. To collect response times, the collection unit can also record the response time for each question and the overall response time. To collect response patterns, the collection unit can also record the learner's choice of options and tendency of incorrect answers. By collecting data such as learning history, test results, response time, and answer patterns, detailed information about the learner can be obtained. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the learner's data into AI and have the AI ​​collect the data.

[0031] The analysis unit can analyze the collected data and analyze the learner's learning style and level of understanding. The analysis unit can, for example, use data mining technology to classify the learner's learning style into visual, auditory, experiential, etc. The analysis unit can also use statistical analysis techniques to evaluate the learner's level of understanding based on test scores, the rate at which questions are answered correctly, etc. For example, the analysis unit can use data mining technology to classify the learner's learning style into visual, auditory, experiential, etc. The analysis unit can also use statistical analysis techniques to evaluate the learner's level of understanding based on test scores, the rate at which questions are answered correctly, etc. In this way, by analyzing the collected data, the learner's learning style and level of understanding can be understood. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into AI and have the AI ​​analyze the data.

[0032] The identification unit can identify the learner's weak points based on the analysis results. For example, the identification unit can identify that the learner makes many incorrect answers in a specific area based on the analysis results. The identification unit can also identify that the learner does not understand a specific concept. For example, the identification unit can identify that the learner makes many incorrect answers in a specific area based on the analysis results. The identification unit can also identify that the learner does not understand a specific concept. This makes it possible to identify the learner's weak points based on the analysis results and provide effective learning support. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input the analysis results to AI and cause the AI ​​to identify the learner's weak points.

[0033] The providing unit can provide customized learning materials and practice questions to strengthen the identified weaknesses. For example, if a learner has weaknesses in a specific area, the providing unit can provide practice questions specialized for that area. The providing unit can also provide learning materials with a difficulty level adjusted according to the learner's level of understanding. For example, if a learner has weaknesses in a specific area, the providing unit can provide practice questions specialized for that area. The providing unit can also provide learning materials with a difficulty level adjusted according to the learner's level of understanding. In this way, by providing customized learning materials and practice questions to strengthen the identified weaknesses, the learner's level of understanding is improved. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input customized learning materials and practice questions into AI and cause the AI ​​to provide the learning materials and practice questions.

[0034] The adjustment unit can adjust the difficulty level according to the progress of the provided learning materials and practice problems. For example, the adjustment unit increases the difficulty level of the problems according to the learner's progress. The adjustment unit can also set a time limit for the learner to answer the problems according to the learner's progress. For example, the adjustment unit increases the difficulty level of the problems according to the learner's progress. The adjustment unit can also set a time limit for the learner to answer the problems according to the learner's progress. In this way, by adjusting the difficulty level according to the progress of the provided learning materials and practice problems, the learner can proceed with learning at an optimal pace. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the progress of the provided learning materials and practice problems into AI and cause the AI ​​to adjust the difficulty level.

[0035] The collection unit can analyze the learner's past learning history and select an appropriate data collection method. For example, the collection unit prioritizes the use of learning methods that the learner has frequently used in the past for data collection. The collection unit can also identify effective learning time periods from the learner's past learning history and collect data during those time periods. The collection unit can also select a data collection method that matches a specific learning style based on the learner's past learning history. In this way, the optimal data collection method can be selected by analyzing the learner's past learning history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the learner's past learning history into AI and have the AI ​​select the data collection method.

[0036] When collecting data, the collection unit can filter the data based on the learner's current learning situation or areas of interest. For example, the collection unit collects only data related to the task the learner is currently working on. The collection unit can also prioritize the collection of highly relevant data based on the learner's areas of interest. The collection unit can also filter and collect only necessary data, taking into account the learner's current learning situation. In this way, highly relevant data can be collected by filtering data based on the learner's current learning situation and areas of interest. 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 into AI and have the AI ​​perform data filtering.

[0037] When collecting data, the collection unit can select the optimal collection means depending on the learner's input method. For example, if the learner is using voice input, the collection unit can prioritize collecting voice data. Furthermore, if the learner is using text input, the collection unit can also prioritize collecting text data. Furthermore, if the learner is using image input, the collection unit can also prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means 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 into AI and have the AI ​​select the optimal collection means.

[0038] When collecting data, the collection unit can 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 can prioritize collecting data related to that area. Furthermore, if the learner is traveling, the collection unit can prioritize collecting data related to the destination. Furthermore, if the learner is in a specific location, the collection unit can prioritize collecting data related to that location. In this way, by taking the learner's geographical location information into consideration, highly relevant data can be collected preferentially. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the learner's geographical location information into AI and cause the AI ​​to collect highly relevant data.

[0039] When collecting data, the collection unit can analyze the learner's social media activities and collect related data. For example, the collection unit collects related data based on information shared by the learner on social media. The collection unit can also analyze the learner's social media activities and collect data related to learning. The collection unit can also collect related data by referring to the activities of the learner's friends on social media. In this way, related data can be collected by analyzing the learner's social media activities. 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 AI and have the AI ​​collect the data.

[0040] The collection unit can customize the collection method by reflecting the learner's past feedback when collecting data. For example, the collection unit adjusts the data collection method based on feedback provided by the learner in the past. The collection unit can also customize the type of data to be collected by reflecting the learner's past feedback. The collection unit can also adjust the timing of data collection by referring to the learner's past feedback. In this way, the collection method can be customized by reflecting 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 into AI and have the AI ​​customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the learner's level of importance. For example, if the learner is preparing for an important test, the analysis unit provides detailed analysis results. The analysis unit can also provide simple analysis results if the learner is engaged in daily study. The analysis unit can also provide detailed analysis results related to a specific task if the learner is working on that task. In this way, by adjusting the level of detail of the analysis based on the learner's level of importance, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the learner's level of importance into AI and have the AI ​​adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the learner's category. For example, if the learner is studying mathematics, the analysis unit can apply an analysis algorithm dedicated to mathematics. Furthermore, if the learner is studying a language, the analysis unit can also apply an analysis algorithm dedicated to language. Furthermore, if the learner is studying science, the analysis unit can also apply an analysis algorithm dedicated to science. In this way, by applying different analysis algorithms depending on the learner's category, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the learner's category into AI and have the AI ​​apply the 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, adjusts the current analysis result based on the learner's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the learner's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the learner's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the learner's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the learner's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the learner's submission time. For example, if the learner's submission deadline is approaching, the analysis unit performs analysis as a priority. The analysis unit can also postpone analysis if the learner's submission deadline is far away. The analysis unit can also perform analysis urgently if the learner's submission deadline has passed. In this way, by determining the priority of analysis based on the learner's submission time, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the learner's submission time into AI and have the AI ​​determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the learner's relevance. For example, the analysis unit prioritizes analysis of data related to the task the learner is currently working on. The analysis unit can also prioritize analysis of highly relevant data based on the learner's areas of interest. The analysis unit can also prioritize analysis of necessary data taking into account the learner's current learning situation. In this way, by adjusting the order of analysis based on the learner's relevance, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the learner's relevance to AI and have the AI ​​adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the learner's level of expertise. For example, if the learner is a beginner, the analysis unit can provide analysis results that avoid technical terminology. Furthermore, if the learner is an intermediate learner, the analysis unit can provide analysis results that use technical terminology appropriately. Furthermore, if the learner is an advanced learner, the analysis unit can provide detailed analysis results that use a lot of technical terminology. By adjusting the use of technical terminology in the analysis according to the learner's level of expertise, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the learner's level of expertise into AI and have the AI ​​adjust the use of technical terminology.

