system

Abstract

The system according to this embodiment aims to automatically generate individually customized review questions and retests based on test results and to provide real-time feedback. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, a feedback unit, a proposal unit, and a support unit. The reception unit captures the test results. The analysis unit analyzes the test results captured by the reception unit. The generation unit automatically generates review questions and retests based on the results analyzed by the analysis unit. The feedback unit provides real-time feedback based on the review questions and retests generated by the generation unit. The proposal unit proposes an individualized learning plan based on the feedback provided by the feedback unit. The support unit supports daily learning based on the learning plan proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it has not been fully carried out to automatically generate customized review questions and retests based on test results and provide real-time feedback, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically generate customized review questions and retests based on test results and provide real-time feedback.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a feedback unit, a proposal unit, and a support unit. The reception unit captures test results. The analysis unit analyzes the test results captured by the reception unit. The generation unit automatically generates review questions and retests based on the results analyzed by the analysis unit. The feedback unit provides real-time feedback based on the review questions and retests generated by the generation unit. The proposal unit proposes an individualized learning plan based on the feedback provided by the feedback unit. The support unit supports daily learning based on the learning plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate individually customized review questions and retests based on test results and provide real-time feedback. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

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

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

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

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

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

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

[0028] (Example of form 1) The learning support system according to an embodiment of the present invention is a system in which AI analyzes test results captured with a smartphone and automatically generates customized review questions and retests. This learning support system begins with the user taking a picture of the test results with their smartphone. Next, the AI ​​analyzes the captured test results and identifies mistakes. The AI ​​automatically generates review questions and retests tailored to each individual student. This system provides real-time feedback and proposes an individualized learning plan. Furthermore, it supports daily learning like a private tutor, providing advice and additional practice problems tailored to individual needs. For example, the user takes a picture of the test results with their smartphone. At this time, the image of the test results is input to the AI. For example, if the user takes a picture of the results of a math test, the image is sent to the AI. Next, the AI ​​analyzes the captured test results. The AI ​​digitizes the test results using optical character recognition (OCR) technology and analyzes incorrect answers using natural language processing (NLP). For example, it identifies the parts of a math problem that were answered incorrectly and analyzes the cause. The AI ​​automatically generates review questions and retests tailored to each individual student. For example, it generates review questions related to the parts of a math problem that were answered incorrectly and creates a retest. This enables students to learn effectively to overcome their weaknesses. Furthermore, the system provides real-time feedback. For example, within seconds of taking a picture of a test result, the AI ​​analyzes it and provides feedback, explaining where mistakes were made and why. It also proposes individualized learning plans, providing learning methods tailored to each student. This system supports daily learning like a private tutor. For example, instead of busy parents, the AI ​​agent supports children's daily learning, providing advice and additional practice problems tailored to their individual needs. This makes it easy for parents and teachers to track progress. In this way, the AI-powered home learning support agent can improve student learning effectiveness by providing automatic analysis of test results, generation of customized learning plans, real-time feedback, and daily learning support. As a result, the learning support system can improve student learning effectiveness.

[0029] The learning support system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a feedback unit, a proposal unit, and a support unit. The reception unit receives a photo of the test results taken by the user with a smartphone. For example, the reception unit uses the smartphone's camera to take a photo of the test results and acquires the image. The analysis unit analyzes the test results captured by the reception unit. For example, the analysis unit uses optical character recognition (OCR) technology to digitize the test results and uses natural language processing (NLP) to analyze incorrect answers. The generation unit automatically generates review questions and retests based on the results analyzed by the analysis unit. For example, the generation unit generates review questions and retests tailored to each individual student. The feedback unit provides real-time feedback based on the review questions and retests generated by the generation unit. For example, the feedback unit provides feedback on the analysis results within a few seconds of the test results being taken. The proposal unit proposes an individualized learning plan based on the feedback provided by the feedback unit. For example, the proposal unit provides a learning method tailored to each student. The support unit supports daily learning based on the learning plan proposed by the proposal unit. For example, the support unit provides advice and additional practice problems tailored to individual needs. This allows the learning support system according to the embodiment to consistently handle everything from capturing and analyzing test results, generating review problems and retests, providing feedback, proposing individual learning plans, and offering daily learning support.

[0030] The reception unit receives test results from users via smartphone. For example, the reception unit uses the smartphone's camera to capture the test results and acquires the images. Specifically, when a user takes a picture of the test results with their smartphone camera, the reception unit automatically recognizes the image and saves it in the appropriate format. It also has a function to automatically adjust the image resolution, brightness, and shooting angle to acquire the image in the optimal state. Furthermore, the reception unit provides the user with guidelines and advice on shooting methods to ensure that the captured images are properly recognized. For example, it can display a stabilization function to prevent camera shake during shooting and guidelines to maintain the appropriate distance and angle. This allows users to easily and accurately capture test results and import them into the system. The reception unit also has a function to capture multiple test results at once, recognize them individually, and save them. This allows users to efficiently process multiple test results at once. In addition, the reception unit automatically uploads the captured images to a cloud server, making them accessible to the analysis and generation units. This ensures that the entire system processes quickly and efficiently.

[0031] The analysis department analyzes the test results captured by the reception department. For example, the analysis department digitizes the test results using optical character recognition (OCR) technology and analyzes incorrect answers using natural language processing (NLP). Specifically, it extracts textual information from the test result images using OCR technology and converts it into digital data. This allows for the accurate digitization of handwritten and printed test results. Next, it uses NLP technology to analyze the digitized test results and identify patterns and causes of incorrect answers. For example, it can analyze the content, frequency, and trends of incorrect answers to understand what kinds of problems students struggle with. Furthermore, the analysis department can evaluate students' learning status and progress based on past test results and learning history. This allows it to provide analysis results tailored to each student's learning needs. In addition, the analysis department can utilize AI technology to analyze the causes of incorrect answers in more detail and provide specific areas for improvement and learning advice. For example, if there are many incorrect answers to a particular problem, it can point out the lack of basic knowledge or understanding related to that problem and suggest appropriate learning methods. This allows the analysis department to maximize students' learning effectiveness and provide efficient learning support.

[0032] The generation unit automatically generates review questions and retests based on the results analyzed by the analysis unit. For example, the generation unit generates review questions and retests tailored to individual students. Specifically, based on the patterns and causes of incorrect answers provided by the analysis unit, it creates review questions that focus on areas and problems that students struggle with. The generation unit can use AI technology to adjust the difficulty and format of questions according to the student's level of understanding and learning progress. For example, by gradually increasing the difficulty from basic to applied problems, students can deepen their understanding. The generation unit can also evaluate the student's learning effectiveness through retests and provide additional review questions as needed. Retests are administered after students have completed the review questions to confirm their understanding. Based on the results of the retests, the generation unit can perform more detailed analysis and continuously monitor the student's learning progress. This allows the generation unit to provide optimal learning support for each student and support effective learning. Furthermore, the generation unit can increase students' motivation to learn by providing questions that match their interests and concerns. For example, it can generate questions related to themes and topics that students are interested in, making learning enjoyable. This allows the generation unit to stimulate students' motivation and provide effective learning support.

