system

Abstract

The system according to this embodiment aims to provide students with individually optimized problem sets based on their mock exam results. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and an update unit. The reception unit uploads the results of the mock exam. The analysis unit analyzes the results of the mock exam uploaded by the reception unit and identifies the student's weaknesses and strengths. The generation unit generates an optimal set of problems based on the weaknesses and strengths identified by the analysis unit. The update unit updates the set of problems generated by the generation unit and adaptively adjusts the difficulty level in accordance with the student's progress.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, 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 to individually provide an optimal question set based on the mock exam results of students, and there is room for improvement.

[0005] The system according to the embodiment aims to individually provide an optimal question set based on the mock exam results of students.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an update unit. The reception unit uploads the results of the mock exam. The analysis unit analyzes the results of the mock exam uploaded by the reception unit and identifies the student's weaknesses and strengths. The generation unit generates an optimal set of practice problems based on the weaknesses and strengths identified by the analysis unit. The update unit updates the set of practice problems generated by the generation unit and adaptively adjusts the difficulty level to match the student's progress. [Effects of the Invention]

[0007] The system according to this embodiment can provide students with individually optimized problem sets based on their mock exam results. [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 multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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 learning support system according to an embodiment of the present invention is a system in which a student uploads the results of their mock exams, and a generating AI analyzes these results to identify the student's weaknesses and strengths. This learning support system generates an optimal set of problems to strengthen the student's weaknesses and maintain their strengths. The generated set of problems is adjusted to each student's abilities. In addition, each set of problems is updated from time to time, and the difficulty level is adaptively adjusted to match the student's progress. The set of problems is created using audio, images, and text. First, the student uploads the results of their mock exams to the system. At this time, the mock exam results are input into the generating AI. The generating AI analyzes the input mock exam results and identifies the student's weaknesses and strengths. For example, if the score is low in a particular area of ​​mathematics, that area is identified as a weakness. Next, the generating AI generates an optimal set of problems for the student based on the identified weaknesses and strengths. The generated set of problems is designed to strengthen the student's weaknesses and maintain their strengths. For example, a set of problems containing many problems in a particular area of ​​mathematics is generated. Furthermore, the generated set of problems is updated from time to time to match the student's progress. The generating AI updates students' learning data daily and updates the problem sets accordingly. This ensures that students are always provided with problems that are appropriate to their learning progress. For example, if a student becomes able to solve problems in a particular area, the difficulty level of problems in that area is adaptively adjusted. This mechanism allows students to learn efficiently. By repeatedly studying their weaknesses, students can efficiently improve their academic ability. Furthermore, since the generated problem sets are created in audio, image, and text formats, students can learn in a variety of ways. For example, they can solve problems while listening to audio explanations. Thus, this invention, utilizing generating AI, is a service that analyzes each student's learning weaknesses and strengths and creates individual problem sets based on that analysis, realizing efficient and personalized learning. As a result, the learning support system can automatically analyze students' mock exam results and provide individually optimized problem sets to support efficient learning.

[0029] The learning support system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an update unit. The reception unit receives mock exam results from students. Students can upload mock exam results, for example, through a web interface or a mobile application. The reception unit stores the uploaded mock exam results in a database and passes them to the analysis unit. The analysis unit uses a generation AI to analyze the uploaded mock exam results and identify the students' weaknesses and strengths. The analysis unit uses, for example, statistical analysis or machine learning algorithms to analyze the mock exam results in detail. The analysis unit uses a generation AI to analyze the students' score distribution and correct answer rates and identify their weaknesses and strengths in specific subjects or problem types. The generation unit generates an optimal set of problems based on the weaknesses and strengths identified by the analysis unit. The generation unit uses a generation AI to generate a set of problems that strengthen the students' weaknesses and maintain their strengths. For example, the generation unit generates a set of problems that contains many problems in a specific area of ​​mathematics. The generation unit uses a generation AI to create the set of problems in audio, images, and text. The update unit updates the problem sets generated by the generation unit, adaptively adjusting the difficulty level to match the student's progress. The update unit uses generation AI to update the student's learning data daily and updates the problem sets based on that data. For example, if a student becomes able to solve problems in a particular area, the update unit adaptively adjusts the difficulty level of the problems in that area. As a result, the learning support system according to this embodiment can support efficient learning by automatically analyzing the student's mock exam results and providing individually optimized problem sets.

[0030] The reception department handles the uploading of mock exam results by students. Students can upload their mock exam results via a web interface or mobile application, for example. Specifically, students access a dedicated website, log in, and then access a form for uploading their mock exam results. This form includes fields for entering mock exam results, which students can manually enter or upload scanned results. If using a mobile application, students download the app, log in, and upload their mock exam results. The app also includes a function to scan mock exam results using the camera and automatically convert them into text data. The reception department stores the uploaded mock exam results in a database and passes them to the analysis department. The database is securely stored, ensuring the safe storage of students' personal information and performance data. The reception department also has a function to verify data integrity and check for fraudulent or incomplete data. This allows the reception department to provide a simple and secure environment for students to upload their mock exam results, enabling a smooth transition to the next analysis step.

[0031] The analysis department uses generative AI to analyze uploaded mock exam results and identify students' strengths and weaknesses. For example, the analysis department uses statistical analysis and machine learning algorithms to analyze the mock exam results in detail. Specifically, the generative AI analyzes students' score distribution and correct answer rates to identify weaknesses and strengths in specific subjects and problem types. For instance, when analyzing mathematics mock exam results, it analyzes the correct answer rate and time taken for each problem to evaluate understanding in specific areas (e.g., algebra or geometry). The generative AI can also use natural language processing technology to analyze students' answers and evaluate their quality and logical consistency. Furthermore, the analysis department refers to past mock exam results and learning history to evaluate long-term learning trends and progress. This allows the analysis department to comprehensively understand students' learning situations and provide feedback tailored to their individual learning needs. The analysis results are provided to students in visually easy-to-understand graphs and charts, allowing them to intuitively understand their learning progress and challenges. This allows the analysis unit to provide crucial information to maximize students' learning effectiveness and offer specific guidance for the next learning steps.

