System

Abstract

An object of the system according to the embodiment is to specify in which halfway expression the learner cannot understand, analyze the root cause, and provide an appropriate problem and explanation.SOLUTION: A system according to an embodiment includes a note reading unit, an insufficient understanding specifying unit, a root cause specifying unit, a problem providing unit, and an explanation unit. The note reader reads a note or an answer sheet. An insufficient understanding specifying part analyzes the data of the notebook or the answer sheet read by the notebook reading part, and specifies in which way the learner cannot understand. The root cause specifying unit analyzes a root cause of the insufficient understanding specified by the insufficient understanding specifying unit. The problem providing unit provides a problem for removing the root cause specified by the root cause specifying unit. The explanation unit explains the question provided by the question providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to identify which intermediate steps a learner is unable to understand and to analyze the root cause.

[0005] The system according to the embodiment aims to identify which intermediate steps a learner does not understand, analyze the root cause, and provide appropriate questions and explanations. [Means for solving the problem]

[0006] The system according to the embodiment includes a note reading unit, a lack of understanding identifying unit, a root cause identifying unit, a question providing unit, and an explanation unit. The note reading unit reads a notebook or an answer sheet. The lack of understanding identifying unit analyzes the data in the notebook or answer sheet read by the note reading unit and identifies which intermediate steps the learner does not understand. The root cause identifying unit analyzes the root cause of the lack of understanding identified by the lack of understanding identifying unit. The question providing unit provides questions to eliminate the root cause identified by the root cause identifying unit. The explanation unit provides explanations for the questions provided by the question providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify which intermediate steps a learner does not understand, analyze the root cause, and provide appropriate questions and explanations. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The learning support system according to an embodiment of the present invention automatically reads the mathematical solution process written by the learner, and the generation AI identifies the lack of understanding, analyzes the root cause, and provides the problem and explanation. This allows the learning support system to efficiently compensate for the learner's lack of understanding and improve their academic ability in mathematics.

[0029] A learning support system according to an embodiment includes a note reader, a lack of understanding identification unit, a root cause identification unit, a question provision unit, and an explanation unit. The note reader reads a learner's notes or answer sheets. For example, the handwritten notes may be scanned using a scanner and saved as digital data. Alternatively, the notes may be photographed using a camera and saved as image data. Furthermore, the note reader converts handwritten characters into text data using OCR technology. For example, the note reader scans handwritten notes using a high-resolution scanner and converts them into text information using OCR technology. Image data captured using a camera can also be converted into text data using OCR technology. The lack of understanding identification unit analyzes the data in the notes or answer sheets read by the note reader and identifies which intermediate steps the learner does not understand. For example, the lack of understanding identification unit detects calculation errors or logical leaps. Furthermore, the lack of understanding identification unit checks the learner's answer process one by one and compares them with the correct answer process to identify areas of lack of understanding. The root cause identification unit analyzes the root cause of the lack of understanding identified by the understanding-deficiency identification unit. For example, the root cause identification unit identifies whether the cause is a basic calculation error, a misunderstanding of a concept, or a misreading of the problem. The root cause identification unit also oversees the learner's entire solution process and clarifies where the misunderstanding occurs. The problem provision unit provides problems to eliminate the root cause identified by the root cause identification unit. For example, if the cause is a basic calculation error, the problem provision unit provides calculation practice problems. If the cause is a conceptual misunderstanding, the problem provision unit provides problems to help the learner understand the concept. The explanation unit provides explanations for the problems provided by the problem provision unit. For example, the explanation unit provides detailed explanations on how to solve the calculation practice problems. The explanation unit also provides explanations for problems to help the learner understand the concept. In this way, the learning support system according to the embodiment can efficiently compensate for the learner's lack of understanding and improve their academic ability in mathematics. For example, the correct answer rate and answer time for the problems solved by the learner are recorded to evaluate the learner's level of understanding. The next problem and explanation content to be provided can be adjusted according to the learner's progress.

[0030] The note reader can analyze a learner's handwriting or writing habits to evaluate the learner's level of understanding of the answer process. The note reader, for example, collects the learner's handwriting data and analyzes its characteristics to evaluate the learner's level of understanding of the answer process. For example, it analyzes changes in character size, inclination, and writing pressure to use as an index of understanding. The note reader also analyzes the learner's writing habits to evaluate the learner's level of understanding of the answer process. For example, it analyzes patterns in how specific symbols and numbers are written to use as an index of understanding. The note reader also analyzes the learner's handwriting and writing habits to evaluate the learner's level of understanding of the answer process. For example, it identifies points in the answer where the writing pressure weakens and evaluates a lack of understanding in those points. In this way, by analyzing the learner's handwriting and writing habits, the learner's level of understanding of the answer process can be more accurately evaluated.

[0031] The note reader can collect handwriting data of a learner in advance and individually optimize handwriting recognition accuracy. The note reader, for example, collects handwriting data of a learner in advance and individually optimizes a handwriting recognition model based on that data. For example, the note reader can learn the different character shapes and writing style characteristics of each learner. The note reader can also collect handwriting data of a learner and individually optimize a handwriting recognition model based on that data. For example, to improve recognition accuracy for specific characters or symbols, the note reader can learn the learner's writing patterns. The note reader can also collect handwriting data of a learner in advance and individually optimize a handwriting recognition model based on that data. For example, a recognition model can be constructed that takes into account the learner's writing pressure and writing speed. In this way, by collecting handwriting data of a learner in advance and individually optimizing handwriting recognition accuracy, recognition accuracy can be improved.

[0032] The note reader, in addition to reading notes and answer sheets, can also collect data in which a learner orally explains their answer process using voice input and use it for analysis. For example, in addition to reading notes and answer sheets, the note reader can collect audio data in which a learner orally explains their answer process and use it for analysis. For example, the oral explanation can be converted into text data using voice recognition technology. The note reader can also collect audio data in which a learner orally explains their answer process and use that data for analysis. For example, the note reader can evaluate the level of understanding of the answer process from the audio data. In addition to reading notes and answer sheets, the note reader can also collect audio data in which a learner orally explains their answer process and use it for analysis. For example, the audio data can be analyzed to evaluate the logical consistency of the answer process. In this way, by collecting data in which a learner orally explains their answer process using voice input and using it for analysis, the level of understanding of the answer process can be evaluated in more detail.

