system

Abstract

The system according to this embodiment aims to propose and provide optimal learning content and programs based on students' learning patterns and interests. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a monitoring unit. The collection unit collects student learning data and survey results. The analysis unit analyzes the data collected by the collection unit to identify students' learning patterns and interests. The proposal unit proposes and provides optimal learning content and programs based on the learning patterns and interests identified by the analysis unit. The monitoring unit monitors the progress of the learning content and programs proposed and provided by the proposal unit in real time.

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 steps of 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 as a 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 conventional technology, it is difficult for students to conduct learning that suits their interests and future career paths, and there is a risk of a decline in learning motivation and a mismatch in career path selection.

[0005] The system according to the embodiment aims to propose and provide optimal learning content and programs based on the learning patterns, interests, and concerns of students.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a monitoring unit. The data collection unit collects student learning data and survey results. The analysis unit analyzes the data collected by the data collection unit to identify students' learning patterns and interests. The proposal unit proposes and provides optimal learning content and programs based on the learning patterns and interests identified by the analysis unit. The monitoring unit monitors the progress of the learning content and programs proposed and provided by the proposal unit in real time. [Effects of the Invention]

[0007] The system according to this embodiment can suggest and provide optimal learning content and programs based on students' learning patterns and interests. [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 signed storage is one or more non-volatile storage devices that store various programs and various parameters. 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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 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 career navigation agent system according to an embodiment of the present invention is an AI agent that autonomously analyzes students' learning patterns and interests. This career navigation agent system utilizes learning data and survey results to propose and provide optimal learning content and programs tailored to future careers and paths. Furthermore, the career navigation agent system allows teachers and parents to monitor learning progress in real time and provide appropriate support. For example, the career navigation agent system collects students' learning data and survey results. For example, it collects information such as which subjects students are interested in and what kind of career they aspire to. This information is input into the AI ​​agent. Next, the career navigation agent system uses the AI ​​agent to analyze the collected information and identify the student's learning patterns and interests. For example, if a student is interested in mathematics, the AI ​​agent will propose learning content related to mathematics to that student. Furthermore, the career navigation agent system allows the AI ​​agent to propose and provide optimal learning content and programs tailored to future careers and paths. For example, a student aiming to become a doctor will be provided with learning content related to medicine. In this way, students can learn in a way that suits their interests and future paths. In addition, the career navigation agent system allows teachers and parents to monitor learning progress in real time. For example, information such as the student's progress in each subject and the learning content they are using can be checked in real time. This allows teachers and parents to provide appropriate support. As a result, the Career Navigation Agent System enables students to maximize their potential and study efficiently at a pace that suits them. Furthermore, because learning is directly related to their future careers, the accuracy of their career choices improves. Ultimately, this leads to the development of individuals who can utilize their individual talents and thrive in society. As a result, the Career Navigation Agent System can suggest and provide optimal learning content and programs based on students' learning patterns and interests, and monitor their progress in real time.

[0029] The career navigation agent system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a monitoring unit. The data collection unit collects student learning data and survey results. The data collection unit collects information such as which subjects students are interested in and what kind of career they aspire to. The data collection unit can collect information on students' interests and career goals, for example, through surveys. The data collection unit can also collect data such as students' grades, attendance, and assignment submission status in order to collect learning data. For example, the data collection unit collects information such as subjects students have selected and club activities they have participated in. The analysis unit analyzes the data collected by the data collection unit to identify students' learning patterns and interests. For example, the analysis unit can analyze the distribution of students' learning time and trends in learning methods based on the collected information. The analysis unit can also analyze survey results to identify students' interests. For example, the analysis unit identifies which subjects students are interested in and what kind of career they aspire to. The proposal unit proposes and provides optimal learning content and programs based on the learning patterns and interests identified by the analysis unit. The proposal department can, for example, suggest mathematics-related learning content if a student is interested in mathematics. The proposal department can also provide learning content and programs tailored to the student's future career and path. For example, it can provide medical-related learning content to a student aiming to become a doctor. The monitoring department monitors the progress of the learning content and programs suggested and provided by the proposal department in real time. The monitoring department can, for example, check in real time information such as the student's progress in each subject and the types of learning content they are using. The monitoring department can, for example, monitor the student's learning progress and provide appropriate support. As a result, the career navigation agent system according to this embodiment can suggest and provide optimal learning content and programs based on the student's learning patterns and interests, and monitor their progress in real time.

[0030] The data collection department collects student learning data and survey results. Specifically, it collects information such as which subjects students are interested in and what career aspirations they have. For example, the department can collect information on students' interests and career goals through surveys. Surveys are conducted via online forms and mobile apps and are designed to be easy for students to respond to. The department can also collect data such as students' grades, attendance, and assignment submission status in order to collect learning data. This data is automatically retrieved from the school's Learning Management System (LMS) and electronic attendance registers. For example, the department collects information such as the subjects students have chosen and the club activities they have participated in. This allows the department to comprehensively collect data on students' learning history and extracurricular activities, and to gain a detailed understanding of students' interests and concerns. Furthermore, the department also collects data on students' activity on online learning platforms. For example, it collects information such as which materials students used, for how long, what problems they tackled, and what grades they achieved. This allows the department to track students' learning behavior in detail and understand their learning progress and comprehension. The data collection unit centrally manages this data, making it accessible to the analysis and proposal units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes data collected by the data collection unit to identify students' learning patterns and interests. Specifically, based on the collected information, it can analyze the distribution of students' study time and trends in their learning methods. For example, it can analyze when students study the most and which subjects they dedicate the most time to. The analysis unit can also analyze survey results to identify students' interests. For example, it can identify which subjects students are interested in and what kind of careers they aspire to. The analysis unit uses AI to analyze data and extract patterns and trends. For example, it can use machine learning algorithms to cluster students' learning behavior and identify groups of students with similar learning patterns. It can also use natural language processing technology to extract students' interests from free-response answers in surveys. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term fluctuations in learning patterns and interests. For example, it can analyze trends in grades in specific subjects based on past performance data to evaluate the effectiveness of learning. In addition, the analysis unit can use anomaly detection algorithms to detect unusual learning patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term learning management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Proposal Department proposes and provides optimal learning content and programs based on learning patterns and interests identified by the Analysis Department. Specifically, if a student is interested in mathematics, the Proposal Department can propose mathematics-related learning content. For example, it can provide online mathematics courses, practice problem sets, and related video materials. The Proposal Department can also provide learning content and programs tailored to future careers and paths. For example, a student aiming to become a doctor would be provided with medical-related learning content. This includes materials on anatomy and physiology, and practical training programs in medical settings. The Proposal Department uses AI to select the most suitable learning content for each student and provides individually customized learning plans. For example, it uses a recommendation system to suggest the most suitable materials and programs based on the student's past learning history and interests. The Proposal Department can also monitor the student's learning progress and adjust the learning plan as needed. For example, if a student is struggling in a particular subject, it can provide additional supplementary materials related to that subject. Furthermore, the Proposal Department can collect student feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, the department evaluates how much students used the proposed materials and what effects they had, and incorporates this into future proposals. This allows the proposal department to provide each student with the most suitable learning content and programs, maximizing learning effectiveness.

