system

Abstract

The system according to this embodiment aims to provide a customized learning experience that is tailored to each student's individual learning needs and style. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a selection unit, a provision unit, a monitoring unit, and a feedback unit. The acquisition unit acquires information about students' learning needs and styles. The analysis unit analyzes the learning needs and styles based on the information acquired by the acquisition unit. The selection unit selects learning content and activities based on the results of the analysis by the analysis unit. The provision unit provides the learning content and activities selected by the selection unit. The monitoring unit monitors the progress of the learning content and activities provided by the provision unit. The feedback unit provides feedback based on the progress monitored by the monitoring unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, a customized learning experience according to the individual learning needs and styles of students has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a customized learning experience according to the individual learning needs and styles of students.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a selection unit, a provision unit, a monitoring unit, and a feedback unit. The acquisition unit acquires information about students' learning needs and styles. The analysis unit analyzes the learning needs and styles based on the information acquired by the acquisition unit. The selection unit selects learning content and activities based on the results of the analysis by the analysis unit. The provision unit provides the learning content and activities selected by the selection unit. The monitoring unit monitors the progress of the learning content and activities provided by the provision unit. The feedback unit provides feedback based on the progress monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide a customized learning experience tailored to each student's individual learning needs and style. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. 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] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

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

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

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

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

[0028] (Example of form 1) The learning support system according to an embodiment of the present invention is a system that provides a customized learning experience to students and learners. This learning support system begins when an educational institution or individual instructor accesses the platform and inputs information about the student's learning needs and style. Next, the system automatically selects the optimal learning content and activities based on the input information. Furthermore, the system monitors the student's learning progress in real time and adjusts the learning content and activities as needed. This allows students to receive a learning experience that suits them, improving their learning effectiveness. In addition, educational institutions and individual instructors can more easily grasp the student's learning progress and enable more effective instruction. For example, an educational institution or individual instructor accesses the platform and inputs information such as the student's strong and weak subjects, learning pace, and preferred learning methods. This information is stored on the platform. Next, the platform automatically selects the optimal learning content and activities based on the input information. For example, for strong subjects, it provides more advanced learning content, and for weak subjects, it provides content that allows students to learn carefully from the basics. It also manages progress according to the learning pace and provides feedback at the appropriate time. Furthermore, the platform monitors the student's learning progress in real time and adjusts the learning content and activities as needed. For example, if a student is struggling with a particular task, additional support content can be provided to help them progress. In this way, the platform provides a customized learning experience tailored to each student's learning needs and style. This allows students to learn at their own pace, improving their learning effectiveness. Furthermore, educational institutions and individual instructors can more easily understand students' learning progress, enabling more effective instruction. In summary, learning support systems can provide a customized learning experience tailored to students' learning needs and styles, thereby improving learning effectiveness.

[0029] The learning support system according to this embodiment comprises an acquisition unit, an analysis unit, a selection unit, a provision unit, a monitoring unit, and a feedback unit. The acquisition unit acquires information about the student's learning needs and style. The student's learning needs and style include, but are not limited to, examples such as strong and weak subjects, learning pace, and preferred learning methods. For example, the acquisition unit acquires the student's strong and weak subjects based on test scores, self-assessments, teacher evaluations, etc. The acquisition unit can also evaluate the student's learning pace based on learning time, progress speed, and level of understanding, etc. Furthermore, the acquisition unit can acquire the student's preferred learning methods based on types such as visual learning, auditory learning, and practical learning. The analysis unit analyzes the learning needs and style based on the information acquired by the acquisition unit. For example, the analysis unit uses AI to analyze the student's learning needs and style. For example, the analysis unit uses AI to take information such as the student's strong and weak subjects, learning pace, and preferred learning methods as input and outputs the learning needs and style. The selection unit selects learning content and activities based on the results analyzed by the analysis unit. For example, the selection unit selects advanced learning content for subjects the student excels at and content that allows them to learn from the basics for subjects they struggle with. The selection unit uses AI to select learning content and activities. For example, the selection unit uses AI to take the results analyzed by the analysis unit as input and outputs learning content and activities. The provision unit provides the learning content and activities selected by the selection unit. For example, the provision unit provides learning content and activities through web applications or mobile applications. For example, the provision unit can also send learning content and activities via email. The monitoring unit monitors the progress of the learning content and activities provided by the provision unit. For example, the monitoring unit monitors the students' learning status in real time. For example, the monitoring unit uses AI to monitor the students' learning status. For example, the monitoring unit uses AI to take the students' learning status as input and outputs the progress status. The feedback unit provides feedback based on the progress status monitored by the monitoring unit.The feedback unit provides feedback at appropriate times, for example. The feedback unit provides feedback using AI, for example. The feedback unit uses AI to take progress as input and outputs feedback, for example. As a result, the learning support system according to the embodiment can provide a customized learning experience tailored to the student's learning needs and style, thereby improving learning effectiveness.

[0030] The data acquisition unit can acquire information about students' strengths and weaknesses in subjects, learning pace, and preferred learning methods. For example, the unit can acquire students' strengths and weaknesses based on test scores, self-assessments, and teacher evaluations. For instance, it can acquire students' test scores from a database to determine their strengths and weaknesses. It can also acquire students' self-assessments in the form of questionnaires to determine their strengths and weaknesses. Furthermore, it can determine strengths and weaknesses based on teacher evaluations. For example, a teacher can evaluate a student's learning progress and input the evaluation results into the data acquisition unit. The data acquisition unit can, for example, evaluate a student's learning pace based on study time, progress speed, and comprehension. For instance, it can record students' study time and evaluate their learning pace based on that data. It can also monitor students' progress speed and evaluate their learning pace based on that data. Furthermore, the data acquisition unit can evaluate students' comprehension based on test results and self-assessments. For example, it can analyze students' test results and evaluate their comprehension. The data acquisition unit can, for example, acquire students' learning method preferences based on types such as visual learning, auditory learning, and practical learning. For instance, the unit can acquire students' learning method preferences through questionnaires and determine their preferences based on that data. The unit can also analyze students' learning history to determine their learning method preferences. For example, the unit can record the learning methods students have used in the past and determine their learning method preferences based on that data. This allows the unit to provide more appropriate learning content based on students' detailed learning needs and styles.

[0031] The analysis unit can analyze learning needs and styles based on the information acquired by the acquisition unit. For example, the analysis unit can use AI to analyze students' learning needs and styles. For instance, the analysis unit's AI takes information such as students' strong and weak subjects, learning pace, and preferred learning methods as input and outputs learning needs and styles. For example, the analysis unit's AI can analyze students' strong and weak subjects and provide advanced learning content for their strong subjects and content that allows them to learn from the basics for their weak subjects. The analysis unit can also use AI to analyze students' learning pace, manage their progress, and provide feedback at appropriate times. Furthermore, the analysis unit can use AI to analyze students' preferred learning methods and provide visual content to visual learners, auditory content to auditory learners, and practical content to practical learners. This allows the analysis unit to accurately analyze students' learning needs and styles and select the most suitable learning content.

[0032] The selection unit can select advanced learning content for subjects in which the student excels, and content that allows students to learn from the basics for subjects in which they struggle, based on the results of the analysis performed by the analysis unit. For example, the selection unit can select advanced learning content for subjects in which the student excels. For example, the selection unit can provide more advanced problems and assignments for subjects in which the student excels. The selection unit can also provide content that includes applied and advanced material for subjects in which the student excels. Furthermore, the selection unit can provide content that deepens specialized knowledge and skills for subjects in which the student excels. For example, the selection unit can provide specialized books and academic papers for subjects in which the student excels. For example, the selection unit can select content that allows students to learn from the basics for subjects in which they struggle. For example, the selection unit can provide basic problems and assignments for subjects in which they struggle. Furthermore, the selection unit can provide content that carefully explains basic concepts and principles for subjects in which they struggle. Furthermore, the selection unit can provide content that includes repetitive learning and practice problems for subjects in which they struggle. For example, the selection unit can provide basic problem sets and practice problems for subjects in which they struggle. This allows the selection department to provide students with optimal learning content tailored to their strengths and weaknesses in different subjects.

[0033] The monitoring unit can monitor students' learning progress in real time. For example, the monitoring unit can monitor students' learning progress in real time. For example, the monitoring unit can record the time students spend viewing learning content and their progress. The monitoring unit can also evaluate how well students understand the learning content. For example, the monitoring unit can analyze students' answers and submissions to the learning content and evaluate their level of understanding. Furthermore, the monitoring unit can monitor how students react to the learning content. For example, the monitoring unit can record and analyze comments and feedback students make about the learning content. This allows the monitoring unit to grasp students' learning progress in real time and provide appropriate feedback.