[0033] The note reader unit can grasp the steps and methods of the answer in more detail by having the learner record a video of the answering process and analyzing the video data. For example, the note reader unit can grasp the steps and methods of the answer in more detail by having the learner record a video of the answering process and analyze the video data. For example, it can use video analysis technology to automatically extract each step of the answer. The note reader unit can also grasp the steps and methods of the answer in more detail by recording a video of the answering process and analyzing the data. For example, it can visualize the flow and steps of the answer from the video data. The note reader unit can also grasp the steps and methods of the answer in more detail by having the learner record a video of the answering process and analyze the video data. For example, it can analyze the video data and identify areas of insufficient understanding during the answering process. In this way, the note reader unit can grasp the steps and methods of the answer in more detail by having the learner record a video of the answering process and analyze the video data.

[0034] When analyzing a learner's answer process, the understanding deficiency identification unit can find specific patterns and trends by comparing it with past answer data. For example, the understanding deficiency identification unit analyzes a learner's answer process and compares it with past answer data to find specific patterns and trends. For example, it identifies parts where the same mistakes are repeated. The understanding deficiency identification unit also compares it with past answer data to find specific patterns and trends in the learner's answer process. For example, it identifies a lack of understanding in a specific question format. The understanding deficiency identification unit also analyzes a learner's answer process and compares it with past answer data to find specific patterns and trends. For example, it analyzes fluctuations in answer speed and accuracy. In this way, by comparing it with past answer data, it is possible to find specific patterns and trends and identify a lack of understanding.

[0035] The understanding deficiency identifying unit can monitor the learner's answer process in real time and provide immediate feedback. The understanding deficiency identifying unit, for example, monitors the learner's answer process in real time and immediately identifies areas of understanding deficiency and provides feedback. For example, if an error is detected during the answer, it is immediately pointed out. The understanding deficiency identifying unit also monitors the learner's answer process in real time and identifies areas of understanding deficiency and provides feedback. For example, if there is a logical leap in the middle of the answer, it is immediately pointed out. The understanding deficiency identifying unit also monitors the learner's answer process in real time and identifies areas of understanding deficiency and provides feedback. For example, if a calculation error is detected, it prompts the learner to make an immediate correction. In this way, by monitoring the answer process in real time and providing immediate feedback, it is possible to quickly make up for lack of understanding.

[0036] The understanding deficiency identification unit can compare the answering processes of different learners and identify common areas of understanding deficiency. The understanding deficiency identification unit, for example, compares the answering processes of different learners and identifies common areas of understanding deficiency. For example, it identifies areas where multiple learners make mistakes on the same question. The understanding deficiency identification unit also compares the answering processes of learners and identifies common areas of understanding deficiency. For example, it identifies an understanding deficiency in a specific concept or skill. The understanding deficiency identification unit also compares the answering processes of different learners and identifies common areas of understanding deficiency. For example, it identifies an understanding deficiency in a specific question format. In this way, by comparing the answering processes of different learners, common areas of understanding deficiency can be identified, enabling efficient learning support.

[0037] The understanding deficiency identification unit can also analyze the answering process of different subjects and evaluate the overall level of learning comprehension. The understanding deficiency identification unit, for example, analyzes the answering process of different subjects and evaluates the overall level of learning comprehension. For example, it compares the answering processes of arithmetic and science and identifies common areas of understanding deficiency. The understanding deficiency identification unit also analyzes the answering process of different subjects, such as science and social studies, and evaluates the overall level of learning comprehension. For example, it identifies common areas of understanding deficiency across different subjects. The understanding deficiency identification unit also analyzes the answering process of different subjects and evaluates the overall level of learning comprehension. For example, it compares the answering processes of arithmetic and social studies and identifies trends in understanding deficiencies. In this way, the overall level of learning comprehension can be evaluated by analyzing the answering processes of different subjects.

[0038] The root cause identification unit can analyze the entire answering process of the learner and identify in detail the root cause of the lack of understanding of a particular concept or skill. The root cause identification unit, for example, analyzes the entire answering process of the learner and identifies the root cause of the lack of understanding of a particular concept or skill. For example, it identifies basic calculation errors or conceptual misunderstandings. The root cause identification unit can also analyze the entire answering process of the learner and identify the root cause of the lack of understanding of a particular concept or skill. For example, it identifies misreading of the question or logical leaps. The root cause identification unit can also analyze the entire answering process of the learner and identify the root cause of the lack of understanding of a particular concept or skill. For example, it analyzes in detail the lack of understanding at a particular step. In this way, by analyzing the entire answering process, it is possible to identify in detail the root cause of the lack of understanding of a particular concept or skill.

[0039] The root cause identification unit can identify long-term trends in insufficient understanding by referring to the learner's past learning history or grade data. The root cause identification unit can, for example, identify long-term trends in insufficient understanding by referring to the learner's past learning history or grade data. For example, it can analyze past test results or homework grades. The root cause identification unit can also identify long-term trends in insufficient understanding by referring to the learner's past learning history and grade data. For example, it can identify insufficient understanding in a specific unit or theme. The root cause identification unit can also identify long-term trends in insufficient understanding by referring to the learner's past learning history and grade data. For example, it can analyze fluctuations in learning progress and grades. In this way, by referring to the learner's past learning history and grade data, it can identify long-term trends in insufficient understanding.

[0040] The root cause identification unit can integrate data from different learners and perform cloud-based data analysis to identify a common root cause. The root cause identification unit, for example, integrates data from different learners and performs cloud-based data analysis to identify a common root cause. For example, data is aggregated and analyzed on the cloud. The root cause identification unit also integrates learner data on a cloud basis and performs data analysis to identify a common root cause. For example, big data analysis technology is used to identify common areas of insufficient understanding. The root cause identification unit also integrates data from different learners on a cloud basis and performs data analysis to identify a common root cause. For example, data mining technology on the cloud is used to identify common areas of insufficient understanding. In this way, by integrating data from different learners and performing cloud-based data analysis, a common root cause can be identified.

