System

Abstract

Depending on embodiments, a system may automatically generate a wordbook, a workbook, or a reference material based on content of a note.SOLUTION: A system includes an image analysis unit, a wordbook generation unit, a question collection generation unit, and a reference document generation unit. The image analysis unit analyzes the image of the notebook. The wordbook generation unit generates a wordbook based on the content of the note analyzed by the image analysis unit. The question collection generation unit generates a question collection based on the content of the note analyzed by the image analysis unit. The reference document generation section generates a reference document based on the content of the note analyzed by the image analysis section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology had the problem of taking time and effort to manually create vocabulary lists, workbooks, and reference materials based on the contents of notes.

[0005] The system according to the embodiment aims to automatically generate vocabulary books, workbooks, and reference materials based on the contents of notes. [Means for solving the problem]

[0006] The system according to the embodiment includes an image analysis unit, a flashcard generation unit, a question collection generation unit, and a reference generation unit. The image analysis unit analyzes an image of a notebook. The flashcard generation unit generates a flashcard based on the content of the notebook analyzed by the image analysis unit. The question collection generation unit generates a question collection based on the content of the notebook analyzed by the image analysis unit. The reference generation unit generates references based on the content of the notebook analyzed by the image analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate vocabulary books, workbooks, and reference materials based on the contents of notes. [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 learning support system according to the embodiment of the present invention is a system that automatically analyzes photographed notes and generates vocabulary books, workbooks, and reference materials based on the content of the notes using a generation AI. This allows the learning support system to effectively support the user's learning.

[0029] A learning support system according to an embodiment includes an image analysis unit, a vocabulary list generation unit, a question collection generation unit, and a reference generation unit. The image analysis unit analyzes images of notes. For example, the image analysis unit analyzes images of handwritten or printed notes to recognize characters and diagrams. The image analysis unit can also convert characters in the images into text data using OCR technology. For example, the image of a handwritten note can be scanned and text information can be extracted using OCR technology. The vocabulary list generation unit generates a vocabulary list based on the contents of the notes analyzed by the image analysis unit. For example, the generation AI extracts important words and phrases from the analyzed text data and compiles them into a vocabulary list. The vocabulary list also includes word meanings and usage examples. For example, the generation AI analyzes English notes and generates a vocabulary list containing important words and their meanings. The question collection generation unit generates a quiz based on the contents of the notes analyzed by the image analysis unit. For example, the generation AI creates questions related to important events or people from the analyzed text data. The quiz also includes options and answers. For example, a history notebook may be analyzed to generate multiple-choice questions about important events. The reference generation unit generates references based on the contents of the notes analyzed by the image analysis unit. For example, the generation AI may suggest related papers and books from the analyzed text data. The references may also include information such as the book title, author name, and publication year. For example, a science notebook may be analyzed to provide summaries of related papers. This allows the learning support system according to the embodiment to effectively support users' learning. For example, the user may study efficiently based on photographed notes. The user may create a vocabulary book for an English exam or a question bank for a history exam. The system may also easily find references needed for scientific research.

[0030] The image analysis unit analyzes the writing pressure or stroke of handwritten characters and learns the user's writing habits to improve accuracy. The image analysis unit, for example, analyzes the writing pressure of handwritten characters and learns the user's writing habits. For example, it detects the strength of writing pressure and improves the accuracy of character recognition based on that. The image analysis unit also analyzes the characteristics of handwriting and learns the user's writing habits. For example, it learns how to write specific characters and how to connect them, and improves recognition accuracy. The image analysis unit also analyzes the slant and size of handwritten characters and learns the user's writing habits. For example, it detects patterns in the slant and size of characters and improves the accuracy of character recognition based on that. In this way, the accuracy of character recognition can be improved by learning the user's writing habits.

[0031] The image analysis unit can recognize the layout or color usage of a page and reflect visual information in the text data. The image analysis unit, for example, recognizes the layout of a page and reflects visual information in the text data. For example, it analyzes the positions of paragraphs and headings and reflects them in the text data. The image analysis unit also recognizes color usage and reflects visual information in the text data. For example, if important parts are color-coded, it reflects that information in the text data. The image analysis unit also recognizes the layout and color usage of a page and reflects visual information in the text data. For example, it analyzes the positions and colors of diagrams and illustrations and reflects them in the text data. In this way, by reflecting visual information in the text data, more accurate analysis results can be provided.

[0032] The image analysis unit can simultaneously analyze multiple notebook or textbook pages and provide integrated related information. The image analysis unit, for example, simultaneously analyzes multiple notebook or textbook pages and provides integrated related information. For example, the contents of different notes on the same topic can be integrated and displayed. The image analysis unit can also simultaneously analyze multiple notebook or textbook pages and provide integrated related information. For example, the contents of the same chapter in different textbooks can be integrated and displayed. The image analysis unit can also simultaneously analyze multiple notebook or textbook pages and provide integrated related information. For example, the contents of the same topic in different notes can be integrated and displayed. This allows for integrated provision of information from multiple notebooks or textbooks, thereby improving learning efficiency.

[0033] The image analysis unit can use voice input when analyzing the image of the note, and reflect the content dictated by the user in the text data. The image analysis unit can use voice input when analyzing the image of the note, and reflect the content dictated by the user in the text data. For example, the content of the note can be supplemented with voice. The image analysis unit can also use voice input when analyzing the image of the note, and reflect the content dictated by the user in the text data. For example, the content of the note can be explained by voice. The image analysis unit can also use voice input when analyzing the image of the note, and reflect the content dictated by the user in the text data. For example, the content of the note can be supplemented with voice. In this way, by using voice input in combination, more detailed text data can be generated.

