System

Abstract

An object of a system according to an embodiment is to propose an optimal learning point to a student based on a test result.SOLUTION: A system according to an embodiment includes a scan unit, an analysis unit, and a proposal unit. The scan unit scans the test result. The analysis unit analyzes the test result scanned by the scan unit. The proposal unit proposes an optimum learning point to the student based on the result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately suggest optimal learning points to students based on test results, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest optimal learning points to students based on test results. [Means for solving the problem]

[0006] The system according to the embodiment includes a scanning unit, an analyzing unit, and a suggesting unit. The scanning unit scans test results. The analyzing unit analyzes the test results scanned by the scanning unit. The suggesting unit suggests optimal learning points for students based on the results analyzed by the analyzing unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal learning points to students based on test results. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The educational support system according to an embodiment of the present invention is a system that scans test results, analyzes them using a generative AI, and suggests learning points. This allows the educational support system to identify students' weaknesses and suggest optimal learning points.

[0029] An education support system according to an embodiment includes a scanning unit, an analysis unit, and a suggestion unit. The scanning unit scans test results. For example, the scanning unit scans test papers using a scanner and saves the images as image data. The scanning unit can also photograph test papers using a smartphone camera and upload the images to an app. The scanning unit can also read printed test results using OCR technology. For example, the scanning unit scans handwritten answer sheets using a high-resolution scanner and converts them into text information using OCR technology. When using a smartphone camera, the app automatically corrects the image and performs character recognition. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The analysis unit uses a generation AI to analyze the test results scanned by the scanning unit. For example, the generation AI analyzes the test results using a text generation AI (e.g., LLM). The generation AI can also analyze the content of the test results using a multimodal generation AI. If there are many mistakes in a particular question type, the generation AI suggests learning points related to that question type. For example, the generation AI uses a text generation AI to pick out particularly important information from test results and perform analysis based on that information. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information from test results and perform analysis based on that information. The suggestion unit suggests optimal study points for students based on the results analyzed by the analysis unit. For example, the suggestion unit suggests additional practice questions for specific problem types and reference materials to deepen understanding. The suggestion unit can also use the generation AI to create individual study plans. The suggestion unit can also use the generation AI to monitor students' learning progress and provide regular feedback. For example, the suggestion unit suggests additional practice questions for specific problem types. The suggestion unit uses the generation AI to suggest additional practice questions for specific problem types and reference materials to deepen understanding. This allows the education support system according to the embodiment to identify students' weaknesses and suggest optimal study points.For example, the output unit can display suggested learning points to students and teachers via a web or mobile application, print the results using a printer if they prefer paper feedback, or send the results via email, providing quick feedback by sending the results directly to students and parents.

[0030] The scanning unit can take a photo of the test paper using a smartphone camera and upload the image to the app. For example, the scanning unit can take a photo of the test paper using a smartphone camera and upload the image to the app. For example, the generation AI recognizes the handwritten characters on the scanned test paper and automatically corrects typos and typing errors. For example, if a "5" is misrecognized as an "S" in a math test, the context is analyzed to correct it to the correct number. To improve the accuracy of handwriting recognition, the generation AI also learns from past data and automatically corrects typos and typing errors. For example, it learns frequently used character patterns and accurately corrects them. The generation AI also recognizes handwritten characters during scanning and provides a function to automatically correct typos and typing errors. For example, if a user misspells "their" as "there" in an English test, the context is analyzed to correct the mistake. This eliminates the need for scanning by simply taking a photo of the test paper using a smartphone camera and uploading it to the app.

[0031] If there are many mistakes on a particular question type, the analysis unit can suggest learning points related to that question type. For example, if there are many mistakes on a particular question type, the analysis unit can suggest learning points related to that question type. For example, the generation AI can automatically correct distortions in scanned test paper images. For example, it can accurately correct images taken at an angle, making them easier to read. The generation AI can also automatically remove shadows from scanned images, improving the accuracy of test result reading. For example, it can analyze shadows caused by light reflection and remove them from the image. The generation AI also provides a function to automatically correct image distortions and shadows to improve the quality of scanned images. For example, it can straighten crooked test papers to make them flat, allowing them to be read accurately. This effectively addresses students' weak points by suggesting learning points related to that question type if there are many mistakes on a particular question type.

[0032] The suggestion unit can suggest additional practice problems for a specific problem type or reference materials to deepen understanding. The suggestion unit suggests, for example, additional practice problems for a specific problem type or reference materials to deepen understanding. For example, the generation AI analyzes the student's facial expressions during scanning and uses an emotion estimation function to detect stress or anxiety. For example, it reads tension from the facial expression and provides advice to relax. The generation AI also analyzes the student's tone of voice during scanning and uses an emotion estimation function to detect stress or anxiety. For example, it reads tension from the tone of voice and provides advice to relax. The emotion estimation function also analyzes the student's emotions during scanning and provides advice to reduce stress or anxiety. For example, it suggests deep breathing or simple exercises to relax. In this way, the suggestion unit deepens the student's understanding by suggesting additional practice problems or reference materials for a specific problem type.

[0033] The analysis unit can analyze scanned test results and identify students' weaknesses and areas of strength. For example, the analysis unit analyzes scanned test results and identifies students' weaknesses and areas of strength. For example, by combining scanned data with voice input, the generation AI can input test results via voice. For example, a student can input their test score or incorrect questions via voice. The generation AI can also combine scanned data with voice input to support the input of test results via voice. For example, a student can read out their test score via voice, and the generation AI can input that data. The generation AI can also combine scanned data with voice input to enable the input of test results via voice. For example, a student can explain the questions they got wrong via voice, and the generation AI can input that data. This makes it possible to analyze scanned test results and identify students' weaknesses and areas of strength, enabling individual learning support.

[0034] The suggestion unit can use the generative AI to create an individual study plan for each student. The suggestion unit, for example, uses the generative AI to create an individual study plan for each student. For example, the generative AI automatically recognizes the layout of a test paper when scanning, and can handle tests of different formats. For example, it can accurately read multiple-choice and written tests. The generative AI also automatically recognizes the layout of a test paper when scanning, providing a function that can handle tests of different formats. For example, it can accurately read test papers created by different schools or teachers. The generative AI also automatically recognizes the layout of a test paper when scanning, and can handle tests of different formats. For example, it can accurately read test papers for different subjects or grades. This makes it possible to create an individual study plan using the generative AI, enabling optimal learning support for each student.

[0035] The scanning unit can automatically correct image distortion and shadows to improve the quality of scanned images. The scanning unit, for example, automatically corrects image distortion and shadows to improve the quality of scanned images. For example, the generation AI automatically corrects image distortion in scanned test paper. For example, it accurately corrects images taken at an angle to make them easier to read. The generation AI also automatically removes shadows from scanned images to improve the accuracy of reading test results. For example, it analyzes shadows caused by light reflection and removes them from the image. The generation AI also provides a function to automatically correct image distortion and shadows to improve the quality of scanned images. For example, it straightens out a curved test paper to make it flat and read it accurately. This automatically corrects image distortion and shadows to improve the quality of scanned images, thereby improving the accuracy of reading data.

[0036] The scanning unit can combine scanned data with voice input, enabling test results to be entered via voice. The scanning unit can, for example, combine scanned data with voice input, enabling test results to be entered via voice. For example, by combining scanned data with voice input, the generation AI can input test results via voice. For example, a student can input their test score or incorrect questions via voice. The generation AI can also combine scanned data with voice input to support the input of test results via voice. For example, a student can read out their test score via voice, and the generation AI can input that data. The generation AI can also combine scanned data with voice input, enabling test results to be entered via voice. For example, a student can explain the questions they got wrong via voice, and the generation AI can input that data. In this way, by combining scanned data with voice input, test results can also be entered via voice.

