system

Abstract

The system according to the embodiment aims to make the contents of textbooks easier to understand visually through 3D images. [Solution] A system according to an embodiment includes an acquisition unit, a generation unit, and an operation unit. The acquisition unit acquires the content of a textbook. The generation unit generates a 3D image based on the data acquired by the acquisition unit. The operation unit allows a user to freely operate the 3D image generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to visually understand the contents of textbooks, making it difficult to attract learning interest.

[0005] The system according to the embodiment aims to make the contents of textbooks easier to understand visually through 3D images. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a generation unit, and an operation unit. The acquisition unit acquires the content of a textbook. The generation unit generates a 3D image based on the data acquired by the acquisition unit. The operation unit allows a user to freely operate the 3D image generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can make the contents of textbooks easier to understand visually through 3D images. [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) An educational support system according to an embodiment of the present invention converts textbook content into 3D images that users can manipulate freely. The educational support system acquires textbook content, generates 3D images based on the acquired data, and provides an interface that allows users to freely manipulate the generated 3D images. For example, in a history class, a user can freely walk around a castle and get a close look at the paintings on the walls. In a chemistry experiment, a user can manipulate glass tubes and chemicals on the screen and check the results of the experiment. In this way, the educational support system provides services that make schoolwork more enjoyable and easy to understand. For example, an acquisition unit is provided that acquires data for converting textbook content into 3D images. Next, a generation unit is provided that generates 3D images based on the acquired data. An operation unit is provided that allows users to freely manipulate the generated 3D images. The acquisition unit acquires the textbook content, passes the data to the generation unit to generate 3D images, and the operation unit provides the generated 3D images to the user. The generation unit includes an algorithm that automatically converts textbook content into 3D images. The operation unit provides an interface that allows users to freely manipulate the 3D images. This allows the educational support system to convert the contents of textbooks into 3D images that users can freely manipulate, deepening their understanding of the learning process.

[0029] The education support system according to the embodiment includes an acquisition unit, a generation unit, and an operation unit. The acquisition unit acquires the content of a textbook. The content of the textbook includes, but is not limited to, text, charts, diagrams, exercises, and the like. For example, the acquisition unit digitizes and acquires the content of the textbook using scanning technology. The acquisition unit can also directly acquire the content of the textbook provided in digital format. The acquisition unit can also read the content of a printed textbook using OCR technology. For example, the acquisition unit scans the content of the textbook with a high-resolution scanner and converts it into text information using OCR technology. The digital content of the textbook can be directly acquired if it is provided in a specific file format. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The generation unit generates a 3D image based on the content of the textbook. For example, the generation unit includes an algorithm that automatically converts the content of the textbook into a 3D image. For example, the generation unit analyzes the content of the textbook using a machine learning algorithm and generates a 3D image. The generation unit can also convert the content of the textbook into a 3D image using a rule-based algorithm. For example, the generation unit receives the content of a textbook as input and generates a 3D image using an algorithm that outputs a 3D image. The operation unit provides an interface that allows a user to freely operate the 3D image generated by the generation unit. The operation unit allows a user to operate the 3D image using, for example, a GUI (Graphical User Interface). The operation unit can also allow a user to operate the 3D image using a touch screen. Furthermore, the operation unit can also allow a user to operate the 3D image using voice control. For example, the operation unit provides an interface that allows a user to rotate, zoom, and move the 3D image. As a result, the education support system according to the embodiment can convert the content of a textbook into a 3D image and allow a user to freely operate it, thereby deepening learning comprehension.

[0030] The generation unit may include an algorithm that automatically converts the textbook content into 3D video. The generation unit may, for example, use a machine learning algorithm to analyze the textbook content and generate 3D video. For example, the generation unit may use an algorithm that takes the textbook content as input and outputs 3D video to generate 3D video. The generation unit may also use a rule-based algorithm to convert the textbook content into 3D video. For example, the generation unit may analyze the textbook content and generate 3D video based on rules. This allows for efficient generation of 3D video by automatically converting the textbook content into 3D video. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may generate 3D video using an AI model that takes the textbook content as input and outputs 3D video.

[0031] The operation unit can provide an interface that allows the user to freely operate the 3D image. The operation unit allows the user to operate the 3D image using, for example, a GUI (Graphical User Interface). For example, the operation unit provides an interface that allows the user to rotate, zoom, and move the 3D image. The operation unit can also allow the user to operate the 3D image using a touch screen. For example, the operation unit provides an interface that allows the user to operate the 3D image using touch gestures. The operation unit can also allow the user to operate the 3D image using voice operation. For example, the operation unit provides an interface that allows the user to operate the 3D image using voice commands. This allows the user to freely operate the 3D image, thereby deepening learning understanding. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without AI. For example, the operation unit can provide an operation interface using an AI model that inputs the user's operation history and outputs an optimal operation interface.

[0032] The operation unit can provide an interface that allows a user to freely walk around a castle in a history class. The operation unit can provide an interface that allows a user to freely walk around a castle in a history class. For example, the operation unit provides a navigation function that allows a user to freely move around the castle. The operation unit can also provide a function that allows a user to select a specific location in the castle and display detailed information. For example, the operation unit allows a user to select a room or corridor in the castle and display historical information about that location. The operation unit can also provide a zoom function that allows a user to get a closer look at a painting on a wall in the castle. For example, the operation unit provides an interface that allows a user to enlarge a painting on a wall to check the details. This allows a user to freely walk around the castle in a history class, thereby deepening their understanding of the learning. Some or all of the above-described processing in the operation unit can be performed using, for example, AI, or can be performed without AI. For example, the operation unit can provide a navigation function using an AI model that inputs a user's movement history and outputs an optimal navigation route.