[0033] The feedback unit provides real-time feedback based on review questions and retests generated by the generation unit. For example, the feedback unit provides analysis results within seconds of a test being taken. Specifically, after a student completes review questions or a retest, the feedback unit immediately determines the correctness of the answers and provides detailed explanations and advice. The feedback unit utilizes AI technology to analyze students' answer patterns and error tendencies, providing individually customized feedback. For example, if a student frequently makes mistakes on a particular question, it will point out the lack of foundational knowledge or understanding related to that question and suggest specific areas for improvement. Furthermore, the feedback unit can provide feedback at the appropriate time according to the student's learning progress and level of understanding. For example, if a student achieves a certain learning goal, it can send words of praise and encouragement to boost their motivation. In addition, the feedback unit can monitor students' reactions and actions to feedback, continuously improving the accuracy and effectiveness of the feedback content. This allows the feedback unit to provide optimal feedback for each student and effectively support their learning.

[0034] The Proposal Department proposes individualized learning plans based on feedback provided by the Feedback Department. For example, the Proposal Department provides learning methods tailored to each student. Specifically, it creates learning plans that match the student's learning situation and goals based on the analysis results and feedback provided by the Feedback Department. The Proposal Department can utilize AI technology to analyze students' learning history and progress and propose optimal learning methods and materials. For example, if a student lacks understanding in a particular field, it can provide materials and practice problems related to that field to support efficient learning. Furthermore, the Proposal Department can increase students' motivation to learn by proposing learning plans that match their interests and concerns. For example, it can create learning plans related to themes and topics that students are interested in, making learning enjoyable. In addition, the Proposal Department can provide flexible learning plans that match students' learning styles and paces. For example, it can propose learning plans that meet students' needs, such as plans for intensive learning in a short period or plans for continuous learning over a long period. In this way, the Proposal Department can provide the optimal learning plan for each student and provide effective learning support.

[0035] The support department provides daily learning support based on the learning plan proposed by the proposal department. For example, the support department provides advice and additional practice problems tailored to individual needs. Specifically, based on the learning plan provided by the proposal department, the support department monitors the student's learning progress and understanding, and provides appropriate support as needed. The support department can utilize AI technology to grasp the student's learning status in real time and provide individually customized advice and practice problems. For example, if a student lacks understanding of a particular problem, additional practice problems related to that problem can be provided to deepen their understanding. The support department can also send messages of praise and encouragement to boost the student's motivation to learn. For example, if a student achieves a certain learning goal, a message of praise or encouragement can be sent to increase their motivation to learn. Furthermore, the support department can continuously improve the accuracy and effectiveness of its support based on student feedback. This allows the support department to provide optimal support to each student and provide effective learning support.

[0036] The analysis unit can digitize test results using image recognition technology and analyze incorrect answers using natural language processing. For example, the analysis unit can digitize test results using optical character recognition (OCR). For example, the analysis unit can convert the image of the test results into text data using OCR technology. The analysis unit can also analyze incorrect answers using natural language processing (NLP). For example, the analysis unit can analyze the text data using NLP technology to identify the cause of the incorrect answer. By digitizing the test results and analyzing the incorrect answers, more accurate analysis becomes possible. 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 image data of the test results into a generating AI and have the generating AI perform the generation of text data from the image data.

[0037] The generation unit can automatically generate review questions and retests tailored to individual students. For example, the generation unit generates review questions tailored to individual students. For example, the generation unit selects appropriate review questions based on the student's learning history and grades. The generation unit can also automatically generate retests. For example, the generation unit creates retests related to questions the student answered incorrectly. This enables effective learning by automatically generating review questions and retests tailored to individual students. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning history data into a generation AI and have the generation AI perform the generation of review questions and retests.

[0038] The feedback unit can provide feedback on the analysis results within seconds of capturing the test results. For example, the feedback unit provides feedback on the analysis results within seconds of capturing the test results. For example, the feedback unit provides feedback to the user immediately after the analysis of the test results is completed. This enables rapid learning improvement by providing feedback in real time. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the analysis result data into a generating AI and have the generating AI perform the generation of feedback.

[0039] The suggestion department can provide learning methods tailored to each student. For example, the suggestion department can propose the optimal learning method based on the student's learning history and grades. This improves the student's learning effectiveness by proposing an individualized learning plan. Some or all of the above processing in the suggestion department may be performed using AI, or not. For example, the suggestion department can input the student's learning history data into a generating AI and have the generating AI produce learning method suggestions.

[0040] The support department can provide advice and additional practice problems tailored to individual needs. For example, the support department can provide advice tailored to individual needs. For example, the support department can provide appropriate advice based on the student's learning goals and areas of difficulty. The support department can also provide additional practice problems. For example, the support department can provide additional practice problems according to the student's learning progress. In this way, by providing support tailored to individual needs, daily learning is effectively supported. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the student's learning data into a generating AI and have the generating AI perform the generation of advice and additional practice problems.

[0041] The reception desk can analyze the user's past test result shooting history and select the optimal shooting method. For example, the reception desk can prioritize suggesting shooting methods that the user has succeeded with in the past. It can also suggest avoiding shooting methods that the user has failed with in the past. Furthermore, the reception desk can suggest the most efficient shooting method based on the user's past shooting history. In this way, the optimal shooting method can be selected by analyzing past shooting history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's shooting history data into a generating AI and have the generating AI select the optimal shooting method.

[0042] The reception unit can filter test results when capturing them based on the user's current learning status and areas of interest. For example, the reception unit can prioritize capturing test results related to the subject the user is currently studying. It can also prioritize capturing test results related to the user's areas of interest. Furthermore, the reception unit can capture only the necessary test results according to the user's learning progress. This allows only the necessary test results to be captured by filtering based on the user's learning status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's learning status data into a generating AI and have the generating AI perform the filtering.

[0043] The reception unit can prioritize capturing test results that are highly relevant based on the user's geographical location. For example, if the user is at school, the reception unit will prioritize capturing test results taken at school. Similarly, if the user is at home, the reception unit can prioritize capturing test results taken at home. Furthermore, if the user is at a library, the reception unit can prioritize capturing test results taken at the library. This allows for efficient capture by prioritizing the capture of highly relevant test results based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant test results.

[0044] The reception unit can analyze the user's social media activity when capturing test results and capture relevant test results. For example, the reception unit can prioritize capturing test results that the user has shared on social media. It can also prioritize capturing test results that the user is discussing on social media. Furthermore, the reception unit can suggest relevant test results based on the user's social media activity. This allows for the efficient capture of relevant test results by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI select relevant test results.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the test results during the analysis. For example, the analysis unit performs a detailed analysis for important test results. It can also perform a simplified analysis for less important test results. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the test results. This allows for efficient analysis by adjusting the level of detail based on the importance of the test results. 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 test result importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the category of the test results during analysis. For example, the analysis unit can apply a mathematical formula analysis algorithm to mathematics test results. It can also apply a grammar analysis algorithm to English test results. Furthermore, it can apply a scientific data analysis algorithm to science test results. By applying different analysis algorithms depending on the category of the test results, more accurate analysis becomes possible. 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 test results into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the submission date of the test results. For example, the analysis unit may prioritize the analysis of recently submitted test results. It can also postpone the analysis of older test results. Furthermore, the analysis unit can adjust the order of analysis based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the test 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 the test result submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the test results during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant test results. It can also postpone the analysis of less relevant test results. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the test results. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the test 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 the relevance data of the test results into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0049] The generation unit can adjust the level of detail of review questions and retests based on the importance of the test results during generation. For example, the generation unit can create detailed review questions based on important test results. It can also simplify review questions based on less important test results. Furthermore, the generation unit can adjust the level of detail of review questions and retests according to the importance of the test results. This allows for efficient learning by adjusting the level of detail of review questions and retests based on the importance of the test results. 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 importance data of the test results into a generation AI and have the generation AI perform the adjustment of the level of detail of review questions and retests.