[0041] The root cause identification unit can analyze the influence of different educational systems or curricula and propose an optimal learning method. The root cause identification unit, for example, analyzes the influence of different educational systems or curricula and proposes an optimal learning method. For example, it analyzes the characteristics of each educational system and proposes an optimal method for the learner. The root cause identification unit also analyzes the influence of the educational system or curriculum and proposes an optimal learning method. For example, it compares the effects of different curricula and identifies an optimal learning method. The root cause identification unit also analyzes the influence of different educational systems or curricula and proposes an optimal learning method. For example, it analyzes the advantages and disadvantages of each system and proposes an optimal method for the learner. In this way, by analyzing the influence of different educational systems and curricula, an optimal learning method can be proposed.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The note reader can analyze a learner's handwriting or writing habits to evaluate the learner's level of understanding of the answer process. For example, the learner's level of understanding of the answer process can be evaluated by collecting handwriting data and analyzing its characteristics. For example, the size, angle, and changes in writing pressure of characters can be analyzed and used as an index of understanding. The note reader can also analyze a learner's writing habits to evaluate the learner's level of understanding of the answer process. For example, the note reader can analyze patterns in writing specific symbols and numbers and use these as an index of understanding. The note reader can also analyze a learner's handwriting or writing habits to evaluate the learner's level of understanding of the answer process. For example, the note reader can identify points in the answer where writing pressure weakens and evaluate a lack of understanding in those points. In this way, by analyzing a learner's handwriting or writing habits, the learner's level of understanding of the answer process can be more accurately evaluated.

[0044] The note reader can collect handwriting data of a learner in advance and individually optimize handwriting recognition accuracy. For example, the note reader can collect handwriting data of a learner in advance and individually optimize a handwriting recognition model based on that data. For example, the note reader can learn the different character shapes and writing style characteristics of each learner. The note reader can also collect handwriting data of a learner and individually optimize a handwriting recognition model based on that data. For example, the note reader can learn the learner's writing patterns to improve recognition accuracy for specific characters and symbols. The note reader can also collect handwriting data of a learner in advance and individually optimize a handwriting recognition model based on that data. For example, a recognition model can be constructed that takes into account the learner's writing pressure and writing speed. In this way, by collecting handwriting data of a learner in advance and individually optimizing handwriting recognition accuracy, recognition accuracy can be improved.

[0045] In addition to reading notes and answer sheets, the note reader can also collect data in which a learner orally explains their answer process using voice input and use it for analysis. For example, in addition to reading notes and answer sheets, it can collect audio data in which a learner orally explains their answer process and use it for analysis. For example, it can use voice recognition technology to convert the oral explanation into text data. The note reader can also collect audio data in which a learner orally explains their answer process and use that data for analysis. For example, it can evaluate the level of understanding of the answer process from the audio data. In addition to reading notes and answer sheets, the note reader can also collect audio data in which a learner orally explains their answer process and use it for analysis. For example, it can analyze the audio data and evaluate the logical consistency of the answer process. In this way, by collecting data in which a learner orally explains their answer process using voice input and using it for analysis, it is possible to evaluate the level of understanding of the answer process in more detail.

[0046] The note reader unit can grasp the steps and methods of the answer in more detail by having the learner record a video of the answering process and analyze the video data. For example, the learner can record a video of the answering process and analyze the video data to grasp the steps and methods of the answer in more detail. For example, video analysis technology can be used to automatically extract each step of the answer. The note reader unit can also record a video of the answering process and analyze the data to grasp the steps and methods of the answer in more detail. For example, the flow and steps of the answer can be visualized from the video data. The note reader unit can also record a video of the answering process and analyze the video data to grasp the steps and methods of the answer in more detail. For example, the video data can be analyzed to identify areas of insufficient understanding during the answering process. In this way, the learner can record a video of the answering process and analyze the video data to grasp the steps and methods of the answer in more detail.

[0047] When analyzing a learner's answer process, the understanding deficiency identification unit can find specific patterns and trends by comparing it with past answer data. For example, by analyzing a learner's answer process and comparing it with past answer data, specific patterns and trends can be found. For example, it can identify parts where the same mistakes are repeated. The understanding deficiency identification unit can also find specific patterns and trends by comparing it with past answer data. For example, it can identify a lack of understanding in a specific question format. The understanding deficiency identification unit can also find specific patterns and trends by analyzing a learner's answer process and comparing it with past answer data. For example, it can analyze fluctuations in answer speed and accuracy. In this way, by comparing it with past answer data, it can find specific patterns and trends and identify a lack of understanding.

[0048] The understanding deficiency identifying unit can monitor the learner's answer process in real time and provide instant feedback. For example, it can monitor the learner's answer process in real time, instantly identify areas of understanding deficiency, and provide feedback. For example, if an error is detected during the answer, it can be pointed out immediately. The understanding deficiency identifying unit can also monitor the learner's answer process in real time, identify areas of understanding deficiency, and provide feedback. For example, it can be pointed out immediately if there is a logical leap in the answer. The understanding deficiency identifying unit can also monitor the learner's answer process in real time, identify areas of understanding deficiency, and provide feedback. For example, if a calculation error is detected, it can be prompted to make an immediate correction. In this way, by monitoring the answer process in real time and providing instant feedback, it is possible to quickly make up for lack of understanding.