[0033] The monitoring department monitors the progress of learning content and programs proposed and provided by the proposal department in real time. Specifically, it can check in real time information such as the progress students are making in each subject and what learning content they are using. For example, the monitoring department can monitor students' learning progress and provide appropriate support. For instance, if a student is falling behind in a particular subject, it can propose additional support or remedial lessons. The monitoring department can also analyze students' learning behavior and evaluate the effectiveness of their learning. For example, it can analyze which materials students used, for how long, and what grades they achieved to evaluate the effectiveness of their learning. Furthermore, the monitoring department can collect student feedback and use it to improve learning plans. For example, it can collect feedback on how students felt about the proposed materials and what improvements they think could be made, and incorporate this into future proposals. The monitoring department can use AI to analyze learning progress in real time and detect abnormal learning patterns and problems early. For example, if a student's grades suddenly drop in a particular subject, the monitoring department can identify the cause and take appropriate measures. Furthermore, the monitoring department can centrally manage and share students' learning data with teachers and parents. This allows the monitoring department to comprehensively understand students' learning progress and provide appropriate support.

[0034] The data collection unit can collect information such as which subjects students are interested in and what kind of career they aspire to. For example, the data collection unit can collect information on students' interests and career goals through questionnaires. The data collection unit can also collect information such as the subjects students have chosen and the club activities they have participated in. Furthermore, the data collection unit can collect data such as students' grades, attendance, and assignment submission status. By collecting information on students' interests and career goals, it becomes possible to propose more appropriate learning content and programs. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input questionnaire results into a generating AI and have the generating AI identify students' interests and career goals.

[0035] The analysis unit can analyze the collected information to identify students' learning patterns and interests. For example, the analysis unit can analyze the distribution of students' study time and trends in their learning methods based on the collected information. The analysis unit can also analyze survey results to identify students' interests and concerns. Furthermore, the analysis unit can identify which subjects students are interested in and what kind of careers they aspire to, based on the collected information. In this way, by analyzing the collected information, students' learning patterns and interests can be identified. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into a generating AI and have the generating AI perform the identification of students' learning patterns and interests.

[0036] The suggestion unit can propose and provide optimal learning content and programs based on identified learning patterns and interests. For example, if a student is interested in mathematics, the suggestion unit will propose mathematics-related learning content. The suggestion unit can also provide learning content and programs tailored to future careers and paths. For example, a student aiming to become a doctor will be provided with medical-related learning content. This allows for the proposal and provision of optimal learning content and programs based on identified learning patterns and interests. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input identified learning patterns and interests into a generating AI and have the generating AI propose optimal learning content and programs.

[0037] The monitoring unit can grasp information in real time, such as the progress students are making in each subject and the learning content they are using. For example, the monitoring unit can monitor students' learning progress and provide appropriate support. The monitoring unit can check information in real time, such as the progress students are making in each subject and the learning content they are using. This allows for appropriate support by understanding students' learning progress in real time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input students' learning progress into a generating AI and have the generating AI perform real-time progress monitoring.

[0038] The suggestion department can provide learning content and programs tailored to future careers and paths. For example, the suggestion department can provide medical-related learning content to students aiming to become doctors. For example, the suggestion department can also provide engineering-related learning content to students aiming to become engineers. Furthermore, the suggestion department can provide learning programs tailored to future careers and paths. For example, the suggestion department can provide programs to help students acquire the skills and knowledge necessary for their desired career. This improves the accuracy of career choices by providing learning content and programs tailored to future careers and paths. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without AI. For example, the suggestion department can input the student's career goals into a generating AI and have the generating AI provide the optimal learning content and programs.

[0039] The data collection unit can analyze a student's past learning history and select the optimal data collection method. For example, the data collection unit may prioritize collecting learning methods that were effective for the student in the past. It can also avoid collecting learning methods that the student struggled with in the past. Furthermore, the data collection unit can select the most efficient data collection method based on the student's past learning history. In this way, the optimal data collection method can be selected by analyzing the student's past learning history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's past learning history into a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can filter learning data based on the student's current learning status and areas of interest. For example, the data collection unit can prioritize collecting data in areas that the student is currently interested in. The data collection unit can also collect only the necessary data according to the student's current learning status. Furthermore, the data collection unit can filter and collect highly relevant data based on the student's areas of interest. This allows for the collection of highly relevant data by filtering the data based on the student's current learning status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's current learning status and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data based on the student's geographical location information when collecting learning data. For example, if a student lives in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if a student is traveling, the data collection unit can also prioritize the collection of data related to their travel destination. Furthermore, if a student is interested in a specific region, the data collection unit can prioritize the collection of data related to that region. This allows for the collection of more appropriate data by collecting highly relevant data based on the student's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0042] The data collection unit can analyze students' social media activities and collect relevant data when collecting learning data. For example, the data collection unit can collect data based on the interests and passions that students share on social media. The data collection unit can also collect relevant data based on the content of accounts that students follow. Furthermore, the data collection unit can collect data based on the groups and events that students participate in on social media. This allows for the collection of relevant data by analyzing students' social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input students' social media activities into a generating AI and have the generating AI collect the relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the training data during the analysis. For example, the analysis unit can perform a detailed analysis on important training data. For example, the analysis unit can perform a concise analysis on less important training data. The analysis unit can also adjust the depth of the analysis according to the importance of the training data. By adjusting the level of detail of the analysis based on the importance of the training data, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the training data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the training data during analysis. For example, the analysis unit can apply a numerical analysis algorithm to mathematical data. For example, the analysis unit can also apply a text analysis algorithm to literary data. Furthermore, the analysis unit can apply an experimental data analysis algorithm to scientific data. By applying different analysis algorithms depending on the category of the training data, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of the training data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the submission timing of the training data during analysis. For example, the analysis unit may prioritize the analysis of recently submitted training data. It may also prioritize the analysis of training data with an approaching submission deadline. Furthermore, the analysis unit can determine the order of analysis based on the submission timing. This allows for the provision of more appropriate analysis results by determining the priority of analysis based on the submission timing of the training data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission timing of the training data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the training data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant training data. For example, the analysis unit may postpone the analysis of less relevant training data. The analysis unit can also adjust the order of analysis based on the relevance of the training data. By adjusting the order of analysis based on the relevance of the training data, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the training data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the learning content. For example, it can provide detailed suggestions for important learning content, and concise suggestions for less important learning content. It can also adjust the depth of its suggestions according to the importance of the learning content. By adjusting the level of detail of suggestions based on the importance of the learning content, it can provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the importance of the learning content into a generating AI and have the generating AI adjust the level of detail of the suggestions.