[0037] The scanning unit automatically recognizes the layout of the test paper and can accommodate tests of different formats. For example, the generation AI automatically recognizes the layout of the test paper when scanning, making it compatible with different test formats. For example, it can accurately read multiple-choice and written tests. The generation AI also automatically recognizes the layout of the test paper when scanning, providing a function that can accommodate different test formats. For example, it can accurately read test papers created by different schools or teachers. The generation AI also automatically recognizes the layout of the test paper when scanning, making it compatible with different test formats. For example, it can accurately read test papers for different subjects or grades. This allows the system to automatically recognize the layout of the test paper and accommodate different test formats, making it possible to accommodate a wide range of test formats.

[0038] The analysis unit can suggest learning points that match the student's learning style based on the analysis results. For example, the analysis unit suggests learning points that match the student's learning style based on the analysis results. For example, the generation AI suggests learning points that match the student's learning style based on the analysis results. For example, for visual students, it suggests learning materials that make extensive use of diagrams and graphs. The generation AI also analyzes the student's learning style and suggests learning points based on that. For example, it suggests audio materials or podcasts for auditory students. The generation AI also suggests learning points that match the student's learning style based on the analysis results. For example, it suggests experiments or hands-on activities for tactile students. In this way, effective learning is supported by suggesting learning points that match the student's learning style based on the analysis results.

[0039] The analysis unit can compare past test results, analyze progress and trends, and suggest study points. For example, the analysis unit compares past test results, analyzes progress and trends, and suggests study points. For example, the generation AI compares past test results with current test results, analyzes progress and trends, and suggests study points. For example, it evaluates whether questions that were difficult in the past have improved. The generation AI also analyzes past test results and suggests study points based on progress and trends. For example, it analyzes changes in understanding of specific question types. The generation AI also compares past test results with current test results, analyzes progress and trends, and suggests study points. For example, it evaluates changes in areas of strength and weakness. In this way, by comparing with past test results, analyzing progress and trends, and suggesting study points, the generation AI supports the student's growth.

[0040] The analysis unit can make suggestions for group learning or pair learning based on the analysis results, thereby promoting collaborative learning. The analysis unit, for example, makes suggestions for group learning or pair learning based on the analysis results, thereby promoting collaborative learning. For example, the generation AI makes suggestions for group learning or pair learning based on the analysis results, thereby promoting collaborative learning. For example, matching students with the same weaknesses. The generation AI also makes suggestions for group learning or pair learning to promote collaborative learning based on the analysis results, thereby promoting collaborative learning. For example, matching students with different areas of expertise. The generation AI also makes suggestions for group learning or pair learning based on the analysis results, thereby promoting collaborative learning. For example, making suggestions for working together on a specific task. In this way, group learning or pair learning is suggested based on the analysis results, promoting collaborative learning, and supporting students learning from each other.

[0041] The suggestion unit can use the generation AI to consider the student's lifestyle and schedule and suggest the optimal study time slot. The suggestion unit, for example, uses the generation AI to consider the student's lifestyle and schedule and suggest the optimal study time slot. For example, the generation AI analyzes the student's lifestyle and schedule and suggests the optimal study time slot. For example, it takes into account school class times and club activity schedules. The generation AI also analyzes the student's lifestyle and suggests the optimal study time slot based on that. For example, it suggests nighttime study times for night owls. The generation AI also analyzes the student's schedule and suggests the optimal study time slot. For example, it suggests study times on weekends and holidays. In this way, the generation AI considers the student's lifestyle and schedule and suggests the optimal study time slot, supporting efficient learning.

[0042] The suggestion unit can use the generative AI to create a learning plan that reflects the student's interests and concerns, thereby improving the enjoyment of learning. The suggestion unit, for example, uses the generative AI to create a learning plan that reflects the student's interests and concerns, thereby improving the enjoyment of learning. For example, the generative AI analyzes the student's interests and creates a learning plan based on them. For example, it suggests learning materials related to favorite subjects or themes. The generative AI also creates a learning plan that reflects the student's interests and improves the enjoyment of learning. For example, it suggests learning activities related to hobbies or special skills. The generative AI also analyzes the student's interests and creates a learning plan based on them. For example, it suggests learning materials that incorporate favorite characters or stories. In this way, the generative AI creates a learning plan that reflects the student's interests and improves the enjoyment of learning, thereby increasing the student's motivation to learn.

[0043] The suggestion unit can use the generative AI to incorporate physical exercise and relaxation time into the study plan to support overall health. The suggestion unit, for example, uses the generative AI to incorporate physical exercise and relaxation time into the study plan to support overall health. For example, the generative AI incorporates physical exercise into the study plan to support overall health. For example, it suggests short exercises or stretches. The generative AI also incorporates relaxation time into the study plan to support overall health. For example, it suggests time for meditation or deep breathing. The generative AI also incorporates physical exercise and relaxation time into the study plan to support overall health. For example, it suggests regular breaks. In this way, the generative AI incorporates physical exercise and relaxation time into the study plan to support overall health, thereby maintaining the health of the student.

[0044] The suggestion unit can use the generation AI to suggest tasks for parents and children to work on together in the study plan, thereby enhancing learning support at home. The suggestion unit, for example, uses the generation AI to suggest tasks for parents and children to work on together in the study plan, thereby enhancing learning support at home. For example, the generation AI suggests tasks for parents and children to work on together in the study plan, thereby enhancing learning support at home. For example, it suggests puzzles and quizzes for parents and children to solve together. The generation AI also suggests tasks for parents and children to work on together in the study plan, thereby enhancing learning support at home. For example, it suggests experiments and crafts for parents and children to do together. The generation AI also suggests tasks for parents and children to work on together in the study plan, thereby enhancing learning support at home. For example, it suggests books and stories for parents and children to read together. In this way, the generation AI can suggest tasks for parents and children to work on together in the study plan, thereby enhancing learning support at home, thereby supporting the student's learning at home as well.

[0045] The suggestion unit can use the generation AI to monitor learning progress in real time and provide immediate feedback. The suggestion unit, for example, uses the generation AI to monitor learning progress in real time and provide immediate feedback. For example, the generation AI monitors learning progress in real time and provides immediate feedback. For example, immediately displays whether the answer is correct or incorrect after a practice problem is answered. The generation AI also monitors learning progress in real time and provides immediate feedback. For example, immediately provides explanations for any questions that arise during learning. The generation AI also monitors learning progress in real time and provides immediate feedback. For example, suggests the next step depending on the learning progress. In this way, the generation AI can be used to monitor learning progress in real time and provide immediate feedback, thereby quickly supporting the student's learning.

[0046] The suggestion unit can use the generation AI to automatically suggest the next learning step based on the learning progress. The suggestion unit, for example, uses the generation AI to automatically suggest the next learning step based on the learning progress. For example, the generation AI automatically suggests the next learning step based on the learning progress. For example, the generation AI automatically suggests additional practice problems for a specific problem type. Also, the generation AI automatically suggests the next learning step based on the learning progress. For example, the generation AI automatically suggests supplementary learning materials for areas where understanding is low. Also, the generation AI automatically suggests the next learning step based on the learning progress. For example, the generation AI automatically suggests advanced tasks to further improve areas of strength. In this way, by using the generation AI to automatically suggest the next learning step based on the learning progress, the student's learning can be efficiently advanced.

