system

Abstract

An object of the system according to the embodiment is to automatically generate a confirmation test only by uploading a material.SOLUTION: A system includes a material upload part, an analysis part, and a test generation part. The material uploading unit uploads a material such as a PDF. The analysis part analyzes the material uploaded by the material upload part. The test generation unit generates a checking test based on the content of the material analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, creating confirmation tests based on documents is a time-consuming and labor-intensive process, and there is room for improvement in terms of work efficiency.

[0005] The system according to the embodiment aims to automatically generate a confirmation test simply by uploading materials. [Means for solving the problem]

[0006] The system according to the embodiment includes a material uploading unit, an analysis unit, and a test generation unit. The material uploading unit uploads materials such as PDFs. The analysis unit analyzes the materials uploaded by the material uploading unit. The test generation unit generates a confirmation test based on the content of the materials analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate a confirmation test simply by uploading materials. [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 automatic confirmation test generation system according to an embodiment of the present invention is a system in which a generation AI automatically generates confirmation tests simply by uploading documents such as PDFs. This enables the automatic confirmation test generation system to improve business efficiency in companies and can also be used for personal learning purposes.

[0029] The automatic confirmation test generation system according to the embodiment includes a material uploading unit, an analysis unit, and a test generation unit. The material uploading unit uploads materials such as PDFs. For example, a user uploads materials in PDF format to the system. The material uploading unit can also upload materials in image format or audio data format. For example, a user uploads materials in image format, and the system analyzes the content. The material uploading unit can also upload materials in audio data format. For example, a user uploads audio data, and the system analyzes the content. The analysis unit analyzes the materials uploaded by the material uploading unit. For example, a generation AI analyzes materials in PDF format and extracts important points and keywords. The analysis unit can also analyze materials in image format and extract important information. For example, the generation AI analyzes image data and extracts text information. The analysis unit can also analyze materials in audio data format and extract important information. For example, the generation AI analyzes audio data and extracts text information. The test generation unit generates a confirmation test based on the content of the materials analyzed by the analysis unit. For example, the generation AI creates multiple-choice questions and written questions based on keywords and important points extracted. The test generation unit can also adjust the difficulty of questions based on the content of the materials analyzed by the generation AI. For example, the generation AI analyzes the content of the materials and generates questions of different difficulty levels. The test generation unit can also add questions that focus on specific items based on the content of the materials analyzed by the generation AI. For example, the generation AI analyzes the content of the materials and generates questions related to specific items. This allows the automatic confirmation test generation system according to the embodiment to automatically generate confirmation tests simply by uploading materials. For example, companies can conduct efficient training when introducing new products, services, or new systems. Individuals can study at their own pace and effectively acquire knowledge.

[0030] The analysis unit summarizes the contents of the documents and can suggest themes for the confirmation test based on the summary. For example, when documents are uploaded, the analysis unit allows the generation AI to automatically summarize the contents and suggest themes for the confirmation test based on the summarized information. For example, the main points of the documents are extracted and themes such as "features of the new product" and "usage instructions" are set based on them. In addition, when documents are uploaded, the analysis unit allows the generation AI to analyze the contents and generate a summary. The confirmation test themes are suggested based on that summary. For example, keywords contained in the summary are extracted and themes such as "safety precautions" and "operating procedures" are set based on them. In addition, when documents are uploaded, the analysis unit allows the generation AI to automatically summarize the documents and suggest themes for the confirmation test based on the summary. For example, themes such as "product advantages" and "market competitiveness" are set from the summarized information. This makes it possible to suggest themes for the confirmation test based on the document summary.

[0031] The analysis unit can evaluate the reliability of materials and filter out unreliable information. For example, when materials are uploaded to the analysis unit, the generation AI analyzes the content and evaluates its reliability. For example, it calculates a reliability score based on the reliability of the information source and the accuracy of the citations, and filters out unreliable information. In addition, when the analysis unit analyzes the materials, the generation AI evaluates their reliability and automatically excludes unreliable information. For example, it evaluates reliability based on the source of the information and the frequency of citations, and filters out unreliable parts. In addition, when materials are uploaded to the analysis unit, the generation AI analyzes their content and evaluates their reliability. For example, it calculates a reliability score based on the source of the information and the accuracy of the citations, and filters out unreliable information. This makes it possible to filter out unreliable information.

