System

Abstract

A system is provided.SOLUTION: A system includes an image acquisition unit that scans a test answer sheet, a data transfer unit that transfers scanned image data to a server on a cloud, an image analysis unit that receives the scanned image data and performs character recognition, a scoring unit that scores text data recognized by the image analysis unit, a result transfer unit that transmits a scoring result from the server to a printer, and a printing unit that prints the scoring result by the printer and outputs the scoring result together with the answer sheet.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] Educational institutions and cram schools require a great deal of time and effort to grade tests and provide explanations to students. Grading large numbers of answer sheets manually, in particular, reduces accuracy and efficiency, increasing the burden on teachers. It is also difficult to grasp each student's academic ability trends and provide appropriate feedback and study plans. Given these circumstances, there is a need to improve the efficiency and accuracy of test grading and provide effective learning support. [Means for solving the problem]

[0005] The present invention solves the above problem by providing a system including an image acquisition means for scanning test answer sheets, a data transfer means for transferring scanned image data to a server on the cloud, an image analysis means for receiving the scanned image data and performing character recognition, a scoring means for scoring the text data recognized by the image analysis means, a result transfer means for sending the scoring results from the server to a printer, and a printing means for printing the scoring results on the printer and outputting them together with the answer sheets.

[0006] Furthermore, the cloud server is equipped with a summary display means that displays compiled scoring result data in real time, a learning support means that analyzes students' answer trends and provides explanations and study plans, a test creation support means that generates new test questions and suggests similar questions, and an interactive explanation means that interacts with students via their terminals and provides detailed explanations, thereby realizing comprehensive educational support.

[0007] "Image acquisition means" refers to a device or system that scans a test answer sheet and generates image data.

[0008] A "data transfer means" is a device or system that transfers scanned image data to a server on the cloud.

[0009] An "image analysis means" is a device or system that converts received scanned image data into text data using character recognition technology.

[0010] The "scoring means" is a device or system that compares the analyzed text data with a correct answer database, determines whether the answer is correct, and calculates the score.

[0011] The "result transfer means" is a device or system that transmits the scoring results from a server on the cloud to a printer.

[0012] The "printing means" is a device or system that prints and outputs the received marking results on an answer sheet.

[0013] The "summary display means" is a device or system that displays the summed score result data in real time on a server on the cloud.

[0014] A "learning support tool" is a device or system that analyzes students' answering tendencies and provides explanations for incorrect answers and future learning plans.

[0015] A "test creation support means" is a device or system that generates new test questions and suggests similar questions.

[0016] An "interactive explanation means" is a device or system that interacts with students through their terminals and provides detailed explanations. [Brief explanation of the drawings]

[0017] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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, a 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), and an APU (Accelerated Processing Unit).

[0021] 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.

[0022] 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.

[0023] 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), Bluetooth (registered trademark), etc.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0029] 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.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention relates to a test marking system used in educational institutions, cram schools, etc., and provides a system consisting of an image acquisition means, a data transfer means, an image analysis means, a marking means, a result transfer means, a printing means, a tally display means, a learning support means, a test creation support means, and an interactive explanation means.

[0039] Specific Examples

[0040] 1. Image acquisition method

[0041] The user (teacher) places the test answer sheet in the printer and starts the scanning operation. The printer scans the answer sheet and generates image data of the answer sheet. This image acquisition means transfers all answer information as electronic data to a server on the cloud.

[0042] 2. Means of data transfer

[0043] The printer transfers the generated image data to a cloud server via the Internet. This data transfer means allows the image data to be uploaded to the cloud server quickly and safely.

[0044] 3. Image analysis methods

[0045] The server passes the received image data to an image analysis module on the cloud. The image analysis module uses OCR (Optical Character Recognition) technology to recognize the characters in the image and convert them into text data. This allows the contents of the answer sheet to be extracted as text data.

[0046] 4. Scoring Method

[0047] Gemini (an AI system) uses text data to compare it with a pre-set database of correct answers and determine whether each answer is correct. It also calculates the score and total score, enabling fast and accurate scoring.

[0048] 5. Means of transferring results

[0049] The server transmits the generated scoring result data to the printer, and as a result, the printer is ready to print the scoring result based on the received data.

[0050] 6. Printing means

[0051] The printer prints the received marking results on the answer sheet and outputs it together with the answer sheet. By this printing means, the answer sheet on which the marking results are written can be obtained without manual work.

[0052] 7. Aggregate display method

[0053] The server stores all test result data in a cloud database. Users (teachers, etc.) can log in to the cloud management screen and view nationwide test results in real time. A summary display method allows for quick understanding of overall educational trends.

[0054] 8. Learning Support Tools

[0055] Gemini analyzes each student's marks and generates explanations for incorrect answers and correct answers. It also provides future study plans and advice. This learning support tool helps students understand their weaknesses and study more effectively.

[0056] 9. Test Creation Support Tools

[0057] Users can access a cloud-based server and input new test questions. Based on the input questions, the server will suggest similar questions and generate questions according to difficulty. This test creation support method allows teachers to efficiently create high-quality tests.

[0058] 10. Interactive Explanation Tools

[0059] Users (students) can access Gemini on the cloud from their devices (PCs, tablets, etc.) and ask questions about specific issues. Gemini then provides detailed explanations in text or audio to the user's questions. This interactive explanation method enables individualized learning support.

[0060] Specific examples

[0061] For example, in the case of marking a math test in a middle school, the process would be as follows:

[0062] 1. The user (teacher) places the answer sheet in the printer and starts scanning.

[0063] 2. The printer transfers the image data to the server.

[0064] 3. The server passes the image data to the image analysis module, where character recognition is performed using OCR technology.

[0065] 4. The image analysis module provides the text data to Gemini for scoring.

[0066] 5. Gemini sends the scoring results to the server, which then forwards them to the printer.

[0067] 6. The printer prints out the scoring results.

[0068] 7. The server stores all results in a database and provides nationwide aggregate data in real time.

[0069] 8. Gemini generates explanations for incorrect answers and future study plans, which are then printed out by the printer.

[0070] 9. The user (teacher) accesses the server to create a new test.

[0071] 10. The user (student) uses the device to ask Gemini questions and receive explanations.

[0072] By using each of the above methods, test grading at educational institutions will become more efficient, and appropriate feedback and learning support will be provided to each student.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The user (teacher) places the test answer sheet in the printer and starts the scanning operation by selecting the scan button on the printer's operation screen.

[0076] Step 2:

[0077] The printer scans the answer sheet and generates image data, which is temporarily stored in the internal memory.

[0078] Step 3:

[0079] The printer transfers the generated image data to a server on the cloud. Specifically, the printer uploads the image data to a specified server URL via the Internet.

[0080] Step 4:

[0081] The server receives the image data sent from the printer. The receiving module temporarily stores the image data and then passes it to the image analysis module.

[0082] Step 5:

[0083] The image analysis module uses OCR technology to extract text information from the received image data, and in this process, each answer is individually identified and converted into text data.

[0084] Step 6:

[0085] The image analysis module sends the converted text data to Gemini (an AI system), which analyzes the received text data and compares it with a pre-registered correct answer database.

[0086] Step 7:

[0087] Gemini compares the analyzed data with the correct answer data to determine whether each answer is correct, calculates the score for each question, and calculates the total score.

[0088] Step 8:

[0089] Gemini generates the scoring results as data and sends it to the server, where a results transfer module prepares the data for sending to the printer.

[0090] Step 9:

[0091] The server sends the received score data to the printer, which analyzes the data and prints the results on the answer sheet.

[0092] Step 10:

[0093] The printer prints the results of the scoring onto the answer sheet and outputs it along with the answer sheet. This printing process allows the correctness and score of each answer to be visually confirmed.

[0094] Step 11:

[0095] The server stores all scoring data in a cloud database, which is later used for aggregation and analysis.

[0096] Step 12:

[0097] Users (teachers) can log in through a cloud-based management screen and view nationwide test results and statistical information in real time.

[0098] Step 13:

[0099] Gemini analyzes each student's answering tendencies and generates explanations about the reasons for incorrect answers, the percentage of correct answers, and points that require particular attention. This content also includes future study plans and advice.

[0100] Step 14:

[0101] The server sends the explanations and study plan data generated by Gemini to the printer, which then prints it onto the answer sheet.

[0102] Step 15:

[0103] Users (teachers) input new test questions into the system, and a server on the cloud helps by suggesting similar questions and generating questions according to difficulty.

[0104] Step 16:

[0105] Users (students) access Gemini from their devices and type or speak questions about specific problems. Gemini analyzes the user's questions and provides detailed explanations in text or voice.

[0106] These steps will enable educational institutions to automate test marking processes, significantly improving teaching efficiency and student learning outcomes.

[0107] Example 1

[0108] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0109] Conventional test scoring systems require the manual analysis and scoring of scanned image data from answer sheets, which is time-consuming and labor-intensive. Furthermore, they lack the functionality to automatically provide individual students with answering trends and study plans, placing a heavy burden on educators. Furthermore, they lacked support functions for creating new test questions, making it difficult to create effective tests.

[0110] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0111] In this invention, the server includes an image acquisition means for scanning test answer sheets, a data transfer means for transferring the scanned image data to a cloud server, an image analysis means for receiving the scanned image data and performing character recognition, an artificial intelligence means for grading the text data recognized by the image analysis means, a result transfer means for sending the grading results from the server to an output device, a printing means for printing the grading results on the output device and outputting them together with the answer sheets, and a learning support means for analyzing the grading results for each student and providing explanations for incorrect answers and a learning plan. This enables fast and accurate automatic grading, individual learning support, and support for creating new test questions.

[0112] The "image acquisition means" is a means for scanning the test answer sheet and generating image data.

[0113] "Data transfer means" refers to a means for transferring scanned image data to a server on the cloud.

[0114] The "image analysis means" is a means for performing character recognition on the received scanned image data and converting it into text data.

[0115] The "artificial intelligence means" is a means for scoring based on the text data recognized by the image analysis means.

[0116] The "result transfer means" is a means for transmitting the grading results from the server to the output device.

[0117] "Printing means" refers to a means for printing and physically outputting the marking results on the answer sheet.

[0118] "Learning support tools" are tools for analyzing each student's marks and providing explanations for incorrect answers and future learning plans.

[0119] The "summary display means" is a means for displaying the scoring result data stored on a server on the cloud in real time.

[0120] "Test creation support tools" are tools that help educators create new test questions by suggesting similar questions and generating questions according to difficulty.

[0121] This invention is a system for automating test marking in educational institutions and learning facilities. This system is composed of an image acquisition means, a data transfer means, an image analysis means, an artificial intelligence means, a result transfer means, a printing means, a learning support means, a tally display means, and a test creation support means.

[0122] Image Acquisition Method

[0123] The user (teacher) places the test answer sheet on the printer and starts the scanning operation. The printer scans the answer sheet and generates image data. This image data is then transferred to a server on the cloud. For example, a general multi-function printer can be used.

[0124] Data Transfer Method

[0125] The printer transfers the generated image data to a cloud server via the Internet, using common data communication protocols (such as SSL / TLS) to ensure secure and fast data transmission.

[0126] Image analysis methods

[0127] The server passes the received image data to an image analysis module on the cloud. The image analysis module uses OCR (Optical Character Recognition) technology to recognize characters in the image and convert them into text data. An example of OCR software is Tesseract.

[0128] Artificial Intelligence Tools

[0129] The server has an artificial intelligence means for scoring the text data. This artificial intelligence means compares the text data with a database of correct answers to determine whether each answer is correct or incorrect. It also calculates the score and calculates the total score. Using an AI system like Gemini allows for fast and accurate scoring.

[0130] Result transfer method

[0131] The server transmits the generated scoring result data to an output device, for example, the server transmits the scoring result to a printer and prepares it for printing.

[0132] printing means

[0133] The printer prints the received marking results on the answer sheet and outputs it, thus providing an answer sheet with the marking results written on it without any manual work.

[0134] Learning support tools

[0135] The AI ​​tool analyzes each student's marks and provides explanations for incorrect answers and future study plans. This learning support allows students to identify their weaknesses and study more effectively. For example, explanations generated by Gemini are used.

[0136] Aggregation display method

[0137] The server stores all grading data in a cloud database, and users (teachers) can view test results in real time from the administration screen, enabling rapid data analysis to grasp trends across the entire educational institution.

[0138] Test Creation Support Tools

[0139] Users (teachers) can access a server on the cloud and input new test questions. Based on the input questions, the server will suggest similar questions and help generate questions according to difficulty. These support functions make it possible to efficiently create high-quality tests.

[0140] Specific examples

[0141] For example, in the case of marking a math test in a middle school, the process would be as follows:

[0142] 1. The user (teacher) places the answer sheet in the printer and starts scanning.

[0143] 2. The printer transfers the image data to the server.

[0144] 3. The server passes the image data to the image analysis module, where character recognition is performed using OCR technology.

[0145] 4. The image analysis module provides the text data to the artificial intelligence means for scoring.

[0146] 5. The artificial intelligence means sends the scoring results to the server, which forwards them to the printer.

[0147] 6. The printer prints out the scoring results.

[0148] 7. The server stores all results in a database and provides nationwide aggregate data in real time.

[0149] 8. An artificial intelligence tool generates explanations for incorrect answers and future study plans, which are then printed out by a printer.

[0150] 9. The user (teacher) accesses the server to create a new test.

[0151] 10. The user (student) uses the terminal to ask questions to the artificial intelligence means and receives explanations.

[0152] Prompt Sentence Examples

[0153] If a tutor were to output the results of an analysis of a math test's incorrect answers, the prompt to the AI ​​would be:

[0154] For the math test results below, generate an explanation for each student's incorrect answer.

[0155] Question 1: 3 2 + 1

[0156] Correct Answer: 7

[0157] Student A's answer: 6 (False)

[0158] Student B's answer: 7 (correct)

[0159] Student C's answer: 8 (False)

[0160] Question 2: 5 + 4 2

[0161] Correct answer: 13

[0162] Student A's answer: 10 (incorrect)

[0163] Student B's answer: 13 (correct)

[0164] Student C's answer: 12 (False)

[0165] Your explanation should include detailed explanations of the correct way to answer each question, the reasons for incorrect answers, and a future study plan for each student.

[0166] By using the above methods, test grading at educational institutions will become more efficient, and appropriate feedback and learning support will be provided to each student.

[0167] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0168] Step 1:

[0169] The user (teacher) places the test answer sheet on the printer and starts the scanning operation. The printer scans the answer sheet and generates image data. This image data is transferred to a server on the cloud. Specifically, the user presses the "scan" button on the printer's operation panel, and the printer reads the answer sheet using an optical sensor. The generated image data is sent to the server via the network.

[0170] Input: Physical test answer sheet

[0171] Output: High-resolution image data of the answer sheet

[0172] Step 2:

[0173] The server receives the image data sent from the printer. The data is securely transferred using an encryption protocol (e.g., SSL / TLS). Specifically, the server listens on a specific port, accepts connection requests from the printer, and receives data.

[0174] Input: Image data of the answer sheet transferred from the printer

[0175] Output: Image data stored on a cloud server

[0176] Step 3:

[0177] The server passes the received image data to an image analysis module in the cloud. The image analysis module uses OCR technology to recognize the characters on the answer sheet and convert them into text data. Specifically, the server inputs the image data into an OCR engine (e.g., Tesseract) and extracts text information from the image.

[0178] Input: Image data stored on a cloud server

[0179] Output: Text data

[0180] Step 4:

[0181] The server passes the text data to the artificial intelligence means and requests grading. The artificial intelligence means compares the text data with a correct answer database, determines whether each answer is correct, and calculates the score. Specifically, the artificial intelligence means (e.g., an AI grading system) compares the answers to each question with pre-registered correct answers and determines whether they are correct. It then calculates the points and calculates the total score.

[0182] Input: Text data generated by OCR

[0183] Output: Scoring result data (correctness and score for each answer)

[0184] Step 5:

[0185] The server then sends the generated scoring result data to the printer. This data includes each student's score and whether their answer was correct or incorrect. Specifically, the server establishes a network connection to send the scoring result data to the printer, encrypts it, and sends the data.

[0186] Input: Grade result data

[0187] Output: The graded results data sent to the printer

[0188] Step 6:

[0189] The printer prints the received marking results on the answer sheet and outputs it. Specifically, the printer stores the received data in its internal memory and prints the marking results on the answer sheet according to the control program. Once printing is complete, the answer sheet with the added evaluation information is ejected.

[0190] Input: Marking result data sent to the printer

[0191] Output: Answer sheet with evaluation information printed on it

[0192] Step 7:

[0193] The server stores all the grading result data in a database on the cloud. Users (teachers) can view the test results in real time from the management screen. Specifically, the server stores the result data in a database and allows users to access it using a web browser.

[0194] Input: Grade result data

[0195] Output: Result data stored in a database on the cloud

[0196] Step 8:

[0197] The AI ​​tool analyzes each student's marks and provides explanations for incorrect answers and future study plans. Specifically, the AI ​​model analyzes each student's incorrect answer patterns, and the generative AI model generates optimal explanations and study advice.

[0198] Input: Grading result data for each student

[0199] Output: Explanation of incorrect answers and study plan

[0200] Step 9:

[0201] The user (teacher) accesses a cloud-based server and inputs new test questions. Based on the input questions, the server suggests similar questions and generates questions according to difficulty. Specifically, the educator inputs questions through a web interface, and the server makes suggestions by comparing them with an existing question database.

[0202] Input: Input data for new test questions

[0203] Output: Suggested similar problems

[0204] Step 10:

[0205] The user (student) uses a device to access the AI ​​means on the cloud and ask a question about a specific problem. The AI ​​means then provides a detailed explanation in text or voice in response to the question. Specifically, the question is analyzed, and the generative AI model generates the most appropriate explanation and provides it to the user.

[0206] Input: Student question data

[0207] Output: Detailed explanatory text or audio

[0208] This will make test marking more efficient at educational institutions and enable appropriate feedback and learning support for each student.

[0209] (Application example 1)

[0210] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0211] While traditional test scoring systems are required to improve the efficiency of scoring, they still require manual feedback and learning support. Furthermore, there is a lack of systems that can identify individual learning trends and weaknesses and provide educational support based on those trends. Furthermore, it is difficult to display real-time aggregated scores or provide feedback using AI.

[0212] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0213] In this invention, the server includes an image acquisition means, a data transfer means, an image analysis means, and a learning support means using a generative AI model, which enables fast and accurate test scoring, individual learning trend analysis and feedback, and real-time summary display.

[0214] "Image acquisition means" refers to a device or system that scans a test answer sheet and generates image data thereof.

[0215] The "data transfer means" is a device or system that transfers the generated image data to a server on the cloud.

[0216] The "image analysis means" is a device or system that performs character recognition on the received scanned image data and converts it into text data.

[0217] The "scoring means" is a device or system that scores text data recognized by the image analysis means by comparing it with a pre-set correct answer database.

[0218] The "result transfer means" is a device or system that transmits the scoring results from a server on the cloud to the information display device.

[0219] The "printing means" is a device or system that outputs the marking results together with the answer sheet on the information display device.

[0220] The "summary display means" is a device or system that displays the score result data that has been summed up on a server on the cloud in real time.

[0221] A "learning support tool" is a device or system that uses a generative AI model to provide individualized learning feedback based on the student's answers.

[0222] A "generative AI model" is a type of artificial intelligence technology that automatically generates individual suggestions and feedback based on specific answer data.

[0223] A "prompt" is a text format used to input specific answer data into an AI model.

[0224] This invention relates to a test marking system used in educational institutions, cram schools, etc., and includes an image acquisition means, a data transfer means, an image analysis means, a marking means, a result transfer means, a printing means, a summary display means, a learning support means, a generative AI model, and a prompt sentence generation function. Specifically, this system is implemented using the following system configuration and operating procedures.

[0225] System Configuration

[0226] 1. Image acquisition method:

[0227] The user (teacher) places the test answer sheet in the printer and starts the scanning operation. The printer scans the answer sheet and generates image data of the answer sheet. This image acquisition means transfers all answer information as electronic data to a server on the cloud.

[0228] 2. Means of data transfer:

[0229] The printer transfers the generated image data to a cloud server via the Internet. This data transfer means allows the image data to be uploaded to the cloud server quickly and safely.

[0230] 3. Image analysis methods:

[0231] The server passes the received image data to an image analysis module on the cloud. The image analysis module uses OCR (Optical Character Recognition) technology to recognize the characters in the image and convert them into text data. This allows the contents of the answer sheet to be extracted as text data.

[0232] 4. Scoring Procedure:

[0233] The server compares the text data with a pre-set database of correct answers to determine whether each answer is correct. It also calculates the score and total score, enabling fast and accurate scoring.

[0234] 5. Means of transfer of results:

[0235] The server transmits the generated grading result data to the information display device, which then becomes ready to print the grading result based on the received data by the transfer means.

[0236] 6. Printing means:

[0237] The information display device prints the received marking results on the answer sheet and outputs it together with the answer sheet. By this printing means, the answer sheet on which the marking results are written can be obtained without any manual work.

[0238] 7. Aggregate display method:

[0239] The server stores all test result data in a cloud database. Users (teachers, etc.) can log in to the cloud management screen and view nationwide test results in real time. A summary display method allows for quick understanding of overall educational trends.

[0240] 8. Learning Support Tools:

[0241] The server analyzes each student's marks and generates explanations for incorrect answers and correct answers. It also provides future study plans and advice. This learning support tool helps students understand their weaknesses and study more effectively.

[0242] 9. Generative AI model and prompt generation function:

[0243] The server uses a generative AI model to provide personalized learning feedback based on the student's answers. Through the prompt generation function, a text format is generated to input specific answer data into the AI ​​model, which then automatically generates personalized suggestions and feedback.

[0244] Specific examples

[0245] For example, in the case of mathematics test marking at an educational institution, the process might look like this:

[0246] The user (teacher) places the answer sheet in the printer and starts scanning.

[0247] The printer transfers the image data to the cloud server.

[0248] The server passes the image data to the image analysis module, which uses OCR technology to recognize characters.

[0249] The image analysis module provides the text data to the server for scoring.

[0250] The server sends the results of the scoring to the information display device and prints them out.

[0251] The server stores all results in a database and provides nationwide aggregate data in real time.

[0252] The server uses the generative AI model to generate learning feedback for each student, for example by forming prompts like:

[0253] Prompt Sentence Examples

[0254] Student Name: Taro Yamada

[0255] Test Subject: Mathematics

[0256] problem:

[0257] 1. 5 + 3 = ?

[0258] 2. 10 - 2 = ?

[0259] answer:

[0260] 1. 8

[0261] 2.8

[0262] Based on this prompt, the generative AI model automatically generates individualized suggestions and feedback to provide to students.

[0263] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0264] Step 1:

[0265] The user (teacher) places the test answer sheet in the printer and starts the scanning operation. The printer scans the answer sheet and generates its image data.

[0266] Input: Test answer sheet

[0267] Output: Image data

[0268] Specific operation: The user places the answer sheet in the printer's scanner and presses the scan button. The printer scans the answer sheet and generates image data.

[0269] Step 2:

[0270] The printer transfers the generated image data to a server on the cloud via the Internet.

[0271] Input: Image data

[0272] Output: Image data stored on a cloud server

[0273] Specific operation: The printer's data transfer module uploads the generated image data to a cloud server via a secure internet connection.