[0047] The suggestion unit can use the generation AI to match students at the same progress level with each other based on their learning progress, thereby promoting peer support. The suggestion unit, for example, uses the generation AI to match students at the same progress level with each other based on their learning progress, thereby promoting peer support. For example, the generation AI matches students at the same progress level with each other based on their learning progress, thereby promoting peer support. For example, it groups students working on the same problem. The generation AI also matches students at the same progress level with each other to promote peer support based on their learning progress, for example, it creates pairs who will teach each other. The generation AI also matches students at the same progress level with each other based on their learning progress, thereby promoting peer support. For example, it creates groups to work on assignments together. In this way, the generation AI matches students at the same progress level with each other based on their learning progress, thereby promoting peer support, thereby supporting students learning from each other.

[0048] The suggestion unit can also use the generation AI to provide feedback to parents and teachers based on the learning progress, thereby strengthening support at home and at school. The suggestion unit, for example, uses the generation AI to provide feedback to parents and teachers based on the learning progress, thereby strengthening support at home and at school. For example, the generation AI provides feedback to parents and teachers based on the learning progress, thereby strengthening support at home and at school. For example, the suggestion unit periodically reports the learning progress. The generation AI also provides feedback to parents and teachers based on the learning progress, thereby strengthening support at home and at school. For example, it provides advice on specific problems. The generation AI also provides feedback to parents and teachers based on the learning progress, thereby strengthening support at home and at school. For example, it suggests adjustments to the learning plan. In this way, the generation AI provides feedback to parents and teachers based on the learning progress, thereby strengthening support at home and at school, thereby providing comprehensive support for the student's learning.

[0049] The suggestion unit can use the generation AI to describe in detail the student's learning style and progress trends in the report, and provide it in a format that is easy for teachers and parents to understand. For example, the suggestion unit can use the generation AI to describe in detail the student's learning style and progress trends in the report, and provide it in a format that is easy for teachers and parents to understand. For example, the generation AI can specify learning styles such as visual and auditory. The generation AI can also describe in detail the student's progress trends in the report, and provide it in a format that is easy for teachers and parents to understand. For example, the generation AI can display strong and weak areas in a graph. The generation AI can also describe in detail the student's learning style and progress trends in the report, and provide it in a format that is easy for teachers and parents to understand. For example, the generation AI can display learning progress in chronological order. In this way, the generation AI can describe in detail the student's learning style and progress trends in the report, and provide it in a format that is easy for teachers and parents to understand, making it easier for them to grasp the student's learning situation.

[0050] The suggestion unit can use the generation AI to display the student's learning history in chronological order in a report, making it possible to grasp long-term changes in learning. The suggestion unit, for example, uses the generation AI to display the student's learning history in chronological order in a report, making it possible to grasp long-term changes in learning. For example, the generation AI displays the student's learning history in chronological order in a report, making it possible to grasp long-term changes in learning. For example, past test results and learning progress are displayed in graphs. The generation AI also displays the student's learning history in chronological order in a report, making it possible to grasp long-term changes in learning. For example, learning progress is displayed in monthly summary form. The generation AI also displays the student's learning history in chronological order in a report, making it possible to grasp long-term changes in learning. For example, it shows changes in understanding of a specific subject or theme. In this way, the generation AI can display the student's learning history in chronological order in a report, making it possible to grasp long-term changes in learning, thereby visualizing the student's growth.

[0051] The suggestion unit can use the generation AI to make the report multilingual and provide it in a format that is easy to understand for parents who speak different languages. The suggestion unit, for example, uses the generation AI to make the report multilingual and provide it in a format that is easy to understand for parents who speak different languages. For example, the generation AI makes the report multilingual and provides it in a format that is easy to understand for parents who speak different languages. For example, the generation AI creates a report in multiple languages, such as English, Spanish, and Chinese. The generation AI also makes the report multilingual and provides it in a format that is easy to understand for parents who speak different languages. For example, it performs automatic translation according to the parent's language setting. The generation AI also makes the report multilingual and provides it in a format that is easy to understand for parents who speak different languages. For example, it highlights important parts of the report in multiple languages. In this way, support for parents is enhanced by using the generation AI to make the report multilingual and provide it in a format that is easy to understand for parents who speak different languages.

[0052] The suggestion unit can use the generation AI to make extensive use of visual elements (graphs and charts) in the report, and provide it in a format that is visually easy to understand. For example, the suggestion unit uses the generation AI to make extensive use of visual elements (graphs and charts) in the report, and provide it in a format that is visually easy to understand. For example, the generation AI makes extensive use of visual elements (graphs and charts) in the report, and provides it in a format that is visually easy to understand. For example, learning progress is displayed in a bar graph or pie chart. The generation AI also makes extensive use of visual elements in the report, and provides it in a format that is visually easy to understand. For example, strong and weak areas are displayed in a color-coded chart. The generation AI also makes extensive use of visual elements in the report, and provides it in a format that is visually easy to understand. For example, a line graph is used to show learning history over time. In this way, the generation AI makes extensive use of visual elements (graphs and charts) in the report, and provides it in a format that is visually easy to understand, making it easier for parents and teachers to grasp the student's learning situation.

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

[0054] The educational support system can also be equipped with a learning style analysis unit that analyzes students' learning styles. Based on the student's learning history and test results, the learning style analysis unit determines whether the student is visual, auditory, or tactile. For example, it can suggest learning materials that make extensive use of diagrams and graphs to visual students, and audio materials and podcasts to auditory students. It can also suggest experiments and hands-on activities to tactile students. This makes it possible to provide each student with the optimal learning method and maximize learning effectiveness.

[0055] The educational support system can further include a progress monitoring unit that monitors students' learning progress in real time. The progress monitoring unit records the correctness of questions answered by students and the time it took to answer them in real time, and provides immediate feedback. For example, it can immediately display whether a student answered a practice question and suggest supplementary learning materials for areas where students have little understanding. It can also immediately provide explanations for any questions that arise during the course of study. This allows for prompt support for students' learning and promotes efficient learning.

[0056] The educational support system can also be equipped with a schedule analysis unit that takes into account students' lifestyles and schedules. The schedule analysis unit analyzes students' lifestyles, school class times, and club activity schedules to suggest optimal study times. For example, it can suggest nighttime study times for night owls, as well as study times on weekends and holidays. This makes it possible to support efficient learning that matches students' lifestyles.

[0057] The educational support system can further include an interest analysis unit that creates a learning plan that reflects the student's interests. The interest analysis unit analyzes the student's interests and creates a learning plan based on them. For example, it can suggest learning materials related to a student's favorite subjects or themes, and suggest learning activities related to a student's hobbies or special skills. It can also suggest learning materials that incorporate a student's favorite characters or stories. This can increase the student's motivation to learn and make learning more enjoyable.