[0033] The operation unit can provide an interface that allows a user to manipulate glass tubes and chemicals on a screen and check the results of a chemistry experiment. For example, the operation unit can provide an interface that allows a user to manipulate glass tubes and chemicals on a screen and check the results of a chemistry experiment. For example, the operation unit can provide an interface that allows a user to select a glass tube and mix chemicals. The operation unit can also provide an interface that allows a user to heat chemicals. For example, the operation unit can provide a temperature adjustment function for a user to heat chemicals. The operation unit can also provide an interface that allows a user to check the results of an experiment. For example, the operation unit can provide a function that allows a user to display the results of an experiment as a graph or chart. This allows a user to manipulate glass tubes and chemicals on a screen and check the results of an experiment, thereby deepening learning understanding. Some or all of the above-described processing in the operation unit can be performed using, for example, AI, or can be performed without AI. For example, the operation unit can provide an experiment interface using an AI model that inputs a user's operation history and outputs an optimal experiment procedure.

[0034] When acquiring textbook content, the acquisition unit can select the content to acquire based on the user's learning progress. For example, the acquisition unit evaluates the user's learning progress and selects the textbook content based on that progress. For example, if the user is learning content for the first time, the acquisition unit acquires the content starting from the basics. Furthermore, if the user is reviewing content they have already learned, the acquisition unit can acquire content focusing on important points. Furthermore, if the user wants to study a specific unit in depth, the acquisition unit can acquire detailed content related to that unit. For example, the acquisition unit evaluates the user's learning progress based on test results, study time, progress, etc., and selects optimal content. This allows for enhanced learning effectiveness by selecting content to acquire based on the user's learning progress. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input the user's learning progress data into a generation AI and cause the generation AI to select optimal content.

[0035] When acquiring textbook content, the acquisition unit can analyze the user's past learning history and select the optimal acquisition method. The acquisition unit, for example, analyzes the user's past learning history and acquires textbook content based on that history. For example, the acquisition unit prioritizes acquiring content that the user previously struggled with. The acquisition unit can also skip content in which the user is strong and focus on weaker parts. Furthermore, the acquisition unit can suggest the most effective learning order based on the user's learning history and acquire content according to that order. For example, the acquisition unit analyzes the user's learning log, test results, study time, etc., and selects the optimal acquisition method. By analyzing the user's past learning history and selecting the optimal acquisition method, the effectiveness of learning can be improved. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can input the user's learning history data into a generation AI and have the generation AI select the optimal acquisition method.

[0036] When acquiring textbook content, the acquisition unit can customize the acquired content based on the user's learning style. The acquisition unit, for example, evaluates the user's learning style and customizes the textbook content based on the style. For example, if the user prefers visual learning, the acquisition unit can acquire content that includes many diagrams and illustrations. Furthermore, if the user prefers auditory learning, the acquisition unit can acquire content that includes audio commentary. Furthermore, if the user prefers practical learning, the acquisition unit can acquire content that includes many experiments and exercises. For example, the acquisition unit can evaluate the user's learning style based on survey results, past behavioral history, etc., and customize the optimal content. This allows the learning effect to be enhanced by customizing the acquired content based on the user's learning style. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input the user's learning style data into a generation AI and cause the generation AI to customize the optimal content.

[0037] When acquiring textbook content, the acquisition unit can acquire related additional information based on the user's interests. For example, the acquisition unit evaluates the user's interests and acquires textbook content based on those interests. For example, if the user is interested in history, the acquisition unit can acquire related historical background and episodes. Furthermore, if the user is interested in science, the acquisition unit can acquire the latest scientific research and discoveries. Furthermore, if the user is interested in literature, the acquisition unit can acquire information on related literary works and authors. For example, the acquisition unit can evaluate the user's interests based on survey results, past behavioral history, etc., and acquire optimal additional information. This allows for the acquisition of relevant additional information based on the user's interests, thereby enhancing the effectiveness of learning. Some or all of the above-described processing by the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input user interest data into a generation AI and cause the generation AI to acquire optimal additional information.

[0038] When generating 3D images, the generation unit can adjust the level of detail of the generated images based on the importance of the textbook content. For example, the generation unit evaluates the importance of the textbook content and adjusts the level of detail of the 3D images based on the importance. For example, the generation unit generates detailed 3D images for important content. The generation unit can also generate simplified 3D images for supplementary content. Furthermore, the generation unit can generate 3D images including detailed explanations for content that the user finds particularly difficult to understand. For example, the generation unit evaluates the importance of the textbook content based on the exam scope, learning goals, educational curriculum, etc., and adjusts the optimal level of detail. This allows for adjusting the level of detail based on the importance of the textbook content, thereby improving the effectiveness of learning. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit may input importance data of the textbook content into the generation AI and cause the generation AI to adjust the optimal level of detail.

[0039] When generating 3D images, the generation unit can apply different generation algorithms depending on the textbook category. For example, the generation unit evaluates the textbook category and applies different generation algorithms depending on the category. For example, the generation unit applies an algorithm that emphasizes historical background and episodes to history content. The generation unit can also apply an algorithm that visually reproduces experiments and phenomena to science content. Furthermore, the generation unit can apply an algorithm that reproduces narrative scenes and characters to literary content. For example, the generation unit evaluates the textbook category based on subject, theme, difficulty level, etc., and applies the optimal generation algorithm. This allows for improved learning effectiveness by applying different generation algorithms depending on the textbook category. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input textbook category data into the generation AI and cause the generation AI to apply the optimal generation algorithm.