[0274] Step 3:

[0275] The server passes the received image data to an image analysis module on the cloud, where character recognition is performed using OCR technology.

[0276] Input: Image data stored on a cloud server

[0277] Output: Text data

[0278] Specific operation: The server passes the image data to the image analysis module, which uses OCR technology to perform character recognition and extract text data from the image data.

[0279] Step 4:

[0280] The server compares the text data with a pre-set database of correct answers to determine whether each answer is correct, and also calculates the score and total score.

[0281] Input: Text data, correct answer database

[0282] Output: Grade result data

[0283] Specific operation: The server's scoring module compares the text data with a correct answer database, determines whether each question is correct, and calculates the score.

[0284] Step 5:

[0285] The server transmits the generated score result data to the information display device.

[0286] Input: Grade result data

[0287] Output: The score result data sent to the information display device

[0288] Specific operation: The graded result data is sent to the information display device, and the graded results are displayed and prepared for printing.

[0289] Step 6:

[0290] The information display device prints the received marking results on the answer sheet and outputs them together with the answer sheet.

[0291] Input: Grade result data sent to the information display device

[0292] Output: Printed answer sheet

[0293] Specific operation: The printing module of the information display device prints the received results on an answer sheet and outputs it in a form that can be taken out by the user.

[0294] Step 7:

[0295] The server stores all test result data in a cloud database, and users can log in to the cloud management screen to view test results from across the country in real time.

[0296] Input: Grade result data

[0297] Output: Aggregated data stored in the database, aggregated data displayed on the cloud management screen

[0298] Specific operation: Scoring result data is stored in a cloud database, allowing users to view the aggregated results in real time from the management screen.

[0299] Step 8:

[0300] The server uses the generative AI model to generate personalized learning feedback based on the student's answers. Through the prompt generation function, a text format is generated to input specific answer data into the AI ​​model.

[0301] Input: Scoring result data, prompt

[0302] Output: Learning feedback

[0303] Specific operation: Prompt sentences are generated based on the scoring results, and these are input into an AI model to generate feedback for each student.

[0304] Step 9:

[0305] The generated learning feedback is provided to the user (student).

[0306] Input: Learning feedback

[0307] Output: Feedback information provided to students

[0308] What it does: Allows students to view feedback on their devices and displays learning plans and advice.

[0309] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0310] The present invention relates to a test marking system used in educational institutions, cram schools, etc., and provides a system comprising an image acquisition means, a data transfer means, an image analysis means, a marking means, a result transfer means, a printing means, a summary display means, a learning support means, a test creation support means, an interactive explanation means, and an emotion engine.

[0311] Specific Examples

[0312] 1. Image acquisition method

[0313] The user (teacher) places the test answer sheet in the printer and starts the scanning operation. The printer scans the answer sheet and generates image data of the answer sheet. This image acquisition means transfers all answer information as electronic data to a server on the cloud.

[0314] 2. Means of data transfer

[0315] The printer transfers the generated image data to a cloud server via the Internet. This data transfer means allows the image data to be uploaded to the cloud server quickly and safely.

[0316] 3. Image analysis methods

[0317] The server passes the received image data to the image analysis module, which uses OCR (Optical Character Recognition) technology to recognize the characters in the image and convert them into text data, thereby extracting the contents of the answer sheet as text data.

[0318] 4. Scoring Method

[0319] The server sends the analyzed text data to Gemini (an AI system). Gemini analyzes the received text data and compares it with a database of pre-registered correct answers. This determines whether each answer is correct, calculates the score, and calculates the total score.

[0320] 5. Means of transferring results

[0321] The server transmits the generated scoring result data to the printer, and as a result, the printer is ready to print the scoring result based on the received data.

[0322] 6. Printing means

[0323] The printer prints the received marking results on the answer sheet and outputs it together with the answer sheet. By this printing means, the answer sheet on which the marking results are written can be obtained without manual work.

[0324] 7. Aggregate display method

[0325] The server stores all test result data in a cloud database. Users (teachers, etc.) can log in to the cloud management screen and view nationwide test results in real time. A summary display method allows for quick understanding of overall educational trends.

[0326] 8. Learning Support Tools

[0327] Gemini analyzes each student's marks and generates explanations for incorrect answers and correct answers. It also provides future study plans and advice. This learning support tool helps students understand their weaknesses and study more effectively.

[0328] 9. Test Creation Support Tools

[0329] Users can access a cloud-based server and input new test questions. Based on the input questions, the server will suggest similar questions and generate questions according to difficulty. This test creation support method allows teachers to efficiently create high-quality tests.

[0330] 10. Interactive Explanation Tools

[0331] Users (students) can access Gemini on the cloud from their devices (PCs, tablets, etc.) and ask questions about specific issues. Gemini then provides detailed explanations in text or audio to the user's questions. This interactive explanation method enables individualized learning support.

[0332] 11. Emotion Engine

[0333] The server is equipped with an emotion engine that can recognize users' emotions in real time. For example, it can read emotions from the facial expressions and tone of voice displayed by students while answering questions and provide appropriate feedback. This emotion engine is used in conjunction with interactive explanation tools and learning support tools to increase users' motivation.

[0334] Specifically, when a user uses the interactive explanation tool, the server analyzes their emotions using an emotion engine, and if they appear unsure about their answers, the server will explain in a gentler tone. Furthermore, the learning support tool can also adjust the difficulty and content of the learning plan based on the user's emotions. This maximizes students' learning efficiency and improves their learning persistence.

[0335] Specific examples

[0336] For example, in the case of marking a math test in a middle school, the process would be as follows:

[0337] 1. The user (teacher) places the answer sheet in the printer and starts scanning.

[0338] 2. The printer transfers the image data to the server.

[0339] 3. The server passes the image data to the image analysis module, where character recognition is performed using OCR technology.

[0340] 4. The image analysis module provides the text data to Gemini for scoring.

[0341] 5. Gemini sends the scoring results to the server, which then forwards them to the printer.

[0342] 6. The printer prints out the scoring results.

[0343] 7. The server stores all results in a database and provides nationwide aggregate data in real time.

[0344] 8. Gemini generates explanations for incorrect answers and future study plans, which are then printed out by the printer.

[0345] 9. The user (teacher) accesses the server to create a new test.

[0346] 10. The user (student) uses the device to ask Gemini questions and receive explanations.

[0347] 11. The server uses an emotion engine to recognize the user's emotions and provide appropriate feedback or adjust the learning plan.

[0348] These methods will automate test marking in educational institutions, significantly improving teaching efficiency and student learning effectiveness. In particular, the combination of emotion engines will further personalize individual learning support, resulting in more effective education.

[0349] The processing flow will be explained below.

[0350] Step 1:

[0351] The user (teacher) places the test answer sheet in the printer and starts the scanning operation by selecting the scan button on the printer's operation screen.

[0352] Step 2:

[0353] The printer scans the answer sheet and generates image data, which is temporarily stored in the internal memory.

[0354] Step 3:

[0355] The printer transfers the generated image data to a server on the cloud. Specifically, the printer uploads the image data to a specified server URL via the Internet.

[0356] Step 4:

[0357] The server receives the image data sent from the printer. The receiving module temporarily stores the image data and then passes it to the image analysis module.

[0358] Step 5:

[0359] The image analysis module uses OCR technology to extract text information from the received image data, and in this process, each answer is individually identified and converted into text data.

[0360] Step 6:

[0361] The image analysis module then sends the converted text data to Gemini, which then analyzes the received text data and compares it with a pre-registered database of correct answers.

[0362] Step 7:

[0363] Gemini compares the analyzed data with the correct answer data to determine whether each answer is correct, calculates the score for each question, and calculates the total score.

[0364] Step 8:

[0365] Gemini generates the scoring results as data and sends it to the server, where a results transfer module prepares the data for sending to the printer.

[0366] Step 9:

[0367] The server sends the received score data to the printer, which analyzes the data and prints the results on the answer sheet.

[0368] Step 10:

[0369] The printer prints the results of the scoring onto the answer sheet and outputs it along with the answer sheet. This printing process allows the correctness and score of each answer to be visually confirmed.

[0370] Step 11:

[0371] The server stores all scoring data in a cloud database, which is later used for aggregation and analysis.

[0372] Step 12:

[0373] Users (teachers) can log in to a cloud-based management screen and view nationwide test results and statistical information in real time. On the management screen, they can check the overall correct answer rate, trends in incorrect answers, and the causes of incorrect answers on specific questions.

[0374] Step 13:

[0375] Gemini analyzes each student's answering habits, generates explanations for incorrect answers and explanations for correct answers, and provides future study plans and advice. These explanations and study plans are stored on a cloud server.

[0376] Step 14:

[0377] The server sends the explanations and study plan data generated by Gemini to a printer, which prints them on the answer sheets. The printed explanations and study plans are then added to the answer sheets returned to the students.

[0378] Step 15:

[0379] The user (teacher) accesses a server on the cloud to input new test questions into the system. Based on the input questions, the server suggests similar questions and generates questions according to difficulty.

[0380] Step 16:

[0381] Users (students) access Gemini from their devices and type or speak questions about specific problems. Gemini then provides detailed explanations of the user's questions in text or voice.

[0382] Step 17:

[0383] The server is equipped with an emotion engine that analyzes the facial expressions and tone of voice of students in real time while they are filling out answer sheets or interacting with Gemini. This emotion data is stored in the cloud.

[0384] Step 18:

[0385] Gemini adjusts dialogue based on emotional data provided by its emotion engine. For example, if a student appears anxious, it will provide explanations in a more understandable and gentle tone. Furthermore, in its learning support tools, it adjusts the difficulty of the lesson plan based on emotional data to maintain student motivation.

[0386] This will streamline test marking at educational institutions, significantly improving teaching efficiency and student learning effectiveness. In particular, the combination of the emotion engine will further personalize individual learning support, resulting in more effective education.

[0387] Example 2

[0388] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0389] Conventional test marking systems require manual marking, placing a heavy burden on teachers. Furthermore, they tend to provide insufficient individual learning support to students, resulting in a decline in learning effectiveness. Furthermore, because learning feedback that takes into account the user's emotions is not provided, it is difficult to maintain student motivation. There is a need to solve these issues and provide efficient and effective educational support.

[0390] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0391] In this invention, the server includes an image acquisition unit that scans test answer sheets, a data transfer unit that transfers the scanned image data to an information processing device on the cloud, and an image analysis unit that receives the scanned image data and performs optical character recognition, thereby enabling automatic test scoring and individual learning support.

[0392] The "image acquisition means" is a device that scans the test answer sheet and generates image data thereof.

[0393] The "data transfer means" is a device or system that transfers the generated scanned image data to an information processing device on the cloud.

[0394] An "image analysis means" is a device or system that receives scanned image data and converts it into text data using optical character recognition (OCR) technology.

[0395] The "evaluation means" is a device or system that analyzes and evaluates the text data recognized by the image analysis means.

[0396] The "result transfer means" is a device or system that transmits the evaluation results from the information processing device to the printing device.

[0397] "Printing means" refers to a device that prints out the evaluation results and outputs them together with the answer sheet.

[0398] An "emotion recognition means" is a device or system that recognizes the user's emotions and provides appropriate feedback based on that information.

[0399] A "learning support tool" is a device or system that provides an individual learning plan and appropriate learning support to students.

[0400] The "aggregation display means" is a device or system that displays the evaluation result data aggregated by the information processing device on the cloud in real time.

[0401] This invention is specifically embodied as a test marking system for use in educational institutions, cram schools, etc. The system mainly comprises an image acquisition means, a data transfer means, an image analysis means, an evaluation means, a result transfer means, a printing means, an emotion recognition means, a learning support means, and a summary display means.

[0402] 1. Image acquisition method

[0403] The user (teacher) places the test answer sheet into a scanner or multifunction printer and starts the scanning operation. Specifically, the scanner function of the printer is used to generate image data of the answer sheet. This image acquisition means acquires all of the students' answer information as electronic data and transfers it to a cloud server via the Internet.

[0404] 2. Means of data transfer

[0405] The printer or scanner transfers the generated image data to a cloud server via the Internet. The transfer process uses encryption technology, ensuring the data is uploaded to the server quickly and securely. This data transfer method converts physical paper into electronic data.

[0406] 3. Image analysis methods

[0407] The server passes the received image data to its internal image analysis module. Specifically, it applies the path of the image file stored on the server to the image analysis module. The image analysis module uses OCR (optical character recognition) technology to recognize the characters in the image and convert the results into text data. The OCR engine used here operates with high accuracy and can even recognize handwritten characters to a certain extent.

[0408] 4. Evaluation Methods

[0409] The server sends the analyzed text data to an evaluation module, which compares the text data with a pre-registered correct answer database and determines whether each answer is correct or incorrect. At this stage, partial points are considered and multiple answers are allowed, achieving a scoring system similar to that of a human. Once the correctness determination is complete, a total score and individual feedback are generated.

[0410] 5. Means of transferring results

[0411] The server then sends the generated evaluation result data to the printer. Specifically, communication from the server to the printer is automatic, and the printer receives the data and prepares to print the results.

[0412] 6. Printing means

[0413] The printer prints the received evaluation results on the answer sheet and outputs it together with the answer sheet. Here, not only is the answer correct or incorrect, but feedback on the incorrect parts is also printed together. This printing means allows the answer sheet with the evaluation results to be obtained without any manual work.

[0414] 7. Aggregate display method

[0415] The server stores all assessment result data in a cloud database. Users (teachers and administrators) can log in to the cloud management screen and view nationwide test results in real time. This aggregate display method allows them to see at a glance what grade trends are occurring in each region and school.

[0416] 8. Learning Support Tools

[0417] The evaluation module analyzes each student's evaluation results and generates explanations for incorrect answers and correct answers. It also provides future study plans and advice, which students can access via the cloud. This learning support tool helps students understand their weaknesses and study more effectively.

[0418] 9. Emotion recognition means

[0419] The server is equipped with an emotion engine that works in conjunction with the interactive explanation and learning support tools to analyze the user's emotions. Specifically, it analyzes facial expressions and vocal tone when answering questions, and adjusts the tone of feedback and explanations based on the results. For example, if a student appears anxious, the system will use a gentler tone when explaining.

[0420] Specific examples

[0421] For example, in the case of a middle school math test grading exercise, the exercise proceeds as follows:

[0422] 1. The user (teacher) places the answer sheet in the printer and presses the scan button to start scanning.

[0423] 2. The image data generated by the printer is transferred to the server via the Internet.

[0424] 3. The server receives the image data and passes it to the analysis module, where it uses OCR technology to recognize the characters.

[0425] 4. The server sends the OCR results to the evaluation module, which compares them with a database and scores them.

[0426] 5. The server sends the evaluation results to the printer, which prints them out.

[0427] 6. The server stores all results in a cloud database and provides real-time nationwide aggregate data to the management screen.

[0428] 7. The evaluation module generates explanations for incorrect answers and future learning plans, which are then made available to students via the cloud.

[0429] 8. The user (teacher) accesses the server to create a new test and enters the questions.

[0430] 9. The user (student) asks questions to the evaluation module from the terminal and receives an explanation.

[0431] 10. The server uses an emotion engine to analyze the user's emotions and provide appropriate feedback.

[0432] Example prompt sentence:

[0433] "Please explain the system for marking middle school math tests. Please provide a detailed explanation of the step-by-step process and the techniques used."

[0434] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0435] Step 1:

[0436] The user (teacher) places a test answer sheet into the printer and initiates the scanning operation. The printer scans the answer sheet and stores the generated image data in its internal memory. The input for this step is the physical answer sheet, and the output is the scanned image data. The specific action is for the user to press the scan button on the printer.

[0437] Step 2:

[0438] The printer transfers the image data stored in its internal memory to a cloud server via the Internet. This data is encrypted before transmission, making it highly secure. The input in step 2 is the image data stored in the printer, and the output is the image data uploaded to the server. Specifically, the printer automatically starts transferring data.

[0439] Step 3:

[0440] The server passes the received image data to the image analysis module. The server saves the image data in a specific directory and applies that path to the image analysis module. The input of step 3 is the path of the image data saved on the server, and the output is the raw data passed to the image analysis module. Specifically, the server executes the built-in script.

[0441] Step 4:

[0442] The image analysis module uses OCR (optical character recognition) technology to extract character information from image data and convert it into text data. The input of step 4 is the image data passed to the image analysis module, and the output is text data. Specifically, the OCR engine scans the image data and generates a text file containing the recognized characters.

[0443] Step 5:

[0444] The server sends the analyzed text data to the evaluation module. The evaluation module compares the text data with a database of pre-registered correct answers and determines whether each answer is correct. The input in step 5 is the text data, and the output is the evaluation result. Specifically, the evaluation module compares the text data with the correct answer data and calculates the score.

[0445] Step 6:

[0446] The server sends the generated evaluation result data to the printer. The input here is the evaluation result data, and the output is the data sent to the printer. In concrete terms, the server transfers the evaluation result data to the printer using a specific communication protocol.

[0447] Step 7:

[0448] The printer prints the received evaluation results on the answer sheet and outputs the answer sheet along with the feedback. The input of step 7 is the evaluation result data sent to the printer, and the output is the printed answer sheet. In concrete terms, the printer prints the evaluation results and feedback on the paper.

[0449] Step 8:

[0450] The server stores all evaluation result data in a database on the cloud. Users (teachers and administrators) can log in to the cloud management screen and view nationwide test results in real time. The input for step 8 is the evaluation result data, and the output is the aggregated data stored in the database. Specifically, the server runs a script that periodically updates the database.

[0451] Step 9:

[0452] The evaluation module analyzes the evaluation results for each student and generates explanations for incorrect answers and correct answers. Based on this, it also provides future study plans and advice. The input of Step 9 is the evaluation result data for each student, and the output is individual explanations and study plans. Specifically, the evaluation module uses an algorithm to perform the analysis.

[0453] Step 10:

[0454] The user (teacher) accesses a server on the cloud and inputs new test questions. Based on the input questions, the server suggests similar questions and adjusts the difficulty level. The input in step 10 is the new test question data, and the output is the generated test questions. Specifically, the server searches the database and suggests related questions.

[0455] Step 11:

[0456] The user (student) uses a terminal to access the evaluation module on the cloud and ask a question about a specific problem. The evaluation module provides a detailed explanation in text or audio. The input of step 11 is the question data from the user, and the output is the generated explanation data. In concrete terms, the evaluation module responds to the question using natural language processing.

[0457] Step 12:

[0458] The server uses an emotion engine to analyze the user's emotions and provides appropriate feedback based on that information. The emotion engine analyzes the student's facial expressions and tone of voice and generates feedback according to that state. The input of step 12 is the user's emotion data, and the output is adjusted feedback. Specifically, the emotion engine uses a machine learning algorithm to analyze emotions.

[0459] These steps will enable automated test scoring and personalized learning support, improving the quality and efficiency of education.

[0460] (Application example 2)

[0461] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0462] Conventional test scoring systems used in educational institutions and cram schools require many steps, such as image acquisition, data transfer, image analysis, and scoring, making efficient and rapid processing difficult. Furthermore, quality control in factories requires a lot of manual work due to a lack of automation, limiting improvements in production efficiency and quality. Furthermore, training and employee motivation management within factories are also inadequate. To solve these issues, a means is needed to integrate both systems and perform processing efficiently and with high accuracy.

[0463] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0464] In this invention, the server includes an image acquisition means, a data transfer means, an image analysis means, a scoring means, a result transfer means, a printing means, a quality assessment means, and a result display means. This not only improves the efficiency and accuracy of test scoring at educational institutions and cram schools, but also automates quality control work in factories, reducing manual work and improving production efficiency. Furthermore, having the ability to display results in real time on a cloud-based server not only enables rapid data collection and analysis, but also enables employee motivation management, thereby improving overall work efficiency and quality.

[0465] "Image capture means" means a device or mechanism that captures an image of an object or document of interest by scanning or photographing it.

[0466] "Data transfer means" refers to a device or means for transferring acquired image data to a remote computer system such as a server.

[0467] "Image analysis means" refers to software or algorithms that analyze acquired image data and perform character recognition, shape recognition, etc.

[0468] A "scoring means" is a device or system that determines whether a question is correct or incorrect and calculates a score based on the analyzed text data.

[0469] "Results Transfer Means" means the mechanism or system that transfers the scoring results data from the server to a printer or other output device.

[0470] The "printing means" is a device or mechanism that directly prints out onto paper or the like based on the data transferred by the result transfer means.

[0471] "Quality assessment means" refers to devices or algorithms used to evaluate the quality of a product and determine whether it passes or fails.

[0472] The "result display means" is a device or system for displaying the quality assessment results and grading results on the terminals of the factory manager or teacher.

[0473] "Aggregation display means" refers to a function or system for displaying data aggregated on a server on the cloud in real time.

[0474] "Support tools" are functions and algorithms that provide learning plans and suggestions for improving production processes based on analyzed data.

[0475] The present invention is a system for achieving efficient business processing and quality control in educational institutions and factories. Specifically, the system comprises an image acquisition means, a data transfer means, an image analysis means, a scoring means, a result transfer means, a printing means, a quality judgment means, a result display means, a tally display means, and a support means.

[0476] 1. Image acquisition method

[0477] The server is connected to image capture devices such as cameras and scanners, which are used to scan test answer sheets in educational institutions or take images of products in factories, and the captured image data is stored at high resolution for further processing.

[0478] 2. Means of data transfer

[0479] Image data acquired by the image acquisition device is transferred at high speed to a cloud server. Specifically, it is sent via the Internet using a secure protocol. A database system is running on the server to properly store the received data.

[0480] 3. Image analysis methods

[0481] The server analyzes the received image data using OCR (Optical Character Recognition) technology. This analysis utilizes software libraries such as TensorFlow and OpenCV. For example, in the case of test answer sheets, handwritten characters are converted into text data, and in the case of factories, product shapes and character information are identified.

[0482] 4. Scoring Method

[0483] The text data obtained by the image analysis means is analyzed by a server-side AI system. At the educational institution, the results are compared with pre-registered correct answer data and graded. High-precision grading is possible using an AI model (for example, a generative AI model using TensorFlow). At the factory, the pass / fail of the product is determined based on the analyzed data.

[0484] 5. Means of transferring results

[0485] The server transfers the grading results and quality assessment results to a printer or management terminal via the Internet, allowing the results to be printed out at the educational institution and displayed in real time on the manager's terminal at the factory.

[0486] 6. Printing means

[0487] The printer prints the marking results on the answer sheets based on the received data. Even in factories, it is possible to print out the quality assessment results and keep a record. This allows results to be checked without manual work, improving work efficiency.

[0488] 7. Results display means

[0489] The server displays the scoring results and quality assessment results in real time through the result display means. Teachers at educational institutions can view nationwide test results from the management screen, and factory managers can view quality control data in real time.

[0490] 8. Aggregate display method

[0491] The server displays the aggregated data on the cloud, allowing educational institutions to aggregate student grade data and analyze learning trends, while factories can collect quality data and analyze production trends.The aggregated data is displayed in dashboard format to help operators make decisions.

[0492] 9. Support methods

[0493] The server uses the generative AI model to provide learning plans and suggestions for improving production processes based on the analysis results. This function allows educational institutions to analyze students' weaknesses and present effective learning plans, and factories to propose specific measures for quality improvement.