[0058] The educational support system can also be equipped with a history display section that displays a student's learning history in chronological order. The history display section displays a student's past test results and learning progress in chronological order, allowing them to grasp long-term learning trends. For example, past test results and learning progress can be displayed in graphs, and learning progress can be summarized by month. It can also show changes in understanding of specific subjects or themes. This makes it possible to visualize a student's growth and increase their motivation to learn.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The scanning unit scans the test results. For example, the scanning unit scans the test paper with a scanner and saves it as image data. The scanning unit can also take a photo of the test paper using a smartphone camera and upload the image to the app. The scanning unit can also read printed test results using OCR technology. For example, handwritten answers can be scanned with a high-resolution scanner and converted into text information using OCR technology. When using a smartphone camera, the app automatically corrects the image and performs character recognition. OCR technology recognizes printed characters with high accuracy and converts them into digital text. Step 2: In the analysis unit, the generation AI analyzes the test results scanned by the scanning unit. For example, the generation AI analyzes the test results using a text generation AI (e.g., LLM). The generation AI can also analyze the content of the test results using a multimodal generation AI. If there are many mistakes in a particular question type, the generation AI will suggest learning points related to that question type. For example, the generation AI uses a text generation AI to pick out particularly important information from the test results and perform analysis based on that. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information from the test results and perform analysis based on that. Step 3: The suggestion unit suggests optimal learning points for the student based on the results of the analysis by the analysis unit. For example, the suggestion unit suggests additional practice questions for a specific problem type or reference materials to deepen understanding. The suggestion unit can also create an individual learning plan using the generation AI. Furthermore, the suggestion unit can also monitor the student's learning progress using the generation AI and provide regular feedback. For example, the suggestion unit suggests additional practice questions for a specific problem type. The suggestion unit uses the generation AI to suggest additional practice questions for a specific problem type or reference materials to deepen understanding. This allows the education support system according to the embodiment to identify the student's weaknesses and suggest optimal learning points.

[0061] (Example 2) The educational support system according to an embodiment of the present invention is a system that scans test results, analyzes them using a generative AI, and suggests learning points. This allows the educational support system to identify students' weaknesses and suggest optimal learning points.

[0062] An education support system according to an embodiment includes a scanning unit, an analysis unit, and a suggestion unit. The scanning unit scans test results. For example, the scanning unit scans test papers using a scanner and saves the images as image data. The scanning unit can also photograph test papers using a smartphone camera and upload the images to an app. The scanning unit can also read printed test results using OCR technology. For example, the scanning unit scans handwritten answer sheets using a high-resolution scanner and converts them into text information using OCR technology. When using a smartphone camera, the app automatically corrects the image and performs character recognition. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The analysis unit uses a generation AI to analyze the test results scanned by the scanning unit. For example, the generation AI analyzes the test results using a text generation AI (e.g., LLM). The generation AI can also analyze the content of the test results using a multimodal generation AI. If there are many mistakes in a particular question type, the generation AI suggests learning points related to that question type. For example, the generation AI uses a text generation AI to pick out particularly important information from test results and perform analysis based on that information. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information from test results and perform analysis based on that information. The suggestion unit suggests optimal study points for students based on the results analyzed by the analysis unit. For example, the suggestion unit suggests additional practice questions for specific problem types and reference materials to deepen understanding. The suggestion unit can also use the generation AI to create individual study plans. The suggestion unit can also use the generation AI to monitor students' learning progress and provide regular feedback. For example, the suggestion unit suggests additional practice questions for specific problem types. The suggestion unit uses the generation AI to suggest additional practice questions for specific problem types and reference materials to deepen understanding. This allows the education support system according to the embodiment to identify students' weaknesses and suggest optimal study points.For example, the output unit can display suggested learning points to students and teachers via a web or mobile application, print the results using a printer if they prefer paper feedback, or send the results via email, providing quick feedback by sending the results directly to students and parents.

[0063] The scanning unit can take a photo of the test paper using a smartphone camera and upload the image to the app. For example, the scanning unit can take a photo of the test paper using a smartphone camera and upload the image to the app. For example, the generation AI recognizes the handwritten characters on the scanned test paper and automatically corrects typos and typing errors. For example, if a "5" is misrecognized as an "S" in a math test, the context is analyzed to correct it to the correct number. To improve the accuracy of handwriting recognition, the generation AI also learns from past data and automatically corrects typos and typing errors. For example, it learns frequently used character patterns and accurately corrects them. The generation AI also recognizes handwritten characters during scanning and provides a function to automatically correct typos and typing errors. For example, if a user misspells "their" as "there" in an English test, the context is analyzed to correct the mistake. This eliminates the need for scanning by simply taking a photo of the test paper using a smartphone camera and uploading it to the app.

[0064] If there are many mistakes on a particular question type, the analysis unit can suggest learning points related to that question type. For example, if there are many mistakes on a particular question type, the analysis unit can suggest learning points related to that question type. For example, the generation AI can automatically correct distortions in scanned test paper images. For example, it can accurately correct images taken at an angle, making them easier to read. The generation AI can also automatically remove shadows from scanned images, improving the accuracy of test result reading. For example, it can analyze shadows caused by light reflection and remove them from the image. The generation AI also provides a function to automatically correct image distortions and shadows to improve the quality of scanned images. For example, it can straighten crooked test papers to make them flat, allowing them to be read accurately. This effectively addresses students' weak points by suggesting learning points related to that question type if there are many mistakes on a particular question type.

[0065] The suggestion unit can suggest additional practice problems for a specific problem type or reference materials to deepen understanding. The suggestion unit suggests, for example, additional practice problems for a specific problem type or reference materials to deepen understanding. For example, the generation AI analyzes the student's facial expressions during scanning and uses an emotion estimation function to detect stress or anxiety. For example, it reads tension from the facial expression and provides advice to relax. The generation AI also analyzes the student's tone of voice during scanning and uses an emotion estimation function to detect stress or anxiety. For example, it reads tension from the tone of voice and provides advice to relax. The emotion estimation function also analyzes the student's emotions during scanning and provides advice to reduce stress or anxiety. For example, it suggests deep breathing or simple exercises to relax. In this way, the suggestion unit deepens the student's understanding by suggesting additional practice problems or reference materials for a specific problem type.

[0066] The analysis unit can analyze scanned test results and identify students' weaknesses and areas of strength. For example, the analysis unit analyzes scanned test results and identifies students' weaknesses and areas of strength. For example, by combining scanned data with voice input, the generation AI can input test results via voice. For example, a student can input their test score or incorrect questions via voice. The generation AI can also combine scanned data with voice input to support the input of test results via voice. For example, a student can read out their test score via voice, and the generation AI can input that data. The generation AI can also combine scanned data with voice input to enable the input of test results via voice. For example, a student can explain the questions they got wrong via voice, and the generation AI can input that data. This makes it possible to analyze scanned test results and identify students' weaknesses and areas of strength, enabling individual learning support.

[0067] The suggestion unit can use the generative AI to create an individual study plan for each student. The suggestion unit, for example, uses the generative AI to create an individual study plan for each student. For example, the generative AI automatically recognizes the layout of a test paper when scanning, and can handle tests of different formats. For example, it can accurately read multiple-choice and written tests. The generative AI also automatically recognizes the layout of a test paper when scanning, providing a function that can handle tests of different formats. For example, it can accurately read test papers created by different schools or teachers. The generative AI also automatically recognizes the layout of a test paper when scanning, and can handle tests of different formats. For example, it can accurately read test papers for different subjects or grades. This makes it possible to create an individual study plan using the generative AI, enabling optimal learning support for each student.

[0068] The suggestion unit can use the generation AI to monitor the student's learning progress and provide regular feedback. For example, the suggestion unit uses the generation AI to monitor the student's learning progress and provide regular feedback. For example, the generation AI analyzes the student's facial expression when scanned and provides positive feedback using the emotion estimation function. For example, if a smile is detected, it displays "Good job." The generation AI also analyzes the student's tone of voice when scanned and provides positive feedback using the emotion estimation function. For example, if a bright tone of voice is detected, it displays "Great." The emotion estimation function also analyzes the student's emotion when scanned and provides positive feedback. For example, if a concentrated expression is detected, it displays "Keep it up." In this way, the generation AI is used to monitor the student's learning progress and provide regular feedback, thereby continuously supporting the student's learning.

