system

Abstract

The system according to the embodiment aims to accurately and efficiently mark written tests. [Solution] The system according to the embodiment comprises a reading unit, a data transfer unit, a learning unit, an analysis unit, and a feedback providing unit. The reading unit reads the written answers of the examinee. The data transfer unit converts the written answers read by the reading unit into text data. The learning unit learns correct answers and related fields and backgrounds in advance. The analysis unit compares and analyzes the written answers of the examinee based on the information learned by the learning unit. The feedback providing unit provides the examinee with the analysis results obtained by the analysis unit.

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] With conventional techniques, it is difficult to accurately and efficiently evaluate examinees' answers in scoring written tests, and there is room for improvement.

[0005] The system according to the embodiment aims to accurately and efficiently mark written tests. [Means for solving the problem]

[0006] The system according to the embodiment comprises a reading unit, a data transfer unit, a learning unit, an analysis unit, and a feedback providing unit. The reading unit reads the written answers of the examinee. The data transfer unit converts the written answers read by the reading unit into text data. The learning unit learns the correct answers and related fields and backgrounds in advance. The analysis unit compares and analyzes the written answers of the examinee based on the information learned by the learning unit. The feedback providing unit provides the examinee with the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately and efficiently score written tests. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The written test scoring system according to an embodiment of the present invention uses OCR technology and AI to streamline the scoring of written tests, providing benefits to both test setters and test takers. This written test scoring system first uses OCR technology to read test takers' written responses and convert them into text data. Next, an AI system pre-trained in correct answers and related fields and background compares and analyzes the test takers' written responses and scores them. The system outputs not only correct and incorrect answers, but also the strengths and weaknesses of incorrect answers along with the analysis results. This reduces the burden on test setters and test graders and provides high-value-added feedback to test takers. For example, OCR technology can be used to read test takers' written responses and convert them into text data. Accurate interpretation of handwritten characters and diagrams is also required. For example, OCR technology can be used to convert handwritten sentences and formulas into digital data, enabling subsequent analysis by AI. Next, an AI system pre-trained in correct answers and related fields and background compares and analyzes the test takers' written responses. AI compares test takers' answers with the correct answers and determines which parts are correct and which parts are incorrect. For example, in a history essay question, AI compares the test taker's answers with the correct historical facts to evaluate their accuracy. Furthermore, when an incorrect answer is given, AI outputs the analysis results, highlighting the strengths and weaknesses. This allows test takers to specifically understand which parts of their answers were good and which parts need improvement. For example, in a mathematics essay question, AI analyzes the test taker's answering process and points out any calculation errors that may have been made despite the correct approach. This system reduces the burden on test setters and graders. Because AI automatically grades questions, manual grading is no longer necessary, significantly reducing time and effort. It also provides test takers with valuable feedback. By identifying specific areas for improvement and strengths, test takers can clarify their learning direction. In this way, the use of OCR technology and AI can streamline the grading of written exams, creating a system that benefits both test setters and test takers. This allows the written test scoring system to efficiently read test takers' written responses, convert them into text data, and provide accurate analysis and feedback.

[0029] The written test scoring system according to the embodiment includes a reading unit, a data transfer unit, a learning unit, an analysis unit, and a feedback providing unit. The reading unit reads the test taker's written response. The test taker's written response may include, but is not limited to, handwritten characters and diagrams. The reading unit may read the handwritten characters and diagrams using, for example, OCR technology. The reading unit may also directly read written responses submitted in digital format. The reading unit may also read printed written responses using OCR technology. For example, the reading unit may scan handwritten characters with a high-resolution scanner and convert them into text information using OCR technology. Digital written responses submitted in a specific file format may also be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The data transfer unit converts the written response read by the reading unit into text data. Examples of text data include, but are not limited to, PDF format, image format, and text format. For example, the data transfer unit may scan the handwritten written response and save it as image data. The data transfer unit then converts the image data into text data using OCR technology. The data transfer unit can also capture handwritten written responses using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The data transfer unit can also write handwritten written responses with a dedicated digital pen, which then converts the responses into digital data in real time. For example, a sensor detects the pen's movement and saves it as text data. The learning unit pre-learns correct answers and related fields and backgrounds. The learning unit learns correct answers and related fields and backgrounds using, for example, a machine learning algorithm. Examples of learning data include, but are not limited to, past test questions and sample answers, related literature, and databases. The learning unit learns correct answers and related fields and backgrounds using, for example, a machine learning algorithm, to improve the accuracy of the analysis by the analysis unit. The analysis unit compares and analyzes the test taker's written responses based on the information learned by the learning unit.The analysis unit, for example, compares the examinee's written answer with the correct answer and determines which parts are correct and which parts are incorrect. The analysis unit, for example, uses a comparison algorithm to calculate the similarity between the examinee's written answer and the correct answer. The analysis unit can also analyze the content of the examinee's written answer and not only determine whether the answer is correct or incorrect, but also analyze the strengths and weaknesses of an incorrect answer. For example, the analysis unit analyzes the logical structure and expression of the examinee's written answer and provides specific feedback. The feedback providing unit provides the analysis results obtained by the analysis unit to the examinee. The feedback providing unit provides, for example, feedback that specifically indicates the strengths and weaknesses of the examinee's written answer. The feedback providing unit, for example, generates feedback in text format and provides it to the examinee. The feedback providing unit can also provide visual feedback such as graphs and charts. For example, the feedback providing unit displays the evaluation results of the examinee's written answer as graphs or charts, providing them in a visually easy-to-understand format. This allows the written test scoring system according to the embodiment to efficiently read the examinee's written answer, convert it into text data, and accurately analyze and provide feedback.

[0030] The reading unit can read the examinee's handwritten text and diagrams using OCR technology. Examples of OCR technologies include, but are not limited to, Tesseract and ABBYY FineReader. For example, the reading unit can scan handwritten text using a high-resolution scanner and convert it into text information using OCR technology. The reading unit can also scan handwritten diagrams and convert the contents of the diagrams into digital data using OCR technology. For example, the reading unit can scan handwritten graphs and charts and convert them into digital data using OCR technology. The reading unit can also scan handwritten mathematical formulas and convert the contents of the formulas into digital data using OCR technology. For example, the reading unit can scan handwritten mathematical formulas and convert them into digital data using OCR technology. This allows the handwritten text and diagrams to be accurately read. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without AI. For example, the reading unit can input handwritten text and diagram data into a generation AI and have the generation AI convert the contents of the text and diagrams into digital data.

[0031] The data transfer unit can convert the scanned written response into text data. Examples of text data include, but are not limited to, PDF, image, and text formats. For example, the data transfer unit scans the handwritten written response and saves it as image data. The data transfer unit then converts the image data into text data using OCR technology. The data transfer unit can also capture a photo of the handwritten written response using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The data transfer unit can also write the handwritten written response with a dedicated digital pen, which can then convert the image data into digital data in real time. For example, a sensor can detect the pen's movement and save it as character data. This allows the written response to be accurately converted into text data. Some or all of the above-described processing in the data transfer unit can be performed using, for example, AI, or without AI. For example, the data transfer unit can input image data of the handwritten written response into a generation AI and have the generation AI convert the image data into text data.

