System

Abstract

An object of the system according to the embodiment is to provide an interactive learning experience by displaying the content of a teaching material in a visually easy-to-understand manner.SOLUTION: A system includes a 3D model display part, an animation display part, and an interactive operation part. A 3D model display part displays the contents of the teaching material as a 3D model. The animation display unit displays the content of the teaching material as an animation. The interactive control allows the user to interact with the content of the educational material.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has limited methods for visually displaying the content of educational materials in an easy-to-understand manner, leaving room for improvement in terms of maximizing learning effectiveness.

[0005] The system according to the embodiment aims to display the contents of educational materials in a visually easy-to-understand manner and to provide an interactive learning experience. [Means for solving the problem]

[0006] The system according to the embodiment includes a 3D model display unit, an animation display unit, and an interactive operation unit. The 3D model display unit displays the content of the teaching material as a 3D model. The animation display unit displays the content of the teaching material as an animation. The interactive operation unit allows a user to interact with the content of the teaching material. [Effects of the Invention]

[0007] The system according to the embodiment can display the contents of the educational material in a visually easy-to-understand manner and provide an interactive learning experience. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The smart glasses according to the embodiment of the present invention are a system that displays educational content in the form of 3D models and animations within the user's field of view, providing an interactive learning experience. This allows the smart glasses to provide users with a visual and interactive learning experience.

[0029] Smart glasses according to an embodiment include a 3D model display unit, an animation display unit, and an interactive operation unit. The 3D model display unit displays the content of educational materials as a 3D model. For example, when learning about an ancient building in a history class, a 3D model of the building can be displayed within the field of view, allowing the user to walk around and observe it from various angles. The 3D model display unit allows a generation AI to receive prompts containing instructions from the user as input information and generate a 3D model based on the prompts. The animation display unit displays the content of educational materials as animation. For example, when learning about the process of cell division in a science class, the process can be visually illustrated using animation. The animation display unit allows a generation AI to receive prompts containing instructions from the user as input information and generate animations based on the prompts. The interactive operation unit allows a user to interact with the content of educational materials. For example, when learning about the properties of solid shapes in a mathematics class, a user can manipulate the 3D model within the field of view to calculate angles and areas. The interactive operation unit allows a generation AI to receive prompts containing instructions from the user as input information and perform interactive operations based on the prompts. This allows the smart glasses according to the embodiment to provide users with a visual and interactive learning experience.

[0030] The 3D model display unit can use eye-tracking data to automatically enlarge and display detailed parts of the model in accordance with eye movement. For example, the 3D model display unit analyzes the user's eye-tracking data in real time, and when the user's gaze focuses on a specific part, automatically enlarges that part. For example, when observing the details of an ancient building in a history class, the part that the user focuses on is enlarged and displayed. This allows the user to enlarge and display detailed parts simply by moving their eyes.

[0031] The 3D model display unit can refer to the user's past learning history and display additional information related to the learning content in real time. For example, the 3D model display unit analyzes the user's past learning history and displays additional information related to the content currently being learned in real time. For example, when learning about ancient buildings in a history class, the 3D model display unit can display related historical background learned in the past. This allows related information to be displayed based on the user's learning history.

[0032] The 3D model display unit can have a multi-user function that allows multiple users to simultaneously observe the same model and learn collaboratively. For example, in a history class, multiple students can simultaneously observe an ancient building and learn while exchanging opinions. This allows multiple users to learn collaboratively.

[0033] The 3D model display unit works in conjunction with a device that provides physical tactile feedback, allowing the user to touch and feel the model. The 3D model display unit, for example, works in conjunction with a device that provides physical tactile feedback, allowing the user to touch and feel the 3D model. For example, in a history class, the texture of an ancient building can be felt tactilely. This allows the user to feel the model tactilely.

[0034] The animation display unit can automatically adjust the speed and level of detail of the animation according to the user's learning progress. The animation display unit has a function for analyzing the user's learning progress and automatically adjusting the speed and level of detail of the animation accordingly. For example, when learning about the process of cell division in a science class, the animation speed is adjusted according to the learning progress. This allows the animation speed and level of detail to be adjusted according to the user's learning progress.

[0035] The animation display unit can incorporate real-time responses to user questions into the animation. The animation display unit, for example, has a function for incorporating real-time responses to user questions into the animation. For example, if a question is asked about the process of cell division in a science class, the response to the question is displayed in the animation. This allows the response to the user's question to be incorporated into the animation.