[0032] The document upload unit supports not only PDFs but also images and audio data, enabling multimodal document analysis. The document upload unit, for example, allows document upload formats to be not only PDFs but also images and audio data. For example, when image data is uploaded, the generation AI analyzes the content and suggests themes for the confirmation test. The document upload unit also diversifies the document upload format and allows document upload formats to be also image and audio data. For example, when audio data is uploaded, the generation AI analyzes the content and suggests themes for the confirmation test. The document upload unit also allows document upload formats to be not only PDFs but also images and audio data. For example, when image data is uploaded, the generation AI analyzes the content and suggests themes for the confirmation test. This enables multimodal document analysis.

[0033] The document uploading unit allows documents in different languages ​​to be uploaded simultaneously, and the generation AI can automatically translate and analyze them. The document uploading unit adds a function for uploading documents in different languages ​​simultaneously, and the generation AI can automatically translate and analyze them. For example, if documents in English and Japanese are uploaded simultaneously, the generation AI will automatically translate them and generate a confirmation test based on the analysis results. The document uploading unit also adds a function for uploading documents in different languages ​​simultaneously, and the generation AI can automatically translate and analyze them. For example, if documents in French and Chinese are uploaded simultaneously, the generation AI will automatically translate them and generate a confirmation test based on the analysis results. The document uploading unit also adds a function for uploading documents in different languages ​​simultaneously, and the generation AI can automatically translate and analyze them. For example, if documents in Spanish and German are uploaded simultaneously, the generation AI will automatically translate them and generate a confirmation test based on the analysis results. This allows documents in different languages ​​to be automatically translated and analyzed.

[0034] The analysis unit can perform more accurate keyword extraction by referring to related external databases. For example, when the generation AI analyzes the contents of a document, the analysis unit can perform more accurate keyword extraction by referring to related external databases. For example, it can refer to a patent database to extract technical keywords. Furthermore, when the generation AI analyzes the contents of a document, the analysis unit can perform more accurate keyword extraction by referring to related external databases. For example, it can refer to an academic paper database to extract specialized keywords. Furthermore, when the generation AI analyzes the contents of a document, the analysis unit can perform more accurate keyword extraction by referring to related external databases. For example, it can refer to a patent database to extract technical keywords. This allows for more accurate keyword extraction by referring to external databases.

[0035] The analysis unit can automatically generate related visual data based on the extracted keywords to help understand the material. The analysis unit can automatically generate related visual data based on, for example, keywords extracted by the generation AI. For example, it can generate charts and graphs based on the extracted keywords to help understand the material. The analysis unit can also automatically generate related visual data based on keywords extracted by the generation AI. For example, it can generate charts and graphs based on the extracted keywords to help understand the material. The analysis unit can also automatically generate related visual data based on keywords extracted by the generation AI. For example, it can generate charts and graphs based on the extracted keywords to help understand the material. In this way, visual data can be automatically generated to help understand the material.

[0036] The analysis unit can automatically search for related video content based on the extracted keywords and provide it together with the materials. The analysis unit can, for example, automatically search for related video content based on keywords extracted by the generation AI and provide it together with the materials. For example, it can search for educational videos related to the extracted keywords and provide them together with the materials. The analysis unit can also automatically search for related video content based on keywords extracted by the generation AI and provide it together with the materials. For example, it can search for educational videos related to the extracted keywords and provide them together with the materials. The analysis unit can also automatically search for related video content based on keywords extracted by the generation AI and provide it together with the materials. For example, it can search for educational videos related to the extracted keywords and provide them together with the materials. This makes it possible to automatically search for related video content and provide it together with the materials.

[0037] The analysis unit analyzes materials from different industries and fields and extracts common keywords to gain new insights. For example, the generation AI in the analysis unit analyzes materials from different industries and fields and extracts common keywords. For example, the generation AI analyzes materials from the technical field and the marketing field and extracts common keywords to gain new insights. The analysis unit also analyzes materials from different industries and fields and extracts common keywords. For example, the generation AI analyzes materials from the medical field and the education field and extracts common keywords to gain new insights. The analysis unit also analyzes materials from different industries and fields and extracts common keywords. For example, the generation AI analyzes materials from the technical field and the marketing field and extracts common keywords to gain new insights. This allows new insights to be gained by analyzing materials from different industries and fields.

[0038] The test generation unit can refer to past test results and automatically adjust questions according to the test taker's level of understanding. In the test generation unit, for example, the generation AI refers to past test results and automatically adjusts questions according to the test taker's level of understanding. For example, it may focus on questions with a low correct answer rate in the past. In addition, the test generation unit analyzes past test results and automatically adjusts questions according to the test taker's level of understanding. For example, it may set more questions in areas with a low level of understanding. In addition, the test generation unit refers to past test results and automatically adjusts questions according to the test taker's level of understanding. For example, it may re-set questions that the test taker got wrong in past tests. This makes it possible to automatically adjust questions according to the test taker's level of understanding.