[0049] The understanding deficiency identification unit can compare the answering processes of different learners and identify common areas of understanding deficiency. For example, it compares the answering processes of different learners and identifies common areas of understanding deficiency. For example, it identifies areas where multiple learners make mistakes on the same question. The understanding deficiency identification unit also compares the answering processes of different learners and identifies common areas of understanding deficiency. For example, it identifies a lack of understanding of a specific concept or skill. The understanding deficiency identification unit also compares the answering processes of different learners and identifies common areas of understanding deficiency. For example, it identifies a lack of understanding in a specific question format. In this way, by comparing the answering processes of different learners, it is possible to identify common areas of understanding deficiency and provide efficient learning support.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The note reader reads the notes or answer sheets written by the learner. For example, handwritten notes can be scanned using a scanner and saved as digital data. Alternatively, notes can be photographed using a camera and saved as image data. The note reader then converts handwritten characters into text data using OCR technology. For example, the note reader scans handwritten notes using a high-resolution scanner and converts them into text information using OCR technology. Image data photographed using a camera can also be converted into text data using OCR technology. Step 2: The incomplete understanding identification unit analyzes the data from the notes or answer sheets read by the note reading unit and identifies which intermediate steps the learner does not understand. For example, the incomplete understanding identification unit detects calculation errors or logical leaps. The incomplete understanding identification unit also checks the learner's answer process one by one and compares it with the correct answer process to identify areas of incomplete understanding. Step 3: The root cause identification unit analyzes the root causes of the lack of understanding identified by the lack of understanding identification unit. For example, the root cause identification unit may identify that the cause is a basic calculation error, a misunderstanding of a concept, or a misreading of the problem. The root cause identification unit also oversees the learner's entire answer process and clarifies where the misunderstanding occurred. Step 4: The problem provider provides problems to eliminate the root cause identified by the root cause identification unit. For example, if the cause is a basic calculation error, the problem provider provides calculation practice problems. Also, if the cause is a misunderstanding of a concept, the problem provider provides problems to help the user understand the concept. Step 5: The explanation unit provides explanations of the problems provided by the problem providing unit. For example, the explanation unit provides detailed explanations on how to solve calculation practice problems. The explanation unit also provides explanations of problems to help students understand concepts. This allows the learning support system according to the embodiment to efficiently compensate for a learner's lack of understanding and improve their academic ability in arithmetic. For example, the correct answer rate and answer time of the problems solved by the learner are recorded to evaluate the learner's level of understanding. The next problem and explanation to be provided can be adjusted according to the learner's progress.

[0052] (Example 2) The learning support system according to an embodiment of the present invention automatically reads the mathematical solution process written by the learner, and the generation AI identifies the lack of understanding, analyzes the root cause, and provides the problem and explanation. This allows the learning support system to efficiently compensate for the learner's lack of understanding and improve their academic ability in mathematics.

[0053] A learning support system according to an embodiment includes a note reader, a lack of understanding identification unit, a root cause identification unit, a question provision unit, and an explanation unit. The note reader reads a learner's notes or answer sheets. For example, the handwritten notes may be scanned using a scanner and saved as digital data. Alternatively, the notes may be photographed using a camera and saved as image data. Furthermore, the note reader converts handwritten characters into text data using OCR technology. For example, the note reader scans handwritten notes using a high-resolution scanner and converts them into text information using OCR technology. Image data captured using a camera can also be converted into text data using OCR technology. The lack of understanding identification unit analyzes the data in the notes or answer sheets read by the note reader and identifies which intermediate steps the learner does not understand. For example, the lack of understanding identification unit detects calculation errors or logical leaps. Furthermore, the lack of understanding identification unit checks the learner's answer process one by one and compares them with the correct answer process to identify areas of lack of understanding. The root cause identification unit analyzes the root cause of the lack of understanding identified by the understanding-deficiency identification unit. For example, the root cause identification unit identifies whether the cause is a basic calculation error, a misunderstanding of a concept, or a misreading of the problem. The root cause identification unit also oversees the learner's entire solution process and clarifies where the misunderstanding occurs. The problem provision unit provides problems to eliminate the root cause identified by the root cause identification unit. For example, if the cause is a basic calculation error, the problem provision unit provides calculation practice problems. If the cause is a conceptual misunderstanding, the problem provision unit provides problems to help the learner understand the concept. The explanation unit provides explanations for the problems provided by the problem provision unit. For example, the explanation unit provides detailed explanations on how to solve the calculation practice problems. The explanation unit also provides explanations for problems to help the learner understand the concept. In this way, the learning support system according to the embodiment can efficiently compensate for the learner's lack of understanding and improve their academic ability in mathematics. For example, the correct answer rate and answer time for the problems solved by the learner are recorded to evaluate the learner's level of understanding. The next problem and explanation content to be provided can be adjusted according to the learner's progress.

[0054] The note reader can analyze a learner's handwriting or writing habits to evaluate the learner's level of understanding of the answer process. The note reader, for example, collects the learner's handwriting data and analyzes its characteristics to evaluate the learner's level of understanding of the answer process. For example, it analyzes changes in character size, inclination, and writing pressure to use as an index of understanding. The note reader also analyzes the learner's writing habits to evaluate the learner's level of understanding of the answer process. For example, it analyzes patterns in how specific symbols and numbers are written to use as an index of understanding. The note reader also analyzes the learner's handwriting and writing habits to evaluate the learner's level of understanding of the answer process. For example, it identifies points in the answer where the writing pressure weakens and evaluates a lack of understanding in those points. In this way, by analyzing the learner's handwriting and writing habits, the learner's level of understanding of the answer process can be more accurately evaluated.

[0055] The note reader can collect handwriting data of a learner in advance and individually optimize handwriting recognition accuracy. The note reader, for example, collects handwriting data of a learner in advance and individually optimizes a handwriting recognition model based on that data. For example, the note reader can learn the different character shapes and writing style characteristics of each learner. The note reader can also collect handwriting data of a learner and individually optimize a handwriting recognition model based on that data. For example, to improve recognition accuracy for specific characters or symbols, the note reader can learn the learner's writing patterns. The note reader can also collect handwriting data of a learner in advance and individually optimize a handwriting recognition model based on that data. For example, a recognition model can be constructed that takes into account the learner's writing pressure and writing speed. In this way, by collecting handwriting data of a learner in advance and individually optimizing handwriting recognition accuracy, recognition accuracy can be improved.