[0032] The learning unit can learn the correct answers and related fields and backgrounds in advance. The learning unit learns the correct answers and related fields and backgrounds, for example, using a machine learning algorithm. Learning data includes, but is not limited to, past test questions and sample answers, related literature, and databases. The learning unit learns the correct answers and related fields and backgrounds, for example, using a machine learning algorithm, and improves the accuracy of analysis by the analysis unit. By learning the correct answers and related fields and backgrounds in advance, the accuracy of analysis is improved. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without AI. For example, the learning unit may input data on the correct answers and related fields and backgrounds into the generation AI and have the generation AI learn the data.

[0033] The analysis unit can compare the test taker's written response with the correct answer and determine whether it is correct or incorrect. For example, the analysis unit can compare the test taker's written response with the correct answer and determine which parts are correct and which parts are incorrect. For example, the analysis unit can use a comparison algorithm to calculate the similarity between the test taker's written response and the correct answer. The analysis unit can also analyze the content of the test taker's written response and not only determine whether it is correct or incorrect, but also analyze the strengths and weaknesses of an incorrect answer. For example, the analysis unit can analyze the logical structure and expression of the test taker's written response and provide specific feedback. This allows for accurate evaluation of the test taker's written response. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the test taker's written response and the correct answer into a generation AI and have the generation AI compare and analyze them.

[0034] The feedback providing unit can provide the examinee with the good points and shortcomings of an incorrect answer along with the analysis results. For example, the feedback providing unit provides feedback that specifically indicates the good points and shortcomings of the examinee's written answer. For example, the feedback providing unit generates feedback in text format and provides it to the examinee. The feedback providing unit can also provide visual feedback such as graphs and charts. For example, the feedback providing unit displays the evaluation results of the examinee's written answer as a graph or chart, providing it in a visually easy-to-understand format. This makes it possible to provide specific feedback to the examinee. Some or all of the above-mentioned processing by the feedback providing unit may be performed using, or without, AI. For example, the feedback providing unit can input the analysis results obtained by the analysis unit to a generation AI and cause the generation AI to generate feedback.

[0035] The reading unit may incorporate multidimensional scanning technology to accurately read not only handwritten characters but also charts and graphs. For example, the reading unit may scan a handwritten graph at high resolution and convert it into digital data. The reading unit may also scan from multiple angles to accurately read even the finer details of the chart. The reading unit may also scan a color image and accurately recognize color differences to convert them into digital data. This allows for accurate reading of handwritten characters and charts. Examples of multidimensional scanning technology include, but are not limited to, 3D scanning and multispectral scanning. Some or all of the above-described processing in the reading unit may be performed using, or without, AI. For example, the reading unit may input data of handwritten charts and graphs into a generation AI and have the generation AI convert the data into digital data.

[0036] The reading unit analyzes the examinee's writing pressure and handwriting characteristics during reading, enabling more accurate text data conversion. The reading unit, for example, analyzes the strength of writing pressure and reproduces the shading of characters. The reading unit can also recognize handwriting characteristics and accurately convert the shape of individual characters into data. The reading unit can also detect changes in writing pressure and identify areas that need rewriting or correction. This allows for more accurate text data conversion by analyzing writing pressure and handwriting characteristics. Analysis of writing pressure and handwriting characteristics includes, but is not limited to, a writing pressure sensor and a handwriting analysis algorithm. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without AI. For example, the reading unit can input the examinee's writing pressure and handwriting data into a generation AI and have the generation AI perform the analysis.

[0037] When reading, the reading unit can prioritize reading highly relevant answers by taking into account the examinee's geographical location information. For example, if the examinee lives in a particular area, the reading unit can prioritize reading answers related to that area. Furthermore, if the examinee attends a particular school, the reading unit can prioritize reading answers related to that school's curriculum. Furthermore, if the examinee has a particular cultural background, the reading unit can prioritize reading answers related to that culture. This allows highly relevant answers to be prioritized by taking into account the geographical location information. Examples of geographical location information acquisition include, but are not limited to, GPS data and location information services. Some or all of the above-described processing in the reading unit may be performed using, or without, AI. For example, the reading unit can input the examinee's geographical location information into the generation AI and cause the generation AI to prioritize highly relevant answers.

[0038] During reading, the reading unit can analyze the test taker's social media activity and read relevant answers. For example, the reading unit can prioritize reading answers related to topics frequently mentioned by the test taker on social media. The reading unit can also prioritize reading answers related to accounts the test taker follows on social media. The reading unit can also prioritize reading answers related to groups the test taker participates in on social media. In this way, by analyzing social media activity, relevant answers can be prioritized. Analysis of social media activity includes, for example, analyzing the content of posts and evaluating the frequency of activity, but is not limited to such examples. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the test taker's social media activity data into a generation AI and have the generation AI determine the priority of relevant answers.

[0039] The data transfer unit can incorporate technology that preserves the font and style of handwritten characters when converting the data into text data. For example, the data transfer unit preserves the font of handwritten characters when converting the data into digital data. The data transfer unit can also reflect the handwritten style (italics, bold, etc.) in the digital data. The data transfer unit can also faithfully reproduce the size and arrangement of handwritten characters in the digital data. This allows for more accurate conversion to text data by preserving the font and style of handwritten characters. Examples of font and style preservation include, but are not limited to, character recognition algorithms and data formats. Some or all of the above-described processing in the data transfer unit may be performed using, or without, AI. For example, the data transfer unit can input handwritten character data to a generation AI and cause the generation AI to preserve the font and style.

[0040] The data transfer unit can automatically tag data based on the content of the examinee's answers when transferring the data. For example, the data transfer unit performs tagging based on keywords contained in the examinee's answers. The data transfer unit can also perform tagging based on the theme of the examinee's answers. The data transfer unit can also perform tagging based on the structure of the examinee's answers (introduction, body, conclusion). This makes it easier to organize data by automatically tagging based on the content of the answers. Specific tagging methods and criteria include, but are not limited to, tagging algorithms and data used. Some or all of the above-mentioned processing in the data transfer unit can be performed using, for example, AI, or without AI. For example, the data transfer unit can input the examinee's answer data into a generation AI and have the generation AI perform tagging.

[0041] When transferring data, the data transfer unit can prioritize the transfer of highly relevant data by taking into account the examinee's geographical location information. For example, if the examinee lives in a particular area, the data transfer unit can prioritize the transfer of data related to that area. Furthermore, if the examinee attends a particular school, the data transfer unit can prioritize the transfer of data related to that school's curriculum. Furthermore, if the examinee has a particular cultural background, the data transfer unit can prioritize the transfer of data related to that culture. This allows the prioritized transfer of highly relevant data by taking into account the geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. Some or all of the above-described processing in the data transfer unit may be performed using, or without, AI. For example, the data transfer unit can input the examinee's geographical location information into a generation AI and have the generation AI determine the priority of highly relevant data.

[0042] When transferring data, the data transfer unit can analyze the test taker's social media activity and transfer relevant data. For example, the data transfer unit can prioritize transferring data related to topics frequently mentioned by the test taker on social media. The data transfer unit can also prioritize transferring data related to accounts the test taker follows on social media. The data transfer unit can also prioritize transferring data related to groups the test taker participates in on social media. This allows for the prioritized transfer of relevant data by analyzing social media activity. Analysis of social media activity includes, but is not limited to, analyzing posted content and evaluating activity frequency. Some or all of the above-described processing in the data transfer unit can be performed using, for example, AI, or without AI. For example, the data transfer unit can input the test taker's social media activity data into a generation AI and have the generation AI determine the priority of relevant data.

