system

Abstract

The system according to this embodiment aims to streamline grade processing and reduce the workload of faculty and staff. [Solution] The system according to the embodiment comprises an acquisition unit, a reading unit, a scoring unit, an analysis unit, and a generation unit. The acquisition unit acquires an image of the answer sheet. The reading unit reads handwritten characters from the image acquired by the acquisition unit. The scoring unit scores the answer sheet by comparing the characters read by the reading unit with the model answer. The analysis unit analyzes the student's strengths and weaknesses based on the scoring results obtained by the scoring unit. The generation unit visualizes the analysis results obtained by the analysis unit and generates feedback.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that a lot of time is required for result processing and the workload of teaching staff becomes high.

[0005] The system according to the embodiment aims to improve the efficiency of result processing and reduce the workload of teaching staff.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a reading unit, a scoring unit, an analysis unit, and a generation unit. The acquisition unit acquires an image of the answer sheet. The reading unit reads handwritten characters from the image acquired by the acquisition unit. The scoring unit scores the answer sheet by comparing the characters read by the reading unit with the model answer. The analysis unit analyzes the student's strengths and weaknesses based on the scoring results obtained by the scoring unit. The generation unit visualizes the analysis results obtained by the analysis unit and generates feedback. [Effects of the Invention]

[0007] The system according to this embodiment can streamline grade processing and reduce the workload of faculty and staff. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The educational support system according to an embodiment of the present invention is a system for reducing the daily workload of teachers and staff and improving the quality of education. This educational support system uses a generating AI to read handwritten characters from answer sheets photographed with a camera, compares them with model answers, and automatically scores tests. Furthermore, the educational support system analyzes and visualizes students' strengths and weaknesses and generates feedback. For example, the educational support system inputs an image of an answer sheet photographed with a camera into the generating AI. The generating AI reads the handwritten characters and scores the test by comparing them with model answers. For example, in the case of a mathematics test, the generating AI analyzes the answer to each question and determines whether it is correct or not. This automates the manual scoring work performed by teachers, saving a significant amount of time. Next, the educational support system uses the generating AI to analyze students' strengths and weaknesses based on the scoring results. The generating AI identifies the category of each question and compiles the score rate for each category. For example, in the case of a mathematics test for first-year junior high school students, the score rate is calculated for each category such as "positive and negative numbers," "spatial geometry," and "equations." This allows for a clear understanding of which categories students excel in and which they struggle with. Furthermore, the educational support system uses generative AI to visualize the analysis results and generate feedback. For example, it displays categories with high and low score rates in a graph, making students' strengths and weaknesses immediately apparent. The feedback also includes specific advice. For instance, it might advise, "You do well with positive and negative number problems, but you seem to have difficulty with equations. Let's improve your scoring ability by solving more equation practice problems." This reduces the workload of teachers, allowing them to focus on more important tasks, such as lesson preparation and communication with students. It also improves the quality of learning by providing individualized feedback to students. This leads to an improvement in the quality of education and contributes to solving problems such as teacher health issues and increased turnover rates. In short, the educational support system reduces the daily workload of teachers and staff and improves the quality of education.

[0029] The educational support system according to this embodiment comprises an acquisition unit, a reading unit, a scoring unit, an analysis unit, and a generation unit. The acquisition unit acquires an image of the answer sheet. The acquisition unit can, for example, acquire an image of the answer sheet taken with a camera. The acquisition unit can, for example, acquire an image of the answer sheet using a high-resolution camera. The acquisition unit can also, for example, acquire an image of the answer sheet using a high-frame-rate camera. The acquisition unit can, for example, adjust the angle and distance of the camera to acquire the optimal image. The reading unit reads handwritten characters from the image acquired by the acquisition unit. The reading unit can, for example, read handwritten characters with high accuracy. The reading unit can, for example, read handwritten characters such as cursive, block letters, and numbers. The reading unit can, for example, adjust the reading accuracy according to the size and pressure of the handwritten characters. The scoring unit scores the answer by comparing the characters read by the reading unit with a model answer. The scoring unit can, for example, determine whether the answer is correct by comparing it with a model answer. The scoring unit can, for example, score according to criteria for partial credit. The scoring unit can, for example, evaluate the logical consistency and accuracy of expression of the answers. The analysis unit analyzes students' strengths and weaknesses based on the scoring results obtained by the scoring unit. The analysis unit can, for example, identify the category of each question and aggregate the score rate for each category. The analysis unit can, for example, predict current scores by referring to past performance data. The analysis unit can, for example, evaluate relative strengths and weaknesses by comparing them with the performance data of other students. The generation unit visualizes the analysis results obtained by the analysis unit and generates feedback. The generation unit can, for example, display categories with high and low score rates in a graph. The generation unit can, for example, generate feedback that includes specific advice. The generation unit can, for example, provide personalized advice by referring to past feedback history. As a result, the educational support system according to this embodiment can reduce the daily workload of teachers and staff and improve the quality of education.

[0030] The acquisition unit acquires images of answer sheets. For example, the acquisition unit can acquire images of answer sheets taken with a camera. Specifically, the acquisition unit can use a high-resolution camera to clearly capture even the smallest details of the answer sheet. This ensures that handwritten characters and symbols are clearly recorded, allowing for accurate subsequent processing. Furthermore, by using a high-frame-rate camera, the acquisition unit can quickly capture multiple answer sheets in succession. This makes it possible to digitize a large number of answer sheets in a short time. The acquisition unit also has a function to automatically adjust the camera angle and distance, maintaining optimal shooting conditions and ensuring that high-quality images are always acquired. For example, by fine-tuning the camera position, the entire answer sheet is illuminated uniformly, minimizing the effects of shadows and reflections. In addition, the acquisition unit has a function to evaluate the image quality in real time during image acquisition and retake images as needed. This allows the acquisition unit to always acquire the best possible image, improving the accuracy of subsequent processing.

[0031] The reading unit reads handwritten characters from images acquired by the acquisition unit. The reading unit can, for example, read handwritten characters with high accuracy. Specifically, it uses optical character recognition (OCR) technology to convert handwritten characters in an image into digital data. The reading unit supports various fonts and character types, including cursive, block letters, and numbers, and can flexibly handle different handwriting styles and writing methods. Furthermore, the reading unit has a function to adjust reading accuracy according to the size and pressure of the handwritten characters. For example, if the characters are small and difficult to read, it improves reading accuracy by enlarging the image and adjusting the contrast to clarify the outline of the characters. Also, if the darkness of the characters differs due to varying pressure, it corrects the difference in darkness to achieve accurate character recognition. By combining these functions, the reading unit can maximize the accuracy of handwritten character reading and ensure the accuracy of subsequent scoring processes.

[0032] The scoring unit scores the answers by comparing the characters read by the reading unit with the model answer. For example, the scoring unit can determine whether an answer is correct by comparing it with the model answer. Specifically, the scoring unit uses an algorithm that compares the read answer with the model answer and determines whether they match. The scoring unit can score according to the criteria for partial credit, and can award partial credit if part of the answer is correct. Furthermore, the scoring unit also has a function to evaluate the logical consistency and accuracy of expression of the answer. For example, in the case of written questions, it evaluates whether the content of the answer is logically consistent and whether the expression is appropriate, and calculates an overall score. In addition, the scoring unit can register multiple model answers and can flexibly handle different answer patterns. As a result, the scoring unit can achieve accurate and fair scoring and appropriately evaluate students' performance.

[0033] The analysis department analyzes students' strengths and weaknesses based on the scoring results obtained by the scoring department. For example, the analysis department can identify the category of each question and compile the score rate for each category. Specifically, the analysis department classifies each question into categories such as mathematics, science, and language, and calculates the score rate for each category. This makes it possible to clearly understand which categories students excel in and which they struggle with. Furthermore, the analysis department has a function to predict current performance by referring to past performance data. For example, it can analyze the trends in a student's performance based on past test results and performance trends, and predict future performance. The analysis department can also evaluate relative strengths and weaknesses by comparing them with the performance data of other students. This allows for an understanding of where a student stands in the class and grade level, and can be used as a reference for developing individualized instructional plans. Through these functions, the analysis department can comprehensively evaluate students' learning situations and build a foundation for providing effective learning support.