[0032] The generation unit generates optimal problem sets based on the weaknesses and strengths identified by the analysis unit. Using a generative AI, the generation unit creates problem sets designed to strengthen students' weaknesses and maintain their strengths. Specifically, the generative AI selects problems that focus on specific areas or problem types to reinforce students' weaknesses. For example, it might generate a problem set containing many problems from a particular area of ​​mathematics. The generative AI adjusts the difficulty and format of the problems to create an optimal set tailored to the student's level of understanding. The generation unit uses the generative AI to create problem sets using audio, images, and text. For example, it provides problems with many diagrams and graphs for students who prefer visual learning, and problems with audio explanations for students who prefer auditory learning. The generated problem sets are customized to the student's learning style and needs, supporting effective learning. Furthermore, the generation unit regularly updates the problem set content to reflect the latest learning trends and curricula. This allows the generation unit to consistently provide students with the most optimal learning resources, maximizing learning effectiveness.

[0033] The update unit updates the problem sets generated by the generation unit, adaptively adjusting the difficulty level to match the students' progress. Using a generation AI, the update unit updates student learning data daily and updates the problem sets accordingly. Specifically, when a student becomes able to solve problems in a particular area, it adaptively adjusts the difficulty level of problems in that area. For example, if a student can solve basic algebra problems, it provides intermediate-level problems to guide them to the next stage. The generation AI analyzes the student's answer history and learning patterns to adjust the difficulty level of problems at the optimal time. Furthermore, the update unit collects student feedback to improve the content and format of the problem sets. For example, if a student finds a particular problem difficult to understand, it adds an explanation of that problem or provides problems that explain the same concept from a different approach. The update unit monitors students' learning progress in real time and updates the problem sets as needed, always providing the optimal learning environment. This allows the update unit to maximize student learning effectiveness and support efficient learning.

[0034] The generation unit can adjust the generated problem sets to match the students' abilities. For example, the generation unit can adjust the difficulty level of the problem sets based on the students' past grades and mock exam results. The generation unit uses a generation AI to generate problem sets that are appropriate for the students' abilities. For example, the generation unit can reduce the number of problems in areas where the student excels and increase the number of problems in areas where the student struggles. The generation unit can also use the generation AI to adjust the content of the problem sets to match the students' learning progress. This maximizes learning effectiveness by providing problem sets that are appropriate for the students' abilities. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the results of the students' mock exams into the generation AI, which can then adjust the content of the problem sets.

[0035] The update unit can periodically update the generated problem sets and adaptively adjust the difficulty level to match the students' progress. For example, the update unit can update the students' learning data daily and update the problem sets based on that data. The update unit can use a generative AI to adjust the content of the problem sets according to the students' progress. For example, if a student becomes able to solve problems in a particular area, the update unit can adaptively adjust the difficulty level of problems in that area. The update unit can also use a generative AI to update the content of the problem sets in real time. This allows the problem sets to be updated according to the students' progress, and problems of an appropriate difficulty level to always be provided. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input student learning data into a generative AI, and the generative AI can update the content of the problem sets.

[0036] The generation unit can create problem sets in audio, images, and text. For example, the generation unit can generate problem sets that include audio explanations. The generation unit uses a generation AI to create the content of problem sets in audio, images, and text. For example, the generation unit can generate problem sets that make extensive use of images and diagrams. The generation unit can also use a generation AI to generate text-based problem sets. This allows for learning tailored to students' learning styles by providing problem sets in diverse formats. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the content of a problem set into a generation AI, which can then create the problem set in audio, images, and text.

[0037] The reception desk can analyze a student's past mock exam result upload history and select the optimal upload method. For example, the reception desk can prioritize suggesting upload methods the student has used in the past (manual, voice input, etc.). The reception desk can suggest the optimal time slot based on the student's past upload history for specific time slots. The reception desk can also analyze a student's past upload failure history and suggest methods that are less likely to fail. This improves upload efficiency by suggesting the optimal upload method based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the student's upload history data into a generating AI, which can then select the optimal upload method.

[0038] The reception desk can filter mock exam results when they are uploaded, based on the student's current learning status and areas of interest. For example, the reception desk can prioritize uploading mock exam results related to the subject the student is currently studying. The reception desk can also prioritize uploading mock exam results in areas of interest the student. The reception desk can also upload only the necessary mock exam results according to the student's learning progress. This supports efficient learning by prioritizing the upload of mock exam results that match the student's learning status and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input student learning status data into a generating AI, which can then perform the filtering.

[0039] The reception desk can prioritize uploading highly relevant mock exam results based on the student's geographical location when the student uploads their results. For example, if a student is in a specific region, the reception desk can prioritize uploading mock exam results related to that region. If a student is traveling, the reception desk can prioritize uploading mock exam results related to their travel destination. If a student is at home, the reception desk can also prioritize uploading mock exam results related to their home study. This allows for efficient learning by prioritizing the uploading of highly relevant mock exam results based on geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the student's geographical location data into a generating AI, which can then select highly relevant mock exam results.

[0040] The reception desk can analyze students' social media activity when they upload mock exam results and upload relevant results. For example, if a student mentions a specific subject on social media, the reception desk can prioritize uploading mock exam results related to that subject. The reception desk can also upload relevant mock exam results based on learning objectives shared by students on social media. The reception desk can also upload relevant mock exam results based on information from educational accounts that students follow on social media. This allows for efficient learning by uploading relevant mock exam results based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input student social media activity data into a generating AI, which can then select relevant mock exam results.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the mock exam results. For example, the analysis unit can perform a detailed analysis on important mock exam results. For less important mock exam results, it can perform a simplified analysis. The analysis unit can also perform a particularly detailed analysis on mock exam results related to the students' learning objectives. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the mock exam results. 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 mock exam result data into a generating AI, which can then adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the mock exam during analysis. For example, the analysis unit can apply a mathematical formula analysis algorithm to the results of a mathematics mock exam. For the results of an English mock exam, the analysis unit can apply a grammatical analysis algorithm. For the results of a science mock exam, the analysis unit can also apply an experimental data analysis algorithm. By applying an analysis algorithm appropriate to the category of the mock exam, highly accurate analysis can be achieved. 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 category data of the mock exam into a generating AI, which can then select an appropriate analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on the submission date of the mock exams. For example, the analysis unit may prioritize the analysis of recently submitted mock exam results. The analysis unit may also prioritize the analysis of mock exam results submitted by students within a specific deadline. The analysis unit can also determine the priority based on mock exam results submitted by students in the past. This allows for efficient analysis by determining the priority of analysis based on the submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input mock exam submission date data into a generating AI, which can then determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the mock exams during the analysis process. For example, the analysis unit can prioritize the analysis of mock exam results related to the student's learning objectives. The analysis unit can also prioritize the analysis of mock exam results related to the student's current learning situation. The analysis unit can also prioritize the analysis of mock exam results related to the student's areas of interest. This allows for efficient analysis by adjusting the order of analysis based on relevance. 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 relevance data of the mock exams into a generating AI, which can then adjust the order of analysis.