[0036] The animation display unit has a function of selecting different learning modes, allowing the user to learn in a quiz format or a simulation format. The animation display unit has a function of selecting different learning modes, allowing the user to learn in a quiz format or a simulation format. For example, in a science class, the process of cell division can be learned in a quiz format. This allows the user to learn in different learning modes.

[0037] The animation display unit allows users to create their own animation scenarios and share them with other users. The animation display unit has a function that allows users to create their own animation scenarios and share them with other users. For example, in a science class, a student can create a scenario about the process of cell division and share it with other students. This allows users to create their own scenarios and share them with other users.

[0038] The interactive operation unit can use user gesture recognition to operate 3D models and animations. The interactive operation unit has a function for operating 3D models using user gesture recognition, for example. For example, in a history class, a 3D model of an ancient building can be rotated or scaled with hand movements. This allows the user to operate the 3D model or animation with gestures.

[0039] The interactive operation unit can analyze the user's learning history and automatically suggest what to learn next. The interactive operation unit, for example, has a function to analyze the user's learning history and automatically suggest what to learn next. For example, after learning about ancient buildings in a history class, the system can suggest the culture and lifestyle of that era. This allows the system to suggest what to learn next based on the user's learning history.

[0040] The interactive operation unit may have a dashboard function that allows the user to visualize their own learning progress and set goals. The interactive operation unit may have, for example, a dashboard function that allows the user to visualize their own learning progress and set goals. For example, the content learned in a history class may be visualized, and the content to be learned next may be set as a goal. This allows the user to visualize their own learning progress and set goals.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] Smart glasses also have a voice recognition function, allowing users to control 3D models and animations in response to voice commands. For example, when a 3D model of an ancient building is displayed in a history class, the user can issue the voice command "rotate," causing the 3D model to rotate. Similarly, when an animation of cell division is displayed in a science class, the user can issue the voice command "play," causing the animation to play. This allows users to intuitively control the device using voice commands.

[0043] The smart glasses also have an environmental recognition module that can adjust the display content according to the user's surroundings. For example, when used outdoors, the display brightness is automatically adjusted according to the ambient brightness. When used indoors, the volume of the audio guide is adjusted according to the ambient sound volume. This allows users to continue learning comfortably in any environment.

[0044] Smart glasses are also equipped with a biometric recognition unit, which can customize learning content based on the user's biological information. For example, they can monitor the user's heart rate and electrical skin response, and if the user's stress level is high, they can display relaxing content. If the user's concentration level is high, they can provide more difficult tasks. This allows them to provide an optimal learning experience based on the user's biological information.

[0045] Smart glasses also have augmented reality (AR) capabilities, allowing users to enjoy learning experiences that blend the real and virtual worlds. For example, in a history class, a 3D model of an ancient building could be superimposed on a real landscape, or in a science class, an animation of cell division could be superimposed on a real microscope image. This allows users to enjoy a learning experience that blends the real and virtual worlds.

[0046] The smart glasses also have an audio guide section, allowing users to receive audio guidance about the content they are learning. For example, when a 3D model of an ancient building is displayed in a history class, an audio guide about the building can be played. Or, when an animation of cell division is displayed in a science class, an audio guide about the process can be played. This allows users to have a learning experience that combines sight and sound.

[0047] Smart glasses can also analyze a user's learning progress and automatically suggest what to learn next. For example, after learning about ancient buildings in a history class, the glasses can suggest the culture and lifestyle of that era. Or, after learning about cell division in a science class, the glasses can suggest the structure and function of cells. This allows the glasses to suggest what to learn next based on the user's learning progress.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The 3D model display unit displays the content of the teaching material as a 3D model. For example, when learning about an ancient building in a history class, a 3D model of the building can be displayed within the user's field of view, allowing the user to walk around it and observe it from various angles. The 3D model display unit also uses a generation AI that receives prompts containing instructions from the user as input information and generates a 3D model based on the prompts. Step 2: The animation display unit displays the content of the teaching material as an animation. For example, when learning about the process of cell division in a science class, the process can be visually shown through animation. The generation AI in the animation display unit receives prompts containing instructions from the user as input information and generates animations based on those prompts. Step 3: The interactive operation unit allows users to interact with the content of the learning material. For example, when learning the properties of solid shapes in a math class, users can manipulate the 3D model in their field of view to calculate angles and areas. The interactive operation unit also allows the generation AI to receive prompts containing instructions from the user as input information and perform interactive operations based on those prompts.