[0048] The proposal unit can apply different proposal algorithms depending on the category of the learning content during the proposal process. For example, the proposal unit can apply a numerical analysis algorithm to mathematical content. For example, it can also apply a text analysis algorithm to literary content. Furthermore, it can apply an experimental data analysis algorithm to scientific content. By applying different proposal algorithms depending on the category of the learning content, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the learning content into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0049] The proposal unit can determine the priority of proposals based on the submission timing of the learning content. For example, the proposal unit may prioritize proposals for learning content with an approaching submission deadline. It may also prioritize proposals for learning content that has been recently submitted. Furthermore, the proposal unit can determine the order of proposals based on the submission timing. This allows for the provision of more appropriate proposals by prioritizing proposals based on the submission timing of the learning content. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the submission timing of the learning content into a generating AI and have the generating AI determine the priority of proposals.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the learning content during the suggestion process. For example, the suggestion unit may prioritize suggesting highly relevant learning content. For example, the suggestion unit may postpone suggesting less relevant learning content. The suggestion unit can also adjust the order of suggestions based on the relevance of the learning content. This allows for the provision of more appropriate suggestions by adjusting the order of suggestions based on the relevance of the learning content. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of the learning content into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0051] The monitoring unit can predict the current learning status by referring to past learning data during monitoring. For example, the monitoring unit predicts the current learning progress based on past learning data. The monitoring unit can also evaluate the current learning status by referring to past learning data. Furthermore, the monitoring unit can analyze past learning data and predict the current learning status. In this way, the current learning status can be predicted by referring to past learning data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input past learning data into a generating AI and have the generating AI perform a prediction of the current learning status.

[0052] The monitoring unit can apply different monitoring methods to each category of training data during monitoring. For example, the monitoring unit can apply a monitoring method using numerical analysis to mathematical data. For example, the monitoring unit can also apply a monitoring method using text analysis to literary data. Furthermore, the monitoring unit can apply a monitoring method using experimental data analysis to scientific data. This allows for more appropriate monitoring by applying different monitoring methods to each category of training data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the categories of training data into a generating AI and have the generating AI execute the application of different monitoring methods.

[0053] The monitoring unit can analyze changes in monitoring based on the submission timing of training data during monitoring. For example, the monitoring unit may prioritize monitoring training data with an approaching submission deadline. It may also prioritize monitoring recently submitted training data. Furthermore, the monitoring unit can determine the order of monitoring based on the submission timing. This allows for more appropriate monitoring by analyzing changes in monitoring based on the submission timing of training data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the training data submission timing into a generating AI and have the generating AI perform the analysis of changes in monitoring.

[0054] The monitoring unit can analyze the monitoring by referring to relevant market data for the training data during monitoring. For example, the monitoring unit can evaluate the current learning progress based on the relevant market data for the training data. The monitoring unit can also predict the current learning status by referring to relevant market data for the training data. Furthermore, the monitoring unit can analyze the relevant market data for the training data and evaluate the current learning status. This allows for more appropriate monitoring by referring to relevant market data for the training data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevant market data for the training data into a generating AI and have the generating AI perform the monitoring analysis.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The career navigation agent system can also include a feedback unit. The feedback unit collects feedback from students on the suggestions and learning content they receive and sends it to the analysis unit. For example, it can collect information such as how well students understood the suggested learning content and their impressions of it. The feedback unit can also collect information on areas for improvement that students felt the suggested learning content could have. This allows the feedback unit to help the suggestion unit propose more appropriate learning content based on student feedback.

[0057] The career navigation agent system can also include a schedule management unit. This unit manages students' learning schedules and suggests optimal study times. For example, it can suggest efficient study times considering students' class schedules and extracurricular activity schedules. Furthermore, the schedule management unit can adjust schedules according to students' learning progress. For instance, if a student is behind in a particular subject, the schedule can be adjusted to allocate more time to that subject. In this way, the schedule management unit can support students in learning efficiently.

[0058] The career navigation agent system can also include a performance evaluation unit. This unit evaluates students' learning outcomes and provides feedback. For example, it can assess learning progress based on students' test results and assignment submission status. The performance evaluation unit can also suggest specific areas for improvement in students' learning outcomes. Furthermore, it can compare students' learning outcomes with those of other students and provide relative evaluations. This allows the performance evaluation unit to help students objectively understand their own learning outcomes and identify areas for improvement.

[0059] The career navigation agent system can also include a career counseling department. This department provides a function where students can consult about their future careers. For example, it can provide information about occupations that students are interested in. Furthermore, the career counseling department can provide advice to help students understand their strengths and weaknesses and make appropriate career choices. In addition, the career counseling department can propose learning plans to help students acquire the skills and knowledge necessary for their future careers. In this way, the career counseling department can support students in thinking deeply about their careers and making appropriate choices.