[0043] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. For example, the learning unit selects an optimal learning pattern based on the past learning data. The learning unit can also analyze the past learning data and improve the learning algorithm. The learning unit can also evaluate the progress of learning by referring to the past learning data. This makes it possible to optimize the learning algorithm by referring to the past learning data. Reference to the past learning data includes, for example, a database structure and a reference algorithm, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the past learning data into a generation AI and cause the generation AI to optimize the learning algorithm.

[0044] During learning, the learning unit can integrate data from different fields to perform multifaceted learning. For example, the learning unit can integrate data from different fields to perform comprehensive learning. The learning unit can also compare data from different fields and analyze their interrelationships. The learning unit can also discover new knowledge by combining data from different fields. This enables multifaceted learning by integrating data from different fields. Specific methods and standards for data integration include, but are not limited to, data mining techniques and integration algorithms. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data from different fields into a generation AI and have the generation AI integrate the data.

[0045] During learning, the learning unit can weight the learning data taking into account the examinee's geographic location information. For example, if the examinee lives in a particular area, the learning unit can emphasize learning data related to that area. Furthermore, if the examinee attends a particular school, the learning unit can emphasize learning data related to that school's curriculum. Furthermore, if the examinee has a particular cultural background, the learning unit can emphasize learning data related to that culture. Thus, by taking geographic location information into consideration, highly relevant learning data can be emphasized. Examples of geographic location information acquisition include, but are not limited to, GPS data and location information services. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input the examinee's geographic location information into a generation AI and have the generation AI weight the learning data.

[0046] During learning, the learning unit can analyze the test taker's social media activity and use related data for learning. For example, the learning unit can use data related to topics frequently mentioned by the test taker on social media for learning. The learning unit can also use data related to accounts the test taker follows on social media for learning. The learning unit can also use data related to groups the test taker participates in on social media for learning. In this way, by analyzing social media activity, related data can be used for learning. Analysis of social media activity includes, for example, analyzing posted content and evaluating activity frequency, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the test taker's social media activity data into a generation AI and cause the generation AI to learn related data.

[0047] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the examinees' answers. For example, the analysis unit analyzes the interrelationships between the examinees' answers and focuses on analyzing highly relevant parts. The analysis unit can also evaluate the overall consistency by taking into account the interrelationships between the examinees' answers. The analysis unit can also improve the accuracy of the analysis based on the interrelationships between the examinees' answers. In this way, the accuracy of the analysis is improved by taking into account the interrelationships between the answers. Specific analysis methods and standards for the interrelationships between answers include, but are not limited to, correlation analysis and network analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the examinee's answer data into a generation AI and have the generation AI analyze the interrelationships.

[0048] The analysis unit can perform the analysis while taking into account the examinee's attribute information. The analysis unit can perform the analysis while taking into account, for example, the examinee's age and gender. The analysis unit can also perform the analysis while taking into account the examinee's educational background and occupation. The analysis unit can also perform the analysis while taking into account the examinee's interests and concerns. By taking the attribute information into account, more accurate analysis is possible. Specific methods for acquiring and using the attribute information include, but are not limited to, questionnaire data and profile information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the examinee's attribute information into a generation AI and have the generation AI perform the analysis.

[0049] The analysis unit can take into account the geographical distribution of test takers during analysis. For example, if a test taker lives in a particular area, the analysis unit can emphasize data related to that area. Furthermore, if a test taker attends a particular school, the analysis unit can emphasize data related to that school's curriculum. Furthermore, if a test taker has a particular cultural background, the analysis unit can emphasize data related to that culture. By taking geographical distribution into consideration, analysis can emphasize highly relevant data. Specific methods for acquiring and using geographical distribution include, but are not limited to, GPS data and location information services. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input test taker geographical distribution data into a generation AI and have the generation AI perform the analysis.

[0050] The analysis unit can improve the accuracy of the analysis by referring to related literature during the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to related academic papers during the analysis. The analysis unit can also improve the accuracy of the analysis by referring to related books during the analysis. The analysis unit can also improve the accuracy of the analysis by referring to related online resources during the analysis. This improves the accuracy of the analysis by referring to related literature. Specific reference methods and standards for related literature include, but are not limited to, literature databases and reference algorithms. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input related literature data into a generation AI and have the generation AI perform the analysis.

[0051] When providing feedback, the feedback providing unit can provide optimal feedback by referring to the examinee's past answer history. For example, the feedback providing unit can evaluate the examinee's progress based on the examinee's past answer history and provide feedback. The feedback providing unit can also analyze the examinee's past answer history and specifically point out areas for improvement. The feedback providing unit can also suggest the examinee's next learning step by referring to the examinee's past answer history. In this way, optimal feedback can be provided by referring to the past answer history. Specific reference methods and standards for the past answer history include, but are not limited to, database structures and reference algorithms. Some or all of the above-mentioned processing in the feedback providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback providing unit can input the examinee's past answer history data into a generation AI and cause the generation AI to generate optimal feedback.

[0052] When providing feedback, the feedback providing unit can customize the feedback by taking into account the test taker's attribute information. The feedback providing unit customizes the feedback by taking into account, for example, the test taker's age and gender. The feedback providing unit can also customize the feedback by taking into account the test taker's educational background and occupation. The feedback providing unit can also customize the feedback by taking into account the test taker's interests and concerns. In this way, by taking into account the attribute information, more appropriate feedback can be provided. Specific methods for acquiring and using the attribute information include, for example, questionnaire data and profile information, but are not limited to these examples. Some or all of the above-mentioned processing in the feedback providing unit may be performed using, or without, AI. For example, the feedback providing unit can input the test taker's attribute information into a generation AI and cause the generation AI to customize the feedback.

[0053] When providing feedback, the feedback providing unit can provide optimal feedback by taking into account the test taker's geographical location information. For example, if the test taker lives in a specific area, the feedback providing unit can provide feedback related to that area. Furthermore, if the test taker attends a specific school, the feedback providing unit can provide feedback related to the school's curriculum. Furthermore, if the test taker has a specific cultural background, the feedback providing unit can provide feedback related to that culture. This allows for providing highly relevant feedback by taking the geographical location information into consideration. Specific methods for acquiring and using the geographical location information include, but are not limited to, GPS data and location information services. Some or all of the above-described processing by the feedback providing unit may be performed using, or without, AI. For example, the feedback providing unit can input the test taker's geographical location information into a generation AI and cause the generation AI to generate optimal feedback.

[0054] When providing feedback, the feedback providing unit can analyze the test taker's social media activity and provide relevant feedback. For example, the feedback providing unit can provide feedback related to topics frequently mentioned by the test taker on social media. The feedback providing unit can also provide feedback related to accounts the test taker follows on social media. The feedback providing unit can also provide feedback related to groups the test taker participates in on social media. This makes it possible to provide relevant feedback by analyzing social media activity. Specific analysis methods and criteria for social media activity include, but are not limited to, analyzing posted content and evaluating activity frequency. Some or all of the above-described processing by the feedback providing unit can be performed using, or without, AI. For example, the feedback providing unit can input the test taker's social media activity data into a generation AI and cause the generation AI to generate relevant feedback.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The written test scoring system can further include a speech recognition unit. When a test taker gives an oral response, the speech recognition unit can convert the response into text data. For example, when a test taker gives an oral response during a test, the speech is recorded and converted into text data using speech recognition technology. The speech recognition unit can also analyze the test taker's pronunciation and intonation to evaluate the accuracy of the response. This allows for efficient scoring of oral responses.