[0050] The generation unit can apply different generation algorithms depending on the category of the test results during generation. For example, the generation unit can apply a mathematical formula generation algorithm to mathematics test results. It can also apply a grammar generation algorithm to English test results. Furthermore, it can apply a scientific data generation algorithm to science test results. By applying different generation algorithms depending on the category of the test results, it becomes possible to generate more accurate review questions and retests. 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 category data of the test results into a generation AI and have the generation AI execute the application of the generation algorithm.

[0051] The generation unit can determine the priority of review questions and retests based on the submission date of test results during the generation process. For example, the generation unit can prioritize generating review questions based on recently submitted test results. It can also postpone generating review questions based on older test results. Furthermore, the generation unit can adjust the priority of review questions and retests based on the submission date. This enables efficient learning by determining the priority of review questions and retests based on the submission date of test results. 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 test result submission date data into a generation AI and have the generation AI determine the priority of review questions and retests.

[0052] The generation unit can adjust the order of review questions and retests based on the relevance of test results during generation. For example, the generation unit can prioritize generating review questions based on highly relevant test results. It can also postpone generating review questions based on less relevant test results. Furthermore, the generation unit can adjust the order of review questions and retests based on the relevance of test results. This allows for more efficient learning by adjusting the order of review questions and retests based on the relevance of test results. 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 relevance data of the test results into a generation AI and have the generation AI perform the adjustment of the order of review questions and retests.

[0053] The feedback unit can adjust the level of detail in the feedback based on the importance of the test results. For example, the feedback unit can provide detailed feedback for important test results, while simplifying feedback for less important test results. The feedback unit can also adjust the level of detail in the feedback according to the importance of the test results. This allows for efficient feedback by adjusting the level of detail in the feedback based on the importance of the test results. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input test result importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the feedback.

[0054] The feedback unit can apply different feedback algorithms depending on the category of the test result during the feedback process. For example, the feedback unit can apply a mathematical formula analysis algorithm to the results of a mathematics test. It can also apply a grammar analysis algorithm to the results of an English test. Furthermore, it can apply a scientific data analysis algorithm to the results of a science test. By applying different feedback algorithms depending on the category of the test result, more accurate feedback becomes possible. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the category data of the test result into a generating AI and have the generating AI execute the application of the feedback algorithm.

[0055] The feedback unit can prioritize feedback based on the submission date of the test results. For example, the feedback unit may prioritize feedback on recently submitted test results. It can also postpone feedback on older test results. Furthermore, the feedback unit can adjust the order of feedback based on the submission date. This enables efficient feedback by prioritizing feedback based on the submission date of the test results. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input test result submission date data into a generating AI and have the generating AI determine the priority of feedback.

[0056] The feedback unit can adjust the order of feedback based on the relevance of the test results. For example, the feedback unit can prioritize feedback on highly relevant test results. It can also postpone feedback on less relevant test results. Furthermore, the feedback unit can adjust the order of feedback based on the relevance of the test results. This allows for efficient feedback by adjusting the order of feedback based on the relevance of the test results. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input relevance data of the test results into a generating AI and have the generating AI perform the adjustment of the feedback order.

[0057] The proposal unit can adjust the level of detail in the learning plan based on the importance of the test results when making a proposal. For example, the proposal unit will create a detailed learning plan based on important test results. It can also simplify the learning plan based on less important test results. Furthermore, the proposal unit can adjust the level of detail in the learning plan according to the importance of the test results. This allows for more efficient learning by adjusting the level of detail in the learning plan based on the importance of the test results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance data of the test results into a generating AI and have the generating AI perform the adjustment of the level of detail in the learning plan.

[0058] The suggestion unit can apply different suggestion algorithms depending on the category of the test results when making suggestions. For example, the suggestion unit can apply a mathematical formula analysis algorithm to mathematics test results. It can also apply a grammar analysis algorithm to English test results. Furthermore, it can apply a scientific data analysis algorithm to science test results. By applying different suggestion algorithms depending on the category of the test results, it becomes possible to propose more accurate learning plans. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the category data of the test results into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0059] The proposal unit can prioritize learning plans based on the submission timing of test results when making a proposal. For example, the proposal unit will prioritize suggesting learning plans based on recently submitted test results. It can also postpone learning plans based on older test results. Furthermore, the proposal unit can adjust the priority of learning plans based on the submission timing. This enables efficient learning by prioritizing learning plans based on the submission timing of test results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input test result submission timing data into a generating AI and have the generating AI perform the determination of learning plan priorities.

[0060] The suggestion unit can adjust the order of learning plans based on the relevance of test results when making suggestions. For example, the suggestion unit can prioritize suggesting learning plans based on highly relevant test results. It can also postpone suggesting learning plans based on less relevant test results. Furthermore, the suggestion unit can adjust the order of learning plans based on the relevance of test results. This allows for more efficient learning by adjusting the order of learning plans based on the relevance of test results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance data of test results into a generating AI and have the generating AI perform the adjustment of the order of learning plans.

[0061] The support unit can adjust the level of detail of support based on the importance of the test results during support. For example, the support unit can provide detailed support based on important test results. Conversely, the support unit can simplify support based on less important test results. The support unit can also adjust the level of detail of support according to the importance of the test results. This allows for efficient support by adjusting the level of detail of support based on the importance of the test results. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input test result importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of support.

[0062] The support unit can apply different support algorithms depending on the category of the test results during support. For example, the support unit can apply a mathematical formula analysis algorithm to mathematics test results. It can also apply a grammar analysis algorithm to English test results. Furthermore, it can apply a scientific data analysis algorithm to science test results. By applying different support algorithms depending on the category of the test results, more accurate support becomes possible. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the category data of the test results into a generating AI and have the generating AI execute the application of the support algorithm.

[0063] The support department can prioritize support based on the submission date of test results. For example, the support department may prioritize support based on recently submitted test results. It may also postpone support based on older test results. Furthermore, the support department can adjust the support priority based on the submission date. This enables efficient support by prioritizing support based on the submission date of test results. Some or all of the above processing in the support department may be performed using AI, for example, or not. For example, the support department can input test result submission date data into a generating AI and have the generating AI determine the support priority.

[0064] The support unit can adjust the order of support based on the relevance of test results during support. For example, the support unit can prioritize support based on highly relevant test results. It can also postpone support based on less relevant test results. Furthermore, the support unit can adjust the order of support based on the relevance of test results. This allows for efficient support by adjusting the order of support based on the relevance of test results. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input relevance data of test results into a generating AI and have the generating AI perform the adjustment of the order of support.

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

[0066] The reception desk can analyze the user's learning history and suggest the optimal time for taking photos. For example, if a user has previously taken photos during times when their concentration is high, that time slot will be prioritized for suggestion. It can also analyze the ambient noise of locations where the user has previously taken photos and suggest quieter locations. Furthermore, it can prompt the user to take photos at appropriate times according to their learning progress. By suggesting the optimal time for taking photos based on the user's learning history, more effective learning support becomes possible.

[0067] The analysis unit can analyze the user's learning style and select the optimal analysis method. For example, if the user prefers visual learning, it can provide analysis results using graphs and diagrams. If the user prefers auditory learning, it can provide analysis results in audio format. Furthermore, if the user prefers tactile learning, it can provide interactive analysis results. By selecting an analysis method that matches the user's learning style, more effective learning support becomes possible.