[0039] The test generation unit adds a function that allows users to provide feedback on the generated confirmation test, and can improve the test content based on that feedback. The test generation unit, for example, adds a function that allows users to provide feedback on the confirmation test generated by the generation AI. For example, it collects opinions on the difficulty and content of the questions. The test generation unit also improves the content of the confirmation test based on user feedback. For example, it adjusts the difficulty of the questions by reflecting the feedback. The test generation unit also adds a function that allows users to provide feedback on the confirmation test generated by the generation AI, and improves the test content based on that feedback. For example, it modifies the content of the questions by reflecting the user's opinions. In this way, the test content can be improved based on user feedback.

[0040] The test generation unit can automatically translate the generated confirmation test into different languages ​​to accommodate international test takers. The test generation unit, for example, automatically translates the confirmation test generated by the generation AI into different languages ​​to accommodate international test takers. For example, translating into English, Japanese, French, etc. The test generation unit also automatically translates the confirmation test generated by the generation AI to accommodate test takers of different languages. For example, translating into Spanish, German, Chinese, etc. The test generation unit also automatically translates the confirmation test generated by the generation AI into different languages ​​to accommodate international test takers. For example, translating into English, Japanese, French, etc. This allows automatic translation into different languages ​​to accommodate international test takers.

[0041] The test generation unit can provide the generated confirmation test in a visual note or mind map format to make it easier to understand visually. For example, the test generation unit can provide the confirmation test generated by the generation AI in a visual note format to make it easier to understand visually. For example, important points are indicated with diagrams and icons. The test generation unit can also provide the confirmation test generated by the generation AI in a mind map format to make it easier to understand visually. For example, related keywords and concepts are visually organized. The test generation unit can also provide the confirmation test generated by the generation AI in a visual note or mind map format to make it easier to understand visually. For example, important points are indicated with diagrams and icons. This can make it easier to understand visually by providing it in a visual note or mind map format.

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

[0043] The automatic confirmation test generation system can further include a history tracking unit that tracks the user's learning history. The history tracking unit records the tests the user has taken in the past and the content of their studies, and can customize the content of the next test based on this. For example, it can focus on questions in areas where the user has previously struggled. The history tracking unit can also visualize the user's learning progress and show which areas the user's understanding has progressed in. Furthermore, the history tracking unit can analyze trends in questions that the user has answered correctly in the past and present similar questions. This can maximize the user's learning effectiveness.

[0044] When analyzing the content of a document, the analysis unit can refer to related social media data and extract keywords that reflect the latest trends and topics. For example, it can collect related posts from social media such as Twitter and Facebook and compare them with the content of the document to extract important keywords. The analysis unit can also evaluate how the content of the document is being received based on social media data and prioritize extracting keywords that have received a lot of positive responses. Furthermore, the analysis unit can analyze social media data and extract keywords that reflect the latest trends related to the content of the document. This ensures that the content of the document is in line with the latest trends.

[0045] The automatic confirmation test generation system can further incorporate gamification elements. For example, the test generator can allow users to earn points or badges each time they clear a test. The test generator can also provide a ranking function that allows users to compete with other users. Furthermore, the test generator can provide rewards each time a user achieves a specific goal. This can increase users' motivation to learn and make learning more enjoyable.

[0046] The automatic confirmation test generation system can also provide customization functions according to the user's learning style. For example, if the user prefers visual learning, the test generation unit can provide questions in visual note or mind map format. If the user prefers auditory learning, the test generation unit can provide questions with audio commentary. Furthermore, if the user prefers hands-on learning, the test generation unit can provide simulation questions. This makes it possible to provide an optimal learning experience according to the user's learning style.

[0047] The automatic confirmation test generation system can further include a feedback collection unit that improves the test content based on user feedback. The feedback collection unit can collect opinions on the difficulty and content of questions after the user takes the test. For example, if the user feels that a question is too difficult, the feedback collection unit can collect that opinion and reflect it in the content of the next test. In addition, if the user is dissatisfied with a particular question, the feedback collection unit can collect that opinion and improve the content of the question. Furthermore, the feedback collection unit can adjust the test format and question presentation method based on the user's opinion. This allows the test content to be continuously improved based on user feedback.