[0045] The generation unit can adjust the level of detail in the problem sets based on the importance of the student's weaknesses and strengths during generation. For example, the generation unit can generate problem sets with detailed explanations for the student's weaknesses. For the student's strengths, it can generate simplified problem sets. The generation unit can also generate particularly detailed problem sets for areas related to the student's learning objectives. This allows for efficient learning by adjusting the level of detail in the problem sets according to the importance of weaknesses and strengths. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the student's weaknesses and strengths into a generation AI, which can then adjust the level of detail in the problem sets.

[0046] The generation unit can apply different generation algorithms depending on the student's learning style during generation. For example, for visual learners, the generation unit can generate problem sets that make extensive use of images and diagrams. For auditory learners, the generation unit can generate problem sets that include audio explanations. For tactile learners, the generation unit can also generate interactive problem sets. This allows for efficient learning by applying a generation algorithm tailored to the learning style. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning style data into a generation AI, which can then select an appropriate generation algorithm.

[0047] The generation unit can determine the priority of problem sets based on the student's learning progress during generation. For example, the generation unit can prioritize generating problem sets related to the subject the student is currently studying. The generation unit can also prioritize generating problem sets related to subjects the student has struggled with in the past. The generation unit can also prioritize generating problem sets related to the student's learning goals. This allows for efficient learning by prioritizing problem sets based on learning progress. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning progress data into a generation AI, which can then determine the priority of problem sets.

[0048] The generation unit can adjust the order of the problem sets based on the student's learning history during generation. For example, the generation unit can generate problem sets in the optimal order based on the order in which the student has solved problems in the past. The generation unit can adjust the order of the problem sets according to the student's learning progress. The generation unit can also adjust the order of the problem sets based on the student's areas of interest. This allows for efficient learning by adjusting the order of problem sets based on the learning history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the student's learning history data into a generation AI, which can then adjust the order of the problem sets.

[0049] The update unit can analyze students' learning progress and select the optimal update method during the update process. For example, if a student is behind in a particular subject, the update unit will prioritize updating the problem sets for that subject. If a student is ahead in a particular area, the update unit can add problem sets for that area. The update unit can also select the optimal update method based on the student's learning objectives. This allows for efficient learning by selecting the optimal update method based on learning progress. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input student learning progress data into a generating AI, which can then select the optimal update method.

[0050] The update unit can adaptively adjust the difficulty level of the problem sets based on the student's learning history during updates. For example, the update unit can adjust the difficulty level of the problem sets based on the student's accuracy rate on problems they have previously solved. If a student is weak in a particular area, the update unit can lower the difficulty level of problems in that area. If a student is strong in a particular area, the update unit can also raise the difficulty level of problems in that area. In this way, by adjusting the difficulty level of the problem sets based on the learning history, efficient learning can be supported. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the student's learning history data into a generating AI, which can then adjust the difficulty level of the problem sets.

[0051] The update unit can select the optimal update method based on the student's geographical location information during the update process. For example, if the student is in a specific region, the update unit can include questions related to that region. If the student is traveling, the update unit can include questions related to the travel destination. If the student is at home, the update unit can include questions related to home learning. This allows for efficient learning by selecting the optimal update method based on geographical location information. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the student's geographical location data into a generating AI, which can then select the optimal update method.

[0052] The update unit can analyze students' social media activity during updates to suggest updated content for the problem sets. For example, if a student mentions a specific subject on social media, the update unit can include questions related to that subject. The update unit can also include questions related to learning objectives shared by students on social media. Furthermore, the update unit can include questions related to educational accounts that students follow on social media. This allows for efficient learning by suggesting updates based on social media activity. Some or all of the above processing in the update unit may be performed using AI, for example, or not. For example, the update unit can input student social media activity data into a generating AI, which can then suggest updated content.

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

[0054] The reception desk can analyze students' learning history and suggest the optimal upload time. For example, based on a student's past upload history at a specific time, it can prompt them to upload at a similar time. It can also analyze a student's past upload failure history and suggest times when failures are less likely. Furthermore, it can suggest the optimal upload time according to the student's learning progress. In this way, by suggesting the optimal upload time based on the student's learning history, the efficiency of uploads can be improved.

[0055] The analysis unit can analyze a student's learning history and select the optimal analysis algorithm. For example, if a student has struggled with a particular subject in the past, a specialized analysis algorithm for that subject can be applied. Furthermore, for subjects where the student excels, a more detailed analysis algorithm can be selected. Additionally, the optimal analysis algorithm can be selected based on the student's learning objectives. This allows for highly accurate analysis by selecting the most suitable algorithm based on the student's learning history.

[0056] The generation unit can analyze students' learning history and select the optimal method for generating problem sets. For example, if a student has previously learned effectively using a particular type of problem set, that format can be prioritized. It can also avoid problem sets in formats that students have struggled with in the past. Furthermore, it can select the optimal method for generating problem sets based on the student's learning objectives. In this way, by selecting the optimal method for generating problem sets based on the student's learning history, it can support efficient learning.

[0057] The update function can analyze students' learning history and suggest the optimal update timing. For example, based on a student's past learning history at specific time slots, it can prompt them to update at similar times. It can also analyze the times when students have been most focused on learning in the past and suggest updates during those times. Furthermore, it can suggest the optimal update timing according to the student's learning progress. In this way, by suggesting the optimal update timing based on the student's learning history, it can support efficient learning.