[0494] Examples of concrete examples and prompts

[0495] For example, automated inspections of products are carried out in factories, which identify defects in real time and notify managers, and in educational institutions, tests taken by students are quickly graded and the results are made available.

[0496] Example prompts to input to a generative AI model:

[0497] "Please analyze this image data and determine the quality of the product."

[0498] "Use an AI model to determine whether this product meets your quality standards."

[0499] This will realize a system that efficiently grades tests in educational institutions and performs quality control in factories.

[0500] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0501] Step 1:

[0502] The image acquisition means captures images of the product using a camera installed on the factory robot. The user (operator) sets the camera in the appropriate position and inputs the command to start capturing. The acquired image data generates an output that is sent to the server as high-resolution electronic data.

[0503] Step 2:

[0504] The server receives the acquired image data and stores it in a database on the cloud. A data transfer method is used for this purpose. The server transfers the data using a secure protocol and processes it for storage in the database. The input is image data, and the output is data stored in the database on the cloud.

[0505] Step 3:

[0506] The server uses image analysis means to analyze the stored image data. Specifically, it uses the OpenCV library to perform character recognition (OCR) and shape recognition of the image. The input is the image data on the cloud, and the output is the analyzed text data and shape data.

[0507] Step 4:

[0508] The analyzed text and shape data is sent to an AI system on the server. The AI ​​system uses a generative AI model to analyze this data and determine its quality. The input is text and shape data, and the output is a "pass" or "fail" result. Specifically, matching and evaluation are performed based on quality standards.

[0509] Step 5:

[0510] The server transfers the obtained quality assessment results to the terminal of the factory manager. The results are displayed on the manager's screen in real time via the Internet using a result transfer means. The input is the quality assessment results, and the output is the display screen of the manager's terminal.

[0511] Step 6:

[0512] The server uses a means of tallying and displaying the quality assessment results in real time in the form of a dashboard, allowing the administrator to grasp the overall quality status at a glance. The input is the assessment result data for each product, and the output is a dashboard of the aggregated assessment status.

[0513] Step 7:

[0514] The server uses support tools to generate improvement proposals based on the judged data. The generative AI model proposes optimal improvement measures, taking past data into account. The input is quality judgment data and past production data, and the output is a report of improvement proposals and advice. Managers can then improve the production process based on this report.

[0515] Step 8:

[0516] The user (administrator) implements the proposed improvement plan and feeds the results back to the server. The fed-back data is used as learning material for future generative AI models, improving the accuracy of the system. The input is the result data of the implemented improvement plan, and the output is an updated AI model.

[0517] The above steps will enable efficient business processing and quality control in educational institutions and factories. An example of a prompt sentence would be, "Please use the AI ​​model to determine whether this product meets the quality standards." This would enable highly accurate judgments to be made by giving the AI ​​system instructions.

[0518] 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.

[0519] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0520] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0521] [Second embodiment]

[0522] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0523] 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.

[0524] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0525] 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.

[0526] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0527] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0528] 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.

[0529] 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.

[0530] 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 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.

[0531] 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.

[0532] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0533] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0534] The present invention relates to a test marking system used in educational institutions, cram schools, etc., and provides a system consisting of an image acquisition means, a data transfer means, an image analysis means, a marking means, a result transfer means, a printing means, a tally display means, a learning support means, a test creation support means, and an interactive explanation means.

[0535] Specific Examples

[0536] 1. Image acquisition method

[0537] The user (teacher) places the test answer sheet in the printer and starts the scanning operation. The printer scans the answer sheet and generates image data of the answer sheet. This image acquisition means transfers all answer information as electronic data to a server on the cloud.

[0538] 2. Means of data transfer

[0539] The printer transfers the generated image data to a cloud server via the Internet. This data transfer means allows the image data to be uploaded to the cloud server quickly and safely.

[0540] 3. Image analysis methods

[0541] The server passes the received image data to an image analysis module on the cloud. The image analysis module uses OCR (Optical Character Recognition) technology to recognize the characters in the image and convert them into text data. This allows the contents of the answer sheet to be extracted as text data.

[0542] 4. Scoring Method

[0543] Gemini (an AI system) uses text data to compare it with a pre-set database of correct answers and determine whether each answer is correct. It also calculates the score and total score, enabling fast and accurate scoring.

[0544] 5. Means of transferring results

[0545] The server transmits the generated scoring result data to the printer, and as a result, the printer is ready to print the scoring result based on the received data.

[0546] 6. Printing means

[0547] The printer prints the received marking results on the answer sheet and outputs it together with the answer sheet. By this printing means, the answer sheet on which the marking results are written can be obtained without manual work.

[0548] 7. Aggregate display method

[0549] The server stores all test result data in a cloud database. Users (teachers, etc.) can log in to the cloud management screen and view nationwide test results in real time. A summary display method allows for quick understanding of overall educational trends.

[0550] 8. Learning Support Tools

[0551] Gemini analyzes each student's marks and generates explanations for incorrect answers and correct answers. It also provides future study plans and advice. This learning support tool helps students understand their weaknesses and study more effectively.

[0552] 9. Test Creation Support Tools

[0553] Users can access a cloud-based server and input new test questions. Based on the input questions, the server will suggest similar questions and generate questions according to difficulty. This test creation support method allows teachers to efficiently create high-quality tests.

[0554] 10. Interactive Explanation Tools

[0555] Users (students) can access Gemini on the cloud from their devices (PCs, tablets, etc.) and ask questions about specific issues. Gemini then provides detailed explanations in text or audio to the user's questions. This interactive explanation method enables individualized learning support.

[0556] Specific examples

[0557] For example, in the case of marking a math test in a middle school, the process would be as follows:

[0558] 1. The user (teacher) places the answer sheet in the printer and starts scanning.

[0559] 2. The printer transfers the image data to the server.

[0560] 3. The server passes the image data to the image analysis module, where character recognition is performed using OCR technology.

[0561] 4. The image analysis module provides the text data to Gemini for scoring.

[0562] 5. Gemini sends the scoring results to the server, which then forwards them to the printer.

[0563] 6. The printer prints out the scoring results.

[0564] 7. The server stores all results in a database and provides nationwide aggregate data in real time.

[0565] 8. Gemini generates explanations for incorrect answers and future study plans, which are then printed out by the printer.

[0566] 9. The user (teacher) accesses the server to create a new test.

[0567] 10. The user (student) uses the device to ask Gemini questions and receive explanations.

[0568] By using each of the above methods, test grading at educational institutions will become more efficient, and appropriate feedback and learning support will be provided to each student.

[0569] The processing flow will be explained below.

[0570] Step 1:

[0571] The user (teacher) places the test answer sheet in the printer and starts the scanning operation by selecting the scan button on the printer's operation screen.

[0572] Step 2:

[0573] The printer scans the answer sheet and generates image data, which is temporarily stored in the internal memory.

[0574] Step 3:

[0575] The printer transfers the generated image data to a server on the cloud. Specifically, the printer uploads the image data to a specified server URL via the Internet.

[0576] Step 4:

[0577] The server receives the image data sent from the printer. The receiving module temporarily stores the image data and then passes it to the image analysis module.

[0578] Step 5:

[0579] The image analysis module uses OCR technology to extract text information from the received image data, and in this process, each answer is individually identified and converted into text data.

[0580] Step 6:

[0581] The image analysis module sends the converted text data to Gemini (an AI system), which analyzes the received text data and compares it with a pre-registered correct answer database.

[0582] Step 7:

[0583] Gemini compares the analyzed data with the correct answer data to determine whether each answer is correct, calculates the score for each question, and calculates the total score.

[0584] Step 8:

[0585] Gemini generates the scoring results as data and sends it to the server, where a results transfer module prepares the data for sending to the printer.

[0586] Step 9:

[0587] The server sends the received score data to the printer, which analyzes the data and prints the results on the answer sheet.

[0588] Step 10:

[0589] The printer prints the results of the scoring onto the answer sheet and outputs it along with the answer sheet. This printing process allows the correctness and score of each answer to be visually confirmed.

[0590] Step 11:

[0591] The server stores all scoring data in a cloud database, which is later used for aggregation and analysis.

[0592] Step 12:

[0593] Users (teachers) can log in through a cloud-based management screen and view nationwide test results and statistical information in real time.

[0594] Step 13:

[0595] Gemini analyzes each student's answering tendencies and generates explanations about the reasons for incorrect answers, the percentage of correct answers, and points that require particular attention. This content also includes future study plans and advice.

[0596] Step 14:

[0597] The server sends the explanations and study plan data generated by Gemini to the printer, which then prints it onto the answer sheet.

[0598] Step 15:

[0599] Users (teachers) input new test questions into the system, and a server on the cloud helps by suggesting similar questions and generating questions according to difficulty.

[0600] Step 16:

[0601] Users (students) access Gemini from their devices and type or speak questions about specific problems. Gemini analyzes the user's questions and provides detailed explanations in text or voice.

[0602] These steps will enable educational institutions to automate test marking processes, significantly improving teaching efficiency and student learning outcomes.

[0603] Example 1

[0604] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0605] Conventional test scoring systems require the manual analysis and scoring of scanned image data from answer sheets, which is time-consuming and labor-intensive. Furthermore, they lack the functionality to automatically provide individual students with answering trends and study plans, placing a heavy burden on educators. Furthermore, they lacked support functions for creating new test questions, making it difficult to create effective tests.

[0606] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0607] In this invention, the server includes an image acquisition means for scanning test answer sheets, a data transfer means for transferring the scanned image data to a cloud server, an image analysis means for receiving the scanned image data and performing character recognition, an artificial intelligence means for grading the text data recognized by the image analysis means, a result transfer means for sending the grading results from the server to an output device, a printing means for printing the grading results on the output device and outputting them together with the answer sheets, and a learning support means for analyzing the grading results for each student and providing explanations for incorrect answers and a learning plan. This enables fast and accurate automatic grading, individual learning support, and support for creating new test questions.

[0608] The "image acquisition means" is a means for scanning the test answer sheet and generating image data.

[0609] "Data transfer means" refers to a means for transferring scanned image data to a server on the cloud.

[0610] The "image analysis means" is a means for performing character recognition on the received scanned image data and converting it into text data.

[0611] The "artificial intelligence means" is a means for scoring based on the text data recognized by the image analysis means.

[0612] The "result transfer means" is a means for transmitting the grading results from the server to the output device.

[0613] "Printing means" refers to a means for printing and physically outputting the marking results on the answer sheet.

[0614] "Learning support tools" are tools for analyzing each student's marks and providing explanations for incorrect answers and future learning plans.

[0615] The "summary display means" is a means for displaying the scoring result data stored on a server on the cloud in real time.

[0616] "Test creation support tools" are tools that help educators create new test questions by suggesting similar questions and generating questions according to difficulty.

[0617] This invention is a system for automating test marking in educational institutions and learning facilities. This system is composed of an image acquisition means, a data transfer means, an image analysis means, an artificial intelligence means, a result transfer means, a printing means, a learning support means, a tally display means, and a test creation support means.

[0618] Image Acquisition Method

[0619] The user (teacher) places the test answer sheet on the printer and starts the scanning operation. The printer scans the answer sheet and generates image data. This image data is then transferred to a server on the cloud. For example, a general multi-function printer can be used.

[0620] Data Transfer Method

[0621] The printer transfers the generated image data to a cloud server via the Internet, using common data communication protocols (such as SSL / TLS) to ensure secure and fast data transmission.

[0622] Image analysis methods

[0623] The server passes the received image data to an image analysis module on the cloud. The image analysis module uses OCR (Optical Character Recognition) technology to recognize characters in the image and convert them into text data. An example of OCR software is Tesseract.

[0624] Artificial Intelligence Tools

[0625] The server has an artificial intelligence means for scoring the text data. This artificial intelligence means compares the text data with a database of correct answers to determine whether each answer is correct or incorrect. It also calculates the score and calculates the total score. Using an AI system like Gemini allows for fast and accurate scoring.

[0626] Result transfer method

[0627] The server transmits the generated scoring result data to an output device, for example, the server transmits the scoring result to a printer and prepares it for printing.

[0628] printing means

[0629] The printer prints the received marking results on the answer sheet and outputs it, thus providing an answer sheet with the marking results written on it without any manual work.

[0630] Learning support tools

[0631] The AI ​​tool analyzes each student's marks and provides explanations for incorrect answers and future study plans. This learning support allows students to identify their weaknesses and study more effectively. For example, explanations generated by Gemini are used.

[0632] Aggregation display method

[0633] The server stores all grading data in a cloud database, and users (teachers) can view test results in real time from the administration screen, enabling rapid data analysis to grasp trends across the entire educational institution.

[0634] Test Creation Support Tools

[0635] Users (teachers) can access a server on the cloud and input new test questions. Based on the input questions, the server will suggest similar questions and help generate questions according to difficulty. These support functions make it possible to efficiently create high-quality tests.

[0636] Specific examples

[0637] For example, in the case of marking a math test in a middle school, the process would be as follows:

[0638] 1. The user (teacher) places the answer sheet in the printer and starts scanning.

[0639] 2. The printer transfers the image data to the server.

[0640] 3. The server passes the image data to the image analysis module, where character recognition is performed using OCR technology.

[0641] 4. The image analysis module provides the text data to the artificial intelligence means for scoring.

[0642] 5. The artificial intelligence means sends the scoring results to the server, which forwards them to the printer.

[0643] 6. The printer prints out the scoring results.

[0644] 7. The server stores all results in a database and provides nationwide aggregate data in real time.

[0645] 8. An artificial intelligence tool generates explanations for incorrect answers and future study plans, which are then printed out by a printer.

[0646] 9. The user (teacher) accesses the server to create a new test.

[0647] 10. The user (student) uses the terminal to ask questions to the artificial intelligence means and receives explanations.

[0648] Prompt Sentence Examples

[0649] If a tutor were to output the results of an analysis of a math test's incorrect answers, the prompt to the AI ​​would be:

[0650] For the math test results below, generate an explanation for each student's incorrect answer.

[0651] Question 1: 3 2 + 1

[0652] Correct Answer: 7

[0653] Student A's answer: 6 (False)

[0654] Student B's answer: 7 (correct)

[0655] Student C's answer: 8 (False)

[0656] Question 2: 5 + 4 2

[0657] Correct answer: 13

[0658] Student A's answer: 10 (incorrect)

[0659] Student B's answer: 13 (correct)

[0660] Student C's answer: 12 (False)

[0661] Your explanation should include detailed explanations of the correct way to answer each question, the reasons for incorrect answers, and a future study plan for each student.

[0662] By using the above methods, test grading at educational institutions will become more efficient, and appropriate feedback and learning support will be provided to each student.

[0663] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0664] Step 1:

[0665] The user (teacher) places the test answer sheet on the printer and starts the scanning operation. The printer scans the answer sheet and generates image data. This image data is transferred to a server on the cloud. Specifically, the user presses the "scan" button on the printer's operation panel, and the printer reads the answer sheet using an optical sensor. The generated image data is sent to the server via the network.

[0666] Input: Physical test answer sheet

[0667] Output: High-resolution image data of the answer sheet

[0668] Step 2:

[0669] The server receives the image data sent from the printer. The data is securely transferred using an encryption protocol (e.g., SSL / TLS). Specifically, the server listens on a specific port, accepts connection requests from the printer, and receives data.

[0670] Input: Image data of the answer sheet transferred from the printer

[0671] Output: Image data stored on a cloud server

[0672] Step 3:

[0673] The server passes the received image data to an image analysis module in the cloud. The image analysis module uses OCR technology to recognize the characters on the answer sheet and convert them into text data. Specifically, the server inputs the image data into an OCR engine (e.g., Tesseract) and extracts text information from the image.

[0674] Input: Image data stored on a cloud server

[0675] Output: Text data

[0676] Step 4:

[0677] The server passes the text data to the artificial intelligence means and requests grading. The artificial intelligence means compares the text data with a correct answer database, determines whether each answer is correct, and calculates the score. Specifically, the artificial intelligence means (e.g., an AI grading system) compares the answers to each question with pre-registered correct answers and determines whether they are correct. It then calculates the points and calculates the total score.

[0678] Input: Text data generated by OCR

[0679] Output: Scoring result data (correctness and score for each answer)

[0680] Step 5:

[0681] The server then sends the generated scoring result data to the printer. This data includes each student's score and whether their answer was correct or incorrect. Specifically, the server establishes a network connection to send the scoring result data to the printer, encrypts it, and sends the data.

[0682] Input: Grade result data

[0683] Output: The graded results data sent to the printer

[0684] Step 6:

[0685] The printer prints the received marking results on the answer sheet and outputs it. Specifically, the printer stores the received data in its internal memory and prints the marking results on the answer sheet according to the control program. Once printing is complete, the answer sheet with the added evaluation information is ejected.

[0686] Input: Marking result data sent to the printer

[0687] Output: Answer sheet with evaluation information printed on it

[0688] Step 7:

[0689] The server stores all the grading result data in a database on the cloud. Users (teachers) can view the test results in real time from the management screen. Specifically, the server stores the result data in a database and allows users to access it using a web browser.

[0690] Input: Grade result data

[0691] Output: Result data stored in a database on the cloud

[0692] Step 8:

[0693] The AI ​​tool analyzes each student's marks and provides explanations for incorrect answers and future study plans. Specifically, the AI ​​model analyzes each student's incorrect answer patterns, and the generative AI model generates optimal explanations and study advice.

[0694] Input: Grading result data for each student

[0695] Output: Explanation of incorrect answers and study plan

[0696] Step 9:

[0697] The user (teacher) accesses a cloud-based server and inputs new test questions. Based on the input questions, the server suggests similar questions and generates questions according to difficulty. Specifically, the educator inputs questions through a web interface, and the server makes suggestions by comparing them with an existing question database.

[0698] Input: Input data for new test questions

[0699] Output: Suggested similar problems

[0700] Step 10:

[0701] The user (student) uses a device to access the AI ​​means on the cloud and ask a question about a specific problem. The AI ​​means then provides a detailed explanation in text or voice in response to the question. Specifically, the question is analyzed, and the generative AI model generates the most appropriate explanation and provides it to the user.

[0702] Input: Student question data

[0703] Output: Detailed explanatory text or audio

[0704] This will make test marking more efficient at educational institutions and enable appropriate feedback and learning support for each student.

[0705] (Application example 1)

[0706] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0707] While traditional test scoring systems are required to improve the efficiency of scoring, they still require manual feedback and learning support. Furthermore, there is a lack of systems that can identify individual learning trends and weaknesses and provide educational support based on those trends. Furthermore, it is difficult to display real-time aggregated scores or provide feedback using AI.

[0708] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0709] In this invention, the server includes an image acquisition means, a data transfer means, an image analysis means, and a learning support means using a generative AI model, which enables fast and accurate test scoring, individual learning trend analysis and feedback, and real-time summary display.

[0710] "Image acquisition means" refers to a device or system that scans a test answer sheet and generates image data thereof.

[0711] The "data transfer means" is a device or system that transfers the generated image data to a server on the cloud.

[0712] The "image analysis means" is a device or system that performs character recognition on the received scanned image data and converts it into text data.

[0713] The "scoring means" is a device or system that scores text data recognized by the image analysis means by comparing it with a pre-set correct answer database.

[0714] The "result transfer means" is a device or system that transmits the scoring results from a server on the cloud to the information display device.

[0715] The "printing means" is a device or system that outputs the marking results together with the answer sheet on the information display device.

[0716] The "summary display means" is a device or system that displays the score result data that has been summed up on a server on the cloud in real time.

[0717] A "learning support tool" is a device or system that uses a generative AI model to provide individualized learning feedback based on the student's answers.

[0718] A "generative AI model" is a type of artificial intelligence technology that automatically generates individual suggestions and feedback based on specific answer data.

[0719] A "prompt" is a text format used to input specific answer data into an AI model.

[0720] This invention relates to a test marking system used in educational institutions, cram schools, etc., and includes an image acquisition means, a data transfer means, an image analysis means, a marking means, a result transfer means, a printing means, a summary display means, a learning support means, a generative AI model, and a prompt sentence generation function. Specifically, this system is implemented using the following system configuration and operating procedures.

[0721] System Configuration

[0722] 1. Image acquisition method:

[0723] The user (teacher) places the test answer sheet in the printer and starts the scanning operation. The printer scans the answer sheet and generates image data of the answer sheet. This image acquisition means transfers all answer information as electronic data to a server on the cloud.

[0724] 2. Means of data transfer:

[0725] The printer transfers the generated image data to a cloud server via the Internet. This data transfer means allows the image data to be uploaded to the cloud server quickly and safely.

[0726] 3. Image analysis methods:

[0727] The server passes the received image data to an image analysis module on the cloud. The image analysis module uses OCR (Optical Character Recognition) technology to recognize the characters in the image and convert them into text data. This allows the contents of the answer sheet to be extracted as text data.

[0728] 4. Scoring Procedure:

[0729] The server compares the text data with a pre-set database of correct answers to determine whether each answer is correct. It also calculates the score and total score, enabling fast and accurate scoring.

[0730] 5. Means of transfer of results:

[0731] The server transmits the generated grading result data to the information display device, which then becomes ready to print the grading result based on the received data by the transfer means.

[0732] 6. Printing means:

[0733] The information display device prints the received marking results on the answer sheet and outputs it together with the answer sheet. By this printing means, the answer sheet on which the marking results are written can be obtained without any manual work.

[0734] 7. Aggregate display method:

[0735] The server stores all test result data in a cloud database. Users (teachers, etc.) can log in to the cloud management screen and view nationwide test results in real time. A summary display method allows for quick understanding of overall educational trends.

[0736] 8. Learning Support Tools:

[0737] The server analyzes each student's marks and generates explanations for incorrect answers and correct answers. It also provides future study plans and advice. This learning support tool helps students understand their weaknesses and study more effectively.

[0738] 9. Generative AI model and prompt generation function:

[0739] The server uses a generative AI model to provide personalized learning feedback based on the student's answers. Through the prompt generation function, a text format is generated to input specific answer data into the AI ​​model, which then automatically generates personalized suggestions and feedback.

[0740] Specific examples

[0741] For example, in the case of mathematics test marking at an educational institution, the process might look like this:

[0742] The user (teacher) places the answer sheet in the printer and starts scanning.

[0743] The printer transfers the image data to the cloud server.

[0744] The server passes the image data to the image analysis module, which uses OCR technology to recognize characters.

[0745] The image analysis module provides the text data to the server for scoring.

[0746] The server sends the results of the scoring to the information display device and prints them out.

[0747] The server stores all results in a database and provides nationwide aggregate data in real time.

[0748] The server uses the generative AI model to generate learning feedback for each student, for example by forming prompts like:

[0749] Prompt Sentence Examples

[0750] Student Name: Taro Yamada

[0751] Test Subject: Mathematics

[0752] problem:

[0753] 1. 5 + 3 = ?

[0754] 2. 10 - 2 = ?

[0755] answer:

[0756] 1. 8

[0757] 2.8

[0758] Based on this prompt, the generative AI model automatically generates individualized suggestions and feedback to provide to students.

[0759] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0760] Step 1:

[0761] The user (teacher) places the test answer sheet in the printer and starts the scanning operation. The printer scans the answer sheet and generates its image data.