[0047] The identification unit can improve the accuracy of identification based on the mutual relationships between learners during identification. For example, if a learner is studying collaboratively with other learners, the identification unit can improve the accuracy of identification by taking into account the mutual relationships. Furthermore, if a learner is competing with other learners, the identification unit can improve the accuracy of identification by taking into account the competitive relationships. Furthermore, if a learner is cooperating with other learners, the identification unit can improve the accuracy of identification by taking into account the cooperative relationships. In this way, the accuracy of identification can be improved by taking into account the mutual relationships between learners. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the mutual relationships between learners into AI and cause the AI ​​to improve the accuracy of identification.

[0048] The identification unit can perform identification by taking into consideration the learner's attribute information. For example, the identification unit can perform identification by taking into consideration the learner's age. The identification unit can also perform identification by taking into consideration the learner's gender. The identification unit can also perform identification by taking into consideration the learner's learning style. In this way, by taking into consideration the learner's attribute information, more appropriate identification results can be provided. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input the learner's attribute information into AI and have the AI ​​perform the identification.

[0049] During identification, the identification unit can assign a specific weight based on the learner's submission frequency. For example, if a learner submits frequently, the identification unit assigns a specific weight based on the submission frequency. Furthermore, if a learner submits infrequently, the identification unit can assign a specific weight based on the submission frequency. Furthermore, if a learner submits at a certain frequency, the identification unit can assign a specific weight based on the submission frequency. In this way, by assigning a specific weight based on the learner's submission frequency, more appropriate identification results can be provided. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the learner's submission frequency into AI and have the AI ​​perform the specific weighting.

[0050] The identification unit can perform identification taking into account the geographical distribution of learners. For example, if a learner is in a specific area, the identification unit can perform identification related to that area. Furthermore, if a learner is traveling, the identification unit can perform identification related to the destination. Furthermore, if a learner is in a specific location, the identification unit can perform identification related to that location. In this way, by taking the geographical distribution of learners into consideration, more appropriate identification results can be provided. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the geographical distribution of learners into AI and have the AI ​​perform the identification.

[0051] The identification unit can improve the accuracy of identification by referring to the learner's related literature during identification. The identification unit can improve the accuracy of identification, for example, based on literature referenced by the learner. The identification unit can also improve the accuracy of identification by analyzing the learner's related literature. The identification unit can also improve the accuracy of identification based on the learner's past literature reference history. In this way, the accuracy of identification can be improved by referring to the learner's related literature. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the learner's related literature into AI and have the AI ​​improve the accuracy of identification.

[0052] The identification unit can perform identification taking into account the market value of the learner. For example, the identification unit determines the priority of identification based on the market value of the learner. The identification unit can also improve the accuracy of identification by taking into account the market value of the learner. The identification unit can also display the identification result by referring to the market value of the learner. In this way, by taking the market value of the learner into consideration, more appropriate identification results can be provided. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the market value of the learner into AI and have the AI ​​perform the identification.

[0053] The providing unit can adjust the level of detail of the provided information based on the learner's level of importance when providing the information. For example, if the learner is preparing for an important test, the providing unit can provide detailed teaching materials and practice questions. Furthermore, if the learner is engaged in daily study, the providing unit can also provide simple teaching materials and practice questions. Furthermore, if the learner is working on a specific task, the providing unit can also provide detailed teaching materials and practice questions related to the task. In this way, by adjusting the level of detail of the provided information based on the learner's level of importance, more appropriate teaching materials and practice questions can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the learner's level of importance to AI and cause the AI ​​to adjust the level of detail of the provided information.

[0054] The providing unit can apply different provision algorithms depending on the learner's category when providing the learning materials. For example, if the learner is studying mathematics, the providing unit can apply a provision algorithm dedicated to mathematics. Furthermore, if the learner is studying a language, the providing unit can also apply a provision algorithm dedicated to language. Furthermore, if the learner is studying science, the providing unit can also apply a provision algorithm dedicated to science. In this way, by applying different provision algorithms depending on the learner's category, more appropriate learning materials and practice problems can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the learner's category into AI and cause the AI ​​to apply the provision algorithm.

[0055] The providing unit can improve the accuracy of the provision by referring to the learner's past provision results when providing the content. The providing unit, for example, adjusts the current content to be provided based on the learner's past provision results. The providing unit can also optimize the provision algorithm by referring to the learner's past provision results. The providing unit can also improve the accuracy of the provision by using the learner's past provision results. In this way, the accuracy of the provision can be improved by referring to the learner's past provision results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the learner's past provision results into AI and cause the AI ​​to improve the accuracy of the provision.

[0056] The providing unit can determine the priority of provision based on the learner's submission time when providing the teaching materials and practice problems. For example, if the learner's submission deadline is approaching, the providing unit can provide the teaching materials and practice problems preferentially. Furthermore, if the learner's submission deadline is far away, the providing unit can also provide the teaching materials and practice problems later. Furthermore, if the learner's submission deadline has passed, the providing unit can also provide the teaching materials and practice problems urgently. In this way, by determining the priority of provision based on the learner's submission time, more appropriate teaching materials and practice problems can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the learner's submission time into AI and have the AI ​​determine the priority of provision.

[0057] The providing unit can adjust the order of provision based on the relevance of the learner when providing the teaching materials and practice problems. For example, the providing unit can prioritize providing teaching materials and practice problems related to the task the learner is currently working on. The providing unit can also prioritize providing highly relevant teaching materials and practice problems based on the learner's area of ​​interest. The providing unit can also prioritize providing necessary teaching materials and practice problems by taking into account the learner's current learning situation. In this way, by adjusting the order of provision based on the relevance of the learner, more appropriate teaching materials and practice problems can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the learner's relevance to AI and have the AI ​​adjust the order of provision.

[0058] The providing unit can adjust the use of technical terminology provided during provision according to the learner's level of expertise. For example, if the learner is a beginner, the providing unit can provide teaching materials and practice problems while avoiding technical terminology. Furthermore, if the learner is an intermediate learner, the providing unit can provide teaching materials and practice problems using technical terminology moderately. Furthermore, if the learner is an advanced learner, the providing unit can provide detailed teaching materials and practice problems using a lot of technical terminology. In this way, by adjusting the use of technical terminology provided according to the learner's level of expertise, more appropriate teaching materials and practice problems can be provided. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the learner's level of expertise into AI and cause the AI ​​to adjust the use of technical terminology.

[0059] During adjustment, the adjustment unit can analyze the learner's past learning history and select the optimal difficulty adjustment method. The adjustment unit selects the optimal difficulty adjustment method based on, for example, the learner's past learning history. The adjustment unit can also analyze the learner's past learning history and select an effective difficulty adjustment method. The adjustment unit can also optimize the difficulty adjustment method by referring to the learner's past learning history. In this way, the optimal difficulty adjustment method can be selected by analyzing the learner's past learning history. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the learner's past learning history into AI and have the AI ​​select the difficulty adjustment method.