[0068] The generation unit can adjust the content of review questions and retests based on the user's learning objectives. For example, if a user is studying for a specific exam, it will prioritize generating questions related to that exam. It can also generate questions related to a specific skill if the user wants to improve that skill. Furthermore, if the user wants to achieve results in a short period, it can generate effective questions for that short time. This allows for more effective learning support by adjusting the content of review questions and retests based on the user's learning objectives.

[0069] The feedback unit can analyze the user's learning history and select the optimal feedback method. For example, if the user previously preferred visual feedback, it can provide feedback using graphs and diagrams. If the user previously preferred auditory feedback, it can provide audio feedback. Furthermore, if the user previously preferred haptic feedback, it can provide interactive feedback. This allows for more effective learning support by selecting the optimal feedback method based on the user's learning history.

[0070] The suggestion function can analyze a user's learning history and propose the optimal learning plan. For example, it can prioritize suggesting learning methods that have been effective for the user in the past. It can also suggest avoiding learning methods that the user has struggled with in the past. Furthermore, it can propose an appropriate learning plan according to the user's learning progress. This enables more effective learning support by suggesting the optimal learning plan based on the user's learning history.

[0071] The support department can analyze a user's learning history and select the most suitable support method. For example, it can prioritize providing support methods that have been effective for the user in the past. It can also suggest avoiding support methods that the user has struggled with in the past. Furthermore, it can provide appropriate support methods according to the user's learning progress. By selecting the most suitable support method based on the user's learning history, more effective learning support becomes possible.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The reception desk takes a picture of the test results with the user's smartphone. For example, the reception desk takes a picture of the test results using the smartphone's camera and acquires the image. Step 2: The analysis unit analyzes the test results captured by the reception unit. For example, the analysis unit uses optical character recognition (OCR) technology to digitize the test results and uses natural language processing (NLP) to analyze incorrect answers. Step 3: The generation unit automatically generates review questions and retests based on the results analyzed by the analysis unit. For example, the generation unit generates review questions and retests tailored to each individual student. Step 4: The feedback unit provides real-time feedback based on the review questions and retests generated by the generation unit. For example, the feedback unit provides feedback on the analysis results within seconds of capturing the test results. Step 5: The proposal team proposes individualized learning plans based on the feedback provided by the feedback team. For example, the proposal team provides learning methods tailored to each student. Step 6: The support team provides daily learning support based on the learning plan proposed by the suggestion team. For example, the support team provides advice and additional practice problems tailored to individual needs.

[0074] (Example of form 2) The learning support system according to an embodiment of the present invention is a system in which AI analyzes test results captured with a smartphone and automatically generates customized review questions and retests. This learning support system begins with the user taking a picture of the test results with their smartphone. Next, the AI ​​analyzes the captured test results and identifies mistakes. The AI ​​automatically generates review questions and retests tailored to each individual student. This system provides real-time feedback and proposes an individualized learning plan. Furthermore, it supports daily learning like a private tutor, providing advice and additional practice problems tailored to individual needs. For example, the user takes a picture of the test results with their smartphone. At this time, the image of the test results is input to the AI. For example, if the user takes a picture of the results of a math test, the image is sent to the AI. Next, the AI ​​analyzes the captured test results. The AI ​​digitizes the test results using optical character recognition (OCR) technology and analyzes incorrect answers using natural language processing (NLP). For example, it identifies the parts of a math problem that were answered incorrectly and analyzes the cause. The AI ​​automatically generates review questions and retests tailored to each individual student. For example, it generates review questions related to the parts of a math problem that were answered incorrectly and creates a retest. This enables students to learn effectively to overcome their weaknesses. Furthermore, the system provides real-time feedback. For example, within seconds of taking a picture of a test result, the AI ​​analyzes it and provides feedback, explaining where mistakes were made and why. It also proposes individualized learning plans, providing learning methods tailored to each student. This system supports daily learning like a private tutor. For example, instead of busy parents, the AI ​​agent supports children's daily learning, providing advice and additional practice problems tailored to their individual needs. This makes it easy for parents and teachers to track progress. In this way, the AI-powered home learning support agent can improve student learning effectiveness by providing automatic analysis of test results, generation of customized learning plans, real-time feedback, and daily learning support. As a result, the learning support system can improve student learning effectiveness.

[0075] The learning support system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, a feedback unit, a proposal unit, and a support unit. The reception unit receives a photo of the test results taken by the user with a smartphone. For example, the reception unit uses the smartphone's camera to take a photo of the test results and acquires the image. The analysis unit analyzes the test results captured by the reception unit. For example, the analysis unit uses optical character recognition (OCR) technology to digitize the test results and uses natural language processing (NLP) to analyze incorrect answers. The generation unit automatically generates review questions and retests based on the results analyzed by the analysis unit. For example, the generation unit generates review questions and retests tailored to each individual student. The feedback unit provides real-time feedback based on the review questions and retests generated by the generation unit. For example, the feedback unit provides feedback on the analysis results within a few seconds of the test results being taken. The proposal unit proposes an individualized learning plan based on the feedback provided by the feedback unit. For example, the proposal unit provides a learning method tailored to each student. The support unit supports daily learning based on the learning plan proposed by the proposal unit. For example, the support unit provides advice and additional practice problems tailored to individual needs. This allows the learning support system according to the embodiment to consistently handle everything from capturing and analyzing test results, generating review problems and retests, providing feedback, proposing individual learning plans, and offering daily learning support.

[0076] The reception unit receives test results from users via smartphone. For example, the reception unit uses the smartphone's camera to capture the test results and acquires the images. Specifically, when a user takes a picture of the test results with their smartphone camera, the reception unit automatically recognizes the image and saves it in the appropriate format. It also has a function to automatically adjust the image resolution, brightness, and shooting angle to acquire the image in the optimal state. Furthermore, the reception unit provides the user with guidelines and advice on shooting methods to ensure that the captured images are properly recognized. For example, it can display a stabilization function to prevent camera shake during shooting and guidelines to maintain the appropriate distance and angle. This allows users to easily and accurately capture test results and import them into the system. The reception unit also has a function to capture multiple test results at once, recognize them individually, and save them. This allows users to efficiently process multiple test results at once. In addition, the reception unit automatically uploads the captured images to a cloud server, making them accessible to the analysis and generation units. This ensures that the entire system processes quickly and efficiently.

[0077] The analysis department analyzes the test results captured by the reception department. For example, the analysis department digitizes the test results using optical character recognition (OCR) technology and analyzes incorrect answers using natural language processing (NLP). Specifically, it extracts textual information from the test result images using OCR technology and converts it into digital data. This allows for the accurate digitization of handwritten and printed test results. Next, it uses NLP technology to analyze the digitized test results and identify patterns and causes of incorrect answers. For example, it can analyze the content, frequency, and trends of incorrect answers to understand what kinds of problems students struggle with. Furthermore, the analysis department can evaluate students' learning status and progress based on past test results and learning history. This allows it to provide analysis results tailored to each student's learning needs. In addition, the analysis department can utilize AI technology to analyze the causes of incorrect answers in more detail and provide specific areas for improvement and learning advice. For example, if there are many incorrect answers to a particular problem, it can point out the lack of basic knowledge or understanding related to that problem and suggest appropriate learning methods. This allows the analysis department to maximize students' learning effectiveness and provide efficient learning support.