[0034] The generation unit visualizes the analysis results obtained by the analysis unit and generates feedback. For example, the generation unit can display categories with high and low score rates in a graph. Specifically, the generation unit displays the score rate for each category in a visual format such as a bar graph or pie chart, so that students and teachers can understand it at a glance. Furthermore, the generation unit has a function to generate feedback that includes specific advice. For example, for categories with low score rates, it provides specific advice on what learning methods are effective and which materials should be used. In addition, the generation unit can provide personalized advice by referring to past feedback history. This allows for the provision of optimal feedback tailored to each student's learning situation, thereby improving the quality of learning. Furthermore, the generation unit can periodically update the feedback content and provide appropriate advice according to changes in students' performance. In this way, the generation unit can realize continuous learning support and support the improvement of students' performance.

[0035] The acquisition unit acquires an image of the answer sheet captured by the camera. The acquisition unit can acquire an image of the answer sheet using, for example, a high-resolution camera. The acquisition unit can also acquire an image of the answer sheet using, for example, a high-frame-rate camera. The acquisition unit can acquire the optimal image by, for example, adjusting the angle and distance of the camera. This makes it possible to digitize handwritten answer sheets by acquiring images captured by the camera. Some or all of the above processing in the acquisition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the acquisition unit can input an image of the answer sheet captured by the camera into a generation AI and have the generation AI perform the image acquisition.

[0036] The reading unit reads handwritten characters. The reading unit can, for example, read handwritten characters with high accuracy. The reading unit can, for example, read handwritten characters such as cursive, block letters, and numbers. The reading unit can, for example, adjust the reading accuracy according to the size and pressure of the handwritten characters. This allows the contents of an answer sheet to be digitized by reading the handwritten characters. Some or all of the above processing in the reading unit may be performed using, for example, a generation AI, or without a generation AI. For example, the reading unit can input handwritten characters into a generation AI and have the generation AI perform the character reading.

[0037] The scoring unit scores by comparing the answers with model answers. The scoring unit can, for example, determine whether an answer is correct by comparing it with a model answer. The scoring unit can, for example, score according to criteria for partial credit. The scoring unit can, for example, evaluate the logical consistency and accuracy of expression of the answer. This allows the scoring process to be automated by scoring by comparing the answers with model answers. Some or all of the above processes in the scoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scoring unit can input data for comparison with model answers into a generative AI and have the generative AI perform the scoring.

[0038] The analysis unit identifies the category of each problem and compiles the score percentages for each category. The analysis unit can, for example, identify the category of each problem and compile the score percentages for each category. The analysis unit can, for example, predict current performance by referring to past performance data. The analysis unit can, for example, evaluate relative strengths and weaknesses by comparing them with the performance data of other students. This allows for the analysis of a student's strengths and weaknesses by compiling the score percentages for each category of each problem. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data for identifying the category of each problem into a generative AI and have the generative AI perform the calculation of the score percentages for each category.

[0039] The generation unit displays categories with high and low score rates in a graph and generates feedback that includes specific advice. The generation unit can, for example, display categories with high and low score rates in a graph. The generation unit can, for example, generate feedback that includes specific advice. The generation unit can, for example, refer to past feedback history to provide personalized advice. This improves the quality of student learning by displaying categories with high and low score rates in a graph and generating feedback that includes specific advice. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data for displaying categories with high and low score rates in a graph into a generation AI and have the generation AI perform the generation of feedback.

[0040] The acquisition unit analyzes the acquisition history of past answer sheets and selects the optimal acquisition method. The acquisition unit can, for example, select the most efficient camera angle and distance from the past acquisition history. The acquisition unit can, for example, set the optimal lighting conditions based on the past acquisition history. The acquisition unit can, for example, analyze the past acquisition history and select the optimal image resolution. In this way, the optimal acquisition method can be selected by analyzing the past acquisition history. Some or all of the above processing in the acquisition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the acquisition unit can input past acquisition history data into a generation AI and have the generation AI perform the selection of the optimal acquisition method.

[0041] The acquisition unit filters answer sheets based on the type of exam and subject when acquiring them. For example, the acquisition unit can acquire only answer sheets for a specific subject depending on the type of exam. For example, the acquisition unit can apply different acquisition methods for each subject. For example, the acquisition unit can determine the priority of answer sheets to acquire based on the type of exam and subject. This allows for efficient acquisition of answer sheets by filtering based on the type of exam and subject. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input data on the type of exam and subject into a generating AI and have the generating AI perform the filtering.

[0042] The acquisition unit, when acquiring answer sheets, prioritizes acquiring answer sheets with high relevance, taking into account the geographical location information of the examination venue. For example, the acquisition unit can prioritize acquiring nearby answer sheets based on the geographical location information of the examination venue. For example, the acquisition unit can prioritize acquiring answer sheets from a specific region, taking into account the geographical location information of the examination venue. For example, the acquisition unit can prioritize acquiring answer sheets with high relevance based on the geographical location information of the examination venue. In this way, by considering the geographical location information of the examination venue, it is possible to prioritize acquiring answer sheets with high relevance. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input the geographical location information of the examination venue into a generating AI and have the generating AI acquire answer sheets with high relevance.

[0043] The acquisition unit acquires answer sheets, including comments and notes from the exam proctor. The acquisition unit can, for example, acquire answer sheets including comments and notes from the exam proctor. The acquisition unit can, for example, adjust the method of acquiring answer sheets based on comments and notes from the exam proctor. The acquisition unit can, for example, adjust the timing of acquiring answer sheets, taking into account comments and notes from the exam proctor. This enriches the information on the answer sheets by including comments and notes from the exam proctor. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input comments and notes from the exam proctor into a generating AI and have the generating AI acquire the answer sheets.

[0044] The reading unit optimizes the reading algorithm according to the size and pressure of the characters when reading handwritten characters. For example, the reading unit can adjust the reading algorithm according to the size of the characters. For example, the reading unit can optimize the reading algorithm according to the pressure of the characters. For example, the reading unit can optimize the reading algorithm by considering both the size and pressure of the characters. By optimizing the reading algorithm according to the size and pressure of the characters, the reading accuracy can be improved. Some or all of the above processing in the reading unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reading unit can input data on the size of the characters and the pressure of the characters into a generative AI and have the generative AI perform the optimization of the reading algorithm.

[0045] The reading unit changes its reading method when reading handwritten characters, depending on the arrangement and format of the answer fields. For example, the reading unit can change the reading method depending on the arrangement of the answer fields. For example, the reading unit can adjust the reading method depending on the format of the answer fields. For example, the reading unit can optimize the reading method by considering both the arrangement and format of the answer fields. This improves reading accuracy by changing the reading method depending on the arrangement and format of the answer fields. Some or all of the above processing in the reading unit may be performed using, for example, a generation AI, or without a generation AI. For example, the reading unit can input data on the arrangement and format of the answer fields into a generation AI and have the generation AI execute the change in the reading method.

[0046] The reading unit improves reading accuracy by taking into account dirt and creases on the answer sheet when reading handwritten characters. For example, the reading unit can detect dirt on the answer sheet and improve reading accuracy. For example, the reading unit can optimize reading accuracy by taking into account creases on the answer sheet. For example, the reading unit can improve reading accuracy by taking into account both dirt and creases. In this way, reading accuracy can be improved by taking into account dirt and creases on the answer sheet. Some or all of the above processing in the reading unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the reading unit can input data on dirt and creases on the answer sheet into a generating AI and have the generating AI perform the improvement of reading accuracy.

[0047] The reading unit reads notes and comments written on the back of the answer sheet when reading handwritten characters. For example, the reading unit can read notes written on the back of the answer sheet. For example, the reading unit can read comments written on the back of the answer sheet. For example, the reading unit can improve reading accuracy by taking into account notes and comments written on the back of the answer sheet. This allows for a more accurate understanding of the answer content by also reading notes and comments written on the back of the answer sheet. Some or all of the above processing in the reading unit may be performed using, for example, a generation AI, or without a generation AI. For example, the reading unit can input data of notes and comments written on the back of the answer sheet into a generation AI and have the generation AI perform the reading.

[0048] The scoring unit adjusts the level of detail in scoring, taking into account partial credit for the answers. The scoring unit can, for example, adjust the level of detail in scoring by considering partial credit for the answers. The scoring unit can, for example, optimize the level of detail in scoring according to the distribution of partial credit. The scoring unit can, for example, adjust the balance between considering partial credit and the level of detail. This allows the level of detail in scoring to be adjusted by considering partial credit for the answers. Some or all of the above processing in the scoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the scoring unit can input partial credit data into a generative AI and have the generative AI perform the adjustment of the level of detail in scoring.