[0050] (Example 2) The smart glasses according to the embodiment of the present invention are a system that displays educational content in the form of 3D models and animations within the user's field of view, providing an interactive learning experience. This allows the smart glasses to provide users with a visual and interactive learning experience.

[0051] Smart glasses according to an embodiment include a 3D model display unit, an animation display unit, and an interactive operation unit. The 3D model display unit displays the content of educational materials as a 3D model. For example, when learning about an ancient building in a history class, a 3D model of the building can be displayed within the field of view, allowing the user to walk around and observe it from various angles. The 3D model display unit allows a generation AI to receive prompts containing instructions from the user as input information and generate a 3D model based on the prompts. The animation display unit displays the content of educational materials as animation. For example, when learning about the process of cell division in a science class, the process can be visually illustrated using animation. The animation display unit allows a generation AI to receive prompts containing instructions from the user as input information and generate animations based on the prompts. The interactive operation unit allows a user to interact with the content of educational materials. For example, when learning about the properties of solid shapes in a mathematics class, a user can manipulate the 3D model within the field of view to calculate angles and areas. The interactive operation unit allows a generation AI to receive prompts containing instructions from the user as input information and perform interactive operations based on the prompts. This allows the smart glasses according to the embodiment to provide users with a visual and interactive learning experience.

[0052] The 3D model display unit can use eye-tracking data to automatically enlarge and display detailed parts of the model in accordance with eye movement. For example, the 3D model display unit analyzes the user's eye-tracking data in real time, and when the user's gaze focuses on a specific part, automatically enlarges that part. For example, when observing the details of an ancient building in a history class, the part that the user focuses on is enlarged and displayed. This allows the user to enlarge and display detailed parts simply by moving their eyes.

[0053] The 3D model display unit can refer to the user's past learning history and display additional information related to the learning content in real time. For example, the 3D model display unit analyzes the user's past learning history and displays additional information related to the content currently being learned in real time. For example, when learning about ancient buildings in a history class, the 3D model display unit can display related historical background learned in the past. This allows related information to be displayed based on the user's learning history.

[0054] The 3D model display unit can use the emotion estimation function to identify parts of the image that the user is interested in and automatically display detailed information or supplementary explanations related to those parts. For example, if a user is interested in a particular part of an ancient building in a history class, the 3D model display unit can display detailed historical background information about that part. This allows detailed information or supplementary explanations to be displayed according to the user's interests.

[0055] The 3D model display unit can have a multi-user function that allows multiple users to simultaneously observe the same model and learn collaboratively. For example, in a history class, multiple students can simultaneously observe an ancient building and learn while exchanging opinions. This allows multiple users to learn collaboratively.

[0056] The 3D model display unit works in conjunction with a device that provides physical tactile feedback, allowing the user to touch and feel the model. The 3D model display unit, for example, works in conjunction with a device that provides physical tactile feedback, allowing the user to touch and feel the 3D model. For example, in a history class, the texture of an ancient building can be felt tactilely. This allows the user to feel the model tactilely.

[0057] The 3D model display unit can be provided with a function that uses an emotion estimation function to share with other users portions of the model that the user has shown interest in, thereby promoting collaborative learning. The 3D model display unit can be provided with a function that uses, for example, an emotion estimation function to share with other users portions of the model that the user has shown interest in. For example, in a history class, a student can share with other students portions of an ancient building that the student has shown interest in, and study together. This allows the user to share portions of the model that the student has shown interest in with other users, promoting collaborative learning.

[0058] The animation display unit can automatically adjust the speed and level of detail of the animation according to the user's learning progress. The animation display unit has a function for analyzing the user's learning progress and automatically adjusting the speed and level of detail of the animation accordingly. For example, when learning about the process of cell division in a science class, the animation speed is adjusted according to the learning progress. This allows the animation speed and level of detail to be adjusted according to the user's learning progress.

[0059] The animation display unit can incorporate real-time responses to user questions into the animation. The animation display unit, for example, has a function for incorporating real-time responses to user questions into the animation. For example, if a question is asked about the process of cell division in a science class, the response to the question is displayed in the animation. This allows the response to the user's question to be incorporated into the animation.