[0048] The automatic confirmation test generation system can further provide customization functions according to the user's learning environment. For example, if the user is using a mobile device, the test generation unit can provide a mobile-friendly interface. Also, if the user is using a desktop device, the test generation unit can provide an interface optimized for a large screen. Furthermore, if the user is learning in an offline environment, the test generation unit can provide a test that can be used offline. This makes it possible to provide an optimal learning experience according to the user's learning environment.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The document uploading unit uploads documents such as PDFs. For example, a user uploads documents in PDF format to the system. The document uploading unit can also upload documents in image format or audio data format. For example, a user uploads documents in image format, and the system analyzes the contents. The document uploading unit can also upload documents in audio data format. For example, a user uploads audio data, and the system analyzes the contents. Step 2: The analysis unit analyzes the materials uploaded by the material uploading unit. For example, the generation AI analyzes materials in PDF format and extracts important points and keywords. The analysis unit can also analyze materials in image format and extract important information. For example, the generation AI analyzes image data and extracts text information. The analysis unit can also analyze materials in audio data format and extract important information. For example, the generation AI analyzes audio data and extracts text information. Step 3: The test generation unit generates a confirmation test based on the content of the materials analyzed by the analysis unit. For example, it creates multiple-choice questions and written questions based on the keywords and important points extracted by the generation AI. The test generation unit can also adjust the difficulty of the questions based on the content of the materials analyzed by the generation AI. For example, the generation AI analyzes the content of the materials and generates questions of different levels of difficulty. The test generation unit can also add questions that focus on specific items based on the content of the materials analyzed by the generation AI. For example, the generation AI analyzes the content of the materials and generates questions related to specific items.

[0051] (Example 2) The automatic confirmation test generation system according to an embodiment of the present invention is a system in which a generation AI automatically generates confirmation tests simply by uploading documents such as PDFs. This enables the automatic confirmation test generation system to improve business efficiency in companies and can also be used for personal learning purposes.

[0052] The automatic confirmation test generation system according to the embodiment includes a material uploading unit, an analysis unit, and a test generation unit. The material uploading unit uploads materials such as PDFs. For example, a user uploads materials in PDF format to the system. The material uploading unit can also upload materials in image format or audio data format. For example, a user uploads materials in image format, and the system analyzes the content. The material uploading unit can also upload materials in audio data format. For example, a user uploads audio data, and the system analyzes the content. The analysis unit analyzes the materials uploaded by the material uploading unit. For example, a generation AI analyzes materials in PDF format and extracts important points and keywords. The analysis unit can also analyze materials in image format and extract important information. For example, the generation AI analyzes image data and extracts text information. The analysis unit can also analyze materials in audio data format and extract important information. For example, the generation AI analyzes audio data and extracts text information. The test generation unit generates a confirmation test based on the content of the materials analyzed by the analysis unit. For example, the generation AI creates multiple-choice questions and written questions based on keywords and important points extracted. The test generation unit can also adjust the difficulty of questions based on the content of the materials analyzed by the generation AI. For example, the generation AI analyzes the content of the materials and generates questions of different difficulty levels. The test generation unit can also add questions that focus on specific items based on the content of the materials analyzed by the generation AI. For example, the generation AI analyzes the content of the materials and generates questions related to specific items. This allows the automatic confirmation test generation system according to the embodiment to automatically generate confirmation tests simply by uploading materials. For example, companies can conduct efficient training when introducing new products, services, or new systems. Individuals can study at their own pace and effectively acquire knowledge.

[0053] The analysis unit summarizes the contents of the documents and can suggest themes for the confirmation test based on the summary. For example, when documents are uploaded, the analysis unit allows the generation AI to automatically summarize the contents and suggest themes for the confirmation test based on the summarized information. For example, the main points of the documents are extracted and themes such as "features of the new product" and "usage instructions" are set based on them. In addition, when documents are uploaded, the analysis unit allows the generation AI to analyze the contents and generate a summary. The confirmation test themes are suggested based on that summary. For example, keywords contained in the summary are extracted and themes such as "safety precautions" and "operating procedures" are set based on them. In addition, when documents are uploaded, the analysis unit allows the generation AI to automatically summarize the documents and suggest themes for the confirmation test based on the summary. For example, themes such as "product advantages" and "market competitiveness" are set from the summarized information. This makes it possible to suggest themes for the confirmation test based on the document summary.

[0054] The analysis unit can evaluate the reliability of materials and filter out unreliable information. For example, when materials are uploaded to the analysis unit, the generation AI analyzes the content and evaluates its reliability. For example, it calculates a reliability score based on the reliability of the information source and the accuracy of the citations, and filters out unreliable information. In addition, when the analysis unit analyzes the materials, the generation AI evaluates their reliability and automatically excludes unreliable information. For example, it evaluates reliability based on the source of the information and the frequency of citations, and filters out unreliable parts. In addition, when materials are uploaded to the analysis unit, the generation AI analyzes their content and evaluates their reliability. For example, it calculates a reliability score based on the source of the information and the accuracy of the citations, and filters out unreliable information. This makes it possible to filter out unreliable information.