[0034] The vocabulary list generation unit can analyze the frequency of use or importance of words and provide them with priorities. The vocabulary list generation unit, for example, analyzes the frequency of use of words and provides them with priorities. For example, frequently used words are displayed at the top. The vocabulary list generation unit also analyzes the importance of words and provides them with priorities. For example, words that are likely to appear on exams are displayed at the top. The vocabulary list generation unit also analyzes the frequency of use or importance of words and provides them with priorities. For example, frequently used words and important words are displayed in a balanced manner. In this way, efficient learning can be supported by prioritizing words based on their frequency of use or importance.

[0035] The vocabulary list generation unit can automatically generate example sentences or phrases related to the vocabulary list to support learning based on assumed usage scenarios. The vocabulary list generation unit, for example, automatically generates example sentences related to the vocabulary list to support learning based on assumed usage scenarios. For example, it provides example sentences for everyday conversations or business situations. The vocabulary list generation unit also automatically generates phrases related to the vocabulary list to support learning based on assumed usage scenarios. For example, it provides phrases that can be used when traveling or shopping. The vocabulary list generation unit also automatically generates example sentences and phrases related to the vocabulary list to support learning based on assumed usage scenarios. For example, it provides example sentences and phrases for exam preparation. In this way, by automatically generating example sentences and phrases, it is possible to support learning based on assumed usage scenarios.

[0036] The vocabulary list generation unit can simultaneously generate vocabulary lists in different languages ​​to support multilingual learning. The vocabulary list generation unit, for example, simultaneously generates vocabulary lists in different languages. For example, English and Spanish vocabulary lists are simultaneously provided. The vocabulary list generation unit can also simultaneously generate vocabulary lists in different languages ​​to support multilingual learning. For example, French and German vocabulary lists are simultaneously provided. The vocabulary list generation unit can also simultaneously generate vocabulary lists in different languages ​​to support multilingual learning. For example, Japanese and Chinese vocabulary lists are simultaneously provided. This makes it possible to support multilingual learning by simultaneously generating vocabulary lists in different languages.

[0037] The vocabulary book generation unit can provide the vocabulary book in a game format, allowing the user to study while having fun. The vocabulary book generation unit, for example, provides the vocabulary book in a game format, allowing the user to study while having fun. For example, a game is provided in the form of a quiz to help the user memorize words. The vocabulary book generation unit also provides the vocabulary book in a game format, allowing the user to study while having fun. For example, a game is provided in the form of a puzzle to help the user memorize words. The vocabulary book generation unit also provides the vocabulary book in a game format, allowing the user to study while having fun. For example, a game is provided in which the user memorizes words in a battle format. In this way, by providing the vocabulary book in a game format, the user can study while having fun.

[0038] The question set generation unit can automatically adjust the difficulty of questions and provide questions that match the user's learning level. The question set generation unit, for example, automatically adjusts the difficulty of questions and provides questions that match the user's learning level. For example, it provides beginner, intermediate, and advanced questions. The question set generation unit also adjusts the difficulty level based on the user's past learning data. For example, it sets the difficulty level of questions based on the past correct answer rate. The question set generation unit also monitors the user's answer status in real time and adjusts the difficulty level. For example, it increases the difficulty level each time an answer is correct. In this way, by automatically adjusting the difficulty level of questions, it is possible to provide questions that match the user's learning level.

[0039] The problem set generation unit can provide a comprehensive problem set that combines problems from different subjects or fields. The problem set generation unit, for example, provides a comprehensive problem set that combines problems from different subjects. For example, a problem set that combines problems from mathematics and English is provided. The problem set generation unit also provides a comprehensive problem set that combines problems from different fields. For example, a problem set that combines problems from science and history is provided. The problem set generation unit also provides a comprehensive problem set that combines problems from different subjects or fields. For example, a problem set that combines problems from literature and geography is provided. In this way, by providing a comprehensive problem set that combines problems from different subjects or fields, it is possible to support comprehensive learning.

[0040] The question set generation unit can provide the question set in an interactive quiz format, allowing the user to study while having fun. The question set generation unit, for example, provides the question set in an interactive quiz format, allowing the user to study while having fun. For example, it provides a multiple-choice quiz. The question set generation unit also provides the question set in an interactive quiz format, allowing the user to study while having fun. For example, it provides a time attack quiz. The question set generation unit also provides the question set in an interactive quiz format, allowing the user to study while having fun. For example, it provides a battle-style quiz. In this way, by providing the question set in an interactive quiz format, the user can study while having fun.

[0041] The reference generation unit can automatically generate summaries of related papers or books to support information gathering. The reference generation unit, for example, automatically generates summaries of related papers to support information gathering. For example, it briefly summarizes the main points of a paper. The reference generation unit also automatically generates summaries of related books to support information gathering. For example, it summarizes the main points of a book. The reference generation unit also automatically generates summaries of related papers or books to support information gathering. For example, it provides a combination of summaries of a paper and a book. This makes it possible to support efficient information gathering by automatically generating summaries of related papers or books.

[0042] The reference generation unit can link databases or online resources related to the references to provide information access. The reference generation unit, for example, links databases related to the references to provide information access. For example, it links academic paper databases. The reference generation unit also links online resources related to the references to provide information access. For example, it links e-book resources. The reference generation unit also links databases or online resources related to the references to provide information access. For example, it links academic paper databases to e-book resources. In this way, by linking databases and online resources, it is possible to provide a wide range of information access.

[0043] The bibliography generation unit can simultaneously provide documents in different languages ​​to support learning from a global perspective. The bibliography generation unit, for example, simultaneously provides documents in different languages. For example, English and Japanese documents are simultaneously provided. The bibliography generation unit can also simultaneously provide documents in different languages ​​to support learning from a global perspective. For example, French and German documents are simultaneously provided. The bibliography generation unit can also simultaneously provide documents in different languages ​​to support learning from a global perspective. For example, Chinese and Spanish documents are simultaneously provided. This makes it possible to simultaneously provide documents in different languages ​​to support learning from a global perspective.