[0069] The suggestion unit can use the generation AI to provide a report of the student's learning status to teachers and parents. For example, the suggestion unit uses the generation AI to provide a report of the student's learning status to teachers and parents. For example, the generation AI analyzes the student's facial expression when scanned and provides positive feedback using the emotion estimation function. For example, if a smile is detected, it displays "Good job." The generation AI also analyzes the student's tone of voice when scanned and provides positive feedback using the emotion estimation function. For example, if a bright tone of voice is detected, it displays "Excellent." The emotion estimation function also analyzes the student's emotion when scanned and provides positive feedback. For example, if a concentrated facial expression is detected, it displays "Keep it up." In this way, by using the generation AI to provide a report of the student's learning status, it becomes easier for teachers and parents to understand the student's learning status.

[0070] The scanning unit can automatically correct image distortion and shadows to improve the quality of scanned images. The scanning unit, for example, automatically corrects image distortion and shadows to improve the quality of scanned images. For example, the generation AI automatically corrects image distortion in scanned test paper. For example, it accurately corrects images taken at an angle to make them easier to read. The generation AI also automatically removes shadows from scanned images to improve the accuracy of reading test results. For example, it analyzes shadows caused by light reflection and removes them from the image. The generation AI also provides a function to automatically correct image distortion and shadows to improve the quality of scanned images. For example, it straightens out a curved test paper to make it flat and read it accurately. This automatically corrects image distortion and shadows to improve the quality of scanned images, thereby improving the accuracy of reading data.

[0071] The scanning unit can use the emotion estimation function to analyze the student's emotions at the time of scanning and provide advice to reduce stress and anxiety. For example, the scanning unit uses the emotion estimation function to analyze the student's emotions at the time of scanning and provide advice to reduce stress and anxiety. For example, the generation AI analyzes the student's facial expression at the time of scanning and uses the emotion estimation function to detect stress or anxiety. For example, it reads tension from the facial expression and provides advice to relax. The generation AI also analyzes the student's tone of voice at the time of scanning and uses the emotion estimation function to detect stress or anxiety. For example, it reads tension from the tone of voice and provides advice to relax. The emotion estimation function also analyzes the student's emotions at the time of scanning and provides advice to reduce stress and anxiety. For example, it suggests deep breathing or simple exercises to relax. In this way, the emotion estimation function is used to analyze the student's emotions at the time of scanning and provides advice to reduce stress and anxiety, thereby reducing the psychological burden on the student.

[0072] The scanning unit can combine scanned data with voice input, enabling test results to be entered via voice. The scanning unit can, for example, combine scanned data with voice input, enabling test results to be entered via voice. For example, by combining scanned data with voice input, the generation AI can input test results via voice. For example, a student can input their test score or incorrect questions via voice. The generation AI can also combine scanned data with voice input to support the input of test results via voice. For example, a student can read out their test score via voice, and the generation AI can input that data. The generation AI can also combine scanned data with voice input, enabling test results to be entered via voice. For example, a student can explain the questions they got wrong via voice, and the generation AI can input that data. In this way, by combining scanned data with voice input, test results can also be entered via voice.

[0073] The scanning unit automatically recognizes the layout of the test paper and can accommodate tests of different formats. For example, the generation AI automatically recognizes the layout of the test paper when scanning, making it compatible with different test formats. For example, it can accurately read multiple-choice and written tests. The generation AI also automatically recognizes the layout of the test paper when scanning, providing a function that can accommodate different test formats. For example, it can accurately read test papers created by different schools or teachers. The generation AI also automatically recognizes the layout of the test paper when scanning, making it compatible with different test formats. For example, it can accurately read test papers for different subjects or grades. This allows the system to automatically recognize the layout of the test paper and accommodate different test formats, making it possible to accommodate a wide range of test formats.

[0074] The scanning unit can use the emotion estimation function to analyze the student's emotion at the time of scanning and provide positive feedback. For example, the scanning unit uses the emotion estimation function to analyze the student's emotion at the time of scanning and provide positive feedback. For example, the generation AI analyzes the student's facial expression at the time of scanning and provides positive feedback using the emotion estimation function. For example, if a smile is detected, it displays "Good job." The generation AI also analyzes the student's tone of voice at the time of scanning and provides positive feedback using the emotion estimation function. For example, if a bright tone of voice is detected, it displays "Great." The emotion estimation function also analyzes the student's emotion at the time of scanning and provides positive feedback. For example, if a concentrated expression is detected, it displays "Keep it up." In this way, the emotion estimation function can be used to analyze the student's emotion at the time of scanning and provide positive feedback, thereby improving student motivation.

[0075] The analysis unit can suggest learning points that match the student's learning style based on the analysis results. For example, the analysis unit suggests learning points that match the student's learning style based on the analysis results. For example, the generation AI suggests learning points that match the student's learning style based on the analysis results. For example, for visual students, it suggests learning materials that make extensive use of diagrams and graphs. The generation AI also analyzes the student's learning style and suggests learning points based on that. For example, it suggests audio materials or podcasts for auditory students. The generation AI also suggests learning points that match the student's learning style based on the analysis results. For example, it suggests experiments or hands-on activities for tactile students. In this way, effective learning is supported by suggesting learning points that match the student's learning style based on the analysis results.

[0076] The analysis unit can compare past test results, analyze progress and trends, and suggest study points. For example, the analysis unit compares past test results, analyzes progress and trends, and suggests study points. For example, the generation AI compares past test results with current test results, analyzes progress and trends, and suggests study points. For example, it evaluates whether questions that were difficult in the past have improved. The generation AI also analyzes past test results and suggests study points based on progress and trends. For example, it analyzes changes in understanding of specific question types. The generation AI also compares past test results with current test results, analyzes progress and trends, and suggests study points. For example, it evaluates changes in areas of strength and weakness. In this way, by comparing with past test results, analyzing progress and trends, and suggesting study points, the generation AI supports the student's growth.

[0077] The analysis unit can use the emotion estimation function to evaluate the emotional impact that learning points based on the analysis results have on students and make optimal suggestions. The analysis unit, for example, uses the emotion estimation function to evaluate the emotional impact that learning points based on the analysis results have on students and make optimal suggestions. For example, the generation AI evaluates the emotional impact that learning points based on the analysis results have on students and makes optimal suggestions. For example, a suggestion is made to avoid problem types that are likely to cause stress. The emotion estimation function also evaluates the emotional impact that learning points based on the analysis results have on students and makes optimal suggestions. For example, a suggestion is made to suggest learning points that elicit positive emotions. The generation AI also evaluates the emotional impact that learning points based on the analysis results have on students and makes optimal suggestions. For example, a suggestion is made to suggest learning points that will increase motivation. In this way, the emotion estimation function is used to evaluate the emotional impact that learning points based on the analysis results have on students and makes optimal suggestions, thereby maintaining students' motivation to learn.

[0078] The analysis unit can make suggestions for group learning or pair learning based on the analysis results, thereby promoting collaborative learning. The analysis unit, for example, makes suggestions for group learning or pair learning based on the analysis results, thereby promoting collaborative learning. For example, the generation AI makes suggestions for group learning or pair learning based on the analysis results, thereby promoting collaborative learning. For example, matching students with the same weaknesses. The generation AI also makes suggestions for group learning or pair learning to promote collaborative learning based on the analysis results, thereby promoting collaborative learning. For example, matching students with different areas of expertise. The generation AI also makes suggestions for group learning or pair learning based on the analysis results, thereby promoting collaborative learning. For example, making suggestions for working together on a specific task. In this way, group learning or pair learning is suggested based on the analysis results, promoting collaborative learning, and supporting students learning from each other.