[0060] During adjustment, the adjustment unit can customize the difficulty adjustment means based on the learner's current learning situation. The adjustment unit customizes the difficulty adjustment means based on, for example, the task the learner is currently working on. The adjustment unit can also select the optimal difficulty adjustment means by taking the learner's current learning situation into consideration. The adjustment unit can also customize the difficulty adjustment means based on the learner's current learning situation. This enables more appropriate difficulty adjustment by customizing the difficulty adjustment means based on the learner's current learning situation. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the learner's current learning situation into AI and have the AI ​​customize the difficulty adjustment means.

[0061] During adjustment, the adjustment unit can improve the difficulty level adjustment method by reflecting learner feedback. The adjustment unit, for example, improves the difficulty level adjustment method based on learner feedback. The adjustment unit can also optimize the difficulty level adjustment method by referring to learner's past feedback. The adjustment unit can also improve the difficulty level adjustment method by reflecting learner feedback. In this way, the difficulty level adjustment method can be improved by reflecting learner feedback. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input learner feedback into AI and cause the AI ​​to improve the difficulty level adjustment method.

[0062] During adjustment, the adjustment unit can select the optimal difficulty adjustment method by taking into account the learner's geographical location information. For example, if the learner is in a specific area, the adjustment unit selects a difficulty adjustment method related to that area. Furthermore, if the learner is traveling, the adjustment unit can also select a difficulty adjustment method related to the learner's destination. Furthermore, if the learner is in a specific location, the adjustment unit can also select a difficulty adjustment method related to that location. In this way, the optimal difficulty adjustment method can be selected by taking into account the learner's geographical location information. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the learner's geographical location information into AI and have the AI ​​select the difficulty adjustment method.

[0063] During adjustment, the adjustment unit can analyze the learner's social media activity and suggest a means for adjusting the difficulty level. For example, the adjustment unit analyzes the learner's social media activity and suggests an optimal means for adjusting the difficulty level. The adjustment unit can also suggest a means for adjusting the difficulty level by referring to the activity of the learner's friends on social media. The adjustment unit can also suggest a means for adjusting the difficulty level based on the content of the learner's posts on social media. In this way, an optimal means for adjusting the difficulty level can be suggested by analyzing the learner's social media activity. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the learner's social media activity into AI and have the AI ​​execute the suggestion of a means for adjusting the difficulty level.

[0064] During adjustment, the adjustment unit can customize the difficulty level adjustment method by reflecting the learner's past feedback. The adjustment unit customizes the difficulty level adjustment method based on, for example, the learner's past feedback. The adjustment unit can also optimize the difficulty level adjustment method by referring to the learner's past feedback. The adjustment unit can also customize the difficulty level adjustment method by reflecting the learner's past feedback. In this way, the difficulty level adjustment method can be customized by reflecting the learner's past feedback. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the learner's past feedback into AI and have the AI ​​customize the difficulty level adjustment method.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The education system can further include a health management unit that monitors the learner's health condition. The health management unit collects data such as the learner's heart rate, blood pressure, and sleep patterns, and monitors the learner's health condition in real time. For example, if a learner is feeling stressed, it can detect fluctuations in their heart rate and blood pressure and encourage them to take appropriate breaks. It can also analyze the learner's sleep patterns and adjust their study schedule if they are not getting enough rest. This makes it possible to provide a learning environment that takes the learner's health condition into consideration.

[0067] The educational system can further include a social assessment unit that evaluates the learner's social skills. The social assessment unit evaluates the learner's communication ability and cooperation ability and adjusts learning activities based on the results. For example, if a learner has difficulty cooperating in group work, the system can increase the time for individual learning. It can also provide special teaching materials and exercises to improve the learner's communication ability. This makes it possible to support the learner in improving their social skills.

[0068] The education system can further include a creativity evaluation unit that evaluates the learner's creativity. The creativity evaluation unit evaluates the learner's creative thinking and problem-solving ability and adjusts learning activities based on the evaluation. For example, if a learner receives a high evaluation for a creative task, the unit can provide a more advanced creative task. The unit can also provide special teaching materials and exercises to stimulate the learner's creativity. This makes it possible to support the learner in improving their creativity.

[0069] The educational system can further include a motivation evaluation unit that evaluates the learner's motivation. The motivation evaluation unit evaluates the learner's motivation and interest in learning and adjusts learning activities based on this. For example, if a learner shows a high interest in a particular field, it can provide learning materials and exercises related to that field. It can also introduce a special reward system to maintain the learner's motivation. This makes it possible to support the learner's motivation.

[0070] The education system can further include a learning suggestion unit that suggests learning methods according to the learner's learning style. The learning suggestion unit can suggest visual learning materials if the learner is visually inclined. It can also suggest audio learning materials if the learner is auditory inclined. It can also suggest practical learning activities if the learner is experiential inclined. This makes it possible to suggest optimal learning methods according to the learner's learning style.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The collection unit collects learner data. Learner data includes learning history, test results, response time, answer patterns, etc. For example, to collect learning history, the collection unit records the learner's past learning activities, study time, and learning content. To collect test results, the collection unit can also record the learner's test scores, correct answer rate, and tendency of incorrect answers. Furthermore, to collect response time, the collection unit can also record the response time for each question and the overall response time. Step 2: The analysis unit analyzes the data collected by the collection unit to analyze the learner's learning style and level of comprehension. The analysis is carried out using data mining technology and statistical analysis methods. For example, the analysis unit can use data mining technology to classify the learner's learning style into visual, auditory, experiential, etc. Statistical analysis methods can also be used to evaluate the learner's level of comprehension based on test scores and the percentage of correct answers to questions. Step 3: The identification unit identifies the learner's weaknesses based on the analysis results obtained by the analysis unit. Weaknesses are identified by factors such as the rate of incorrect answers to specific questions or a lack of understanding of specific concepts. For example, based on the analysis results, the identification unit identifies that the learner makes many incorrect answers in a specific area or does not understand a specific concept. Step 4: The provision unit provides customized learning materials and exercises to strengthen the weaknesses identified by the identification unit. The customized learning materials and exercises are provided with content and difficulty levels adjusted according to the learner's weaknesses. For example, if a learner has weaknesses in a specific area, exercises specialized for that area are provided. It is also possible to provide learning materials with difficulty levels adjusted according to the learner's level of understanding. Step 5: The adjustment unit adjusts the difficulty level according to the progress of the learning materials and practice problems provided by the provision unit. The difficulty level is adjusted based on the complexity of the problems and the time limit for answering. For example, the learner can increase the difficulty level of the problems according to their progress. It is also possible for the learner to set a time limit for answering according to their progress.

[0073] (Example 2) An educational system according to an embodiment of the present invention analyzes the learning style and comprehension level of each individual student and dynamically adjusts the content of educational materials based on the analysis. This educational system collects, analyzes, identifies, provides, and adjusts learner data to provide an optimal learning experience for each individual learner. For example, the educational system collects data such as the learner's past learning history, test results, response time, and answer patterns, and analyzes it using AI. The AI ​​then identifies the learner's weaknesses and provides customized educational materials and practice problems to strengthen them. For example, if a learner has weaknesses in a specific area of ​​mathematics, the AI ​​provides practice problems specialized for that area. Furthermore, the difficulty level is adjusted according to the learner's progress, allowing the learner to progress at an optimal pace. This allows the learner to efficiently progress through learning at their own pace and deepen their understanding. This allows the educational system to provide an optimal learning experience for each individual learner.