[0034] The feedback department can provide appropriate feedback based on the progress monitored by the monitoring department. For example, the feedback department can provide feedback at the appropriate time. For instance, if a student is struggling with a particular task, the feedback department can provide additional support content. The feedback department can also provide feedback that fosters a sense of accomplishment when a student completes learning content. For example, the feedback department can provide words of praise and encouragement when a student completes learning content. Furthermore, the feedback department can offer specific advice and suggestions for improvement regarding the student's answers and submissions to the learning content. For example, the feedback department can provide specific suggestions for improvement and advice for future assignments regarding a student's submitted report. This allows the feedback department to provide appropriate feedback tailored to the student's learning progress.

[0035] The acquisition unit can analyze a student's past learning history and select the optimal acquisition method. For example, the acquisition unit selects the optimal acquisition method based on the learning methods the student has used in the past. For example, the acquisition unit retrieves a student's past learning history from a database and selects an effective acquisition method. For example, the acquisition unit selects an effective acquisition timing from a student's past learning history. For example, the acquisition unit analyzes a student's learning history and selects the most effective acquisition method. For example, the acquisition unit identifies a student's learning method preferences and effective learning methods based on their past learning history and selects the optimal acquisition method based on that information. In this way, the acquisition unit can acquire the optimal learning needs based on the student's past learning history.

[0036] The acquisition unit can filter learning needs based on the student's current learning environment and areas of interest. For example, the acquisition unit can acquire appropriate learning needs based on the student's current learning environment. For example, the acquisition unit can acquire learning needs considering the student's learning location, the devices used, and the surrounding noise level. For example, the acquisition unit can acquire relevant learning needs based on the student's areas of interest. For example, the acquisition unit can acquire the student's areas of interest in the form of a questionnaire and filter learning needs based on that information. For example, the acquisition unit can acquire optimal learning needs considering the student's learning environment and areas of interest. For example, the acquisition unit can acquire the student's learning environment and areas of interest from a database and filter learning needs based on that information. In this way, the acquisition unit can acquire optimal learning needs based on the student's current learning environment and areas of interest.

[0037] The acquisition unit can prioritize the acquisition of highly relevant information by considering the student's geographical location when acquiring learning needs. For example, the acquisition unit acquires relevant learning needs based on the student's current location. For example, the acquisition unit acquires the student's geographical location information using GPS data, IP address, or regional code, and filters learning needs based on that information. For example, the acquisition unit acquires optimal learning needs by considering the student's geographical location. For example, the acquisition unit acquires region-specific learning needs based on the student's location information. For example, the acquisition unit acquires region-specific learning needs based on the student's location information. As a result, the acquisition unit can acquire highly relevant learning needs based on the student's geographical location information.

[0038] The acquisition unit can analyze students' social media activity and obtain relevant information when acquiring learning needs. For example, the acquisition unit analyzes students' social media activity and acquires learning needs based on their interests. For example, the acquisition unit acquires relevant learning needs based on students' social media posts. For example, the acquisition unit analyzes students' social media activity history and acquires optimal learning needs. For example, the acquisition unit acquires students' social media activity from a database and filters learning needs based on that information. For example, the acquisition unit acquires relevant learning needs based on students' social media activity. For example, the acquisition unit acquires relevant learning needs based on students' social media posts. In this way, the acquisition unit can acquire relevant learning needs based on students' social media activity.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the learning needs during the analysis. For example, the analysis unit performs a detailed analysis for high-importance learning needs. For example, the analysis unit evaluates the importance of the learning needs and adjusts the level of detail of the analysis based on the evaluation results. For example, the analysis unit performs a concise analysis for low-importance learning needs. For example, the analysis unit evaluates the importance of the learning needs and adjusts the level of detail of the analysis based on the evaluation results. For example, the analysis unit adjusts the level of detail of the analysis according to the importance of the learning needs. For example, the analysis unit evaluates the importance of the learning needs and adjusts the level of detail of the analysis based on the evaluation results. In this way, the analysis unit can provide a detailed analysis according to the importance of the learning needs.

[0040] The analysis unit can apply different analysis algorithms depending on the learning style during analysis. For example, the analysis unit performs analysis using graphs and diagrams for visual learners. For example, the analysis unit performs analysis using visual information for visual learners. For example, the analysis unit performs audio analysis for auditory learners. For example, the analysis unit performs analysis using audio information for auditory learners. For example, the analysis unit performs practical analysis for experiential learners. For example, the analysis unit performs analysis using practical information for experiential learners. This allows the analysis unit to apply the optimal analysis algorithm according to the student's learning style.

[0041] The analysis unit can determine the priority of analysis based on the submission timing of learning needs during the analysis process. For example, the analysis unit will prioritize analysis of learning needs that have been submitted earlier. For example, the analysis unit will determine the priority based on the submission date and time of the learning needs. For example, the analysis unit will postpone analysis of learning needs that have been submitted later. For example, the analysis unit will determine the priority based on the frequency of the learning needs submission. For example, the analysis unit will determine the priority of analysis according to the submission timing of the learning needs. For example, the analysis unit will determine the priority based on the submission date and time of the learning needs. This allows the analysis unit to perform analysis with a priority order according to the submission timing of the learning needs.

[0042] The analysis unit can adjust the order of analysis based on the relevance of learning needs during the analysis process. For example, the analysis unit prioritizes analysis of highly relevant learning needs. For example, the analysis unit adjusts the order based on related topics or common goals of the learning needs. For example, the analysis unit postpones analysis of less relevant learning needs. For example, the analysis unit adjusts the order based on whether or not the learning needs have prior knowledge. For example, the analysis unit adjusts the order of analysis according to the relevance of the learning needs. For example, the analysis unit adjusts the order based on related topics or common goals of the learning needs. This allows the analysis unit to perform analyses in an order that corresponds to the relevance of the learning needs.

[0043] The selection unit can improve the accuracy of its selection process by considering the interrelationships of learning content. For example, the selection unit analyzes the interrelationships of learning content and selects highly relevant content. For example, the selection unit improves the accuracy of its selection based on the relevant topics and the presence or absence of prior knowledge of the learning content. For example, the selection unit selects the optimal content by considering the interrelationships of learning content. For example, the selection unit improves the accuracy of its selection based on the relevant topics and the presence or absence of prior knowledge of the learning content. For example, the selection unit improves the accuracy of its selection based on the interrelationships of learning content. For example, the selection unit improves the accuracy of its selection based on the relevant topics and the presence or absence of prior knowledge of the learning content. As a result, the selection unit can select the optimal content based on the interrelationships of learning content.

[0044] The selection unit can make selections by considering the attribute information of the learning content submitter. For example, the selection unit can select the most suitable content by considering the expertise of the learning content submitter. For example, the selection unit can make selections based on the expertise and experience of the learning content submitter. For example, the selection unit can select highly reliable content by considering the experience of the learning content submitter. For example, the selection unit can make selections based on the expertise and experience of the learning content submitter. For example, the selection unit can improve the accuracy of selections by using the attribute information of the learning content submitter. For example, the selection unit can make selections based on the expertise and experience of the learning content submitter. As a result, the selection unit can select highly reliable content based on the attribute information of the learning content submitter.

[0045] The selection unit can make selections considering the geographical distribution of learning content. For example, the selection unit can analyze the geographical distribution of learning content and select highly relevant content. For example, the selection unit can make selections based on the regional distribution of learning content. For example, the selection unit can select the optimal content considering the geographical distribution of learning content. For example, the selection unit can make selections based on the regional distribution of learning content. For example, the selection unit can improve the accuracy of selections based on the geographical distribution of learning content. For example, the selection unit can make selections based on the regional distribution of learning content. As a result, the selection unit can select the optimal content based on the geographical distribution of learning content.

[0046] The selection unit can improve the accuracy of its selection by referring to relevant literature for the learning content during the selection process. For example, the selection unit can select highly reliable content by referring to relevant literature for the learning content. For example, the selection unit can make selections based on relevant academic papers, books, and web articles for the learning content. For example, the selection unit can select the optimal content based on relevant literature for the learning content. For example, the selection unit can make selections based on relevant academic papers, books, and web articles for the learning content. For example, the selection unit can improve the accuracy of its selection by considering relevant literature for the learning content. For example, the selection unit can make selections based on relevant academic papers, books, and web articles for the learning content. As a result, the selection unit can select highly reliable content based on relevant literature for the learning content.

[0047] The service provider can adjust the level of detail provided based on the importance of the learning content at the time of delivery. For example, the service provider can provide detailed explanations for highly important learning content. For example, the service provider can evaluate the importance of the learning content and adjust the level of detail based on the evaluation results. For example, the service provider can provide concise explanations for less important learning content. For example, the service provider can evaluate the importance of the learning content and adjust the level of detail based on the evaluation results. For example, the service provider can adjust the level of detail provided according to the importance of the learning content. For example, the service provider can evaluate the importance of the learning content and adjust the level of detail based on the evaluation results. This allows the service provider to provide detailed information according to the importance of the learning content.