[0044] The reference generation unit may provide the references in an interactive format, allowing the user to directly add notes and comments. For example, the reference generation unit may provide the references in an interactive format, allowing the user to directly add notes. For example, the reference generation unit may provide a function for adding notes to specific parts of the document. The reference generation unit may also provide the references in an interactive format, allowing the user to directly add comments. For example, the reference generation unit may provide a function for adding comments to specific parts of the document. The reference generation unit may also provide the references in an interactive format, allowing the user to directly add notes and comments. For example, the reference generation unit may provide a function for adding notes and comments to the entire document. Thus, by providing the references in an interactive format, the user may directly add notes and comments.

[0045] The reference generation unit can automatically generate summaries of related papers or books when analyzing the contents of the notes, thereby supporting information gathering. The reference generation unit, for example, analyzes the contents of the notes and automatically generates summaries of related papers to support information gathering. For example, it briefly summarizes the main points of the papers. The reference generation unit can also analyze the contents of the notes and automatically generate summaries of related books to support information gathering. For example, it can summarize the main points of the books. The reference generation unit can also analyze the contents of the notes and automatically generate summaries of related papers or books to support information gathering. For example, it can provide a combination of summaries of papers and books. This can support efficient information gathering by automatically generating summaries of related papers or books when analyzing the contents of the notes.

[0046] The reference generation unit can link databases or online resources related to the references to provide information access. The reference generation unit, for example, links databases related to the references to provide information access. For example, it links academic paper databases. The reference generation unit also links online resources related to the references to provide information access. For example, it links e-book resources. The reference generation unit also links databases or online resources related to the references to provide information access. For example, it links academic paper databases to e-book resources. In this way, by linking databases and online resources, it is possible to provide a wide range of information access.

[0047] The bibliography generation unit can simultaneously provide documents in different languages ​​to support learning from a global perspective. The bibliography generation unit, for example, simultaneously provides documents in different languages. For example, English and Japanese documents are simultaneously provided. The bibliography generation unit can also simultaneously provide documents in different languages ​​to support learning from a global perspective. For example, French and German documents are simultaneously provided. The bibliography generation unit can also simultaneously provide documents in different languages ​​to support learning from a global perspective. For example, Chinese and Spanish documents are simultaneously provided. This makes it possible to simultaneously provide documents in different languages ​​to support learning from a global perspective.

[0048] The reference generation unit may provide the references in an interactive format, allowing the user to directly add notes and comments. For example, the reference generation unit may provide the references in an interactive format, allowing the user to directly add notes. For example, the reference generation unit may provide a function for adding notes to specific parts of the document. The reference generation unit may also provide the references in an interactive format, allowing the user to directly add comments. For example, the reference generation unit may provide a function for adding comments to specific parts of the document. The reference generation unit may also provide the references in an interactive format, allowing the user to directly add notes and comments. For example, the reference generation unit may provide a function for adding notes and comments to the entire document. Thus, by providing the references in an interactive format, the user may directly add notes and comments.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The learning assistance system can further include a progress management unit that tracks the user's learning progress. The progress management unit records the content and time the user has studied and visualizes the learning progress. For example, it can display a graph showing how much the user has studied for each unit. The progress management unit can also analyze the user's learning pace and propose an appropriate study plan. For example, it can create an efficient study schedule based on the time remaining until the exam date. Furthermore, the progress management unit can recommend the next content to study based on the user's learning history. This allows the user to understand their own learning situation and progress effectively in their studies.

[0051] The learning support system can further include a learning style analysis unit that analyzes the user's learning style. The learning style analysis unit analyzes the user's learning method and preferences and suggests the most suitable learning method. For example, for a user who prefers visual learning, learning materials that make extensive use of charts and illustrations can be suggested. For a user who prefers auditory learning, audio learning materials and podcasts can be suggested. Furthermore, the learning style analysis unit can analyze the user's learning patterns and suggest effective study times. This allows the user to find the best learning method for themselves and progress with their studies efficiently.

[0052] The learning support system can further include a reward system to improve users' motivation to learn. The reward system awards points or badges when users achieve their learning goals. For example, rewards are given when users continue studying for a certain period of time or when they clear a specific unit. The reward system can also provide a ranking function that allows users to compete with each other. For example, a ranking can be displayed based on study time or achievement level, and special rewards can be given to users who rank highly. Furthermore, the reward system can provide a function that allows users to use the points they have earned to purchase items that are useful for studying. This allows users to maintain their motivation while enjoying learning.

[0053] The learning support system can further include an environment adjustment unit for optimizing the user's learning environment. The environment adjustment unit monitors the user's learning environment and provides an optimal environment. For example, it provides a function for adjusting the brightness and volume of lighting. The environment adjustment unit can also provide environmental sounds and music to improve the user's concentration. For example, it can play sounds of nature or relaxing music. Furthermore, the environment adjustment unit can monitor the user's learning posture and provide advice on maintaining appropriate posture. This allows the user to concentrate on their studies in a comfortable environment.

[0054] The learning support system may further include a sharing unit that enables a user to share their learning content with other users. The sharing unit provides a function for users to share notes, vocabulary books, and workbooks created by the user with other users. For example, users studying the same subject can share notes and provide feedback to each other. The sharing unit may also provide a function for users to upload their learning content to social networking sites or cloud storage. For example, users can share their learning results on social networking sites and receive encouragement from friends and followers. Furthermore, the sharing unit may provide a search function that enables users to refer to the learning content of other users. This allows users to share their learning content with other users and learn from each other.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The image analysis unit analyzes the image of the note. For example, it analyzes images of handwritten or printed notes and recognizes characters and diagrams. It can also use OCR technology to convert characters in the image into text data. For example, it can scan an image of a handwritten note and use OCR technology to extract the character information. Step 2: The flashcard generator generates a flashcard based on the contents of the notes analyzed by the image analyzer. For example, it extracts important words and phrases from the analyzed text data and compiles them into a flashcard. The flashcard also includes the meanings and usage examples of the words. For example, it analyzes English notes and generates a flashcard containing important words and their meanings. Step 3: The question set generator generates a question set based on the contents of the notes analyzed by the image analyzer. For example, it creates questions about important events or people from the analyzed text data. The question set also includes options and answers. For example, it analyzes history notes and generates multiple-choice questions about important events. Step 4: The bibliography generator generates a bibliography based on the contents of the notes analyzed by the image analyzer. For example, it suggests related papers and books from the analyzed text data. The bibliography also includes information such as book titles, author names, and publication years. For example, it analyzes scientific notes and provides summaries of related papers.