[0079] The analysis unit can use the emotion estimation function to evaluate the emotional impact that the learning points based on the analysis results have on the student and provide positive feedback. The analysis unit, for example, uses the emotion estimation function to evaluate the emotional impact that the learning points based on the analysis results have on the student and provide positive feedback. For example, the emotion estimation function is used to evaluate the emotional impact that the learning points based on the analysis results have on the student and provide positive feedback. For example, feedback that emphasizes successful experiences is provided. Further, the generation AI evaluates the emotional impact that the learning points based on the analysis results have on the student and provides positive feedback. For example, feedback that makes the student feel a sense of accomplishment is provided. Further, the emotion estimation function is used to evaluate the emotional impact that the learning points based on the analysis results have on the student and provide positive feedback. For example, feedback that includes words of encouragement is provided. In this way, the emotion estimation function is used to evaluate the emotional impact that the learning points based on the analysis results have on the student and positive feedback is provided, thereby maintaining the student's motivation to learn.

[0080] The suggestion unit can use the generation AI to consider the student's lifestyle and schedule and suggest the optimal study time slot. The suggestion unit, for example, uses the generation AI to consider the student's lifestyle and schedule and suggest the optimal study time slot. For example, the generation AI analyzes the student's lifestyle and schedule and suggests the optimal study time slot. For example, it takes into account school class times and club activity schedules. The generation AI also analyzes the student's lifestyle and suggests the optimal study time slot based on that. For example, it suggests nighttime study times for night owls. The generation AI also analyzes the student's schedule and suggests the optimal study time slot. For example, it suggests study times on weekends and holidays. In this way, the generation AI considers the student's lifestyle and schedule and suggests the optimal study time slot, supporting efficient learning.

[0081] The suggestion unit can use the generative AI to create a learning plan that reflects the student's interests and concerns, thereby improving the enjoyment of learning. The suggestion unit, for example, uses the generative AI to create a learning plan that reflects the student's interests and concerns, thereby improving the enjoyment of learning. For example, the generative AI analyzes the student's interests and creates a learning plan based on them. For example, it suggests learning materials related to favorite subjects or themes. The generative AI also creates a learning plan that reflects the student's interests and improves the enjoyment of learning. For example, it suggests learning activities related to hobbies or special skills. The generative AI also analyzes the student's interests and creates a learning plan based on them. For example, it suggests learning materials that incorporate favorite characters or stories. In this way, the generative AI creates a learning plan that reflects the student's interests and improves the enjoyment of learning, thereby increasing the student's motivation to learn.

[0082] The suggestion unit can use the emotion estimation function to evaluate the emotional impact of the study plan on the student and make adjustments to reduce stress. The suggestion unit, for example, uses the emotion estimation function to evaluate the emotional impact of the study plan on the student and make adjustments to reduce stress. For example, the suggestion unit uses the emotion estimation function to evaluate the emotional impact of the study plan on the student and make adjustments to reduce stress. For example, the suggestion unit divides burdensome tasks into smaller parts. The generation AI also evaluates the emotional impact of the study plan and makes adjustments to reduce stress. For example, the suggestion unit suggests break times for relaxation. The suggestion unit also uses the emotion estimation function to evaluate the emotional impact of the study plan on the student and make adjustments to reduce stress. For example, the suggestion unit incorporates fun activities into the study plan. In this way, the suggestion unit uses the emotion estimation function to evaluate the emotional impact of the study plan on the student and makes adjustments to reduce stress, thereby reducing the psychological burden on the student.

[0083] The suggestion unit can use the generative AI to incorporate physical exercise and relaxation time into the study plan to support overall health. The suggestion unit, for example, uses the generative AI to incorporate physical exercise and relaxation time into the study plan to support overall health. For example, the generative AI incorporates physical exercise into the study plan to support overall health. For example, it suggests short exercises or stretches. The generative AI also incorporates relaxation time into the study plan to support overall health. For example, it suggests time for meditation or deep breathing. The generative AI also incorporates physical exercise and relaxation time into the study plan to support overall health. For example, it suggests regular breaks. In this way, the generative AI incorporates physical exercise and relaxation time into the study plan to support overall health, thereby maintaining the health of the student.

[0084] The suggestion unit can use the generation AI to suggest tasks for parents and children to work on together in the study plan, thereby enhancing learning support at home. The suggestion unit, for example, uses the generation AI to suggest tasks for parents and children to work on together in the study plan, thereby enhancing learning support at home. For example, the generation AI suggests tasks for parents and children to work on together in the study plan, thereby enhancing learning support at home. For example, it suggests puzzles and quizzes for parents and children to solve together. The generation AI also suggests tasks for parents and children to work on together in the study plan, thereby enhancing learning support at home. For example, it suggests experiments and crafts for parents and children to do together. The generation AI also suggests tasks for parents and children to work on together in the study plan, thereby enhancing learning support at home. For example, it suggests books and stories for parents and children to read together. In this way, the generation AI can suggest tasks for parents and children to work on together in the study plan, thereby enhancing learning support at home, thereby supporting the student's learning at home as well.

[0085] The suggestion unit can use the emotion estimation function to evaluate the emotional impact of the learning plan on the student and make adjustments to elicit positive emotions. The suggestion unit, for example, uses the emotion estimation function to evaluate the emotional impact of the learning plan on the student and make adjustments to elicit positive emotions. For example, the suggestion unit uses the emotion estimation function to evaluate the emotional impact of the learning plan on the student and make adjustments to elicit positive emotions. For example, the suggestion unit proposes tasks that emphasize successful experiences. Furthermore, the generation AI evaluates the emotional impact of the learning plan and makes adjustments to elicit positive emotions. For example, the suggestion unit proposes tasks that make the student feel a sense of accomplishment. Furthermore, the suggestion unit uses the emotion estimation function to evaluate the emotional impact of the learning plan on the student and make adjustments to elicit positive emotions. For example, the suggestion unit incorporates fun activities into the learning plan. In this way, the suggestion unit uses the emotion estimation function to evaluate the emotional impact of the learning plan on the student and makes adjustments to elicit positive emotions, thereby increasing the student's motivation to learn.

[0086] The suggestion unit can use the generation AI to monitor learning progress in real time and provide immediate feedback. The suggestion unit, for example, uses the generation AI to monitor learning progress in real time and provide immediate feedback. For example, the generation AI monitors learning progress in real time and provides immediate feedback. For example, immediately displays whether the answer is correct or incorrect after a practice problem is answered. The generation AI also monitors learning progress in real time and provides immediate feedback. For example, immediately provides explanations for any questions that arise during learning. The generation AI also monitors learning progress in real time and provides immediate feedback. For example, suggests the next step depending on the learning progress. In this way, the generation AI can be used to monitor learning progress in real time and provide immediate feedback, thereby quickly supporting the student's learning.

[0087] The suggestion unit can use the generation AI to automatically suggest the next learning step based on the learning progress. The suggestion unit, for example, uses the generation AI to automatically suggest the next learning step based on the learning progress. For example, the generation AI automatically suggests the next learning step based on the learning progress. For example, the generation AI automatically suggests additional practice problems for a specific problem type. Also, the generation AI automatically suggests the next learning step based on the learning progress. For example, the generation AI automatically suggests supplementary learning materials for areas where understanding is low. Also, the generation AI automatically suggests the next learning step based on the learning progress. For example, the generation AI automatically suggests advanced tasks to further improve areas of strength. In this way, by using the generation AI to automatically suggest the next learning step based on the learning progress, the student's learning can be efficiently advanced.