[0048] The service provider can apply different delivery algorithms depending on the category of the learning content at the time of delivery. For example, the service provider can provide experimental videos for science-related learning content. For example, the service provider can select a delivery algorithm based on the category of the learning content. For example, the service provider can provide text-based materials for humanities-related learning content. For example, the service provider can select a delivery algorithm based on the category of the learning content. For example, the service provider can provide visual works for arts-related learning content. For example, the service provider can select a delivery algorithm based on the category of the learning content. This allows the service provider to apply the optimal delivery algorithm according to the category of the learning content.

[0049] The distribution department can determine the priority of distribution based on the submission timing of the learning content. For example, the distribution department will prioritize distribution of learning content submitted earlier. For example, the distribution department will determine the priority based on the submission date and time of the learning content. For example, the distribution department will postpone distribution of learning content submitted later. For example, the distribution department will determine the priority based on the frequency of submission of the learning content. For example, the distribution department will determine the priority of distribution according to the submission timing of the learning content. For example, the distribution department will determine the priority based on the submission date and time of the learning content. This allows the distribution department to provide learning content with a priority order according to the submission timing.

[0050] The provider can adjust the order of delivery based on the relevance of the learning content at the time of delivery. For example, the provider may prioritize the delivery of highly relevant learning content. For example, the provider may adjust the order based on the relevant topics and common goals of the learning content. For example, the provider may postpone the delivery of less relevant learning content. For example, the provider may adjust the order based on whether or not the learning content has prior knowledge. For example, the provider may adjust the order of delivery according to the relevance of the learning content. For example, the provider may adjust the order based on the relevant topics and common goals of the learning content. This allows the provider to deliver learning content in an order that is appropriate to its relevance.

[0051] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships of learning situations during monitoring. For example, the monitoring unit analyzes the interrelationships of learning situations and performs highly relevant monitoring. For example, the monitoring unit improves the accuracy of monitoring based on the relevant topics and the presence or absence of prior knowledge of the learning situations. For example, the monitoring unit performs optimal monitoring by considering the interrelationships of learning situations. For example, the monitoring unit improves the accuracy of monitoring based on the relevant topics and the presence or absence of prior knowledge of the learning situations. For example, the monitoring unit improves the accuracy of monitoring based on the interrelationships of learning situations. For example, the monitoring unit improves the accuracy of monitoring based on the relevant topics and the presence or absence of prior knowledge of the learning situations. As a result, the monitoring unit can perform optimal monitoring based on the interrelationships of learning situations.

[0052] The monitoring unit can perform monitoring while considering the attribute information of the person submitting the learning status. For example, the monitoring unit can perform optimal monitoring by considering the field of expertise of the person submitting the learning status. For example, the monitoring unit can perform monitoring based on the field of expertise and experience of the person submitting the learning status. For example, the monitoring unit can perform highly reliable monitoring by considering the experience of the person submitting the learning status. For example, the monitoring unit can perform monitoring based on the field of expertise and experience of the person submitting the learning status. For example, the monitoring unit can improve the accuracy of monitoring based on the attribute information of the person submitting the learning status. For example, the monitoring unit can perform monitoring based on the field of expertise and experience of the person submitting the learning status. As a result, the monitoring unit can perform highly reliable monitoring based on the attribute information of the person submitting the learning status.

[0053] The monitoring unit can perform monitoring while considering the geographical distribution of learning status. For example, the monitoring unit analyzes the geographical distribution of learning status and performs highly relevant monitoring. For example, the monitoring unit performs monitoring based on the regional distribution of learning status. For example, the monitoring unit performs optimal monitoring by considering the geographical distribution of learning status. For example, the monitoring unit performs monitoring based on the regional distribution of learning status. For example, the monitoring unit improves the accuracy of monitoring based on the geographical distribution of learning status. For example, the monitoring unit performs monitoring based on the regional distribution of learning status. As a result, the monitoring unit can perform optimal monitoring based on the geographical distribution of learning status.

[0054] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature on the learning situation during monitoring. For example, the monitoring unit performs highly reliable monitoring by referring to relevant literature on the learning situation. For example, the monitoring unit performs monitoring based on relevant academic papers, books, and web articles on the learning situation. For example, the monitoring unit performs optimal monitoring based on relevant literature on the learning situation. For example, the monitoring unit performs monitoring based on relevant academic papers, books, and web articles on the learning situation. For example, the monitoring unit improves the accuracy of monitoring by considering relevant literature on the learning situation. For example, the monitoring unit performs monitoring based on relevant academic papers, books, and web articles on the learning situation. As a result, the monitoring unit can perform highly reliable monitoring based on relevant literature on the learning situation.

[0055] The feedback unit can adjust the level of detail in the feedback based on the importance of the learning situation. For example, the feedback unit provides detailed feedback for high-importance learning situations. For example, the feedback unit evaluates the importance of the learning situation and adjusts the level of detail based on the evaluation result. For example, the feedback unit provides concise feedback for low-importance learning situations. For example, the feedback unit evaluates the importance of the learning situation and adjusts the level of detail based on the evaluation result. For example, the feedback unit adjusts the level of detail in the feedback according to the importance of the learning situation. For example, the feedback unit evaluates the importance of the learning situation and adjusts the level of detail based on the evaluation result. In this way, the feedback unit can provide detailed feedback according to the importance of the learning situation.

[0056] The feedback unit can apply different feedback algorithms depending on the category of the learning situation during feedback. For example, the feedback unit provides feedback based on experimental results for science-related learning situations. For example, the feedback unit selects a feedback algorithm based on the category of the learning situation. For example, the feedback unit provides text-based feedback for humanities-related learning situations. For example, the feedback unit selects a feedback algorithm based on the category of the learning situation. For example, the feedback unit provides feedback based on visual works for arts-related learning situations. For example, the feedback unit selects a feedback algorithm based on the category of the learning situation. This allows the feedback unit to apply the most appropriate feedback algorithm according to the category of the learning situation.

[0057] The feedback unit can prioritize feedback based on when the learning progress is submitted. For example, the feedback unit will prioritize feedback for learning progress submitted earlier. For example, the feedback unit will prioritize based on the submission date and time of the learning progress. For example, the feedback unit will postpone feedback for learning progress submitted later. For example, the feedback unit will prioritize based on the frequency of the learning progress submission. For example, the feedback unit will prioritize feedback according to when the learning progress is submitted. For example, the feedback unit will prioritize based on the submission date and time of the learning progress. This allows the feedback unit to provide feedback with priority according to when the learning progress is submitted.

[0058] The feedback unit can adjust the order of feedback based on the relevance of the learning situations. For example, the feedback unit prioritizes providing feedback to highly relevant learning situations. For example, the feedback unit adjusts the order based on the relevant topics or common goals of the learning situations. For example, the feedback unit postpones providing feedback to less relevant learning situations. For example, the feedback unit adjusts the order based on whether or not the learning situation has prior knowledge. For example, the feedback unit adjusts the order of feedback according to the relevance of the learning situations. For example, the feedback unit adjusts the order based on the relevant topics or common goals of the learning situations. In this way, the feedback unit can provide feedback in an order that corresponds to the relevance of the learning situations.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The data acquisition unit can consider not only learning history but also past learning outcomes and evaluations when acquiring information about students' learning needs and styles. For example, the unit can retrieve students' past test scores and teacher evaluations from a database, and use this to more accurately understand their learning needs. The unit can also analyze the results of learning activities and projects that students have participated in in the past to identify their learning style and areas of strength. Furthermore, the unit can acquire the usage history of learning tools and resources that students have used in the past, and use this to provide the most suitable learning content. As a result, the unit can acquire more appropriate learning needs by taking into account students' past learning outcomes and evaluations.

[0061] The analysis unit can also consider learners' goals and motivation when analyzing learning needs and styles based on information acquired by the acquisition unit. For example, the analysis unit can use students' set learning goals and future career goals as input data and customize learning content based on this. The analysis unit can also acquire students' learning motivation and enthusiasm through questionnaires and self-assessments and reflect this in the analysis. Furthermore, the analysis unit can consider goals and successes that students have achieved in the past and select learning content based on this. As a result, the analysis unit can provide more effective learning content while taking into account students' goals and motivation.

[0062] The selection unit can also consider learners' interests and preferences when selecting learning content and activities based on the results analyzed by the analysis unit. For example, the selection unit can obtain topics and themes that students are interested in through questionnaires and self-assessments, and customize learning content based on this. The selection unit can also consider club activities and hobbies that students have participated in in the past and provide learning content related to these. Furthermore, the selection unit can select learning content that reflects the latest research and trends in areas that students are interested in. In this way, the selection unit can provide more engaging learning content while taking students' interests and preferences into account.