[0060] The Career Navigation Agent System can also include a Data Security Department. This department provides functions to securely protect students' personal information and learning data. For example, it can perform data encryption and access control. Furthermore, the Data Security Department can provide monitoring functions to prevent unauthorized access and data breaches. In addition, the Data Security Department can conduct regular security checks to ensure that student data is properly managed. This allows the Data Security Department to securely protect students' personal information and learning data, enabling them to use the system with peace of mind.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection department collects student learning data and survey results. For example, it collects information such as which subjects students are interested in and what kind of career they aspire to. The data collection department can collect information about students' interests and career goals through surveys. In addition, the data collection department can also collect data such as students' grades, attendance, and assignment submission status in order to collect learning data. For example, it collects information such as the subjects students have chosen and the club activities they have participated in. Step 2: The analysis unit analyzes the data collected by the collection unit to identify students' learning patterns and interests. For example, based on the collected information, it can analyze the distribution of students' study time and trends in their learning methods. It can also analyze survey results to identify students' interests and concerns. For example, it can identify which subjects students are interested in and what kind of careers they aspire to. Step 3: The proposal department proposes and provides optimal learning content and programs based on the learning patterns and interests identified by the analysis department. For example, if a student is interested in mathematics, the proposal department can suggest mathematics-related learning content. It can also provide learning content and programs tailored to the student's future career and path. For example, a student aspiring to become a doctor can be provided with medical-related learning content. Step 4: The monitoring department monitors the progress of learning content and programs proposed and provided by the proposal department in real time. For example, it can check in real time information such as the progress students have made in each subject and what learning content they are using. The monitoring department can monitor students' learning progress and provide appropriate support.

[0063] (Example of form 2) The career navigation agent system according to an embodiment of the present invention is an AI agent that autonomously analyzes students' learning patterns and interests. This career navigation agent system utilizes learning data and survey results to propose and provide optimal learning content and programs tailored to future careers and paths. Furthermore, the career navigation agent system allows teachers and parents to monitor learning progress in real time and provide appropriate support. For example, the career navigation agent system collects students' learning data and survey results. For example, it collects information such as which subjects students are interested in and what kind of career they aspire to. This information is input into the AI ​​agent. Next, the career navigation agent system uses the AI ​​agent to analyze the collected information and identify the student's learning patterns and interests. For example, if a student is interested in mathematics, the AI ​​agent will propose learning content related to mathematics to that student. Furthermore, the career navigation agent system allows the AI ​​agent to propose and provide optimal learning content and programs tailored to future careers and paths. For example, a student aiming to become a doctor will be provided with learning content related to medicine. In this way, students can learn in a way that suits their interests and future paths. In addition, the career navigation agent system allows teachers and parents to monitor learning progress in real time. For example, information such as the student's progress in each subject and the learning content they are using can be checked in real time. This allows teachers and parents to provide appropriate support. As a result, the Career Navigation Agent System enables students to maximize their potential and study efficiently at a pace that suits them. Furthermore, because learning is directly related to their future careers, the accuracy of their career choices improves. Ultimately, this leads to the development of individuals who can utilize their individual talents and thrive in society. As a result, the Career Navigation Agent System can suggest and provide optimal learning content and programs based on students' learning patterns and interests, and monitor their progress in real time.

[0064] The career navigation agent system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a monitoring unit. The data collection unit collects student learning data and survey results. The data collection unit collects information such as which subjects students are interested in and what kind of career they aspire to. The data collection unit can collect information on students' interests and career goals, for example, through surveys. The data collection unit can also collect data such as students' grades, attendance, and assignment submission status in order to collect learning data. For example, the data collection unit collects information such as subjects students have selected and club activities they have participated in. The analysis unit analyzes the data collected by the data collection unit to identify students' learning patterns and interests. For example, the analysis unit can analyze the distribution of students' learning time and trends in learning methods based on the collected information. The analysis unit can also analyze survey results to identify students' interests. For example, the analysis unit identifies which subjects students are interested in and what kind of career they aspire to. The proposal unit proposes and provides optimal learning content and programs based on the learning patterns and interests identified by the analysis unit. The proposal department can, for example, suggest mathematics-related learning content if a student is interested in mathematics. The proposal department can also provide learning content and programs tailored to the student's future career and path. For example, it can provide medical-related learning content to a student aiming to become a doctor. The monitoring department monitors the progress of the learning content and programs suggested and provided by the proposal department in real time. The monitoring department can, for example, check in real time information such as the student's progress in each subject and the types of learning content they are using. The monitoring department can, for example, monitor the student's learning progress and provide appropriate support. As a result, the career navigation agent system according to this embodiment can suggest and provide optimal learning content and programs based on the student's learning patterns and interests, and monitor their progress in real time.