[0040] When generating 3D images, the generation unit can determine the generation priority based on the submission date of the textbook content. For example, the generation unit evaluates the submission date of the textbook content and determines the generation priority of the 3D image based on the submission date. For example, the generation unit prioritizes the generation of 3D images for content with an upcoming submission deadline. The generation unit can also postpone the generation of content with a distant submission deadline. Furthermore, when the submission deadlines are the same, the generation unit can prioritize the generation of content with higher importance. For example, the generation unit evaluates the submission date of textbook content based on the end of the semester or a project deadline, and determines the optimal priority. Thus, determining the generation priority based on the submission date of textbook content can improve learning effectiveness. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input submission date data of textbook content into the generation AI and cause the generation AI to determine the optimal priority.

[0041] When generating 3D images, the generation unit can adjust the order of generation based on the relevance of the textbook content. The generation unit, for example, evaluates the relevance of the textbook content and adjusts the order of generation of the 3D images based on the relevance. For example, the generation unit generates highly relevant content consecutively to smooth the learning flow. The generation unit can also generate less relevant content later. Furthermore, the generation unit can prioritize the generation of content that is particularly interesting to the user. For example, the generation unit evaluates the relevance of the textbook content based on the relevance of the topic or the connection between previous and subsequent content, and adjusts the optimal order. This allows for improved learning effectiveness by adjusting the order of generation based on the relevance of the textbook content. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input relevance data of the textbook content into the generation AI and cause the generation AI to adjust the optimal order.

[0042] When displaying an operation interface, the operation unit can select an optimal display method by referring to the user's past operation history. For example, the operation unit references the user's past operation history and selects a display method for the operation interface based on the history. For example, the operation unit prioritizes providing a display method that the user has previously preferred. The operation unit can also suggest the most efficient display method based on the user's past operation history. Furthermore, the operation unit can eliminate display methods that the user has previously avoided and provide an optimal display method. For example, the operation unit evaluates the user's operation history based on click history, operation time, frequency of use, etc., and selects an optimal display method. By selecting an optimal display method by referring to the user's past operation history, the effectiveness of learning can be enhanced. Some or all of the above-described processing in the operation unit may be performed using, or without, AI. For example, the operation unit can input the user's operation history data into a generation AI and have the generation AI select an optimal display method.

[0043] The operation unit can customize the operation method based on the user's learning progress when displaying the operation interface. The operation unit, for example, evaluates the user's learning progress and customizes the operation method of the operation interface based on the progress. For example, the operation unit provides a basic operation method when the user uses the device for the first time. The operation unit can also provide a detailed operation method when the user is already familiar with the operation. Furthermore, the operation unit can gradually customize the operation method according to the user's learning progress. For example, the operation unit evaluates the user's learning progress based on test results, study time, progress status, etc., and customizes the optimal operation method. This allows the learning effect to be improved by customizing the operation method based on the user's learning progress. Some or all of the above-mentioned processing in the operation unit may be performed using, or without, AI. For example, the operation unit can input the user's learning progress data into a generation AI and have the generation AI customize the optimal operation method.

[0044] When displaying the operation interface, the operation unit can select the optimal display method based on the user's device information. The operation unit, for example, evaluates the user's device information and selects the display method for the operation interface based on that information. For example, if the user is using a smartphone, the operation unit provides a display method tailored to the screen size. Furthermore, if the user is using a tablet, the operation unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the operation unit can provide a display method including detailed information. For example, the operation unit evaluates the user's device information based on the device type, specifications, OS, etc., and selects the optimal display method. This selection of the optimal display method based on the user's device information can enhance the effectiveness of learning. Some or all of the above-described processing in the operation unit may be performed using, for example, AI, or may be performed without AI. For example, the operation unit can input the user's device information data into a generation AI and have the generation AI select the optimal display method.

[0045] The operation unit can customize the operation method based on the user's learning style when displaying the operation interface. The operation unit, for example, evaluates the user's learning style and customizes the operation method of the operation interface based on the style. For example, if the user prefers visual learning, the operation unit can provide an operation method that includes many diagrams and illustrations. Furthermore, if the user prefers auditory learning, the operation unit can also provide an operation method that includes audio guidance. Furthermore, if the user prefers hands-on learning, the operation unit can also provide an interactive operation method. For example, the operation unit can evaluate the user's learning style based on survey results, past behavioral history, etc., and customize the optimal operation method. This allows the effectiveness of learning to be improved by customizing the operation method based on the user's learning style. Some or all of the above-described processing in the operation unit may be performed using, for example, AI, or may be performed without AI. For example, the operation unit can input the user's learning style data into a generation AI and have the generation AI customize the optimal operation method.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The educational support system may further include a difficulty level adjustment unit that automatically adjusts the difficulty of the learning content based on the user's learning progress. For example, the difficulty level adjustment unit may analyze the user's past learning history and test results to provide learning content of an appropriate level of difficulty. Furthermore, if the user feels that they have difficulty with a particular piece of content, the difficulty level adjustment unit may gradually lower the difficulty level so that the content can be more easily understood. Furthermore, for content in which the user excels, the difficulty level adjustment unit may increase the depth of learning by providing more advanced questions and challenges. This may provide an optimal learning experience according to the user's learning progress.

[0048] The education support system may further include a presentation method adjustment unit that customizes the presentation method of the learning content based on the user's learning style. For example, if the user prefers visual learning, the presentation method adjustment unit may provide learning materials that include many diagrams and illustrations. If the user prefers auditory learning, the presentation method adjustment unit may also provide learning materials that include audio commentary. Furthermore, if the user prefers hands-on learning, the presentation method adjustment unit may also provide interactive exercises and simulations. This makes it possible to provide an optimal learning experience according to the user's learning style.

[0049] The education support system may further include a review timing adjustment unit that automatically adjusts the timing of reviewing learning content based on the user's learning progress. For example, if the user tends to forget specific content, the review timing adjustment unit may create a schedule to periodically review that content. The review timing adjustment unit may also reduce the frequency of reviewing content that the user has already mastered. Furthermore, the review timing adjustment unit may also set the timing for reviewing new content immediately after the user learns that content. This makes it possible to provide an optimal review experience according to the user's learning progress.