[0057] (Example 2) The learning support system according to the embodiment of the present invention is a system that automatically analyzes photographed notes and generates vocabulary books, workbooks, and reference materials based on the content of the notes using a generation AI. This allows the learning support system to effectively support the user's learning.

[0058] A learning support system according to an embodiment includes an image analysis unit, a vocabulary list generation unit, a question collection generation unit, and a reference generation unit. The image analysis unit analyzes images of notes. For example, the image analysis unit analyzes images of handwritten or printed notes to recognize characters and diagrams. The image analysis unit can also convert characters in the images into text data using OCR technology. For example, the image of a handwritten note can be scanned and text information can be extracted using OCR technology. The vocabulary list generation unit generates a vocabulary list based on the contents of the notes analyzed by the image analysis unit. For example, the generation AI extracts important words and phrases from the analyzed text data and compiles them into a vocabulary list. The vocabulary list also includes word meanings and usage examples. For example, the generation AI analyzes English notes and generates a vocabulary list containing important words and their meanings. The question collection generation unit generates a quiz based on the contents of the notes analyzed by the image analysis unit. For example, the generation AI creates questions related to important events or people from the analyzed text data. The quiz also includes options and answers. For example, a history notebook may be analyzed to generate multiple-choice questions about important events. The reference generation unit generates references based on the contents of the notes analyzed by the image analysis unit. For example, the generation AI may suggest related papers and books from the analyzed text data. The references may also include information such as the book title, author name, and publication year. For example, a science notebook may be analyzed to provide summaries of related papers. This allows the learning support system according to the embodiment to effectively support users' learning. For example, the user may study efficiently based on photographed notes. The user may create a vocabulary book for an English exam or a question bank for a history exam. The system may also easily find references needed for scientific research.

[0059] The image analysis unit analyzes the writing pressure or stroke of handwritten characters and learns the user's writing habits to improve accuracy. The image analysis unit, for example, analyzes the writing pressure of handwritten characters and learns the user's writing habits. For example, it detects the strength of writing pressure and improves the accuracy of character recognition based on that. The image analysis unit also analyzes the characteristics of handwriting and learns the user's writing habits. For example, it learns how to write specific characters and how to connect them, and improves recognition accuracy. The image analysis unit also analyzes the slant and size of handwritten characters and learns the user's writing habits. For example, it detects patterns in the slant and size of characters and improves the accuracy of character recognition based on that. In this way, the accuracy of character recognition can be improved by learning the user's writing habits.

[0060] The image analysis unit can recognize the layout or color usage of a page and reflect visual information in the text data. The image analysis unit, for example, recognizes the layout of a page and reflects visual information in the text data. For example, it analyzes the positions of paragraphs and headings and reflects them in the text data. The image analysis unit also recognizes color usage and reflects visual information in the text data. For example, if important parts are color-coded, it reflects that information in the text data. The image analysis unit also recognizes the layout and color usage of a page and reflects visual information in the text data. For example, it analyzes the positions and colors of diagrams and illustrations and reflects them in the text data. In this way, by reflecting visual information in the text data, more accurate analysis results can be provided.

[0061] The image analysis unit can use the emotion estimation function to estimate the user's emotions while studying from the contents of the notes and provide analysis results based on the emotions. The image analysis unit, for example, analyzes the contents of the notes and uses the emotion estimation function to estimate the user's emotions while studying. For example, it highlights parts with strong positive emotions. The image analysis unit also analyzes the contents of the notes and uses the emotion estimation function to estimate the user's emotions while studying. For example, it suggests improvements for parts with strong negative emotions. The image analysis unit also analyzes the contents of the notes and uses the emotion estimation function to estimate the user's emotions while studying. For example, it provides study advice based on the emotions. This allows for improved learning effectiveness by providing analysis results that take into account the user's emotions while studying.

[0062] The image analysis unit can simultaneously analyze multiple notebook or textbook pages and provide integrated related information. The image analysis unit, for example, simultaneously analyzes multiple notebook or textbook pages and provides integrated related information. For example, the contents of different notes on the same topic can be integrated and displayed. The image analysis unit can also simultaneously analyze multiple notebook or textbook pages and provide integrated related information. For example, the contents of the same chapter in different textbooks can be integrated and displayed. The image analysis unit can also simultaneously analyze multiple notebook or textbook pages and provide integrated related information. For example, the contents of the same topic in different notes can be integrated and displayed. This allows for integrated provision of information from multiple notebooks or textbooks, thereby improving learning efficiency.

[0063] The image analysis unit can use voice input when analyzing the image of the note, and reflect the content dictated by the user in the text data. The image analysis unit can use voice input when analyzing the image of the note, and reflect the content dictated by the user in the text data. For example, the content of the note can be supplemented with voice. The image analysis unit can also use voice input when analyzing the image of the note, and reflect the content dictated by the user in the text data. For example, the content of the note can be explained by voice. The image analysis unit can also use voice input when analyzing the image of the note, and reflect the content dictated by the user in the text data. For example, the content of the note can be supplemented with voice. In this way, by using voice input in combination, more detailed text data can be generated.