[0088] The suggestion unit can use the generation AI to match students at the same progress level with each other based on their learning progress, thereby promoting peer support. The suggestion unit, for example, uses the generation AI to match students at the same progress level with each other based on their learning progress, thereby promoting peer support. For example, the generation AI matches students at the same progress level with each other based on their learning progress, thereby promoting peer support. For example, it groups students working on the same problem. The generation AI also matches students at the same progress level with each other to promote peer support based on their learning progress, for example, it creates pairs who will teach each other. The generation AI also matches students at the same progress level with each other based on their learning progress, thereby promoting peer support. For example, it creates groups to work on assignments together. In this way, the generation AI matches students at the same progress level with each other based on their learning progress, thereby promoting peer support, thereby supporting students learning from each other.

[0089] The suggestion unit can also use the generation AI to provide feedback to parents and teachers based on the learning progress, thereby strengthening support at home and at school. The suggestion unit, for example, uses the generation AI to provide feedback to parents and teachers based on the learning progress, thereby strengthening support at home and at school. For example, the generation AI provides feedback to parents and teachers based on the learning progress, thereby strengthening support at home and at school. For example, the suggestion unit periodically reports the learning progress. The generation AI also provides feedback to parents and teachers based on the learning progress, thereby strengthening support at home and at school. For example, it provides advice on specific problems. The generation AI also provides feedback to parents and teachers based on the learning progress, thereby strengthening support at home and at school. For example, it suggests adjustments to the learning plan. In this way, the generation AI provides feedback to parents and teachers based on the learning progress, thereby strengthening support at home and at school, thereby providing comprehensive support for the student's learning.

[0090] The suggestion unit can use the emotion estimation function to evaluate the emotional impact of the feedback on the student and provide positive feedback. The suggestion unit, for example, uses the emotion estimation function to evaluate the emotional impact of the feedback on the student and provide positive feedback. For example, the emotion estimation function is used to evaluate the emotional impact of the feedback on the student and provide positive feedback. For example, feedback that emphasizes successful experiences is provided. The generation AI also evaluates the emotional impact of the feedback and provides positive feedback. For example, feedback that makes the student feel a sense of accomplishment is provided. The emotion estimation function is also used to evaluate the emotional impact of the feedback on the student and provide positive feedback. For example, feedback that includes words of encouragement is provided. In this way, the emotion estimation function is used to evaluate the emotional impact of the feedback on the student and positive feedback is provided, thereby increasing the student's motivation to learn.

[0091] The suggestion unit can use the generation AI to describe in detail the student's learning style and progress trends in the report, and provide it in a format that is easy for teachers and parents to understand. For example, the suggestion unit can use the generation AI to describe in detail the student's learning style and progress trends in the report, and provide it in a format that is easy for teachers and parents to understand. For example, the generation AI can specify learning styles such as visual and auditory. The generation AI can also describe in detail the student's progress trends in the report, and provide it in a format that is easy for teachers and parents to understand. For example, the generation AI can display strong and weak areas in a graph. The generation AI can also describe in detail the student's learning style and progress trends in the report, and provide it in a format that is easy for teachers and parents to understand. For example, the generation AI can display learning progress in chronological order. In this way, the generation AI can describe in detail the student's learning style and progress trends in the report, and provide it in a format that is easy for teachers and parents to understand, making it easier for them to grasp the student's learning situation.

[0092] The suggestion unit can use the generation AI to display the student's learning history in chronological order in a report, making it possible to grasp long-term changes in learning. The suggestion unit, for example, uses the generation AI to display the student's learning history in chronological order in a report, making it possible to grasp long-term changes in learning. For example, the generation AI displays the student's learning history in chronological order in a report, making it possible to grasp long-term changes in learning. For example, past test results and learning progress are displayed in graphs. The generation AI also displays the student's learning history in chronological order in a report, making it possible to grasp long-term changes in learning. For example, learning progress is displayed in monthly summary form. The generation AI also displays the student's learning history in chronological order in a report, making it possible to grasp long-term changes in learning. For example, it shows changes in understanding of a specific subject or theme. In this way, the generation AI can display the student's learning history in chronological order in a report, making it possible to grasp long-term changes in learning, thereby visualizing the student's growth.

[0093] The suggestion unit can use the emotion estimation function to evaluate the emotional impact of the report on teachers and parents and provide positive feedback. For example, the suggestion unit uses the emotion estimation function to evaluate the emotional impact of the report on teachers and parents and provide positive feedback. For example, the emotion estimation function is used to evaluate the emotional impact of the report on teachers and parents and provide positive feedback. For example, feedback that emphasizes the student's growth is provided. The generation AI also evaluates the emotional impact of the report and provides positive feedback. For example, feedback that recognizes the student's efforts is provided. The emotion estimation function is also used to evaluate the emotional impact of the report on teachers and parents and provide positive feedback. For example, feedback that praises the student's progress is provided. In this way, by using the emotion estimation function to evaluate the emotional impact of the report on teachers and parents and providing positive feedback, the motivation of teachers and parents to provide support is increased.

[0094] The suggestion unit can use the generation AI to make the report multilingual and provide it in a format that is easy to understand for parents who speak different languages. The suggestion unit, for example, uses the generation AI to make the report multilingual and provide it in a format that is easy to understand for parents who speak different languages. For example, the generation AI makes the report multilingual and provides it in a format that is easy to understand for parents who speak different languages. For example, the generation AI creates a report in multiple languages, such as English, Spanish, and Chinese. The generation AI also makes the report multilingual and provides it in a format that is easy to understand for parents who speak different languages. For example, it performs automatic translation according to the parent's language setting. The generation AI also makes the report multilingual and provides it in a format that is easy to understand for parents who speak different languages. For example, it highlights important parts of the report in multiple languages. In this way, support for parents is enhanced by using the generation AI to make the report multilingual and provide it in a format that is easy to understand for parents who speak different languages.

[0095] The suggestion unit can use the generation AI to make extensive use of visual elements (graphs and charts) in the report, and provide it in a format that is visually easy to understand. For example, the suggestion unit uses the generation AI to make extensive use of visual elements (graphs and charts) in the report, and provide it in a format that is visually easy to understand. For example, the generation AI makes extensive use of visual elements (graphs and charts) in the report, and provides it in a format that is visually easy to understand. For example, learning progress is displayed in a bar graph or pie chart. The generation AI also makes extensive use of visual elements in the report, and provides it in a format that is visually easy to understand. For example, strong and weak areas are displayed in a color-coded chart. The generation AI also makes extensive use of visual elements in the report, and provides it in a format that is visually easy to understand. For example, a line graph is used to show learning history over time. In this way, the generation AI makes extensive use of visual elements (graphs and charts) in the report, and provides it in a format that is visually easy to understand, making it easier for parents and teachers to grasp the student's learning situation.

[0096] The suggestion unit can use the emotion estimation function to evaluate the emotional impact of the report on teachers and parents and provide positive feedback. For example, the suggestion unit uses the emotion estimation function to evaluate the emotional impact of the report on teachers and parents and provide positive feedback. For example, the emotion estimation function is used to evaluate the emotional impact of the report on teachers and parents and provide positive feedback. For example, feedback that emphasizes the student's growth is provided. The generation AI also evaluates the emotional impact of the report and provides positive feedback. For example, feedback that recognizes the student's efforts is provided. The emotion estimation function is also used to evaluate the emotional impact of the report on teachers and parents and provide positive feedback. For example, feedback that praises the student's progress is provided. In this way, by using the emotion estimation function to evaluate the emotional impact of the report on teachers and parents and providing positive feedback, the motivation of teachers and parents to provide support is increased.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The educational support system can also be equipped with a learning style analysis unit that analyzes students' learning styles. Based on the student's learning history and test results, the learning style analysis unit determines whether the student is visual, auditory, or tactile. For example, it can suggest learning materials that make extensive use of diagrams and graphs to visual students, and audio materials and podcasts to auditory students. It can also suggest experiments and hands-on activities to tactile students. This makes it possible to provide each student with the optimal learning method and maximize learning effectiveness.