[0050] The education support system may further include a progress speed adjustment unit that automatically adjusts the progress speed of the learning content based on the user's learning history. For example, if it takes a long time for the user to understand a particular content, the progress speed adjustment unit may progress the content slowly. Alternatively, if the user quickly understands a particular content, the progress speed adjustment unit may progress the content quickly. Furthermore, when the user reviews a particular content, the progress speed adjustment unit may progress the content at an appropriate speed. This makes it possible to provide an optimal learning progress speed according to the user's learning history.

[0051] The education support system may further include a related information providing unit that automatically provides related information about the learning content based on the user's learning history. For example, if the user is learning a specific content, the related information providing unit may provide additional information related to that content. The related information providing unit may also provide reference materials that will help the user understand the specific content. Furthermore, if the user wants to learn more about a specific content, the related information providing unit may also provide detailed information related to that content. This makes it possible to provide optimal related information according to the user's learning history.

[0052] The education support system can further include an evaluation method adjustment unit that automatically adjusts the evaluation method for learning content based on the user's learning progress. For example, if the user understands specific content, the evaluation method adjustment unit can make the evaluation for that content stricter. Also, if the user does not understand specific content, the evaluation method adjustment unit can make the evaluation for that content lenient. Furthermore, when the user reviews specific content, the evaluation method adjustment unit can appropriately adjust the evaluation for that content. This makes it possible to provide an optimal evaluation method according to the user's learning progress.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The acquisition unit acquires the contents of the textbook. The contents of the textbook include text, charts, exercises, etc. The acquisition unit digitizes and acquires the contents of the textbook using scanning technology. Alternatively, the acquisition unit can directly acquire the contents of the textbook provided in digital format. Furthermore, the acquisition unit can read the contents of a printed textbook using OCR technology. For example, the contents can be scanned using a high-resolution scanner and converted into text information using OCR technology. Step 2: The generator generates a 3D image based on the textbook content acquired by the acquirer. The generator includes an algorithm that automatically converts the textbook content into a 3D image. For example, the generator analyzes the textbook content using a machine learning algorithm or a rule-based algorithm to generate a 3D image. Step 3: The operation unit provides an interface that allows the user to freely operate the 3D image generated by the generation unit. The operation unit allows the user to operate the 3D image using a GUI (Graphical User Interface), a touch screen, or voice control. For example, it provides an interface that allows the user to rotate, zoom, and move the 3D image.

[0055] (Example 2) An educational support system according to an embodiment of the present invention converts textbook content into 3D images that users can manipulate freely. The educational support system acquires textbook content, generates 3D images based on the acquired data, and provides an interface that allows users to freely manipulate the generated 3D images. For example, in a history class, a user can freely walk around a castle and get a close look at the paintings on the walls. In a chemistry experiment, a user can manipulate glass tubes and chemicals on the screen and check the results of the experiment. In this way, the educational support system provides services that make schoolwork more enjoyable and easy to understand. For example, an acquisition unit is provided that acquires data for converting textbook content into 3D images. Next, a generation unit is provided that generates 3D images based on the acquired data. An operation unit is provided that allows users to freely manipulate the generated 3D images. The acquisition unit acquires the textbook content, passes the data to the generation unit to generate 3D images, and the operation unit provides the generated 3D images to the user. The generation unit includes an algorithm that automatically converts textbook content into 3D images. The operation unit provides an interface that allows users to freely manipulate the 3D images. This allows the educational support system to convert the contents of textbooks into 3D images that users can freely manipulate, deepening their understanding of the learning process.

[0056] The education support system according to the embodiment includes an acquisition unit, a generation unit, and an operation unit. The acquisition unit acquires the content of a textbook. The content of the textbook includes, but is not limited to, text, charts, diagrams, exercises, and the like. For example, the acquisition unit digitizes and acquires the content of the textbook using scanning technology. The acquisition unit can also directly acquire the content of the textbook provided in digital format. The acquisition unit can also read the content of a printed textbook using OCR technology. For example, the acquisition unit scans the content of the textbook with a high-resolution scanner and converts it into text information using OCR technology. The digital content of the textbook can be directly acquired if it is provided in a specific file format. OCR technology recognizes printed characters with high accuracy and converts them into digital text. The generation unit generates a 3D image based on the content of the textbook. For example, the generation unit includes an algorithm that automatically converts the content of the textbook into a 3D image. For example, the generation unit analyzes the content of the textbook using a machine learning algorithm and generates a 3D image. The generation unit can also convert the content of the textbook into a 3D image using a rule-based algorithm. For example, the generation unit receives the content of a textbook as input and generates a 3D image using an algorithm that outputs a 3D image. The operation unit provides an interface that allows a user to freely operate the 3D image generated by the generation unit. The operation unit allows a user to operate the 3D image using, for example, a GUI (Graphical User Interface). The operation unit can also allow a user to operate the 3D image using a touch screen. Furthermore, the operation unit can also allow a user to operate the 3D image using voice control. For example, the operation unit provides an interface that allows a user to rotate, zoom, and move the 3D image. As a result, the education support system according to the embodiment can convert the content of a textbook into a 3D image and allow a user to freely operate it, thereby deepening learning comprehension.

[0057] The generation unit may include an algorithm that automatically converts the textbook content into 3D video. The generation unit may, for example, use a machine learning algorithm to analyze the textbook content and generate 3D video. For example, the generation unit may use an algorithm that takes the textbook content as input and outputs 3D video to generate 3D video. The generation unit may also use a rule-based algorithm to convert the textbook content into 3D video. For example, the generation unit may analyze the textbook content and generate 3D video based on rules. This allows for efficient generation of 3D video by automatically converting the textbook content into 3D video. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may generate 3D video using an AI model that takes the textbook content as input and outputs 3D video.