[0049] The scoring unit applies an algorithm to evaluate the logical consistency and accuracy of expression of the answer during scoring. For example, the scoring unit may apply an algorithm to evaluate the logical consistency of the answer. For example, the scoring unit may apply an algorithm to evaluate the accuracy of expression of the answer. For example, the scoring unit may apply an algorithm to evaluate both logical consistency and accuracy of expression. This improves the accuracy of scoring by evaluating the logical consistency and accuracy of expression of the answer. Some or all of the above processing in the scoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scoring unit may input data on the logical consistency and accuracy of expression of the answer into a generative AI and have the generative AI perform the evaluation.

[0050] The grading unit modifies the grading criteria during grading, taking into account the submission timing of answers and the difficulty level of the exam. For example, the grading unit can change the grading criteria according to the submission timing of answers. For example, the grading unit can adjust the grading criteria according to the difficulty level of the exam. For example, the grading unit can optimize the grading criteria by considering both the submission timing and the difficulty level. This allows for appropriate changes to the grading criteria by considering the submission timing of answers and the difficulty level of the exam. Some or all of the above processes in the grading unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the grading unit can input data on the submission timing of answers and the difficulty level of the exam into a generative AI and have the generative AI execute the changes to the grading criteria.

[0051] The scoring unit improves the accuracy of scoring by referring to relevant literature and reference materials for the answers during the scoring process. For example, the scoring unit can improve the accuracy of scoring by referring to relevant literature for the answers. For example, the scoring unit can optimize the accuracy of scoring based on reference materials for the answers. For example, the scoring unit can improve the accuracy of scoring by referring to both relevant literature and reference materials. In this way, the accuracy of scoring can be improved by referring to relevant literature and reference materials for the answers. Some or all of the above processing in the scoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the scoring unit can input data on relevant literature and reference materials for the answers into a generative AI and have the generative AI perform the task of improving the accuracy of scoring.

[0052] The analysis unit predicts current performance by referring to past performance data during analysis. For example, the analysis unit can predict current performance based on past performance data. For example, the analysis unit can analyze performance trends by referring to past performance data. For example, the analysis unit can predict performance fluctuations based on past performance data. In this way, current performance can be predicted by referring to past performance data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input past performance data into a generative AI and have the generative AI perform a prediction of current performance.

[0053] The analysis unit evaluates strengths and weaknesses by considering the difficulty level and question trends of each problem during the analysis. For example, the analysis unit can evaluate strengths and weaknesses by considering the difficulty level of each problem. For example, the analysis unit can analyze strengths and weaknesses based on question trends. For example, the analysis unit can evaluate strengths and weaknesses by considering both difficulty level and question trends. This allows for an accurate evaluation of strengths and weaknesses by considering the difficulty level and question trends of each problem. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input data on the difficulty level and question trends of each problem into a generative AI and have the generative AI perform the evaluation of strengths and weaknesses.

[0054] The analysis unit applies different analytical methods depending on the type of test and subject during the analysis. For example, the analysis unit can apply different analytical methods depending on the type of test. For example, the analysis unit can apply different analytical methods depending on the subject. For example, the analysis unit can apply the optimal analytical method by considering both the type of test and the subject. This allows for more accurate analysis by applying different analytical methods depending on the type of test and subject. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the type of test and subject into a generative AI and have the generative AI execute the application of different analytical methods.

[0055] The analysis unit evaluates relative strengths and weaknesses by comparing the performance data of other students during the analysis. For example, the analysis unit can evaluate relative strengths and weaknesses by comparing the performance data of other students. For example, the analysis unit can evaluate the relative position of performance based on the performance data of other students. For example, the analysis unit can analyze trends in strengths and weaknesses by comparing the performance data of other students. This allows for the evaluation of relative strengths and weaknesses by comparing the performance data of other students. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the performance data of other students into a generative AI and have the generative AI perform the evaluation of relative strengths and weaknesses.

[0056] The generation unit includes specific areas for improvement and learning advice when generating feedback. For example, the generation unit can include specific areas for improvement in the feedback. For example, the generation unit can include learning advice in the feedback. For example, the generation unit can provide feedback that includes both areas for improvement and learning advice. This improves the quality of user learning by including specific areas for improvement and learning advice. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data on specific areas for improvement and learning advice into a generation AI and have the generation AI perform the generation of feedback.

[0057] The generation unit provides personalized advice by referring to past feedback history when generating feedback. For example, the generation unit can provide personalized advice based on past feedback history. For example, the generation unit can provide optimal advice by referring to feedback history. For example, the generation unit can provide personalized feedback based on past feedback history and current performance. This allows for the provision of personalized advice by referring to past feedback history. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past feedback history into a generation AI and have the generation AI perform the provision of personalized advice.

[0058] The generation unit displays feedback visually in an easy-to-understand manner using graphs and charts during the generation process. For example, the generation unit can display feedback visually in an easy-to-understand manner using graphs. For example, the generation unit can display feedback visually in an easy-to-understand manner using charts. For example, the generation unit can display feedback visually in an easy-to-understand manner using both graphs and charts. This makes it possible to display feedback visually in an easy-to-understand manner using graphs and charts. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input graph and chart data into a generation AI and have the generation AI perform a visually easy-to-understand display.

[0059] The generation unit provides feedback that includes success stories and reference materials from other students. For example, the generation unit can include success stories from other students in the feedback. For example, the generation unit can include reference materials in the feedback. For example, the generation unit can provide feedback that includes both success stories and reference materials. This can improve the user's motivation to learn by including success stories and reference materials from other students. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data on success stories and reference materials from other students into a generation AI and have the generation AI perform the generation of feedback.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] In addition to the acquisition, reading, scoring, analysis, and generation units, the educational support system may also include a notification unit. The notification unit is responsible for notifying students and teachers of scoring results and feedback, for example. The notification unit can quickly notify students of scoring results using methods such as email or app push notifications. The notification unit can highlight important points when notifying students of feedback content, for example. The notification unit can adjust the timing of notifications to send them at the time most convenient for students and teachers. As a result, adding a notification unit improves the usability of the educational support system by enabling the rapid and effective communication of scoring results and feedback.

[0062] The educational support system may include an image correction unit in addition to the image acquisition unit. The image correction unit is responsible for correcting distortions and tilts in the images of answer sheets acquired by the acquisition unit. The image correction unit can, for example, detect and automatically correct image distortions. The image correction unit can, for example, detect image tilts and correct them to be horizontal. The image correction unit can, for example, adjust the brightness and contrast of the image to improve the accuracy of character reading. As a result, adding an image correction unit improves the quality of acquired images and increases the accuracy of the reading unit.

[0063] The educational support system may include a handwriting recognition unit in addition to the reading unit. The handwriting recognition unit, for example, is responsible for recognizing handwritten characters read by the reading unit with high accuracy. The handwriting recognition unit can, for example, distinguish between different cursive and block letters. The handwriting recognition unit can, for example, adjust its recognition accuracy according to the size and pressure of the characters. The handwriting recognition unit can, for example, analyze the shape and characteristics of the characters to recognize them accurately. As a result, adding a handwriting recognition unit improves the accuracy of handwriting recognition and also improves the accuracy of the scoring unit.

[0064] The educational support system may include a partial credit evaluation unit in addition to the scoring unit. The partial credit evaluation unit is responsible for evaluating partial credit for answers scored by the scoring unit. For example, the partial credit evaluation unit can award partial credit when part of an answer is correct. For example, the partial credit evaluation unit can evaluate the logical consistency and partial accuracy of an answer. For example, the partial credit evaluation unit can set criteria for partial credit and score based on those criteria. By adding a partial credit evaluation unit, it becomes possible to evaluate the partial accuracy of answers and achieve fairer scoring.