[0762] Input: Test answer sheet

[0763] Output: Image data

[0764] Specific operation: The user places the answer sheet in the printer's scanner and presses the scan button. The printer scans the answer sheet and generates image data.

[0765] Step 2:

[0766] The printer transfers the generated image data to a server on the cloud via the Internet.

[0767] Input: Image data

[0768] Output: Image data stored on a cloud server

[0769] Specific operation: The printer's data transfer module uploads the generated image data to a cloud server via a secure internet connection.

[0770] Step 3:

[0771] The server passes the received image data to an image analysis module on the cloud, where character recognition is performed using OCR technology.

[0772] Input: Image data stored on a cloud server

[0773] Output: Text data

[0774] Specific operation: The server passes the image data to the image analysis module, which uses OCR technology to perform character recognition and extract text data from the image data.

[0775] Step 4:

[0776] The server compares the text data with a pre-set database of correct answers to determine whether each answer is correct, and also calculates the score and total score.

[0777] Input: Text data, correct answer database

[0778] Output: Grade result data

[0779] Specific operation: The server's scoring module compares the text data with a correct answer database, determines whether each question is correct, and calculates the score.

[0780] Step 5:

[0781] The server transmits the generated score result data to the information display device.

[0782] Input: Grade result data

[0783] Output: The score result data sent to the information display device

[0784] Specific operation: The graded result data is sent to the information display device, and the graded results are displayed and prepared for printing.

[0785] Step 6:

[0786] The information display device prints the received marking results on the answer sheet and outputs them together with the answer sheet.

[0787] Input: Grade result data sent to the information display device

[0788] Output: Printed answer sheet

[0789] Specific operation: The printing module of the information display device prints the received results on an answer sheet and outputs it in a form that can be taken out by the user.

[0790] Step 7:

[0791] The server stores all test result data in a cloud database, and users can log in to the cloud management screen to view test results from across the country in real time.

[0792] Input: Grade result data

[0793] Output: Aggregated data stored in the database, aggregated data displayed on the cloud management screen

[0794] Specific operation: Scoring result data is stored in a cloud database, allowing users to view the aggregated results in real time from the management screen.

[0795] Step 8:

[0796] The server uses the generative AI model to generate personalized learning feedback based on the student's answers. Through the prompt generation function, a text format is generated to input specific answer data into the AI ​​model.

[0797] Input: Scoring result data, prompt

[0798] Output: Learning feedback

[0799] Specific operation: Prompt sentences are generated based on the scoring results, and these are input into an AI model to generate feedback for each student.

[0800] Step 9:

[0801] The generated learning feedback is provided to the user (student).

[0802] Input: Learning feedback

[0803] Output: Feedback information provided to students

[0804] What it does: Allows students to view feedback on their devices and displays learning plans and advice.

[0805] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0806] The present invention relates to a test marking system used in educational institutions, cram schools, etc., and provides a system comprising an image acquisition means, a data transfer means, an image analysis means, a marking means, a result transfer means, a printing means, a summary display means, a learning support means, a test creation support means, an interactive explanation means, and an emotion engine.

[0807] Specific Examples

[0808] 1. Image acquisition method

[0809] The user (teacher) places the test answer sheet in the printer and starts the scanning operation. The printer scans the answer sheet and generates image data of the answer sheet. This image acquisition means transfers all answer information as electronic data to a server on the cloud.

[0810] 2. Means of data transfer

[0811] The printer transfers the generated image data to a cloud server via the Internet. This data transfer means allows the image data to be uploaded to the cloud server quickly and safely.

[0812] 3. Image analysis methods

[0813] The server passes the received image data to the image analysis module, which uses OCR (Optical Character Recognition) technology to recognize the characters in the image and convert them into text data, thereby extracting the contents of the answer sheet as text data.

[0814] 4. Scoring Method

[0815] The server sends the analyzed text data to Gemini (an AI system). Gemini analyzes the received text data and compares it with a database of pre-registered correct answers. This determines whether each answer is correct, calculates the score, and calculates the total score.

[0816] 5. Means of transferring results

[0817] The server transmits the generated scoring result data to the printer, and as a result, the printer is ready to print the scoring result based on the received data.

[0818] 6. Printing means

[0819] The printer prints the received marking results on the answer sheet and outputs it together with the answer sheet. By this printing means, the answer sheet on which the marking results are written can be obtained without manual work.

[0820] 7. Aggregate display method

[0821] The server stores all test result data in a cloud database. Users (teachers, etc.) can log in to the cloud management screen and view nationwide test results in real time. A summary display method allows for quick understanding of overall educational trends.

[0822] 8. Learning Support Tools

[0823] Gemini analyzes each student's marks and generates explanations for incorrect answers and correct answers. It also provides future study plans and advice. This learning support tool helps students understand their weaknesses and study more effectively.

[0824] 9. Test Creation Support Tools

[0825] Users can access a cloud-based server and input new test questions. Based on the input questions, the server will suggest similar questions and generate questions according to difficulty. This test creation support method allows teachers to efficiently create high-quality tests.

[0826] 10. Interactive Explanation Tools

[0827] Users (students) can access Gemini on the cloud from their devices (PCs, tablets, etc.) and ask questions about specific issues. Gemini then provides detailed explanations in text or audio to the user's questions. This interactive explanation method enables individualized learning support.

[0828] 11. Emotion Engine

[0829] The server is equipped with an emotion engine that can recognize users' emotions in real time. For example, it can read emotions from the facial expressions and tone of voice displayed by students while answering questions and provide appropriate feedback. This emotion engine is used in conjunction with interactive explanation tools and learning support tools to increase users' motivation.

[0830] Specifically, when a user uses the interactive explanation tool, the server analyzes their emotions using an emotion engine, and if they appear unsure about their answers, the server will explain in a gentler tone. Furthermore, the learning support tool can also adjust the difficulty and content of the learning plan based on the user's emotions. This maximizes students' learning efficiency and improves their learning persistence.

[0831] Specific examples

[0832] For example, in the case of marking a math test in a middle school, the process would be as follows:

[0833] 1. The user (teacher) places the answer sheet in the printer and starts scanning.

[0834] 2. The printer transfers the image data to the server.

[0835] 3. The server passes the image data to the image analysis module, where character recognition is performed using OCR technology.

[0836] 4. The image analysis module provides the text data to Gemini for scoring.

[0837] 5. Gemini sends the scoring results to the server, which then forwards them to the printer.

[0838] 6. The printer prints out the scoring results.

[0839] 7. The server stores all results in a database and provides nationwide aggregate data in real time.

[0840] 8. Gemini generates explanations for incorrect answers and future study plans, which are then printed out by the printer.

[0841] 9. The user (teacher) accesses the server to create a new test.

[0842] 10. The user (student) uses the device to ask Gemini questions and receive explanations.

[0843] 11. The server uses an emotion engine to recognize the user's emotions and provide appropriate feedback or adjust the learning plan.

[0844] These methods will automate test marking in educational institutions, significantly improving teaching efficiency and student learning effectiveness. In particular, the combination of emotion engines will further personalize individual learning support, resulting in more effective education.

[0845] The processing flow will be explained below.

[0846] Step 1:

[0847] The user (teacher) places the test answer sheet in the printer and starts the scanning operation by selecting the scan button on the printer's operation screen.

[0848] Step 2:

[0849] The printer scans the answer sheet and generates image data, which is temporarily stored in the internal memory.

[0850] Step 3:

[0851] The printer transfers the generated image data to a server on the cloud. Specifically, the printer uploads the image data to a specified server URL via the Internet.

[0852] Step 4:

[0853] The server receives the image data sent from the printer. The receiving module temporarily stores the image data and then passes it to the image analysis module.

[0854] Step 5:

[0855] The image analysis module uses OCR technology to extract text information from the received image data, and in this process, each answer is individually identified and converted into text data.

[0856] Step 6:

[0857] The image analysis module then sends the converted text data to Gemini, which then analyzes the received text data and compares it with a pre-registered database of correct answers.

[0858] Step 7:

[0859] Gemini compares the analyzed data with the correct answer data to determine whether each answer is correct, calculates the score for each question, and calculates the total score.

[0860] Step 8:

[0861] Gemini generates the scoring results as data and sends it to the server, where a results transfer module prepares the data for sending to the printer.

[0862] Step 9:

[0863] The server sends the received score data to the printer, which analyzes the data and prints the results on the answer sheet.

[0864] Step 10:

[0865] The printer prints the results of the scoring onto the answer sheet and outputs it along with the answer sheet. This printing process allows the correctness and score of each answer to be visually confirmed.

[0866] Step 11:

[0867] The server stores all scoring data in a cloud database, which is later used for aggregation and analysis.

[0868] Step 12:

[0869] Users (teachers) can log in to a cloud-based management screen and view nationwide test results and statistical information in real time. On the management screen, they can check the overall correct answer rate, trends in incorrect answers, and the causes of incorrect answers on specific questions.

[0870] Step 13:

[0871] Gemini analyzes each student's answering habits, generates explanations for incorrect answers and explanations for correct answers, and provides future study plans and advice. These explanations and study plans are stored on a cloud server.

[0872] Step 14:

[0873] The server sends the explanations and study plan data generated by Gemini to a printer, which prints them on the answer sheets. The printed explanations and study plans are then added to the answer sheets returned to the students.

[0874] Step 15:

[0875] The user (teacher) accesses a server on the cloud to input new test questions into the system. Based on the input questions, the server suggests similar questions and generates questions according to difficulty.

[0876] Step 16:

[0877] Users (students) access Gemini from their devices and type or speak questions about specific problems. Gemini then provides detailed explanations of the user's questions in text or voice.

[0878] Step 17:

[0879] The server is equipped with an emotion engine that analyzes the facial expressions and tone of voice of students in real time while they are filling out answer sheets or interacting with Gemini. This emotion data is stored in the cloud.

[0880] Step 18:

[0881] Gemini adjusts dialogue based on emotional data provided by its emotion engine. For example, if a student appears anxious, it will provide explanations in a more understandable and gentle tone. Furthermore, in its learning support tools, it adjusts the difficulty of the lesson plan based on emotional data to maintain student motivation.

[0882] This will streamline test marking at educational institutions, significantly improving teaching efficiency and student learning effectiveness. In particular, the combination of the emotion engine will further personalize individual learning support, resulting in more effective education.

[0883] Example 2

[0884] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0885] Conventional test marking systems require manual marking, placing a heavy burden on teachers. Furthermore, they tend to provide insufficient individual learning support to students, resulting in a decline in learning effectiveness. Furthermore, because learning feedback that takes into account the user's emotions is not provided, it is difficult to maintain student motivation. There is a need to solve these issues and provide efficient and effective educational support.

[0886] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0887] In this invention, the server includes an image acquisition unit that scans test answer sheets, a data transfer unit that transfers the scanned image data to an information processing device on the cloud, and an image analysis unit that receives the scanned image data and performs optical character recognition, thereby enabling automatic test scoring and individual learning support.

[0888] The "image acquisition means" is a device that scans the test answer sheet and generates image data thereof.

[0889] The "data transfer means" is a device or system that transfers the generated scanned image data to an information processing device on the cloud.

[0890] An "image analysis means" is a device or system that receives scanned image data and converts it into text data using optical character recognition (OCR) technology.

[0891] The "evaluation means" is a device or system that analyzes and evaluates the text data recognized by the image analysis means.

[0892] The "result transfer means" is a device or system that transmits the evaluation results from the information processing device to the printing device.

[0893] "Printing means" refers to a device that prints out the evaluation results and outputs them together with the answer sheet.

[0894] An "emotion recognition means" is a device or system that recognizes the user's emotions and provides appropriate feedback based on that information.

[0895] A "learning support tool" is a device or system that provides an individual learning plan and appropriate learning support to students.

[0896] The "aggregation display means" is a device or system that displays the evaluation result data aggregated by the information processing device on the cloud in real time.

[0897] This invention is specifically embodied as a test marking system for use in educational institutions, cram schools, etc. The system mainly comprises an image acquisition means, a data transfer means, an image analysis means, an evaluation means, a result transfer means, a printing means, an emotion recognition means, a learning support means, and a summary display means.

[0898] 1. Image acquisition method

[0899] The user (teacher) places the test answer sheet into a scanner or multifunction printer and starts the scanning operation. Specifically, the scanner function of the printer is used to generate image data of the answer sheet. This image acquisition means acquires all of the students' answer information as electronic data and transfers it to a cloud server via the Internet.

[0900] 2. Means of data transfer

[0901] The printer or scanner transfers the generated image data to a cloud server via the Internet. The transfer process uses encryption technology, ensuring the data is uploaded to the server quickly and securely. This data transfer method converts physical paper into electronic data.

[0902] 3. Image analysis methods

[0903] The server passes the received image data to its internal image analysis module. Specifically, it applies the path of the image file stored on the server to the image analysis module. The image analysis module uses OCR (optical character recognition) technology to recognize the characters in the image and convert the results into text data. The OCR engine used here operates with high accuracy and can even recognize handwritten characters to a certain extent.

[0904] 4. Evaluation Methods

[0905] The server sends the analyzed text data to an evaluation module, which compares the text data with a pre-registered correct answer database and determines whether each answer is correct or incorrect. At this stage, partial points are considered and multiple answers are allowed, achieving a scoring system similar to that of a human. Once the correctness determination is complete, a total score and individual feedback are generated.

[0906] 5. Means of transferring results

[0907] The server then sends the generated evaluation result data to the printer. Specifically, communication from the server to the printer is automatic, and the printer receives the data and prepares to print the results.

[0908] 6. Printing means

[0909] The printer prints the received evaluation results on the answer sheet and outputs it together with the answer sheet. Here, not only is the answer correct or incorrect, but feedback on the incorrect parts is also printed together. This printing means allows the answer sheet with the evaluation results to be obtained without any manual work.

[0910] 7. Aggregate display method

[0911] The server stores all assessment result data in a cloud database. Users (teachers and administrators) can log in to the cloud management screen and view nationwide test results in real time. This aggregate display method allows them to see at a glance what grade trends are occurring in each region and school.

[0912] 8. Learning Support Tools

[0913] The evaluation module analyzes each student's evaluation results and generates explanations for incorrect answers and correct answers. It also provides future study plans and advice, which students can access via the cloud. This learning support tool helps students understand their weaknesses and study more effectively.

[0914] 9. Emotion recognition means

[0915] The server is equipped with an emotion engine that works in conjunction with the interactive explanation and learning support tools to analyze the user's emotions. Specifically, it analyzes facial expressions and vocal tone when answering questions, and adjusts the tone of feedback and explanations based on the results. For example, if a student appears anxious, the system will use a gentler tone when explaining.

[0916] Specific examples

[0917] For example, in the case of a middle school math test grading exercise, the exercise proceeds as follows:

[0918] 1. The user (teacher) places the answer sheet in the printer and presses the scan button to start scanning.

[0919] 2. The image data generated by the printer is transferred to the server via the Internet.

[0920] 3. The server receives the image data and passes it to the analysis module, where it uses OCR technology to recognize the characters.

[0921] 4. The server sends the OCR results to the evaluation module, which compares them with a database and scores them.

[0922] 5. The server sends the evaluation results to the printer, which prints them out.

[0923] 6. The server stores all results in a cloud database and provides real-time nationwide aggregate data to the management screen.

[0924] 7. The evaluation module generates explanations for incorrect answers and future learning plans, which are then made available to students via the cloud.

[0925] 8. The user (teacher) accesses the server to create a new test and enters the questions.

[0926] 9. The user (student) asks questions to the evaluation module from the terminal and receives an explanation.

[0927] 10. The server uses an emotion engine to analyze the user's emotions and provide appropriate feedback.

[0928] Example prompt sentence:

[0929] "Please explain the system for marking middle school math tests. Please provide a detailed explanation of the step-by-step process and the techniques used."

[0930] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0931] Step 1:

[0932] The user (teacher) places a test answer sheet into the printer and initiates the scanning operation. The printer scans the answer sheet and stores the generated image data in its internal memory. The input for this step is the physical answer sheet, and the output is the scanned image data. The specific action is for the user to press the scan button on the printer.

[0933] Step 2:

[0934] The printer transfers the image data stored in its internal memory to a cloud server via the Internet. This data is encrypted before transmission, making it highly secure. The input in step 2 is the image data stored in the printer, and the output is the image data uploaded to the server. Specifically, the printer automatically starts transferring data.

[0935] Step 3:

[0936] The server passes the received image data to the image analysis module. The server saves the image data in a specific directory and applies that path to the image analysis module. The input of step 3 is the path of the image data saved on the server, and the output is the raw data passed to the image analysis module. Specifically, the server executes the built-in script.

[0937] Step 4:

[0938] The image analysis module uses OCR (optical character recognition) technology to extract character information from image data and convert it into text data. The input of step 4 is the image data passed to the image analysis module, and the output is text data. Specifically, the OCR engine scans the image data and generates a text file containing the recognized characters.

[0939] Step 5:

[0940] The server sends the analyzed text data to the evaluation module. The evaluation module compares the text data with a database of pre-registered correct answers and determines whether each answer is correct. The input in step 5 is the text data, and the output is the evaluation result. Specifically, the evaluation module compares the text data with the correct answer data and calculates the score.

[0941] Step 6:

[0942] The server sends the generated evaluation result data to the printer. The input here is the evaluation result data, and the output is the data sent to the printer. In concrete terms, the server transfers the evaluation result data to the printer using a specific communication protocol.

[0943] Step 7:

[0944] The printer prints the received evaluation results on the answer sheet and outputs the answer sheet along with the feedback. The input of step 7 is the evaluation result data sent to the printer, and the output is the printed answer sheet. In concrete terms, the printer prints the evaluation results and feedback on the paper.

[0945] Step 8:

[0946] The server stores all evaluation result data in a database on the cloud. Users (teachers and administrators) can log in to the cloud management screen and view nationwide test results in real time. The input for step 8 is the evaluation result data, and the output is the aggregated data stored in the database. Specifically, the server runs a script that periodically updates the database.

[0947] Step 9:

[0948] The evaluation module analyzes the evaluation results for each student and generates explanations for incorrect answers and correct answers. Based on this, it also provides future study plans and advice. The input of Step 9 is the evaluation result data for each student, and the output is individual explanations and study plans. Specifically, the evaluation module uses an algorithm to perform the analysis.

[0949] Step 10:

[0950] The user (teacher) accesses a server on the cloud and inputs new test questions. Based on the input questions, the server suggests similar questions and adjusts the difficulty level. The input in step 10 is the new test question data, and the output is the generated test questions. Specifically, the server searches the database and suggests related questions.

[0951] Step 11:

[0952] The user (student) uses a terminal to access the evaluation module on the cloud and ask a question about a specific problem. The evaluation module provides a detailed explanation in text or audio. The input of step 11 is the question data from the user, and the output is the generated explanation data. In concrete terms, the evaluation module responds to the question using natural language processing.

[0953] Step 12:

[0954] The server uses an emotion engine to analyze the user's emotions and provides appropriate feedback based on that information. The emotion engine analyzes the student's facial expressions and tone of voice and generates feedback according to that state. The input of step 12 is the user's emotion data, and the output is adjusted feedback. Specifically, the emotion engine uses a machine learning algorithm to analyze emotions.

[0955] These steps will enable automated test scoring and personalized learning support, improving the quality and efficiency of education.

[0956] (Application example 2)

[0957] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0958] Conventional test scoring systems used in educational institutions and cram schools require many steps, such as image acquisition, data transfer, image analysis, and scoring, making efficient and rapid processing difficult. Furthermore, quality control in factories requires a lot of manual work due to a lack of automation, limiting improvements in production efficiency and quality. Furthermore, training and employee motivation management within factories are also inadequate. To solve these issues, a means is needed to integrate both systems and perform processing efficiently and with high accuracy.

[0959] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0960] In this invention, the server includes an image acquisition means, a data transfer means, an image analysis means, a scoring means, a result transfer means, a printing means, a quality assessment means, and a result display means. This not only improves the efficiency and accuracy of test scoring at educational institutions and cram schools, but also automates quality control work in factories, reducing manual work and improving production efficiency. Furthermore, having the ability to display results in real time on a cloud-based server not only enables rapid data collection and analysis, but also enables employee motivation management, thereby improving overall work efficiency and quality.

[0961] "Image capture means" means a device or mechanism that captures an image of an object or document of interest by scanning or photographing it.

[0962] "Data transfer means" refers to a device or means for transferring acquired image data to a remote computer system such as a server.

[0963] "Image analysis means" refers to software or algorithms that analyze acquired image data and perform character recognition, shape recognition, etc.

[0964] A "scoring means" is a device or system that determines whether a question is correct or incorrect and calculates a score based on the analyzed text data.

[0965] "Results Transfer Means" means the mechanism or system that transfers the scoring results data from the server to a printer or other output device.

[0966] The "printing means" is a device or mechanism that directly prints out onto paper or the like based on the data transferred by the result transfer means.

[0967] "Quality assessment means" refers to devices or algorithms used to evaluate the quality of a product and determine whether it passes or fails.

[0968] The "result display means" is a device or system for displaying the quality assessment results and grading results on the terminals of the factory manager or teacher.

[0969] "Aggregation display means" refers to a function or system for displaying data aggregated on a server on the cloud in real time.

[0970] "Support tools" are functions and algorithms that provide learning plans and suggestions for improving production processes based on analyzed data.

[0971] The present invention is a system for achieving efficient business processing and quality control in educational institutions and factories. Specifically, the system comprises an image acquisition means, a data transfer means, an image analysis means, a scoring means, a result transfer means, a printing means, a quality judgment means, a result display means, a tally display means, and a support means.

[0972] 1. Image acquisition method

[0973] The server is connected to image capture devices such as cameras and scanners, which are used to scan test answer sheets in educational institutions or take images of products in factories, and the captured image data is stored at high resolution for further processing.

[0974] 2. Means of data transfer

[0975] Image data acquired by the image acquisition device is transferred at high speed to a cloud server. Specifically, it is sent via the Internet using a secure protocol. A database system is running on the server to properly store the received data.

[0976] 3. Image analysis methods

[0977] The server analyzes the received image data using OCR (Optical Character Recognition) technology. This analysis utilizes software libraries such as TensorFlow and OpenCV. For example, in the case of test answer sheets, handwritten characters are converted into text data, and in the case of factories, product shapes and character information are identified.

[0978] 4. Scoring Method

[0979] The text data obtained by the image analysis means is analyzed by a server-side AI system. At the educational institution, the results are compared with pre-registered correct answer data and graded. High-precision grading is possible using an AI model (for example, a generative AI model using TensorFlow). At the factory, the pass / fail of the product is determined based on the analyzed data.

[0980] 5. Means of transferring results

[0981] The server transfers the grading results and quality assessment results to a printer or management terminal via the Internet, allowing the results to be printed out at the educational institution and displayed in real time on the manager's terminal at the factory.

[0982] 6. Printing means

[0983] The printer prints the marking results on the answer sheets based on the received data. Even in factories, it is possible to print out the quality assessment results and keep a record. This allows results to be checked without manual work, improving work efficiency.

[0984] 7. Results display means

[0985] The server displays the scoring results and quality assessment results in real time through the result display means. Teachers at educational institutions can view nationwide test results from the management screen, and factory managers can view quality control data in real time.