[0065] The data collection department collects student learning data and survey results. Specifically, it collects information such as which subjects students are interested in and what career aspirations they have. For example, the department can collect information on students' interests and career goals through surveys. Surveys are conducted via online forms and mobile apps and are designed to be easy for students to respond to. The department can also collect data such as students' grades, attendance, and assignment submission status in order to collect learning data. This data is automatically retrieved from the school's Learning Management System (LMS) and electronic attendance registers. For example, the department collects information such as the subjects students have chosen and the club activities they have participated in. This allows the department to comprehensively collect data on students' learning history and extracurricular activities, and to gain a detailed understanding of students' interests and concerns. Furthermore, the department also collects data on students' activity on online learning platforms. For example, it collects information such as which materials students used, for how long, what problems they tackled, and what grades they achieved. This allows the department to track students' learning behavior in detail and understand their learning progress and comprehension. The data collection unit centrally manages this data, making it accessible to the analysis and proposal units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The analysis unit analyzes data collected by the data collection unit to identify students' learning patterns and interests. Specifically, based on the collected information, it can analyze the distribution of students' study time and trends in their learning methods. For example, it can analyze when students study the most and which subjects they dedicate the most time to. The analysis unit can also analyze survey results to identify students' interests. For example, it can identify which subjects students are interested in and what kind of careers they aspire to. The analysis unit uses AI to analyze data and extract patterns and trends. For example, it can use machine learning algorithms to cluster students' learning behavior and identify groups of students with similar learning patterns. It can also use natural language processing technology to extract students' interests from free-response answers in surveys. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term fluctuations in learning patterns and interests. For example, it can analyze trends in grades in specific subjects based on past performance data to evaluate the effectiveness of learning. In addition, the analysis unit can use anomaly detection algorithms to detect unusual learning patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term learning management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0067] The Proposal Department proposes and provides optimal learning content and programs based on learning patterns and interests identified by the Analysis Department. Specifically, if a student is interested in mathematics, the Proposal Department can propose mathematics-related learning content. For example, it can provide online mathematics courses, practice problem sets, and related video materials. The Proposal Department can also provide learning content and programs tailored to future careers and paths. For example, a student aiming to become a doctor would be provided with medical-related learning content. This includes materials on anatomy and physiology, and practical training programs in medical settings. The Proposal Department uses AI to select the most suitable learning content for each student and provides individually customized learning plans. For example, it uses a recommendation system to suggest the most suitable materials and programs based on the student's past learning history and interests. The Proposal Department can also monitor the student's learning progress and adjust the learning plan as needed. For example, if a student is struggling in a particular subject, it can provide additional supplementary materials related to that subject. Furthermore, the Proposal Department can collect student feedback and continuously improve the accuracy and effectiveness of its suggestions. For example, the department evaluates how much students used the proposed materials and what effects they had, and incorporates this into future proposals. This allows the proposal department to provide each student with the most suitable learning content and programs, maximizing learning effectiveness.

[0068] The monitoring department monitors the progress of learning content and programs proposed and provided by the proposal department in real time. Specifically, it can check in real time information such as the progress students are making in each subject and what learning content they are using. For example, the monitoring department can monitor students' learning progress and provide appropriate support. For instance, if a student is falling behind in a particular subject, it can propose additional support or remedial lessons. The monitoring department can also analyze students' learning behavior and evaluate the effectiveness of their learning. For example, it can analyze which materials students used, for how long, and what grades they achieved to evaluate the effectiveness of their learning. Furthermore, the monitoring department can collect student feedback and use it to improve learning plans. For example, it can collect feedback on how students felt about the proposed materials and what improvements they think could be made, and incorporate this into future proposals. The monitoring department can use AI to analyze learning progress in real time and detect abnormal learning patterns and problems early. For example, if a student's grades suddenly drop in a particular subject, the monitoring department can identify the cause and take appropriate measures. Furthermore, the monitoring department can centrally manage and share students' learning data with teachers and parents. This allows the monitoring department to comprehensively understand students' learning progress and provide appropriate support.

[0069] The data collection unit can collect information such as which subjects students are interested in and what kind of career they aspire to. For example, the data collection unit can collect information on students' interests and career goals through questionnaires. The data collection unit can also collect information such as the subjects students have chosen and the club activities they have participated in. Furthermore, the data collection unit can collect data such as students' grades, attendance, and assignment submission status. By collecting information on students' interests and career goals, it becomes possible to propose more appropriate learning content and programs. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input questionnaire results into a generating AI and have the generating AI identify students' interests and career goals.

[0070] The analysis unit can analyze the collected information to identify students' learning patterns and interests. For example, the analysis unit can analyze the distribution of students' study time and trends in their learning methods based on the collected information. The analysis unit can also analyze survey results to identify students' interests and concerns. Furthermore, the analysis unit can identify which subjects students are interested in and what kind of careers they aspire to, based on the collected information. In this way, by analyzing the collected information, students' learning patterns and interests can be identified. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected information into a generating AI and have the generating AI perform the identification of students' learning patterns and interests.

[0071] The suggestion unit can propose and provide optimal learning content and programs based on identified learning patterns and interests. For example, if a student is interested in mathematics, the suggestion unit will propose mathematics-related learning content. The suggestion unit can also provide learning content and programs tailored to future careers and paths. For example, a student aiming to become a doctor will be provided with medical-related learning content. This allows for the proposal and provision of optimal learning content and programs based on identified learning patterns and interests. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input identified learning patterns and interests into a generating AI and have the generating AI propose optimal learning content and programs.

[0072] The monitoring unit can grasp information in real time, such as the progress students are making in each subject and the learning content they are using. For example, the monitoring unit can monitor students' learning progress and provide appropriate support. The monitoring unit can check information in real time, such as the progress students are making in each subject and the learning content they are using. This allows for appropriate support by understanding students' learning progress in real time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input students' learning progress into a generating AI and have the generating AI perform real-time progress monitoring.

[0073] The suggestion department can provide learning content and programs tailored to future careers and paths. For example, the suggestion department can provide medical-related learning content to students aiming to become doctors. For example, the suggestion department can also provide engineering-related learning content to students aiming to become engineers. Furthermore, the suggestion department can provide learning programs tailored to future careers and paths. For example, the suggestion department can provide programs to help students acquire the skills and knowledge necessary for their desired career. This improves the accuracy of career choices by providing learning content and programs tailored to future careers and paths. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without AI. For example, the suggestion department can input the student's career goals into a generating AI and have the generating AI provide the optimal learning content and programs.

[0074] The data collection unit can estimate students' emotions and adjust the timing of data collection based on the estimated emotions. For example, if a student is feeling stressed, the data collection unit can collect data during times when the student is relaxed. For example, if a student is concentrating, the data collection unit can collect data at that time. Also, if a student is tired, the data collection unit can collect data after a break. By adjusting the timing of data collection based on students' emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input student emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0075] The data collection unit can analyze a student's past learning history and select the optimal data collection method. For example, the data collection unit may prioritize collecting learning methods that were effective for the student in the past. It can also avoid collecting learning methods that the student struggled with in the past. Furthermore, the data collection unit can select the most efficient data collection method based on the student's past learning history. In this way, the optimal data collection method can be selected by analyzing the student's past learning history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's past learning history into a generating AI and have the generating AI select the optimal data collection method.

[0076] The data collection unit can filter learning data based on the student's current learning status and areas of interest. For example, the data collection unit can prioritize collecting data in areas that the student is currently interested in. The data collection unit can also collect only the necessary data according to the student's current learning status. Furthermore, the data collection unit can filter and collect highly relevant data based on the student's areas of interest. This allows for the collection of highly relevant data by filtering the data based on the student's current learning status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's current learning status and areas of interest into a generating AI and have the generating AI perform the filtering.