[0063] The monitoring unit can take into account the learner's health and fatigue level when monitoring their learning progress in real time. For example, the monitoring unit collects health data such as the student's heart rate, sleep duration, and exercise level using sensors, and evaluates their learning progress based on this data. Furthermore, if the student is tired or stressed, the monitoring unit can adjust the learning pace and encourage breaks. In addition, the monitoring unit can adjust the difficulty and amount of learning content according to the student's health condition. This allows the monitoring unit to provide more appropriate learning support while taking into account the student's health and fatigue level.

[0064] The feedback unit can select a feedback method that suits the learner's learning style and preferences when providing feedback based on the progress monitored by the monitoring unit. For example, the feedback unit can provide feedback using graphs and diagrams for visual learners and audio messages for auditory learners. It can also provide specific action plans and practical advice for practical learners. Furthermore, the feedback unit can consider the student's learning history and past feedback to provide individually customized feedback. This allows the feedback unit to provide optimal feedback tailored to each student's learning style and preferences.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The acquisition department acquires information about students' learning needs and styles. Specifically, it acquires information such as their strengths and weaknesses in subjects, learning pace, and preferred learning methods based on test scores, self-assessments, teacher evaluations, study time and progress speed, comprehension level, visual learning, auditory learning, and practical learning. Step 2: The analysis unit analyzes learning needs and styles based on the information acquired by the acquisition unit. For example, using AI, it takes information such as preferred subjects, subjects the student excels at, learning pace, and preferred learning methods as input and outputs learning needs and styles. Step 3: The selection unit selects learning content and activities based on the results analyzed by the analysis unit. For example, it selects advanced learning content for subjects the student excels at and content that allows them to learn from the basics for subjects they struggle with. Using AI, the analysis results are taken as input and the learning content and activities are output. Step 4: The delivery department provides the learning content and activities selected by the selection department. For example, this can be done through a web application, a mobile application, or by sending it via email. Step 5: The monitoring unit monitors the progress of learning content and activities provided by the delivery unit. For example, it monitors students' learning progress in real time and outputs progress information using AI. Step 6: The feedback unit provides feedback based on the progress monitored by the monitoring unit. For example, it provides feedback using AI at the appropriate time and outputs feedback with the progress status as input.

[0067] (Example of form 2) The learning support system according to an embodiment of the present invention is a system that provides a customized learning experience to students and learners. This learning support system begins when an educational institution or individual instructor accesses the platform and inputs information about the student's learning needs and style. Next, the system automatically selects the optimal learning content and activities based on the input information. Furthermore, the system monitors the student's learning progress in real time and adjusts the learning content and activities as needed. This allows students to receive a learning experience that suits them, improving their learning effectiveness. In addition, educational institutions and individual instructors can more easily grasp the student's learning progress and enable more effective instruction. For example, an educational institution or individual instructor accesses the platform and inputs information such as the student's strong and weak subjects, learning pace, and preferred learning methods. This information is stored on the platform. Next, the platform automatically selects the optimal learning content and activities based on the input information. For example, for strong subjects, it provides more advanced learning content, and for weak subjects, it provides content that allows students to learn carefully from the basics. It also manages progress according to the learning pace and provides feedback at the appropriate time. Furthermore, the platform monitors the student's learning progress in real time and adjusts the learning content and activities as needed. For example, if a student is struggling with a particular task, additional support content can be provided to help them progress. In this way, the platform provides a customized learning experience tailored to each student's learning needs and style. This allows students to learn at their own pace, improving their learning effectiveness. Furthermore, educational institutions and individual instructors can more easily understand students' learning progress, enabling more effective instruction. In summary, learning support systems can provide a customized learning experience tailored to students' learning needs and styles, thereby improving learning effectiveness.

[0068] The learning support system according to this embodiment comprises an acquisition unit, an analysis unit, a selection unit, a provision unit, a monitoring unit, and a feedback unit. The acquisition unit acquires information about the student's learning needs and style. The student's learning needs and style include, but are not limited to, examples such as strong and weak subjects, learning pace, and preferred learning methods. For example, the acquisition unit acquires the student's strong and weak subjects based on test scores, self-assessments, teacher evaluations, etc. The acquisition unit can also evaluate the student's learning pace based on learning time, progress speed, and level of understanding, etc. Furthermore, the acquisition unit can acquire the student's preferred learning methods based on types such as visual learning, auditory learning, and practical learning. The analysis unit analyzes the learning needs and style based on the information acquired by the acquisition unit. For example, the analysis unit uses AI to analyze the student's learning needs and style. For example, the analysis unit uses AI to take information such as the student's strong and weak subjects, learning pace, and preferred learning methods as input and outputs the learning needs and style. The selection unit selects learning content and activities based on the results analyzed by the analysis unit. For example, the selection unit selects advanced learning content for subjects the student excels at and content that allows them to learn from the basics for subjects they struggle with. The selection unit uses AI to select learning content and activities. For example, the selection unit uses AI to take the results analyzed by the analysis unit as input and outputs learning content and activities. The provision unit provides the learning content and activities selected by the selection unit. For example, the provision unit provides learning content and activities through web applications or mobile applications. For example, the provision unit can also send learning content and activities via email. The monitoring unit monitors the progress of the learning content and activities provided by the provision unit. For example, the monitoring unit monitors the students' learning status in real time. For example, the monitoring unit uses AI to monitor the students' learning status. For example, the monitoring unit uses AI to take the students' learning status as input and outputs the progress status. The feedback unit provides feedback based on the progress status monitored by the monitoring unit.The feedback unit provides feedback at appropriate times, for example. The feedback unit provides feedback using AI, for example. The feedback unit uses AI to take progress as input and outputs feedback, for example. As a result, the learning support system according to the embodiment can provide a customized learning experience tailored to the student's learning needs and style, thereby improving learning effectiveness.

[0069] The data acquisition unit can acquire information about students' strengths and weaknesses in subjects, learning pace, and preferred learning methods. For example, the unit can acquire students' strengths and weaknesses based on test scores, self-assessments, and teacher evaluations. For instance, it can acquire students' test scores from a database to determine their strengths and weaknesses. It can also acquire students' self-assessments in the form of questionnaires to determine their strengths and weaknesses. Furthermore, it can determine strengths and weaknesses based on teacher evaluations. For example, a teacher can evaluate a student's learning progress and input the evaluation results into the data acquisition unit. The data acquisition unit can, for example, evaluate a student's learning pace based on study time, progress speed, and comprehension. For instance, it can record students' study time and evaluate their learning pace based on that data. It can also monitor students' progress speed and evaluate their learning pace based on that data. Furthermore, the data acquisition unit can evaluate students' comprehension based on test results and self-assessments. For example, it can analyze students' test results and evaluate their comprehension. The data acquisition unit can, for example, acquire students' learning method preferences based on types such as visual learning, auditory learning, and practical learning. For instance, the unit can acquire students' learning method preferences through questionnaires and determine their preferences based on that data. The unit can also analyze students' learning history to determine their learning method preferences. For example, the unit can record the learning methods students have used in the past and determine their learning method preferences based on that data. This allows the unit to provide more appropriate learning content based on students' detailed learning needs and styles.

[0070] The analysis unit can analyze learning needs and styles based on the information acquired by the acquisition unit. For example, the analysis unit can use AI to analyze students' learning needs and styles. For instance, the analysis unit's AI takes information such as students' strong and weak subjects, learning pace, and preferred learning methods as input and outputs learning needs and styles. For example, the analysis unit's AI can analyze students' strong and weak subjects and provide advanced learning content for their strong subjects and content that allows them to learn from the basics for their weak subjects. The analysis unit can also use AI to analyze students' learning pace, manage their progress, and provide feedback at appropriate times. Furthermore, the analysis unit can use AI to analyze students' preferred learning methods and provide visual content to visual learners, auditory content to auditory learners, and practical content to practical learners. This allows the analysis unit to accurately analyze students' learning needs and styles and select the most suitable learning content.

[0071] The selection unit can select advanced learning content for subjects in which the student excels, and content that allows students to learn from the basics for subjects in which they struggle, based on the results of the analysis performed by the analysis unit. For example, the selection unit can select advanced learning content for subjects in which the student excels. For example, the selection unit can provide more advanced problems and assignments for subjects in which the student excels. The selection unit can also provide content that includes applied and advanced material for subjects in which the student excels. Furthermore, the selection unit can provide content that deepens specialized knowledge and skills for subjects in which the student excels. For example, the selection unit can provide specialized books and academic papers for subjects in which the student excels. For example, the selection unit can select content that allows students to learn from the basics for subjects in which they struggle. For example, the selection unit can provide basic problems and assignments for subjects in which they struggle. Furthermore, the selection unit can provide content that carefully explains basic concepts and principles for subjects in which they struggle. Furthermore, the selection unit can provide content that includes repetitive learning and practice problems for subjects in which they struggle. For example, the selection unit can provide basic problem sets and practice problems for subjects in which they struggle. This allows the selection department to provide students with optimal learning content tailored to their strengths and weaknesses in different subjects.