[0986] 8. Aggregate display method

[0987] The server displays the aggregated data on the cloud, allowing educational institutions to aggregate student grade data and analyze learning trends, while factories can collect quality data and analyze production trends.The aggregated data is displayed in dashboard format to help operators make decisions.

[0988] 9. Support methods

[0989] The server uses the generative AI model to provide learning plans and suggestions for improving production processes based on the analysis results. This function allows educational institutions to analyze students' weaknesses and present effective learning plans, and factories to propose specific measures for quality improvement.

[0990] Examples of concrete examples and prompts

[0991] For example, automated inspections of products are carried out in factories, which identify defects in real time and notify managers, and in educational institutions, tests taken by students are quickly graded and the results are made available.

[0992] Example prompts to input to a generative AI model:

[0993] "Please analyze this image data and determine the quality of the product."

[0994] "Use an AI model to determine whether this product meets your quality standards."

[0995] This will realize a system that efficiently grades tests in educational institutions and performs quality control in factories.

[0996] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0997] Step 1:

[0998] The image acquisition means captures images of the product using a camera installed on the factory robot. The user (operator) sets the camera in the appropriate position and inputs the command to start capturing. The acquired image data generates an output that is sent to the server as high-resolution electronic data.

[0999] Step 2:

[1000] The server receives the acquired image data and stores it in a database on the cloud. A data transfer method is used for this purpose. The server transfers the data using a secure protocol and processes it for storage in the database. The input is image data, and the output is data stored in the database on the cloud.

[1001] Step 3:

[1002] The server uses image analysis means to analyze the stored image data. Specifically, it uses the OpenCV library to perform character recognition (OCR) and shape recognition of the image. The input is the image data on the cloud, and the output is the analyzed text data and shape data.

[1003] Step 4:

[1004] The analyzed text and shape data is sent to an AI system on the server. The AI ​​system uses a generative AI model to analyze this data and determine its quality. The input is text and shape data, and the output is a "pass" or "fail" result. Specifically, matching and evaluation are performed based on quality standards.

[1005] Step 5:

[1006] The server transfers the obtained quality assessment results to the terminal of the factory manager. The results are displayed on the manager's screen in real time via the Internet using a result transfer means. The input is the quality assessment results, and the output is the display screen of the manager's terminal.

[1007] Step 6:

[1008] The server uses a means of tallying and displaying the quality assessment results in real time in the form of a dashboard, allowing the administrator to grasp the overall quality status at a glance. The input is the assessment result data for each product, and the output is a dashboard of the aggregated assessment status.

[1009] Step 7:

[1010] The server uses support tools to generate improvement proposals based on the judged data. The generative AI model proposes optimal improvement measures, taking past data into account. The input is quality judgment data and past production data, and the output is a report of improvement proposals and advice. Managers can then improve the production process based on this report.

[1011] Step 8:

[1012] The user (administrator) implements the proposed improvement plan and feeds the results back to the server. The fed-back data is used as learning material for future generative AI models, improving the accuracy of the system. The input is the result data of the implemented improvement plan, and the output is an updated AI model.

[1013] The above steps will enable efficient business processing and quality control in educational institutions and factories. An example of a prompt sentence would be, "Please use the AI ​​model to determine whether this product meets the quality standards." This would enable highly accurate judgments to be made by giving the AI ​​system instructions.

[1014] 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.

[1015] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1016] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1017] [Third embodiment]

[1018] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1019] 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.

[1020] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1021] 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.

[1022] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1023] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1024] 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.

[1025] 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.

[1026] 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 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.

[1027] 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.

[1028] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1029] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1030] The present invention relates to a test marking system used in educational institutions, cram schools, etc., and provides a system consisting of an image acquisition means, a data transfer means, an image analysis means, a marking means, a result transfer means, a printing means, a tally display means, a learning support means, a test creation support means, and an interactive explanation means.

[1031] Specific Examples

[1032] 1. Image acquisition method

[1033] The user (teacher) places the test answer sheet in the printer and starts the scanning operation. The printer scans the answer sheet and generates image data of the answer sheet. This image acquisition means transfers all answer information as electronic data to a server on the cloud.

[1034] 2. Means of data transfer

[1035] The printer transfers the generated image data to a cloud server via the Internet. This data transfer means allows the image data to be uploaded to the cloud server quickly and safely.

[1036] 3. Image analysis methods

[1037] The server passes the received image data to an image analysis module on the cloud. The image analysis module uses OCR (Optical Character Recognition) technology to recognize the characters in the image and convert them into text data. This allows the contents of the answer sheet to be extracted as text data.

[1038] 4. Scoring Method

[1039] Gemini (an AI system) uses text data to compare it with a pre-set database of correct answers and determine whether each answer is correct. It also calculates the score and total score, enabling fast and accurate scoring.

[1040] 5. Means of transferring results

[1041] The server transmits the generated scoring result data to the printer, and as a result, the printer is ready to print the scoring result based on the received data.

[1042] 6. Printing means

[1043] The printer prints the received marking results on the answer sheet and outputs it together with the answer sheet. By this printing means, the answer sheet on which the marking results are written can be obtained without manual work.

[1044] 7. Aggregate display method

[1045] The server stores all test result data in a cloud database. Users (teachers, etc.) can log in to the cloud management screen and view nationwide test results in real time. A summary display method allows for quick understanding of overall educational trends.

[1046] 8. Learning Support Tools

[1047] Gemini analyzes each student's marks and generates explanations for incorrect answers and correct answers. It also provides future study plans and advice. This learning support tool helps students understand their weaknesses and study more effectively.

[1048] 9. Test Creation Support Tools

[1049] Users can access a cloud-based server and input new test questions. Based on the input questions, the server will suggest similar questions and generate questions according to difficulty. This test creation support method allows teachers to efficiently create high-quality tests.

[1050] 10. Interactive Explanation Tools

[1051] Users (students) can access Gemini on the cloud from their devices (PCs, tablets, etc.) and ask questions about specific issues. Gemini then provides detailed explanations in text or audio to the user's questions. This interactive explanation method enables individualized learning support.

[1052] Specific examples

[1053] For example, in the case of marking a math test in a middle school, the process would be as follows:

[1054] 1. The user (teacher) places the answer sheet in the printer and starts scanning.

[1055] 2. The printer transfers the image data to the server.

[1056] 3. The server passes the image data to the image analysis module, where character recognition is performed using OCR technology.

[1057] 4. The image analysis module provides the text data to Gemini for scoring.

[1058] 5. Gemini sends the scoring results to the server, which then forwards them to the printer.

[1059] 6. The printer prints out the scoring results.

[1060] 7. The server stores all results in a database and provides nationwide aggregate data in real time.

[1061] 8. Gemini generates explanations for incorrect answers and future study plans, which are then printed out by the printer.

[1062] 9. The user (teacher) accesses the server to create a new test.

[1063] 10. The user (student) uses the device to ask Gemini questions and receive explanations.

[1064] By using each of the above methods, test grading at educational institutions will become more efficient, and appropriate feedback and learning support will be provided to each student.

[1065] The processing flow will be explained below.

[1066] Step 1:

[1067] The user (teacher) places the test answer sheet in the printer and starts the scanning operation by selecting the scan button on the printer's operation screen.

[1068] Step 2:

[1069] The printer scans the answer sheet and generates image data, which is temporarily stored in the internal memory.

[1070] Step 3:

[1071] The printer transfers the generated image data to a server on the cloud. Specifically, the printer uploads the image data to a specified server URL via the Internet.

[1072] Step 4:

[1073] The server receives the image data sent from the printer. The receiving module temporarily stores the image data and then passes it to the image analysis module.

[1074] Step 5:

[1075] The image analysis module uses OCR technology to extract text information from the received image data, and in this process, each answer is individually identified and converted into text data.

[1076] Step 6:

[1077] The image analysis module sends the converted text data to Gemini (an AI system), which analyzes the received text data and compares it with a pre-registered correct answer database.

[1078] Step 7:

[1079] Gemini compares the analyzed data with the correct answer data to determine whether each answer is correct, calculates the score for each question, and calculates the total score.

[1080] Step 8:

[1081] Gemini generates the scoring results as data and sends it to the server, where a results transfer module prepares the data for sending to the printer.

[1082] Step 9:

[1083] The server sends the received score data to the printer, which analyzes the data and prints the results on the answer sheet.

[1084] Step 10:

[1085] The printer prints the results of the scoring onto the answer sheet and outputs it along with the answer sheet. This printing process allows the correctness and score of each answer to be visually confirmed.

[1086] Step 11:

[1087] The server stores all scoring data in a cloud database, which is later used for aggregation and analysis.

[1088] Step 12:

[1089] Users (teachers) can log in through a cloud-based management screen and view nationwide test results and statistical information in real time.

[1090] Step 13:

[1091] Gemini analyzes each student's answering tendencies and generates explanations about the reasons for incorrect answers, the percentage of correct answers, and points that require particular attention. This content also includes future study plans and advice.

[1092] Step 14:

[1093] The server sends the explanations and study plan data generated by Gemini to the printer, which then prints it onto the answer sheet.

[1094] Step 15:

[1095] Users (teachers) input new test questions into the system, and a server on the cloud helps by suggesting similar questions and generating questions according to difficulty.

[1096] Step 16:

[1097] Users (students) access Gemini from their devices and type or speak questions about specific problems. Gemini analyzes the user's questions and provides detailed explanations in text or voice.

[1098] These steps will enable educational institutions to automate test marking processes, significantly improving teaching efficiency and student learning outcomes.

[1099] Example 1

[1100] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1101] Conventional test scoring systems require the manual analysis and scoring of scanned image data from answer sheets, which is time-consuming and labor-intensive. Furthermore, they lack the functionality to automatically provide individual students with answering trends and study plans, placing a heavy burden on educators. Furthermore, they lacked support functions for creating new test questions, making it difficult to create effective tests.

[1102] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1103] In this invention, the server includes an image acquisition means for scanning test answer sheets, a data transfer means for transferring the scanned image data to a cloud server, an image analysis means for receiving the scanned image data and performing character recognition, an artificial intelligence means for grading the text data recognized by the image analysis means, a result transfer means for sending the grading results from the server to an output device, a printing means for printing the grading results on the output device and outputting them together with the answer sheets, and a learning support means for analyzing the grading results for each student and providing explanations for incorrect answers and a learning plan. This enables fast and accurate automatic grading, individual learning support, and support for creating new test questions.

[1104] The "image acquisition means" is a means for scanning the test answer sheet and generating image data.

[1105] "Data transfer means" refers to a means for transferring scanned image data to a server on the cloud.

[1106] The "image analysis means" is a means for performing character recognition on the received scanned image data and converting it into text data.

[1107] The "artificial intelligence means" is a means for scoring based on the text data recognized by the image analysis means.

[1108] The "result transfer means" is a means for transmitting the grading results from the server to the output device.

[1109] "Printing means" refers to a means for printing and physically outputting the marking results on the answer sheet.

[1110] "Learning support tools" are tools for analyzing each student's marks and providing explanations for incorrect answers and future learning plans.

[1111] The "summary display means" is a means for displaying the scoring result data stored on a server on the cloud in real time.

[1112] "Test creation support tools" are tools that help educators create new test questions by suggesting similar questions and generating questions according to difficulty.

[1113] This invention is a system for automating test marking in educational institutions and learning facilities. This system is composed of an image acquisition means, a data transfer means, an image analysis means, an artificial intelligence means, a result transfer means, a printing means, a learning support means, a tally display means, and a test creation support means.

[1114] Image Acquisition Method

[1115] The user (teacher) places the test answer sheet on the printer and starts the scanning operation. The printer scans the answer sheet and generates image data. This image data is then transferred to a server on the cloud. For example, a general multi-function printer can be used.

[1116] Data Transfer Method

[1117] The printer transfers the generated image data to a cloud server via the Internet, using common data communication protocols (such as SSL / TLS) to ensure secure and fast data transmission.

[1118] Image analysis methods

[1119] The server passes the received image data to an image analysis module on the cloud. The image analysis module uses OCR (Optical Character Recognition) technology to recognize characters in the image and convert them into text data. An example of OCR software is Tesseract.

[1120] Artificial Intelligence Tools

[1121] The server has an artificial intelligence means for scoring the text data. This artificial intelligence means compares the text data with a database of correct answers to determine whether each answer is correct or incorrect. It also calculates the score and calculates the total score. Using an AI system like Gemini allows for fast and accurate scoring.

[1122] Result transfer method

[1123] The server transmits the generated scoring result data to an output device, for example, the server transmits the scoring result to a printer and prepares it for printing.

[1124] printing means

[1125] The printer prints the received marking results on the answer sheet and outputs it, thus providing an answer sheet with the marking results written on it without any manual work.

[1126] Learning support tools

[1127] The AI ​​tool analyzes each student's marks and provides explanations for incorrect answers and future study plans. This learning support allows students to identify their weaknesses and study more effectively. For example, explanations generated by Gemini are used.

[1128] Aggregation display method

[1129] The server stores all grading data in a cloud database, and users (teachers) can view test results in real time from the administration screen, enabling rapid data analysis to grasp trends across the entire educational institution.

[1130] Test Creation Support Tools

[1131] Users (teachers) can access a server on the cloud and input new test questions. Based on the input questions, the server will suggest similar questions and help generate questions according to difficulty. These support functions make it possible to efficiently create high-quality tests.

[1132] Specific examples

[1133] For example, in the case of marking a math test in a middle school, the process would be as follows:

[1134] 1. The user (teacher) places the answer sheet in the printer and starts scanning.

[1135] 2. The printer transfers the image data to the server.

[1136] 3. The server passes the image data to the image analysis module, where character recognition is performed using OCR technology.

[1137] 4. The image analysis module provides the text data to the artificial intelligence means for scoring.

[1138] 5. The artificial intelligence means sends the scoring results to the server, which forwards them to the printer.

[1139] 6. The printer prints out the scoring results.

[1140] 7. The server stores all results in a database and provides nationwide aggregate data in real time.

[1141] 8. An artificial intelligence tool generates explanations for incorrect answers and future study plans, which are then printed out by a printer.

[1142] 9. The user (teacher) accesses the server to create a new test.

[1143] 10. The user (student) uses the terminal to ask questions to the artificial intelligence means and receives explanations.

[1144] Prompt Sentence Examples

[1145] If a tutor were to output the results of an analysis of a math test's incorrect answers, the prompt to the AI ​​would be:

[1146] For the math test results below, generate an explanation for each student's incorrect answer.

[1147] Question 1: 3 2 + 1

[1148] Correct Answer: 7

[1149] Student A's answer: 6 (False)

[1150] Student B's answer: 7 (correct)

[1151] Student C's answer: 8 (False)

[1152] Question 2: 5 + 4 2

[1153] Correct answer: 13

[1154] Student A's answer: 10 (incorrect)

[1155] Student B's answer: 13 (correct)

[1156] Student C's answer: 12 (False)

[1157] Your explanation should include detailed explanations of the correct way to answer each question, the reasons for incorrect answers, and a future study plan for each student.

[1158] By using the above methods, test grading at educational institutions will become more efficient, and appropriate feedback and learning support will be provided to each student.

[1159] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1160] Step 1:

[1161] The user (teacher) places the test answer sheet on the printer and starts the scanning operation. The printer scans the answer sheet and generates image data. This image data is transferred to a server on the cloud. Specifically, the user presses the "scan" button on the printer's operation panel, and the printer reads the answer sheet using an optical sensor. The generated image data is sent to the server via the network.

[1162] Input: Physical test answer sheet

[1163] Output: High-resolution image data of the answer sheet

[1164] Step 2:

[1165] The server receives the image data sent from the printer. The data is securely transferred using an encryption protocol (e.g., SSL / TLS). Specifically, the server listens on a specific port, accepts connection requests from the printer, and receives data.

[1166] Input: Image data of the answer sheet transferred from the printer

[1167] Output: Image data stored on a cloud server

[1168] Step 3:

[1169] The server passes the received image data to an image analysis module in the cloud. The image analysis module uses OCR technology to recognize the characters on the answer sheet and convert them into text data. Specifically, the server inputs the image data into an OCR engine (e.g., Tesseract) and extracts text information from the image.

[1170] Input: Image data stored on a cloud server

[1171] Output: Text data

[1172] Step 4:

[1173] The server passes the text data to the artificial intelligence means and requests grading. The artificial intelligence means compares the text data with a correct answer database, determines whether each answer is correct, and calculates the score. Specifically, the artificial intelligence means (e.g., an AI grading system) compares the answers to each question with pre-registered correct answers and determines whether they are correct. It then calculates the points and calculates the total score.

[1174] Input: Text data generated by OCR

[1175] Output: Scoring result data (correctness and score for each answer)

[1176] Step 5:

[1177] The server then sends the generated scoring result data to the printer. This data includes each student's score and whether their answer was correct or incorrect. Specifically, the server establishes a network connection to send the scoring result data to the printer, encrypts it, and sends the data.

[1178] Input: Grade result data

[1179] Output: The graded results data sent to the printer

[1180] Step 6:

[1181] The printer prints the received marking results on the answer sheet and outputs it. Specifically, the printer stores the received data in its internal memory and prints the marking results on the answer sheet according to the control program. Once printing is complete, the answer sheet with the added evaluation information is ejected.

[1182] Input: Marking result data sent to the printer

[1183] Output: Answer sheet with evaluation information printed on it

[1184] Step 7:

[1185] The server stores all the grading result data in a database on the cloud. Users (teachers) can view the test results in real time from the management screen. Specifically, the server stores the result data in a database and allows users to access it using a web browser.

[1186] Input: Grade result data

[1187] Output: Result data stored in a database on the cloud

[1188] Step 8:

[1189] The AI ​​tool analyzes each student's marks and provides explanations for incorrect answers and future study plans. Specifically, the AI ​​model analyzes each student's incorrect answer patterns, and the generative AI model generates optimal explanations and study advice.

[1190] Input: Grading result data for each student

[1191] Output: Explanation of incorrect answers and study plan

[1192] Step 9:

[1193] The user (teacher) accesses a cloud-based server and inputs new test questions. Based on the input questions, the server suggests similar questions and generates questions according to difficulty. Specifically, the educator inputs questions through a web interface, and the server makes suggestions by comparing them with an existing question database.

[1194] Input: Input data for new test questions

[1195] Output: Suggested similar problems

[1196] Step 10:

[1197] The user (student) uses a device to access the AI ​​means on the cloud and ask a question about a specific problem. The AI ​​means then provides a detailed explanation in text or voice in response to the question. Specifically, the question is analyzed, and the generative AI model generates the most appropriate explanation and provides it to the user.

[1198] Input: Student question data

[1199] Output: Detailed explanatory text or audio

[1200] This will make test marking more efficient at educational institutions and enable appropriate feedback and learning support for each student.

[1201] (Application example 1)

[1202] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1203] While traditional test scoring systems are required to improve the efficiency of scoring, they still require manual feedback and learning support. Furthermore, there is a lack of systems that can identify individual learning trends and weaknesses and provide educational support based on those trends. Furthermore, it is difficult to display real-time aggregated scores or provide feedback using AI.

[1204] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1205] In this invention, the server includes an image acquisition means, a data transfer means, an image analysis means, and a learning support means using a generative AI model, which enables fast and accurate test scoring, individual learning trend analysis and feedback, and real-time summary display.

[1206] "Image acquisition means" refers to a device or system that scans a test answer sheet and generates image data thereof.

[1207] The "data transfer means" is a device or system that transfers the generated image data to a server on the cloud.

[1208] The "image analysis means" is a device or system that performs character recognition on the received scanned image data and converts it into text data.

[1209] The "scoring means" is a device or system that scores text data recognized by the image analysis means by comparing it with a pre-set correct answer database.

[1210] The "result transfer means" is a device or system that transmits the scoring results from a server on the cloud to the information display device.

[1211] The "printing means" is a device or system that outputs the marking results together with the answer sheet on the information display device.

[1212] The "summary display means" is a device or system that displays the score result data that has been summed up on a server on the cloud in real time.

[1213] A "learning support tool" is a device or system that uses a generative AI model to provide individualized learning feedback based on the student's answers.

[1214] A "generative AI model" is a type of artificial intelligence technology that automatically generates individual suggestions and feedback based on specific answer data.

[1215] A "prompt" is a text format used to input specific answer data into an AI model.

[1216] This invention relates to a test marking system used in educational institutions, cram schools, etc., and includes an image acquisition means, a data transfer means, an image analysis means, a marking means, a result transfer means, a printing means, a summary display means, a learning support means, a generative AI model, and a prompt sentence generation function. Specifically, this system is implemented using the following system configuration and operating procedures.

[1217] System Configuration

[1218] 1. Image acquisition method:

[1219] The user (teacher) places the test answer sheet in the printer and starts the scanning operation. The printer scans the answer sheet and generates image data of the answer sheet. This image acquisition means transfers all answer information as electronic data to a server on the cloud.

[1220] 2. Means of data transfer:

[1221] The printer transfers the generated image data to a cloud server via the Internet. This data transfer means allows the image data to be uploaded to the cloud server quickly and safely.

[1222] 3. Image analysis methods:

[1223] The server passes the received image data to an image analysis module on the cloud. The image analysis module uses OCR (Optical Character Recognition) technology to recognize the characters in the image and convert them into text data. This allows the contents of the answer sheet to be extracted as text data.

[1224] 4. Scoring Procedure:

[1225] The server compares the text data with a pre-set database of correct answers to determine whether each answer is correct. It also calculates the score and total score, enabling fast and accurate scoring.

[1226] 5. Means of transfer of results:

[1227] The server transmits the generated grading result data to the information display device, which then becomes ready to print the grading result based on the received data by the transfer means.

[1228] 6. Printing means:

[1229] The information display device prints the received marking results on the answer sheet and outputs it together with the answer sheet. By this printing means, the answer sheet on which the marking results are written can be obtained without any manual work.

[1230] 7. Aggregate display method:

[1231] The server stores all test result data in a cloud database. Users (teachers, etc.) can log in to the cloud management screen and view nationwide test results in real time. A summary display method allows for quick understanding of overall educational trends.

[1232] 8. Learning Support Tools:

[1233] The server analyzes each student's marks and generates explanations for incorrect answers and correct answers. It also provides future study plans and advice. This learning support tool helps students understand their weaknesses and study more effectively.

[1234] 9. Generative AI model and prompt generation function:

[1235] The server uses a generative AI model to provide personalized learning feedback based on the student's answers. Through the prompt generation function, a text format is generated to input specific answer data into the AI ​​model, which then automatically generates personalized suggestions and feedback.

[1236] Specific examples

[1237] For example, in the case of mathematics test marking at an educational institution, the process might look like this:

[1238] The user (teacher) places the answer sheet in the printer and starts scanning.

[1239] The printer transfers the image data to the cloud server.

[1240] The server passes the image data to the image analysis module, which uses OCR technology to recognize characters.

[1241] The image analysis module provides the text data to the server for scoring.

[1242] The server sends the results of the scoring to the information display device and prints them out.

[1243] The server stores all results in a database and provides nationwide aggregate data in real time.

[1244] The server uses the generative AI model to generate learning feedback for each student, for example by forming prompts like:

[1245] Prompt Sentence Examples

[1246] Student Name: Taro Yamada

[1247] Test Subject: Mathematics

[1248] problem:

[1249] 1. 5 + 3 = ?

[1250] 2. 10 - 2 = ?