[0058] The reception desk can select the optimal upload method based on the student's geographical location. For example, if a student is in a specific region, they can prioritize uploading mock exam results related to that region. Similarly, if a student is traveling, they can prioritize uploading mock exam results related to their travel destination. Furthermore, if a student is at home, they can prioritize uploading mock exam results related to their home study. This allows for efficient learning by prioritizing the upload of highly relevant mock exam results based on geographical location.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception department receives the mock exam results from students. Students can upload their mock exam results via a web interface or mobile application. The reception department stores the uploaded mock exam results in a database and passes them on to the analysis department. Step 2: The analysis department uses generative AI to analyze the uploaded mock exam results and identify students' weaknesses and strengths. The analysis department uses statistical analysis and machine learning algorithms to analyze the mock exam results in detail and identify weaknesses and strengths in specific subjects and question types. Step 3: The generation unit generates an optimal set of problems based on the weaknesses and strengths identified by the analysis unit. The generation unit uses a generation AI to generate a set of problems that strengthen the student's weaknesses and maintain their strengths. For example, it might generate a set of problems that contain many problems from a specific area of ​​mathematics. The generation unit creates the set of problems using audio, images, and text. Step 4: The update unit updates the problem sets generated by the generation unit, adaptively adjusting the difficulty level to match the students' progress. The update unit uses generation AI to update students' learning data daily and updates the problem sets accordingly. For example, if a student becomes able to solve problems in a particular area, the difficulty level of the problems in that area will be adaptively adjusted.

[0061] (Example of form 2) The learning support system according to an embodiment of the present invention is a system in which a student uploads the results of their mock exams, and a generating AI analyzes these results to identify the student's weaknesses and strengths. This learning support system generates an optimal set of problems to strengthen the student's weaknesses and maintain their strengths. The generated set of problems is adjusted to each student's abilities. In addition, each set of problems is updated from time to time, and the difficulty level is adaptively adjusted to match the student's progress. The set of problems is created using audio, images, and text. First, the student uploads the results of their mock exams to the system. At this time, the mock exam results are input into the generating AI. The generating AI analyzes the input mock exam results and identifies the student's weaknesses and strengths. For example, if the score is low in a particular area of ​​mathematics, that area is identified as a weakness. Next, the generating AI generates an optimal set of problems for the student based on the identified weaknesses and strengths. The generated set of problems is designed to strengthen the student's weaknesses and maintain their strengths. For example, a set of problems containing many problems in a particular area of ​​mathematics is generated. Furthermore, the generated set of problems is updated from time to time to match the student's progress. The generating AI updates students' learning data daily and updates the problem sets accordingly. This ensures that students are always provided with problems that are appropriate to their learning progress. For example, if a student becomes able to solve problems in a particular area, the difficulty level of problems in that area is adaptively adjusted. This mechanism allows students to learn efficiently. By repeatedly studying their weaknesses, students can efficiently improve their academic ability. Furthermore, since the generated problem sets are created in audio, image, and text formats, students can learn in a variety of ways. For example, they can solve problems while listening to audio explanations. Thus, this invention, utilizing generating AI, is a service that analyzes each student's learning weaknesses and strengths and creates individual problem sets based on that analysis, realizing efficient and personalized learning. As a result, the learning support system can automatically analyze students' mock exam results and provide individually optimized problem sets to support efficient learning.

[0062] The learning support system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and an update unit. The reception unit receives mock exam results from students. Students can upload mock exam results, for example, through a web interface or a mobile application. The reception unit stores the uploaded mock exam results in a database and passes them to the analysis unit. The analysis unit uses a generation AI to analyze the uploaded mock exam results and identify the students' weaknesses and strengths. The analysis unit uses, for example, statistical analysis or machine learning algorithms to analyze the mock exam results in detail. The analysis unit uses a generation AI to analyze the students' score distribution and correct answer rates and identify their weaknesses and strengths in specific subjects or problem types. The generation unit generates an optimal set of problems based on the weaknesses and strengths identified by the analysis unit. The generation unit uses a generation AI to generate a set of problems that strengthen the students' weaknesses and maintain their strengths. For example, the generation unit generates a set of problems that contains many problems in a specific area of ​​mathematics. The generation unit uses a generation AI to create the set of problems in audio, images, and text. The update unit updates the problem sets generated by the generation unit, adaptively adjusting the difficulty level to match the student's progress. The update unit uses generation AI to update the student's learning data daily and updates the problem sets based on that data. For example, if a student becomes able to solve problems in a particular area, the update unit adaptively adjusts the difficulty level of the problems in that area. As a result, the learning support system according to this embodiment can support efficient learning by automatically analyzing the student's mock exam results and providing individually optimized problem sets.

[0063] The reception department handles the uploading of mock exam results by students. Students can upload their mock exam results via a web interface or mobile application, for example. Specifically, students access a dedicated website, log in, and then access a form for uploading their mock exam results. This form includes fields for entering mock exam results, which students can manually enter or upload scanned results. If using a mobile application, students download the app, log in, and upload their mock exam results. The app also includes a function to scan mock exam results using the camera and automatically convert them into text data. The reception department stores the uploaded mock exam results in a database and passes them to the analysis department. The database is securely stored, ensuring the safe storage of students' personal information and performance data. The reception department also has a function to verify data integrity and check for fraudulent or incomplete data. This allows the reception department to provide a simple and secure environment for students to upload their mock exam results, enabling a smooth transition to the next analysis step.

[0064] The analysis department uses generative AI to analyze uploaded mock exam results and identify students' strengths and weaknesses. For example, the analysis department uses statistical analysis and machine learning algorithms to analyze the mock exam results in detail. Specifically, the generative AI analyzes students' score distribution and correct answer rates to identify weaknesses and strengths in specific subjects and problem types. For instance, when analyzing mathematics mock exam results, it analyzes the correct answer rate and time taken for each problem to evaluate understanding in specific areas (e.g., algebra or geometry). The generative AI can also use natural language processing technology to analyze students' answers and evaluate their quality and logical consistency. Furthermore, the analysis department refers to past mock exam results and learning history to evaluate long-term learning trends and progress. This allows the analysis department to comprehensively understand students' learning situations and provide feedback tailored to their individual learning needs. The analysis results are provided to students in visually easy-to-understand graphs and charts, allowing them to intuitively understand their learning progress and challenges. This allows the analysis unit to provide crucial information to maximize students' learning effectiveness and offer specific guidance for the next learning steps.