[0064] The image analysis unit can use the emotion estimation function to monitor the user's emotions in real time when analyzing the note image and reflect them in the analysis results. For example, the image analysis unit can use the emotion estimation function to monitor the user's emotions in real time when analyzing the note image and reflect them in the analysis results. For example, it can highlight parts with strong positive emotions. The image analysis unit can also use the emotion estimation function to monitor the user's emotions in real time when analyzing the note image and reflect them in the analysis results. For example, it can suggest improvements to parts with strong negative emotions. The image analysis unit can also use the emotion estimation function to monitor the user's emotions in real time when analyzing the note image and reflect them in the analysis results. For example, it can provide learning advice based on emotions. In this way, by monitoring the user's emotions in real time, it is possible to provide more appropriate analysis results.

[0065] The vocabulary list generation unit can analyze the frequency of use or importance of words and provide them with priorities. The vocabulary list generation unit, for example, analyzes the frequency of use of words and provides them with priorities. For example, frequently used words are displayed at the top. The vocabulary list generation unit also analyzes the importance of words and provides them with priorities. For example, words that are likely to appear on exams are displayed at the top. The vocabulary list generation unit also analyzes the frequency of use or importance of words and provides them with priorities. For example, frequently used words and important words are displayed in a balanced manner. In this way, efficient learning can be supported by prioritizing words based on their frequency of use or importance.

[0066] The vocabulary list generation unit can automatically generate example sentences or phrases related to the vocabulary list to support learning based on assumed usage scenarios. The vocabulary list generation unit, for example, automatically generates example sentences related to the vocabulary list to support learning based on assumed usage scenarios. For example, it provides example sentences for everyday conversations or business situations. The vocabulary list generation unit also automatically generates phrases related to the vocabulary list to support learning based on assumed usage scenarios. For example, it provides phrases that can be used when traveling or shopping. The vocabulary list generation unit also automatically generates example sentences and phrases related to the vocabulary list to support learning based on assumed usage scenarios. For example, it provides example sentences and phrases for exam preparation. In this way, by automatically generating example sentences and phrases, it is possible to support learning based on assumed usage scenarios.

[0067] The flashcard generation unit can use the emotion estimation function to estimate the user's emotion from the content of the flashcard and generate a flashcard based on the emotion. The flashcard generation unit, for example, analyzes the content of the flashcard and estimates the user's emotion using the emotion estimation function. For example, words associated with strong positive emotions are preferentially displayed. The flashcard generation unit also analyzes the content of the flashcard and estimates the user's emotion using the emotion estimation function. For example, words associated with strong negative emotions are suggested for improvement. The flashcard generation unit also analyzes the content of the flashcard and estimates the user's emotion using the emotion estimation function. For example, study advice based on the emotion is provided. In this way, generating a flashcard based on the user's emotion can increase motivation to study.

[0068] The vocabulary list generation unit can simultaneously generate vocabulary lists in different languages ​​to support multilingual learning. The vocabulary list generation unit, for example, simultaneously generates vocabulary lists in different languages. For example, English and Spanish vocabulary lists are simultaneously provided. The vocabulary list generation unit can also simultaneously generate vocabulary lists in different languages ​​to support multilingual learning. For example, French and German vocabulary lists are simultaneously provided. The vocabulary list generation unit can also simultaneously generate vocabulary lists in different languages ​​to support multilingual learning. For example, Japanese and Chinese vocabulary lists are simultaneously provided. This makes it possible to support multilingual learning by simultaneously generating vocabulary lists in different languages.

[0069] The vocabulary book generation unit can provide the vocabulary book in a game format, allowing the user to study while having fun. The vocabulary book generation unit, for example, provides the vocabulary book in a game format, allowing the user to study while having fun. For example, a game is provided in the form of a quiz to help the user memorize words. The vocabulary book generation unit also provides the vocabulary book in a game format, allowing the user to study while having fun. For example, a game is provided in the form of a puzzle to help the user memorize words. The vocabulary book generation unit also provides the vocabulary book in a game format, allowing the user to study while having fun. For example, a game is provided in which the user memorizes words in a battle format. In this way, by providing the vocabulary book in a game format, the user can study while having fun.

[0070] The vocabulary list generation unit can use the emotion estimation function to monitor the user's emotions in real time when generating the vocabulary list, and provide content that will increase motivation to study. For example, the vocabulary list generation unit can use the emotion estimation function to monitor the user's emotions in real time when generating the vocabulary list, and provide content that will increase motivation to study. For example, words that evoke strong positive emotions can be preferentially displayed. The vocabulary list generation unit can also use the emotion estimation function to monitor the user's emotions in real time when generating the vocabulary list, and provide content that will increase motivation to study. For example, words that evoke strong negative emotions can be suggested for improvement. The vocabulary list generation unit can also use the emotion estimation function to monitor the user's emotions in real time when generating the vocabulary list, and provide content that will increase motivation to study. For example, study advice based on emotions can be provided. In this way, content that will increase motivation to study can be provided by monitoring the user's emotions in real time.

[0071] The question set generation unit can automatically adjust the difficulty of questions and provide questions that match the user's learning level. The question set generation unit, for example, automatically adjusts the difficulty of questions and provides questions that match the user's learning level. For example, it provides beginner, intermediate, and advanced questions. The question set generation unit also adjusts the difficulty level based on the user's past learning data. For example, it sets the difficulty level of questions based on the past correct answer rate. The question set generation unit also monitors the user's answer status in real time and adjusts the difficulty level. For example, it increases the difficulty level each time an answer is correct. In this way, by automatically adjusting the difficulty level of questions, it is possible to provide questions that match the user's learning level.