[1251] answer:

[1252] 1. 8

[1253] 2.8

[1254] Based on this prompt, the generative AI model automatically generates individualized suggestions and feedback to provide to students.

[1255] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1256] Step 1:

[1257] The user (teacher) places the test answer sheet in the printer and starts the scanning operation. The printer scans the answer sheet and generates its image data.

[1258] Input: Test answer sheet

[1259] Output: Image data

[1260] Specific operation: The user places the answer sheet in the printer's scanner and presses the scan button. The printer scans the answer sheet and generates image data.

[1261] Step 2:

[1262] The printer transfers the generated image data to a server on the cloud via the Internet.

[1263] Input: Image data

[1264] Output: Image data stored on a cloud server

[1265] Specific operation: The printer's data transfer module uploads the generated image data to a cloud server via a secure internet connection.

[1266] Step 3:

[1267] The server passes the received image data to an image analysis module on the cloud, where character recognition is performed using OCR technology.

[1268] Input: Image data stored on a cloud server

[1269] Output: Text data

[1270] Specific operation: The server passes the image data to the image analysis module, which uses OCR technology to perform character recognition and extract text data from the image data.

[1271] Step 4:

[1272] The server compares the text data with a pre-set database of correct answers to determine whether each answer is correct, and also calculates the score and total score.

[1273] Input: Text data, correct answer database

[1274] Output: Grade result data

[1275] Specific operation: The server's scoring module compares the text data with a correct answer database, determines whether each question is correct, and calculates the score.

[1276] Step 5:

[1277] The server transmits the generated score result data to the information display device.

[1278] Input: Grade result data

[1279] Output: The score result data sent to the information display device

[1280] Specific operation: The graded result data is sent to the information display device, and the graded results are displayed and prepared for printing.

[1281] Step 6:

[1282] The information display device prints the received marking results on the answer sheet and outputs them together with the answer sheet.

[1283] Input: Grade result data sent to the information display device

[1284] Output: Printed answer sheet

[1285] Specific operation: The printing module of the information display device prints the received results on an answer sheet and outputs it in a form that can be taken out by the user.

[1286] Step 7:

[1287] The server stores all test result data in a cloud database, and users can log in to the cloud management screen to view test results from across the country in real time.

[1288] Input: Grade result data

[1289] Output: Aggregated data stored in the database, aggregated data displayed on the cloud management screen

[1290] Specific operation: Scoring result data is stored in a cloud database, allowing users to view the aggregated results in real time from the management screen.

[1291] Step 8:

[1292] The server uses the generative AI model to generate personalized learning feedback based on the student's answers. Through the prompt generation function, a text format is generated to input specific answer data into the AI ​​model.

[1293] Input: Scoring result data, prompt

[1294] Output: Learning feedback

[1295] Specific operation: Prompt sentences are generated based on the scoring results, and these are input into an AI model to generate feedback for each student.

[1296] Step 9:

[1297] The generated learning feedback is provided to the user (student).

[1298] Input: Learning feedback

[1299] Output: Feedback information provided to students

[1300] What it does: Allows students to view feedback on their devices and displays learning plans and advice.

[1301] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1302] The present invention relates to a test marking system used in educational institutions, cram schools, etc., and provides a system comprising an image acquisition means, a data transfer means, an image analysis means, a marking means, a result transfer means, a printing means, a summary display means, a learning support means, a test creation support means, an interactive explanation means, and an emotion engine.

[1303] Specific Examples

[1304] 1. Image acquisition method

[1305] The user (teacher) places the test answer sheet in the printer and starts the scanning operation. The printer scans the answer sheet and generates image data of the answer sheet. This image acquisition means transfers all answer information as electronic data to a server on the cloud.

[1306] 2. Means of data transfer

[1307] The printer transfers the generated image data to a cloud server via the Internet. This data transfer means allows the image data to be uploaded to the cloud server quickly and safely.

[1308] 3. Image analysis methods

[1309] The server passes the received image data to the image analysis module, which uses OCR (Optical Character Recognition) technology to recognize the characters in the image and convert them into text data, thereby extracting the contents of the answer sheet as text data.

[1310] 4. Scoring Method

[1311] The server sends the analyzed text data to Gemini (an AI system). Gemini analyzes the received text data and compares it with a database of pre-registered correct answers. This determines whether each answer is correct, calculates the score, and calculates the total score.

[1312] 5. Means of transferring results

[1313] The server transmits the generated scoring result data to the printer, and as a result, the printer is ready to print the scoring result based on the received data.

[1314] 6. Printing means

[1315] The printer prints the received marking results on the answer sheet and outputs it together with the answer sheet. By this printing means, the answer sheet on which the marking results are written can be obtained without manual work.

[1316] 7. Aggregate display method

[1317] The server stores all test result data in a cloud database. Users (teachers, etc.) can log in to the cloud management screen and view nationwide test results in real time. A summary display method allows for quick understanding of overall educational trends.

[1318] 8. Learning Support Tools

[1319] Gemini analyzes each student's marks and generates explanations for incorrect answers and correct answers. It also provides future study plans and advice. This learning support tool helps students understand their weaknesses and study more effectively.

[1320] 9. Test Creation Support Tools

[1321] Users can access a cloud-based server and input new test questions. Based on the input questions, the server will suggest similar questions and generate questions according to difficulty. This test creation support method allows teachers to efficiently create high-quality tests.

[1322] 10. Interactive Explanation Tools

[1323] Users (students) can access Gemini on the cloud from their devices (PCs, tablets, etc.) and ask questions about specific issues. Gemini then provides detailed explanations in text or audio to the user's questions. This interactive explanation method enables individualized learning support.

[1324] 11. Emotion Engine

[1325] The server is equipped with an emotion engine that can recognize users' emotions in real time. For example, it can read emotions from the facial expressions and tone of voice displayed by students while answering questions and provide appropriate feedback. This emotion engine is used in conjunction with interactive explanation tools and learning support tools to increase users' motivation.

[1326] Specifically, when a user uses the interactive explanation tool, the server analyzes their emotions using an emotion engine, and if they appear unsure about their answers, the server will explain in a gentler tone. Furthermore, the learning support tool can also adjust the difficulty and content of the learning plan based on the user's emotions. This maximizes students' learning efficiency and improves their learning persistence.

[1327] Specific examples

[1328] For example, in the case of marking a math test in a middle school, the process would be as follows:

[1329] 1. The user (teacher) places the answer sheet in the printer and starts scanning.

[1330] 2. The printer transfers the image data to the server.

[1331] 3. The server passes the image data to the image analysis module, where character recognition is performed using OCR technology.

[1332] 4. The image analysis module provides the text data to Gemini for scoring.

[1333] 5. Gemini sends the scoring results to the server, which then forwards them to the printer.

[1334] 6. The printer prints out the scoring results.

[1335] 7. The server stores all results in a database and provides nationwide aggregate data in real time.

[1336] 8. Gemini generates explanations for incorrect answers and future study plans, which are then printed out by the printer.

[1337] 9. The user (teacher) accesses the server to create a new test.

[1338] 10. The user (student) uses the device to ask Gemini questions and receive explanations.

[1339] 11. The server uses an emotion engine to recognize the user's emotions and provide appropriate feedback or adjust the learning plan.

[1340] These methods will automate test marking in educational institutions, significantly improving teaching efficiency and student learning effectiveness. In particular, the combination of emotion engines will further personalize individual learning support, resulting in more effective education.

[1341] The processing flow will be explained below.

[1342] Step 1:

[1343] The user (teacher) places the test answer sheet in the printer and starts the scanning operation by selecting the scan button on the printer's operation screen.

[1344] Step 2:

[1345] The printer scans the answer sheet and generates image data, which is temporarily stored in the internal memory.

[1346] Step 3:

[1347] The printer transfers the generated image data to a server on the cloud. Specifically, the printer uploads the image data to a specified server URL via the Internet.

[1348] Step 4:

[1349] The server receives the image data sent from the printer. The receiving module temporarily stores the image data and then passes it to the image analysis module.

[1350] Step 5:

[1351] The image analysis module uses OCR technology to extract text information from the received image data, and in this process, each answer is individually identified and converted into text data.

[1352] Step 6:

[1353] The image analysis module then sends the converted text data to Gemini, which then analyzes the received text data and compares it with a pre-registered database of correct answers.

[1354] Step 7:

[1355] Gemini compares the analyzed data with the correct answer data to determine whether each answer is correct, calculates the score for each question, and calculates the total score.

[1356] Step 8:

[1357] Gemini generates the scoring results as data and sends it to the server, where a results transfer module prepares the data for sending to the printer.

[1358] Step 9:

[1359] The server sends the received score data to the printer, which analyzes the data and prints the results on the answer sheet.

[1360] Step 10:

[1361] The printer prints the results of the scoring onto the answer sheet and outputs it along with the answer sheet. This printing process allows the correctness and score of each answer to be visually confirmed.

[1362] Step 11:

[1363] The server stores all scoring data in a cloud database, which is later used for aggregation and analysis.

[1364] Step 12:

[1365] Users (teachers) can log in to a cloud-based management screen and view nationwide test results and statistical information in real time. On the management screen, they can check the overall correct answer rate, trends in incorrect answers, and the causes of incorrect answers on specific questions.

[1366] Step 13:

[1367] Gemini analyzes each student's answering habits, generates explanations for incorrect answers and explanations for correct answers, and provides future study plans and advice. These explanations and study plans are stored on a cloud server.

[1368] Step 14:

[1369] The server sends the explanations and study plan data generated by Gemini to a printer, which prints them on the answer sheets. The printed explanations and study plans are then added to the answer sheets returned to the students.

[1370] Step 15:

[1371] The user (teacher) accesses a server on the cloud to input new test questions into the system. Based on the input questions, the server suggests similar questions and generates questions according to difficulty.

[1372] Step 16:

[1373] Users (students) access Gemini from their devices and type or speak questions about specific problems. Gemini then provides detailed explanations of the user's questions in text or voice.

[1374] Step 17:

[1375] The server is equipped with an emotion engine that analyzes the facial expressions and tone of voice of students in real time while they are filling out answer sheets or interacting with Gemini. This emotion data is stored in the cloud.

[1376] Step 18:

[1377] Gemini adjusts dialogue based on emotional data provided by its emotion engine. For example, if a student appears anxious, it will provide explanations in a more understandable and gentle tone. Furthermore, in its learning support tools, it adjusts the difficulty of the lesson plan based on emotional data to maintain student motivation.

[1378] This will streamline test marking at educational institutions, significantly improving teaching efficiency and student learning effectiveness. In particular, the combination of the emotion engine will further personalize individual learning support, resulting in more effective education.

[1379] Example 2

[1380] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1381] Conventional test marking systems require manual marking, placing a heavy burden on teachers. Furthermore, they tend to provide insufficient individual learning support to students, resulting in a decline in learning effectiveness. Furthermore, because learning feedback that takes into account the user's emotions is not provided, it is difficult to maintain student motivation. There is a need to solve these issues and provide efficient and effective educational support.

[1382] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1383] In this invention, the server includes an image acquisition unit that scans test answer sheets, a data transfer unit that transfers the scanned image data to an information processing device on the cloud, and an image analysis unit that receives the scanned image data and performs optical character recognition, thereby enabling automatic test scoring and individual learning support.

[1384] The "image acquisition means" is a device that scans the test answer sheet and generates image data thereof.

[1385] The "data transfer means" is a device or system that transfers the generated scanned image data to an information processing device on the cloud.

[1386] An "image analysis means" is a device or system that receives scanned image data and converts it into text data using optical character recognition (OCR) technology.

[1387] The "evaluation means" is a device or system that analyzes and evaluates the text data recognized by the image analysis means.

[1388] The "result transfer means" is a device or system that transmits the evaluation results from the information processing device to the printing device.

[1389] "Printing means" refers to a device that prints out the evaluation results and outputs them together with the answer sheet.

[1390] An "emotion recognition means" is a device or system that recognizes the user's emotions and provides appropriate feedback based on that information.

[1391] A "learning support tool" is a device or system that provides an individual learning plan and appropriate learning support to students.

[1392] The "aggregation display means" is a device or system that displays the evaluation result data aggregated by the information processing device on the cloud in real time.

[1393] This invention is specifically embodied as a test marking system for use in educational institutions, cram schools, etc. The system mainly comprises an image acquisition means, a data transfer means, an image analysis means, an evaluation means, a result transfer means, a printing means, an emotion recognition means, a learning support means, and a summary display means.

[1394] 1. Image acquisition method

[1395] The user (teacher) places the test answer sheet into a scanner or multifunction printer and starts the scanning operation. Specifically, the scanner function of the printer is used to generate image data of the answer sheet. This image acquisition means acquires all of the students' answer information as electronic data and transfers it to a cloud server via the Internet.

[1396] 2. Means of data transfer

[1397] The printer or scanner transfers the generated image data to a cloud server via the Internet. The transfer process uses encryption technology, ensuring the data is uploaded to the server quickly and securely. This data transfer method converts physical paper into electronic data.

[1398] 3. Image analysis methods

[1399] The server passes the received image data to its internal image analysis module. Specifically, it applies the path of the image file stored on the server to the image analysis module. The image analysis module uses OCR (optical character recognition) technology to recognize the characters in the image and convert the results into text data. The OCR engine used here operates with high accuracy and can even recognize handwritten characters to a certain extent.

[1400] 4. Evaluation Methods

[1401] The server sends the analyzed text data to an evaluation module, which compares the text data with a pre-registered correct answer database and determines whether each answer is correct or incorrect. At this stage, partial points are considered and multiple answers are allowed, achieving a scoring system similar to that of a human. Once the correctness determination is complete, a total score and individual feedback are generated.

[1402] 5. Means of transferring results

[1403] The server then sends the generated evaluation result data to the printer. Specifically, communication from the server to the printer is automatic, and the printer receives the data and prepares to print the results.

[1404] 6. Printing means

[1405] The printer prints the received evaluation results on the answer sheet and outputs it together with the answer sheet. Here, not only is the answer correct or incorrect, but feedback on the incorrect parts is also printed together. This printing means allows the answer sheet with the evaluation results to be obtained without any manual work.

[1406] 7. Aggregate display method

[1407] The server stores all assessment result data in a cloud database. Users (teachers and administrators) can log in to the cloud management screen and view nationwide test results in real time. This aggregate display method allows them to see at a glance what grade trends are occurring in each region and school.

[1408] 8. Learning Support Tools

[1409] The evaluation module analyzes each student's evaluation results and generates explanations for incorrect answers and correct answers. It also provides future study plans and advice, which students can access via the cloud. This learning support tool helps students understand their weaknesses and study more effectively.

[1410] 9. Emotion recognition means

[1411] The server is equipped with an emotion engine that works in conjunction with the interactive explanation and learning support tools to analyze the user's emotions. Specifically, it analyzes facial expressions and vocal tone when answering questions, and adjusts the tone of feedback and explanations based on the results. For example, if a student appears anxious, the system will use a gentler tone when explaining.

[1412] Specific examples

[1413] For example, in the case of a middle school math test grading exercise, the exercise proceeds as follows:

[1414] 1. The user (teacher) places the answer sheet in the printer and presses the scan button to start scanning.

[1415] 2. The image data generated by the printer is transferred to the server via the Internet.

[1416] 3. The server receives the image data and passes it to the analysis module, where it uses OCR technology to recognize the characters.

[1417] 4. The server sends the OCR results to the evaluation module, which compares them with a database and scores them.

[1418] 5. The server sends the evaluation results to the printer, which prints them out.

[1419] 6. The server stores all results in a cloud database and provides real-time nationwide aggregate data to the management screen.

[1420] 7. The evaluation module generates explanations for incorrect answers and future learning plans, which are then made available to students via the cloud.

[1421] 8. The user (teacher) accesses the server to create a new test and enters the questions.

[1422] 9. The user (student) asks questions to the evaluation module from the terminal and receives an explanation.

[1423] 10. The server uses an emotion engine to analyze the user's emotions and provide appropriate feedback.

[1424] Example prompt sentence:

[1425] "Please explain the system for marking middle school math tests. Please provide a detailed explanation of the step-by-step process and the techniques used."

[1426] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1427] Step 1:

[1428] The user (teacher) places a test answer sheet into the printer and initiates the scanning operation. The printer scans the answer sheet and stores the generated image data in its internal memory. The input for this step is the physical answer sheet, and the output is the scanned image data. The specific action is for the user to press the scan button on the printer.

[1429] Step 2:

[1430] The printer transfers the image data stored in its internal memory to a cloud server via the Internet. This data is encrypted before transmission, making it highly secure. The input in step 2 is the image data stored in the printer, and the output is the image data uploaded to the server. Specifically, the printer automatically starts transferring data.

[1431] Step 3:

[1432] The server passes the received image data to the image analysis module. The server saves the image data in a specific directory and applies that path to the image analysis module. The input of step 3 is the path of the image data saved on the server, and the output is the raw data passed to the image analysis module. Specifically, the server executes the built-in script.

[1433] Step 4:

[1434] The image analysis module uses OCR (optical character recognition) technology to extract character information from image data and convert it into text data. The input of step 4 is the image data passed to the image analysis module, and the output is text data. Specifically, the OCR engine scans the image data and generates a text file containing the recognized characters.

[1435] Step 5:

[1436] The server sends the analyzed text data to the evaluation module. The evaluation module compares the text data with a database of pre-registered correct answers and determines whether each answer is correct. The input in step 5 is the text data, and the output is the evaluation result. Specifically, the evaluation module compares the text data with the correct answer data and calculates the score.

[1437] Step 6:

[1438] The server sends the generated evaluation result data to the printer. The input here is the evaluation result data, and the output is the data sent to the printer. In concrete terms, the server transfers the evaluation result data to the printer using a specific communication protocol.

[1439] Step 7:

[1440] The printer prints the received evaluation results on the answer sheet and outputs the answer sheet along with the feedback. The input of step 7 is the evaluation result data sent to the printer, and the output is the printed answer sheet. In concrete terms, the printer prints the evaluation results and feedback on the paper.

[1441] Step 8:

[1442] The server stores all evaluation result data in a database on the cloud. Users (teachers and administrators) can log in to the cloud management screen and view nationwide test results in real time. The input for step 8 is the evaluation result data, and the output is the aggregated data stored in the database. Specifically, the server runs a script that periodically updates the database.

[1443] Step 9:

[1444] The evaluation module analyzes the evaluation results for each student and generates explanations for incorrect answers and correct answers. Based on this, it also provides future study plans and advice. The input of Step 9 is the evaluation result data for each student, and the output is individual explanations and study plans. Specifically, the evaluation module uses an algorithm to perform the analysis.

[1445] Step 10:

[1446] The user (teacher) accesses a server on the cloud and inputs new test questions. Based on the input questions, the server suggests similar questions and adjusts the difficulty level. The input in step 10 is the new test question data, and the output is the generated test questions. Specifically, the server searches the database and suggests related questions.

[1447] Step 11:

[1448] The user (student) uses a terminal to access the evaluation module on the cloud and ask a question about a specific problem. The evaluation module provides a detailed explanation in text or audio. The input of step 11 is the question data from the user, and the output is the generated explanation data. In concrete terms, the evaluation module responds to the question using natural language processing.

[1449] Step 12:

[1450] The server uses an emotion engine to analyze the user's emotions and provides appropriate feedback based on that information. The emotion engine analyzes the student's facial expressions and tone of voice and generates feedback according to that state. The input of step 12 is the user's emotion data, and the output is adjusted feedback. Specifically, the emotion engine uses a machine learning algorithm to analyze emotions.

[1451] These steps will enable automated test scoring and personalized learning support, improving the quality and efficiency of education.

[1452] (Application example 2)

[1453] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1454] Conventional test scoring systems used in educational institutions and cram schools require many steps, such as image acquisition, data transfer, image analysis, and scoring, making efficient and rapid processing difficult. Furthermore, quality control in factories requires a lot of manual work due to a lack of automation, limiting improvements in production efficiency and quality. Furthermore, training and employee motivation management within factories are also inadequate. To solve these issues, a means is needed to integrate both systems and perform processing efficiently and with high accuracy.

[1455] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1456] In this invention, the server includes an image acquisition means, a data transfer means, an image analysis means, a scoring means, a result transfer means, a printing means, a quality assessment means, and a result display means. This not only improves the efficiency and accuracy of test scoring at educational institutions and cram schools, but also automates quality control work in factories, reducing manual work and improving production efficiency. Furthermore, having the ability to display results in real time on a cloud-based server not only enables rapid data collection and analysis, but also enables employee motivation management, thereby improving overall work efficiency and quality.

[1457] "Image capture means" means a device or mechanism that captures an image of an object or document of interest by scanning or photographing it.

[1458] "Data transfer means" refers to a device or means for transferring acquired image data to a remote computer system such as a server.

[1459] "Image analysis means" refers to software or algorithms that analyze acquired image data and perform character recognition, shape recognition, etc.

[1460] A "scoring means" is a device or system that determines whether a question is correct or incorrect and calculates a score based on the analyzed text data.

[1461] "Results Transfer Means" means the mechanism or system that transfers the scoring results data from the server to a printer or other output device.

[1462] The "printing means" is a device or mechanism that directly prints out onto paper or the like based on the data transferred by the result transfer means.

[1463] "Quality assessment means" refers to devices or algorithms used to evaluate the quality of a product and determine whether it passes or fails.

[1464] The "result display means" is a device or system for displaying the quality assessment results and grading results on the terminals of the factory manager or teacher.

[1465] "Aggregation display means" refers to a function or system for displaying data aggregated on a server on the cloud in real time.

[1466] "Support tools" are functions and algorithms that provide learning plans and suggestions for improving production processes based on analyzed data.

[1467] The present invention is a system for achieving efficient business processing and quality control in educational institutions and factories. Specifically, the system comprises an image acquisition means, a data transfer means, an image analysis means, a scoring means, a result transfer means, a printing means, a quality judgment means, a result display means, a tally display means, and a support means.

[1468] 1. Image acquisition method

[1469] The server is connected to image capture devices such as cameras and scanners, which are used to scan test answer sheets in educational institutions or take images of products in factories, and the captured image data is stored at high resolution for further processing.

[1470] 2. Means of data transfer

[1471] Image data acquired by the image acquisition device is transferred at high speed to a cloud server. Specifically, it is sent via the Internet using a secure protocol. A database system is running on the server to properly store the received data.

[1472] 3. Image analysis methods

[1473] The server analyzes the received image data using OCR (Optical Character Recognition) technology. This analysis utilizes software libraries such as TensorFlow and OpenCV. For example, in the case of test answer sheets, handwritten characters are converted into text data, and in the case of factories, product shapes and character information are identified.

[1474] 4. Scoring Method

[1475] The text data obtained by the image analysis means is analyzed by a server-side AI system. At the educational institution, the results are compared with pre-registered correct answer data and graded. High-precision grading is possible using an AI model (for example, a generative AI model using TensorFlow). At the factory, the pass / fail of the product is determined based on the analyzed data.

[1476] 5. Means of transferring results

[1477] The server transfers the grading results and quality assessment results to a printer or management terminal via the Internet, allowing the results to be printed out at the educational institution and displayed in real time on the manager's terminal at the factory.

[1478] 6. Printing means

[1479] The printer prints the marking results on the answer sheets based on the received data. Even in factories, it is possible to print out the quality assessment results and keep a record. This allows results to be checked without manual work, improving work efficiency.