[0056] The note reader uses the emotion estimation function to estimate the learner's emotional state from the writing pressure and speed of characters written on a notebook or answer sheet, and can evaluate the learner's stress level during learning. The note reader, for example, analyzes the writing pressure and speed of characters written on a notebook or answer sheet to estimate the learner's emotional state. For example, if the writing pressure is strong and the writing speed is fast, it is determined that the learner is under high stress. The note reader also uses the emotion estimation function to estimate the learner's emotional state from the writing pressure and speed of characters written on a notebook or answer sheet. For example, if the writing pressure is weak and the writing speed is slow, it is determined that the learner is relaxed. The note reader also analyzes the writing pressure and speed of characters written on a notebook or answer sheet to estimate the learner's emotional state. For example, it analyzes changes in writing pressure and speed to evaluate fluctuations in stress level. In this way, the learner's emotional state can be estimated and the learner's stress level can be evaluated, thereby understanding the learner's psychological state.

[0057] The note reader, in addition to reading notes and answer sheets, can also collect data in which a learner orally explains their answer process using voice input and use it for analysis. For example, in addition to reading notes and answer sheets, the note reader can collect audio data in which a learner orally explains their answer process and use it for analysis. For example, the oral explanation can be converted into text data using voice recognition technology. The note reader can also collect audio data in which a learner orally explains their answer process and use that data for analysis. For example, the note reader can evaluate the level of understanding of the answer process from the audio data. In addition to reading notes and answer sheets, the note reader can also collect audio data in which a learner orally explains their answer process and use it for analysis. For example, the audio data can be analyzed to evaluate the logical consistency of the answer process. In this way, by collecting data in which a learner orally explains their answer process using voice input and using it for analysis, the level of understanding of the answer process can be evaluated in more detail.

[0058] The note reader unit can grasp the steps and methods of the answer in more detail by having the learner record a video of the answering process and analyzing the video data. For example, the note reader unit can grasp the steps and methods of the answer in more detail by having the learner record a video of the answering process and analyze the video data. For example, it can use video analysis technology to automatically extract each step of the answer. The note reader unit can also grasp the steps and methods of the answer in more detail by recording a video of the answering process and analyzing the data. For example, it can visualize the flow and steps of the answer from the video data. The note reader unit can also grasp the steps and methods of the answer in more detail by having the learner record a video of the answering process and analyze the video data. For example, it can analyze the video data and identify areas of insufficient understanding during the answering process. In this way, the note reader unit can grasp the steps and methods of the answer in more detail by having the learner record a video of the answering process and analyze the video data.

[0059] The note reader uses the emotion estimation function to analyze the learner's facial expression and tone of voice when writing in a notebook or answer sheet, and can evaluate the learner's motivation to learn. The note reader, for example, uses the emotion estimation function to analyze the learner's facial expression when writing in a notebook or answer sheet, and evaluate the learner's motivation to learn. For example, facial expression recognition technology is used to detect smiling or serious expressions. The note reader also analyzes the tone of voice when the learner writes in a notebook or answer sheet, and evaluates the learner's motivation to learn. For example, a high tone of voice is determined to indicate high motivation. The note reader also uses the emotion estimation function to analyze the learner's facial expression and tone of voice when writing in a notebook or answer sheet, and evaluate the learner's motivation to learn. For example, the change in facial expression and tone of voice is analyzed to evaluate fluctuations in motivation. In this way, the learner's motivation to learn can be evaluated by analyzing the learner's facial expression and tone of voice.

[0060] When analyzing a learner's answer process, the understanding deficiency identification unit can find specific patterns and trends by comparing it with past answer data. For example, the understanding deficiency identification unit analyzes a learner's answer process and compares it with past answer data to find specific patterns and trends. For example, it identifies parts where the same mistakes are repeated. The understanding deficiency identification unit also compares it with past answer data to find specific patterns and trends in the learner's answer process. For example, it identifies a lack of understanding in a specific question format. The understanding deficiency identification unit also analyzes a learner's answer process and compares it with past answer data to find specific patterns and trends. For example, it analyzes fluctuations in answer speed and accuracy. In this way, by comparing it with past answer data, it is possible to find specific patterns and trends and identify a lack of understanding.

[0061] The understanding deficiency identifying unit can monitor the learner's answer process in real time and provide immediate feedback. The understanding deficiency identifying unit, for example, monitors the learner's answer process in real time and immediately identifies areas of understanding deficiency and provides feedback. For example, if an error is detected during the answer, it is immediately pointed out. The understanding deficiency identifying unit also monitors the learner's answer process in real time and identifies areas of understanding deficiency and provides feedback. For example, if there is a logical leap in the middle of the answer, it is immediately pointed out. The understanding deficiency identifying unit also monitors the learner's answer process in real time and identifies areas of understanding deficiency and provides feedback. For example, if a calculation error is detected, it prompts the learner to make an immediate correction. In this way, by monitoring the answer process in real time and providing immediate feedback, it is possible to quickly make up for lack of understanding.

[0062] The insufficient understanding identification unit can use the emotion estimation function to identify parts of a specific intermediate formula where the learner feels stressed or anxious, and evaluate the learner's insufficient understanding of those parts. The insufficient understanding identification unit can, for example, use the emotion estimation function to identify parts of a specific intermediate formula where the learner feels stressed or anxious, and evaluate the learner's insufficient understanding of those parts. For example, it can analyze changes in writing pressure and facial expression. The insufficient understanding identification unit can also use the emotion estimation function to identify parts of a specific intermediate formula where the learner feels stressed or anxious, and evaluate the learner's insufficient understanding of those parts. For example, it can analyze changes in voice tone and speed. The insufficient understanding identification unit can also use the emotion estimation function to identify parts of a specific intermediate formula where the learner feels stressed or anxious, and evaluate the learner's insufficient understanding of those parts. For example, it can analyze changes in facial expression and voice while the learner is answering the question. This makes it possible to identify the learner's insufficient understanding taking into account the learner's psychological state by identifying the stress or anxiety the learner feels in a specific intermediate formula and evaluating the learner's insufficient understanding of those parts.

[0063] The understanding deficiency identification unit can compare the answering processes of different learners and identify common areas of understanding deficiency. The understanding deficiency identification unit, for example, compares the answering processes of different learners and identifies common areas of understanding deficiency. For example, it identifies areas where multiple learners make mistakes on the same question. The understanding deficiency identification unit also compares the answering processes of learners and identifies common areas of understanding deficiency. For example, it identifies an understanding deficiency in a specific concept or skill. The understanding deficiency identification unit also compares the answering processes of different learners and identifies common areas of understanding deficiency. For example, it identifies an understanding deficiency in a specific question format. In this way, by comparing the answering processes of different learners, common areas of understanding deficiency can be identified, enabling efficient learning support.