[0065] The generation unit generates optimal problem sets based on the weaknesses and strengths identified by the analysis unit. Using a generative AI, the generation unit creates problem sets designed to strengthen students' weaknesses and maintain their strengths. Specifically, the generative AI selects problems that focus on specific areas or problem types to reinforce students' weaknesses. For example, it might generate a problem set containing many problems from a particular area of ​​mathematics. The generative AI adjusts the difficulty and format of the problems to create an optimal set tailored to the student's level of understanding. The generation unit uses the generative AI to create problem sets using audio, images, and text. For example, it provides problems with many diagrams and graphs for students who prefer visual learning, and problems with audio explanations for students who prefer auditory learning. The generated problem sets are customized to the student's learning style and needs, supporting effective learning. Furthermore, the generation unit regularly updates the problem set content to reflect the latest learning trends and curricula. This allows the generation unit to consistently provide students with the most optimal learning resources, maximizing learning effectiveness.

[0066] The update unit updates the problem sets generated by the generation unit, adaptively adjusting the difficulty level to match the students' progress. Using a generation AI, the update unit updates student learning data daily and updates the problem sets accordingly. Specifically, when a student becomes able to solve problems in a particular area, it adaptively adjusts the difficulty level of problems in that area. For example, if a student can solve basic algebra problems, it provides intermediate-level problems to guide them to the next stage. The generation AI analyzes the student's answer history and learning patterns to adjust the difficulty level of problems at the optimal time. Furthermore, the update unit collects student feedback to improve the content and format of the problem sets. For example, if a student finds a particular problem difficult to understand, it adds an explanation of that problem or provides problems that explain the same concept from a different approach. The update unit monitors students' learning progress in real time and updates the problem sets as needed, always providing the optimal learning environment. This allows the update unit to maximize student learning effectiveness and support efficient learning.

[0067] The generation unit can adjust the generated problem sets to match the students' abilities. For example, the generation unit can adjust the difficulty level of the problem sets based on the students' past grades and mock exam results. The generation unit uses a generation AI to generate problem sets that are appropriate for the students' abilities. For example, the generation unit can reduce the number of problems in areas where the student excels and increase the number of problems in areas where the student struggles. The generation unit can also use the generation AI to adjust the content of the problem sets to match the students' learning progress. This maximizes learning effectiveness by providing problem sets that are appropriate for the students' abilities. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the results of the students' mock exams into the generation AI, which can then adjust the content of the problem sets.

[0068] The update unit can periodically update the generated problem sets and adaptively adjust the difficulty level to match the students' progress. For example, the update unit can update the students' learning data daily and update the problem sets based on that data. The update unit can use a generative AI to adjust the content of the problem sets according to the students' progress. For example, if a student becomes able to solve problems in a particular area, the update unit can adaptively adjust the difficulty level of problems in that area. The update unit can also use a generative AI to update the content of the problem sets in real time. This allows the problem sets to be updated according to the students' progress, and problems of an appropriate difficulty level to always be provided. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input student learning data into a generative AI, and the generative AI can update the content of the problem sets.

[0069] The generation unit can create problem sets in audio, images, and text. For example, the generation unit can generate problem sets that include audio explanations. The generation unit uses a generation AI to create the content of problem sets in audio, images, and text. For example, the generation unit can generate problem sets that make extensive use of images and diagrams. The generation unit can also use a generation AI to generate text-based problem sets. This allows for learning tailored to students' learning styles by providing problem sets in diverse formats. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the content of a problem set into a generation AI, which can then create the problem set in audio, images, and text.

[0070] The reception desk can estimate a student's emotions and adjust the timing of uploading mock exam results based on the estimated emotions. For example, if a student is feeling stressed, the reception desk can encourage them to upload during a time when they can relax. If a student is concentrating, the reception desk can encourage them to upload immediately. If a student is tired, the reception desk can also encourage them to upload after a break. By adjusting the upload timing according to the student's emotions, stress can be reduced and uploads can be made more efficient. 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 reception desk may be performed using AI or not using AI. For example, the reception desk can input student facial expression data into a generative AI, which can then estimate emotions.

[0071] The reception desk can analyze a student's past mock exam result upload history and select the optimal upload method. For example, the reception desk can prioritize suggesting upload methods the student has used in the past (manual, voice input, etc.). The reception desk can suggest the optimal time slot based on the student's past upload history for specific time slots. The reception desk can also analyze a student's past upload failure history and suggest methods that are less likely to fail. This improves upload efficiency by suggesting the optimal upload method based on past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the student's upload history data into a generating AI, which can then select the optimal upload method.

[0072] The reception desk can filter mock exam results when they are uploaded, based on the student's current learning status and areas of interest. For example, the reception desk can prioritize uploading mock exam results related to the subject the student is currently studying. The reception desk can also prioritize uploading mock exam results in areas of interest the student. The reception desk can also upload only the necessary mock exam results according to the student's learning progress. This supports efficient learning by prioritizing the upload of mock exam results that match the student's learning status and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input student learning status data into a generating AI, which can then perform the filtering.

[0073] The reception desk can estimate a student's emotions and determine the priority of the mock exam results to upload based on the estimated emotions. For example, if a student is feeling anxious, the reception desk will prioritize uploading important mock exam results. If a student is relaxed, the reception desk can upload all mock exam results equally. If a student is in a hurry, the reception desk can upload only the most important mock exam results. This allows for efficient uploads by prioritizing uploads according to the student's emotions. 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 reception desk may be performed using AI or not. For example, the reception desk can input student emotion data into a generative AI, which can then determine the upload priority.

[0074] The reception desk can prioritize uploading highly relevant mock exam results based on the student's geographical location when the student uploads their results. For example, if a student is in a specific region, the reception desk can prioritize uploading mock exam results related to that region. If a student is traveling, the reception desk can prioritize uploading mock exam results related to their travel destination. If a student is at home, the reception desk can also prioritize uploading mock exam results related to their home study. This allows for efficient learning by prioritizing the uploading of highly relevant mock exam results based on geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the student's geographical location data into a generating AI, which can then select highly relevant mock exam results.