[0072] The question set generation unit can use the emotion estimation function to estimate the user's emotion from the content of the question set and generate a question set based on the emotion. The question set generation unit, for example, analyzes the content of the question set and estimates the user's emotion using the emotion estimation function. For example, questions that evoke strong positive emotions are preferentially displayed. The question set generation unit also analyzes the content of the question set and estimates the user's emotion using the emotion estimation function. For example, questions that evoke strong negative emotions are suggested for improvement. The question set generation unit also analyzes the content of the question set and estimates the user's emotion using the emotion estimation function. For example, study advice based on the emotion is provided. In this way, generating a question set based on the user's emotion can increase motivation to study.

[0073] The problem set generation unit can provide a comprehensive problem set that combines problems from different subjects or fields. The problem set generation unit, for example, provides a comprehensive problem set that combines problems from different subjects. For example, a problem set that combines problems from mathematics and English is provided. The problem set generation unit also provides a comprehensive problem set that combines problems from different fields. For example, a problem set that combines problems from science and history is provided. The problem set generation unit also provides a comprehensive problem set that combines problems from different subjects or fields. For example, a problem set that combines problems from literature and geography is provided. In this way, by providing a comprehensive problem set that combines problems from different subjects or fields, it is possible to support comprehensive learning.

[0074] The question set generation unit can provide the question set in an interactive quiz format, allowing the user to study while having fun. The question set generation unit, for example, provides the question set in an interactive quiz format, allowing the user to study while having fun. For example, it provides a multiple-choice quiz. The question set generation unit also provides the question set in an interactive quiz format, allowing the user to study while having fun. For example, it provides a time attack quiz. The question set generation unit also provides the question set in an interactive quiz format, allowing the user to study while having fun. For example, it provides a battle-style quiz. In this way, by providing the question set in an interactive quiz format, the user can study while having fun.

[0075] The question set generation unit can use the emotion estimation function to monitor the user's emotions in real time when generating a question set, and provide content that will increase motivation to study. For example, the question set generation unit can use the emotion estimation function to monitor the user's emotions in real time when generating a question set, and provide content that will increase motivation to study. For example, questions that evoke strong positive emotions can be preferentially displayed. The question set generation unit can also use the emotion estimation function to monitor the user's emotions in real time when generating a question set, and provide content that will increase motivation to study. For example, questions that evoke strong negative emotions can be suggested for improvement. The question set generation unit can also use the emotion estimation function to monitor the user's emotions in real time when generating a question set, and provide content that will increase motivation to study. For example, study advice based on emotions can be provided. In this way, content that will increase motivation to study can be provided by monitoring the user's emotions in real time.

[0076] The reference generation unit can automatically generate summaries of related papers or books to support information gathering. The reference generation unit, for example, automatically generates summaries of related papers to support information gathering. For example, it briefly summarizes the main points of a paper. The reference generation unit also automatically generates summaries of related books to support information gathering. For example, it summarizes the main points of a book. The reference generation unit also automatically generates summaries of related papers or books to support information gathering. For example, it provides a combination of summaries of a paper and a book. This makes it possible to support efficient information gathering by automatically generating summaries of related papers or books.

[0077] The reference generation unit can link databases or online resources related to the references to provide information access. The reference generation unit, for example, links databases related to the references to provide information access. For example, it links academic paper databases. The reference generation unit also links online resources related to the references to provide information access. For example, it links e-book resources. The reference generation unit also links databases or online resources related to the references to provide information access. For example, it links academic paper databases to e-book resources. In this way, by linking databases and online resources, it is possible to provide a wide range of information access.

[0078] The reference document generation unit can use the emotion estimation function to estimate the user's emotion from the content of the reference document and generate reference documents based on the emotion. The reference document generation unit, for example, analyzes the content of the reference document and estimates the user's emotion using the emotion estimation function. For example, it preferentially displays documents with strong positive emotions. The reference document generation unit also analyzes the content of the reference document and estimates the user's emotion using the emotion estimation function. For example, it suggests improvements for documents with strong negative emotions. The reference document generation unit also analyzes the content of the reference document and estimates the user's emotion using the emotion estimation function. For example, it provides study advice based on the emotion. In this way, by generating reference documents based on the user's emotion, it is possible to increase motivation to study.

[0079] The bibliography generation unit can simultaneously provide documents in different languages ​​to support learning from a global perspective. The bibliography generation unit, for example, simultaneously provides documents in different languages. For example, English and Japanese documents are simultaneously provided. The bibliography generation unit can also simultaneously provide documents in different languages ​​to support learning from a global perspective. For example, French and German documents are simultaneously provided. The bibliography generation unit can also simultaneously provide documents in different languages ​​to support learning from a global perspective. For example, Chinese and Spanish documents are simultaneously provided. This makes it possible to simultaneously provide documents in different languages ​​to support learning from a global perspective.

[0080] The reference generation unit may provide the references in an interactive format, allowing the user to directly add notes and comments. For example, the reference generation unit may provide the references in an interactive format, allowing the user to directly add notes. For example, the reference generation unit may provide a function for adding notes to specific parts of the document. The reference generation unit may also provide the references in an interactive format, allowing the user to directly add comments. For example, the reference generation unit may provide a function for adding comments to specific parts of the document. The reference generation unit may also provide the references in an interactive format, allowing the user to directly add notes and comments. For example, the reference generation unit may provide a function for adding notes and comments to the entire document. Thus, by providing the references in an interactive format, the user may directly add notes and comments.

[0081] The reference document generation unit can use the emotion estimation function to monitor the user's emotions in real time when generating reference documents, and provide content that will increase their motivation to study. For example, the reference document generation unit can use the emotion estimation function to monitor the user's emotions in real time when generating reference documents, and provide content that will increase their motivation to study. For example, documents that evoke strong positive emotions can be preferentially displayed. The reference document generation unit can also use the emotion estimation function to monitor the user's emotions in real time when generating reference documents, and provide content that will increase their motivation to study. For example, it can suggest improvements for documents that evoke strong negative emotions. The reference document generation unit can also use the emotion estimation function to monitor the user's emotions in real time when generating reference documents, and provide content that will increase their motivation to study. For example, it can provide study advice based on emotions. In this way, content that will increase their motivation to study can be provided by monitoring the user's emotions in real time.