[0074] The educational system according to the embodiment includes a collection unit, an analysis unit, an identification unit, a provision unit, and an adjustment unit. The collection unit collects data on the learner. The learner's data includes, but is not limited to, learning history, test results, response time, and answer patterns. For example, the collection unit records the learner's past learning activities, study time, and learning content to collect the learning history. The collection unit can also record the learner's test scores, correct answer rate, and tendency of incorrect answers to collect test results. Furthermore, the collection unit can also record the response time for each question and the overall response time to collect response time. The analysis unit analyzes the data collected by the collection unit to analyze the learner's learning style and level of understanding. The analysis is performed using, for example, data mining technology or statistical analysis method, but is not limited to, examples. For example, the analysis unit uses data mining technology to classify the learner's learning style into visual, auditory, experiential, etc. The analysis unit can also use statistical analysis techniques to evaluate the learner's level of understanding based on test scores, the rate of correct answers to questions, etc. The identification unit identifies the learner's weak points based on the analysis results obtained by the analysis unit. Weak points can be identified, for example, by the rate of incorrect answers to specific questions or a lack of understanding of a specific concept, but examples are not limited to these. For example, the identification unit can identify that the learner makes many incorrect answers in a specific field based on the analysis results. The identification unit can also identify that the learner does not understand a specific concept. The provision unit provides customized learning materials and practice problems to strengthen the weak points identified by the identification unit. The customized learning materials and practice problems can be provided by, for example, adjusting the content and difficulty level according to the learner's weak points, but examples are not limited to these. For example, if the learner has a weakness in a specific field, the provision unit can provide practice problems specialized for that field. The provision unit can also provide learning materials with adjusted difficulty level according to the learner's level of understanding. The adjustment unit adjusts the difficulty level according to the progress of the learning materials and practice problems provided by the provision unit. The difficulty level can be adjusted, for example, by the complexity of the questions and the time limit for answering, but examples are not limited to these. The adjustment unit increases the difficulty of the questions according to the learner's progress, for example.The adjustment unit can also set a time limit for each learner to answer questions depending on their progress. This allows the education system according to the embodiment to provide an optimal learning experience for each individual learner.

[0075] The collection unit can collect at least one of the following data: learning history, test results, response time, and answer patterns. For example, to collect learning history, the collection unit records the learner's past learning activities, study time, and learning content. To collect test results, the collection unit can also record the learner's test scores, correct answer rate, and tendency of incorrect answers. To collect response times, the collection unit can also record the response time for each question and the overall response time. To collect response patterns, the collection unit can also record the learner's choice of options and tendency of incorrect answers. By collecting data such as learning history, test results, response time, and answer patterns, detailed information about the learner can be obtained. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the learner's data into AI and have the AI ​​collect the data.

[0076] The analysis unit can analyze the collected data and analyze the learner's learning style and level of understanding. The analysis unit can, for example, use data mining technology to classify the learner's learning style into visual, auditory, experiential, etc. The analysis unit can also use statistical analysis techniques to evaluate the learner's level of understanding based on test scores, the rate at which questions are answered correctly, etc. For example, the analysis unit can use data mining technology to classify the learner's learning style into visual, auditory, experiential, etc. The analysis unit can also use statistical analysis techniques to evaluate the learner's level of understanding based on test scores, the rate at which questions are answered correctly, etc. In this way, by analyzing the collected data, the learner's learning style and level of understanding can be understood. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into AI and have the AI ​​analyze the data.

[0077] The identification unit can identify the learner's weak points based on the analysis results. For example, the identification unit can identify that the learner makes many incorrect answers in a specific area based on the analysis results. The identification unit can also identify that the learner does not understand a specific concept. For example, the identification unit can identify that the learner makes many incorrect answers in a specific area based on the analysis results. The identification unit can also identify that the learner does not understand a specific concept. This makes it possible to identify the learner's weak points based on the analysis results and provide effective learning support. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input the analysis results to AI and cause the AI ​​to identify the learner's weak points.

[0078] The providing unit can provide customized learning materials and practice questions to strengthen the identified weaknesses. For example, if a learner has weaknesses in a specific area, the providing unit can provide practice questions specialized for that area. The providing unit can also provide learning materials with a difficulty level adjusted according to the learner's level of understanding. For example, if a learner has weaknesses in a specific area, the providing unit can provide practice questions specialized for that area. The providing unit can also provide learning materials with a difficulty level adjusted according to the learner's level of understanding. In this way, by providing customized learning materials and practice questions to strengthen the identified weaknesses, the learner's level of understanding is improved. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input customized learning materials and practice questions into AI and cause the AI ​​to provide the learning materials and practice questions.

[0079] The adjustment unit can adjust the difficulty level according to the progress of the provided learning materials and practice problems. For example, the adjustment unit increases the difficulty level of the problems according to the learner's progress. The adjustment unit can also set a time limit for the learner to answer the problems according to the learner's progress. For example, the adjustment unit increases the difficulty level of the problems according to the learner's progress. The adjustment unit can also set a time limit for the learner to answer the problems according to the learner's progress. In this way, by adjusting the difficulty level according to the progress of the provided learning materials and practice problems, the learner can proceed with learning at an optimal pace. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the progress of the provided learning materials and practice problems into AI and cause the AI ​​to adjust the difficulty level.

[0080] The education system further includes a collection unit that estimates the learner's emotions and adjusts the timing of data collection according to the estimated learner's emotions. For example, if the learner is stressed, the collection unit reduces the frequency of data collection and collects data in a relaxed state. Furthermore, if the learner is concentrating, the collection unit can increase the frequency of data collection and collect more detailed data. Furthermore, if the learner is tired, the collection unit can temporarily stop data collection and resume it after a break. This allows for more appropriate data collection by adjusting the timing of data collection based on the learner's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or without an AI. For example, the collection unit may input the learner's emotional data into an AI and have the AI ​​adjust the timing of data collection.

[0081] The collection unit can analyze the learner's past learning history and select an appropriate data collection method. For example, the collection unit prioritizes the use of learning methods that the learner has frequently used in the past for data collection. The collection unit can also identify effective learning time periods from the learner's past learning history and collect data during those time periods. The collection unit can also select a data collection method that matches a specific learning style based on the learner's past learning history. In this way, the optimal data collection method can be selected by analyzing the learner's past learning history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the learner's past learning history into AI and have the AI ​​select the data collection method.

[0082] When collecting data, the collection unit can filter the data based on the learner's current learning situation or areas of interest. For example, the collection unit collects only data related to the task the learner is currently working on. The collection unit can also prioritize the collection of highly relevant data based on the learner's areas of interest. The collection unit can also filter and collect only necessary data, taking into account the learner's current learning situation. In this way, highly relevant data can be collected by filtering data based on the learner's current learning situation and areas of interest. 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 into AI and have the AI ​​perform data filtering.