[0078] The generation unit automatically generates review questions and retests based on the results analyzed by the analysis unit. For example, the generation unit generates review questions and retests tailored to individual students. Specifically, based on the patterns and causes of incorrect answers provided by the analysis unit, it creates review questions that focus on areas and problems that students struggle with. The generation unit can use AI technology to adjust the difficulty and format of questions according to the student's level of understanding and learning progress. For example, by gradually increasing the difficulty from basic to applied problems, students can deepen their understanding. The generation unit can also evaluate the student's learning effectiveness through retests and provide additional review questions as needed. Retests are administered after students have completed the review questions to confirm their understanding. Based on the results of the retests, the generation unit can perform more detailed analysis and continuously monitor the student's learning progress. This allows the generation unit to provide optimal learning support for each student and support effective learning. Furthermore, the generation unit can increase students' motivation to learn by providing questions that match their interests and concerns. For example, it can generate questions related to themes and topics that students are interested in, making learning enjoyable. This allows the generation unit to stimulate students' motivation and provide effective learning support.

[0079] The feedback unit provides real-time feedback based on review questions and retests generated by the generation unit. For example, the feedback unit provides analysis results within seconds of a test being taken. Specifically, after a student completes review questions or a retest, the feedback unit immediately determines the correctness of the answers and provides detailed explanations and advice. The feedback unit utilizes AI technology to analyze students' answer patterns and error tendencies, providing individually customized feedback. For example, if a student frequently makes mistakes on a particular question, it will point out the lack of foundational knowledge or understanding related to that question and suggest specific areas for improvement. Furthermore, the feedback unit can provide feedback at the appropriate time according to the student's learning progress and level of understanding. For example, if a student achieves a certain learning goal, it can send words of praise and encouragement to boost their motivation. In addition, the feedback unit can monitor students' reactions and actions to feedback, continuously improving the accuracy and effectiveness of the feedback content. This allows the feedback unit to provide optimal feedback for each student and effectively support their learning.

[0080] The Proposal Department proposes individualized learning plans based on feedback provided by the Feedback Department. For example, the Proposal Department provides learning methods tailored to each student. Specifically, it creates learning plans that match the student's learning situation and goals based on the analysis results and feedback provided by the Feedback Department. The Proposal Department can utilize AI technology to analyze students' learning history and progress and propose optimal learning methods and materials. For example, if a student lacks understanding in a particular field, it can provide materials and practice problems related to that field to support efficient learning. Furthermore, the Proposal Department can increase students' motivation to learn by proposing learning plans that match their interests and concerns. For example, it can create learning plans related to themes and topics that students are interested in, making learning enjoyable. In addition, the Proposal Department can provide flexible learning plans that match students' learning styles and paces. For example, it can propose learning plans that meet students' needs, such as plans for intensive learning in a short period or plans for continuous learning over a long period. In this way, the Proposal Department can provide the optimal learning plan for each student and provide effective learning support.

[0081] The support department provides daily learning support based on the learning plan proposed by the proposal department. For example, the support department provides advice and additional practice problems tailored to individual needs. Specifically, based on the learning plan provided by the proposal department, the support department monitors the student's learning progress and understanding, and provides appropriate support as needed. The support department can utilize AI technology to grasp the student's learning status in real time and provide individually customized advice and practice problems. For example, if a student lacks understanding of a particular problem, additional practice problems related to that problem can be provided to deepen their understanding. The support department can also send messages of praise and encouragement to boost the student's motivation to learn. For example, if a student achieves a certain learning goal, a message of praise or encouragement can be sent to increase their motivation to learn. Furthermore, the support department can continuously improve the accuracy and effectiveness of its support based on student feedback. This allows the support department to provide optimal support to each student and provide effective learning support.

[0082] The analysis unit can digitize test results using image recognition technology and analyze incorrect answers using natural language processing. For example, the analysis unit can digitize test results using optical character recognition (OCR). For example, the analysis unit can convert the image of the test results into text data using OCR technology. The analysis unit can also analyze incorrect answers using natural language processing (NLP). For example, the analysis unit can analyze the text data using NLP technology to identify the cause of the incorrect answer. By digitizing the test results and analyzing the incorrect answers, more accurate analysis becomes possible. 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 image data of the test results into a generating AI and have the generating AI perform the generation of text data from the image data.

[0083] The generation unit can automatically generate review questions and retests tailored to individual students. For example, the generation unit generates review questions tailored to individual students. For example, the generation unit selects appropriate review questions based on the student's learning history and grades. The generation unit can also automatically generate retests. For example, the generation unit creates retests related to questions the student answered incorrectly. This enables effective learning by automatically generating review questions and retests tailored to individual students. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input student learning history data into a generation AI and have the generation AI perform the generation of review questions and retests.

[0084] The feedback unit can provide feedback on the analysis results within seconds of capturing the test results. For example, the feedback unit provides feedback on the analysis results within seconds of capturing the test results. For example, the feedback unit provides feedback to the user immediately after the analysis of the test results is completed. This enables rapid learning improvement by providing feedback in real time. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the analysis result data into a generating AI and have the generating AI perform the generation of feedback.

[0085] The suggestion department can provide learning methods tailored to each student. For example, the suggestion department can propose the optimal learning method based on the student's learning history and grades. This improves the student's learning effectiveness by proposing an individualized learning plan. Some or all of the above processing in the suggestion department may be performed using AI, or not. For example, the suggestion department can input the student's learning history data into a generating AI and have the generating AI produce learning method suggestions.

[0086] The support department can provide advice and additional practice problems tailored to individual needs. For example, the support department can provide advice tailored to individual needs. For example, the support department can provide appropriate advice based on the student's learning goals and areas of difficulty. The support department can also provide additional practice problems. For example, the support department can provide additional practice problems according to the student's learning progress. In this way, by providing support tailored to individual needs, daily learning is effectively supported. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the student's learning data into a generating AI and have the generating AI perform the generation of advice and additional practice problems.

[0087] The reception desk can estimate the user's emotions and adjust the timing of test result capture based on the estimated emotions. For example, if the user is tense, the reception desk may wait until the user is relaxed before prompting them to take the test. If the user is focused, the reception desk may take the test immediately to maintain their concentration. If the user is tired, the reception desk may suggest taking the test after a break. By adjusting the timing of capture according to the user's emotions, test results can be captured at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0088] The reception desk can analyze the user's past test result shooting history and select the optimal shooting method. For example, the reception desk can prioritize suggesting shooting methods that the user has succeeded with in the past. It can also suggest avoiding shooting methods that the user has failed with in the past. Furthermore, the reception desk can suggest the most efficient shooting method based on the user's past shooting history. In this way, the optimal shooting method can be selected by analyzing past shooting history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's shooting history data into a generating AI and have the generating AI select the optimal shooting method.

[0089] The reception unit can filter test results when capturing them based on the user's current learning status and areas of interest. For example, the reception unit can prioritize capturing test results related to the subject the user is currently studying. It can also prioritize capturing test results related to the user's areas of interest. Furthermore, the reception unit can capture only the necessary test results according to the user's learning progress. This allows only the necessary test results to be captured by filtering based on the user's learning status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's learning status data into a generating AI and have the generating AI perform the filtering.

[0090] The reception desk can estimate the user's emotions and determine the priority of test results to capture based on the estimated emotions. For example, if the user is stressed, the reception desk may start capturing the easier test results first. If the user is relaxed, the reception desk may start capturing the more difficult test results first. If the user is in a hurry, the reception desk may start capturing the most important test results first. This allows for more effective shooting by prioritizing the test results to capture according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of test results to capture.

[0091] The reception unit can prioritize capturing test results that are highly relevant based on the user's geographical location. For example, if the user is at school, the reception unit will prioritize capturing test results taken at school. Similarly, if the user is at home, the reception unit can prioritize capturing test results taken at home. Furthermore, if the user is at a library, the reception unit can prioritize capturing test results taken at the library. This allows for efficient capture by prioritizing the capture of highly relevant test results based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI select highly relevant test results.