[0072] The monitoring unit can monitor students' learning progress in real time. For example, the monitoring unit can monitor students' learning progress in real time. For example, the monitoring unit can record the time students spend viewing learning content and their progress. The monitoring unit can also evaluate how well students understand the learning content. For example, the monitoring unit can analyze students' answers and submissions to the learning content and evaluate their level of understanding. Furthermore, the monitoring unit can monitor how students react to the learning content. For example, the monitoring unit can record and analyze comments and feedback students make about the learning content. This allows the monitoring unit to grasp students' learning progress in real time and provide appropriate feedback.

[0073] The feedback department can provide appropriate feedback based on the progress monitored by the monitoring department. For example, the feedback department can provide feedback at the appropriate time. For instance, if a student is struggling with a particular task, the feedback department can provide additional support content. The feedback department can also provide feedback that fosters a sense of accomplishment when a student completes learning content. For example, the feedback department can provide words of praise and encouragement when a student completes learning content. Furthermore, the feedback department can offer specific advice and suggestions for improvement regarding the student's answers and submissions to the learning content. For example, the feedback department can provide specific suggestions for improvement and advice for future assignments regarding a student's submitted report. This allows the feedback department to provide appropriate feedback tailored to the student's learning progress.

[0074] The acquisition unit can estimate a student's emotions and adjust the timing of acquiring learning needs based on the estimated emotions. For example, if a student is feeling stressed, the acquisition unit will acquire learning needs during a time when the student can relax. For example, the acquisition unit can collect the student's biometric data (heart rate and skin electrical activity) using sensors and estimate emotions using an emotion estimation algorithm. For example, if a student is concentrating, the acquisition unit will acquire learning needs at that time. For example, the acquisition unit can capture the student's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, if a student is tired, the acquisition unit will acquire learning needs after a break. For example, the acquisition unit can record the student's voice and estimate emotions using voice analysis technology. This allows the acquisition unit to acquire learning needs at the optimal timing according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The acquisition unit can analyze a student's past learning history and select the optimal acquisition method. For example, the acquisition unit selects the optimal acquisition method based on the learning methods the student has used in the past. For example, the acquisition unit retrieves a student's past learning history from a database and selects an effective acquisition method. For example, the acquisition unit selects an effective acquisition timing from a student's past learning history. For example, the acquisition unit analyzes a student's learning history and selects the most effective acquisition method. For example, the acquisition unit identifies a student's learning method preferences and effective learning methods based on their past learning history and selects the optimal acquisition method based on that information. In this way, the acquisition unit can acquire the optimal learning needs based on the student's past learning history.

[0076] The acquisition unit can filter learning needs based on the student's current learning environment and areas of interest. For example, the acquisition unit can acquire appropriate learning needs based on the student's current learning environment. For example, the acquisition unit can acquire learning needs considering the student's learning location, the devices used, and the surrounding noise level. For example, the acquisition unit can acquire relevant learning needs based on the student's areas of interest. For example, the acquisition unit can acquire the student's areas of interest in the form of a questionnaire and filter learning needs based on that information. For example, the acquisition unit can acquire optimal learning needs considering the student's learning environment and areas of interest. For example, the acquisition unit can acquire the student's learning environment and areas of interest from a database and filter learning needs based on that information. In this way, the acquisition unit can acquire optimal learning needs based on the student's current learning environment and areas of interest.

[0077] The acquisition unit can estimate a student's emotions and determine the priority of learning needs to acquire based on the estimated emotions. For example, if a student is excited, the acquisition unit prioritizes more difficult learning needs. For example, the acquisition unit collects the student's biometric data (heart rate and skin electrical activity) using sensors and estimates emotions using an emotion estimation algorithm. For example, if a student is relaxed, the acquisition unit prioritizes basic learning needs. For example, the acquisition unit captures the student's facial expressions with a camera and estimates emotions using an emotion estimation algorithm. For example, if a student is tired, the acquisition unit prioritizes easy learning needs. For example, the acquisition unit records the student's voice and estimates emotions using voice analysis technology. This allows the acquisition unit to acquire learning needs with priorities according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The acquisition unit can prioritize the acquisition of highly relevant information by considering the student's geographical location when acquiring learning needs. For example, the acquisition unit acquires relevant learning needs based on the student's current location. For example, the acquisition unit acquires the student's geographical location information using GPS data, IP address, or regional code, and filters learning needs based on that information. For example, the acquisition unit acquires optimal learning needs by considering the student's geographical location. For example, the acquisition unit acquires region-specific learning needs based on the student's location information. For example, the acquisition unit acquires region-specific learning needs based on the student's location information. As a result, the acquisition unit can acquire highly relevant learning needs based on the student's geographical location information.

[0079] The acquisition unit can analyze students' social media activity and obtain relevant information when acquiring learning needs. For example, the acquisition unit analyzes students' social media activity and acquires learning needs based on their interests. For example, the acquisition unit acquires relevant learning needs based on students' social media posts. For example, the acquisition unit analyzes students' social media activity history and acquires optimal learning needs. For example, the acquisition unit acquires students' social media activity from a database and filters learning needs based on that information. For example, the acquisition unit acquires relevant learning needs based on students' social media activity. For example, the acquisition unit acquires relevant learning needs based on students' social media posts. In this way, the acquisition unit can acquire relevant learning needs based on students' social media activity.

[0080] The analysis unit can estimate a student's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if a student is relaxed, the analysis unit provides detailed analysis results. For example, the analysis unit collects the student's biometric data (heart rate and skin electrical activity) using sensors and estimates emotions using an emotion estimation algorithm. For example, if a student is tense, the analysis unit provides concise analysis results. For example, the analysis unit captures the student's facial expressions with a camera and estimates emotions using an emotion estimation algorithm. For example, if a student is excited, the analysis unit provides visually appealing analysis results. For example, the analysis unit records the student's voice and estimates emotions using voice analysis technology. This allows the analysis unit to provide analysis results in the most appropriate presentation for the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the learning needs during the analysis. For example, the analysis unit performs a detailed analysis for high-importance learning needs. For example, the analysis unit evaluates the importance of the learning needs and adjusts the level of detail of the analysis based on the evaluation results. For example, the analysis unit performs a concise analysis for low-importance learning needs. For example, the analysis unit evaluates the importance of the learning needs and adjusts the level of detail of the analysis based on the evaluation results. For example, the analysis unit adjusts the level of detail of the analysis according to the importance of the learning needs. For example, the analysis unit evaluates the importance of the learning needs and adjusts the level of detail of the analysis based on the evaluation results. In this way, the analysis unit can provide a detailed analysis according to the importance of the learning needs.

[0082] The analysis unit can apply different analysis algorithms depending on the learning style during analysis. For example, the analysis unit performs analysis using graphs and diagrams for visual learners. For example, the analysis unit performs analysis using visual information for visual learners. For example, the analysis unit performs audio analysis for auditory learners. For example, the analysis unit performs analysis using audio information for auditory learners. For example, the analysis unit performs practical analysis for experiential learners. For example, the analysis unit performs analysis using practical information for experiential learners. This allows the analysis unit to apply the optimal analysis algorithm according to the student's learning style.

[0083] The analysis unit can estimate a student's emotions and adjust the length of the analysis based on the estimated emotions. For example, if a student is in a hurry, the analysis unit provides a short analysis result. For example, the analysis unit collects the student's biometric data (heart rate and skin electrical activity) with sensors and estimates emotions using an emotion estimation algorithm. For example, if a student is relaxed, the analysis unit provides a detailed analysis result. For example, the analysis unit captures the student's facial expressions with a camera and estimates emotions using an emotion estimation algorithm. For example, if a student is excited, the analysis unit provides a visually appealing analysis result. For example, the analysis unit records the student's voice and estimates emotions using voice analysis technology. This allows the analysis unit to provide analysis results of an optimal length according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The analysis unit can determine the priority of analysis based on the submission timing of learning needs during the analysis process. For example, the analysis unit will prioritize analysis of learning needs that have been submitted earlier. For example, the analysis unit will determine the priority based on the submission date and time of the learning needs. For example, the analysis unit will postpone analysis of learning needs that have been submitted later. For example, the analysis unit will determine the priority based on the frequency of the learning needs submission. For example, the analysis unit will determine the priority of analysis according to the submission timing of the learning needs. For example, the analysis unit will determine the priority based on the submission date and time of the learning needs. This allows the analysis unit to perform analysis with a priority order according to the submission timing of the learning needs.