[0075] The reception desk can analyze students' social media activity when they upload mock exam results and upload relevant results. For example, if a student mentions a specific subject on social media, the reception desk can prioritize uploading mock exam results related to that subject. The reception desk can also upload relevant mock exam results based on learning objectives shared by students on social media. The reception desk can also upload relevant mock exam results based on information from educational accounts that students follow on social media. This allows for efficient learning by uploading relevant mock exam results based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input student social media activity data into a generating AI, which can then select relevant mock exam results.

[0076] 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 nervous, the analysis unit can provide a simple and highly visual analysis result. If the student is relaxed, the analysis unit can provide a detailed analysis result. If the student is excited, the analysis unit can also provide an analysis result with visually stimulating effects. In this way, by adjusting the presentation of the analysis according to the student's emotions, it is possible to provide an easily understandable analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 the student's emotion data into the generative AI, and the generative AI can adjust the presentation of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the mock exam results. For example, the analysis unit can perform a detailed analysis on important mock exam results. For less important mock exam results, it can perform a simplified analysis. The analysis unit can also perform a particularly detailed analysis on mock exam results related to the students' learning objectives. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the mock exam results. 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 mock exam result data into a generating AI, which can then adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of the mock exam during analysis. For example, the analysis unit can apply a mathematical formula analysis algorithm to the results of a mathematics mock exam. For the results of an English mock exam, the analysis unit can apply a grammatical analysis algorithm. For the results of a science mock exam, the analysis unit can also apply an experimental data analysis algorithm. By applying an analysis algorithm appropriate to the category of the mock exam, highly accurate analysis can be achieved. 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 category data of the mock exam into a generating AI, which can then select an appropriate analysis algorithm.

[0079] 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 provide a detailed analysis. If the student is excited, the analysis unit can also provide an analysis with visually stimulating effects. By adjusting the length of the analysis according to the student's emotions, it is possible to provide an easily understandable analysis. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 the generative AI can adjust the length of the analysis.

[0080] The analysis unit can determine the priority of analysis based on the submission date of the mock exams. For example, the analysis unit may prioritize the analysis of recently submitted mock exam results. The analysis unit may also prioritize the analysis of mock exam results submitted by students within a specific deadline. The analysis unit can also determine the priority based on mock exam results submitted by students in the past. This allows for efficient analysis by determining the priority of analysis based on the submission date. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input mock exam submission date data into a generating AI, which can then determine the priority of analysis.

[0081] The analysis unit can adjust the order of analysis based on the relevance of the mock exams during the analysis process. For example, the analysis unit can prioritize the analysis of mock exam results related to the student's learning objectives. The analysis unit can also prioritize the analysis of mock exam results related to the student's current learning situation. The analysis unit can also prioritize the analysis of mock exam results related to the student's areas of interest. This allows for efficient analysis by adjusting the order of analysis based on relevance. 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 relevance data of the mock exams into a generating AI, which can then adjust the order of analysis.

[0082] The generation unit can estimate the student's emotions and adjust the presentation of the generated worksheets based on the estimated emotions. For example, if the student is relaxed, the generation unit can generate worksheets that proceed at a relaxed pace. If the student is in a hurry, the generation unit can generate worksheets that emphasize the shortest route. If the student is excited, the generation unit can also generate worksheets with visually stimulating effects. This maximizes learning effectiveness by adjusting the presentation of the worksheets according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input student emotion data into a generation AI, which can then adjust the presentation of the worksheets.

[0083] The generation unit can adjust the level of detail in the problem sets based on the importance of the student's weaknesses and strengths during generation. For example, the generation unit can generate problem sets with detailed explanations for the student's weaknesses. For the student's strengths, it can generate simplified problem sets. The generation unit can also generate particularly detailed problem sets for areas related to the student's learning objectives. This allows for efficient learning by adjusting the level of detail in the problem sets according to the importance of weaknesses and strengths. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the student's weaknesses and strengths into a generation AI, which can then adjust the level of detail in the problem sets.

[0084] The generation unit can apply different generation algorithms depending on the student's learning style during generation. For example, for visual learners, the generation unit can generate problem sets that make extensive use of images and diagrams. For auditory learners, the generation unit can generate problem sets that include audio explanations. For tactile learners, the generation unit can also generate interactive problem sets. This allows for efficient learning by applying a generation algorithm tailored to the learning style. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning style data into a generation AI, which can then select an appropriate generation algorithm.

[0085] The generation unit can estimate the student's emotions and adjust the length of the generated question sets based on the estimated emotions. For example, if the student is in a hurry, the generation unit can generate short, concise question sets. If the student is relaxed, the generation unit can generate longer question sets with detailed explanations. If the student is excited, the generation unit can also generate question sets with visually stimulating effects. This allows for efficient learning by adjusting the length of the question sets according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input student emotion data into a generation AI, which can then adjust the length of the question sets.

[0086] The generation unit can determine the priority of problem sets based on the student's learning progress during generation. For example, the generation unit can prioritize generating problem sets related to the subject the student is currently studying. The generation unit can also prioritize generating problem sets related to subjects the student has struggled with in the past. The generation unit can also prioritize generating problem sets related to the student's learning goals. This allows for efficient learning by prioritizing problem sets based on learning progress. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning progress data into a generation AI, which can then determine the priority of problem sets.

[0087] The generation unit can adjust the order of the problem sets based on the student's learning history during generation. For example, the generation unit can generate problem sets in the optimal order based on the order in which the student has solved problems in the past. The generation unit can adjust the order of the problem sets according to the student's learning progress. The generation unit can also adjust the order of the problem sets based on the student's areas of interest. This allows for efficient learning by adjusting the order of problem sets based on the learning history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the student's learning history data into a generation AI, which can then adjust the order of the problem sets.