[0057] The written test scoring system may further include a translation unit. If a test taker writes answers in a different language, the translation unit can translate the answers into a specified language. For example, if a test taker writes answers in English, the translation unit translates the answers into Japanese. The translation unit may also use a dictionary database to accurately translate specialized and technical terms. This allows for consistent scoring of answers in different languages.

[0058] The written test scoring system can further include a plug-in unit. The plug-in unit can connect with external educational tools and learning platforms to obtain test takers' learning history and performance data. For example, the plug-in unit can obtain what the test taker has learned on an online learning platform and reflect this in the scoring of the test. The plug-in unit can also use additional learning resources provided by external educational tools to enhance feedback to the test taker. This makes it possible to score tests taking the test taker's learning history into consideration.

[0059] The written test scoring system can further include a performance analysis unit. The performance analysis unit can analyze the examinee's past test results and learning history and compare them with the examinee's current test results. For example, the performance analysis unit can evaluate the examinee's progress in the current test results based on the examinee's past test results. The performance analysis unit can also analyze the examinee's learning patterns and tendencies and suggest future study plans. This allows for a comprehensive evaluation of the examinee's learning progress.

[0060] The written test scoring system can further include a customization section. The customization section can customize the content and feedback of the test according to the individual needs and learning style of the test taker. For example, if the test taker prefers visual learning, the customization section can provide feedback that makes extensive use of charts and graphs. Also, if the test taker prefers auditory learning, the customization section can provide audio feedback. This allows the test taker to receive optimal feedback tailored to their learning style.

[0061] The written test scoring system can further include a progress tracking unit. The progress tracking unit can track the test taker's learning progress in real time and provide feedback at the appropriate time. For example, when a test taker achieves a certain learning goal, the progress tracking unit can notify the test taker of this achievement and suggest the next step. Furthermore, when a test taker falls behind in their studies, the progress tracking unit can analyze the cause and suggest improvement measures. This makes it possible to effectively manage the test taker's learning progress.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reader reads the test taker's written response. The test taker's written response may include handwritten characters and diagrams. The reader uses OCR technology to read handwritten characters and diagrams. It can also directly read written responses submitted in digital format. In addition, printed written responses can also be read using OCR technology. Step 2: The data transfer unit converts the written responses read by the reading unit into text data. Text data can be in PDF, image, or text format. The data transfer unit reads the handwritten written responses using a scanner, saves them as image data, and converts the image data into text data using OCR technology. It can also take a photo of the handwritten written responses using a smartphone camera and convert the image data into text data using a dedicated app. It can also convert handwritten written responses into digital data in real time using a dedicated digital pen. Step 3: The learning unit pre-learns the correct answers and related fields and backgrounds. The learning unit uses a machine learning algorithm to learn the correct answers and related fields and backgrounds. The learning data includes past exam questions, sample answers, related literature, databases, etc. Step 4: The analysis unit compares and analyzes the test taker's written answers based on the information learned by the learning unit. The analysis unit compares the test taker's written answers with the correct answers and determines which parts are correct and which parts are incorrect. It also analyzes the logical structure and expression of the test taker's written answers and provides specific feedback. Step 5: The feedback providing unit provides the test taker with the analysis results obtained by the analysis unit. The feedback providing unit provides specific feedback indicating the good and bad points of the test taker's written answer. In addition, the feedback providing unit can also provide visual feedback such as graphs and charts.

[0064] (Example 2) The written test scoring system according to an embodiment of the present invention uses OCR technology and AI to streamline the scoring of written tests, providing benefits to both test setters and test takers. This written test scoring system first uses OCR technology to read test takers' written responses and convert them into text data. Next, an AI system pre-trained in correct answers and related fields and background compares and analyzes the test takers' written responses and scores them. The system outputs not only correct and incorrect answers, but also the strengths and weaknesses of incorrect answers along with the analysis results. This reduces the burden on test setters and test graders and provides high-value-added feedback to test takers. For example, OCR technology can be used to read test takers' written responses and convert them into text data. Accurate interpretation of handwritten characters and diagrams is also required. For example, OCR technology can be used to convert handwritten sentences and formulas into digital data, enabling subsequent analysis by AI. Next, an AI system pre-trained in correct answers and related fields and background compares and analyzes the test takers' written responses. AI compares test takers' answers with the correct answers and determines which parts are correct and which parts are incorrect. For example, in a history essay question, AI compares the test taker's answers with the correct historical facts to evaluate their accuracy. Furthermore, when an incorrect answer is given, AI outputs the analysis results, highlighting the strengths and weaknesses. This allows test takers to specifically understand which parts of their answers were good and which parts need improvement. For example, in a mathematics essay question, AI analyzes the test taker's answering process and points out any calculation errors that may have been made despite the correct approach. This system reduces the burden on test setters and graders. Because AI automatically grades questions, manual grading is no longer necessary, significantly reducing time and effort. It also provides test takers with valuable feedback. By identifying specific areas for improvement and strengths, test takers can clarify their learning direction. In this way, the use of OCR technology and AI can streamline the grading of written exams, creating a system that benefits both test setters and test takers. This allows the written test scoring system to efficiently read test takers' written responses, convert them into text data, and provide accurate analysis and feedback.

[0065] The written test scoring system according to the embodiment includes a reading unit, a data transfer unit, a learning unit, an analysis unit, and a feedback providing unit. The reading unit reads the test taker's written response. The test taker's written response may include, but is not limited to, handwritten characters and diagrams. The reading unit may read the handwritten characters and diagrams using, for example, OCR technology. The reading unit may also directly read written responses submitted in digital format. The reading unit may also read printed written responses using OCR technology. For example, the reading unit may scan handwritten characters with a high-resolution scanner and convert them into text information using OCR technology. Digital written responses submitted in a specific file format may also be directly read. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The data transfer unit converts the written response read by the reading unit into text data. Examples of text data include, but are not limited to, PDF format, image format, and text format. For example, the data transfer unit may scan the handwritten written response and save it as image data. The data transfer unit then converts the image data into text data using OCR technology. The data transfer unit can also capture handwritten written responses using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The data transfer unit can also write handwritten written responses with a dedicated digital pen, which then converts the responses into digital data in real time. For example, a sensor detects the pen's movement and saves it as text data. The learning unit pre-learns correct answers and related fields and backgrounds. The learning unit learns correct answers and related fields and backgrounds using, for example, a machine learning algorithm. Examples of learning data include, but are not limited to, past test questions and sample answers, related literature, and databases. The learning unit learns correct answers and related fields and backgrounds using, for example, a machine learning algorithm, to improve the accuracy of the analysis by the analysis unit. The analysis unit compares and analyzes the test taker's written responses based on the information learned by the learning unit.The analysis unit, for example, compares the examinee's written answer with the correct answer and determines which parts are correct and which parts are incorrect. The analysis unit, for example, uses a comparison algorithm to calculate the similarity between the examinee's written answer and the correct answer. The analysis unit can also analyze the content of the examinee's written answer and not only determine whether the answer is correct or incorrect, but also analyze the strengths and weaknesses of an incorrect answer. For example, the analysis unit analyzes the logical structure and expression of the examinee's written answer and provides specific feedback. The feedback providing unit provides the analysis results obtained by the analysis unit to the examinee. The feedback providing unit provides, for example, feedback that specifically indicates the strengths and weaknesses of the examinee's written answer. The feedback providing unit, for example, generates feedback in text format and provides it to the examinee. The feedback providing unit can also provide visual feedback such as graphs and charts. For example, the feedback providing unit displays the evaluation results of the examinee's written answer as graphs or charts, providing them in a visually easy-to-understand format. This allows the written test scoring system according to the embodiment to efficiently read the examinee's written answer, convert it into text data, and accurately analyze and provide feedback.