[0092] The reception unit can analyze the user's social media activity when capturing test results and capture relevant test results. For example, the reception unit can prioritize capturing test results that the user has shared on social media. It can also prioritize capturing test results that the user is discussing on social media. Furthermore, the reception unit can suggest relevant test results based on the user's social media activity. This allows for the efficient capture of relevant test results by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media data into a generating AI and have the generating AI select relevant test results.

[0093] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can provide a concise analysis result. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0094] The analysis unit can adjust the level of detail of the analysis based on the importance of the test results during the analysis. For example, the analysis unit performs a detailed analysis for important test results. It can also perform a simplified analysis for less important test results. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the test results. This allows for efficient analysis by adjusting the level of detail based on the importance of the test results. 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 test result importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0095] The analysis unit can apply different analysis algorithms depending on the category of the test results during analysis. For example, the analysis unit can apply a mathematical formula analysis algorithm to mathematics test results. It can also apply a grammar analysis algorithm to English test results. Furthermore, it can apply a scientific data analysis algorithm to science test results. By applying different analysis algorithms depending on the category of the test results, more accurate analysis becomes possible. 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 test results into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0096] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. If the user is in a hurry, the analysis unit can provide a brief analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0097] The analysis unit can determine the priority of analysis based on the submission date of the test results. For example, the analysis unit may prioritize the analysis of recently submitted test results. It can also postpone the analysis of older test results. Furthermore, the analysis unit can adjust the order of analysis based on the submission date. This enables efficient analysis by determining the priority of analysis based on the submission date of the test 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 the test result submission date data into a generating AI and have the generating AI perform the determination of the analysis priority.

[0098] The analysis unit can adjust the order of analysis based on the relevance of the test results during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant test results. It can also postpone the analysis of less relevant test results. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of the test results. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the test 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 the relevance data of the test results into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0099] The generation unit can estimate the user's emotions and adjust the generation method for review questions and retests based on the estimated emotions. For example, if the user is nervous, the generation unit can start with easy review questions. If the user is relaxed, the generation unit can also provide more difficult review questions. If the user is in a hurry, the generation unit can provide review questions that can be solved in a short time. By adjusting the generation method for review questions and retests according to the user's emotions, more appropriate learning becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the generation method for review questions and retests.

[0100] The generation unit can adjust the level of detail of review questions and retests based on the importance of the test results during generation. For example, the generation unit can create detailed review questions based on important test results. It can also simplify review questions based on less important test results. Furthermore, the generation unit can adjust the level of detail of review questions and retests according to the importance of the test results. This allows for efficient learning by adjusting the level of detail of review questions and retests based on the importance of the test results. 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 importance data of the test results into a generation AI and have the generation AI perform the adjustment of the level of detail of review questions and retests.

[0101] The generation unit can apply different generation algorithms depending on the category of the test results during generation. For example, the generation unit can apply a mathematical formula generation algorithm to mathematics test results. It can also apply a grammar generation algorithm to English test results. Furthermore, it can apply a scientific data generation algorithm to science test results. By applying different generation algorithms depending on the category of the test results, it becomes possible to generate more accurate review questions and retests. 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 category data of the test results into a generation AI and have the generation AI execute the application of the generation algorithm.

[0102] The generation unit can estimate the user's emotions and adjust the length of review questions and retests based on the estimated emotions. For example, if the user is nervous, the generation unit can provide short, concise review questions. If the user is relaxed, the generation unit can provide detailed review questions. If the user is in a hurry, the generation unit can provide brief review questions. By adjusting the length of review questions and retests according to the user's emotions, more appropriate learning becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a 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 generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the length of review questions and retests.

[0103] The generation unit can determine the priority of review questions and retests based on the submission date of test results during the generation process. For example, the generation unit can prioritize generating review questions based on recently submitted test results. It can also postpone generating review questions based on older test results. Furthermore, the generation unit can adjust the priority of review questions and retests based on the submission date. This enables efficient learning by determining the priority of review questions and retests based on the submission date of test results. 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 test result submission date data into a generation AI and have the generation AI determine the priority of review questions and retests.

[0104] The generation unit can adjust the order of review questions and retests based on the relevance of test results during generation. For example, the generation unit can prioritize generating review questions based on highly relevant test results. It can also postpone generating review questions based on less relevant test results. Furthermore, the generation unit can adjust the order of review questions and retests based on the relevance of test results. This allows for more efficient learning by adjusting the order of review questions and retests based on the relevance of test results. 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 relevance data of the test results into a generation AI and have the generation AI perform the adjustment of the order of review questions and retests.

[0105] The feedback unit can estimate the user's emotions and adjust the way it presents feedback based on those emotions. For example, if the user is nervous, the feedback unit can provide simple and easily understandable feedback. If the user is relaxed, the feedback unit can provide detailed feedback. If the user is in a hurry, the feedback unit can provide concise feedback. By adjusting the way feedback is presented according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, or not. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the way feedback is presented.

[0106] The feedback unit can adjust the level of detail in the feedback based on the importance of the test results. For example, the feedback unit can provide detailed feedback for important test results, while simplifying feedback for less important test results. The feedback unit can also adjust the level of detail in the feedback according to the importance of the test results. This allows for efficient feedback by adjusting the level of detail in the feedback based on the importance of the test results. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input test result importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the feedback.

[0107] The feedback unit can apply different feedback algorithms depending on the category of the test result during the feedback process. For example, the feedback unit can apply a mathematical formula analysis algorithm to the results of a mathematics test. It can also apply a grammar analysis algorithm to the results of an English test. Furthermore, it can apply a scientific data analysis algorithm to the results of a science test. By applying different feedback algorithms depending on the category of the test result, more accurate feedback becomes possible. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the category data of the test result into a generating AI and have the generating AI execute the application of the feedback algorithm.

[0108] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is nervous, the feedback unit can provide short, concise feedback. If the user is relaxed, the feedback unit can provide detailed feedback. If the user is in a hurry, the feedback unit can provide brief feedback. By adjusting the length of the feedback according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into the generative AI and have the generative AI adjust the length of the feedback.

[0109] The feedback unit can prioritize feedback based on the submission date of the test results. For example, the feedback unit may prioritize feedback on recently submitted test results. It can also postpone feedback on older test results. Furthermore, the feedback unit can adjust the order of feedback based on the submission date. This enables efficient feedback by prioritizing feedback based on the submission date of the test results. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input test result submission date data into a generating AI and have the generating AI determine the priority of feedback.

[0110] The feedback unit can adjust the order of feedback based on the relevance of the test results. For example, the feedback unit can prioritize feedback on highly relevant test results. It can also postpone feedback on less relevant test results. Furthermore, the feedback unit can adjust the order of feedback based on the relevance of the test results. This allows for efficient feedback by adjusting the order of feedback based on the relevance of the test results. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input relevance data of the test results into a generating AI and have the generating AI perform the adjustment of the feedback order.

[0111] The suggestion unit can estimate the user's emotions and adjust the method of suggesting learning plans based on the estimated emotions. For example, if the user is nervous, the suggestion unit will suggest a simple and easy-to-understand learning plan. If the user is relaxed, the suggestion unit can also suggest a detailed learning plan. If the user is in a hurry, the suggestion unit can also suggest a concise learning plan. In this way, by adjusting the method of suggesting learning plans according to the user's emotions, a more appropriate learning plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the method of suggesting learning plans.