[0058] The operation unit can provide an interface that allows the user to freely operate the 3D image. The operation unit allows the user to operate the 3D image using, for example, a GUI (Graphical User Interface). For example, the operation unit provides an interface that allows the user to rotate, zoom, and move the 3D image. The operation unit can also allow the user to operate the 3D image using a touch screen. For example, the operation unit provides an interface that allows the user to operate the 3D image using touch gestures. The operation unit can also allow the user to operate the 3D image using voice operation. For example, the operation unit provides an interface that allows the user to operate the 3D image using voice commands. This allows the user to freely operate the 3D image, thereby deepening learning understanding. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without AI. For example, the operation unit can provide an operation interface using an AI model that inputs the user's operation history and outputs an optimal operation interface.

[0059] The operation unit can provide an interface that allows a user to freely walk around a castle in a history class. The operation unit can provide an interface that allows a user to freely walk around a castle in a history class. For example, the operation unit provides a navigation function that allows a user to freely move around the castle. The operation unit can also provide a function that allows a user to select a specific location in the castle and display detailed information. For example, the operation unit allows a user to select a room or corridor in the castle and display historical information about that location. The operation unit can also provide a zoom function that allows a user to get a closer look at a painting on a wall in the castle. For example, the operation unit provides an interface that allows a user to enlarge a painting on a wall to check the details. This allows a user to freely walk around the castle in a history class, thereby deepening their understanding of the learning. Some or all of the above-described processing in the operation unit can be performed using, for example, AI, or can be performed without AI. For example, the operation unit can provide a navigation function using an AI model that inputs a user's movement history and outputs an optimal navigation route.

[0060] The operation unit can provide an interface that allows a user to manipulate glass tubes and chemicals on a screen and check the results of a chemistry experiment. For example, the operation unit can provide an interface that allows a user to manipulate glass tubes and chemicals on a screen and check the results of a chemistry experiment. For example, the operation unit can provide an interface that allows a user to select a glass tube and mix chemicals. The operation unit can also provide an interface that allows a user to heat chemicals. For example, the operation unit can provide a temperature adjustment function for a user to heat chemicals. The operation unit can also provide an interface that allows a user to check the results of an experiment. For example, the operation unit can provide a function that allows a user to display the results of an experiment as a graph or chart. This allows a user to manipulate glass tubes and chemicals on a screen and check the results of an experiment, thereby deepening learning understanding. Some or all of the above-described processing in the operation unit can be performed using, for example, AI, or can be performed without AI. For example, the operation unit can provide an experiment interface using an AI model that inputs a user's operation history and outputs an optimal experiment procedure.

[0061] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring textbook content based on the estimated user's emotions. For example, the acquisition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on changes in facial expression. The acquisition unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the acquisition unit analyzes the tone and speed of the voice and calculates the emotion score. The acquisition unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates the emotion score based on heart rate fluctuations. This allows the timing of acquiring textbook content to be adjusted according to the user's emotions, thereby improving learning effectiveness. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0062] When acquiring textbook content, the acquisition unit can select the content to acquire based on the user's learning progress. For example, the acquisition unit evaluates the user's learning progress and selects the textbook content based on that progress. For example, if the user is learning content for the first time, the acquisition unit acquires the content starting from the basics. Furthermore, if the user is reviewing content they have already learned, the acquisition unit can acquire content focusing on important points. Furthermore, if the user wants to study a specific unit in depth, the acquisition unit can acquire detailed content related to that unit. For example, the acquisition unit evaluates the user's learning progress based on test results, study time, progress, etc., and selects optimal content. This allows for enhanced learning effectiveness by selecting content to acquire based on the user's learning progress. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input the user's learning progress data into a generation AI and cause the generation AI to select optimal content.

[0063] When acquiring textbook content, the acquisition unit can analyze the user's past learning history and select the optimal acquisition method. The acquisition unit, for example, analyzes the user's past learning history and acquires textbook content based on that history. For example, the acquisition unit prioritizes acquiring content that the user previously struggled with. The acquisition unit can also skip content in which the user is strong and focus on weaker parts. Furthermore, the acquisition unit can suggest the most effective learning order based on the user's learning history and acquire content according to that order. For example, the acquisition unit analyzes the user's learning log, test results, study time, etc., and selects the optimal acquisition method. By analyzing the user's past learning history and selecting the optimal acquisition method, the effectiveness of learning can be improved. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can input the user's learning history data into a generation AI and have the generation AI select the optimal acquisition method.

[0064] The acquisition unit can estimate the user's emotions and determine the priority of textbook content to be acquired based on the estimated user emotions. The acquisition unit, for example, captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on changes in facial expression. The acquisition unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the acquisition unit analyzes the tone and speed of the voice and calculates the emotion score. The acquisition unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates the emotion score based on heart rate fluctuations. This can improve learning effectiveness by determining the priority of textbook content to be acquired according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0065] When acquiring textbook content, the acquisition unit can customize the acquired content based on the user's learning style. The acquisition unit, for example, evaluates the user's learning style and customizes the textbook content based on the style. For example, if the user prefers visual learning, the acquisition unit can acquire content that includes many diagrams and illustrations. Furthermore, if the user prefers auditory learning, the acquisition unit can acquire content that includes audio commentary. Furthermore, if the user prefers practical learning, the acquisition unit can acquire content that includes many experiments and exercises. For example, the acquisition unit can evaluate the user's learning style based on survey results, past behavioral history, etc., and customize the optimal content. This allows the learning effect to be enhanced by customizing the acquired content based on the user's learning style. Some or all of the above-described processing in the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input the user's learning style data into a generation AI and cause the generation AI to customize the optimal content.