[0066] The reading unit can read the examinee's handwritten text and diagrams using OCR technology. Examples of OCR technologies include, but are not limited to, Tesseract and ABBYY FineReader. For example, the reading unit can scan handwritten text using a high-resolution scanner and convert it into text information using OCR technology. The reading unit can also scan handwritten diagrams and convert the contents of the diagrams into digital data using OCR technology. For example, the reading unit can scan handwritten graphs and charts and convert them into digital data using OCR technology. The reading unit can also scan handwritten mathematical formulas and convert the contents of the formulas into digital data using OCR technology. For example, the reading unit can scan handwritten mathematical formulas and convert them into digital data using OCR technology. This allows the handwritten text and diagrams to be accurately read. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without AI. For example, the reading unit can input handwritten text and diagram data into a generation AI and have the generation AI convert the contents of the text and diagrams into digital data.

[0067] The data transfer unit can convert the scanned written response into text data. Examples of text data include, but are not limited to, PDF, image, and text formats. For example, the data transfer unit scans the handwritten written response and saves it as image data. The data transfer unit then converts the image data into text data using OCR technology. The data transfer unit can also capture a photo of the handwritten written response using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. The data transfer unit can also write the handwritten written response with a dedicated digital pen, which can then convert the image data into digital data in real time. For example, a sensor can detect the pen's movement and save it as character data. This allows the written response to be accurately converted into text data. Some or all of the above-described processing in the data transfer unit can be performed using, for example, AI, or without AI. For example, the data transfer unit can input image data of the handwritten written response into a generation AI and have the generation AI convert the image data into text data.

[0068] The learning unit can learn the correct answers and related fields and backgrounds in advance. The learning unit learns the correct answers and related fields and backgrounds, for example, using a machine learning algorithm. Learning data includes, but is not limited to, past test questions and sample answers, related literature, and databases. The learning unit learns the correct answers and related fields and backgrounds, for example, using a machine learning algorithm, and improves the accuracy of analysis by the analysis unit. By learning the correct answers and related fields and backgrounds in advance, the accuracy of analysis is improved. Some or all of the above-mentioned processing in the learning unit may be performed, for example, using AI, or may be performed without AI. For example, the learning unit may input data on the correct answers and related fields and backgrounds into the generation AI and have the generation AI learn the data.

[0069] The analysis unit can compare the test taker's written response with the correct answer and determine whether it is correct or incorrect. For example, the analysis unit can compare the test taker's written response with the correct answer and determine which parts are correct and which parts are incorrect. For example, the analysis unit can use a comparison algorithm to calculate the similarity between the test taker's written response and the correct answer. The analysis unit can also analyze the content of the test taker's written response and not only determine whether it is correct or incorrect, but also analyze the strengths and weaknesses of an incorrect answer. For example, the analysis unit can analyze the logical structure and expression of the test taker's written response and provide specific feedback. This allows for accurate evaluation of the test taker's written response. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the test taker's written response and the correct answer into a generation AI and have the generation AI compare and analyze them.

[0070] The feedback providing unit can provide the examinee with the good points and shortcomings of an incorrect answer along with the analysis results. For example, the feedback providing unit provides feedback that specifically indicates the good points and shortcomings of the examinee's written answer. For example, the feedback providing unit generates feedback in text format and provides it to the examinee. The feedback providing unit can also provide visual feedback such as graphs and charts. For example, the feedback providing unit displays the evaluation results of the examinee's written answer as a graph or chart, providing it in a visually easy-to-understand format. This makes it possible to provide specific feedback to the examinee. Some or all of the above-mentioned processing by the feedback providing unit may be performed using, or without, AI. For example, the feedback providing unit can input the analysis results obtained by the analysis unit to a generation AI and cause the generation AI to generate feedback.

[0071] The reading unit can estimate the examinee's emotions and adjust the timing of reading based on the estimated emotions. For example, if the examinee is nervous, the reading unit allows the examinee time to relax before starting reading. Furthermore, if the examinee is concentrating, the reading unit can quickly read the examinee's voice without changing their state. Furthermore, if the examinee is tired, the reading unit can take a break and then resume reading. This allows reading to be performed at the optimal timing depending on the examinee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reading unit may be performed using AI, or without AI. For example, the reading unit can input the examinee's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0072] The reading unit may incorporate multidimensional scanning technology to accurately read not only handwritten characters but also charts and graphs. For example, the reading unit may scan a handwritten graph at high resolution and convert it into digital data. The reading unit may also scan from multiple angles to accurately read even the finer details of the chart. The reading unit may also scan a color image and accurately recognize color differences to convert them into digital data. This allows for accurate reading of handwritten characters and charts. Examples of multidimensional scanning technology include, but are not limited to, 3D scanning and multispectral scanning. Some or all of the above-described processing in the reading unit may be performed using, or without, AI. For example, the reading unit may input data of handwritten charts and graphs into a generation AI and have the generation AI convert the data into digital data.

[0073] The reading unit analyzes the examinee's writing pressure and handwriting characteristics during reading, enabling more accurate text data conversion. The reading unit, for example, analyzes the strength of writing pressure and reproduces the shading of characters. The reading unit can also recognize handwriting characteristics and accurately convert the shape of individual characters into data. The reading unit can also detect changes in writing pressure and identify areas that need rewriting or correction. This allows for more accurate text data conversion by analyzing writing pressure and handwriting characteristics. Analysis of writing pressure and handwriting characteristics includes, but is not limited to, a writing pressure sensor and a handwriting analysis algorithm. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without AI. For example, the reading unit can input the examinee's writing pressure and handwriting data into a generation AI and have the generation AI perform the analysis.

[0074] The reading unit can estimate the examinee's emotions and determine the reading priority based on the estimated examinee's emotions. For example, if the examinee is anxious, the reading unit can prioritize reading important parts. If the examinee is relaxed, the reading unit can also read the entire text evenly. If the examinee is tired, the reading unit can start reading from the easier parts. This allows reading to be performed with optimal priority according to the examinee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reading unit can be performed using AI, for example, or without AI. For example, the reading unit can input the examinee's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0075] When reading, the reading unit can prioritize reading highly relevant answers by taking into account the examinee's geographical location information. For example, if the examinee lives in a particular area, the reading unit can prioritize reading answers related to that area. Furthermore, if the examinee attends a particular school, the reading unit can prioritize reading answers related to that school's curriculum. Furthermore, if the examinee has a particular cultural background, the reading unit can prioritize reading answers related to that culture. This allows highly relevant answers to be prioritized by taking into account the geographical location information. Examples of geographical location information acquisition include, but are not limited to, GPS data and location information services. Some or all of the above-described processing in the reading unit may be performed using, or without, AI. For example, the reading unit can input the examinee's geographical location information into the generation AI and cause the generation AI to prioritize highly relevant answers.

[0076] During reading, the reading unit can analyze the test taker's social media activity and read relevant answers. For example, the reading unit can prioritize reading answers related to topics frequently mentioned by the test taker on social media. The reading unit can also prioritize reading answers related to accounts the test taker follows on social media. The reading unit can also prioritize reading answers related to groups the test taker participates in on social media. In this way, by analyzing social media activity, relevant answers can be prioritized. Analysis of social media activity includes, for example, analyzing the content of posts and evaluating the frequency of activity, but is not limited to such examples. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the test taker's social media activity data into a generation AI and have the generation AI determine the priority of relevant answers.