[0085] The analysis unit can adjust the order of analysis based on the relevance of learning needs during the analysis process. For example, the analysis unit prioritizes analysis of highly relevant learning needs. For example, the analysis unit adjusts the order based on related topics or common goals of the learning needs. For example, the analysis unit postpones analysis of less relevant learning needs. For example, the analysis unit adjusts the order based on whether or not the learning needs have prior knowledge. For example, the analysis unit adjusts the order of analysis according to the relevance of the learning needs. For example, the analysis unit adjusts the order based on related topics or common goals of the learning needs. This allows the analysis unit to perform analyses in an order that corresponds to the relevance of the learning needs.

[0086] The selection unit can estimate a student's emotions and adjust the selection criteria based on the estimated emotions. For example, if a student is relaxed, the selection unit will select challenging content. For example, the selection unit will collect the student's biometric data (heart rate and skin electrical activity) using sensors and estimate emotions using an emotion estimation algorithm. For example, if a student is nervous, the selection unit will select basic content. For example, the selection unit will capture the student's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, if a student is excited, the selection unit will select visually appealing content. For example, the selection unit will record the student's voice and estimate emotions using voice analysis technology. This allows the selection unit to select learning content based on criteria that match the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The selection unit can improve the accuracy of its selection process by considering the interrelationships of learning content. For example, the selection unit analyzes the interrelationships of learning content and selects highly relevant content. For example, the selection unit improves the accuracy of its selection based on the relevant topics and the presence or absence of prior knowledge of the learning content. For example, the selection unit selects the optimal content by considering the interrelationships of learning content. For example, the selection unit improves the accuracy of its selection based on the relevant topics and the presence or absence of prior knowledge of the learning content. For example, the selection unit improves the accuracy of its selection based on the interrelationships of learning content. For example, the selection unit improves the accuracy of its selection based on the relevant topics and the presence or absence of prior knowledge of the learning content. As a result, the selection unit can select the optimal content based on the interrelationships of learning content.

[0088] The selection unit can make selections by considering the attribute information of the learning content submitter. For example, the selection unit can select the most suitable content by considering the expertise of the learning content submitter. For example, the selection unit can make selections based on the expertise and experience of the learning content submitter. For example, the selection unit can select highly reliable content by considering the experience of the learning content submitter. For example, the selection unit can make selections based on the expertise and experience of the learning content submitter. For example, the selection unit can improve the accuracy of selections by using the attribute information of the learning content submitter. For example, the selection unit can make selections based on the expertise and experience of the learning content submitter. As a result, the selection unit can select highly reliable content based on the attribute information of the learning content submitter.

[0089] The selection unit can estimate a student's emotions and adjust the order in which it displays the selection results based on the estimated emotions. For example, if a student is relaxed, the selection unit may display the results in an order that includes detailed information. For example, the selection unit may collect the student's biometric data (heart rate and skin electrical activity) using sensors and estimate their emotions using an emotion estimation algorithm. For example, if a student is tense, the selection unit may display the results in an order that includes concise information. For example, the selection unit may capture the student's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, if a student is excited, the selection unit may display the results in a visually appealing order. For example, the selection unit may record the student's voice and estimate their emotions using voice analysis technology. This allows the selection unit to display the selection results in an order that corresponds to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The selection unit can make selections considering the geographical distribution of learning content. For example, the selection unit can analyze the geographical distribution of learning content and select highly relevant content. For example, the selection unit can make selections based on the regional distribution of learning content. For example, the selection unit can select the optimal content considering the geographical distribution of learning content. For example, the selection unit can make selections based on the regional distribution of learning content. For example, the selection unit can improve the accuracy of selections based on the geographical distribution of learning content. For example, the selection unit can make selections based on the regional distribution of learning content. As a result, the selection unit can select the optimal content based on the geographical distribution of learning content.

[0091] The selection unit can improve the accuracy of its selection by referring to relevant literature for the learning content during the selection process. For example, the selection unit can select highly reliable content by referring to relevant literature for the learning content. For example, the selection unit can make selections based on relevant academic papers, books, and web articles for the learning content. For example, the selection unit can select the optimal content based on relevant literature for the learning content. For example, the selection unit can make selections based on relevant academic papers, books, and web articles for the learning content. For example, the selection unit can improve the accuracy of its selection by considering relevant literature for the learning content. For example, the selection unit can make selections based on relevant academic papers, books, and web articles for the learning content. As a result, the selection unit can select highly reliable content based on relevant literature for the learning content.

[0092] The service provider can estimate a student's emotions and adjust the presentation of the learning content based on the estimated emotions. For example, if a student is relaxed, the service provider can provide content with detailed explanations. For example, the service provider can collect the student's biometric data (heart rate and skin electrical activity) using sensors and estimate emotions using an emotion estimation algorithm. For example, if a student is nervous, the service provider can provide content with concise explanations. For example, the service provider can capture the student's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, if a student is excited, the service provider can provide visually appealing content. For example, the service provider can record the student's voice and estimate emotions using voice analysis technology. This allows the service provider to deliver learning content in the most appropriate presentation for the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The service provider can adjust the level of detail provided based on the importance of the learning content at the time of delivery. For example, the service provider can provide detailed explanations for highly important learning content. For example, the service provider can evaluate the importance of the learning content and adjust the level of detail based on the evaluation results. For example, the service provider can provide concise explanations for less important learning content. For example, the service provider can evaluate the importance of the learning content and adjust the level of detail based on the evaluation results. For example, the service provider can adjust the level of detail provided according to the importance of the learning content. For example, the service provider can evaluate the importance of the learning content and adjust the level of detail based on the evaluation results. This allows the service provider to provide detailed information according to the importance of the learning content.

[0094] The service provider can apply different delivery algorithms depending on the category of the learning content at the time of delivery. For example, the service provider can provide experimental videos for science-related learning content. For example, the service provider can select a delivery algorithm based on the category of the learning content. For example, the service provider can provide text-based materials for humanities-related learning content. For example, the service provider can select a delivery algorithm based on the category of the learning content. For example, the service provider can provide visual works for arts-related learning content. For example, the service provider can select a delivery algorithm based on the category of the learning content. This allows the service provider to apply the optimal delivery algorithm according to the category of the learning content.

[0095] The service provider can estimate a student's emotions and adjust the length of the learning content provided based on the estimated emotions. For example, if a student is in a hurry, the service provider can provide short content. For example, the service provider can collect the student's biometric data (heart rate and skin electrical activity) using sensors and estimate emotions using an emotion estimation algorithm. For example, if a student is relaxed, the service provider can provide detailed content. For example, the service provider can capture the student's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, if a student is excited, the service provider can provide visually engaging content. For example, the service provider can record the student's voice and estimate emotions using voice analysis technology. This allows the service provider to provide learning content at the optimal length according to the student's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The distribution department can determine the priority of distribution based on the submission timing of the learning content. For example, the distribution department will prioritize distribution of learning content submitted earlier. For example, the distribution department will determine the priority based on the submission date and time of the learning content. For example, the distribution department will postpone distribution of learning content submitted later. For example, the distribution department will determine the priority based on the frequency of submission of the learning content. For example, the distribution department will determine the priority of distribution according to the submission timing of the learning content. For example, the distribution department will determine the priority based on the submission date and time of the learning content. This allows the distribution department to provide learning content with a priority order according to the submission timing.

[0097] The provider can adjust the order of delivery based on the relevance of the learning content at the time of delivery. For example, the provider may prioritize the delivery of highly relevant learning content. For example, the provider may adjust the order based on the relevant topics and common goals of the learning content. For example, the provider may postpone the delivery of less relevant learning content. For example, the provider may adjust the order based on whether or not the learning content has prior knowledge. For example, the provider may adjust the order of delivery according to the relevance of the learning content. For example, the provider may adjust the order based on the relevant topics and common goals of the learning content. This allows the provider to deliver learning content in an order that is appropriate to its relevance.

[0098] The monitoring unit can estimate a student's emotions and adjust monitoring criteria based on the estimated emotions. For example, if a student is relaxed, the monitoring unit performs detailed monitoring. For example, the monitoring unit collects the student's biometric data (heart rate and skin electrical activity) using sensors and estimates emotions using an emotion estimation algorithm. For example, if a student is tense, the monitoring unit performs concise monitoring. For example, the monitoring unit captures the student's facial expressions with a camera and estimates emotions using an emotion estimation algorithm. For example, if a student is excited, the monitoring unit performs visually engaging monitoring. For example, the monitoring unit records the student's voice and estimates emotions using voice analysis technology. This allows the monitoring unit to perform monitoring based on criteria appropriate to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships of learning situations during monitoring. For example, the monitoring unit analyzes the interrelationships of learning situations and performs highly relevant monitoring. For example, the monitoring unit improves the accuracy of monitoring based on the relevant topics and the presence or absence of prior knowledge of the learning situations. For example, the monitoring unit performs optimal monitoring by considering the interrelationships of learning situations. For example, the monitoring unit improves the accuracy of monitoring based on the relevant topics and the presence or absence of prior knowledge of the learning situations. For example, the monitoring unit improves the accuracy of monitoring based on the interrelationships of learning situations. For example, the monitoring unit improves the accuracy of monitoring based on the relevant topics and the presence or absence of prior knowledge of the learning situations. As a result, the monitoring unit can perform optimal monitoring based on the interrelationships of learning situations.