[0064] The understanding deficiency identification unit can also analyze the answering process of different subjects and evaluate the overall level of learning comprehension. The understanding deficiency identification unit, for example, analyzes the answering process of different subjects and evaluates the overall level of learning comprehension. For example, it compares the answering processes of arithmetic and science and identifies common areas of understanding deficiency. The understanding deficiency identification unit also analyzes the answering process of different subjects, such as science and social studies, and evaluates the overall level of learning comprehension. For example, it identifies common areas of understanding deficiency across different subjects. The understanding deficiency identification unit also analyzes the answering process of different subjects and evaluates the overall level of learning comprehension. For example, it compares the answering processes of arithmetic and social studies and identifies trends in understanding deficiencies. In this way, the overall level of learning comprehension can be evaluated by analyzing the answering processes of different subjects.

[0065] The understanding deficiency identification unit can use the emotion estimation function to provide feedback to reinforce the positive emotions felt by the learner during the answering process, thereby compensating for the lack of understanding. The understanding deficiency identification unit can, for example, use the emotion estimation function to provide feedback to reinforce the positive emotions felt by the learner during the answering process. For example, a message emphasizing a successful experience can be displayed. The understanding deficiency identification unit can also provide feedback to reinforce the positive emotions felt by the learner during the answering process, thereby compensating for the lack of understanding. For example, a message of praise or encouragement can be displayed. The understanding deficiency identification unit can also use the emotion estimation function to provide feedback to reinforce the positive emotions felt by the learner during the answering process, thereby compensating for the lack of understanding. For example, an interactive element can be added to elicit positive emotions. This can reinforce the positive emotions felt by the learner during the answering process, thereby compensating for the lack of understanding.

[0066] The root cause identification unit can analyze the entire answering process of the learner and identify in detail the root cause of the lack of understanding of a particular concept or skill. The root cause identification unit, for example, analyzes the entire answering process of the learner and identifies the root cause of the lack of understanding of a particular concept or skill. For example, it identifies basic calculation errors or conceptual misunderstandings. The root cause identification unit can also analyze the entire answering process of the learner and identify the root cause of the lack of understanding of a particular concept or skill. For example, it identifies misreading of the question or logical leaps. The root cause identification unit can also analyze the entire answering process of the learner and identify the root cause of the lack of understanding of a particular concept or skill. For example, it analyzes in detail the lack of understanding at a particular step. In this way, by analyzing the entire answering process, it is possible to identify in detail the root cause of the lack of understanding of a particular concept or skill.

[0067] The root cause identification unit can identify long-term trends in insufficient understanding by referring to the learner's past learning history or grade data. The root cause identification unit can, for example, identify long-term trends in insufficient understanding by referring to the learner's past learning history or grade data. For example, it can analyze past test results or homework grades. The root cause identification unit can also identify long-term trends in insufficient understanding by referring to the learner's past learning history and grade data. For example, it can identify insufficient understanding in a specific unit or theme. The root cause identification unit can also identify long-term trends in insufficient understanding by referring to the learner's past learning history and grade data. For example, it can analyze fluctuations in learning progress and grades. In this way, by referring to the learner's past learning history and grade data, it can identify long-term trends in insufficient understanding.

[0068] The root cause identification unit can use the emotion estimation function to identify the anxiety or stress the learner feels about a particular concept or skill and propose measures to alleviate those emotions. For example, the root cause identification unit can use the emotion estimation function to identify the anxiety or stress the learner feels about a particular concept or skill and propose measures to alleviate those emotions. For example, it can suggest breathing techniques to help the learner relax. The root cause identification unit can also use the emotion estimation function to identify the anxiety or stress the learner feels about a particular concept or skill and propose measures to alleviate those emotions. For example, it can provide positive feedback. The root cause identification unit can also use the emotion estimation function to identify the anxiety or stress the learner feels about a particular concept or skill and propose measures to alleviate those emotions. For example, it can provide an interactive game to alleviate stress. In this way, the learner's psychological burden can be reduced by identifying the anxiety or stress the learner feels about a particular concept or skill and proposing measures to alleviate those emotions.

[0069] The root cause identification unit can integrate data from different learners and perform cloud-based data analysis to identify a common root cause. The root cause identification unit, for example, integrates data from different learners and performs cloud-based data analysis to identify a common root cause. For example, data is aggregated and analyzed on the cloud. The root cause identification unit also integrates learner data on a cloud basis and performs data analysis to identify a common root cause. For example, big data analysis technology is used to identify common areas of insufficient understanding. The root cause identification unit also integrates data from different learners on a cloud basis and performs data analysis to identify a common root cause. For example, data mining technology on the cloud is used to identify common areas of insufficient understanding. In this way, by integrating data from different learners and performing cloud-based data analysis, a common root cause can be identified.

[0070] The root cause identification unit can analyze the influence of different educational systems or curricula and propose an optimal learning method. The root cause identification unit, for example, analyzes the influence of different educational systems or curricula and proposes an optimal learning method. For example, it analyzes the characteristics of each educational system and proposes an optimal method for the learner. The root cause identification unit also analyzes the influence of the educational system or curriculum and proposes an optimal learning method. For example, it compares the effects of different curricula and identifies an optimal learning method. The root cause identification unit also analyzes the influence of different educational systems or curricula and proposes an optimal learning method. For example, it analyzes the advantages and disadvantages of each system and proposes an optimal method for the learner. In this way, by analyzing the influence of different educational systems and curricula, an optimal learning method can be proposed.