[0077] The data transfer unit can estimate the examinee's emotions and adjust the timing of data transfer based on the estimated examinee's emotions. For example, if the examinee is relaxed, the data transfer unit can immediately transfer data. If the examinee is nervous, the data transfer unit can also transfer data after a short delay. If the examinee is tired, the data transfer unit can also transfer data after a break. This allows data transfer to be performed at the optimal timing depending on the examinee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the data transfer unit can be performed using AI, for example, or without AI. For example, the data transfer unit can input the examinee's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0078] The data transfer unit can incorporate technology that preserves the font and style of handwritten characters when converting the data into text data. For example, the data transfer unit preserves the font of handwritten characters when converting the data into digital data. The data transfer unit can also reflect the handwritten style (italics, bold, etc.) in the digital data. The data transfer unit can also faithfully reproduce the size and arrangement of handwritten characters in the digital data. This allows for more accurate conversion to text data by preserving the font and style of handwritten characters. Examples of font and style preservation include, but are not limited to, character recognition algorithms and data formats. Some or all of the above-described processing in the data transfer unit may be performed using, or without, AI. For example, the data transfer unit can input handwritten character data to a generation AI and cause the generation AI to preserve the font and style.

[0079] The data transfer unit can automatically tag data based on the content of the examinee's answers when transferring the data. For example, the data transfer unit performs tagging based on keywords contained in the examinee's answers. The data transfer unit can also perform tagging based on the theme of the examinee's answers. The data transfer unit can also perform tagging based on the structure of the examinee's answers (introduction, body, conclusion). This makes it easier to organize data by automatically tagging based on the content of the answers. Specific tagging methods and criteria include, but are not limited to, tagging algorithms and data used. Some or all of the above-mentioned processing in the data transfer unit can be performed using, for example, AI, or without AI. For example, the data transfer unit can input the examinee's answer data into a generation AI and have the generation AI perform tagging.

[0080] The data transfer unit can estimate the examinee's emotions and determine the priority of data transfer based on the estimated examinee's emotions. For example, if the examinee is anxious, the data transfer unit can prioritize transferring important data. Furthermore, if the examinee is relaxed, the data transfer unit can transfer all data evenly. Furthermore, if the examinee is tired, the data transfer unit can start transferring simple data first. This allows data transfer to be performed with optimal priority according to the examinee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data transfer unit can be performed using, for example, AI, or without AI. For example, the data transfer unit can input the examinee's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0081] When transferring data, the data transfer unit can prioritize the transfer of highly relevant data by taking into account the examinee's geographical location information. For example, if the examinee lives in a particular area, the data transfer unit can prioritize the transfer of data related to that area. Furthermore, if the examinee attends a particular school, the data transfer unit can prioritize the transfer of data related to that school's curriculum. Furthermore, if the examinee has a particular cultural background, the data transfer unit can prioritize the transfer of data related to that culture. This allows the prioritized transfer of highly relevant data by taking into account the geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. Some or all of the above-described processing in the data transfer unit may be performed using, or without, AI. For example, the data transfer unit can input the examinee's geographical location information into a generation AI and have the generation AI determine the priority of highly relevant data.

[0082] When transferring data, the data transfer unit can analyze the test taker's social media activity and transfer relevant data. For example, the data transfer unit can prioritize transferring data related to topics frequently mentioned by the test taker on social media. The data transfer unit can also prioritize transferring data related to accounts the test taker follows on social media. The data transfer unit can also prioritize transferring data related to groups the test taker participates in on social media. This allows for the prioritized transfer of relevant data by analyzing social media activity. Analysis of social media activity includes, but is not limited to, analyzing posted content and evaluating activity frequency. Some or all of the above-described processing in the data transfer unit can be performed using, for example, AI, or without AI. For example, the data transfer unit can input the test taker's social media activity data into a generation AI and have the generation AI determine the priority of relevant data.

[0083] The learning unit can estimate the examinee's emotions and select learning data based on the estimated examinee's emotions. For example, if the examinee is relaxed, the learning unit can select detailed learning data. Also, if the examinee is nervous, the learning unit can select concise learning data. Also, if the examinee is tired, the learning unit can select learning data after a break. This allows optimal learning data to be selected according to the examinee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the learning unit can be performed using, for example, AI, or without AI. For example, the learning unit can input the examinee's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0084] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. For example, the learning unit selects an optimal learning pattern based on the past learning data. The learning unit can also analyze the past learning data and improve the learning algorithm. The learning unit can also evaluate the progress of learning by referring to the past learning data. This makes it possible to optimize the learning algorithm by referring to the past learning data. Reference to the past learning data includes, for example, a database structure and a reference algorithm, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the past learning data into a generation AI and cause the generation AI to optimize the learning algorithm.

[0085] During learning, the learning unit can integrate data from different fields to perform multifaceted learning. For example, the learning unit can integrate data from different fields to perform comprehensive learning. The learning unit can also compare data from different fields and analyze their interrelationships. The learning unit can also discover new knowledge by combining data from different fields. This enables multifaceted learning by integrating data from different fields. Specific methods and standards for data integration include, but are not limited to, data mining techniques and integration algorithms. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data from different fields into a generation AI and have the generation AI integrate the data.

[0086] The learning unit can estimate the examinee's emotions and adjust the frequency of study based on the estimated emotions. For example, if the examinee is relaxed, the learning unit can increase the frequency of study. Also, if the examinee is nervous, the learning unit can reduce the frequency of study. Also, if the examinee is tired, the learning unit can resume study after a break. This allows the examinee to study at an optimal frequency depending on the examinee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit can be performed using AI, for example, or without AI. For example, the learning unit can input the examinee's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0087] During learning, the learning unit can weight the learning data taking into account the examinee's geographic location information. For example, if the examinee lives in a particular area, the learning unit can emphasize learning data related to that area. Furthermore, if the examinee attends a particular school, the learning unit can emphasize learning data related to that school's curriculum. Furthermore, if the examinee has a particular cultural background, the learning unit can emphasize learning data related to that culture. Thus, by taking geographic location information into consideration, highly relevant learning data can be emphasized. Examples of geographic location information acquisition include, but are not limited to, GPS data and location information services. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit can input the examinee's geographic location information into a generation AI and have the generation AI weight the learning data.

[0088] During learning, the learning unit can analyze the test taker's social media activity and use related data for learning. For example, the learning unit can use data related to topics frequently mentioned by the test taker on social media for learning. The learning unit can also use data related to accounts the test taker follows on social media for learning. The learning unit can also use data related to groups the test taker participates in on social media for learning. In this way, by analyzing social media activity, related data can be used for learning. Analysis of social media activity includes, for example, analyzing posted content and evaluating activity frequency, but is not limited to such examples. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the test taker's social media activity data into a generation AI and cause the generation AI to learn related data.