[0088] The update unit can estimate the student's emotions and adjust the frequency of updates to the problem set based on the estimated emotions. For example, if the student is stressed, the update unit can reduce the update frequency to alleviate the burden. If the student is relaxed, the update unit can maintain the normal update frequency. If the student is excited, the update unit can also increase the update frequency to enhance their motivation to learn. In this way, by adjusting the update frequency according to the student's emotions, efficient learning can be supported. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 update unit may be performed using AI, for example, or not using AI. For example, the update unit can input student emotion data into the generative AI, and the generative AI can adjust the update frequency.

[0089] The update unit can analyze students' learning progress and select the optimal update method during the update process. For example, if a student is behind in a particular subject, the update unit will prioritize updating the problem sets for that subject. If a student is ahead in a particular area, the update unit can add problem sets for that area. The update unit can also select the optimal update method based on the student's learning objectives. This allows for efficient learning by selecting the optimal update method based on learning progress. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input student learning progress data into a generating AI, which can then select the optimal update method.

[0090] The update unit can adaptively adjust the difficulty level of the problem sets based on the student's learning history during updates. For example, the update unit can adjust the difficulty level of the problem sets based on the student's accuracy rate on problems they have previously solved. If a student is weak in a particular area, the update unit can lower the difficulty level of problems in that area. If a student is strong in a particular area, the update unit can also raise the difficulty level of problems in that area. In this way, by adjusting the difficulty level of the problem sets based on the learning history, efficient learning can be supported. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the student's learning history data into a generating AI, which can then adjust the difficulty level of the problem sets.

[0091] The update unit can estimate a student's emotions and adjust the content of the problem set based on the estimated emotions. For example, if a student is stressed, the update unit can include more easy problems. If a student is relaxed, the update unit can include problems of normal difficulty. If a student is excited, the update unit can include more difficult problems. This allows for efficient learning by adjusting the content according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the update unit may be performed using AI, or not using AI. For example, the update unit can input student emotion data into a generative AI, which can then adjust the content of the update.

[0092] The update unit can select the optimal update method based on the student's geographical location information during the update process. For example, if the student is in a specific region, the update unit can include questions related to that region. If the student is traveling, the update unit can include questions related to the travel destination. If the student is at home, the update unit can include questions related to home learning. This allows for efficient learning by selecting the optimal update method based on geographical location information. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the student's geographical location data into a generating AI, which can then select the optimal update method.

[0093] The update unit can analyze students' social media activity during updates to suggest updated content for the problem sets. For example, if a student mentions a specific subject on social media, the update unit can include questions related to that subject. The update unit can also include questions related to learning objectives shared by students on social media. Furthermore, the update unit can include questions related to educational accounts that students follow on social media. This allows for efficient learning by suggesting updates based on social media activity. Some or all of the above processing in the update unit may be performed using AI, for example, or not. For example, the update unit can input student social media activity data into a generating AI, which can then suggest updated content.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The reception desk can analyze students' learning history and suggest the optimal upload time. For example, based on a student's past upload history at a specific time, it can prompt them to upload at a similar time. It can also analyze a student's past upload failure history and suggest times when failures are less likely. Furthermore, it can suggest the optimal upload time according to the student's learning progress. In this way, by suggesting the optimal upload time based on the student's learning history, the efficiency of uploads can be improved.

[0096] The analysis unit can analyze a student's learning history and select the optimal analysis algorithm. For example, if a student has struggled with a particular subject in the past, a specialized analysis algorithm for that subject can be applied. Furthermore, for subjects where the student excels, a more detailed analysis algorithm can be selected. Additionally, the optimal analysis algorithm can be selected based on the student's learning objectives. This allows for highly accurate analysis by selecting the most suitable algorithm based on the student's learning history.

[0097] The generation unit can analyze students' learning history and select the optimal method for generating problem sets. For example, if a student has previously learned effectively using a particular type of problem set, that format can be prioritized. It can also avoid problem sets in formats that students have struggled with in the past. Furthermore, it can select the optimal method for generating problem sets based on the student's learning objectives. In this way, by selecting the optimal method for generating problem sets based on the student's learning history, it can support efficient learning.

[0098] The update function can analyze students' learning history and suggest the optimal update timing. For example, based on a student's past learning history at specific time slots, it can prompt them to update at similar times. It can also analyze the times when students have been most focused on learning in the past and suggest updates during those times. Furthermore, it can suggest the optimal update timing according to the student's learning progress. In this way, by suggesting the optimal update timing based on the student's learning history, it can support efficient learning.

[0099] The reception desk can estimate students' emotions and adjust the timing of mock exam result uploads based on those estimates. For example, if a student is feeling stressed, they can be encouraged to upload during a time when they can relax. If a student is concentrating, they can be encouraged to upload immediately. Furthermore, if a student is tired, they can be encouraged to upload after a break. By adjusting the upload timing according to students' emotions, stress can be reduced and uploads can be made more efficient.

[0100] The analysis unit can estimate the student's emotions and adjust the presentation of the analysis based on those emotions. For example, if the student is nervous, it can provide a simple and easy-to-understand analysis result. If the student is relaxed, it can provide a detailed analysis result. Furthermore, if the student is excited, it can provide an analysis result with visually stimulating effects. In this way, by adjusting the presentation of the analysis according to the student's emotions, it is possible to provide an easily understandable analysis result.

[0101] The generation unit can estimate the student's emotions and adjust the presentation of the generated problem sets based on those emotions. For example, if the student is relaxed, it can generate problem sets that proceed at a leisurely pace. If the student is in a hurry, it can generate problem sets that emphasize the shortest route. Furthermore, if the student is excited, it can generate problem sets with visually stimulating effects. In this way, the learning effect can be maximized by adjusting the presentation of the problem sets according to the student's emotions.

[0102] The update function can estimate students' emotions and adjust the frequency of problem set updates based on those estimates. For example, if a student is stressed, the update frequency can be reduced to alleviate their burden. If a student is relaxed, the normal update frequency can be maintained. Furthermore, if a student is excited, the update frequency can be increased to boost their motivation to learn. In this way, by adjusting the update frequency according to students' emotions, it is possible to support efficient learning.

[0103] The generation unit can estimate the student's emotions and adjust the length of the generated question sets based on those emotions. For example, if the student is in a hurry, it can generate short, concise question sets. If the student is relaxed, it can generate longer question sets with detailed explanations. Furthermore, if the student is excited, it can generate question sets with visually stimulating effects. By adjusting the length of the question sets according to the student's emotions, it can support efficient learning.