[0077] The data collection unit can estimate students' emotions and determine the priority of data to collect based on the estimated emotions. For example, if a student is excited, the data collection unit may prioritize collecting data that is interesting to them. If a student is relaxed, the data collection unit may also prioritize collecting in-depth data. If a student is tired, the data collection unit may also prioritize collecting simple and easy-to-understand data. This allows for the collection of more relevant data by prioritizing data collection based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input student emotion data into a generative AI and have the generative AI determine the priority of the data.

[0078] The data collection unit can prioritize the collection of highly relevant data based on the student's geographical location information when collecting learning data. For example, if a student lives in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if a student is traveling, the data collection unit can also prioritize the collection of data related to their travel destination. Furthermore, if a student is interested in a specific region, the data collection unit can prioritize the collection of data related to that region. This allows for the collection of more appropriate data by collecting highly relevant data based on the student's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0079] The data collection unit can analyze students' social media activities and collect relevant data when collecting learning data. For example, the data collection unit can collect data based on the interests and passions that students share on social media. The data collection unit can also collect relevant data based on the content of accounts that students follow. Furthermore, the data collection unit can collect data based on the groups and events that students participate in on social media. This allows for the collection of relevant data by analyzing students' social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input students' social media activities into a generating AI and have the generating AI collect the relevant data.

[0080] The analysis unit can estimate the 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 can provide detailed analysis results. If the student is nervous, the analysis unit can also provide concise and to-the-point analysis results. Furthermore, if the student is excited, the analysis unit can provide visually appealing analysis results. By adjusting the presentation of the analysis based on the student's emotions, more appropriate analysis results can be provided. 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. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input student emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the training data during the analysis. For example, the analysis unit can perform a detailed analysis on important training data. For example, the analysis unit can perform a concise analysis on less important training data. The analysis unit can also adjust the depth of the analysis according to the importance of the training data. By adjusting the level of detail of the analysis based on the importance of the training data, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the training data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the training data during analysis. For example, the analysis unit can apply a numerical analysis algorithm to mathematical data. For example, the analysis unit can also apply a text analysis algorithm to literary data. Furthermore, the analysis unit can apply an experimental data analysis algorithm to scientific data. By applying different analysis algorithms depending on the category of the training data, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of the training data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0083] The analysis unit can estimate the student's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the student is in a hurry, the analysis unit can provide a short, concise analysis. If the student is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the student is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis based on the student's emotions, more appropriate analysis results can be provided. 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. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input student emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0084] The analysis unit can determine the priority of analysis based on the submission timing of the training data during analysis. For example, the analysis unit may prioritize the analysis of recently submitted training data. It may also prioritize the analysis of training data with an approaching submission deadline. Furthermore, the analysis unit can determine the order of analysis based on the submission timing. This allows for the provision of more appropriate analysis results by determining the priority of analysis based on the submission timing of the training data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the submission timing of the training data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the training data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant training data. For example, the analysis unit may postpone the analysis of less relevant training data. The analysis unit can also adjust the order of analysis based on the relevance of the training data. By adjusting the order of analysis based on the relevance of the training data, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the training data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0086] The suggestion function can estimate a student's emotions and adjust the way it presents the suggestion based on those emotions. For example, if a student is relaxed, the suggestion function may provide a detailed suggestion. If a student is nervous, the suggestion function may also provide a concise and to-the-point suggestion. Furthermore, if a student is excited, the suggestion function may provide a visually appealing suggestion. By adjusting the way the suggestion is presented based on the student's emotions, it is possible to provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion function may be performed using AI or not. For example, the suggestion function can input student emotion data into a generative AI and have the generative AI adjust the way the suggestion is presented.

[0087] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the learning content. For example, it can provide detailed suggestions for important learning content, and concise suggestions for less important learning content. It can also adjust the depth of its suggestions according to the importance of the learning content. By adjusting the level of detail of suggestions based on the importance of the learning content, it can provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the importance of the learning content into a generating AI and have the generating AI adjust the level of detail of the suggestions.

[0088] The proposal unit can apply different proposal algorithms depending on the category of the learning content during the proposal process. For example, the proposal unit can apply a numerical analysis algorithm to mathematical content. For example, it can also apply a text analysis algorithm to literary content. Furthermore, it can apply an experimental data analysis algorithm to scientific content. By applying different proposal algorithms depending on the category of the learning content, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the learning content into a generating AI and have the generating AI execute the application of different proposal algorithms.

[0089] The suggestion unit can estimate a student's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if a student is in a hurry, the suggestion unit can provide a short, concise suggestion. If a student is relaxed, the suggestion unit can also provide a detailed suggestion. Furthermore, if a student is excited, the suggestion unit can provide a visually stimulating suggestion. By adjusting the length of the suggestion based on the student's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input student emotion data into a generative AI and have the generative AI adjust the length of the suggestion.

[0090] The proposal unit can determine the priority of proposals based on the submission timing of the learning content. For example, the proposal unit may prioritize proposals for learning content with an approaching submission deadline. It may also prioritize proposals for learning content that has been recently submitted. Furthermore, the proposal unit can determine the order of proposals based on the submission timing. This allows for the provision of more appropriate proposals by prioritizing proposals based on the submission timing of the learning content. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the submission timing of the learning content into a generating AI and have the generating AI determine the priority of proposals.

[0091] The suggestion unit can adjust the order of suggestions based on the relevance of the learning content during the suggestion process. For example, the suggestion unit may prioritize suggesting highly relevant learning content. For example, the suggestion unit may postpone suggesting less relevant learning content. The suggestion unit can also adjust the order of suggestions based on the relevance of the learning content. This allows for the provision of more appropriate suggestions by adjusting the order of suggestions based on the relevance of the learning content. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance of the learning content into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0092] The monitoring unit can estimate a student's emotions and adjust the display method of monitoring based on the estimated emotions. For example, if a student is relaxed, the monitoring unit can provide detailed monitoring information. If a student is nervous, the monitoring unit can also provide concise and to-the-point monitoring information. Furthermore, if a student is excited, the monitoring unit can provide visually appealing monitoring information. This allows for the provision of more appropriate monitoring information by adjusting the display method of monitoring based on 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. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input student emotion data into a generative AI and have the generative AI adjust the display method of monitoring.