[0089] The analysis unit can estimate the examinee's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the examinee is relaxed, the analysis unit can perform a detailed analysis. If the examinee is nervous, the analysis unit can also perform a brief analysis. If the examinee is tired, the analysis unit can resume analysis after a break. This allows analysis to be performed using optimal criteria depending on the examinee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the examinee's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0090] During analysis, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the examinees' answers. For example, the analysis unit analyzes the interrelationships between the examinees' answers and focuses on analyzing highly relevant parts. The analysis unit can also evaluate the overall consistency by taking into account the interrelationships between the examinees' answers. The analysis unit can also improve the accuracy of the analysis based on the interrelationships between the examinees' answers. In this way, the accuracy of the analysis is improved by taking into account the interrelationships between the answers. Specific analysis methods and standards for the interrelationships between answers include, but are not limited to, correlation analysis and network analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the examinee's answer data into a generation AI and have the generation AI analyze the interrelationships.

[0091] The analysis unit can perform the analysis while taking into account the examinee's attribute information. The analysis unit can perform the analysis while taking into account, for example, the examinee's age and gender. The analysis unit can also perform the analysis while taking into account the examinee's educational background and occupation. The analysis unit can also perform the analysis while taking into account the examinee's interests and concerns. By taking the attribute information into account, more accurate analysis is possible. Specific methods for acquiring and using the attribute information include, but are not limited to, questionnaire data and profile information. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the examinee's attribute information into a generation AI and have the generation AI perform the analysis.

[0092] The analysis unit can estimate the examinee's emotions and adjust the display order of the analysis results based on the estimated examinee's emotions. For example, if the examinee is nervous, the analysis unit can display important results first. Furthermore, if the examinee is relaxed, the analysis unit can display all results evenly. Furthermore, if the examinee is tired, the analysis unit can display the simplest results first. This allows the analysis results to be displayed in the optimal order according to the examinee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the examinee's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0093] The analysis unit can take into account the geographical distribution of test takers during analysis. For example, if a test taker lives in a particular area, the analysis unit can emphasize data related to that area. Furthermore, if a test taker attends a particular school, the analysis unit can emphasize data related to that school's curriculum. Furthermore, if a test taker has a particular cultural background, the analysis unit can emphasize data related to that culture. By taking geographical distribution into consideration, analysis can emphasize highly relevant data. Specific methods for acquiring and using geographical distribution include, but are not limited to, GPS data and location information services. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input test taker geographical distribution data into a generation AI and have the generation AI perform the analysis.

[0094] The analysis unit can improve the accuracy of the analysis by referring to related literature during the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to related academic papers during the analysis. The analysis unit can also improve the accuracy of the analysis by referring to related books during the analysis. The analysis unit can also improve the accuracy of the analysis by referring to related online resources during the analysis. This improves the accuracy of the analysis by referring to related literature. Specific reference methods and standards for related literature include, but are not limited to, literature databases and reference algorithms. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input related literature data into a generation AI and have the generation AI perform the analysis.

[0095] The feedback providing unit can estimate the examinee's emotions and adjust the way the feedback is expressed based on the estimated emotions. For example, if the examinee is nervous, the feedback providing unit can provide gentle feedback. If the examinee is relaxed, the feedback providing unit can also provide detailed feedback. If the examinee is tired, the feedback providing unit can also provide concise feedback. This makes it possible to provide feedback in an optimal way depending on the examinee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback providing unit can be performed using AI, for example, or without AI. For example, the feedback providing unit can input the examinee's emotion data into the generation AI and cause the generation AI to estimate emotions.

[0096] When providing feedback, the feedback providing unit can provide optimal feedback by referring to the examinee's past answer history. For example, the feedback providing unit can evaluate the examinee's progress based on the examinee's past answer history and provide feedback. The feedback providing unit can also analyze the examinee's past answer history and specifically point out areas for improvement. The feedback providing unit can also suggest the examinee's next learning step by referring to the examinee's past answer history. In this way, optimal feedback can be provided by referring to the past answer history. Specific reference methods and standards for the past answer history include, but are not limited to, database structures and reference algorithms. Some or all of the above-mentioned processing in the feedback providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback providing unit can input the examinee's past answer history data into a generation AI and cause the generation AI to generate optimal feedback.

[0097] When providing feedback, the feedback providing unit can customize the feedback by taking into account the test taker's attribute information. The feedback providing unit customizes the feedback by taking into account, for example, the test taker's age and gender. The feedback providing unit can also customize the feedback by taking into account the test taker's educational background and occupation. The feedback providing unit can also customize the feedback by taking into account the test taker's interests and concerns. In this way, by taking into account the attribute information, more appropriate feedback can be provided. Specific methods for acquiring and using the attribute information include, for example, questionnaire data and profile information, but are not limited to these examples. Some or all of the above-mentioned processing in the feedback providing unit may be performed using, or without, AI. For example, the feedback providing unit can input the test taker's attribute information into a generation AI and cause the generation AI to customize the feedback.

[0098] The feedback providing unit can estimate the examinee's emotions and determine the priority of feedback based on the estimated examinee's emotions. For example, if the examinee is anxious, the feedback providing unit can prioritize important feedback. Furthermore, if the examinee is relaxed, the feedback providing unit can provide feedback evenly across the examinee. Furthermore, if the examinee is tired, the feedback providing unit can provide feedback starting with the simplest feedback. This allows feedback to be provided in an optimal order of priority according to the examinee's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback providing unit can be performed using, for example, an AI, or without an AI. For example, the feedback providing unit can input the examinee's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0099] When providing feedback, the feedback providing unit can provide optimal feedback by taking into account the test taker's geographical location information. For example, if the test taker lives in a specific area, the feedback providing unit can provide feedback related to that area. Furthermore, if the test taker attends a specific school, the feedback providing unit can provide feedback related to the school's curriculum. Furthermore, if the test taker has a specific cultural background, the feedback providing unit can provide feedback related to that culture. This allows for providing highly relevant feedback by taking the geographical location information into consideration. Specific methods for acquiring and using the geographical location information include, but are not limited to, GPS data and location information services. Some or all of the above-described processing by the feedback providing unit may be performed using, or without, AI. For example, the feedback providing unit can input the test taker's geographical location information into a generation AI and cause the generation AI to generate optimal feedback.

[0100] When providing feedback, the feedback providing unit can analyze the test taker's social media activity and provide relevant feedback. For example, the feedback providing unit can provide feedback related to topics frequently mentioned by the test taker on social media. The feedback providing unit can also provide feedback related to accounts the test taker follows on social media. The feedback providing unit can also provide feedback related to groups the test taker participates in on social media. This makes it possible to provide relevant feedback by analyzing social media activity. Specific analysis methods and criteria for social media activity include, but are not limited to, analyzing posted content and evaluating activity frequency. Some or all of the above-described processing by the feedback providing unit can be performed using, or without, AI. For example, the feedback providing unit can input the test taker's social media activity data into a generation AI and cause the generation AI to generate relevant feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the reading unit, data transfer unit, learning unit, analysis unit, and feedback providing unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reading unit reads the examinee's written answers using the camera 42 of the smart device 14 and converts them into text data using OCR technology. The data transfer unit transfers the read data to the data processing device 12 via the communication I / F 44 of the smart device 14. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns correct answers and related fields and backgrounds. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and compares and analyzes the examinee's written answers. The feedback providing unit provides the examinee with the analysis results using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the reading unit, data transfer unit, learning unit, analysis unit, and feedback providing unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reading unit reads the examinee's written response using the camera 42 of the smart glasses 214 and converts it into text data using OCR technology. The data transfer unit transfers the read data to the data processing device 12 via the communication I / F 44 of the smart glasses 214. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns correct answers and related fields and backgrounds. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and compares and analyzes the examinee's written response. The feedback providing unit provides the analysis results to the examinee using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the reading unit, data transfer unit, learning unit, analysis unit, and feedback providing unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reading unit reads the examinee's written answers using the camera 42 of the headset terminal 314 and converts them into text data using OCR technology. The data transfer unit transfers the read data to the data processing device 12 via the communication I / F 44 of the headset terminal 314. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns correct answers and related fields and backgrounds. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and compares and analyzes the examinee's written answers. The feedback providing unit provides the examinee with the analysis results using the display 343 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the reading unit, data transfer unit, learning unit, analysis unit, and feedback providing unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reading unit reads the examinee's written response using the camera 42 of the robot 414 and converts it into text data using OCR technology. The data transfer unit transfers the read data to the data processing device 12 via the communication I / F 44 of the robot 414. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns correct answers and related fields and backgrounds. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and compares and analyzes the examinee's written response. The feedback providing unit provides the analysis results to the examinee using the speaker 240 of the robot 414.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The written test scoring system can further include a speech recognition unit. When a test taker gives an oral response, the speech recognition unit can convert the response into text data. For example, when a test taker gives an oral response during a test, the speech is recorded and converted into text data using speech recognition technology. The speech recognition unit can also analyze the test taker's pronunciation and intonation to evaluate the accuracy of the response. This allows for efficient scoring of oral responses.