[0066] When acquiring textbook content, the acquisition unit can acquire related additional information based on the user's interests. For example, the acquisition unit evaluates the user's interests and acquires textbook content based on those interests. For example, if the user is interested in history, the acquisition unit can acquire related historical background and episodes. Furthermore, if the user is interested in science, the acquisition unit can acquire the latest scientific research and discoveries. Furthermore, if the user is interested in literature, the acquisition unit can acquire information on related literary works and authors. For example, the acquisition unit can evaluate the user's interests based on survey results, past behavioral history, etc., and acquire optimal additional information. This allows for the acquisition of relevant additional information based on the user's interests, thereby enhancing the effectiveness of learning. Some or all of the above-described processing by the acquisition unit may be performed using, or without, AI. For example, the acquisition unit can input user interest data into a generation AI and cause the generation AI to acquire optimal additional information.

[0067] The generation unit can estimate the user's emotions and adjust the 3D image generation method based on the estimated user emotions. For example, the generation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expressions. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This allows the learning effect to be improved by adjusting the 3D image generation method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0068] When generating 3D images, the generation unit can adjust the level of detail of the generated images based on the importance of the textbook content. For example, the generation unit evaluates the importance of the textbook content and adjusts the level of detail of the 3D images based on the importance. For example, the generation unit generates detailed 3D images for important content. The generation unit can also generate simplified 3D images for supplementary content. Furthermore, the generation unit can generate 3D images including detailed explanations for content that the user finds particularly difficult to understand. For example, the generation unit evaluates the importance of the textbook content based on the exam scope, learning goals, educational curriculum, etc., and adjusts the optimal level of detail. This allows for adjusting the level of detail based on the importance of the textbook content, thereby improving the effectiveness of learning. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit may input importance data of the textbook content into the generation AI and cause the generation AI to adjust the optimal level of detail.

[0069] When generating 3D images, the generation unit can apply different generation algorithms depending on the textbook category. For example, the generation unit evaluates the textbook category and applies different generation algorithms depending on the category. For example, the generation unit applies an algorithm that emphasizes historical background and episodes to history content. The generation unit can also apply an algorithm that visually reproduces experiments and phenomena to science content. Furthermore, the generation unit can apply an algorithm that reproduces narrative scenes and characters to literary content. For example, the generation unit evaluates the textbook category based on subject, theme, difficulty level, etc., and applies the optimal generation algorithm. This allows for improved learning effectiveness by applying different generation algorithms depending on the textbook category. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input textbook category data into the generation AI and cause the generation AI to apply the optimal generation algorithm.

[0070] The generation unit can estimate the user's emotion and adjust the length of the 3D video based on the estimated user emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates the emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates the emotion score based on heart rate fluctuations. This allows the length of the 3D video to be adjusted according to the user's emotion, thereby improving the effectiveness of learning. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0071] When generating 3D images, the generation unit can determine the generation priority based on the submission date of the textbook content. For example, the generation unit evaluates the submission date of the textbook content and determines the generation priority of the 3D image based on the submission date. For example, the generation unit prioritizes the generation of 3D images for content with an upcoming submission deadline. The generation unit can also postpone the generation of content with a distant submission deadline. Furthermore, when the submission deadlines are the same, the generation unit can prioritize the generation of content with higher importance. For example, the generation unit evaluates the submission date of textbook content based on the end of the semester or a project deadline, and determines the optimal priority. Thus, determining the generation priority based on the submission date of textbook content can improve learning effectiveness. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input submission date data of textbook content into the generation AI and cause the generation AI to determine the optimal priority.

[0072] When generating 3D images, the generation unit can adjust the order of generation based on the relevance of the textbook content. The generation unit, for example, evaluates the relevance of the textbook content and adjusts the order of generation of the 3D images based on the relevance. For example, the generation unit generates highly relevant content consecutively to smooth the learning flow. The generation unit can also generate less relevant content later. Furthermore, the generation unit can prioritize the generation of content that is particularly interesting to the user. For example, the generation unit evaluates the relevance of the textbook content based on the relevance of the topic or the connection between previous and subsequent content, and adjusts the optimal order. This allows for improved learning effectiveness by adjusting the order of generation based on the relevance of the textbook content. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input relevance data of the textbook content into the generation AI and cause the generation AI to adjust the optimal order.

[0073] The operation unit can estimate the user's emotions and adjust the display method of the operation interface based on the estimated user emotions. For example, the operation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the operation unit calculates an emotion score based on changes in facial expressions. The operation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the operation unit analyzes the tone and speed of the voice and calculates an emotion score. The operation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the operation unit calculates an emotion score based on heart rate fluctuations. This allows the effect of learning to be improved by adjusting the display method of the operation interface according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the operation unit can be performed using, for example, AI, or without AI. For example, the operation unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0074] When displaying an operation interface, the operation unit can select an optimal display method by referring to the user's past operation history. For example, the operation unit references the user's past operation history and selects a display method for the operation interface based on the history. For example, the operation unit prioritizes providing a display method that the user has previously preferred. The operation unit can also suggest the most efficient display method based on the user's past operation history. Furthermore, the operation unit can eliminate display methods that the user has previously avoided and provide an optimal display method. For example, the operation unit evaluates the user's operation history based on click history, operation time, frequency of use, etc., and selects an optimal display method. By selecting an optimal display method by referring to the user's past operation history, the effectiveness of learning can be enhanced. Some or all of the above-described processing in the operation unit may be performed using, or without, AI. For example, the operation unit can input the user's operation history data into a generation AI and have the generation AI select an optimal display method.