[0083] When collecting data, the collection unit can select the optimal collection means depending on the learner's input method. For example, if the learner is using voice input, the collection unit can prioritize collecting voice data. Furthermore, if the learner is using text input, the collection unit can also prioritize collecting text data. Furthermore, if the learner is using image input, the collection unit can also prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means 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 into AI and have the AI ​​select the optimal collection means.

[0084] 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. For example, if the learner is feeling stressed, the collection unit can prioritize collecting data related to relaxation. Furthermore, if the learner is concentrating, the collection unit can prioritize collecting data related to learning. Furthermore, if the learner is tired, the collection unit can prioritize collecting data related to rest. This enables more appropriate data collection by determining the priority of data to be collected based on the learner's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the learner's emotion data into an AI and have the AI ​​determine the priority of the data.

[0085] When collecting data, the collection unit can 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 can prioritize collecting data related to that area. Furthermore, if the learner is traveling, the collection unit can prioritize collecting data related to the destination. Furthermore, if the learner is in a specific location, the collection unit can prioritize collecting data related to that location. In this way, by taking the learner's geographical location information into consideration, highly relevant data can be collected preferentially. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the learner's geographical location information into AI and cause the AI ​​to collect highly relevant data.

[0086] When collecting data, the collection unit can analyze the learner's social media activities and collect related data. For example, the collection unit collects related data based on information shared by the learner on social media. The collection unit can also analyze the learner's social media activities and collect data related to learning. The collection unit can also collect related data by referring to the activities of the learner's friends on social media. In this way, related data can be collected by analyzing the learner's social media activities. 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 AI and have the AI ​​collect the data.

[0087] The collection unit can customize the collection method by reflecting the learner's past feedback when collecting data. For example, the collection unit adjusts the data collection method based on feedback provided by the learner in the past. The collection unit can also customize the type of data to be collected by reflecting the learner's past feedback. The collection unit can also adjust the timing of data collection by referring to the learner's past feedback. In this way, the collection method can be customized by reflecting 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 into AI and have the AI ​​customize the collection method.

[0088] The analysis unit can estimate the learner's emotions and adjust the presentation method of the analysis based on the estimated learner's emotions. For example, if the learner is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the learner is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the learner is excited, the analysis unit can provide visually stimulating analysis results. By adjusting the presentation method of the analysis based on the learner's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the learner's emotion data into an AI and have the AI ​​adjust the presentation method of the analysis.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis based on the learner's level of importance. For example, if the learner is preparing for an important test, the analysis unit provides detailed analysis results. The analysis unit can also provide simple analysis results if the learner is engaged in daily study. The analysis unit can also provide detailed analysis results related to a specific task if the learner is working on that task. In this way, by adjusting the level of detail of the analysis based on the learner's level of importance, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the learner's level of importance into AI and have the AI ​​adjust the level of detail of the analysis.

[0090] During analysis, the analysis unit can apply different analysis algorithms depending on the learner's category. For example, if the learner is studying mathematics, the analysis unit can apply an analysis algorithm dedicated to mathematics. Furthermore, if the learner is studying a language, the analysis unit can also apply an analysis algorithm dedicated to language. Furthermore, if the learner is studying science, the analysis unit can also apply an analysis algorithm dedicated to science. In this way, by applying different analysis algorithms depending on the learner's category, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the learner's category into AI and have the AI ​​apply the analysis algorithm.

[0091] 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, adjusts the current analysis result based on the learner's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the learner's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the learner's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the learner's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the learner's past analysis results into AI and have the AI ​​improve the accuracy of the analysis.

[0092] The analysis unit can estimate the learner's emotions and adjust the length of the analysis based on the estimated learner's emotions. For example, if the learner is in a hurry, the analysis unit can provide a short and concise analysis result. If the learner is relaxed, the analysis unit can also provide a detailed analysis result. If the learner is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis based on the learner's emotions, more appropriate analysis results can be provided. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the learner's emotion data into an AI and have the AI ​​adjust the length of the analysis.

[0093] During analysis, the analysis unit can determine the priority of analysis based on the learner's submission time. For example, if the learner's submission deadline is approaching, the analysis unit performs analysis as a priority. The analysis unit can also postpone analysis if the learner's submission deadline is far away. The analysis unit can also perform analysis urgently if the learner's submission deadline has passed. In this way, by determining the priority of analysis based on the learner's submission time, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the learner's submission time into AI and have the AI ​​determine the analysis priority.

[0094] During analysis, the analysis unit can adjust the order of analysis based on the learner's relevance. For example, the analysis unit prioritizes analysis of data related to the task the learner is currently working on. The analysis unit can also prioritize analysis of highly relevant data based on the learner's areas of interest. The analysis unit can also prioritize analysis of necessary data taking into account the learner's current learning situation. In this way, by adjusting the order of analysis based on the learner's relevance, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the learner's relevance to AI and have the AI ​​adjust the order of analysis.

[0095] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the learner's level of expertise. For example, if the learner is a beginner, the analysis unit can provide analysis results that avoid technical terminology. Furthermore, if the learner is an intermediate learner, the analysis unit can provide analysis results that use technical terminology appropriately. Furthermore, if the learner is an advanced learner, the analysis unit can provide detailed analysis results that use a lot of technical terminology. By adjusting the use of technical terminology in the analysis according to the learner's level of expertise, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the learner's level of expertise into AI and have the AI ​​adjust the use of technical terminology.

[0096] The identification unit can estimate the learner's emotions and adjust the identification criteria based on the estimated learner's emotions. For example, the identification unit can apply detailed identification criteria when the learner is relaxed. The identification unit can also apply simple identification criteria when the learner is nervous. The identification unit can also apply visually stimulating identification criteria when the learner is excited. By adjusting the identification criteria based on the learner's emotions, more appropriate identification results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the identification unit can be performed using, for example, an AI, or without an AI. For example, the identification unit can input the learner's emotion data into an AI and have the AI ​​adjust the identification criteria.

[0097] The identification unit can improve the accuracy of identification based on the mutual relationships between learners during identification. For example, if a learner is studying collaboratively with other learners, the identification unit can improve the accuracy of identification by taking into account the mutual relationships. Furthermore, if a learner is competing with other learners, the identification unit can improve the accuracy of identification by taking into account the competitive relationships. Furthermore, if a learner is cooperating with other learners, the identification unit can improve the accuracy of identification by taking into account the cooperative relationships. In this way, the accuracy of identification can be improved by taking into account the mutual relationships between learners. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the mutual relationships between learners into AI and cause the AI ​​to improve the accuracy of identification.

[0098] The identification unit can perform identification by taking into consideration the learner's attribute information. For example, the identification unit can perform identification by taking into consideration the learner's age. The identification unit can also perform identification by taking into consideration the learner's gender. The identification unit can also perform identification by taking into consideration the learner's learning style. In this way, by taking into consideration the learner's attribute information, more appropriate identification results can be provided. Some or all of the above-mentioned processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input the learner's attribute information into AI and have the AI ​​perform the identification.

[0099] During identification, the identification unit can assign a specific weight based on the learner's submission frequency. For example, if a learner submits frequently, the identification unit assigns a specific weight based on the submission frequency. Furthermore, if a learner submits infrequently, the identification unit can assign a specific weight based on the submission frequency. Furthermore, if a learner submits at a certain frequency, the identification unit can assign a specific weight based on the submission frequency. In this way, by assigning a specific weight based on the learner's submission frequency, more appropriate identification results can be provided. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the learner's submission frequency into AI and have the AI ​​perform the specific weighting.