[0065] The educational support system may include a performance prediction unit in addition to the analysis unit. The performance prediction unit, for example, is responsible for predicting students' future performance based on data obtained by the analysis unit. The performance prediction unit can, for example, predict current performance by referring to past performance data. The performance prediction unit can, for example, predict a student's relative performance position by comparing it to the performance data of other students. The performance prediction unit can, for example, analyze performance trends and predict future fluctuations in performance. By adding the performance prediction unit, it becomes possible to predict students' future performance and provide appropriate guidance.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The acquisition unit acquires an image of the answer sheet. The acquisition unit can acquire an image of the answer sheet, for example, by taking a picture of it with a camera. The acquisition unit can acquire an image of the answer sheet using, for example, a high-resolution camera. The acquisition unit can also acquire an image of the answer sheet using, for example, a high-frame-rate camera. The acquisition unit can acquire the optimal image by, for example, adjusting the angle and distance of the camera. Step 2: The reading unit reads handwritten characters from the image acquired by the acquisition unit. The reading unit can, for example, read handwritten characters with high accuracy. The reading unit can, for example, read handwritten characters such as cursive, block letters, and numbers. The reading unit can, for example, adjust the reading accuracy according to the size and pressure of the handwritten characters. Step 3: The scoring unit scores the answers by comparing the characters read by the reading unit with the model answer. The scoring unit can, for example, determine whether the answer is correct by comparing it with the model answer. The scoring unit can, for example, score according to the criteria for partial credit. The scoring unit can, for example, evaluate the logical consistency and accuracy of expression of the answer. Step 4: The analysis department analyzes students' strengths and weaknesses based on the scoring results obtained by the scoring department. The analysis department can, for example, identify the category of each question and compile the score percentage for each category. The analysis department can, for example, predict current scores by referring to past performance data. The analysis department can, for example, evaluate relative strengths and weaknesses by comparing them with the performance data of other students. Step 5: The generation unit visualizes the analysis results obtained by the analysis unit and generates feedback. The generation unit can, for example, display categories with high and low score rates in a graph. The generation unit can, for example, generate feedback that includes specific advice. The generation unit can, for example, provide personalized advice by referring to past feedback history.

[0068] (Example of form 2) The educational support system according to an embodiment of the present invention is a system for reducing the daily workload of teachers and staff and improving the quality of education. This educational support system uses a generating AI to read handwritten characters from answer sheets photographed with a camera, compares them with model answers, and automatically scores tests. Furthermore, the educational support system analyzes and visualizes students' strengths and weaknesses and generates feedback. For example, the educational support system inputs an image of an answer sheet photographed with a camera into the generating AI. The generating AI reads the handwritten characters and scores the test by comparing them with model answers. For example, in the case of a mathematics test, the generating AI analyzes the answer to each question and determines whether it is correct or not. This automates the manual scoring work performed by teachers, saving a significant amount of time. Next, the educational support system uses the generating AI to analyze students' strengths and weaknesses based on the scoring results. The generating AI identifies the category of each question and compiles the score rate for each category. For example, in the case of a mathematics test for first-year junior high school students, the score rate is calculated for each category such as "positive and negative numbers," "spatial geometry," and "equations." This allows for a clear understanding of which categories students excel in and which they struggle with. Furthermore, the educational support system uses generative AI to visualize the analysis results and generate feedback. For example, it displays categories with high and low score rates in a graph, making students' strengths and weaknesses immediately apparent. The feedback also includes specific advice. For instance, it might advise, "You do well with positive and negative number problems, but you seem to have difficulty with equations. Let's improve your scoring ability by solving more equation practice problems." This reduces the workload of teachers, allowing them to focus on more important tasks, such as lesson preparation and communication with students. It also improves the quality of learning by providing individualized feedback to students. This leads to an improvement in the quality of education and contributes to solving problems such as teacher health issues and increased turnover rates. In short, the educational support system reduces the daily workload of teachers and staff and improves the quality of education.

[0069] The educational support system according to this embodiment comprises an acquisition unit, a reading unit, a scoring unit, an analysis unit, and a generation unit. The acquisition unit acquires an image of the answer sheet. The acquisition unit can, for example, acquire an image of the answer sheet taken with a camera. The acquisition unit can, for example, acquire an image of the answer sheet using a high-resolution camera. The acquisition unit can also, for example, acquire an image of the answer sheet using a high-frame-rate camera. The acquisition unit can, for example, adjust the angle and distance of the camera to acquire the optimal image. The reading unit reads handwritten characters from the image acquired by the acquisition unit. The reading unit can, for example, read handwritten characters with high accuracy. The reading unit can, for example, read handwritten characters such as cursive, block letters, and numbers. The reading unit can, for example, adjust the reading accuracy according to the size and pressure of the handwritten characters. The scoring unit scores the answer by comparing the characters read by the reading unit with a model answer. The scoring unit can, for example, determine whether the answer is correct by comparing it with a model answer. The scoring unit can, for example, score according to criteria for partial credit. The scoring unit can, for example, evaluate the logical consistency and accuracy of expression of the answers. The analysis unit analyzes students' strengths and weaknesses based on the scoring results obtained by the scoring unit. The analysis unit can, for example, identify the category of each question and aggregate the score rate for each category. The analysis unit can, for example, predict current scores by referring to past performance data. The analysis unit can, for example, evaluate relative strengths and weaknesses by comparing them with the performance data of other students. The generation unit visualizes the analysis results obtained by the analysis unit and generates feedback. The generation unit can, for example, display categories with high and low score rates in a graph. The generation unit can, for example, generate feedback that includes specific advice. The generation unit can, for example, provide personalized advice by referring to past feedback history. As a result, the educational support system according to this embodiment can reduce the daily workload of teachers and staff and improve the quality of education.

[0070] The acquisition unit acquires images of answer sheets. For example, the acquisition unit can acquire images of answer sheets taken with a camera. Specifically, the acquisition unit can use a high-resolution camera to clearly capture even the smallest details of the answer sheet. This ensures that handwritten characters and symbols are clearly recorded, allowing for accurate subsequent processing. Furthermore, by using a high-frame-rate camera, the acquisition unit can quickly capture multiple answer sheets in succession. This makes it possible to digitize a large number of answer sheets in a short time. The acquisition unit also has a function to automatically adjust the camera angle and distance, maintaining optimal shooting conditions and ensuring that high-quality images are always acquired. For example, by fine-tuning the camera position, the entire answer sheet is illuminated uniformly, minimizing the effects of shadows and reflections. In addition, the acquisition unit has a function to evaluate the image quality in real time during image acquisition and retake images as needed. This allows the acquisition unit to always acquire the best possible image, improving the accuracy of subsequent processing.

[0071] The reading unit reads handwritten characters from images acquired by the acquisition unit. The reading unit can, for example, read handwritten characters with high accuracy. Specifically, it uses optical character recognition (OCR) technology to convert handwritten characters in an image into digital data. The reading unit supports various fonts and character types, including cursive, block letters, and numbers, and can flexibly handle different handwriting styles and writing methods. Furthermore, the reading unit has a function to adjust reading accuracy according to the size and pressure of the handwritten characters. For example, if the characters are small and difficult to read, it improves reading accuracy by enlarging the image and adjusting the contrast to clarify the outline of the characters. Also, if the darkness of the characters differs due to varying pressure, it corrects the difference in darkness to achieve accurate character recognition. By combining these functions, the reading unit can maximize the accuracy of handwritten character reading and ensure the accuracy of subsequent scoring processes.

[0072] The scoring unit scores the answers by comparing the characters read by the reading unit with the model answer. For example, the scoring unit can determine whether an answer is correct by comparing it with the model answer. Specifically, the scoring unit uses an algorithm that compares the read answer with the model answer and determines whether they match. The scoring unit can score according to the criteria for partial credit, and can award partial credit if part of the answer is correct. Furthermore, the scoring unit also has a function to evaluate the logical consistency and accuracy of expression of the answer. For example, in the case of written questions, it evaluates whether the content of the answer is logically consistent and whether the expression is appropriate, and calculates an overall score. In addition, the scoring unit can register multiple model answers and can flexibly handle different answer patterns. As a result, the scoring unit can achieve accurate and fair scoring and appropriately evaluate students' performance.

[0073] The analysis department analyzes students' strengths and weaknesses based on the scoring results obtained by the scoring department. For example, the analysis department can identify the category of each question and compile the score rate for each category. Specifically, the analysis department classifies each question into categories such as mathematics, science, and language, and calculates the score rate for each category. This makes it possible to clearly understand which categories students excel in and which they struggle with. Furthermore, the analysis department has a function to predict current performance by referring to past performance data. For example, it can analyze the trends in a student's performance based on past test results and performance trends, and predict future performance. The analysis department can also evaluate relative strengths and weaknesses by comparing them with the performance data of other students. This allows for an understanding of where a student stands in the class and grade level, and can be used as a reference for developing individualized instructional plans. Through these functions, the analysis department can comprehensively evaluate students' learning situations and build a foundation for providing effective learning support.