[0103] The written test scoring system may further include a translation unit. If a test taker writes answers in a different language, the translation unit can translate the answers into a specified language. For example, if a test taker writes answers in English, the translation unit translates the answers into Japanese. The translation unit may also use a dictionary database to accurately translate specialized and technical terms. This allows for consistent scoring of answers in different languages.

[0104] The written test scoring system can further include a plug-in unit. The plug-in unit can connect with external educational tools and learning platforms to obtain test takers' learning history and performance data. For example, the plug-in unit can obtain what the test taker has learned on an online learning platform and reflect this in the scoring of the test. The plug-in unit can also use additional learning resources provided by external educational tools to enhance feedback to the test taker. This makes it possible to score tests taking the test taker's learning history into consideration.

[0105] The written test scoring system may further include a real-time feedback unit. The real-time feedback unit may provide real-time feedback to the test taker while he or she is writing his or her answer. For example, the real-time feedback unit may point out errors or areas for improvement while the test taker is writing his or her answer. The real-time feedback unit may also display encouraging messages if the test taker is progressing in the right direction. This allows the test taker to receive instant feedback and correct their answers during the test.

[0106] The written test scoring system can further include a sentiment analysis unit. The sentiment analysis unit can infer emotions from the content of the examinee's answers and adjust feedback based on those emotions. For example, if the examinee's answers are confident, positive feedback can be provided. Also, if the examinee's answers show anxiety or tension, an encouraging message can be provided. In this way, feedback can be provided that takes the examinee's emotions into consideration.

[0107] The written test scoring system can further include a performance analysis unit. The performance analysis unit can analyze the examinee's past test results and learning history and compare them with the examinee's current test results. For example, the performance analysis unit can evaluate the examinee's progress in the current test results based on the examinee's past test results. The performance analysis unit can also analyze the examinee's learning patterns and tendencies and suggest future study plans. This allows for a comprehensive evaluation of the examinee's learning progress.

[0108] The written test scoring system can further include a collaborative learning unit. The collaborative learning unit can support the process when multiple test takers collaborate to create answers. For example, when test takers create answers while holding a discussion in a group, the collaborative learning unit records and analyzes the content of the discussion. The collaborative learning unit can also evaluate the contribution of each test taker and provide feedback. This can enhance the effectiveness of collaborative learning.

[0109] The written test scoring system may further include a motivation unit. The motivation unit may provide messages and rewards to motivate test takers. For example, if a test taker achieves good results, the motivation unit may display a message of praise. If a test taker is working hard, the motivation unit may also provide an encouraging message. Furthermore, the motivation unit may award badges or points to test takers when they achieve their goals. This may help maintain test takers' motivation and increase their willingness to learn.

[0110] The written test scoring system can further include a customization section. The customization section can customize the content and feedback of the test according to the individual needs and learning style of the test taker. For example, if the test taker prefers visual learning, the customization section can provide feedback that makes extensive use of charts and graphs. Also, if the test taker prefers auditory learning, the customization section can provide audio feedback. This allows the test taker to receive optimal feedback tailored to their learning style.

[0111] The written test scoring system can further include a progress tracking unit. The progress tracking unit can track the test taker's learning progress in real time and provide feedback at the appropriate time. For example, when a test taker achieves a certain learning goal, the progress tracking unit can notify the test taker of this achievement and suggest the next step. Furthermore, when a test taker falls behind in their studies, the progress tracking unit can analyze the cause and suggest improvement measures. This makes it possible to effectively manage the test taker's learning progress.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The reader reads the test taker's written response. The test taker's written response may include handwritten characters and diagrams. The reader uses OCR technology to read handwritten characters and diagrams. It can also directly read written responses submitted in digital format. In addition, printed written responses can also be read using OCR technology. Step 2: The data transfer unit converts the written responses read by the reading unit into text data. Text data can be in PDF, image, or text format. The data transfer unit reads the handwritten written responses using a scanner, saves them as image data, and converts the image data into text data using OCR technology. It can also take a photo of the handwritten written responses using a smartphone camera and convert the image data into text data using a dedicated app. It can also convert handwritten written responses into digital data in real time using a dedicated digital pen. Step 3: The learning unit pre-learns the correct answers and related fields and backgrounds. The learning unit uses a machine learning algorithm to learn the correct answers and related fields and backgrounds. The learning data includes past exam questions, sample answers, related literature, databases, etc. Step 4: The analysis unit compares and analyzes the test taker's written answers based on the information learned by the learning unit. The analysis unit compares the test taker's written answers with the correct answers and determines which parts are correct and which parts are incorrect. It also analyzes the logical structure and expression of the test taker's written answers and provides specific feedback. Step 5: The feedback providing unit provides the test taker with the analysis results obtained by the analysis unit. The feedback providing unit provides specific feedback indicating the good and bad points of the test taker's written answer. In addition, the feedback providing unit can also provide visual feedback such as graphs and charts.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0121] The 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.

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

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

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

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] 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.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] 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.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] 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.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] 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.

[0159] 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.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0177] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0178] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0179] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0183] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reading section for reading the examinee's written answers; a data transfer unit that converts the written response read by the reading unit into text data; A learning section that learns the correct answers and related fields and backgrounds in advance; an analysis unit that compares and analyzes the written responses of examinees based on the information learned by the learning unit; a feedback providing unit that provides the test taker with the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The reading unit Uses OCR technology to read test-takers' handwriting and diagrams 2. The system of claim 1.

3. The data transfer unit Convert the read written answers into text data 2. The system of claim 1.

4. The learning unit Study the correct answers and related fields and background in advance 2. The system of claim 1.

5. The analysis unit The examinee's written answers are compared with the correct answers to determine whether they are correct or incorrect.

2. The system of claim 1.

6. The feedback providing unit: Provide test takers with analysis of their strengths and weaknesses when they answer incorrectly 2. The system of claim 1.

7. The reading unit Estimate the examinee's emotions and adjust the timing of readings based on the estimated emotions of the examinee 2. The system of claim 1.

8. The reading unit Introduce multidimensional scanning technology to accurately read not only handwritten characters but also charts and graphs.

2. The system of claim 1.

Images