[0112] The proposal unit can adjust the level of detail in the learning plan based on the importance of the test results when making a proposal. For example, the proposal unit will create a detailed learning plan based on important test results. It can also simplify the learning plan based on less important test results. Furthermore, the proposal unit can adjust the level of detail in the learning plan according to the importance of the test results. This allows for more efficient learning by adjusting the level of detail in the learning plan based on the importance of the test results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance data of the test results into a generating AI and have the generating AI perform the adjustment of the level of detail in the learning plan.

[0113] The suggestion unit can apply different suggestion algorithms depending on the category of the test results when making suggestions. For example, the suggestion unit can apply a mathematical formula analysis algorithm to mathematics test results. It can also apply a grammar analysis algorithm to English test results. Furthermore, it can apply a scientific data analysis algorithm to science test results. By applying different suggestion algorithms depending on the category of the test results, it becomes possible to propose more accurate learning plans. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the category data of the test results into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0114] The suggestion unit can estimate the user's emotions and adjust the length of the learning plan based on the estimated emotions. For example, if the user is nervous, the suggestion unit will suggest a short, concise learning plan. If the user is relaxed, the suggestion unit can suggest a detailed learning plan. If the user is in a hurry, the suggestion unit can suggest a brief learning plan. By adjusting the length of the learning plan according to the user's emotions, a more appropriate learning plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of the learning plan.

[0115] The proposal unit can prioritize learning plans based on the submission timing of test results when making a proposal. For example, the proposal unit will prioritize suggesting learning plans based on recently submitted test results. It can also postpone learning plans based on older test results. Furthermore, the proposal unit can adjust the priority of learning plans based on the submission timing. This enables efficient learning by prioritizing learning plans based on the submission timing of test results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input test result submission timing data into a generating AI and have the generating AI perform the determination of learning plan priorities.

[0116] The suggestion unit can adjust the order of learning plans based on the relevance of test results when making suggestions. For example, the suggestion unit can prioritize suggesting learning plans based on highly relevant test results. It can also postpone suggesting learning plans based on less relevant test results. Furthermore, the suggestion unit can adjust the order of learning plans based on the relevance of test results. This allows for more efficient learning by adjusting the order of learning plans based on the relevance of test results. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the relevance data of test results into a generating AI and have the generating AI perform the adjustment of the order of learning plans.

[0117] The support unit can estimate the user's emotions and adjust the daily learning support methods based on the estimated emotions. For example, if the user is feeling stressed, the support unit can provide a relaxing learning environment. If the user is relaxed, the support unit can also provide a learning environment that enhances concentration. Furthermore, if the user is in a hurry, the support unit can suggest efficient learning methods. In this way, by adjusting the daily learning support methods according to the user's emotions, more appropriate learning support can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the daily learning support methods.

[0118] The support unit can adjust the level of detail of support based on the importance of the test results during support. For example, the support unit can provide detailed support based on important test results. Conversely, the support unit can simplify support based on less important test results. The support unit can also adjust the level of detail of support according to the importance of the test results. This allows for efficient support by adjusting the level of detail of support based on the importance of the test results. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input test result importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of support.

[0119] The support unit can apply different support algorithms depending on the category of the test results during support. For example, the support unit can apply a mathematical formula analysis algorithm to mathematics test results. It can also apply a grammar analysis algorithm to English test results. Furthermore, it can apply a scientific data analysis algorithm to science test results. By applying different support algorithms depending on the category of the test results, more accurate support becomes possible. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the category data of the test results into a generating AI and have the generating AI execute the application of the support algorithm.

[0120] The support unit can estimate the user's emotions and adjust the length of daily learning support based on the estimated emotions. For example, if the user is stressed, the support unit can provide short, concise support. If the user is relaxed, the support unit can provide detailed support. If the user is in a hurry, the support unit can provide brief support. By adjusting the length of daily learning support according to the user's emotions, more appropriate learning support can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI adjust the length of daily learning support.

[0121] The support department can prioritize support based on the submission date of test results. For example, the support department may prioritize support based on recently submitted test results. It may also postpone support based on older test results. Furthermore, the support department can adjust the support priority based on the submission date. This enables efficient support by prioritizing support based on the submission date of test results. Some or all of the above processing in the support department may be performed using AI, for example, or not. For example, the support department can input test result submission date data into a generating AI and have the generating AI determine the support priority.

[0122] The support unit can adjust the order of support based on the relevance of test results during support. For example, the support unit can prioritize support based on highly relevant test results. It can also postpone support based on less relevant test results. Furthermore, the support unit can adjust the order of support based on the relevance of test results. This allows for efficient support by adjusting the order of support based on the relevance of test results. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input relevance data of test results into a generating AI and have the generating AI perform the adjustment of the order of support.

[0123] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0124] The reception desk can analyze the user's learning history and suggest the optimal time for taking photos. For example, if a user has previously taken photos during times when their concentration is high, that time slot will be prioritized for suggestion. It can also analyze the ambient noise of locations where the user has previously taken photos and suggest quieter locations. Furthermore, it can prompt the user to take photos at appropriate times according to their learning progress. By suggesting the optimal time for taking photos based on the user's learning history, more effective learning support becomes possible.

[0125] The analysis unit can analyze the user's learning style and select the optimal analysis method. For example, if the user prefers visual learning, it can provide analysis results using graphs and diagrams. If the user prefers auditory learning, it can provide analysis results in audio format. Furthermore, if the user prefers tactile learning, it can provide interactive analysis results. By selecting an analysis method that matches the user's learning style, more effective learning support becomes possible.

[0126] The generation unit can adjust the content of review questions and retests based on the user's learning objectives. For example, if a user is studying for a specific exam, it will prioritize generating questions related to that exam. It can also generate questions related to a specific skill if the user wants to improve that skill. Furthermore, if the user wants to achieve results in a short period, it can generate effective questions for that short time. This allows for more effective learning support by adjusting the content of review questions and retests based on the user's learning objectives.

[0127] The feedback unit can analyze the user's learning history and select the optimal feedback method. For example, if the user previously preferred visual feedback, it can provide feedback using graphs and diagrams. If the user previously preferred auditory feedback, it can provide audio feedback. Furthermore, if the user previously preferred haptic feedback, it can provide interactive feedback. This allows for more effective learning support by selecting the optimal feedback method based on the user's learning history.

[0128] The suggestion function can analyze a user's learning history and propose the optimal learning plan. For example, it can prioritize suggesting learning methods that have been effective for the user in the past. It can also suggest avoiding learning methods that the user has struggled with in the past. Furthermore, it can propose an appropriate learning plan according to the user's learning progress. This enables more effective learning support by suggesting the optimal learning plan based on the user's learning history.

[0129] The support department can analyze a user's learning history and select the most suitable support method. For example, it can prioritize providing support methods that have been effective for the user in the past. It can also suggest avoiding support methods that the user has struggled with in the past. Furthermore, it can provide appropriate support methods according to the user's learning progress. By selecting the most suitable support method based on the user's learning history, more effective learning support becomes possible.

[0130] The reception area can estimate the user's emotions and adjust the shooting environment based on those estimates. For example, if the user is nervous, it can provide a relaxing environment. If the user is concentrating, it can provide a quiet environment to maintain that concentration. Furthermore, if the user is tired, it can provide an environment that encourages them to take a break. By adjusting the shooting environment according to the user's emotions, more effective shooting becomes possible.

[0131] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on those emotions. For example, if the user is nervous, it can provide simple and easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results. By adjusting the presentation method of analysis results according to the user's emotions, more effective learning support becomes possible.