[0099] The educational support system can further include a progress monitoring unit that monitors students' learning progress in real time. The progress monitoring unit records the correctness of questions answered by students and the time it took to answer them in real time, and provides immediate feedback. For example, it can immediately display whether a student answered a practice question and suggest supplementary learning materials for areas where students have little understanding. It can also immediately provide explanations for any questions that arise during the course of study. This allows for prompt support for students' learning and promotes efficient learning.

[0100] The educational support system can also be equipped with a schedule analysis unit that takes into account students' lifestyles and schedules. The schedule analysis unit analyzes students' lifestyles, school class times, and club activity schedules to suggest optimal study times. For example, it can suggest nighttime study times for night owls, as well as study times on weekends and holidays. This makes it possible to support efficient learning that matches students' lifestyles.

[0101] The educational support system can further include an interest analysis unit that creates a learning plan that reflects the student's interests. The interest analysis unit analyzes the student's interests and creates a learning plan based on them. For example, it can suggest learning materials related to a student's favorite subjects or themes, and suggest learning activities related to a student's hobbies or special skills. It can also suggest learning materials that incorporate a student's favorite characters or stories. This can increase the student's motivation to learn and make learning more enjoyable.

[0102] The educational support system can also be equipped with a history display section that displays a student's learning history in chronological order. The history display section displays a student's past test results and learning progress in chronological order, allowing them to grasp long-term learning trends. For example, past test results and learning progress can be displayed in graphs, and learning progress can be summarized by month. It can also show changes in understanding of specific subjects or themes. This makes it possible to visualize a student's growth and increase their motivation to learn.

[0103] The suggestion unit can use the generation AI to monitor students' learning progress and provide regular feedback. For example, the generation AI can analyze the student's facial expression when scanned and use the emotion estimation function to provide positive feedback. For example, if a smile is detected, it will display "Good job." The generation AI can also analyze the student's tone of voice when scanned and use the emotion estimation function to provide positive feedback. For example, if a bright tone of voice is detected, it will display "Great." The emotion estimation function can also be used to analyze the student's emotions when scanned and provide positive feedback. For example, if a concentrated expression is detected, it will display "Keep it up." In this way, the generation AI can be used to monitor learning progress and provide regular feedback, thereby continuously supporting students' learning.

[0104] The suggestion unit can use the generation AI to provide teachers and parents with reports on students' learning status. For example, the generation AI can analyze a student's facial expression when scanned and use the emotion estimation function to provide positive feedback. For example, if a smile is detected, it can display "Good job." The generation AI can also analyze the student's tone of voice when scanned and use the emotion estimation function to provide positive feedback. For example, if a bright tone of voice is detected, it can display "Excellent." The emotion estimation function can also be used to analyze a student's emotions when scanned and provide positive feedback. For example, if a concentrated expression is detected, it can display "Keep it up." This makes it easier for teachers and parents to understand a student's learning status by using the generation AI to provide a report on learning status.

[0105] The suggestion unit can use the emotion estimation function to evaluate the emotional impact of the study plan on the student and make adjustments to reduce stress. For example, the emotion estimation function can be used to evaluate the emotional impact of the study plan on the student and make adjustments to reduce stress. For example, the suggestion unit can divide up burdensome tasks and suggest them. The generation AI can also evaluate the emotional impact of the study plan and make adjustments to reduce stress. For example, the suggestion unit can suggest break times for relaxation. The emotion estimation function can also be used to evaluate the emotional impact of the study plan on the student and make adjustments to reduce stress. For example, the suggestion unit can incorporate fun activities into the study plan. In this way, the emotion estimation function can be used to evaluate the emotional impact of the study plan on the student and make adjustments to reduce stress, thereby reducing the psychological burden on the student.

[0106] The suggestion unit can use the emotion estimation function to evaluate the emotional impact that feedback has on students and provide positive feedback. For example, the emotion estimation function can be used to evaluate the emotional impact that feedback has on students and provide positive feedback. For example, feedback that emphasizes successful experiences can be provided. The generation AI can also evaluate the emotional impact of feedback and provide positive feedback. For example, feedback that makes students feel a sense of accomplishment can be provided. The emotion estimation function can also be used to evaluate the emotional impact that feedback has on students and provide positive feedback. For example, feedback that includes words of encouragement can be provided. In this way, the emotion estimation function can be used to evaluate the emotional impact that feedback has on students and provide positive feedback, thereby increasing the students' motivation to learn.

[0107] The suggestion unit can use the emotion estimation function to evaluate the emotional impact the report has on teachers and parents and provide positive feedback. For example, the emotion estimation function can be used to evaluate the emotional impact the report has on teachers and parents and provide positive feedback, for example, feedback that emphasizes the student's growth. The generation AI can also evaluate the emotional impact of the report and provide positive feedback, for example, feedback that recognizes the student's efforts. The emotion estimation function can also be used to evaluate the emotional impact the report has on teachers and parents and provide positive feedback, for example, feedback that praises the student's progress. In this way, using the emotion estimation function to evaluate the emotional impact the report has on teachers and parents and providing positive feedback increases the willingness of teachers and parents to support the report.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The scanning unit scans the test results. For example, the scanning unit scans the test paper with a scanner and saves it as image data. The scanning unit can also take a photo of the test paper using a smartphone camera and upload the image to the app. The scanning unit can also read printed test results using OCR technology. For example, handwritten answers can be scanned with a high-resolution scanner and converted into text information using OCR technology. When using a smartphone camera, the app automatically corrects the image and performs character recognition. OCR technology recognizes printed characters with high accuracy and converts them into digital text. Step 2: In the analysis unit, the generation AI analyzes the test results scanned by the scanning unit. For example, the generation AI analyzes the test results using a text generation AI (e.g., LLM). The generation AI can also analyze the content of the test results using a multimodal generation AI. If there are many mistakes in a particular question type, the generation AI will suggest learning points related to that question type. For example, the generation AI uses a text generation AI to pick out particularly important information from the test results and perform analysis based on that. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to pick out particularly important information from the test results and perform analysis based on that. Step 3: The suggestion unit suggests optimal learning points for the student based on the results of the analysis by the analysis unit. For example, the suggestion unit suggests additional practice questions for a specific problem type or reference materials to deepen understanding. The suggestion unit can also create an individual learning plan using the generation AI. Furthermore, the suggestion unit can also monitor the student's learning progress using the generation AI and provide regular feedback. For example, the suggestion unit suggests additional practice questions for a specific problem type. The suggestion unit uses the generation AI to suggest additional practice questions for a specific problem type or reference materials to deepen understanding. This allows the education support system according to the embodiment to identify the student's weaknesses and suggest optimal learning points.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0144] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0146] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0151] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a scanning unit for scanning the test results; an analysis unit that analyzes the test results scanned by the scanning unit; a suggestion unit that suggests optimal learning points to students based on the results of the analysis by the analysis unit. A system characterized by:

2. The scanning unit Take a photo of the test paper using your smartphone camera and upload the image to the app 2. The system of claim 1.

3. The analysis unit If there are many mistakes on a particular question type, suggest the learning points related to that question type.

2. The system of claim 1.

4. The proposal unit Suggest additional practice questions or further reading for specific problem types 2. The system of claim 1.

5. The analysis unit Analyzing the scanned test results to identify the student's weaknesses and strengths 2. The system of claim 1.

Images