[1480] 7. Results display means

[1481] The server displays the scoring results and quality assessment results in real time through the result display means. Teachers at educational institutions can view nationwide test results from the management screen, and factory managers can view quality control data in real time.

[1482] 8. Aggregate display method

[1483] The server displays the aggregated data on the cloud, allowing educational institutions to aggregate student grade data and analyze learning trends, while factories can collect quality data and analyze production trends.The aggregated data is displayed in dashboard format to help operators make decisions.

[1484] 9. Support methods

[1485] The server uses the generative AI model to provide learning plans and suggestions for improving production processes based on the analysis results. This function allows educational institutions to analyze students' weaknesses and present effective learning plans, and factories to propose specific measures for quality improvement.

[1486] Examples of concrete examples and prompts

[1487] For example, automated inspections of products are carried out in factories, which identify defects in real time and notify managers, and in educational institutions, tests taken by students are quickly graded and the results are made available.

[1488] Example prompts to input to a generative AI model:

[1489] "Please analyze this image data and determine the quality of the product."

[1490] "Use an AI model to determine whether this product meets your quality standards."

[1491] This will realize a system that efficiently grades tests in educational institutions and performs quality control in factories.

[1492] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1493] Step 1:

[1494] The image acquisition means captures images of the product using a camera installed on the factory robot. The user (operator) sets the camera in the appropriate position and inputs the command to start capturing. The acquired image data generates an output that is sent to the server as high-resolution electronic data.

[1495] Step 2:

[1496] The server receives the acquired image data and stores it in a database on the cloud. A data transfer method is used for this purpose. The server transfers the data using a secure protocol and processes it for storage in the database. The input is image data, and the output is data stored in the database on the cloud.

[1497] Step 3:

[1498] The server uses image analysis means to analyze the stored image data. Specifically, it uses the OpenCV library to perform character recognition (OCR) and shape recognition of the image. The input is the image data on the cloud, and the output is the analyzed text data and shape data.

[1499] Step 4:

[1500] The analyzed text and shape data is sent to an AI system on the server. The AI ​​system uses a generative AI model to analyze this data and determine its quality. The input is text and shape data, and the output is a "pass" or "fail" result. Specifically, matching and evaluation are performed based on quality standards.

[1501] Step 5:

[1502] The server transfers the obtained quality assessment results to the terminal of the factory manager. The results are displayed on the manager's screen in real time via the Internet using a result transfer means. The input is the quality assessment results, and the output is the display screen of the manager's terminal.

[1503] Step 6:

[1504] The server uses a means of tallying and displaying the quality assessment results in real time in the form of a dashboard, allowing the administrator to grasp the overall quality status at a glance. The input is the assessment result data for each product, and the output is a dashboard of the aggregated assessment status.

[1505] Step 7:

[1506] The server uses support tools to generate improvement proposals based on the judged data. The generative AI model proposes optimal improvement measures, taking past data into account. The input is quality judgment data and past production data, and the output is a report of improvement proposals and advice. Managers can then improve the production process based on this report.

[1507] Step 8:

[1508] The user (administrator) implements the proposed improvement plan and feeds the results back to the server. The fed-back data is used as learning material for future generative AI models, improving the accuracy of the system. The input is the result data of the implemented improvement plan, and the output is an updated AI model.

[1509] The above steps will enable efficient business processing and quality control in educational institutions and factories. An example of a prompt sentence would be, "Please use the AI ​​model to determine whether this product meets the quality standards." This would enable highly accurate judgments to be made by giving the AI ​​system instructions.

[1510] 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.

[1511] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1512] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1513] [Fourth embodiment]

[1514] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1515] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1516] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1517] 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.

[1518] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1519] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1520] 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.

[1521] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1522] 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.

[1523] 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 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.

[1524] 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.

[1525] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1526] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1527] The present invention relates to a test marking system used in educational institutions, cram schools, etc., and provides a system consisting of an image acquisition means, a data transfer means, an image analysis means, a marking means, a result transfer means, a printing means, a tally display means, a learning support means, a test creation support means, and an interactive explanation means.

[1528] Specific Examples

[1529] 1. Image acquisition method

[1530] The user (teacher) places the test answer sheet in the printer and starts the scanning operation. The printer scans the answer sheet and generates image data of the answer sheet. This image acquisition means transfers all answer information as electronic data to a server on the cloud.

[1531] 2. Means of data transfer

[1532] The printer transfers the generated image data to a cloud server via the Internet. This data transfer means allows the image data to be uploaded to the cloud server quickly and safely.

[1533] 3. Image analysis methods

[1534] The server passes the received image data to an image analysis module on the cloud. The image analysis module uses OCR (Optical Character Recognition) technology to recognize the characters in the image and convert them into text data. This allows the contents of the answer sheet to be extracted as text data.

[1535] 4. Scoring Method

[1536] Gemini (an AI system) uses text data to compare it with a pre-set database of correct answers and determine whether each answer is correct. It also calculates the score and total score, enabling fast and accurate scoring.

[1537] 5. Means of transferring results

[1538] The server transmits the generated scoring result data to the printer, and as a result, the printer is ready to print the scoring result based on the received data.

[1539] 6. Printing means

[1540] The printer prints the received marking results on the answer sheet and outputs it together with the answer sheet. By this printing means, the answer sheet on which the marking results are written can be obtained without manual work.

[1541] 7. Aggregate display method

[1542] The server stores all test result data in a cloud database. Users (teachers, etc.) can log in to the cloud management screen and view nationwide test results in real time. A summary display method allows for quick understanding of overall educational trends.

[1543] 8. Learning Support Tools

[1544] Gemini analyzes each student's marks and generates explanations for incorrect answers and correct answers. It also provides future study plans and advice. This learning support tool helps students understand their weaknesses and study more effectively.

[1545] 9. Test Creation Support Tools

[1546] Users can access a cloud-based server and input new test questions. Based on the input questions, the server will suggest similar questions and generate questions according to difficulty. This test creation support method allows teachers to efficiently create high-quality tests.

[1547] 10. Interactive Explanation Tools

[1548] Users (students) can access Gemini on the cloud from their devices (PCs, tablets, etc.) and ask questions about specific issues. Gemini then provides detailed explanations in text or audio to the user's questions. This interactive explanation method enables individualized learning support.

[1549] Specific examples

[1550] For example, in the case of marking a math test in a middle school, the process would be as follows:

[1551] 1. The user (teacher) places the answer sheet in the printer and starts scanning.

[1552] 2. The printer transfers the image data to the server.

[1553] 3. The server passes the image data to the image analysis module, where character recognition is performed using OCR technology.

[1554] 4. The image analysis module provides the text data to Gemini for scoring.

[1555] 5. Gemini sends the scoring results to the server, which then forwards them to the printer.

[1556] 6. The printer prints out the scoring results.

[1557] 7. The server stores all results in a database and provides nationwide aggregate data in real time.

[1558] 8. Gemini generates explanations for incorrect answers and future study plans, which are then printed out by the printer.

[1559] 9. The user (teacher) accesses the server to create a new test.

[1560] 10. The user (student) uses the device to ask Gemini questions and receive explanations.

[1561] By using each of the above methods, test grading at educational institutions will become more efficient, and appropriate feedback and learning support will be provided to each student.

[1562] The processing flow will be explained below.

[1563] Step 1:

[1564] The user (teacher) places the test answer sheet in the printer and starts the scanning operation by selecting the scan button on the printer's operation screen.

[1565] Step 2:

[1566] The printer scans the answer sheet and generates image data, which is temporarily stored in the internal memory.

[1567] Step 3:

[1568] The printer transfers the generated image data to a server on the cloud. Specifically, the printer uploads the image data to a specified server URL via the Internet.

[1569] Step 4:

[1570] The server receives the image data sent from the printer. The receiving module temporarily stores the image data and then passes it to the image analysis module.

[1571] Step 5:

[1572] The image analysis module uses OCR technology to extract text information from the received image data, and in this process, each answer is individually identified and converted into text data.

[1573] Step 6:

[1574] The image analysis module sends the converted text data to Gemini (an AI system), which analyzes the received text data and compares it with a pre-registered correct answer database.

[1575] Step 7:

[1576] Gemini compares the analyzed data with the correct answer data to determine whether each answer is correct, calculates the score for each question, and calculates the total score.

[1577] Step 8:

[1578] Gemini generates the scoring results as data and sends it to the server, where a results transfer module prepares the data for sending to the printer.

[1579] Step 9:

[1580] The server sends the received score data to the printer, which analyzes the data and prints the results on the answer sheet.

[1581] Step 10:

[1582] The printer prints the results of the scoring onto the answer sheet and outputs it along with the answer sheet. This printing process allows the correctness and score of each answer to be visually confirmed.

[1583] Step 11:

[1584] The server stores all scoring data in a cloud database, which is later used for aggregation and analysis.

[1585] Step 12:

[1586] Users (teachers) can log in through a cloud-based management screen and view nationwide test results and statistical information in real time.

[1587] Step 13:

[1588] Gemini analyzes each student's answering tendencies and generates explanations about the reasons for incorrect answers, the percentage of correct answers, and points that require particular attention. This content also includes future study plans and advice.

[1589] Step 14:

[1590] The server sends the explanations and study plan data generated by Gemini to the printer, which then prints it onto the answer sheet.

[1591] Step 15:

[1592] Users (teachers) input new test questions into the system, and a server on the cloud helps by suggesting similar questions and generating questions according to difficulty.

[1593] Step 16:

[1594] Users (students) access Gemini from their devices and type or speak questions about specific problems. Gemini analyzes the user's questions and provides detailed explanations in text or voice.

[1595] These steps will enable educational institutions to automate test marking processes, significantly improving teaching efficiency and student learning outcomes.

[1596] Example 1

[1597] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1598] Conventional test scoring systems require the manual analysis and scoring of scanned image data from answer sheets, which is time-consuming and labor-intensive. Furthermore, they lack the functionality to automatically provide individual students with answering trends and study plans, placing a heavy burden on educators. Furthermore, they lacked support functions for creating new test questions, making it difficult to create effective tests.

[1599] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1600] In this invention, the server includes an image acquisition means for scanning test answer sheets, a data transfer means for transferring the scanned image data to a cloud server, an image analysis means for receiving the scanned image data and performing character recognition, an artificial intelligence means for grading the text data recognized by the image analysis means, a result transfer means for sending the grading results from the server to an output device, a printing means for printing the grading results on the output device and outputting them together with the answer sheets, and a learning support means for analyzing the grading results for each student and providing explanations for incorrect answers and a learning plan. This enables fast and accurate automatic grading, individual learning support, and support for creating new test questions.

[1601] The "image acquisition means" is a means for scanning the test answer sheet and generating image data.

[1602] "Data transfer means" refers to a means for transferring scanned image data to a server on the cloud.

[1603] The "image analysis means" is a means for performing character recognition on the received scanned image data and converting it into text data.

[1604] The "artificial intelligence means" is a means for scoring based on the text data recognized by the image analysis means.

[1605] The "result transfer means" is a means for transmitting the grading results from the server to the output device.

[1606] "Printing means" refers to a means for printing and physically outputting the marking results on the answer sheet.

[1607] "Learning support tools" are tools for analyzing each student's marks and providing explanations for incorrect answers and future learning plans.

[1608] The "summary display means" is a means for displaying the scoring result data stored on a server on the cloud in real time.

[1609] "Test creation support tools" are tools that help educators create new test questions by suggesting similar questions and generating questions according to difficulty.

[1610] This invention is a system for automating test marking in educational institutions and learning facilities. This system is composed of an image acquisition means, a data transfer means, an image analysis means, an artificial intelligence means, a result transfer means, a printing means, a learning support means, a tally display means, and a test creation support means.

[1611] Image Acquisition Method

[1612] The user (teacher) places the test answer sheet on the printer and starts the scanning operation. The printer scans the answer sheet and generates image data. This image data is then transferred to a server on the cloud. For example, a general multi-function printer can be used.

[1613] Data Transfer Method

[1614] The printer transfers the generated image data to a cloud server via the Internet, using common data communication protocols (such as SSL / TLS) to ensure secure and fast data transmission.

[1615] Image analysis methods

[1616] The server passes the received image data to an image analysis module on the cloud. The image analysis module uses OCR (Optical Character Recognition) technology to recognize characters in the image and convert them into text data. An example of OCR software is Tesseract.

[1617] Artificial Intelligence Tools

[1618] The server has an artificial intelligence means for scoring the text data. This artificial intelligence means compares the text data with a database of correct answers to determine whether each answer is correct or incorrect. It also calculates the score and calculates the total score. Using an AI system like Gemini allows for fast and accurate scoring.

[1619] Result transfer method

[1620] The server transmits the generated scoring result data to an output device, for example, the server transmits the scoring result to a printer and prepares it for printing.

[1621] printing means

[1622] The printer prints the received marking results on the answer sheet and outputs it, thus providing an answer sheet with the marking results written on it without any manual work.

[1623] Learning support tools

[1624] The AI ​​tool analyzes each student's marks and provides explanations for incorrect answers and future study plans. This learning support allows students to identify their weaknesses and study more effectively. For example, explanations generated by Gemini are used.

[1625] Aggregation display method

[1626] The server stores all grading data in a cloud database, and users (teachers) can view test results in real time from the administration screen, enabling rapid data analysis to grasp trends across the entire educational institution.

[1627] Test Creation Support Tools

[1628] Users (teachers) can access a server on the cloud and input new test questions. Based on the input questions, the server will suggest similar questions and help generate questions according to difficulty. These support functions make it possible to efficiently create high-quality tests.

[1629] Specific examples

[1630] For example, in the case of marking a math test in a middle school, the process would be as follows:

[1631] 1. The user (teacher) places the answer sheet in the printer and starts scanning.

[1632] 2. The printer transfers the image data to the server.

[1633] 3. The server passes the image data to the image analysis module, where character recognition is performed using OCR technology.

[1634] 4. The image analysis module provides the text data to the artificial intelligence means for scoring.

[1635] 5. The artificial intelligence means sends the scoring results to the server, which forwards them to the printer.

[1636] 6. The printer prints out the scoring results.

[1637] 7. The server stores all results in a database and provides nationwide aggregate data in real time.

[1638] 8. An artificial intelligence tool generates explanations for incorrect answers and future study plans, which are then printed out by a printer.

[1639] 9. The user (teacher) accesses the server to create a new test.

[1640] 10. The user (student) uses the terminal to ask questions to the artificial intelligence means and receives explanations.

[1641] Prompt Sentence Examples

[1642] If a tutor were to output the results of an analysis of a math test's incorrect answers, the prompt to the AI ​​would be:

[1643] For the math test results below, generate an explanation for each student's incorrect answer.

[1644] Question 1: 3 2 + 1

[1645] Correct Answer: 7

[1646] Student A's answer: 6 (False)

[1647] Student B's answer: 7 (correct)

[1648] Student C's answer: 8 (False)

[1649] Question 2: 5 + 4 2

[1650] Correct answer: 13

[1651] Student A's answer: 10 (incorrect)

[1652] Student B's answer: 13 (correct)

[1653] Student C's answer: 12 (False)

[1654] Your explanation should include detailed explanations of the correct way to answer each question, the reasons for incorrect answers, and a future study plan for each student.

[1655] By using the above methods, test grading at educational institutions will become more efficient, and appropriate feedback and learning support will be provided to each student.

[1656] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1657] Step 1:

[1658] The user (teacher) places the test answer sheet on the printer and starts the scanning operation. The printer scans the answer sheet and generates image data. This image data is transferred to a server on the cloud. Specifically, the user presses the "scan" button on the printer's operation panel, and the printer reads the answer sheet using an optical sensor. The generated image data is sent to the server via the network.

[1659] Input: Physical test answer sheet

[1660] Output: High-resolution image data of the answer sheet

[1661] Step 2:

[1662] The server receives the image data sent from the printer. The data is securely transferred using an encryption protocol (e.g., SSL / TLS). Specifically, the server listens on a specific port, accepts connection requests from the printer, and receives data.

[1663] Input: Image data of the answer sheet transferred from the printer

[1664] Output: Image data stored on a cloud server

[1665] Step 3:

[1666] The server passes the received image data to an image analysis module in the cloud. The image analysis module uses OCR technology to recognize the characters on the answer sheet and convert them into text data. Specifically, the server inputs the image data into an OCR engine (e.g., Tesseract) and extracts text information from the image.

[1667] Input: Image data stored on a cloud server

[1668] Output: Text data

[1669] Step 4:

[1670] The server passes the text data to the artificial intelligence means and requests grading. The artificial intelligence means compares the text data with a correct answer database, determines whether each answer is correct, and calculates the score. Specifically, the artificial intelligence means (e.g., an AI grading system) compares the answers to each question with pre-registered correct answers and determines whether they are correct. It then calculates the points and calculates the total score.

[1671] Input: Text data generated by OCR

[1672] Output: Scoring result data (correctness and score for each answer)

[1673] Step 5:

[1674] The server then sends the generated scoring result data to the printer. This data includes each student's score and whether their answer was correct or incorrect. Specifically, the server establishes a network connection to send the scoring result data to the printer, encrypts it, and sends the data.

[1675] Input: Grade result data

[1676] Output: The graded results data sent to the printer

[1677] Step 6:

[1678] The printer prints the received marking results on the answer sheet and outputs it. Specifically, the printer stores the received data in its internal memory and prints the marking results on the answer sheet according to the control program. Once printing is complete, the answer sheet with the added evaluation information is ejected.

[1679] Input: Marking result data sent to the printer

[1680] Output: Answer sheet with evaluation information printed on it

[1681] Step 7:

[1682] The server stores all the grading result data in a database on the cloud. Users (teachers) can view the test results in real time from the management screen. Specifically, the server stores the result data in a database and allows users to access it using a web browser.

[1683] Input: Grade result data

[1684] Output: Result data stored in a database on the cloud

[1685] Step 8:

[1686] The AI ​​tool analyzes each student's marks and provides explanations for incorrect answers and future study plans. Specifically, the AI ​​model analyzes each student's incorrect answer patterns, and the generative AI model generates optimal explanations and study advice.

[1687] Input: Grading result data for each student

[1688] Output: Explanation of incorrect answers and study plan

[1689] Step 9:

[1690] The user (teacher) accesses a cloud-based server and inputs new test questions. Based on the input questions, the server suggests similar questions and generates questions according to difficulty. Specifically, the educator inputs questions through a web interface, and the server makes suggestions by comparing them with an existing question database.

[1691] Input: Input data for new test questions

[1692] Output: Suggested similar problems

[1693] Step 10:

[1694] The user (student) uses a device to access the AI ​​means on the cloud and ask a question about a specific problem. The AI ​​means then provides a detailed explanation in text or voice in response to the question. Specifically, the question is analyzed, and the generative AI model generates the most appropriate explanation and provides it to the user.

[1695] Input: Student question data

[1696] Output: Detailed explanatory text or audio

[1697] This will make test marking more efficient at educational institutions and enable appropriate feedback and learning support for each student.

[1698] (Application example 1)

[1699] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1700] While traditional test scoring systems are required to improve the efficiency of scoring, they still require manual feedback and learning support. Furthermore, there is a lack of systems that can identify individual learning trends and weaknesses and provide educational support based on those trends. Furthermore, it is difficult to display real-time aggregated scores or provide feedback using AI.

[1701] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1702] In this invention, the server includes an image acquisition means, a data transfer means, an image analysis means, and a learning support means using a generative AI model, which enables fast and accurate test scoring, individual learning trend analysis and feedback, and real-time summary display.

[1703] "Image acquisition means" refers to a device or system that scans a test answer sheet and generates image data thereof.

[1704] The "data transfer means" is a device or system that transfers the generated image data to a server on the cloud.

[1705] The "image analysis means" is a device or system that performs character recognition on the received scanned image data and converts it into text data.

[1706] The "scoring means" is a device or system that scores text data recognized by the image analysis means by comparing it with a pre-set correct answer database.

[1707] The "result transfer means" is a device or system that transmits the scoring results from a server on the cloud to the information display device.

[1708] The "printing means" is a device or system that outputs the marking results together with the answer sheet on the information display device.

[1709] The "summary display means" is a device or system that displays the score result data that has been summed up on a server on the cloud in real time.

[1710] A "learning support tool" is a device or system that uses a generative AI model to provide individualized learning feedback based on the student's answers.

[1711] A "generative AI model" is a type of artificial intelligence technology that automatically generates individual suggestions and feedback based on specific answer data.

[1712] A "prompt" is a text format used to input specific answer data into an AI model.

[1713] This invention relates to a test marking system used in educational institutions, cram schools, etc., and includes an image acquisition means, a data transfer means, an image analysis means, a marking means, a result transfer means, a printing means, a summary display means, a learning support means, a generative AI model, and a prompt sentence generation function. Specifically, this system is implemented using the following system configuration and operating procedures.

[1714] System Configuration

[1715] 1. Image acquisition method:

[1716] The user (teacher) places the test answer sheet in the printer and starts the scanning operation. The printer scans the answer sheet and generates image data of the answer sheet. This image acquisition means transfers all answer information as electronic data to a server on the cloud.

[1717] 2. Means of data transfer:

[1718] The printer transfers the generated image data to a cloud server via the Internet. This data transfer means allows the image data to be uploaded to the cloud server quickly and safely.

[1719] 3. Image analysis methods:

[1720] The server passes the received image data to an image analysis module on the cloud. The image analysis module uses OCR (Optical Character Recognition) technology to recognize the characters in the image and convert them into text data. This allows the contents of the answer sheet to be extracted as text data.

[1721] 4. Scoring Procedure:

[1722] The server compares the text data with a pre-set database of correct answers to determine whether each answer is correct. It also calculates the score and total score, enabling fast and accurate scoring.

[1723] 5. Means of transfer of results:

[1724] The server transmits the generated grading result data to the information display device, which then becomes ready to print the grading result based on the received data by the transfer means.

[1725] 6. Printing means:

[1726] The information display device prints the received marking results on the answer sheet and outputs it together with the answer sheet. By this printing means, the answer sheet on which the marking results are written can be obtained without any manual work.

[1727] 7. Aggregate display method:

[1728] The server stores all test result data in a cloud database. Users (teachers, etc.) can log in to the cloud management screen and view nationwide test results in real time. A summary display method allows for quick understanding of overall educational trends.

[1729] 8. Learning Support Tools:

[1730] The server analyzes each student's marks and generates explanations for incorrect answers and correct answers. It also provides future study plans and advice. This learning support tool helps students understand their weaknesses and study more effectively.

[1731] 9. Generative AI model and prompt generation function:

[1732] The server uses a generative AI model to provide personalized learning feedback based on the student's answers. Through the prompt generation function, a text format is generated to input specific answer data into the AI ​​model, which then automatically generates personalized suggestions and feedback.

[1733] Specific examples

[1734] For example, in the case of mathematics test marking at an educational institution, the process might look like this:

[1735] The user (teacher) places the answer sheet in the printer and starts scanning.

[1736] The printer transfers the image data to the cloud server.

[1737] The server passes the image data to the image analysis module, which uses OCR technology to recognize characters.

[1738] The image analysis module provides the text data to the server for scoring.

[1739] The server sends the results of the scoring to the information display device and prints them out.

[1740] The server stores all results in a database and provides nationwide aggregate data in real time.