[0055] The analysis unit uses the emotion estimation function to analyze the emotional impact that the content of the materials has on test takers and can select materials that elicit positive emotions. For example, when the analysis unit uploads materials, the generation AI uses the emotion estimation function to analyze the content and selects materials that elicit positive emotions in test takers. For example, the analysis unit evaluates the emotional impact that the content of the materials has on test takers and prioritizes selecting materials that elicit positive emotions. In addition, when the analysis unit uploads materials, the generation AI uses the emotion estimation function to analyze the content and selects materials that elicit positive emotions. For example, the analysis unit evaluates the emotional impact that the content of the materials has on test takers and prioritizes selecting materials that elicit positive emotions. In addition, when the analysis unit uploads materials, the generation AI uses the emotion estimation function to analyze the content and selects materials that elicit positive emotions in test takers. For example, the analysis unit evaluates the emotional impact that the content of the materials has on test takers and prioritizes selecting materials that elicit positive emotions. In this way, materials that elicit positive emotions can be selected.

[0056] The document upload unit supports not only PDFs but also images and audio data, enabling multimodal document analysis. The document upload unit, for example, allows document upload formats to be not only PDFs but also images and audio data. For example, when image data is uploaded, the generation AI analyzes the content and suggests themes for the confirmation test. The document upload unit also diversifies the document upload format and allows document upload formats to be also image and audio data. For example, when audio data is uploaded, the generation AI analyzes the content and suggests themes for the confirmation test. The document upload unit also allows document upload formats to be not only PDFs but also images and audio data. For example, when image data is uploaded, the generation AI analyzes the content and suggests themes for the confirmation test. This enables multimodal document analysis.

[0057] The document uploading unit allows documents in different languages ​​to be uploaded simultaneously, and the generation AI can automatically translate and analyze them. The document uploading unit adds a function for uploading documents in different languages ​​simultaneously, and the generation AI can automatically translate and analyze them. For example, if documents in English and Japanese are uploaded simultaneously, the generation AI will automatically translate them and generate a confirmation test based on the analysis results. The document uploading unit also adds a function for uploading documents in different languages ​​simultaneously, and the generation AI can automatically translate and analyze them. For example, if documents in French and Chinese are uploaded simultaneously, the generation AI will automatically translate them and generate a confirmation test based on the analysis results. The document uploading unit also adds a function for uploading documents in different languages ​​simultaneously, and the generation AI can automatically translate and analyze them. For example, if documents in Spanish and German are uploaded simultaneously, the generation AI will automatically translate them and generate a confirmation test based on the analysis results. This allows documents in different languages ​​to be automatically translated and analyzed.

[0058] The material uploading unit can use the emotion estimation function to analyze the user's emotions in real time and recommend the most appropriate material. For example, when materials are uploaded by the material uploading unit, the generation AI uses the emotion estimation function to analyze the user's emotions in real time and recommend the most appropriate material. For example, materials for which the user has positive emotions are preferentially recommended. Furthermore, when materials are uploaded by the material uploading unit, the generation AI uses the emotion estimation function to analyze the user's emotions in real time and recommend the most appropriate material. For example, materials for which the user has positive emotions are preferentially recommended. Furthermore, when materials are uploaded by the material uploading unit, the generation AI uses the emotion estimation function to analyze the user's emotions in real time and recommend the most appropriate material. For example, materials for which the user has positive emotions are preferentially recommended. This makes it possible to recommend the most appropriate material based on the user's emotions.

[0059] The analysis unit can perform more accurate keyword extraction by referring to related external databases. For example, when the generation AI analyzes the contents of a document, the analysis unit can perform more accurate keyword extraction by referring to related external databases. For example, it can refer to a patent database to extract technical keywords. Furthermore, when the generation AI analyzes the contents of a document, the analysis unit can perform more accurate keyword extraction by referring to related external databases. For example, it can refer to an academic paper database to extract specialized keywords. Furthermore, when the generation AI analyzes the contents of a document, the analysis unit can perform more accurate keyword extraction by referring to related external databases. For example, it can refer to a patent database to extract technical keywords. This allows for more accurate keyword extraction by referring to external databases.