[0075] The operation unit can customize the operation method based on the user's learning progress when displaying the operation interface. The operation unit, for example, evaluates the user's learning progress and customizes the operation method of the operation interface based on the progress. For example, the operation unit provides a basic operation method when the user uses the device for the first time. The operation unit can also provide a detailed operation method when the user is already familiar with the operation. Furthermore, the operation unit can gradually customize the operation method according to the user's learning progress. For example, the operation unit evaluates the user's learning progress based on test results, study time, progress status, etc., and customizes the optimal operation method. This allows the learning effect to be improved by customizing the operation method based on the user's learning progress. Some or all of the above-mentioned processing in the operation unit may be performed using, or without, AI. For example, the operation unit can input the user's learning progress data into a generation AI and have the generation AI customize the optimal operation method.

[0076] The operation unit can estimate the user's emotions and adjust the operation procedures of the operation interface based on the estimated user emotions. For example, the operation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the operation unit calculates an emotion score based on changes in facial expressions. The operation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the operation unit analyzes the tone and speed of the voice and calculates an emotion score. The operation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the operation unit calculates an emotion score based on heart rate fluctuations. This allows the operation procedures of the operation interface to be adjusted according to the user's emotions, thereby improving the effectiveness of learning. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the operation unit can be performed using, for example, AI, or without AI. For example, the operation unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0077] When displaying the operation interface, the operation unit can select the optimal display method based on the user's device information. The operation unit, for example, evaluates the user's device information and selects the display method for the operation interface based on that information. For example, if the user is using a smartphone, the operation unit provides a display method tailored to the screen size. Furthermore, if the user is using a tablet, the operation unit can provide a display method optimized for a large screen. Furthermore, if the user is using a desktop, the operation unit can provide a display method including detailed information. For example, the operation unit evaluates the user's device information based on the device type, specifications, OS, etc., and selects the optimal display method. This selection of the optimal display method based on the user's device information can enhance the effectiveness of learning. Some or all of the above-described processing in the operation unit may be performed using, for example, AI, or may be performed without AI. For example, the operation unit can input the user's device information data into a generation AI and have the generation AI select the optimal display method.

[0078] The operation unit can customize the operation method based on the user's learning style when displaying the operation interface. The operation unit, for example, evaluates the user's learning style and customizes the operation method of the operation interface based on the style. For example, if the user prefers visual learning, the operation unit can provide an operation method that includes many diagrams and illustrations. Furthermore, if the user prefers auditory learning, the operation unit can also provide an operation method that includes audio guidance. Furthermore, if the user prefers hands-on learning, the operation unit can also provide an interactive operation method. For example, the operation unit can evaluate the user's learning style based on survey results, past behavioral history, etc., and customize the optimal operation method. This allows the effectiveness of learning to be improved by customizing the operation method based on the user's learning style. Some or all of the above-described processing in the operation unit may be performed using, for example, AI, or may be performed without AI. For example, the operation unit can input the user's learning style data into a generation AI and have the generation AI customize the optimal operation method. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, generation unit, and operation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires the content of the textbook using the camera 42 or scanner of the smart device 14 and digitizes it using the specific processing unit 290 of the data processing device 12. For example, the generation unit executes an algorithm to convert the content of the textbook into a 3D image using the specific processing unit 290 of the data processing device 12. The operation unit provides an interface that allows the user to operate the 3D image using, for example, the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the acquisition unit, generation unit, and operation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires the content of the textbook using the camera 42 or scanner of the smart glasses 214 and digitizes it using the specific processing unit 290 of the data processing device 12. For example, the generation unit executes an algorithm to convert the content of the textbook into a 3D image using the specific processing unit 290 of the data processing device 12. The operation unit provides an interface that allows the user to operate the 3D image using, for example, the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the acquisition unit, generation unit, and operation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit acquires the content of the textbook using the camera 42 or scanner of the headset type terminal 314 and digitizes it using the specific processing unit 290 of the data processing device 12. For example, the generation unit executes an algorithm that converts the content of the textbook into 3D images using the specific processing unit 290 of the data processing device 12. The operation unit provides an interface that allows the user to operate the 3D images using the control unit 46A of the headset type terminal 314, for example. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, generation unit, and operation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires the content of the textbook using the camera 42 or scanner of the robot 414 and digitizes it using the specific processing unit 290 of the data processing device 12. For example, the generation unit executes an algorithm that converts the content of the textbook into a 3D image using the specific processing unit 290 of the data processing device 12. The operation unit provides an interface that allows the user to operate the 3D image using, for example, the control unit 46A of the robot 414.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The educational support system may further include a difficulty level adjustment unit that automatically adjusts the difficulty of the learning content based on the user's learning progress. For example, the difficulty level adjustment unit may analyze the user's past learning history and test results to provide learning content of an appropriate level of difficulty. Furthermore, if the user feels that they have difficulty with a particular piece of content, the difficulty level adjustment unit may gradually lower the difficulty level so that the content can be more easily understood. Furthermore, for content in which the user excels, the difficulty level adjustment unit may increase the depth of learning by providing more advanced questions and challenges. This may provide an optimal learning experience according to the user's learning progress.

[0081] The education support system may further include a presentation method adjustment unit that customizes the presentation method of the learning content based on the user's learning style. For example, if the user prefers visual learning, the presentation method adjustment unit may provide learning materials that include many diagrams and illustrations. If the user prefers auditory learning, the presentation method adjustment unit may also provide learning materials that include audio commentary. Furthermore, if the user prefers hands-on learning, the presentation method adjustment unit may also provide interactive exercises and simulations. This makes it possible to provide an optimal learning experience according to the user's learning style.

[0082] The education support system may further include an emotion order adjustment unit that estimates the user's emotion and adjusts the presentation order of learning content based on the estimated emotion. For example, if the user is tired, the emotion order adjustment unit may present content with a low level of difficulty first. Alternatively, if the user is excited, the emotion order adjustment unit may present content with an interest first. Furthermore, if the user is concentrating, the emotion order adjustment unit may prioritize the presentation of important content. This makes it possible to provide an optimal learning experience according to the user's emotional state.