[0104] The reception desk can select the optimal upload method based on the student's geographical location. For example, if a student is in a specific region, they can prioritize uploading mock exam results related to that region. Similarly, if a student is traveling, they can prioritize uploading mock exam results related to their travel destination. Furthermore, if a student is at home, they can prioritize uploading mock exam results related to their home study. This allows for efficient learning by prioritizing the upload of highly relevant mock exam results based on geographical location.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception department receives the mock exam results from students. Students can upload their mock exam results via a web interface or mobile application. The reception department stores the uploaded mock exam results in a database and passes them on to the analysis department. Step 2: The analysis department uses generative AI to analyze the uploaded mock exam results and identify students' weaknesses and strengths. The analysis department uses statistical analysis and machine learning algorithms to analyze the mock exam results in detail and identify weaknesses and strengths in specific subjects and question types. Step 3: The generation unit generates an optimal set of problems based on the weaknesses and strengths identified by the analysis unit. The generation unit uses a generation AI to generate a set of problems that strengthen the student's weaknesses and maintain their strengths. For example, it might generate a set of problems that contain many problems from a specific area of ​​mathematics. The generation unit creates the set of problems using audio, images, and text. Step 4: The update unit updates the problem sets generated by the generation unit, adaptively adjusting the difficulty level to match the students' progress. The update unit uses generation AI to update students' learning data daily and updates the problem sets accordingly. For example, if a student becomes able to solve problems in a particular area, the difficulty level of the problems in that area will be adaptively adjusted.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and update unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and is used when students upload their mock exam results. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the mock exam results using a generation AI to identify the student's weaknesses and strengths. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal set of questions based on the analysis results. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates the set of questions to match the student's progress and adaptively adjusts the difficulty level. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and update unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and is used when students upload their mock exam results via voice input. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the mock exam results using a generation AI to identify the student's weaknesses and strengths. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates an optimal set of questions based on the analysis results. The update unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which updates the set of questions to match the student's progress and adaptively adjusts the difficulty level. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

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

[0130] The 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.

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

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

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

[0134] Figure 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.

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

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

[0137] In the 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.

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

[0139] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and update unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and is used when students upload their mock exam results via voice input. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the mock exam results using a generation AI to identify the student's weaknesses and strengths. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates an optimal set of questions based on the analysis results. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which updates the set of questions in accordance with the student's progress and adaptively adjusts the difficulty level. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

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

[0146] The 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.

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

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and update unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and is used when students upload their mock exam results via voice input. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the mock exam results using a generation AI to identify the student's weaknesses and strengths. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates an optimal set of questions based on the analysis results. The update unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which updates the set of questions in accordance with the student's progress and adaptively adjusts the difficulty level. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) The reception desk for uploading mock exam results, The analysis unit analyzes the mock exam results uploaded by the reception unit to identify the students' weaknesses and strengths, Based on the weaknesses and strengths identified by the analysis unit, a generation unit generates an optimal set of problems. The system includes an update unit that updates the problem sets generated by the generation unit and adaptively adjusts the difficulty level to match the students' progress. A system characterized by the following features. (Note 2) The generating unit is Adjust the generated problem sets to match the students' abilities. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned update unit is, The generated problem sets are updated periodically, and the difficulty level is adaptively adjusted to match the students' progress. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Create a problem set using audio, images, and text. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates students' emotions and adjusts the timing of uploading mock exam results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the upload history of students' past mock exam results to select the optimal upload method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When uploading mock exam results, the system filters them based on students' current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates students' emotions and prioritizes uploading mock exam results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When uploading mock exam results, the system prioritizes uploading results that are more relevant based on the student's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When uploading mock exam results, the system analyzes students' social media activity and uploads relevant results. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned analysis unit, During the analysis, the level of detail is adjusted based on the importance of the mock exam results. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the mock exam. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on the submission timing of the mock exams. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the order of analysis will be adjusted based on the relevance of the practice tests. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is We estimate students' emotions and adjust the way the problem sets are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the level of detail in the problem sets is adjusted based on the importance of each student's strengths and weaknesses. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, different generation algorithms are applied depending on the student's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates students' emotions and adjusts the length of the generated problem sets based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the priority of the problem sets is determined based on the students' learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the order of the problem sets is adjusted based on the student's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned update unit is, The system estimates students' emotions and adjusts the frequency of updating the problem sets based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned update unit is, During updates, the system analyzes students' learning progress to select the most suitable update method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned update unit is, During updates, the difficulty level of the practice problems will be adaptively adjusted based on the students' learning history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned update unit is, We estimate students' emotions and adjust the content of the problem sets based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned update unit is, During the update process, the optimal update method will be selected based on the student's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned update unit is, When updating the problem sets, we analyze students' social media activity and suggest updates to the problem sets. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0179] 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 reception desk for uploading mock exam results, The analysis unit analyzes the mock exam results uploaded by the reception unit to identify the students' weaknesses and strengths, Based on the weaknesses and strengths identified by the analysis unit, a generation unit generates an optimal set of problems. The system includes an update unit that updates the problem sets generated by the generation unit and adaptively adjusts the difficulty level to match the students' progress. A system characterized by the following features.

2. The generating unit is Adjust the generated problem sets to match the students' abilities. The system according to feature 1.

3. The aforementioned update unit is, The generated problem sets are updated periodically, and the difficulty level is adaptively adjusted to match the students' progress. The system according to feature 1.

4. The generating unit is Create a problem set using audio, images, and text. The system according to feature 1.

5. The aforementioned reception unit is The system estimates students' emotions and adjusts the timing of uploading mock exam results based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the upload history of students' past mock exam results to select the optimal upload method. The system according to feature 1.

7. The aforementioned reception unit is When uploading mock exam results, the system filters them based on students' current learning status and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is The system estimates students' emotions and prioritizes uploading mock exam results based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When uploading mock exam results, the system prioritizes uploading results that are more relevant based on the student's geographical location. The system according to feature 1.

10. The aforementioned reception unit is When uploading mock exam results, the system analyzes students' social media activity and uploads relevant results. The system according to feature 1.

Images