[0060] The analysis unit can automatically generate related visual data based on the extracted keywords to help understand the material. The analysis unit can automatically generate related visual data based on, for example, keywords extracted by the generation AI. For example, it can generate charts and graphs based on the extracted keywords to help understand the material. The analysis unit can also automatically generate related visual data based on keywords extracted by the generation AI. For example, it can generate charts and graphs based on the extracted keywords to help understand the material. The analysis unit can also automatically generate related visual data based on keywords extracted by the generation AI. For example, it can generate charts and graphs based on the extracted keywords to help understand the material. In this way, visual data can be automatically generated to help understand the material.

[0061] The analysis unit can automatically search for related video content based on the extracted keywords and provide it together with the materials. The analysis unit can, for example, automatically search for related video content based on keywords extracted by the generation AI and provide it together with the materials. For example, it can search for educational videos related to the extracted keywords and provide them together with the materials. The analysis unit can also automatically search for related video content based on keywords extracted by the generation AI and provide it together with the materials. For example, it can search for educational videos related to the extracted keywords and provide them together with the materials. The analysis unit can also automatically search for related video content based on keywords extracted by the generation AI and provide it together with the materials. For example, it can search for educational videos related to the extracted keywords and provide them together with the materials. This makes it possible to automatically search for related video content and provide it together with the materials.

[0062] The analysis unit analyzes materials from different industries and fields and extracts common keywords to gain new insights. For example, the generation AI in the analysis unit analyzes materials from different industries and fields and extracts common keywords. For example, the generation AI analyzes materials from the technical field and the marketing field and extracts common keywords to gain new insights. The analysis unit also analyzes materials from different industries and fields and extracts common keywords. For example, the generation AI analyzes materials from the medical field and the education field and extracts common keywords to gain new insights. The analysis unit also analyzes materials from different industries and fields and extracts common keywords. For example, the generation AI analyzes materials from the technical field and the marketing field and extracts common keywords to gain new insights. This allows new insights to be gained by analyzing materials from different industries and fields.

[0063] The analysis unit can monitor the user's emotional reactions to keywords extracted using the emotion estimation function in real time and select the optimal keywords. For example, the analysis unit uses the emotion estimation function on keywords extracted by the generation AI to monitor the user's emotional reactions in real time. For example, it prioritizes selecting keywords with a high number of positive emotional reactions. The analysis unit also uses the emotion estimation function to monitor the user's emotional reactions to the extracted keywords in real time and selects the optimal keywords. For example, it selects keywords based on the user's emotion score. The analysis unit also uses the emotion estimation function on keywords extracted by the generation AI to monitor the user's emotional reactions in real time. For example, it prioritizes selecting keywords with a high number of positive emotional reactions. This makes it possible to monitor the user's emotional reactions in real time and select the optimal keywords.

[0064] The test generation unit can refer to past test results and automatically adjust questions according to the test taker's level of understanding. In the test generation unit, for example, the generation AI refers to past test results and automatically adjusts questions according to the test taker's level of understanding. For example, it may focus on questions with a low correct answer rate in the past. In addition, the test generation unit analyzes past test results and automatically adjusts questions according to the test taker's level of understanding. For example, it may set more questions in areas with a low level of understanding. In addition, the test generation unit refers to past test results and automatically adjusts questions according to the test taker's level of understanding. For example, it may re-set questions that the test taker got wrong in past tests. This makes it possible to automatically adjust questions according to the test taker's level of understanding.

[0065] The test generation unit adds a function that allows users to provide feedback on the generated confirmation test, and can improve the test content based on that feedback. The test generation unit, for example, adds a function that allows users to provide feedback on the confirmation test generated by the generation AI. For example, it collects opinions on the difficulty and content of the questions. The test generation unit also improves the content of the confirmation test based on user feedback. For example, it adjusts the difficulty of the questions by reflecting the feedback. The test generation unit also adds a function that allows users to provide feedback on the confirmation test generated by the generation AI, and improves the test content based on that feedback. For example, it modifies the content of the questions by reflecting the user's opinions. In this way, the test content can be improved based on user feedback.

[0066] The test generation unit can evaluate the emotional impact that a confirmation test generated using the emotion estimation function has on the test taker, and prioritize generating questions that elicit positive emotions. The test generation unit, for example, uses the emotion estimation function on a confirmation test generated by the generation AI to prioritize generating questions that elicit positive emotions in the test taker. For example, questions that elicit positive emotions are selected. The test generation unit also uses the emotion estimation function to evaluate the emotional impact that a generated confirmation test has on the test taker, and prioritize generating questions that elicit positive emotions. For example, questions that elicit positive emotions are selected. The test generation unit also uses the emotion estimation function on a confirmation test generated by the generation AI to prioritize generating questions that elicit positive emotions in the test taker. For example, questions that elicit positive emotions are selected. This makes it possible to prioritize generating questions that elicit positive emotions.