[0083] The education support system may further include a review timing adjustment unit that automatically adjusts the timing of reviewing learning content based on the user's learning progress. For example, if the user tends to forget specific content, the review timing adjustment unit may create a schedule to periodically review that content. The review timing adjustment unit may also reduce the frequency of reviewing content that the user has already mastered. Furthermore, the review timing adjustment unit may also set the timing for reviewing new content immediately after the user learns that content. This makes it possible to provide an optimal review experience according to the user's learning progress.

[0084] The education support system may further include an emotion feedback adjustment unit that estimates the user's emotion and adjusts the feedback method for the learning content based on the estimated emotion. For example, the emotion feedback adjustment unit may display an encouraging message if the user is depressed. The emotion feedback adjustment unit may also display a praising message if the user is happy. Furthermore, the emotion feedback adjustment unit may display a message encouraging the user to relax if the user is impatient. This makes it possible to provide optimal feedback according to the user's emotional state.

[0085] The education support system may further include a progress speed adjustment unit that automatically adjusts the progress speed of the learning content based on the user's learning history. For example, if it takes a long time for the user to understand a particular content, the progress speed adjustment unit may progress the content slowly. Alternatively, if the user quickly understands a particular content, the progress speed adjustment unit may progress the content quickly. Furthermore, when the user reviews a particular content, the progress speed adjustment unit may progress the content at an appropriate speed. This makes it possible to provide an optimal learning progress speed according to the user's learning history.

[0086] The education support system may further include an emotional difficulty adjustment unit that estimates the user's emotions and adjusts the difficulty of the learning content based on the estimated emotions. For example, the emotional difficulty adjustment unit may lower the difficulty level when the user is feeling stressed. The emotional difficulty adjustment unit may also increase the difficulty level when the user is relaxed. Furthermore, the emotional difficulty adjustment unit may maintain an appropriate level of difficulty when the user is concentrating. This makes it possible to provide an optimal learning difficulty level according to the user's emotional state.

[0087] The education support system may further include a related information providing unit that automatically provides related information about the learning content based on the user's learning history. For example, if the user is learning a specific content, the related information providing unit may provide additional information related to that content. The related information providing unit may also provide reference materials that will help the user understand the specific content. Furthermore, if the user wants to learn more about a specific content, the related information providing unit may also provide detailed information related to that content. This makes it possible to provide optimal related information according to the user's learning history.

[0088] The education support system may further include an emotion display adjustment unit that estimates the user's emotion and adjusts the presentation method of the learning content based on the estimated emotion. For example, if the user is tired, the emotion display adjustment unit may provide a screen with a color tone that visually relaxes the user. If the user is excited, the emotion display adjustment unit may also provide a screen with a color tone that enhances concentration. If the user is calm, the emotion display adjustment unit may also provide a screen with a normal color tone. This makes it possible to provide an optimal presentation method of the learning content according to the user's emotional state.

[0089] The education support system can further include an evaluation method adjustment unit that automatically adjusts the evaluation method for learning content based on the user's learning progress. For example, if the user understands specific content, the evaluation method adjustment unit can make the evaluation for that content stricter. Also, if the user does not understand specific content, the evaluation method adjustment unit can make the evaluation for that content lenient. Furthermore, when the user reviews specific content, the evaluation method adjustment unit can appropriately adjust the evaluation for that content. This makes it possible to provide an optimal evaluation method according to the user's learning progress.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The acquisition unit acquires the contents of the textbook. The contents of the textbook include text, charts, exercises, etc. The acquisition unit digitizes and acquires the contents of the textbook using scanning technology. Alternatively, the acquisition unit can directly acquire the contents of the textbook provided in digital format. Furthermore, the acquisition unit can read the contents of a printed textbook using OCR technology. For example, the contents can be scanned using a high-resolution scanner and converted into text information using OCR technology. Step 2: The generator generates a 3D image based on the textbook content acquired by the acquirer. The generator includes an algorithm that automatically converts the textbook content into a 3D image. For example, the generator analyzes the textbook content using a machine learning algorithm or a rule-based algorithm to generate a 3D image. Step 3: The operation unit provides an interface that allows the user to freely operate the 3D image generated by the generation unit. The operation unit allows the user to operate the 3D image using a GUI (Graphical User Interface), a touch screen, or voice control. For example, it provides an interface that allows the user to rotate, zoom, and move the 3D image.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0094] 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.

[0095] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] 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.

[0121] 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.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

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

[0131] The 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.

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

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0162] 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.

[0163] [Explanation of symbols]

[0164] 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 acquisition unit that acquires the contents of the textbook; a generation unit that generates a 3D image based on the data acquired by the acquisition unit; an operation unit that enables a user to freely operate the 3D video generated by the generation unit; A system characterized by:

2. The generation unit Includes algorithms that automatically convert textbook content into 3D images 2. The system of claim 1.

3. The operation unit includes: Provide an interface that allows users to freely manipulate 3D images 2. The system of claim 1.

4. The operation unit includes: Providing an interface that allows users to freely walk around a castle in a history lesson 2. The system of claim 1.

5. The operation unit includes: In chemical experiments, provide an interface that allows users to manipulate glass tubes and chemicals on the screen and check the results of the experiment.

2. The system of claim 1.

6. The acquisition unit Estimates user emotions and adjusts the timing of textbook content acquisition based on the estimated user emotions.

2. The system of claim 1.

7. The acquisition unit When acquiring textbook content, select the content to acquire based on the user's learning progress.

2. The system of claim 1.

8. The acquisition unit When acquiring textbook content, the system analyzes the user's past learning history and selects the optimal acquisition method.

2. The system of claim 1.

Images