[0074] The generation unit visualizes the analysis results obtained by the analysis unit and generates feedback. For example, the generation unit can display categories with high and low score rates in a graph. Specifically, the generation unit displays the score rate for each category in a visual format such as a bar graph or pie chart, so that students and teachers can understand it at a glance. Furthermore, the generation unit has a function to generate feedback that includes specific advice. For example, for categories with low score rates, it provides specific advice on what learning methods are effective and which materials should be used. In addition, the generation unit can provide personalized advice by referring to past feedback history. This allows for the provision of optimal feedback tailored to each student's learning situation, thereby improving the quality of learning. Furthermore, the generation unit can periodically update the feedback content and provide appropriate advice according to changes in students' performance. In this way, the generation unit can realize continuous learning support and support the improvement of students' performance.

[0075] The acquisition unit acquires an image of the answer sheet captured by the camera. The acquisition unit can acquire an image of the answer sheet using, for example, a high-resolution camera. The acquisition unit can also acquire an image of the answer sheet using, for example, a high-frame-rate camera. The acquisition unit can acquire the optimal image by, for example, adjusting the angle and distance of the camera. This makes it possible to digitize handwritten answer sheets by acquiring images captured by the camera. Some or all of the above processing in the acquisition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the acquisition unit can input an image of the answer sheet captured by the camera into a generation AI and have the generation AI perform the image acquisition.

[0076] The reading unit reads handwritten characters. The reading unit can, for example, read handwritten characters with high accuracy. The reading unit can, for example, read handwritten characters such as cursive, block letters, and numbers. The reading unit can, for example, adjust the reading accuracy according to the size and pressure of the handwritten characters. This allows the contents of an answer sheet to be digitized by reading the handwritten characters. Some or all of the above processing in the reading unit may be performed using, for example, a generation AI, or without a generation AI. For example, the reading unit can input handwritten characters into a generation AI and have the generation AI perform the character reading.

[0077] The scoring unit scores by comparing the answers with model answers. The scoring unit can, for example, determine whether an answer is correct by comparing it with a model answer. The scoring unit can, for example, score according to criteria for partial credit. The scoring unit can, for example, evaluate the logical consistency and accuracy of expression of the answer. This allows the scoring process to be automated by scoring by comparing the answers with model answers. Some or all of the above processes in the scoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scoring unit can input data for comparison with model answers into a generative AI and have the generative AI perform the scoring.

[0078] The analysis unit identifies the category of each problem and compiles the score percentages for each category. The analysis unit can, for example, identify the category of each problem and compile the score percentages for each category. The analysis unit can, for example, predict current performance by referring to past performance data. The analysis unit can, for example, evaluate relative strengths and weaknesses by comparing them with the performance data of other students. This allows for the analysis of a student's strengths and weaknesses by compiling the score percentages for each category of each problem. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data for identifying the category of each problem into a generative AI and have the generative AI perform the calculation of the score percentages for each category.

[0079] The generation unit displays categories with high and low score rates in a graph and generates feedback that includes specific advice. The generation unit can, for example, display categories with high and low score rates in a graph. The generation unit can, for example, generate feedback that includes specific advice. The generation unit can, for example, refer to past feedback history to provide personalized advice. This improves the quality of student learning by displaying categories with high and low score rates in a graph and generating feedback that includes specific advice. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input data for displaying categories with high and low score rates in a graph into a generation AI and have the generation AI perform the generation of feedback.

[0080] The acquisition unit estimates the user's emotions and adjusts the timing of image acquisition of the answer sheet based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit can acquire the image at a time when the user can relax. For example, if the user is concentrating, the acquisition unit can acquire the image at an appropriate time to avoid interrupting the work. For example, if the user is tired, the acquisition unit can acquire the image after a break. In this way, the burden on the user can be reduced by adjusting the image acquisition timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the acquisition unit may be performed using a generative AI, for example, or without a generative AI. For example, the acquisition unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The acquisition unit analyzes the acquisition history of past answer sheets and selects the optimal acquisition method. The acquisition unit can, for example, select the most efficient camera angle and distance from the past acquisition history. The acquisition unit can, for example, set the optimal lighting conditions based on the past acquisition history. The acquisition unit can, for example, analyze the past acquisition history and select the optimal image resolution. In this way, the optimal acquisition method can be selected by analyzing the past acquisition history. Some or all of the above processing in the acquisition unit may be performed using, for example, a generation AI, or without a generation AI. For example, the acquisition unit can input past acquisition history data into a generation AI and have the generation AI perform the selection of the optimal acquisition method.

[0082] The acquisition unit filters answer sheets based on the type of exam and subject when acquiring them. For example, the acquisition unit can acquire only answer sheets for a specific subject depending on the type of exam. For example, the acquisition unit can apply different acquisition methods for each subject. For example, the acquisition unit can determine the priority of answer sheets to acquire based on the type of exam and subject. This allows for efficient acquisition of answer sheets by filtering based on the type of exam and subject. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input data on the type of exam and subject into a generating AI and have the generating AI perform the filtering.

[0083] The acquisition unit estimates the user's emotions and determines the priority of the answer sheets to acquire based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit can prioritize acquiring easy answer sheets. For example, if the user is relaxed, the acquisition unit can prioritize acquiring difficult answer sheets. For example, if the user is in a hurry, the acquisition unit can prioritize acquiring answer sheets that can be processed quickly. In this way, the user's burden can be reduced by determining the priority of answer sheets according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the acquisition unit may be performed using a generative AI, or not using a generative AI. For example, the acquisition unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The acquisition unit, when acquiring answer sheets, prioritizes acquiring answer sheets with high relevance, taking into account the geographical location information of the examination venue. For example, the acquisition unit can prioritize acquiring nearby answer sheets based on the geographical location information of the examination venue. For example, the acquisition unit can prioritize acquiring answer sheets from a specific region, taking into account the geographical location information of the examination venue. For example, the acquisition unit can prioritize acquiring answer sheets with high relevance based on the geographical location information of the examination venue. In this way, by considering the geographical location information of the examination venue, it is possible to prioritize acquiring answer sheets with high relevance. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input the geographical location information of the examination venue into a generating AI and have the generating AI acquire answer sheets with high relevance.

[0085] The acquisition unit acquires answer sheets, including comments and notes from the exam proctor. The acquisition unit can, for example, acquire answer sheets including comments and notes from the exam proctor. The acquisition unit can, for example, adjust the method of acquiring answer sheets based on comments and notes from the exam proctor. The acquisition unit can, for example, adjust the timing of acquiring answer sheets, taking into account comments and notes from the exam proctor. This enriches the information on the answer sheets by including comments and notes from the exam proctor. Some or all of the above processing in the acquisition unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the acquisition unit can input comments and notes from the exam proctor into a generating AI and have the generating AI acquire the answer sheets.

[0086] The reading unit estimates the user's emotions and adjusts the accuracy of handwriting recognition based on the estimated emotions. For example, the reading unit can increase recognition accuracy when the user is relaxed. For example, the reading unit can prioritize recognition speed when the user is in a hurry. For example, the reading unit can adjust the balance between recognition accuracy and speed when the user is stressed. This improves recognition accuracy by adjusting recognition accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reading unit may be performed using a generative AI or not. For example, the reading unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The reading unit optimizes the reading algorithm according to the size and pressure of the characters when reading handwritten characters. For example, the reading unit can adjust the reading algorithm according to the size of the characters. For example, the reading unit can optimize the reading algorithm according to the pressure of the characters. For example, the reading unit can optimize the reading algorithm by considering both the size and pressure of the characters. By optimizing the reading algorithm according to the size and pressure of the characters, the reading accuracy can be improved. Some or all of the above processing in the reading unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reading unit can input data on the size of the characters and the pressure of the characters into a generative AI and have the generative AI perform the optimization of the reading algorithm.