[0067] The test generation unit can automatically translate the generated confirmation test into different languages ​​to accommodate international test takers. The test generation unit, for example, automatically translates the confirmation test generated by the generation AI into different languages ​​to accommodate international test takers. For example, translating into English, Japanese, French, etc. The test generation unit also automatically translates the confirmation test generated by the generation AI to accommodate test takers of different languages. For example, translating into Spanish, German, Chinese, etc. The test generation unit also automatically translates the confirmation test generated by the generation AI into different languages ​​to accommodate international test takers. For example, translating into English, Japanese, French, etc. This allows automatic translation into different languages ​​to accommodate international test takers.

[0068] The test generation unit can provide the generated confirmation test in a visual note or mind map format to make it easier to understand visually. For example, the test generation unit can provide the confirmation test generated by the generation AI in a visual note format to make it easier to understand visually. For example, important points are indicated with diagrams and icons. The test generation unit can also provide the confirmation test generated by the generation AI in a mind map format to make it easier to understand visually. For example, related keywords and concepts are visually organized. The test generation unit can also provide the confirmation test generated by the generation AI in a visual note or mind map format to make it easier to understand visually. For example, important points are indicated with diagrams and icons. This can make it easier to understand visually by providing it in a visual note or mind map format.

[0069] The test generation unit monitors the user's emotional response to the generated confirmation test in real time using the emotion estimation function, and continuously searches for optimal test content. The test generation unit, for example, uses the emotion estimation function to monitor the user's emotional response to the generated confirmation test in real time, and continuously searches for optimal test content. For example, questions that evoke a large number of positive emotional responses are prioritized. The test generation unit also monitors the user's emotional response to the generated confirmation test in real time, and continuously searches for optimal test content. For example, the content of the questions is adjusted based on the emotion score. The test generation unit also uses the emotion estimation function to monitor the user's emotional response to the generated confirmation test in real time, and continuously searches for optimal test content. For example, questions that evoke a large number of positive emotional responses are prioritized. This makes it possible to monitor the user's emotional response in real time and continuously search for optimal test content.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The automatic confirmation test generation system can further include a history tracking unit that tracks the user's learning history. The history tracking unit records the tests the user has taken in the past and the content of their studies, and can customize the content of the next test based on this. For example, it can focus on questions in areas where the user has previously struggled. The history tracking unit can also visualize the user's learning progress and show which areas the user's understanding has progressed in. Furthermore, the history tracking unit can analyze trends in questions that the user has answered correctly in the past and present similar questions. This can maximize the user's learning effectiveness.

[0072] When analyzing the content of a document, the analysis unit can refer to related social media data and extract keywords that reflect the latest trends and topics. For example, it can collect related posts from social media such as Twitter and Facebook and compare them with the content of the document to extract important keywords. The analysis unit can also evaluate how the content of the document is being received based on social media data and prioritize extracting keywords that have received a lot of positive responses. Furthermore, the analysis unit can analyze social media data and extract keywords that reflect the latest trends related to the content of the document. This ensures that the content of the document is in line with the latest trends.

[0073] When analyzing the content of the materials, the analysis unit can use the emotion estimation function to evaluate the emotional impact of the material content on the test taker and filter out parts that may cause negative emotions. For example, if the content of the materials is likely to cause stress to the test taker, that part can be excluded. The analysis unit can also use the emotion estimation function to analyze the content of the materials and prioritize parts that elicit positive emotions in the test taker. Furthermore, the analysis unit can use the emotion estimation function to analyze the content of the materials and allow the test taker to study in a relaxed state. This reduces the emotional burden on the test taker and improves learning effectiveness.

[0074] The automatic confirmation test generation system can further incorporate gamification elements. For example, the test generator can allow users to earn points or badges each time they clear a test. The test generator can also provide a ranking function that allows users to compete with other users. Furthermore, the test generator can provide rewards each time a user achieves a specific goal. This can increase users' motivation to learn and make learning more enjoyable.

[0075] When analyzing the content of the materials, the analysis unit can use the emotion estimation function to evaluate the emotional impact that the content of the materials has on the test taker and select materials that elicit positive emotions. For example, if the content of the materials elicits positive emotions in the test taker, the analysis unit can preferentially select those materials. The analysis unit can also use the emotion estimation function to analyze the content of the materials and select materials that elicit positive emotions in the test taker. The analysis unit can also use the emotion estimation function to analyze the content of the materials and select materials that elicit positive emotions in the test taker. This makes it possible to select materials that elicit positive emotions.