[0132] The generation unit can estimate the user's emotions and adjust the difficulty of review questions and retests based on those emotions. For example, if the user is nervous, it can start with easy questions. Conversely, if the user is relaxed, it can provide more difficult questions. Furthermore, if the user is in a hurry, it can provide questions that can be solved in a short time. By adjusting the difficulty of review questions and retests according to the user's emotions, more effective learning support becomes possible.

[0133] The feedback unit can estimate the user's emotions and adjust the timing of feedback based on those emotions. For example, if the user is feeling tense, feedback can be provided at a time when they can relax. If the user is concentrating, feedback can be provided immediately to help maintain their concentration. Furthermore, if the user is tired, feedback can be provided after a break. This allows for more effective learning support by adjusting the timing of feedback according to the user's emotions.

[0134] The following briefly describes the processing flow for example form 2.

[0135] Step 1: The reception desk takes a picture of the test results with the user's smartphone. For example, the reception desk takes a picture of the test results using the smartphone's camera and acquires the image. Step 2: The analysis unit analyzes the test results captured by the reception unit. For example, the analysis unit uses optical character recognition (OCR) technology to digitize the test results and uses natural language processing (NLP) to analyze incorrect answers. Step 3: The generation unit automatically generates review questions and retests based on the results analyzed by the analysis unit. For example, the generation unit generates review questions and retests tailored to each individual student. Step 4: The feedback unit provides real-time feedback based on the review questions and retests generated by the generation unit. For example, the feedback unit provides feedback on the analysis results within seconds of capturing the test results. Step 5: The proposal team proposes individualized learning plans based on the feedback provided by the feedback team. For example, the proposal team provides learning methods tailored to each student. Step 6: The support team provides daily learning support based on the learning plan proposed by the suggestion team. For example, the support team provides advice and additional practice problems tailored to individual needs.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, feedback unit, suggestion unit, and support unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit uses the camera 42 of the smart device 14 to capture the test results and acquire the image. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which digitizes the test results using optical character recognition (OCR) technology and analyzes incorrect answers using natural language processing (NLP). The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates review questions and retests tailored to each individual student. The feedback unit is implemented by the control unit 46A of the smart device 14, which provides feedback of the analysis results within a few seconds of capturing the test results. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12, which provides learning methods tailored to each individual student. The support unit is implemented by the control unit 46A of the smart device 14, which provides advice and additional practice problems tailored to individual needs. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0140] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, feedback unit, suggestion unit, and support 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 uses the camera 42 of the smart glasses 214 to capture the test results and acquire the image. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which digitizes the test results using optical character recognition (OCR) technology and analyzes incorrect answers using natural language processing (NLP). The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates review questions and retests tailored to each individual student. The feedback unit is implemented by the control unit 46A of the smart glasses 214, which provides feedback of the analysis results within a few seconds of capturing the test results. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12, which provides a learning method tailored to each individual student. The support unit is implemented by the control unit 46A of the smart glasses 214, which provides advice and additional practice problems tailored to individual needs. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, feedback unit, suggestion unit, and support unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit uses the camera 42 of the headset terminal 314 to capture the test results and acquire the image. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which digitizes the test results using optical character recognition (OCR) technology and analyzes incorrect answers using natural language processing (NLP). The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which generates review questions and retests tailored to each individual student. The feedback unit is implemented by the control unit 46A of the headset terminal 314, which provides feedback of the analysis results within a few seconds of capturing the test results. The suggestion unit is implemented by the identification processing unit 290 of the data processing unit 12, which provides learning methods tailored to each individual student. The support unit is implemented by the control unit 46A of the headset terminal 314, which provides advice and additional practice problems tailored to individual needs. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0172] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, feedback unit, suggestion unit, and support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit uses the camera 42 of the robot 414 to capture the test results and acquire the images. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which digitizes the test results using optical character recognition (OCR) technology and analyzes incorrect answers using natural language processing (NLP). The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates review questions and retests tailored to each individual student. The feedback unit is implemented by the control unit 46A of the robot 414, which provides feedback of the analysis results within a few seconds of capturing the test results. The suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides learning methods tailored to each individual student. The support unit is implemented by the control unit 46A of the robot 414, which provides advice and additional practice problems tailored to individual needs. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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."

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] (Note 1) The reception area where the test results are photographed, An analysis unit that analyzes the test results captured by the reception unit, A generation unit that automatically generates review questions and retests based on the results of the analysis performed by the aforementioned analysis unit, A feedback unit provides real-time feedback based on the review questions and retests generated by the generation unit, A proposal unit that proposes an individualized learning plan based on the feedback provided by the aforementioned feedback unit, The system includes a support unit that supports daily learning based on the learning plan proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Image recognition technology is used to digitize test results, and natural language processing is used to analyze incorrect answers. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Automatically generates review questions and retests tailored to each individual student. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is The analysis results are provided as feedback within seconds of capturing the test results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We provide learning methods tailored to each individual student. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit is We provide advice and additional practice problems tailored to individual needs. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of test result captures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past test results and shooting history to select the optimal shooting method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When capturing test results, filtering is performed based on the user's current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of test results to capture based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When capturing test results, the system prioritizes capturing highly relevant test results based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When capturing test results, analyze the user's social media activity and capture relevant test results. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the test results were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is We estimate the user's emotions and adjust how review questions and retests are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the level of detail for review questions and retests is adjusted based on the importance of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different generation algorithms are applied depending on the category of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is The system estimates the user's emotions and adjusts the length of review questions and retests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the priority of review questions and retests is determined based on when the test results were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the order of review questions and retests is adjusted based on the relevance of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When providing feedback, adjust the level of detail in the feedback based on the importance of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is During feedback, different feedback algorithms are applied depending on the category of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is It estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, we prioritize feedback based on when the test results were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When providing feedback, adjust the order of feedback based on the relevance of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, It estimates the user's emotions and adjusts how learning plans are suggested based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the learning plan based on the importance of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the category of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the learning plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When making a proposal, prioritize the learning plan based on the timing of test result submission. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, When making a proposal, adjust the order of the learning plan based on the relevance of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned support unit is It estimates the user's emotions and adjusts the daily learning support methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned support unit is During support, we adjust the level of detail based on the importance of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned support unit is During support, different support algorithms are applied depending on the category of the test results. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned support unit is It estimates the user's emotions and adjusts the length of daily learning support based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned support unit is During support, we prioritize support based on when the test results are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned support unit is During support, we adjust the order of support based on the relevance of the test results. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0208] 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 area where the test results are photographed, An analysis unit that analyzes the test results captured by the reception unit, A generation unit that automatically generates review questions and retests based on the results of the analysis performed by the aforementioned analysis unit, A feedback unit provides real-time feedback based on the review questions and retests generated by the generation unit, A proposal unit that proposes an individualized learning plan based on the feedback provided by the aforementioned feedback unit, The system includes a support unit that supports daily learning based on the learning plan proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned analysis unit, Image recognition technology is used to digitize test results, and natural language processing is used to analyze incorrect answers. The system according to feature 1.

3. The generating unit is Automatically generates review questions and retests tailored to each individual student. The system according to feature 1.

4. The aforementioned feedback unit is The analysis results are provided as feedback within seconds of capturing the test results. The system according to feature 1.

5. The aforementioned proposal section is, We provide learning methods tailored to each individual student. The system according to feature 1.

6. The aforementioned support unit is We provide advice and additional practice problems tailored to individual needs. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of test result captures based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is The system analyzes the user's past test results and shooting history to select the optimal shooting method. The system according to feature 1.

Images