[0088] The reading unit changes its reading method when reading handwritten characters, depending on the arrangement and format of the answer fields. For example, the reading unit can change the reading method depending on the arrangement of the answer fields. For example, the reading unit can adjust the reading method depending on the format of the answer fields. For example, the reading unit can optimize the reading method by considering both the arrangement and format of the answer fields. This improves reading accuracy by changing the reading method depending on the arrangement and format of the answer fields. Some or all of the above processing in the reading unit may be performed using, for example, a generation AI, or without a generation AI. For example, the reading unit can input data on the arrangement and format of the answer fields into a generation AI and have the generation AI execute the change in the reading method.

[0089] The reading unit estimates the user's emotions and determines the priority of characters to read based on the estimated emotions. For example, if the user is in a hurry, the reading unit can prioritize reading important characters. For example, if the user is relaxed, the reading unit can read all characters equally. For example, if the user is stressed, the reading unit can adjust the balance between important characters and other characters. This improves reading efficiency by determining the priority of characters to read according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reading unit may be performed using a generative AI, or not using a generative AI. For example, the reading unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The reading unit improves reading accuracy by taking into account dirt and creases on the answer sheet when reading handwritten characters. For example, the reading unit can detect dirt on the answer sheet and improve reading accuracy. For example, the reading unit can optimize reading accuracy by taking into account creases on the answer sheet. For example, the reading unit can improve reading accuracy by taking into account both dirt and creases. In this way, reading accuracy can be improved by taking into account dirt and creases on the answer sheet. Some or all of the above processing in the reading unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the reading unit can input data on dirt and creases on the answer sheet into a generating AI and have the generating AI perform the improvement of reading accuracy.

[0091] The reading unit reads notes and comments written on the back of the answer sheet when reading handwritten characters. For example, the reading unit can read notes written on the back of the answer sheet. For example, the reading unit can read comments written on the back of the answer sheet. For example, the reading unit can improve reading accuracy by taking into account notes and comments written on the back of the answer sheet. This allows for a more accurate understanding of the answer content by also reading notes and comments written on the back of the answer sheet. Some or all of the above processing in the reading unit may be performed using, for example, a generation AI, or without a generation AI. For example, the reading unit can input data of notes and comments written on the back of the answer sheet into a generation AI and have the generation AI perform the reading.

[0092] The scoring unit estimates the user's emotions and adjusts the scoring criteria based on the estimated emotions. For example, if the user is relaxed, the scoring unit can apply strict scoring criteria. For example, if the user is in a hurry, the scoring unit can apply simplified scoring criteria. For example, if the user is stressed, the scoring unit can adjust the balance of the scoring criteria. This allows for more appropriate scoring by adjusting the scoring criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the scoring unit may be performed using a generative AI, or not using a generative AI. For example, the scoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The scoring unit adjusts the level of detail in scoring, taking into account partial credit for the answers. The scoring unit can, for example, adjust the level of detail in scoring by considering partial credit for the answers. The scoring unit can, for example, optimize the level of detail in scoring according to the distribution of partial credit. The scoring unit can, for example, adjust the balance between considering partial credit and the level of detail. This allows the level of detail in scoring to be adjusted by considering partial credit for the answers. Some or all of the above processing in the scoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the scoring unit can input partial credit data into a generative AI and have the generative AI perform the adjustment of the level of detail in scoring.

[0094] The scoring unit applies an algorithm to evaluate the logical consistency and accuracy of expression of the answer during scoring. For example, the scoring unit may apply an algorithm to evaluate the logical consistency of the answer. For example, the scoring unit may apply an algorithm to evaluate the accuracy of expression of the answer. For example, the scoring unit may apply an algorithm to evaluate both logical consistency and accuracy of expression. This improves the accuracy of scoring by evaluating the logical consistency and accuracy of expression of the answer. Some or all of the above processing in the scoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scoring unit may input data on the logical consistency and accuracy of expression of the answer into a generative AI and have the generative AI perform the evaluation.

[0095] The scoring unit estimates the user's emotions and adjusts the display method of the scoring results based on the estimated user emotions. For example, if the user is relaxed, the scoring unit can display detailed scoring results. For example, if the user is in a hurry, the scoring unit can display concise scoring results. For example, if the user is stressed, the scoring unit can adjust the display method of the scoring results. By adjusting the display method of the scoring results according to the user's emotions, it becomes possible to display the results in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the scoring unit may be performed using a generative AI, for example, or without a generative AI. For example, the scoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The grading unit modifies the grading criteria during grading, taking into account the submission timing of answers and the difficulty level of the exam. For example, the grading unit can change the grading criteria according to the submission timing of answers. For example, the grading unit can adjust the grading criteria according to the difficulty level of the exam. For example, the grading unit can optimize the grading criteria by considering both the submission timing and the difficulty level. This allows for appropriate changes to the grading criteria by considering the submission timing of answers and the difficulty level of the exam. Some or all of the above processes in the grading unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the grading unit can input data on the submission timing of answers and the difficulty level of the exam into a generative AI and have the generative AI execute the changes to the grading criteria.

[0097] The scoring unit improves the accuracy of scoring by referring to relevant literature and reference materials for the answers during the scoring process. For example, the scoring unit can improve the accuracy of scoring by referring to relevant literature for the answers. For example, the scoring unit can optimize the accuracy of scoring based on reference materials for the answers. For example, the scoring unit can improve the accuracy of scoring by referring to both relevant literature and reference materials. In this way, the accuracy of scoring can be improved by referring to relevant literature and reference materials for the answers. Some or all of the above processing in the scoring unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the scoring unit can input data on relevant literature and reference materials for the answers into a generative AI and have the generative AI perform the task of improving the accuracy of scoring.

[0098] The analysis unit estimates the user's emotions and adjusts the strengths and weaknesses analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can apply a detailed analysis method. For example, if the user is in a hurry, the analysis unit can apply a simplified analysis method. For example, if the user is stressed, the analysis unit can adjust the balance of the analysis method. By adjusting the analysis method according to the user's emotions, a more appropriate analysis of strengths and weaknesses becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The analysis unit predicts current performance by referring to past performance data during analysis. For example, the analysis unit can predict current performance based on past performance data. For example, the analysis unit can analyze performance trends by referring to past performance data. For example, the analysis unit can predict performance fluctuations based on past performance data. In this way, current performance can be predicted by referring to past performance data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input past performance data into a generative AI and have the generative AI perform a prediction of current performance.

[0100] The analysis unit evaluates strengths and weaknesses by considering the difficulty level and question trends of each problem during the analysis. For example, the analysis unit can evaluate strengths and weaknesses by considering the difficulty level of each problem. For example, the analysis unit can analyze strengths and weaknesses based on question trends. For example, the analysis unit can evaluate strengths and weaknesses by considering both difficulty level and question trends. This allows for an accurate evaluation of strengths and weaknesses by considering the difficulty level and question trends of each problem. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input data on the difficulty level and question trends of each problem into a generative AI and have the generative AI perform the evaluation of strengths and weaknesses.

[0101] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results. For example, if the user is in a hurry, the analysis unit can display concise analysis results. For example, if the user is stressed, the analysis unit can adjust the display method of the analysis results. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to display the results in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0102] The analysis unit applies different analytical methods depending on the type of test and subject during the analysis. For example, the analysis unit can apply different analytical methods depending on the type of test. For example, the analysis unit can apply different analytical methods depending on the subject. For example, the analysis unit can apply the optimal analytical method by considering both the type of test and the subject. This allows for more accurate analysis by applying different analytical methods depending on the type of test and subject. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input data on the type of test and subject into a generative AI and have the generative AI execute the application of different analytical methods.

[0103] The analysis unit evaluates relative strengths and weaknesses by comparing the performance data of other students during the analysis. For example, the analysis unit can evaluate relative strengths and weaknesses by comparing the performance data of other students. For example, the analysis unit can evaluate the relative position of performance based on the performance data of other students. For example, the analysis unit can analyze trends in strengths and weaknesses by comparing the performance data of other students. This allows for the evaluation of relative strengths and weaknesses by comparing the performance data of other students. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the performance data of other students into a generative AI and have the generative AI perform the evaluation of relative strengths and weaknesses.