[0082] The reference generation unit can automatically generate summaries of related papers or books when analyzing the contents of the notes, thereby supporting information gathering. The reference generation unit, for example, analyzes the contents of the notes and automatically generates summaries of related papers to support information gathering. For example, it briefly summarizes the main points of the papers. The reference generation unit can also analyze the contents of the notes and automatically generate summaries of related books to support information gathering. For example, it can summarize the main points of the books. The reference generation unit can also analyze the contents of the notes and automatically generate summaries of related papers or books to support information gathering. For example, it can provide a combination of summaries of papers and books. This can support efficient information gathering by automatically generating summaries of related papers or books when analyzing the contents of the notes.

[0083] The reference generation unit can link databases or online resources related to the references to provide information access. The reference generation unit, for example, links databases related to the references to provide information access. For example, it links academic paper databases. The reference generation unit also links online resources related to the references to provide information access. For example, it links e-book resources. The reference generation unit also links databases or online resources related to the references to provide information access. For example, it links academic paper databases to e-book resources. In this way, by linking databases and online resources, it is possible to provide a wide range of information access.

[0084] The reference generation unit can use the emotion estimation function to estimate the user's emotion from the contents of the notes and generate references based on the emotion. The reference generation unit, for example, analyzes the contents of the notes and estimates the user's emotion using the emotion estimation function. For example, it preferentially displays literature with strong positive emotions. The reference generation unit also analyzes the contents of the notes and estimates the user's emotion using the emotion estimation function. For example, it suggests improvements for literature with strong negative emotions. The reference generation unit also analyzes the contents of the notes and estimates the user's emotion using the emotion estimation function. For example, it provides study advice based on the emotion. In this way, by estimating the user's emotion from the contents of the notes and generating references based on the emotion, it is possible to increase motivation to study.

[0085] The bibliography generation unit can simultaneously provide documents in different languages ​​to support learning from a global perspective. The bibliography generation unit, for example, simultaneously provides documents in different languages. For example, English and Japanese documents are simultaneously provided. The bibliography generation unit can also simultaneously provide documents in different languages ​​to support learning from a global perspective. For example, French and German documents are simultaneously provided. The bibliography generation unit can also simultaneously provide documents in different languages ​​to support learning from a global perspective. For example, Chinese and Spanish documents are simultaneously provided. This makes it possible to simultaneously provide documents in different languages ​​to support learning from a global perspective.

[0086] The reference generation unit may provide the references in an interactive format, allowing the user to directly add notes and comments. For example, the reference generation unit may provide the references in an interactive format, allowing the user to directly add notes. For example, the reference generation unit may provide a function for adding notes to specific parts of the document. The reference generation unit may also provide the references in an interactive format, allowing the user to directly add comments. For example, the reference generation unit may provide a function for adding comments to specific parts of the document. The reference generation unit may also provide the references in an interactive format, allowing the user to directly add notes and comments. For example, the reference generation unit may provide a function for adding notes and comments to the entire document. Thus, by providing the references in an interactive format, the user may directly add notes and comments.

[0087] The reference document generation unit can monitor the user's emotions in real time when analyzing the contents of the notes using the emotion estimation function, and provide content that will increase their motivation to study. For example, the reference document generation unit can monitor the user's emotions in real time when analyzing the contents of the notes using the emotion estimation function, and provide content that will increase their motivation to study. For example, it can preferentially display literature that evokes strong positive emotions. The reference document generation unit can also monitor the user's emotions in real time when analyzing the contents of the notes using the emotion estimation function, and provide content that will increase their motivation to study. For example, it can suggest improvements for literature that evokes strong negative emotions. The reference document generation unit can also monitor the user's emotions in real time when analyzing the contents of the notes using the emotion estimation function, and provide content that will increase their motivation to study. For example, it can provide study advice based on emotions. In this way, it is possible to provide content that will increase their motivation to study by monitoring the user's emotions in real time.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The learning assistance system can further include a progress management unit that tracks the user's learning progress. The progress management unit records the content and time the user has studied and visualizes the learning progress. For example, it can display a graph showing how much the user has studied for each unit. The progress management unit can also analyze the user's learning pace and propose an appropriate study plan. For example, it can create an efficient study schedule based on the time remaining until the exam date. Furthermore, the progress management unit can recommend the next content to study based on the user's learning history. This allows the user to understand their own learning situation and progress effectively in their studies.

[0090] The learning support system can further include a learning style analysis unit that analyzes the user's learning style. The learning style analysis unit analyzes the user's learning method and preferences and suggests the most suitable learning method. For example, for a user who prefers visual learning, learning materials that make extensive use of charts and illustrations can be suggested. For a user who prefers auditory learning, audio learning materials and podcasts can be suggested. Furthermore, the learning style analysis unit can analyze the user's learning patterns and suggest effective study times. This allows the user to find the best learning method for themselves and progress with their studies efficiently.

[0091] The learning support system can further include a reward system to improve users' motivation to learn. The reward system awards points or badges when users achieve their learning goals. For example, rewards are given when users continue studying for a certain period of time or when they clear a specific unit. The reward system can also provide a ranking function that allows users to compete with each other. For example, a ranking can be displayed based on study time or achievement level, and special rewards can be given to users who rank highly. Furthermore, the reward system can provide a function that allows users to use the points they have earned to purchase items that are useful for studying. This allows users to maintain their motivation while enjoying learning.