[0100] The identification unit can estimate the learner's emotions and adjust the order in which the specific results are displayed based on the estimated learner's emotions. For example, if the learner is relaxed, the identification unit can display detailed identified results first. Also, if the learner is nervous, the identification unit can display simple identified results first. Also, if the learner is excited, the identification unit can display visually stimulating identified results first. By adjusting the order in which the specific results are displayed based on the learner's emotions, more appropriate identified results can be provided. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the identification unit can be performed using, for example, an AI, or without an AI. For example, the identification unit can input the learner's emotion data into an AI and have the AI ​​adjust the display order of the identified results.

[0101] The identification unit can perform identification taking into account the geographical distribution of learners. For example, if a learner is in a specific area, the identification unit can perform identification related to that area. Furthermore, if a learner is traveling, the identification unit can perform identification related to the destination. Furthermore, if a learner is in a specific location, the identification unit can perform identification related to that location. In this way, by taking the geographical distribution of learners into consideration, more appropriate identification results can be provided. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the geographical distribution of learners into AI and have the AI ​​perform the identification.

[0102] The identification unit can improve the accuracy of identification by referring to the learner's related literature during identification. The identification unit can improve the accuracy of identification, for example, based on literature referenced by the learner. The identification unit can also improve the accuracy of identification by analyzing the learner's related literature. The identification unit can also improve the accuracy of identification based on the learner's past literature reference history. In this way, the accuracy of identification can be improved by referring to the learner's related literature. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the learner's related literature into AI and have the AI ​​improve the accuracy of identification.

[0103] The identification unit can perform identification taking into account the market value of the learner. For example, the identification unit determines the priority of identification based on the market value of the learner. The identification unit can also improve the accuracy of identification by taking into account the market value of the learner. The identification unit can also display the identification result by referring to the market value of the learner. In this way, by taking the market value of the learner into consideration, more appropriate identification results can be provided. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input the market value of the learner into AI and have the AI ​​perform the identification.

[0104] The providing unit can estimate the learner's emotions and adjust the presentation of the learning materials and exercises to be provided based on the estimated learner's emotions. For example, if the learner is relaxed, the providing unit can provide detailed learning materials and exercises. Furthermore, if the learner is nervous, the providing unit can provide simple, highly visible learning materials and exercises. Furthermore, if the learner is excited, the providing unit can provide visually stimulating learning materials and exercises. This allows for adjusting the presentation of the learning materials and exercises to be provided based on the learner's emotions, thereby providing more appropriate learning materials and exercises. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the learner's emotional data into an AI and have the AI ​​adjust the presentation of the learning materials and exercises.

[0105] The providing unit can adjust the level of detail of the provided information based on the learner's level of importance when providing the information. For example, if the learner is preparing for an important test, the providing unit can provide detailed teaching materials and practice questions. Furthermore, if the learner is engaged in daily study, the providing unit can also provide simple teaching materials and practice questions. Furthermore, if the learner is working on a specific task, the providing unit can also provide detailed teaching materials and practice questions related to the task. In this way, by adjusting the level of detail of the provided information based on the learner's level of importance, more appropriate teaching materials and practice questions can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the learner's level of importance to AI and cause the AI ​​to adjust the level of detail of the provided information.

[0106] The providing unit can apply different provision algorithms depending on the learner's category when providing the learning materials. For example, if the learner is studying mathematics, the providing unit can apply a provision algorithm dedicated to mathematics. Furthermore, if the learner is studying a language, the providing unit can also apply a provision algorithm dedicated to language. Furthermore, if the learner is studying science, the providing unit can also apply a provision algorithm dedicated to science. In this way, by applying different provision algorithms depending on the learner's category, more appropriate learning materials and practice problems can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the learner's category into AI and cause the AI ​​to apply the provision algorithm.

[0107] The providing unit can improve the accuracy of the provision by referring to the learner's past provision results when providing the content. The providing unit, for example, adjusts the current content to be provided based on the learner's past provision results. The providing unit can also optimize the provision algorithm by referring to the learner's past provision results. The providing unit can also improve the accuracy of the provision by using the learner's past provision results. In this way, the accuracy of the provision can be improved by referring to the learner's past provision results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the learner's past provision results into AI and cause the AI ​​to improve the accuracy of the provision.

[0108] The providing unit can estimate the learner's emotions and adjust the length of the learning materials and exercises to be provided based on the estimated learner's emotions. For example, if the learner is in a hurry, the providing unit can provide short, concise learning materials and exercises. Furthermore, if the learner is relaxed, the providing unit can provide longer learning materials and exercises with detailed explanations. Furthermore, if the learner is excited, the providing unit can provide visually stimulating learning materials and exercises. This allows for adjusting the length of the learning materials and exercises to be provided based on the learner's emotions, thereby providing more appropriate learning materials and exercises. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or without an AI. For example, the providing unit can input the learner's emotion data into an AI and have the AI ​​adjust the length of the learning materials and exercises.

[0109] The providing unit can determine the priority of provision based on the learner's submission time when providing the teaching materials and practice problems. For example, if the learner's submission deadline is approaching, the providing unit can provide the teaching materials and practice problems preferentially. Furthermore, if the learner's submission deadline is far away, the providing unit can also provide the teaching materials and practice problems later. Furthermore, if the learner's submission deadline has passed, the providing unit can also provide the teaching materials and practice problems urgently. In this way, by determining the priority of provision based on the learner's submission time, more appropriate teaching materials and practice problems can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the learner's submission time into AI and have the AI ​​determine the priority of provision.

[0110] The providing unit can adjust the order of provision based on the relevance of the learner when providing the teaching materials and practice problems. For example, the providing unit can prioritize providing teaching materials and practice problems related to the task the learner is currently working on. The providing unit can also prioritize providing highly relevant teaching materials and practice problems based on the learner's area of ​​interest. The providing unit can also prioritize providing necessary teaching materials and practice problems by taking into account the learner's current learning situation. In this way, by adjusting the order of provision based on the relevance of the learner, more appropriate teaching materials and practice problems can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the learner's relevance to AI and have the AI ​​adjust the order of provision.

[0111] The providing unit can adjust the use of technical terminology provided during provision according to the learner's level of expertise. For example, if the learner is a beginner, the providing unit can provide teaching materials and practice problems while avoiding technical terminology. Furthermore, if the learner is an intermediate learner, the providing unit can provide teaching materials and practice problems using technical terminology moderately. Furthermore, if the learner is an advanced learner, the providing unit can provide detailed teaching materials and practice problems using a lot of technical terminology. In this way, by adjusting the use of technical terminology provided according to the learner's level of expertise, more appropriate teaching materials and practice problems can be provided. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the learner's level of expertise into AI and cause the AI ​​to adjust the use of technical terminology.