[0100] The monitoring unit can perform monitoring while considering the attribute information of the person submitting the learning status. For example, the monitoring unit can perform optimal monitoring by considering the field of expertise of the person submitting the learning status. For example, the monitoring unit can perform monitoring based on the field of expertise and experience of the person submitting the learning status. For example, the monitoring unit can perform highly reliable monitoring by considering the experience of the person submitting the learning status. For example, the monitoring unit can perform monitoring based on the field of expertise and experience of the person submitting the learning status. For example, the monitoring unit can improve the accuracy of monitoring based on the attribute information of the person submitting the learning status. For example, the monitoring unit can perform monitoring based on the field of expertise and experience of the person submitting the learning status. As a result, the monitoring unit can perform highly reliable monitoring based on the attribute information of the person submitting the learning status.

[0101] The monitoring unit can estimate a student's emotions and adjust the order in which it displays the monitoring results based on the estimated emotions. For example, if a student is relaxed, the monitoring unit displays the results in an order that includes detailed information. For example, the monitoring unit collects the student's biometric data (heart rate and skin electrical activity) using sensors and estimates the emotion using an emotion estimation algorithm. For example, if a student is tense, the monitoring unit displays the results in an order that includes concise information. For example, the monitoring unit captures the student's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. For example, if a student is excited, the monitoring unit displays the results in a visually appealing order. For example, the monitoring unit records the student's voice and estimates the emotion using voice analysis technology. This allows the monitoring unit to display the monitoring results in an order that corresponds to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The monitoring unit can perform monitoring while considering the geographical distribution of learning status. For example, the monitoring unit analyzes the geographical distribution of learning status and performs highly relevant monitoring. For example, the monitoring unit performs monitoring based on the regional distribution of learning status. For example, the monitoring unit performs optimal monitoring by considering the geographical distribution of learning status. For example, the monitoring unit performs monitoring based on the regional distribution of learning status. For example, the monitoring unit improves the accuracy of monitoring based on the geographical distribution of learning status. For example, the monitoring unit performs monitoring based on the regional distribution of learning status. As a result, the monitoring unit can perform optimal monitoring based on the geographical distribution of learning status.

[0103] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature on the learning situation during monitoring. For example, the monitoring unit performs highly reliable monitoring by referring to relevant literature on the learning situation. For example, the monitoring unit performs monitoring based on relevant academic papers, books, and web articles on the learning situation. For example, the monitoring unit performs optimal monitoring based on relevant literature on the learning situation. For example, the monitoring unit performs monitoring based on relevant academic papers, books, and web articles on the learning situation. For example, the monitoring unit improves the accuracy of monitoring by considering relevant literature on the learning situation. For example, the monitoring unit performs monitoring based on relevant academic papers, books, and web articles on the learning situation. As a result, the monitoring unit can perform highly reliable monitoring based on relevant literature on the learning situation.

[0104] The feedback unit can estimate a student's emotions and adjust the way it expresses the feedback based on the estimated emotions. For example, if a student is relaxed, the feedback unit can provide detailed feedback. For example, it can collect the student's biometric data (heart rate and skin electrical activity) using sensors and estimate emotions using an emotion estimation algorithm. For example, if a student is nervous, the feedback unit can provide concise feedback. For example, it can capture the student's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, if a student is excited, the feedback unit can provide visually appealing feedback. For example, it can record the student's voice and estimate emotions using voice analysis technology. This allows the feedback unit to provide feedback in the most appropriate expression for the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The feedback unit can adjust the level of detail in the feedback based on the importance of the learning situation. For example, the feedback unit provides detailed feedback for high-importance learning situations. For example, the feedback unit evaluates the importance of the learning situation and adjusts the level of detail based on the evaluation result. For example, the feedback unit provides concise feedback for low-importance learning situations. For example, the feedback unit evaluates the importance of the learning situation and adjusts the level of detail based on the evaluation result. For example, the feedback unit adjusts the level of detail in the feedback according to the importance of the learning situation. For example, the feedback unit evaluates the importance of the learning situation and adjusts the level of detail based on the evaluation result. In this way, the feedback unit can provide detailed feedback according to the importance of the learning situation.

[0106] The feedback unit can apply different feedback algorithms depending on the category of the learning situation during feedback. For example, the feedback unit provides feedback based on experimental results for science-related learning situations. For example, the feedback unit selects a feedback algorithm based on the category of the learning situation. For example, the feedback unit provides text-based feedback for humanities-related learning situations. For example, the feedback unit selects a feedback algorithm based on the category of the learning situation. For example, the feedback unit provides feedback based on visual works for arts-related learning situations. For example, the feedback unit selects a feedback algorithm based on the category of the learning situation. This allows the feedback unit to apply the most appropriate feedback algorithm according to the category of the learning situation.

[0107] The feedback unit can estimate the student's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the student is in a hurry, the feedback unit will provide short feedback. For example, the feedback unit will collect the student's biometric data (heart rate and skin electrical activity) using sensors and estimate emotions using an emotion estimation algorithm. For example, if the student is relaxed, the feedback unit will provide detailed feedback. For example, the feedback unit will capture the student's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, if the student is excited, the feedback unit will provide visually appealing feedback. For example, the feedback unit will record the student's voice and estimate emotions using voice analysis technology. This allows the feedback unit to provide feedback of the optimal length according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The feedback unit can prioritize feedback based on when the learning progress is submitted. For example, the feedback unit will prioritize feedback for learning progress submitted earlier. For example, the feedback unit will prioritize based on the submission date and time of the learning progress. For example, the feedback unit will postpone feedback for learning progress submitted later. For example, the feedback unit will prioritize based on the frequency of the learning progress submission. For example, the feedback unit will prioritize feedback according to when the learning progress is submitted. For example, the feedback unit will prioritize based on the submission date and time of the learning progress. This allows the feedback unit to provide feedback with priority according to when the learning progress is submitted.

[0109] The feedback unit can adjust the order of feedback based on the relevance of the learning situations. For example, the feedback unit prioritizes providing feedback to highly relevant learning situations. For example, the feedback unit adjusts the order based on the relevant topics or common goals of the learning situations. For example, the feedback unit postpones providing feedback to less relevant learning situations. For example, the feedback unit adjusts the order based on whether or not the learning situation has prior knowledge. For example, the feedback unit adjusts the order of feedback according to the relevance of the learning situations. For example, the feedback unit adjusts the order based on the relevant topics or common goals of the learning situations. In this way, the feedback unit can provide feedback in an order that corresponds to the relevance of the learning situations. === Hard Collateral 1-1 === Each of the multiple elements described above, including the acquisition unit, analysis unit, selection unit, provision unit, monitoring unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires information about students' learning needs and styles using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the learning needs and styles based on the acquired information. The selection unit is implemented in the specific processing unit 290 of the data processing unit 12 and selects learning content and activities based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the smart device 14 and provides the selected learning content and activities. The monitoring unit is implemented in the specific processing unit 46A of the smart device 14 and monitors the progress of the provided learning content and activities. The feedback unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides feedback based on the monitored progress. === Hard Collateral 1-2 === Each of the multiple elements described above, including the acquisition unit, analysis unit, selection unit, provision unit, monitoring unit, and feedback unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires information about the student's learning needs and style using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the learning needs and style based on the acquired information. The selection unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and selects learning content and activities based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides the selected learning content and activities. The monitoring unit is implemented, for example, by the control unit 46A of the smart glasses 214, and monitors the progress of the provided learning content and activities. The feedback unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and provides feedback based on the monitored progress. === Hard Collateral 1-3 === Each of the multiple elements described above, including the acquisition unit, analysis unit, selection unit, provision unit, monitoring unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires information about students' learning needs and styles using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, and analyzes learning needs and styles based on the acquired information. The selection unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, and selects learning content and activities based on the analysis results. The provision unit is implemented in, for example, the control unit 46A of the headset terminal 314, and provides the selected learning content and activities. The monitoring unit is implemented in, for example, the control unit 46A of the headset terminal 314, and monitors the progress of the provided learning content and activities. The feedback unit is implemented in, for example, the specific processing unit 290 of the data processing unit 12, and provides feedback based on the monitored progress. === Hard Collateral 1-4 === Each of the multiple elements described above, including the acquisition unit, analysis unit, selection unit, provision unit, monitoring unit, and feedback unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires information about students' learning needs and styles using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the learning needs and styles based on the acquired information. The selection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and selects learning content and activities based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides the selected learning content and activities. The monitoring unit is implemented, for example, by the control unit 46A of the robot 414, and monitors the progress of the provided learning content and activities. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and provides feedback based on the monitored progress.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The data acquisition unit can consider not only learning history but also past learning outcomes and evaluations when acquiring information about students' learning needs and styles. For example, the unit can retrieve students' past test scores and teacher evaluations from a database, and use this to more accurately understand their learning needs. The unit can also analyze the results of learning activities and projects that students have participated in in the past to identify their learning style and areas of strength. Furthermore, the unit can acquire the usage history of learning tools and resources that students have used in the past, and use this to provide the most suitable learning content. As a result, the unit can acquire more appropriate learning needs by taking into account students' past learning outcomes and evaluations.