[0092] The learning support system can further include an environment adjustment unit for optimizing the user's learning environment. The environment adjustment unit monitors the user's learning environment and provides an optimal environment. For example, it provides a function for adjusting the brightness and volume of lighting. The environment adjustment unit can also provide environmental sounds and music to improve the user's concentration. For example, it can play sounds of nature or relaxing music. Furthermore, the environment adjustment unit can monitor the user's learning posture and provide advice on maintaining appropriate posture. This allows the user to concentrate on their studies in a comfortable environment.

[0093] The learning support system may further include a sharing unit that enables a user to share their learning content with other users. The sharing unit provides a function for users to share notes, vocabulary books, and workbooks created by the user with other users. For example, users studying the same subject can share notes and provide feedback to each other. The sharing unit may also provide a function for users to upload their learning content to social networking sites or cloud storage. For example, users can share their learning results on social networking sites and receive encouragement from friends and followers. Furthermore, the sharing unit may provide a search function that enables users to refer to the learning content of other users. This allows users to share their learning content with other users and learn from each other.

[0094] The learning assistance system can further include an emotion advice unit that estimates the user's emotions while studying and provides learning advice based on the emotions. The emotion advice unit analyzes the user's emotions while studying and provides appropriate advice. For example, if the user is feeling stressed, it can suggest taking a break to relax. Also, if the user is lacking concentration, it can suggest adjusting the environment to improve concentration. Furthermore, the emotion advice unit can adjust the learning content based on the user's emotions. For example, it can provide more difficult questions when the user is feeling strongly positive, and provide easier questions when the user is feeling strongly negative. This allows the user to study in accordance with their emotions.

[0095] The learning assistance system can further include an emotional content unit that estimates the user's emotions during learning and provides learning content based on those emotions. The emotional content unit analyzes the user's emotions during learning and provides appropriate learning content. For example, if the user has positive emotions, it can provide challenging questions. On the other hand, if the user has negative emotions, it can provide relaxing content. Furthermore, the emotional content unit can adjust the learning progress based on the user's emotions. For example, if the user is tired, it can provide learning content that can be completed in a short amount of time. This allows the user to use learning content that matches their emotions.

[0096] The learning assistance system may further include an emotion feedback unit that estimates the user's emotion during learning and provides feedback based on the emotion. The emotion feedback unit analyzes the user's emotion during learning and provides appropriate feedback. For example, if the user has positive emotions, it may provide praising feedback. Also, if the user has negative emotions, it may provide encouraging feedback. Furthermore, the emotion feedback unit may adjust the progress of learning based on the user's emotions. For example, if the user is tired, it may suggest taking a break. This allows the user to receive feedback that is tailored to their emotions.

[0097] The learning assistance system may further include an emotion planning unit that estimates the user's emotions during study and provides a study plan based on the emotions. The emotion planning unit analyzes the user's emotions during study and provides an appropriate study plan. For example, if the user has positive emotions, it may suggest a time period when the user can concentrate on studying. Also, if the user has negative emotions, it may suggest a time period when the user can relax. Furthermore, the emotion planning unit may adjust the progress of study based on the user's emotions. For example, if the user is tired, it may provide a study plan that can be completed in a short amount of time. This allows the user to create a study plan that suits their emotions.

[0098] The learning assistance system may further include an emotional resource unit that estimates the user's emotions during learning and provides learning resources based on the emotions. The emotional resource unit analyzes the user's emotions during learning and provides appropriate learning resources. For example, if the user has positive emotions, it may provide reference materials with a high level of difficulty. On the other hand, if the user has negative emotions, it may provide reference materials that are easy to read. Furthermore, the emotional resource unit may adjust the progress of learning based on the user's emotions. For example, if the user is tired, it may provide reference materials that can be read in a short amount of time. This allows the user to use learning resources that match their emotions.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The image analysis unit analyzes the image of the note. For example, it analyzes images of handwritten or printed notes and recognizes characters and diagrams. It can also use OCR technology to convert characters in the image into text data. For example, it can scan an image of a handwritten note and use OCR technology to extract the character information. Step 2: The flashcard generator generates a flashcard based on the contents of the notes analyzed by the image analyzer. For example, it extracts important words and phrases from the analyzed text data and compiles them into a flashcard. The flashcard also includes the meanings and usage examples of the words. For example, it analyzes English notes and generates a flashcard containing important words and their meanings. Step 3: The question set generator generates a question set based on the contents of the notes analyzed by the image analyzer. For example, it creates questions about important events or people from the analyzed text data. The question set also includes options and answers. For example, it analyzes history notes and generates multiple-choice questions about important events. Step 4: The bibliography generator generates a bibliography based on the contents of the notes analyzed by the image analyzer. For example, it suggests related papers and books from the analyzed text data. The bibliography also includes information such as book titles, author names, and publication years. For example, it analyzes scientific notes and provides summaries of related papers.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0137] The 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.

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

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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, in order to avoid confusion and to 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.

[0167] 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]

[0168] 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. an image analysis unit that analyzes the image of the note; a flashcard generation unit that generates a flashcard based on the content of the note analyzed by the image analysis unit; a question set generation unit that generates a question set based on the contents of the notes analyzed by the image analysis unit; a reference generation unit that generates references based on the contents of the notes analyzed by the image analysis unit. A system characterized by:

2. The image analysis unit Analyzes handwriting pressure or strokes to learn the user's writing habits and improve accuracy 2. The system of claim 1.

3. The image analysis unit Recognizes page layout or color usage and reflects visual information in text data 2. The system of claim 1.

4. The image analysis unit Inferring the user's feelings during study from the contents of notes and providing analysis results based on those feelings 2. The system of claim 1.

5. The image analysis unit Simultaneously analyze multiple pages of the notebook or textbook and provide integrated information related to the pages.

2. The system of claim 1.

6. The image analysis unit When analyzing images of notes, voice input is used in combination, and the contents dictated by the user are reflected in the text data.

2. The system of claim 1.

Images