[0071] The root cause identification unit can use the emotion estimation function to propose a learning approach for reinforcing the positive emotions that the learner feels toward a specific root cause. For example, the root cause identification unit uses the emotion estimation function to propose a learning approach for reinforcing the positive emotions that the learner feels toward a specific root cause. For example, it proposes a learning method that emphasizes successful experiences. The root cause identification unit also uses the emotion estimation function to identify the positive emotions that the learner feels toward a specific root cause and proposes a learning approach for reinforcing those emotions. For example, it provides positive feedback. The root cause identification unit also uses the emotion estimation function to propose a learning approach for reinforcing the positive emotions that the learner feels toward a specific root cause. For example, it adds an interactive element to elicit positive emotions. In this way, the positive emotions that the learner feels toward a specific root cause can be strengthened, thereby increasing motivation to learn and making up for lack of understanding.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The note reader can analyze a learner's handwriting or writing habits to evaluate the learner's level of understanding of the answer process. For example, the learner's level of understanding of the answer process can be evaluated by collecting handwriting data and analyzing its characteristics. For example, the size, angle, and changes in writing pressure of characters can be analyzed and used as an index of understanding. The note reader can also analyze a learner's writing habits to evaluate the learner's level of understanding of the answer process. For example, the note reader can analyze patterns in writing specific symbols and numbers and use these as an index of understanding. The note reader can also analyze a learner's handwriting or writing habits to evaluate the learner's level of understanding of the answer process. For example, the note reader can identify points in the answer where writing pressure weakens and evaluate a lack of understanding in those points. In this way, by analyzing a learner's handwriting or writing habits, the learner's level of understanding of the answer process can be more accurately evaluated.

[0074] The note reader can collect handwriting data of a learner in advance and individually optimize handwriting recognition accuracy. For example, the note reader can collect handwriting data of a learner in advance and individually optimize a handwriting recognition model based on that data. For example, the note reader can learn the different character shapes and writing style characteristics of each learner. The note reader can also collect handwriting data of a learner and individually optimize a handwriting recognition model based on that data. For example, the note reader can learn the learner's writing patterns to improve recognition accuracy for specific characters and symbols. The note reader can also collect handwriting data of a learner in advance and individually optimize a handwriting recognition model based on that data. For example, a recognition model can be constructed that takes into account the learner's writing pressure and writing speed. In this way, by collecting handwriting data of a learner in advance and individually optimizing handwriting recognition accuracy, recognition accuracy can be improved.

[0075] The note reader unit can use the emotion estimation function to estimate a learner's emotional state from the writing pressure and speed of characters written on a notebook or answer sheet, and evaluate the learner's stress level during learning. For example, the note reader unit can analyze the writing pressure and speed of characters written on a notebook or answer sheet to estimate the learner's emotional state. For example, if the writing pressure is strong and the writing speed is fast, it can determine that the learner is stressed. The note reader unit also uses the emotion estimation function to estimate a learner's emotional state from the writing pressure and speed of characters written on a notebook or answer sheet. For example, if the writing pressure is weak and the writing speed is slow, it can determine that the learner is relaxed. The note reader unit also analyzes the writing pressure and speed of characters written on a notebook or answer sheet to estimate the learner's emotional state. For example, it can analyze changes in writing pressure and speed to evaluate fluctuations in stress level. In this way, the learner's emotional state can be estimated and the learner's stress level can be evaluated, thereby understanding the learner's psychological state.

[0076] In addition to reading notes and answer sheets, the note reader can also collect data in which a learner orally explains their answer process using voice input and use it for analysis. For example, in addition to reading notes and answer sheets, it can collect audio data in which a learner orally explains their answer process and use it for analysis. For example, it can use voice recognition technology to convert the oral explanation into text data. The note reader can also collect audio data in which a learner orally explains their answer process and use that data for analysis. For example, it can evaluate the level of understanding of the answer process from the audio data. In addition to reading notes and answer sheets, the note reader can also collect audio data in which a learner orally explains their answer process and use it for analysis. For example, it can analyze the audio data and evaluate the logical consistency of the answer process. In this way, by collecting data in which a learner orally explains their answer process using voice input and using it for analysis, it is possible to evaluate the level of understanding of the answer process in more detail.

[0077] The note reader unit can grasp the steps and methods of the answer in more detail by having the learner record a video of the answering process and analyze the video data. For example, the learner can record a video of the answering process and analyze the video data to grasp the steps and methods of the answer in more detail. For example, video analysis technology can be used to automatically extract each step of the answer. The note reader unit can also record a video of the answering process and analyze the data to grasp the steps and methods of the answer in more detail. For example, the flow and steps of the answer can be visualized from the video data. The note reader unit can also record a video of the answering process and analyze the video data to grasp the steps and methods of the answer in more detail. For example, the video data can be analyzed to identify areas of insufficient understanding during the answering process. In this way, the learner can record a video of the answering process and analyze the video data to grasp the steps and methods of the answer in more detail.

[0078] The note reader unit can use the emotion estimation function to analyze the learner's facial expression and tone of voice when writing in a notebook or answer sheet to evaluate their motivation to learn. For example, the emotion estimation function can be used to analyze the learner's facial expression when writing in a notebook or answer sheet to evaluate their motivation to learn. For example, facial expression recognition technology can be used to detect smiling or serious expressions. The note reader unit can also analyze the tone of voice when the learner writes in a notebook or answer sheet to evaluate their motivation to learn. For example, a high tone of voice can be determined to indicate high motivation. The note reader unit can also use the emotion estimation function to analyze the learner's facial expression and tone of voice when writing in a notebook or answer sheet to evaluate their motivation to learn. For example, it can analyze changes in facial expression and tone of voice to evaluate fluctuations in motivation. In this way, the learner's motivation to learn can be evaluated by analyzing their facial expression and tone of voice.

[0079] When analyzing a learner's answer process, the understanding deficiency identification unit can find specific patterns and trends by comparing it with past answer data. For example, by analyzing a learner's answer process and comparing it with past answer data, specific patterns and trends can be found. For example, it can identify parts where the same mistakes are repeated. The understanding deficiency identification unit can also find specific patterns and trends by comparing it with past answer data. For example, it can identify a lack of understanding in a specific question format. The understanding deficiency identification unit can also find specific patterns and trends by analyzing a learner's answer process and comparing it with past answer data. For example, it can analyze fluctuations in answer speed and accuracy. In this way, by comparing it with past answer data, it can find specific patterns and trends and identify a lack of understanding.