[0060] The animation display unit can use the emotion estimation function to identify a portion that the user found difficult to understand and repeatedly display that portion. The animation display unit has a function to use, for example, the emotion estimation function to identify a portion that the user found difficult to understand and repeatedly display that portion. For example, if the user found a particular stage of cell division difficult to understand in a science class, that portion can be repeatedly displayed. This allows the portion that the user found difficult to understand to be repeatedly displayed.

[0061] The animation display unit has a function of selecting different learning modes, allowing the user to learn in a quiz format or a simulation format. The animation display unit has a function of selecting different learning modes, allowing the user to learn in a quiz format or a simulation format. For example, in a science class, the process of cell division can be learned in a quiz format. This allows the user to learn in different learning modes.

[0062] The animation display unit allows users to create their own animation scenarios and share them with other users. The animation display unit has a function that allows users to create their own animation scenarios and share them with other users. For example, in a science class, a student can create a scenario about the process of cell division and share it with other students. This allows users to create their own scenarios and share them with other users.

[0063] The animation display unit uses the emotion estimation function to automatically generate an animation scenario that is most interesting to the user, thereby improving learning effectiveness. The animation display unit has a function for automatically generating an animation scenario that is most interesting to the user, for example, using the emotion estimation function. For example, for a user who is interested in the process of cell division in a science class, a scenario that shows that process in detail is automatically generated. This allows the automatic generation of a scenario that is most interesting to the user, improving learning effectiveness.

[0064] The interactive operation unit can use user gesture recognition to operate 3D models and animations. The interactive operation unit has a function for operating 3D models using user gesture recognition, for example. For example, in a history class, a 3D model of an ancient building can be rotated or scaled with hand movements. This allows the user to operate the 3D model or animation with gestures.

[0065] The interactive operation unit can analyze the user's learning history and automatically suggest what to learn next. The interactive operation unit, for example, has a function to analyze the user's learning history and automatically suggest what to learn next. For example, after learning about ancient buildings in a history class, the system can suggest the culture and lifestyle of that era. This allows the system to suggest what to learn next based on the user's learning history.

[0066] The interactive operation unit can use the emotion estimation function to identify learning content that the user is interested in and automatically generate additional assignments and quizzes related to that content. The interactive operation unit has a function to identify learning content that the user is interested in and automatically generate additional assignments and quizzes related to that content, for example, using the emotion estimation function. For example, for a user who is interested in ancient buildings in history class, a quiz about those buildings is automatically generated. In this way, additional assignments and quizzes can be automatically generated based on the user's interests.

[0067] The interactive operation unit may have a dashboard function that allows the user to visualize their own learning progress and set goals. The interactive operation unit may have, for example, a dashboard function that allows the user to visualize their own learning progress and set goals. For example, the content learned in a history class may be visualized, and the content to be learned next may be set as a goal. This allows the user to visualize their own learning progress and set goals.

[0068] The interactive operation unit can be provided with a function that uses an emotion estimation function to share with other users the learning content that the user is most interested in, thereby promoting collaborative learning. The interactive operation unit, for example, has a function that uses an emotion estimation function to share with other users the learning content that the user is most interested in. For example, in a history class, a student can share information about ancient buildings that interest him or her with other students and study together. This allows the user to share with other users the learning content that interests them most, thereby promoting collaborative learning.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] Smart glasses also have a voice recognition function, allowing users to control 3D models and animations in response to voice commands. For example, when a 3D model of an ancient building is displayed in a history class, the user can issue the voice command "rotate," causing the 3D model to rotate. Similarly, when an animation of cell division is displayed in a science class, the user can issue the voice command "play," causing the animation to play. This allows users to intuitively control the device using voice commands.

[0071] The smart glasses also have an environmental recognition module that can adjust the display content according to the user's surroundings. For example, when used outdoors, the display brightness is automatically adjusted according to the ambient brightness. When used indoors, the volume of the audio guide is adjusted according to the ambient sound volume. This allows users to continue learning comfortably in any environment.

[0072] Smart glasses are also equipped with a biometric recognition unit, which can customize learning content based on the user's biological information. For example, they can monitor the user's heart rate and electrical skin response, and if the user's stress level is high, they can display relaxing content. If the user's concentration level is high, they can provide more difficult tasks. This allows them to provide an optimal learning experience based on the user's biological information.