[0076] The automatic confirmation test generation system can also provide customization functions according to the user's learning style. For example, if the user prefers visual learning, the test generation unit can provide questions in visual note or mind map format. If the user prefers auditory learning, the test generation unit can provide questions with audio commentary. Furthermore, if the user prefers hands-on learning, the test generation unit can provide simulation questions. This makes it possible to provide an optimal learning experience according to the user's learning style.

[0077] When analyzing the content of the materials, the analysis unit can use the emotion estimation function to evaluate the emotional impact that the content of the materials has on the test taker and select materials that elicit positive emotions. For example, if the content of the materials elicits positive emotions in the test taker, the analysis unit can preferentially select those materials. The analysis unit can also use the emotion estimation function to analyze the content of the materials and select materials that elicit positive emotions in the test taker. The analysis unit can also use the emotion estimation function to analyze the content of the materials and select materials that elicit positive emotions in the test taker. This makes it possible to select materials that elicit positive emotions.

[0078] The automatic confirmation test generation system can further include a feedback collection unit that improves the test content based on user feedback. The feedback collection unit can collect opinions on the difficulty and content of questions after the user takes the test. For example, if the user feels that a question is too difficult, the feedback collection unit can collect that opinion and reflect it in the content of the next test. In addition, if the user is dissatisfied with a particular question, the feedback collection unit can collect that opinion and improve the content of the question. Furthermore, the feedback collection unit can adjust the test format and question presentation method based on the user's opinion. This allows the test content to be continuously improved based on user feedback.

[0079] When analyzing the content of the materials, the analysis unit can use the emotion estimation function to evaluate the emotional impact that the content of the materials has on the test taker and select materials that elicit positive emotions. For example, if the content of the materials elicits positive emotions in the test taker, the analysis unit can preferentially select those materials. The analysis unit can also use the emotion estimation function to analyze the content of the materials and select materials that elicit positive emotions in the test taker. The analysis unit can also use the emotion estimation function to analyze the content of the materials and select materials that elicit positive emotions in the test taker. This makes it possible to select materials that elicit positive emotions.

[0080] The automatic confirmation test generation system can further provide customization functions according to the user's learning environment. For example, if the user is using a mobile device, the test generation unit can provide a mobile-friendly interface. Also, if the user is using a desktop device, the test generation unit can provide an interface optimized for a large screen. Furthermore, if the user is learning in an offline environment, the test generation unit can provide a test that can be used offline. This makes it possible to provide an optimal learning experience according to the user's learning environment.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The document uploading unit uploads documents such as PDFs. For example, a user uploads documents in PDF format to the system. The document uploading unit can also upload documents in image format or audio data format. For example, a user uploads documents in image format, and the system analyzes the contents. The document uploading unit can also upload documents in audio data format. For example, a user uploads audio data, and the system analyzes the contents. Step 2: The analysis unit analyzes the materials uploaded by the material uploading unit. For example, the generation AI analyzes materials in PDF format and extracts important points and keywords. The analysis unit can also analyze materials in image format and extract important information. For example, the generation AI analyzes image data and extracts text information. The analysis unit can also analyze materials in audio data format and extract important information. For example, the generation AI analyzes audio data and extracts text information. Step 3: The test generation unit generates a confirmation test based on the content of the materials analyzed by the analysis unit. For example, it creates multiple-choice questions and written questions based on the keywords and important points extracted by the generation AI. The test generation unit can also adjust the difficulty of the questions based on the content of the materials analyzed by the generation AI. For example, the generation AI analyzes the content of the materials and generates questions of different levels of difficulty. The test generation unit can also add questions that focus on specific items based on the content of the materials analyzed by the generation AI. For example, the generation AI analyzes the content of the materials and generates questions related to specific items.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 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 document upload section for uploading documents such as PDFs, an analysis unit that analyzes the materials uploaded by the material upload unit; a test generation unit that generates a confirmation test based on the content of the material analyzed by the analysis unit. A system characterized by:

2. The analysis unit Summarize the contents of the materials and propose a topic for the confirmation test based on the summary 2. The system of claim 1.

3. The analysis unit Evaluating the reliability of said materials and filtering out said unreliable information 2. The system of claim 1.

4. The analysis unit Analyze the emotional impact of the content of the materials on test takers and select materials that elicit positive emotions.

2. The system of claim 1.

5. The material upload unit In addition to PDF, it also supports image and audio data, enabling multimodal document analysis.

2. The system of claim 1.

Images