[0080] The understanding deficiency identifying unit can monitor the learner's answer process in real time and provide instant feedback. For example, it can monitor the learner's answer process in real time, instantly identify areas of understanding deficiency, and provide feedback. For example, if an error is detected during the answer, it can be pointed out immediately. The understanding deficiency identifying unit can also monitor the learner's answer process in real time, identify areas of understanding deficiency, and provide feedback. For example, it can be pointed out immediately if there is a logical leap in the answer. The understanding deficiency identifying unit can also monitor the learner's answer process in real time, identify areas of understanding deficiency, and provide feedback. For example, if a calculation error is detected, it can be prompted to make an immediate correction. In this way, by monitoring the answer process in real time and providing instant feedback, it is possible to quickly make up for lack of understanding.

[0081] The insufficient understanding identification unit can use the emotion estimation function to identify parts of a specific intermediate formula where the learner feels stressed or anxious, and evaluate the learner's insufficient understanding of those parts. For example, the emotion estimation function can be used to identify parts of a specific intermediate formula where the learner feels stressed or anxious, and evaluate the learner's insufficient understanding of those parts. For example, changes in writing pressure and facial expression can be analyzed. The insufficient understanding identification unit can also use the emotion estimation function to identify parts of a specific intermediate formula where the learner feels stressed or anxious, and evaluate the learner's insufficient understanding of those parts. For example, changes in voice tone and speed can be analyzed. The insufficient understanding identification unit can also use the emotion estimation function to identify parts of a specific intermediate formula where the learner feels stressed or anxious, and evaluate the learner's insufficient understanding of those parts. For example, changes in facial expression and voice can be analyzed while the learner is answering the question. This makes it possible to identify the learner's insufficient understanding of those parts by identifying the stress or anxiety the learner feels in a specific intermediate formula and evaluating the learner's insufficient understanding of those parts, taking the learner's psychological state into consideration.

[0082] The understanding deficiency identification unit can compare the answering processes of different learners and identify common areas of understanding deficiency. For example, it compares the answering processes of different learners and identifies common areas of understanding deficiency. For example, it identifies areas where multiple learners make mistakes on the same question. The understanding deficiency identification unit also compares the answering processes of different learners and identifies common areas of understanding deficiency. For example, it identifies a lack of understanding of a specific concept or skill. The understanding deficiency identification unit also compares the answering processes of different learners and identifies common areas of understanding deficiency. For example, it identifies a lack of understanding in a specific question format. In this way, by comparing the answering processes of different learners, it is possible to identify common areas of understanding deficiency and provide efficient learning support.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The note reader reads the notes or answer sheets written by the learner. For example, handwritten notes can be scanned using a scanner and saved as digital data. Alternatively, notes can be photographed using a camera and saved as image data. The note reader then converts handwritten characters into text data using OCR technology. For example, the note reader scans handwritten notes using a high-resolution scanner and converts them into text information using OCR technology. Image data photographed using a camera can also be converted into text data using OCR technology. Step 2: The incomplete understanding identification unit analyzes the data from the notes or answer sheets read by the note reading unit and identifies which intermediate steps the learner does not understand. For example, the incomplete understanding identification unit detects calculation errors or logical leaps. The incomplete understanding identification unit also checks the learner's answer process one by one and compares it with the correct answer process to identify areas of incomplete understanding. Step 3: The root cause identification unit analyzes the root causes of the lack of understanding identified by the lack of understanding identification unit. For example, the root cause identification unit may identify that the cause is a basic calculation error, a misunderstanding of a concept, or a misreading of the problem. The root cause identification unit also oversees the learner's entire answer process and clarifies where the misunderstanding occurred. Step 4: The problem provider provides problems to eliminate the root cause identified by the root cause identification unit. For example, if the cause is a basic calculation error, the problem provider provides calculation practice problems. Also, if the cause is a misunderstanding of a concept, the problem provider provides problems to help the user understand the concept. Step 5: The explanation unit provides explanations of the problems provided by the problem providing unit. For example, the explanation unit provides detailed explanations on how to solve calculation practice problems. The explanation unit also provides explanations of problems to help students understand concepts. This allows the learning support system according to the embodiment to efficiently compensate for a learner's lack of understanding and improve their academic ability in arithmetic. For example, the correct answer rate and answer time of the problems solved by the learner are recorded to evaluate the learner's level of understanding. The next problem and explanation to be provided can be adjusted according to the learner's progress.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] 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.

[0144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a note reader for reading notes or answer sheets; a comprehension deficiency identification unit that analyzes the data of the notebook or the answer sheet read by the note reading unit and identifies which intermediate formulas the learner does not understand; a root cause identifying unit that analyzes the root cause of the insufficient understanding identified by the insufficient understanding identifying unit; a problem providing unit that provides a problem for removing the root cause identified by the root cause identifying unit; an explanation unit that provides an explanation of the problem provided by the problem providing unit; A system characterized by:

2. The note reading unit Analyzing the learner's handwriting or writing habits and evaluating the learner's understanding of the answering process 2. The system of claim 1.

3. The note reading unit Collecting handwriting data from the learner in advance and individually optimizing handwriting recognition accuracy.

2. The system of claim 1.

4. The note reading unit The emotional state of the learner is estimated from the writing pressure or speed of the characters written on the notebook or the answer sheet, and the stress level of the learning is evaluated.

2. The system of claim 1.

5. The note reading unit In addition to reading the notes or the answer sheets, the data in which the learner verbally explains the answering process using voice input is also collected and used for the analysis.

2. The system of claim 1.

6. The note reading unit The learner records the answering process on video, and by analyzing the video data, the steps and methods of the answer can be understood in more detail.

2. The system of claim 1.

7. The note reading unit The facial expression or the tone of the voice of the learner when writing in the notebook or the answer sheet is analyzed to evaluate the motivation for learning.

2. The system of claim 1.

8. The insufficient understanding identifying unit When analyzing the learner's answering process, compare it with past answer data to find specific patterns or trends.

2. The system of claim 1.

Images