[0073] Smart glasses can also use emotion estimation to identify areas of interest to users and automatically generate quizzes and assignments related to those areas. For example, if a student shows interest in a particular part of an ancient building in history class, a quiz about that part can be automatically generated and provided to the user. Similarly, if a student shows interest in a particular stage of cell division in science class, an assignment related to that stage can be automatically generated. This allows users to deepen their learning based on their interests.

[0074] Smart glasses can also use emotion estimation to identify parts of a lesson that a user finds difficult to understand and automatically display supplementary explanations. For example, if a user has difficulty understanding a particular stage of cell division in a science class, the glasses can display a detailed explanation of that part. Or, if a user has difficulty understanding a particular event in a history class, the glasses can display background information about that event. This helps users understand the parts they find difficult to understand.

[0075] Smart glasses can also use emotion estimation to identify the learning topics that a user is most interested in and automatically recommend videos and articles related to those topics. For example, if a user is interested in ancient buildings in history class, they can recommend a documentary video about those buildings. Or, if a user is interested in cell division in science class, they can recommend the latest research articles on that process. This allows them to provide learning resources based on the user's interests.

[0076] Smart glasses can also use emotion estimation to enable users to share the learning content they are most interested in with other users, promoting collaborative learning. For example, in a history class, a student can share information about ancient buildings that interest them and study together. In a science class, a student who is interested in cell division can share that information with other students and hold a discussion. This allows users to share the learning content that interests them most with other users, promoting collaborative learning.

[0077] Smart glasses also have augmented reality (AR) capabilities, allowing users to enjoy learning experiences that blend the real and virtual worlds. For example, in a history class, a 3D model of an ancient building could be superimposed on a real landscape, or in a science class, an animation of cell division could be superimposed on a real microscope image. This allows users to enjoy a learning experience that blends the real and virtual worlds.

[0078] The smart glasses also have an audio guide section, allowing users to receive audio guidance about the content they are learning. For example, when a 3D model of an ancient building is displayed in a history class, an audio guide about the building can be played. Or, when an animation of cell division is displayed in a science class, an audio guide about the process can be played. This allows users to have a learning experience that combines sight and sound.

[0079] Smart glasses can also analyze a user's learning progress and automatically suggest what to learn next. For example, after learning about ancient buildings in a history class, the glasses can suggest the culture and lifestyle of that era. Or, after learning about cell division in a science class, the glasses can suggest the structure and function of cells. This allows the glasses to suggest what to learn next based on the user's learning progress.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The 3D model display unit displays the content of the teaching material as a 3D model. For example, when learning about an ancient building in a history class, a 3D model of the building can be displayed within the user's field of view, allowing the user to walk around it and observe it from various angles. The 3D model display unit also uses a generation AI that receives prompts containing instructions from the user as input information and generates a 3D model based on the prompts. Step 2: The animation display unit displays the content of the teaching material as an animation. For example, when learning about the process of cell division in a science class, the process can be visually shown through animation. The generation AI in the animation display unit receives prompts containing instructions from the user as input information and generates animations based on those prompts. Step 3: The interactive operation unit allows users to interact with the content of the learning material. For example, when learning the properties of solid shapes in a math class, users can manipulate the 3D model in their field of view to calculate angles and areas. The interactive operation unit also allows the generation AI to receive prompts containing instructions from the user as input information and perform interactive operations based on those prompts.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a 3D model display section that displays the contents of the teaching material as a 3D model; an animation display unit that displays the contents of the teaching material as animation; An interactive operation unit that allows users to interact with the content of the educational material. A system characterized by:

2. The 3D model display unit Using eye-tracking data, the model automatically zooms in on details based on eye movements. The system of claim 1 .

3. The 3D model display unit Equipped with multi-user functionality that allows multiple users to simultaneously observe the same model and learn collaboratively. The system of claim 1 .

4. The animation display unit The speed and level of detail of the animation are automatically adjusted according to the user's learning progress. The system of claim 1 .

5. The interactive operation unit includes: Using gesture recognition of the user to manipulate the 3D model and the animation. The system of claim 1 .

6. The 3D model display unit Using an emotion estimation function, the part in which the user showed interest is identified, and detailed information or supplementary explanations related to that part are automatically displayed. The system of claim 1 .

7. The animation display unit The function of using an emotion estimation function to identify parts that the user found difficult to understand and repeatedly display those parts is provided. The system of claim 1 .

8. The interactive operation unit includes: The emotion estimation function is used to share the learning content that the user is most interested in with other users, promoting collaborative learning. The system of claim 1 .

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