[0093] The monitoring unit can predict the current learning status by referring to past learning data during monitoring. For example, the monitoring unit predicts the current learning progress based on past learning data. The monitoring unit can also evaluate the current learning status by referring to past learning data. Furthermore, the monitoring unit can analyze past learning data and predict the current learning status. In this way, the current learning status can be predicted by referring to past learning data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI. For example, the monitoring unit can input past learning data into a generating AI and have the generating AI perform a prediction of the current learning status.

[0094] The monitoring unit can apply different monitoring methods to each category of training data during monitoring. For example, the monitoring unit can apply a monitoring method using numerical analysis to mathematical data. For example, the monitoring unit can also apply a monitoring method using text analysis to literary data. Furthermore, the monitoring unit can apply a monitoring method using experimental data analysis to scientific data. This allows for more appropriate monitoring by applying different monitoring methods to each category of training data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the categories of training data into a generating AI and have the generating AI execute the application of different monitoring methods.

[0095] The monitoring unit can estimate a student's emotions and adjust the importance of monitoring based on the estimated emotions. For example, if a student is relaxed, the monitoring unit can provide detailed monitoring information. If a student is nervous, the monitoring unit can also provide concise and to-the-point monitoring information. Furthermore, if a student is excited, the monitoring unit can provide visually appealing monitoring information. This allows for the provision of more appropriate monitoring information by adjusting the importance of monitoring based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input student emotion data into a generative AI and have the generative AI adjust the importance of monitoring.

[0096] The monitoring unit can analyze changes in monitoring based on the submission timing of training data during monitoring. For example, the monitoring unit may prioritize monitoring training data with an approaching submission deadline. It may also prioritize monitoring recently submitted training data. Furthermore, the monitoring unit can determine the order of monitoring based on the submission timing. This allows for more appropriate monitoring by analyzing changes in monitoring based on the submission timing of training data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the training data submission timing into a generating AI and have the generating AI perform the analysis of changes in monitoring.

[0097] The monitoring unit can analyze the monitoring by referring to relevant market data for the training data during monitoring. For example, the monitoring unit can evaluate the current learning progress based on the relevant market data for the training data. The monitoring unit can also predict the current learning status by referring to relevant market data for the training data. Furthermore, the monitoring unit can analyze the relevant market data for the training data and evaluate the current learning status. This allows for more appropriate monitoring by referring to relevant market data for the training data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevant market data for the training data into a generating AI and have the generating AI perform the monitoring analysis.

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

[0099] The career navigation agent system can also include a feedback unit. The feedback unit collects feedback from students on the suggestions and learning content they receive and sends it to the analysis unit. For example, it can collect information such as how well students understood the suggested learning content and their impressions of it. The feedback unit can also collect information on areas for improvement that students felt the suggested learning content could have. This allows the feedback unit to help the suggestion unit propose more appropriate learning content based on student feedback.

[0100] The career navigation agent system can also include a schedule management unit. This unit manages students' learning schedules and suggests optimal study times. For example, it can suggest efficient study times considering students' class schedules and extracurricular activity schedules. Furthermore, the schedule management unit can adjust schedules according to students' learning progress. For instance, if a student is behind in a particular subject, the schedule can be adjusted to allocate more time to that subject. In this way, the schedule management unit can support students in learning efficiently.

[0101] The Career Navigation Agent System can also include a Motivation Management Department. This department provides measures to enhance students' motivation to learn. For example, it can offer rewards when students achieve their goals. It can also send regular encouraging messages to maintain students' motivation. Furthermore, it can provide interesting information and topics related to the learning content to help students maintain their interest in learning. In this way, the Motivation Management Department can increase students' motivation to learn and support their continued learning.

[0102] The career navigation agent system can also include a performance evaluation unit. This unit evaluates students' learning outcomes and provides feedback. For example, it can assess learning progress based on students' test results and assignment submission status. The performance evaluation unit can also suggest specific areas for improvement in students' learning outcomes. Furthermore, it can compare students' learning outcomes with those of other students and provide relative evaluations. This allows the performance evaluation unit to help students objectively understand their own learning outcomes and identify areas for improvement.

[0103] The Career Navigation Agent System can also include a Communications Department. This department provides functions to facilitate communication between students, teachers, and parents. For example, students can send learning-related questions to teachers. The Communications Department can also provide parents with information to understand their child's learning progress and provide appropriate support. Furthermore, the Communications Department can provide a platform for students to share learning-related information and help each other. This allows the Communications Department to make it easier for students to receive learning support and improve learning efficiency.

[0104] The career navigation agent system can also be equipped with a reflection section. This section provides functions for students to reflect on their learning and conduct self-assessments. For example, students can record and review their learning progress and achieved goals. The reflection section can also allow students to record their feelings and insights during the learning process. Furthermore, the reflection section can provide support for students to conduct self-assessments and plan their future learning. In this way, the reflection section enables students to objectively reflect on their learning and promote their personal growth.

[0105] The Career Navigation Agent System can also be equipped with an interactive learning section. This section provides interactive learning content to encourage students to engage more actively in their studies. For example, students can review learning content in a quiz format. The interactive learning section can also allow students to acquire practical skills through simulations. Furthermore, the interactive learning section can provide group work where students collaborate with other students to solve problems. This allows students to enjoy learning and enhances the effectiveness of their learning.

[0106] The career navigation agent system can also include a career counseling department. This department provides a function where students can consult about their future careers. For example, it can provide information about occupations that students are interested in. Furthermore, the career counseling department can provide advice to help students understand their strengths and weaknesses and make appropriate career choices. In addition, the career counseling department can propose learning plans to help students acquire the skills and knowledge necessary for their future careers. In this way, the career counseling department can support students in thinking deeply about their careers and making appropriate choices.