[1741] The server uses the generative AI model to generate learning feedback for each student, for example by forming prompts like:

[1742] Prompt Sentence Examples

[1743] Student Name: Taro Yamada

[1744] Test Subject: Mathematics

[1745] problem:

[1746] 1. 5 + 3 = ?

[1747] 2. 10 - 2 = ?

[1748] answer:

[1749] 1. 8

[1750] 2.8

[1751] Based on this prompt, the generative AI model automatically generates individualized suggestions and feedback to provide to students.

[1752] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1753] Step 1:

[1754] The user (teacher) places the test answer sheet in the printer and starts the scanning operation. The printer scans the answer sheet and generates its image data.

[1755] Input: Test answer sheet

[1756] Output: Image data

[1757] Specific operation: The user places the answer sheet in the printer's scanner and presses the scan button. The printer scans the answer sheet and generates image data.

[1758] Step 2:

[1759] The printer transfers the generated image data to a server on the cloud via the Internet.

[1760] Input: Image data

[1761] Output: Image data stored on a cloud server

[1762] Specific operation: The printer's data transfer module uploads the generated image data to a cloud server via a secure internet connection.

[1763] Step 3:

[1764] The server passes the received image data to an image analysis module on the cloud, where character recognition is performed using OCR technology.

[1765] Input: Image data stored on a cloud server

[1766] Output: Text data

[1767] Specific operation: The server passes the image data to the image analysis module, which uses OCR technology to perform character recognition and extract text data from the image data.

[1768] Step 4:

[1769] The server compares the text data with a pre-set database of correct answers to determine whether each answer is correct, and also calculates the score and total score.

[1770] Input: Text data, correct answer database

[1771] Output: Grade result data

[1772] Specific operation: The server's scoring module compares the text data with a correct answer database, determines whether each question is correct, and calculates the score.

[1773] Step 5:

[1774] The server transmits the generated score result data to the information display device.

[1775] Input: Grade result data

[1776] Output: The score result data sent to the information display device

[1777] Specific operation: The graded result data is sent to the information display device, and the graded results are displayed and prepared for printing.

[1778] Step 6:

[1779] The information display device prints the received marking results on the answer sheet and outputs them together with the answer sheet.

[1780] Input: Grade result data sent to the information display device

[1781] Output: Printed answer sheet

[1782] Specific operation: The printing module of the information display device prints the received results on an answer sheet and outputs it in a form that can be taken out by the user.

[1783] Step 7:

[1784] The server stores all test result data in a cloud database, and users can log in to the cloud management screen to view test results from across the country in real time.

[1785] Input: Grade result data

[1786] Output: Aggregated data stored in the database, aggregated data displayed on the cloud management screen

[1787] Specific operation: Scoring result data is stored in a cloud database, allowing users to view the aggregated results in real time from the management screen.

[1788] Step 8:

[1789] The server uses the generative AI model to generate personalized learning feedback based on the student's answers. Through the prompt generation function, a text format is generated to input specific answer data into the AI ​​model.

[1790] Input: Scoring result data, prompt

[1791] Output: Learning feedback

[1792] Specific operation: Prompt sentences are generated based on the scoring results, and these are input into an AI model to generate feedback for each student.

[1793] Step 9:

[1794] The generated learning feedback is provided to the user (student).

[1795] Input: Learning feedback

[1796] Output: Feedback information provided to students

[1797] What it does: Allows students to view feedback on their devices and displays learning plans and advice.

[1798] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1799] The present invention relates to a test marking system used in educational institutions, cram schools, etc., and provides a system comprising an image acquisition means, a data transfer means, an image analysis means, a marking means, a result transfer means, a printing means, a summary display means, a learning support means, a test creation support means, an interactive explanation means, and an emotion engine.

[1800] Specific Examples

[1801] 1. Image acquisition method

[1802] The user (teacher) places the test answer sheet in the printer and starts the scanning operation. The printer scans the answer sheet and generates image data of the answer sheet. This image acquisition means transfers all answer information as electronic data to a server on the cloud.

[1803] 2. Means of data transfer

[1804] The printer transfers the generated image data to a cloud server via the Internet. This data transfer means allows the image data to be uploaded to the cloud server quickly and safely.

[1805] 3. Image analysis methods

[1806] The server passes the received image data to the image analysis module, which uses OCR (Optical Character Recognition) technology to recognize the characters in the image and convert them into text data, thereby extracting the contents of the answer sheet as text data.

[1807] 4. Scoring Method

[1808] The server sends the analyzed text data to Gemini (an AI system). Gemini analyzes the received text data and compares it with a database of pre-registered correct answers. This determines whether each answer is correct, calculates the score, and calculates the total score.

[1809] 5. Means of transferring results

[1810] The server transmits the generated scoring result data to the printer, and as a result, the printer is ready to print the scoring result based on the received data.

[1811] 6. Printing means

[1812] The printer prints the received marking results on the answer sheet and outputs it together with the answer sheet. By this printing means, the answer sheet on which the marking results are written can be obtained without manual work.

[1813] 7. Aggregate display method

[1814] The server stores all test result data in a cloud database. Users (teachers, etc.) can log in to the cloud management screen and view nationwide test results in real time. A summary display method allows for quick understanding of overall educational trends.

[1815] 8. Learning Support Tools

[1816] Gemini analyzes each student's marks and generates explanations for incorrect answers and correct answers. It also provides future study plans and advice. This learning support tool helps students understand their weaknesses and study more effectively.

[1817] 9. Test Creation Support Tools

[1818] Users can access a cloud-based server and input new test questions. Based on the input questions, the server will suggest similar questions and generate questions according to difficulty. This test creation support method allows teachers to efficiently create high-quality tests.

[1819] 10. Interactive Explanation Tools

[1820] Users (students) can access Gemini on the cloud from their devices (PCs, tablets, etc.) and ask questions about specific issues. Gemini then provides detailed explanations in text or audio to the user's questions. This interactive explanation method enables individualized learning support.

[1821] 11. Emotion Engine

[1822] The server is equipped with an emotion engine that can recognize users' emotions in real time. For example, it can read emotions from the facial expressions and tone of voice displayed by students while answering questions and provide appropriate feedback. This emotion engine is used in conjunction with interactive explanation tools and learning support tools to increase users' motivation.

[1823] Specifically, when a user uses the interactive explanation tool, the server analyzes their emotions using an emotion engine, and if they appear unsure about their answers, the server will explain in a gentler tone. Furthermore, the learning support tool can also adjust the difficulty and content of the learning plan based on the user's emotions. This maximizes students' learning efficiency and improves their learning persistence.

[1824] Specific examples

[1825] For example, in the case of marking a math test in a middle school, the process would be as follows:

[1826] 1. The user (teacher) places the answer sheet in the printer and starts scanning.

[1827] 2. The printer transfers the image data to the server.

[1828] 3. The server passes the image data to the image analysis module, where character recognition is performed using OCR technology.

[1829] 4. The image analysis module provides the text data to Gemini for scoring.

[1830] 5. Gemini sends the scoring results to the server, which then forwards them to the printer.

[1831] 6. The printer prints out the scoring results.

[1832] 7. The server stores all results in a database and provides nationwide aggregate data in real time.

[1833] 8. Gemini generates explanations for incorrect answers and future study plans, which are then printed out by the printer.

[1834] 9. The user (teacher) accesses the server to create a new test.

[1835] 10. The user (student) uses the device to ask Gemini questions and receive explanations.

[1836] 11. The server uses an emotion engine to recognize the user's emotions and provide appropriate feedback or adjust the learning plan.

[1837] These methods will automate test marking in educational institutions, significantly improving teaching efficiency and student learning effectiveness. In particular, the combination of emotion engines will further personalize individual learning support, resulting in more effective education.

[1838] The processing flow will be explained below.

[1839] Step 1:

[1840] The user (teacher) places the test answer sheet in the printer and starts the scanning operation by selecting the scan button on the printer's operation screen.

[1841] Step 2:

[1842] The printer scans the answer sheet and generates image data, which is temporarily stored in the internal memory.

[1843] Step 3:

[1844] The printer transfers the generated image data to a server on the cloud. Specifically, the printer uploads the image data to a specified server URL via the Internet.

[1845] Step 4:

[1846] The server receives the image data sent from the printer. The receiving module temporarily stores the image data and then passes it to the image analysis module.

[1847] Step 5:

[1848] The image analysis module uses OCR technology to extract text information from the received image data, and in this process, each answer is individually identified and converted into text data.

[1849] Step 6:

[1850] The image analysis module then sends the converted text data to Gemini, which then analyzes the received text data and compares it with a pre-registered database of correct answers.

[1851] Step 7:

[1852] Gemini compares the analyzed data with the correct answer data to determine whether each answer is correct, calculates the score for each question, and calculates the total score.

[1853] Step 8:

[1854] Gemini generates the scoring results as data and sends it to the server, where a results transfer module prepares the data for sending to the printer.

[1855] Step 9:

[1856] The server sends the received score data to the printer, which analyzes the data and prints the results on the answer sheet.

[1857] Step 10:

[1858] The printer prints the results of the scoring onto the answer sheet and outputs it along with the answer sheet. This printing process allows the correctness and score of each answer to be visually confirmed.

[1859] Step 11:

[1860] The server stores all scoring data in a cloud database, which is later used for aggregation and analysis.

[1861] Step 12:

[1862] Users (teachers) can log in to a cloud-based management screen and view nationwide test results and statistical information in real time. On the management screen, they can check the overall correct answer rate, trends in incorrect answers, and the causes of incorrect answers on specific questions.

[1863] Step 13:

[1864] Gemini analyzes each student's answering habits, generates explanations for incorrect answers and explanations for correct answers, and provides future study plans and advice. These explanations and study plans are stored on a cloud server.

[1865] Step 14:

[1866] The server sends the explanations and study plan data generated by Gemini to a printer, which prints them on the answer sheets. The printed explanations and study plans are then added to the answer sheets returned to the students.

[1867] Step 15:

[1868] The user (teacher) accesses a server on the cloud to input new test questions into the system. Based on the input questions, the server suggests similar questions and generates questions according to difficulty.

[1869] Step 16:

[1870] Users (students) access Gemini from their devices and type or speak questions about specific problems. Gemini then provides detailed explanations of the user's questions in text or voice.

[1871] Step 17:

[1872] The server is equipped with an emotion engine that analyzes the facial expressions and tone of voice of students in real time while they are filling out answer sheets or interacting with Gemini. This emotion data is stored in the cloud.

[1873] Step 18:

[1874] Gemini adjusts dialogue based on emotional data provided by its emotion engine. For example, if a student appears anxious, it will provide explanations in a more understandable and gentle tone. Furthermore, in its learning support tools, it adjusts the difficulty of the lesson plan based on emotional data to maintain student motivation.

[1875] This will streamline test marking at educational institutions, significantly improving teaching efficiency and student learning effectiveness. In particular, the combination of the emotion engine will further personalize individual learning support, resulting in more effective education.

[1876] Example 2

[1877] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1878] Conventional test marking systems require manual marking, placing a heavy burden on teachers. Furthermore, they tend to provide insufficient individual learning support to students, resulting in a decline in learning effectiveness. Furthermore, because learning feedback that takes into account the user's emotions is not provided, it is difficult to maintain student motivation. There is a need to solve these issues and provide efficient and effective educational support.

[1879] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1880] In this invention, the server includes an image acquisition unit that scans test answer sheets, a data transfer unit that transfers the scanned image data to an information processing device on the cloud, and an image analysis unit that receives the scanned image data and performs optical character recognition, thereby enabling automatic test scoring and individual learning support.

[1881] The "image acquisition means" is a device that scans the test answer sheet and generates image data thereof.

[1882] The "data transfer means" is a device or system that transfers the generated scanned image data to an information processing device on the cloud.

[1883] An "image analysis means" is a device or system that receives scanned image data and converts it into text data using optical character recognition (OCR) technology.

[1884] The "evaluation means" is a device or system that analyzes and evaluates the text data recognized by the image analysis means.

[1885] The "result transfer means" is a device or system that transmits the evaluation results from the information processing device to the printing device.

[1886] "Printing means" refers to a device that prints out the evaluation results and outputs them together with the answer sheet.

[1887] An "emotion recognition means" is a device or system that recognizes the user's emotions and provides appropriate feedback based on that information.

[1888] A "learning support tool" is a device or system that provides an individual learning plan and appropriate learning support to students.

[1889] The "aggregation display means" is a device or system that displays the evaluation result data aggregated by the information processing device on the cloud in real time.

[1890] This invention is specifically embodied as a test marking system for use in educational institutions, cram schools, etc. The system mainly comprises an image acquisition means, a data transfer means, an image analysis means, an evaluation means, a result transfer means, a printing means, an emotion recognition means, a learning support means, and a summary display means.

[1891] 1. Image acquisition method

[1892] The user (teacher) places the test answer sheet into a scanner or multifunction printer and starts the scanning operation. Specifically, the scanner function of the printer is used to generate image data of the answer sheet. This image acquisition means acquires all of the students' answer information as electronic data and transfers it to a cloud server via the Internet.

[1893] 2. Means of data transfer

[1894] The printer or scanner transfers the generated image data to a cloud server via the Internet. The transfer process uses encryption technology, ensuring the data is uploaded to the server quickly and securely. This data transfer method converts physical paper into electronic data.

[1895] 3. Image analysis methods

[1896] The server passes the received image data to its internal image analysis module. Specifically, it applies the path of the image file stored on the server to the image analysis module. The image analysis module uses OCR (optical character recognition) technology to recognize the characters in the image and convert the results into text data. The OCR engine used here operates with high accuracy and can even recognize handwritten characters to a certain extent.

[1897] 4. Evaluation Methods

[1898] The server sends the analyzed text data to an evaluation module, which compares the text data with a pre-registered correct answer database and determines whether each answer is correct or incorrect. At this stage, partial points are considered and multiple answers are allowed, achieving a scoring system similar to that of a human. Once the correctness determination is complete, a total score and individual feedback are generated.

[1899] 5. Means of transferring results

[1900] The server then sends the generated evaluation result data to the printer. Specifically, communication from the server to the printer is automatic, and the printer receives the data and prepares to print the results.

[1901] 6. Printing means

[1902] The printer prints the received evaluation results on the answer sheet and outputs it together with the answer sheet. Here, not only is the answer correct or incorrect, but feedback on the incorrect parts is also printed together. This printing means allows the answer sheet with the evaluation results to be obtained without any manual work.

[1903] 7. Aggregate display method

[1904] The server stores all assessment result data in a cloud database. Users (teachers and administrators) can log in to the cloud management screen and view nationwide test results in real time. This aggregate display method allows them to see at a glance what grade trends are occurring in each region and school.

[1905] 8. Learning Support Tools

[1906] The evaluation module analyzes each student's evaluation results and generates explanations for incorrect answers and correct answers. It also provides future study plans and advice, which students can access via the cloud. This learning support tool helps students understand their weaknesses and study more effectively.

[1907] 9. Emotion recognition means

[1908] The server is equipped with an emotion engine that works in conjunction with the interactive explanation and learning support tools to analyze the user's emotions. Specifically, it analyzes facial expressions and vocal tone when answering questions, and adjusts the tone of feedback and explanations based on the results. For example, if a student appears anxious, the system will use a gentler tone when explaining.

[1909] Specific examples

[1910] For example, in the case of a middle school math test grading exercise, the exercise proceeds as follows:

[1911] 1. The user (teacher) places the answer sheet in the printer and presses the scan button to start scanning.

[1912] 2. The image data generated by the printer is transferred to the server via the Internet.

[1913] 3. The server receives the image data and passes it to the analysis module, where it uses OCR technology to recognize the characters.

[1914] 4. The server sends the OCR results to the evaluation module, which compares them with a database and scores them.

[1915] 5. The server sends the evaluation results to the printer, which prints them out.

[1916] 6. The server stores all results in a cloud database and provides real-time nationwide aggregate data to the management screen.

[1917] 7. The evaluation module generates explanations for incorrect answers and future learning plans, which are then made available to students via the cloud.

[1918] 8. The user (teacher) accesses the server to create a new test and enters the questions.

[1919] 9. The user (student) asks questions to the evaluation module from the terminal and receives an explanation.

[1920] 10. The server uses an emotion engine to analyze the user's emotions and provide appropriate feedback.

[1921] Example prompt sentence:

[1922] "Please explain the system for marking middle school math tests. Please provide a detailed explanation of the step-by-step process and the techniques used."

[1923] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1924] Step 1:

[1925] The user (teacher) places a test answer sheet into the printer and initiates the scanning operation. The printer scans the answer sheet and stores the generated image data in its internal memory. The input for this step is the physical answer sheet, and the output is the scanned image data. The specific action is for the user to press the scan button on the printer.

[1926] Step 2:

[1927] The printer transfers the image data stored in its internal memory to a cloud server via the Internet. This data is encrypted before transmission, making it highly secure. The input in step 2 is the image data stored in the printer, and the output is the image data uploaded to the server. Specifically, the printer automatically starts transferring data.

[1928] Step 3:

[1929] The server passes the received image data to the image analysis module. The server saves the image data in a specific directory and applies that path to the image analysis module. The input of step 3 is the path of the image data saved on the server, and the output is the raw data passed to the image analysis module. Specifically, the server executes the built-in script.

[1930] Step 4:

[1931] The image analysis module uses OCR (optical character recognition) technology to extract character information from image data and convert it into text data. The input of step 4 is the image data passed to the image analysis module, and the output is text data. Specifically, the OCR engine scans the image data and generates a text file containing the recognized characters.

[1932] Step 5:

[1933] The server sends the analyzed text data to the evaluation module. The evaluation module compares the text data with a database of pre-registered correct answers and determines whether each answer is correct. The input in step 5 is the text data, and the output is the evaluation result. Specifically, the evaluation module compares the text data with the correct answer data and calculates the score.

[1934] Step 6:

[1935] The server sends the generated evaluation result data to the printer. The input here is the evaluation result data, and the output is the data sent to the printer. In concrete terms, the server transfers the evaluation result data to the printer using a specific communication protocol.

[1936] Step 7:

[1937] The printer prints the received evaluation results on the answer sheet and outputs the answer sheet along with the feedback. The input of step 7 is the evaluation result data sent to the printer, and the output is the printed answer sheet. In concrete terms, the printer prints the evaluation results and feedback on the paper.

[1938] Step 8:

[1939] The server stores all evaluation result data in a database on the cloud. Users (teachers and administrators) can log in to the cloud management screen and view nationwide test results in real time. The input for step 8 is the evaluation result data, and the output is the aggregated data stored in the database. Specifically, the server runs a script that periodically updates the database.

[1940] Step 9:

[1941] The evaluation module analyzes the evaluation results for each student and generates explanations for incorrect answers and correct answers. Based on this, it also provides future study plans and advice. The input of Step 9 is the evaluation result data for each student, and the output is individual explanations and study plans. Specifically, the evaluation module uses an algorithm to perform the analysis.

[1942] Step 10:

[1943] The user (teacher) accesses a server on the cloud and inputs new test questions. Based on the input questions, the server suggests similar questions and adjusts the difficulty level. The input in step 10 is the new test question data, and the output is the generated test questions. Specifically, the server searches the database and suggests related questions.

[1944] Step 11:

[1945] The user (student) uses a terminal to access the evaluation module on the cloud and ask a question about a specific problem. The evaluation module provides a detailed explanation in text or audio. The input of step 11 is the question data from the user, and the output is the generated explanation data. In concrete terms, the evaluation module responds to the question using natural language processing.

[1946] Step 12:

[1947] The server uses an emotion engine to analyze the user's emotions and provides appropriate feedback based on that information. The emotion engine analyzes the student's facial expressions and tone of voice and generates feedback according to that state. The input of step 12 is the user's emotion data, and the output is adjusted feedback. Specifically, the emotion engine uses a machine learning algorithm to analyze emotions.

[1948] These steps will enable automated test scoring and personalized learning support, improving the quality and efficiency of education.

[1949] (Application example 2)

[1950] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1951] Conventional test scoring systems used in educational institutions and cram schools require many steps, such as image acquisition, data transfer, image analysis, and scoring, making efficient and rapid processing difficult. Furthermore, quality control in factories requires a lot of manual work due to a lack of automation, limiting improvements in production efficiency and quality. Furthermore, training and employee motivation management within factories are also inadequate. To solve these issues, a means is needed to integrate both systems and perform processing efficiently and with high accuracy.

[1952] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1953] In this invention, the server includes an image acquisition means, a data transfer means, an image analysis means, a scoring means, a result transfer means, a printing means, a quality assessment means, and a result display means. This not only improves the efficiency and accuracy of test scoring at educational institutions and cram schools, but also automates quality control work in factories, reducing manual work and improving production efficiency. Furthermore, having the ability to display results in real time on a cloud-based server not only enables rapid data collection and analysis, but also enables employee motivation management, thereby improving overall work efficiency and quality.

[1954] "Image capture means" means a device or mechanism that captures an image of an object or document of interest by scanning or photographing it.

[1955] "Data transfer means" refers to a device or means for transferring acquired image data to a remote computer system such as a server.

[1956] "Image analysis means" refers to software or algorithms that analyze acquired image data and perform character recognition, shape recognition, etc.

[1957] A "scoring means" is a device or system that determines whether a question is correct or incorrect and calculates a score based on the analyzed text data.

[1958] "Results Transfer Means" means the mechanism or system that transfers the scoring results data from the server to a printer or other output device.

[1959] The "printing means" is a device or mechanism that directly prints out onto paper or the like based on the data transferred by the result transfer means.

[1960] "Quality assessment means" refers to devices or algorithms used to evaluate the quality of a product and determine whether it passes or fails.

[1961] The "result display means" is a device or system for displaying the quality assessment results and grading results on the terminals of the factory manager or teacher.

[1962] "Aggregation display means" refers to a function or system for displaying data aggregated on a server on the cloud in real time.

[1963] "Support tools" are functions and algorithms that provide learning plans and suggestions for improving production processes based on analyzed data.

[1964] The present invention is a system for achieving efficient business processing and quality control in educational institutions and factories. Specifically, the system comprises an image acquisition means, a data transfer means, an image analysis means, a scoring means, a result transfer means, a printing means, a quality judgment means, a result display means, a tally display means, and a support means.

[1965] 1. Image acquisition method

[1966] The server is connected to image capture devices such as cameras and scanners, which are used to scan test answer sheets in educational institutions or take images of products in factories, and the captured image data is sto...

Claims

1. an image acquisition means for scanning the test answer sheet; a data transfer means for transferring the scanned image data to a server on the cloud; image analysis means for receiving scanned image data and performing character recognition; scoring means for scoring the text data recognized by the image analysis means; a result transfer means for transmitting the scoring results from the server to the printer; a printing means for printing out the marking results together with the answer sheet; A system including:

2. The system according to claim 1 , wherein the server on the cloud comprises a summary display means for displaying the summarized score result data in real time.

3. The system according to claim 1 , wherein the server on the cloud comprises a learning support means for analyzing the student's answer tendency and providing an explanation of the answer and a future learning plan.

4. The system according to claim 1 , wherein the server on the cloud comprises a test creation support means for generating new test questions and proposing similar questions.

5. The system according to claim 1 , wherein the server on the cloud comprises an interactive explanation means for interacting with students through the terminals and providing detailed explanations.

Images