[0104] The generation unit estimates the user's emotions and adjusts the way feedback is expressed based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide detailed feedback. For example, if the user is in a hurry, the generation unit can provide concise feedback. For example, if the user is stressed, the generation unit can adjust the way feedback is expressed. This makes it possible to provide feedback that is easy for the user to understand by adjusting the way feedback is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generative AI, or not using a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0105] The generation unit includes specific areas for improvement and learning advice when generating feedback. For example, the generation unit can include specific areas for improvement in the feedback. For example, the generation unit can include learning advice in the feedback. For example, the generation unit can provide feedback that includes both areas for improvement and learning advice. This improves the quality of user learning by including specific areas for improvement and learning advice. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data on specific areas for improvement and learning advice into a generation AI and have the generation AI perform the generation of feedback.

[0106] The generation unit provides personalized advice by referring to past feedback history when generating feedback. For example, the generation unit can provide personalized advice based on past feedback history. For example, the generation unit can provide optimal advice by referring to feedback history. For example, the generation unit can provide personalized feedback based on past feedback history and current performance. This allows for the provision of personalized advice by referring to past feedback history. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input past feedback history into a generation AI and have the generation AI perform the provision of personalized advice.

[0107] The generation unit estimates the user's emotions and determines the priority of feedback based on the estimated emotions. For example, if the user is relaxed, the generation unit may prioritize detailed feedback. For example, if the user is in a hurry, the generation unit may prioritize concise feedback. For example, if the user is stressed, the generation unit may adjust the priority of feedback. This allows for the provision of important feedback to the user by prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generative AI, or not using a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0108] The generation unit displays feedback visually in an easy-to-understand manner using graphs and charts during the generation process. For example, the generation unit can display feedback visually in an easy-to-understand manner using graphs. For example, the generation unit can display feedback visually in an easy-to-understand manner using charts. For example, the generation unit can display feedback visually in an easy-to-understand manner using both graphs and charts. This makes it possible to display feedback visually in an easy-to-understand manner using graphs and charts. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input graph and chart data into a generation AI and have the generation AI perform a visually easy-to-understand display.

[0109] The generation unit provides feedback that includes success stories and reference materials from other students. For example, the generation unit can include success stories from other students in the feedback. For example, the generation unit can include reference materials in the feedback. For example, the generation unit can provide feedback that includes both success stories and reference materials. This can improve the user's motivation to learn by including success stories and reference materials from other students. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data on success stories and reference materials from other students into a generation AI and have the generation AI perform the generation of feedback.

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

[0111] In addition to the acquisition, reading, scoring, analysis, and generation units, the educational support system may also include a notification unit. The notification unit is responsible for notifying students and teachers of scoring results and feedback, for example. The notification unit can quickly notify students of scoring results using methods such as email or app push notifications. The notification unit can highlight important points when notifying students of feedback content, for example. The notification unit can adjust the timing of notifications to send them at the time most convenient for students and teachers. As a result, adding a notification unit improves the usability of the educational support system by enabling the rapid and effective communication of scoring results and feedback.

[0112] The educational support system may include an image correction unit in addition to the image acquisition unit. The image correction unit is responsible for correcting distortions and tilts in the images of answer sheets acquired by the acquisition unit. The image correction unit can, for example, detect and automatically correct image distortions. The image correction unit can, for example, detect image tilts and correct them to be horizontal. The image correction unit can, for example, adjust the brightness and contrast of the image to improve the accuracy of character reading. As a result, adding an image correction unit improves the quality of acquired images and increases the accuracy of the reading unit.

[0113] The educational support system may include a handwriting recognition unit in addition to the reading unit. The handwriting recognition unit, for example, is responsible for recognizing handwritten characters read by the reading unit with high accuracy. The handwriting recognition unit can, for example, distinguish between different cursive and block letters. The handwriting recognition unit can, for example, adjust its recognition accuracy according to the size and pressure of the characters. The handwriting recognition unit can, for example, analyze the shape and characteristics of the characters to recognize them accurately. As a result, adding a handwriting recognition unit improves the accuracy of handwriting recognition and also improves the accuracy of the scoring unit.

[0114] The educational support system may include a partial credit evaluation unit in addition to the scoring unit. The partial credit evaluation unit is responsible for evaluating partial credit for answers scored by the scoring unit. For example, the partial credit evaluation unit can award partial credit when part of an answer is correct. For example, the partial credit evaluation unit can evaluate the logical consistency and partial accuracy of an answer. For example, the partial credit evaluation unit can set criteria for partial credit and score based on those criteria. By adding a partial credit evaluation unit, it becomes possible to evaluate the partial accuracy of answers and achieve fairer scoring.

[0115] The educational support system may include a performance prediction unit in addition to the analysis unit. The performance prediction unit, for example, is responsible for predicting students' future performance based on data obtained by the analysis unit. The performance prediction unit can, for example, predict current performance by referring to past performance data. The performance prediction unit can, for example, predict a student's relative performance position by comparing it to the performance data of other students. The performance prediction unit can, for example, analyze performance trends and predict future fluctuations in performance. By adding the performance prediction unit, it becomes possible to predict students' future performance and provide appropriate guidance.

[0116] The educational support system can include an emotion estimation function in addition to the generation unit. The generation unit can, for example, estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide detailed feedback. For example, if the user is in a hurry, the generation unit can provide concise feedback. For example, if the user is stressed, the generation unit can adjust the content of the feedback. This makes it possible to provide feedback that is easy for the user to understand by adjusting the content of the feedback according to the user's emotions.

[0117] The educational support system can be equipped with an emotion estimation function in addition to the image acquisition unit. The acquisition unit can, for example, estimate the user's emotions and adjust the timing of image acquisition of answer sheets based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit can acquire images at a time when the user can relax. For example, if the user is concentrating, the acquisition unit can acquire images at an appropriate time to avoid interrupting the work. For example, if the user is tired, the acquisition unit can acquire images after a break. In this way, the burden on the user can be reduced by adjusting the image acquisition timing according to the user's emotions.

[0118] The educational support system can be equipped with an emotion estimation function in addition to the reading unit. The reading unit can, for example, estimate the user's emotions and adjust the accuracy of handwriting recognition based on the estimated emotions. For example, the reading unit can increase recognition accuracy when the user is relaxed. For example, the reading unit can prioritize recognition speed when the user is in a hurry. For example, the reading unit can adjust the balance between recognition accuracy and speed when the user is stressed. In this way, recognition accuracy can be improved by adjusting recognition accuracy according to the user's emotions.

[0119] The educational support system can include an emotion estimation function in addition to a scoring function. The scoring function can, for example, estimate the user's emotions and adjust the scoring criteria based on the estimated emotions. For example, the scoring function can apply strict scoring criteria when the user is relaxed. For example, the scoring function can apply simplified scoring criteria when the user is in a hurry. For example, the scoring function can adjust the balance of scoring criteria when the user is stressed. This allows for more appropriate scoring by adjusting the scoring criteria according to the user's emotions.

[0120] The educational support system can be equipped with an emotion estimation function in addition to the analysis unit. The analysis unit can, for example, estimate the user's emotions and adjust the analysis method for strengths and weaknesses based on the estimated emotions. For example, the analysis unit can apply a detailed analysis method when the user is relaxed. For example, the analysis unit can apply a simplified analysis method when the user is in a hurry. For example, the analysis unit can adjust the balance of the analysis method when the user is stressed. This allows for a more appropriate analysis of strengths and weaknesses by adjusting the analysis method according to the user's emotions.

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

[0122] Step 1: The acquisition unit acquires an image of the answer sheet. The acquisition unit can acquire an image of the answer sheet, for example, by taking a picture of it with a camera. The acquisition unit can acquire an image of the answer sheet using, for example, a high-resolution camera. The acquisition unit can also acquire an image of the answer sheet using, for example, a high-frame-rate camera. The acquisition unit can acquire the optimal image by, for example, adjusting the angle and distance of the camera. Step 2: The reading unit reads handwritten characters from the image acquired by the acquisition unit. The reading unit can, for example, read handwritten characters with high accuracy. The reading unit can, for example, read handwritten characters such as cursive, block letters, and numbers. The reading unit can, for example, adjust the reading accuracy according to the size and pressure of the handwritten characters. Step 3: The scoring unit scores the answers by comparing the characters read by the reading unit with the model answer. The scoring unit can, for example, determine whether the answer is correct by comparing it with the model answer. The scoring unit can, for example, score according to the criteria for partial credit. The scoring unit can, for example, evaluate the logical consistency and accuracy of expression of the answer. Step 4: The analysis department analyzes students' strengths and weaknesses based on the scoring results obtained by the scoring department. The analysis department can, for example, identify the category of each question and compile the score percentage for each category. The analysis department can, for example, predict current scores by referring to past performance data. The analysis department can, for example, evaluate relative strengths and weaknesses by comparing them with the performance data of other students. Step 5: The generation unit visualizes the analysis results obtained by the analysis unit and generates feedback. The generation unit can, for example, display categories with high and low score rates in a graph. The generation unit can, for example, generate feedback that includes specific advice. The generation unit can, for example, provide personalized advice by referring to past feedback history.