[0107] The career navigation agent system can also include a health management department. This department monitors students' health and manages factors that affect their learning. For example, it can record students' sleep patterns and eating habits and assess their health. The health management department can also provide advice on how to relax if students are experiencing stress. Furthermore, it can suggest appropriate exercise plans if students are not getting enough exercise. In this way, the health management department can support students in maintaining their health while learning efficiently.

[0108] The Career Navigation Agent System can also include a Data Security Department. This department provides functions to securely protect students' personal information and learning data. For example, it can perform data encryption and access control. Furthermore, the Data Security Department can provide monitoring functions to prevent unauthorized access and data breaches. In addition, the Data Security Department can conduct regular security checks to ensure that student data is properly managed. This allows the Data Security Department to securely protect students' personal information and learning data, enabling them to use the system with peace of mind.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection department collects student learning data and survey results. For example, it collects information such as which subjects students are interested in and what kind of career they aspire to. The data collection department can collect information about students' interests and career goals through surveys. In addition, the data collection department can also collect data such as students' grades, attendance, and assignment submission status in order to collect learning data. For example, it collects information such as the subjects students have chosen and the club activities they have participated in. Step 2: The analysis unit analyzes the data collected by the collection unit to identify students' learning patterns and interests. For example, based on the collected information, it can analyze the distribution of students' study time and trends in their learning methods. It can also analyze survey results to identify students' interests and concerns. For example, it can identify which subjects students are interested in and what kind of careers they aspire to. Step 3: The proposal department proposes and provides optimal learning content and programs based on the learning patterns and interests identified by the analysis department. For example, if a student is interested in mathematics, the proposal department can suggest mathematics-related learning content. It can also provide learning content and programs tailored to the student's future career and path. For example, a student aspiring to become a doctor can be provided with medical-related learning content. Step 4: The monitoring department monitors the progress of learning content and programs proposed and provided by the proposal department in real time. For example, it can check in real time information such as the progress students have made in each subject and what learning content they are using. The monitoring department can monitor students' learning progress and provide appropriate support.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires 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.

[0112] 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 text generation AI, image generation AI, and multimodal generation AI. 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 with 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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] 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.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect student learning data and survey results, and the control unit 46A transmits the collected data to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the student's learning patterns and interests. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes and provides optimal learning content and programs based on the analysis results. The monitoring unit is implemented in the control unit 46A of the smart device 14, and monitors the progress of the proposed learning content and programs in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires 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.

[0128] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and monitoring unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect student learning data and questionnaire results, and the control unit 46A transmits the collected data to the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the student's learning patterns and interests. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12, and proposes and provides optimal learning content and programs based on the analysis results. The monitoring unit is implemented in the control unit 46A of the smart glasses 214, and monitors the progress of the proposed learning content and programs in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and monitoring unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects student learning data and questionnaire results using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A transmits the collected data to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the student's learning patterns and interests. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes and provides optimal learning content and programs based on the analysis results. The monitoring unit is implemented in the control unit 46A of the headset terminal 314, and monitors the progress of the proposed learning content and programs in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0161] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and monitoring unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to collect student learning data and questionnaire results, and the control unit 46A transmits the collected data to the data processing unit 12. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify the student's learning patterns and interests. The proposal unit is implemented in the identification processing unit 290 of the data processing unit 12, and proposes and provides optimal learning content and programs based on the analysis results. The monitoring unit is implemented in the control unit 46A of the robot 414, and monitors the progress of the proposed learning content and programs in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) The data collection department collects student learning data and survey results, The data collected by the aforementioned collection unit is analyzed by the analysis unit to identify students' learning patterns and interests, Based on the learning patterns and interests identified by the aforementioned analysis unit, the proposal unit proposes and provides optimal learning content and programs. The system includes a monitoring unit that monitors the progress of learning content and programs proposed and provided by the aforementioned proposal unit in real time. A system characterized by the following features. (Note 2) The aforementioned collection unit is Gather information about which subjects students are interested in and what kind of careers they aspire to. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected information is analyzed to identify students' learning patterns and interests. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on identified learning patterns and interests, we propose and provide optimal learning content and programs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned monitoring unit, This system allows us to track students' progress in each subject, their level of understanding, and the types of learning content they are using in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We provide learning content and programs tailored to your future career and path. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates students' emotions and adjusts the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze students' past learning history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting learning data, filtering is performed based on students' current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate students' emotions and prioritize the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting learning data, the system prioritizes collecting data that is highly relevant based on students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting learning data, analyze students' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the students' emotions and adjust the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the students' emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the training data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the training data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, We estimate the students' emotions and adjust the way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, Estimate the students' emotions and adjust the length of the proposal based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on the submission timing of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned monitoring unit, The system estimates students' emotions and adjusts how monitoring is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned monitoring unit, During monitoring, past training data is referenced to predict the current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned monitoring unit, During monitoring, different monitoring methods are applied to each category of training data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned monitoring unit, The system estimates students' emotions and adjusts the importance of monitoring based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned monitoring unit, During monitoring, analyze changes in monitoring based on when the training data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned monitoring unit, During monitoring, the monitoring is analyzed by referring to relevant market data from the training data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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 data collection department collects student learning data and survey results, The data collected by the aforementioned collection unit is analyzed by the analysis unit to identify students' learning patterns and interests, Based on the learning patterns and interests identified by the aforementioned analysis unit, the proposal unit proposes and provides optimal learning content and programs. The system includes a monitoring unit that grasps the progress of the learning content and programs proposed and provided by the aforementioned proposal unit in real time. A system characterized by the following features.

2. The aforementioned collection unit is Gather information about which subjects students are interested in and what kind of careers they aspire to. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed to identify students' learning patterns and interests. The system according to feature 1.

4. The aforementioned proposal section is, Based on identified learning patterns and interests, we propose and provide optimal learning content and programs. The system according to feature 1.

5. The aforementioned monitoring unit, This system allows us to track students' progress in each subject, their level of understanding, and the types of learning content they are using in real time. The system according to feature 1.

6. The aforementioned proposal section is, We provide learning content and programs tailored to your future career and path. The system according to feature 1.

7. The aforementioned collection unit is The system estimates students' emotions and adjusts the timing of data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze students' past learning history and select the optimal data collection method. The system according to feature 1.

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