[0112] The analysis unit can also consider learners' goals and motivation when analyzing learning needs and styles based on information acquired by the acquisition unit. For example, the analysis unit can use students' set learning goals and future career goals as input data and customize learning content based on this. The analysis unit can also acquire students' learning motivation and enthusiasm through questionnaires and self-assessments and reflect this in the analysis. Furthermore, the analysis unit can consider goals and successes that students have achieved in the past and select learning content based on this. As a result, the analysis unit can provide more effective learning content while taking into account students' goals and motivation.

[0113] The selection unit can also consider learners' interests and preferences when selecting learning content and activities based on the results analyzed by the analysis unit. For example, the selection unit can obtain topics and themes that students are interested in through questionnaires and self-assessments, and customize learning content based on this. The selection unit can also consider club activities and hobbies that students have participated in in the past and provide learning content related to these. Furthermore, the selection unit can select learning content that reflects the latest research and trends in areas that students are interested in. In this way, the selection unit can provide more engaging learning content while taking students' interests and preferences into account.

[0114] The monitoring unit can take into account the learner's health and fatigue level when monitoring their learning progress in real time. For example, the monitoring unit collects health data such as the student's heart rate, sleep duration, and exercise level using sensors, and evaluates their learning progress based on this data. Furthermore, if the student is tired or stressed, the monitoring unit can adjust the learning pace and encourage breaks. In addition, the monitoring unit can adjust the difficulty and amount of learning content according to the student's health condition. This allows the monitoring unit to provide more appropriate learning support while taking into account the student's health and fatigue level.

[0115] The feedback unit can select a feedback method that suits the learner's learning style and preferences when providing feedback based on the progress monitored by the monitoring unit. For example, the feedback unit can provide feedback using graphs and diagrams for visual learners and audio messages for auditory learners. It can also provide specific action plans and practical advice for practical learners. Furthermore, the feedback unit can consider the student's learning history and past feedback to provide individually customized feedback. This allows the feedback unit to provide optimal feedback tailored to each student's learning style and preferences.

[0116] The data acquisition unit can estimate a student's emotions and adjust the timing of acquiring learning needs based on the estimated emotions. For example, if a student is feeling stressed, the unit will acquire learning needs during a time when the student can relax. For example, the unit can collect the student's biometric data (heart rate and skin electrical activity) using sensors and estimate emotions using an emotion estimation algorithm. For example, if a student is concentrating, the unit will acquire learning needs at that time. For example, the unit can capture the student's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, if a student is tired, the unit will acquire learning needs after a break. For example, the unit can record the student's voice and estimate emotions using voice analysis technology. This allows the unit to acquire learning needs at the optimal timing according to the student's emotions.

[0117] The analysis unit can estimate a student's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the student is relaxed, the analysis unit provides detailed analysis results. For example, the analysis unit collects the student's biometric data (heart rate and skin electrical activity) using sensors and estimates emotions using an emotion estimation algorithm. If the student is tense, the analysis unit provides concise analysis results. For example, the analysis unit captures the student's facial expressions with a camera and estimates emotions using an emotion estimation algorithm. If the student is excited, the analysis unit provides visually appealing analysis results. For example, the analysis unit records the student's voice and estimates emotions using voice analysis technology. This allows the analysis unit to provide analysis results in the most appropriate presentation method according to the student's emotions.

[0118] The selection unit can estimate the student's emotions and adjust the selection criteria based on those emotions. For example, if the student is relaxed, the selection unit will select challenging content. For example, the selection unit will collect the student's biometric data (heart rate and skin electrical activity) using sensors and estimate their emotions using an emotion estimation algorithm. For example, if the student is nervous, the selection unit will select basic content. For example, the selection unit will capture the student's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, if the student is excited, the selection unit will select visually appealing content. For example, the selection unit will record the student's voice and estimate their emotions using voice analysis technology. This allows the selection unit to select learning content based on criteria that match the student's emotions.

[0119] The service provider can estimate a student's emotions and adjust the presentation of the learning content based on those emotions. For example, if a student is relaxed, the service provider can provide content with detailed explanations. For example, the service provider can collect the student's biometric data (heart rate and skin electrical activity) using sensors and estimate their emotions using an emotion estimation algorithm. For example, if a student is nervous, the service provider can provide content with concise explanations. For example, the service provider can capture the student's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, if a student is excited, the service provider can provide visually appealing content. For example, the service provider can record the student's voice and estimate their emotions using voice analysis technology. This allows the service provider to deliver learning content in the most appropriate presentation style for each student's emotions.

[0120] The feedback unit can estimate a student's emotions and adjust the way it presents the feedback based on those emotions. For example, if a student is relaxed, the feedback unit can provide detailed feedback. For example, the feedback unit can collect the student's biometric data (heart rate and skin electrical activity) using sensors and estimate their emotions using an emotion estimation algorithm. If a student is nervous, the feedback unit can provide concise feedback. For example, the feedback unit can capture the student's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. If a student is excited, the feedback unit can provide visually appealing feedback. For example, the feedback unit can record the student's voice and estimate their emotions using voice analysis technology. This allows the feedback unit to provide feedback in the most appropriate way to express the student's emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The acquisition department acquires information about students' learning needs and styles. Specifically, it acquires information such as their strengths and weaknesses in subjects, learning pace, and preferred learning methods based on test scores, self-assessments, teacher evaluations, study time and progress speed, comprehension level, visual learning, auditory learning, and practical learning. Step 2: The analysis unit analyzes learning needs and styles based on the information acquired by the acquisition unit. For example, using AI, it takes information such as preferred subjects, subjects the student excels at, learning pace, and preferred learning methods as input and outputs learning needs and styles. Step 3: The selection unit selects learning content and activities based on the results analyzed by the analysis unit. For example, it selects advanced learning content for subjects the student excels at and content that allows them to learn from the basics for subjects they struggle with. Using AI, the analysis results are taken as input and the learning content and activities are output. Step 4: The delivery department provides the learning content and activities selected by the selection department. For example, this can be done through a web application, a mobile application, or by sending it via email. Step 5: The monitoring unit monitors the progress of learning content and activities provided by the delivery unit. For example, it monitors students' learning progress in real time and outputs progress information using AI. Step 6: The feedback unit provides feedback based on the progress monitored by the monitoring unit. For example, it provides feedback using AI at the appropriate time and outputs feedback with the progress status as input.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The acquisition department obtains information about students' learning needs and styles, An analysis unit analyzes learning needs and styles based on the information acquired by the acquisition unit, A selection unit selects learning content and activities based on the results analyzed by the aforementioned analysis unit, A provisioning unit that provides learning content and activities selected by the aforementioned selection unit, A monitoring unit monitors the progress of learning content and activities provided by the aforementioned provisioning unit, The system includes a feedback unit that provides feedback based on the progress monitored by the monitoring unit. A system characterized by the following features.

2. The acquisition unit is, We obtain information about students' strengths and weaknesses in subjects, their learning pace, and their preferred learning methods. The system according to feature 1.

3. The aforementioned analysis unit, Based on the information acquired by the aforementioned acquisition unit, learning needs and styles are analyzed. The system according to feature 1.

4. The aforementioned selection unit is Based on the results analyzed by the aforementioned analysis unit, learning content is selected for subjects the user excels at, and content that allows users to learn from the basics is selected for subjects they struggle with. The system according to feature 1.

5. The monitoring unit, Monitor students' learning progress in real time. The system according to feature 1.

6. The aforementioned feedback unit is Based on the progress monitored by the monitoring unit, appropriate feedback is provided. The system according to feature 1.

7. The acquisition unit is, The system estimates students' emotions and adjusts the timing of acquiring learning needs based on those estimated emotions. The system according to feature 1.

8. The acquisition unit is, Analyze students' past learning history and select the most suitable acquisition method. The system according to feature 1.

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