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

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

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

[0126] Each of the multiple elements described above, including the acquisition unit, reading unit, scoring unit, analysis unit, and generation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires an image of the answer sheet using the camera 42 of the smart device 14 and processes the image using the specific processing unit 290 of the data processing unit 12. The reading unit reads handwritten characters using the control unit 46A of the smart device 14 and recognizes the characters using the specific processing unit 290 of the data processing unit 12. The scoring unit scores the answers by comparing them with model answers using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the students' strengths and weaknesses based on the scoring results using the specific processing unit 290 of the data processing unit 12. The generation unit visualizes the analysis results using the control unit 46A of the smart device 14 and generates feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0142] Each of the multiple elements described above, including the acquisition unit, reading unit, scoring unit, analysis unit, and generation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires an image of the answer sheet using the camera 42 of the smart glasses 214 and processes the image using the identification processing unit 290 of the data processing unit 12. The reading unit reads handwritten characters using the control unit 46A of the smart glasses 214 and recognizes the characters using the identification processing unit 290 of the data processing unit 12. The scoring unit scores the answers by comparing them with model answers using the identification processing unit 290 of the data processing unit 12. The analysis unit analyzes the student's strengths and weaknesses based on the scoring results using the identification processing unit 290 of the data processing unit 12. The generation unit visualizes the analysis results using the control unit 46A of the smart glasses 214 and generates feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0158] Each of the multiple elements described above, including the acquisition unit, reading unit, scoring unit, analysis unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires an image of the answer sheet using the camera 42 of the headset terminal 314 and processes the image using the specific processing unit 290 of the data processing unit 12. The reading unit reads handwritten characters using the control unit 46A of the headset terminal 314 and recognizes the characters using the specific processing unit 290 of the data processing unit 12. The scoring unit scores the answers by comparing them with model answers using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the student's strengths and weaknesses based on the scoring results using the specific processing unit 290 of the data processing unit 12. The generation unit visualizes the analysis results using the control unit 46A of the headset terminal 314 and generates feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0175] Each of the multiple elements described above, including the acquisition unit, reading unit, scoring unit, analysis unit, and generation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires an image of the answer sheet using the camera 42 of the robot 414 and processes the image using the specific processing unit 290 of the data processing unit 12. The reading unit reads handwritten characters using the control unit 46A of the robot 414 and recognizes the characters using the specific processing unit 290 of the data processing unit 12. The scoring unit scores the answers by comparing them with model answers using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the students' strengths and weaknesses based on the scoring results using the specific processing unit 290 of the data processing unit 12. The generation unit visualizes the analysis results and generates feedback using the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

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

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0194] (Note 1) An acquisition unit that acquires an image of the answer sheet, A reading unit reads handwritten characters from an image acquired by the acquisition unit, A scoring unit that compares the characters read by the aforementioned reading unit with the model answer and scores the answer, Based on the scoring results obtained by the aforementioned scoring unit, an analysis unit analyzes the students' strengths and weaknesses. The system includes a generation unit that visualizes the analysis results obtained by the analysis unit and generates feedback. A system characterized by the following features. (Note 2) The acquisition unit is, Obtain an image of the answer sheet taken with a camera. The system described in Appendix 1, characterized by the features described herein. (Note 3) The reading unit is Reading handwritten text The system described in Appendix 1, characterized by the features described herein. (Note 4) The scoring unit is, The answers will be graded by comparing them to the model answers. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Identify the category of each question and compile the score percentage for each category. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is The system displays categories with high and low score rates in a graph and generates feedback including specific advice. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of image acquisition of the answer sheet based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Analyze past records of obtaining answer sheets and select the most optimal method of acquisition. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When obtaining answer sheets, filtering is performed based on the type of exam and subject. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, The system estimates the user's emotions and determines the priority of the answer sheets to retrieve based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When obtaining answer sheets, priority will be given to obtaining answer sheets that are highly relevant, taking into account the geographical location of the examination venue. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When obtaining the answer sheet, be sure to also obtain any comments or notes from the exam proctor. The system described in Appendix 1, characterized by the features described herein. (Note 13) The reading unit is The system estimates the user's emotions and adjusts the accuracy of handwriting recognition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The reading unit is When reading handwritten characters, the reading algorithm is optimized according to the size of the characters and the pressure applied. The system described in Appendix 1, characterized by the features described herein. (Note 15) The reading unit is When reading handwritten characters, the reading method is changed depending on the layout and format of the answer field. The system described in Appendix 1, characterized by the features described herein. (Note 16) The reading unit is It estimates the user's emotions and determines the priority of characters to read based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The reading unit is When reading handwritten characters, the system takes into account smudges and creases on the answer sheet to improve reading accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 18) The reading unit is When reading handwritten text, the system also reads notes and comments written on the back of the answer sheet. The system described in Appendix 1, characterized by the features described herein. (Note 19) The scoring unit is, The system estimates the user's emotions and adjusts the scoring criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The scoring unit is, When grading, adjust the level of detail in the grading to take into account partial credit for the answers. The system described in Appendix 1, characterized by the features described herein. (Note 21) The scoring unit is, During grading, an algorithm is applied to evaluate the logical consistency and accuracy of expression of the answer. The system described in Appendix 1, characterized by the features described herein. (Note 22) The scoring unit is, The system estimates the user's emotions and adjusts how the scoring results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The scoring unit is, When grading, the grading criteria will be adjusted to take into account the submission period for answers and the difficulty level of the exam. The system described in Appendix 1, characterized by the features described herein. (Note 24) The scoring unit is, When grading, we will refer to the relevant literature and reference materials for the answers to improve the accuracy of the grading. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis method for strengths and weaknesses based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is During analysis, past performance data is used to predict current performance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is During the analysis, we assess strengths and weaknesses by considering the difficulty level and question trends of each problem. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is During the analysis, different analytical methods are applied depending on the type of test and subject. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit is During the analysis, relative strengths and weaknesses are evaluated by comparing them with the performance data of other students. The system described in Appendix 1, characterized by the features described herein. (Note 31) The generating unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The generating unit is When generating feedback, include specific areas for improvement and learning advice. The system described in Appendix 1, characterized by the features described herein. (Note 33) The generating unit is When generating feedback, it provides personalized advice by referring to past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The generating unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The generating unit is When generating feedback, we use graphs and charts to display it visually and clearly. The system described in Appendix 1, characterized by the features described herein. (Note 36) The generating unit is When generating feedback, we will provide examples of other students' success stories and reference materials. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. An acquisition unit that acquires an image of the answer sheet, A reading unit reads handwritten characters from an image acquired by the acquisition unit, A scoring unit that compares the characters read by the aforementioned reading unit with the model answer and scores the answer, Based on the scoring results obtained by the aforementioned scoring unit, an analysis unit analyzes the students' strengths and weaknesses. The system includes a generation unit that visualizes the analysis results obtained by the analysis unit and generates feedback. A system characterized by the following features.

2. The acquisition unit is, Obtain an image of the answer sheet taken with a camera. The system according to feature 1.

3. The reading unit is Reading handwritten text The system according to feature 1.

4. The scoring unit is, The answers will be graded by comparing them to the model answers. The system according to feature 1.

5. The aforementioned analysis unit is Identify the category of each question and compile the score percentage for each category. The system according to feature 1.

6. The generating unit is The system displays categories with high and low score rates in a graph and generates feedback including specific advice. The system according to feature 1.

7. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of image acquisition of the answer sheet based on the estimated emotions. The system according to feature 1.

8. The acquisition unit is, Analyze past records of obtaining answer sheets and select the most optimal method of acquisition. The system according to feature 1.

9. The acquisition unit is, When obtaining answer sheets, filtering is performed based on the type of exam and subject. The system according to feature 1.

10. The acquisition unit is, The system estimates the user's emotions and determines the priority of the answer sheets to retrieve based on the estimated user emotions. The system according to feature 1.

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