[0112] The adjustment unit can estimate the learner's emotions and adjust the difficulty level adjustment method based on the estimated learner's emotions. For example, the adjustment unit can perform detailed difficulty level adjustment when the learner is relaxed. The adjustment unit can also perform simple difficulty level adjustment when the learner is nervous. The adjustment unit can also perform visually stimulating difficulty level adjustment when the learner is excited. This enables more appropriate difficulty level adjustment by adjusting the difficulty level adjustment method based on the learner's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the adjustment unit can be performed using, for example, an AI, or without an AI. For example, the adjustment unit can input the learner's emotion data into an AI and have the AI ​​adjust the difficulty level adjustment method.

[0113] During adjustment, the adjustment unit can analyze the learner's past learning history and select the optimal difficulty adjustment method. The adjustment unit selects the optimal difficulty adjustment method based on, for example, the learner's past learning history. The adjustment unit can also analyze the learner's past learning history and select an effective difficulty adjustment method. The adjustment unit can also optimize the difficulty adjustment method by referring to the learner's past learning history. In this way, the optimal difficulty adjustment method can be selected by analyzing the learner's past learning history. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the learner's past learning history into AI and have the AI ​​select the difficulty adjustment method.

[0114] During adjustment, the adjustment unit can customize the difficulty adjustment means based on the learner's current learning situation. The adjustment unit customizes the difficulty adjustment means based on, for example, the task the learner is currently working on. The adjustment unit can also select the optimal difficulty adjustment means by taking the learner's current learning situation into consideration. The adjustment unit can also customize the difficulty adjustment means based on the learner's current learning situation. This enables more appropriate difficulty adjustment by customizing the difficulty adjustment means based on the learner's current learning situation. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the learner's current learning situation into AI and have the AI ​​customize the difficulty adjustment means.

[0115] During adjustment, the adjustment unit can improve the difficulty level adjustment method by reflecting learner feedback. The adjustment unit, for example, improves the difficulty level adjustment method based on learner feedback. The adjustment unit can also optimize the difficulty level adjustment method by referring to learner's past feedback. The adjustment unit can also improve the difficulty level adjustment method by reflecting learner feedback. In this way, the difficulty level adjustment method can be improved by reflecting learner feedback. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input learner feedback into AI and cause the AI ​​to improve the difficulty level adjustment method.

[0116] The adjustment unit can estimate the learner's emotions and determine the priority of difficulty levels based on the estimated learner's emotions. For example, if the learner is relaxed, the adjustment unit can prioritize detailed difficulty adjustment. Furthermore, if the learner is nervous, the adjustment unit can prioritize simple difficulty adjustment. Furthermore, if the learner is excited, the adjustment unit can prioritize visually stimulating difficulty adjustment. Thus, by determining the priority of difficulty levels based on the learner's emotions, more appropriate difficulty adjustment is possible. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the learner's emotional data into an AI and have the AI ​​determine the priority of difficulty levels.

[0117] During adjustment, the adjustment unit can select the optimal difficulty adjustment method by taking into account the learner's geographical location information. For example, if the learner is in a specific area, the adjustment unit selects a difficulty adjustment method related to that area. Furthermore, if the learner is traveling, the adjustment unit can also select a difficulty adjustment method related to the learner's destination. Furthermore, if the learner is in a specific location, the adjustment unit can also select a difficulty adjustment method related to that location. In this way, the optimal difficulty adjustment method can be selected by taking into account the learner's geographical location information. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the learner's geographical location information into AI and have the AI ​​select the difficulty adjustment method.

[0118] During adjustment, the adjustment unit can analyze the learner's social media activity and suggest a means for adjusting the difficulty level. For example, the adjustment unit analyzes the learner's social media activity and suggests an optimal means for adjusting the difficulty level. The adjustment unit can also suggest a means for adjusting the difficulty level by referring to the activity of the learner's friends on social media. The adjustment unit can also suggest a means for adjusting the difficulty level based on the content of the learner's posts on social media. In this way, an optimal means for adjusting the difficulty level can be suggested by analyzing the learner's social media activity. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the learner's social media activity into AI and have the AI ​​execute the suggestion of a means for adjusting the difficulty level.

[0119] During adjustment, the adjustment unit can customize the difficulty level adjustment method by reflecting the learner's past feedback. The adjustment unit customizes the difficulty level adjustment method based on, for example, the learner's past feedback. The adjustment unit can also optimize the difficulty level adjustment method by referring to the learner's past feedback. The adjustment unit can also customize the difficulty level adjustment method by reflecting the learner's past feedback. In this way, the difficulty level adjustment method can be customized by reflecting the learner's past feedback. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the learner's past feedback into AI and have the AI ​​customize the difficulty level adjustment method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, identification unit, provision unit, and adjustment unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data on the learner using the camera 42 and microphone 38B of the smart device 14 and records the data using the control unit 46A. The analysis unit, implemented, for example, by the identification processing unit 290 of the data processing device 12, analyzes the collected data. The identification unit, implemented, for example, by the identification processing unit 290 of the data processing device 12, identifies the learner's weaknesses based on the analysis results. The provision unit, implemented, for example, by the control unit 46A of the smart device 14, provides customized learning materials and practice problems. The adjustment unit, implemented, for example, by the identification processing unit 290 of the data processing device 12, adjusts the difficulty level according to the progress of the provided learning materials and practice problems. Furthermore, the collection unit has a function of estimating the learner's emotions and adjusting the timing of data collection according to the estimated emotions. For example, emotions are estimated using an emotion engine or a generation AI and the frequency of data collection is adjusted. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, identification unit, provision unit, and adjustment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data on the learner using the camera 42 and microphone 238 of the smart glasses 214 and records the data using the control unit 46A. The analysis unit, implemented, for example, by the identification processing unit 290 of the data processing device 12, analyzes the collected data. The identification unit, implemented, for example, by the identification processing unit 290 of the data processing device 12, identifies the learner's weaknesses based on the analysis results. The provision unit, implemented, for example, by the control unit 46A of the smart glasses 214, provides customized learning materials and practice problems. The adjustment unit, implemented, for example, by the identification processing unit 290 of the data processing device 12, adjusts the difficulty level according to the progress of the provided learning materials and practice problems. Furthermore, the collection unit has a function of estimating the learner's emotions and adjusting the timing of data collection according to the estimated emotions. For example, emotions are estimated using an emotion engine or a generation AI, and the frequency of data collection is adjusted. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, identification unit, provision unit, and adjustment unit, is implemented, for example, in at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data on the learner using the camera 42 and microphone 238 of the headset-type terminal 314 and records the data using the control unit 46A. The analysis unit, implemented, for example, by the identification processing unit 290 of the data processing device 12, analyzes the collected data. The identification unit, implemented, for example, by the identification processing unit 290 of the data processing device 12, identifies the learner's weaknesses based on the analysis results. The provision unit, implemented, for example, by the control unit 46A of the headset-type terminal 314, provides customized learning materials and practice problems. The adjustment unit, implemented, for example, by the identification processing unit 290 of the data processing device 12, adjusts the difficulty level according to the progress of the provided learning materials and practice problems. Furthermore, the collection unit has a function of estimating the learner's emotions and adjusting the timing of data collection according to the estimated emotions. For example, emotions are estimated using an emotion engine or a generation AI and the frequency of data collection is adjusted. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, identification unit, provision unit, and adjustment unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data on the learner using the camera 42 and microphone 238 of the robot 414 and records the data using the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the collected data. The identification unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies the learner's weaknesses based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides customized learning materials and practice problems. The adjustment unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and adjusts the difficulty level according to the progress of the provided learning materials and practice problems. Furthermore, the collection unit has a function of estimating the learner's emotions and adjusting the timing of data collection according to the estimated emotions. For example, emotions are estimated using an emotion engine or a generation AI and the frequency of data collection is adjusted.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The education system can further include a health management unit that monitors the learner's health condition. The health management unit collects data such as the learner's heart rate, blood pressure, and sleep patterns, and monitors the learner's health condition in real time. For example, if a learner is feeling stressed, it can detect fluctuations in their heart rate and blood pressure and encourage them to take appropriate breaks. It can also analyze the learner's sleep patterns and adjust their study schedule if they are not getting enough rest. This makes it possible to provide a learning environment that takes the learner's health condition into consideration.

[0122] The educational system can further include a social assessment unit that evaluates the learner's social skills. The social assessment unit evaluates the learner's communication ability and cooperation ability and adjusts learning activities based on the results. For example, if a learner has difficulty cooperating in group work, the system can increase the time for individual learning. It can also provide special teaching materials and exercises to improve the learner's communication ability. This makes it possible to support the learner in improving their social skills.

[0123] The education system can further include a creativity evaluation unit that evaluates the learner's creativity. The creativity evaluation unit evaluates the learner's creative thinking and problem-solving ability and adjusts learning activities based on the evaluation. For example, if a learner receives a high evaluation for a creative task, the unit can provide a more advanced creative task. The unit can also provide special teaching materials and exercises to stimulate the learner's creativity. This makes it possible to support the learner in improving their creativity.

[0124] The educational system can further include a motivation evaluation unit that evaluates the learner's motivation. The motivation evaluation unit evaluates the learner's motivation and interest in learning and adjusts learning activities based on this. For example, if a learner shows a high interest in a particular field, it can provide learning materials and exercises related to that field. It can also introduce a special reward system to maintain the learner's motivation. This makes it possible to support the learner's motivation.

[0125] The education system may further include an environment adjustment unit that estimates the learner's emotions and adjusts the learning environment based on the estimated learner's emotions. The environment adjustment unit may provide a quiet learning environment when the learner is relaxed, or may provide a learning environment that provides appropriate stimulation when the learner is concentrating, or may provide a break time for the learner to refresh themselves when the learner is tired. In this way, an optimal learning environment can be provided based on the learner's emotions.

[0126] The educational system may further include a content adjustment unit that estimates the learner's emotions and adjusts the learning content based on the estimated learner's emotions. The content adjustment unit may provide learning materials with a lower level of difficulty if the learner is feeling stressed. Alternatively, the content adjustment unit may provide learning materials with a higher level of difficulty if the learner is relaxed. Alternatively, the content adjustment unit may provide learning materials that are visually stimulating if the learner is excited. In this way, optimal learning content can be provided based on the learner's emotions.

[0127] The education system may further include a pace adjustment unit that estimates the learner's emotions and adjusts the learning pace based on the estimated learner's emotions. The pace adjustment unit may slow down the learning pace when the learner is tired, or may speed up the learning pace when the learner is concentrating, or may proceed with learning at a moderate pace when the learner is relaxed. In this way, an optimal learning pace can be provided based on the learner's emotions.

[0128] The education system may further include a goal setting unit that estimates the learner's emotions and sets learning goals based on the estimated learner's emotions. The goal setting unit may set a challenging goal if the learner is relaxed. Alternatively, it may set an easy-to-achieve goal if the learner is nervous. Alternatively, it may set a visually stimulating goal if the learner is excited. In this way, optimal learning goals may be set based on the learner's emotions.

[0129] The educational system may further include a feedback unit that estimates the learner's emotions and provides learning feedback based on the estimated learner's emotions. The feedback unit may provide detailed feedback when the learner is relaxed, or simple feedback when the learner is nervous, or visually stimulating feedback when the learner is excited. This allows optimal learning feedback to be provided based on the learner's emotions.

[0130] The education system can further include a learning suggestion unit that suggests learning methods according to the learner's learning style. The learning suggestion unit can suggest visual learning materials if the learner is visually inclined. It can also suggest audio learning materials if the learner is auditory inclined. It can also suggest practical learning activities if the learner is experiential inclined. This makes it possible to suggest optimal learning methods according to the learner's learning style.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The collection unit collects learner data. Learner data includes learning history, test results, response time, answer patterns, etc. For example, to collect learning history, the collection unit records the learner's past learning activities, study time, and learning content. To collect test results, the collection unit can also record the learner's test scores, correct answer rate, and tendency of incorrect answers. Furthermore, to collect response time, the collection unit can also record the response time for each question and the overall response time. Step 2: The analysis unit analyzes the data collected by the collection unit to analyze the learner's learning style and level of comprehension. The analysis is carried out using data mining technology and statistical analysis methods. For example, the analysis unit can use data mining technology to classify the learner's learning style into visual, auditory, experiential, etc. Statistical analysis methods can also be used to evaluate the learner's level of comprehension based on test scores and the percentage of correct answers to questions. Step 3: The identification unit identifies the learner's weaknesses based on the analysis results obtained by the analysis unit. Weaknesses are identified by factors such as the rate of incorrect answers to specific questions or a lack of understanding of specific concepts. For example, based on the analysis results, the identification unit identifies that the learner makes many incorrect answers in a specific area or does not understand a specific concept. Step 4: The provision unit provides customized learning materials and exercises to strengthen the weaknesses identified by the identification unit. The customized learning materials and exercises are provided with content and difficulty levels adjusted according to the learner's weaknesses. For example, if a learner has weaknesses in a specific area, exercises specialized for that area are provided. It is also possible to provide learning materials with difficulty levels adjusted according to the learner's level of understanding. Step 5: The adjustment unit adjusts the difficulty level according to the progress of the learning materials and practice problems provided by the provision unit. The difficulty level is adjusted based on the complexity of the problems and the time limit for answering. For example, the learner can increase the difficulty level of the problems according to their progress. It is also possible for the learner to set a time limit for answering according to their progress.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data of learners; an analysis unit that analyzes the data collected by the collection unit and analyzes the learning style and comprehension level of the learner; an identification unit that identifies weaknesses of the learner based on the analysis result obtained by the analysis unit; a providing unit that provides customized learning materials and exercises to strengthen the weak points identified by the identifying unit; an adjustment unit that adjusts the difficulty level according to the progress of the teaching materials and practice questions provided by the provision unit; A system characterized by:

2. The collecting unit Collect at least one of the following data: learning history, test results, response time, and response patterns 2. The system of claim 1.

3. The analysis unit Analyze the collected data to understand the learner's learning style and level of understanding.

2. The system of claim 1.

4. The identification unit Identify learner weaknesses based on analysis results 2. The system of claim 1.

5. The providing unit Providing customized study materials and exercises to strengthen identified weaknesses 2. The system of claim 1.

6. The adjustment unit Adjust the difficulty level according to the progress of the provided materials and exercises 2. The system of claim 1.

7. The collecting unit Estimate the learner's emotions and adjust the timing of data collection according to the estimated learner's emotions.

2. The system of claim 1.

8. The collecting unit Analyze learners' past learning history and select appropriate data collection methods 2. The system of claim 1.

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