System

Abstract

To provide a system according to an embodiment which allows a student who was absent from school to easily catch up with course content and conversations with classmates.SOLUTION: The system includes a recording unit, an analysis unit, and a sending unit. The recording unit records how a class or a break time is going. The analysis unit analyzes the data recorded by the recording unit and extracts important information. The sending unit sends the information extracted by the analysis unit in a batch.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, students who were absent from school had a hard time keeping up with the lesson content and conversations with their classmates, which led to stress.

[0005] The system according to the embodiment aims to make it easier for students who are absent from school to keep up with class content and conversations with classmates. [Means for solving the problem]

[0006] The system according to the embodiment includes a recording unit, an analysis unit, and a transmission unit. The recording unit records the state of a class or a break. The analysis unit analyzes the data recorded by the recording unit and extracts important information. The transmission unit transmits the information extracted by the analysis unit together. Effect of the Invention

[0007] The system according to the embodiment can make it easier for students who are absent from school to keep up with class content and conversations with classmates. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The robot proxy attendance system according to the embodiment of the present invention is a system that utilizes generative AI to solve the problem of students who are absent from school being unable to keep up with the content of lessons or conversations with classmates and feeling stressed. This system installs a small robot on the desk of an absent student, records the state of lessons and break times, and finally sends them together by email. For example, the system installs a small robot on the desk of an absent student. This robot is equipped with a camera and a microphone, and records the teacher's explanation during class and conversations with classmates. Next, the system uses generative AI to analyze the recorded and recorded data and extracts important points. For example, the main points of the lesson and important conversations with classmates. Finally, the system compiles the information extracted by the generative AI and sends it to the student by email. This system allows absent students to understand the content of lessons and conversations with classmates, and to participate in classes smoothly when they return. It also reduces the workload of teachers. Furthermore, this system may be used in the business field. For example, it can be used by employees who were unable to attend a meeting to understand the content of the meeting. As a result, the robot proxy attendance system allows absent students to understand the content of lessons and conversations with classmates, and to participate in classes smoothly when they return.

[0029] The robot proxy attendance system according to the embodiment includes a recording unit, an analysis unit, and a transmission unit. The recording unit records the state of classes and break times. The recording unit is equipped with, for example, a camera and a microphone, and records the teacher's explanation during the class and the conversation with classmates. For example, the recording unit records the teacher's explanation during the class with a high-resolution camera and records the conversation with classmates with a high-quality microphone. The recording unit can also monitor the progress of the class and the behavior of students in real time. The analysis unit uses a generation AI to analyze the data recorded by the recording unit and extract important points. For example, the analysis unit extracts the main points of the class and important conversations with classmates. The generation AI uses a text generation AI (for example, LLM) to extract important information from the video and audio data. The analysis unit can also use the generation AI to summarize the main points of the class and extract important keywords. For example, the generation AI analyzes the content of the teacher's remarks and extracts important keywords. The transmission unit transmits the information extracted by the analysis unit together. The transmission unit, for example, transmits the extracted information to the student by email. The transmission unit can compile the extracted information and transmit it in the form of an email. For example, the transmission unit transmits an email including the main points of the lesson and important conversations to the student. In this way, the robot proxy attendance system according to the embodiment allows an absent student to understand the lesson content and conversations with classmates, and smoothly participate in the lesson when returning. Some or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can transmit information using an AI model that receives the information extracted by the generation AI as input and transmits it in the form of an email.

[0030] The recording unit records the state of lessons and breaks. The recording unit is equipped with, for example, a camera and a microphone, and records the teacher's explanations during the lesson and conversations with classmates. Specifically, the high-resolution camera installed in the recording unit can clearly record the contents of the teacher's blackboard notes and slides, and capture the progress of the lesson in detail. In addition, the camera uses a wide-angle lens to record the state of the entire classroom at once. The high-quality microphone can clearly record not only the teacher's voice, but also the remarks of classmates and the environmental sounds in the classroom. This ensures that the contents of discussions and question-and-answer sessions during the lesson are recorded without omission. In addition, the recording unit has the function of streaming data in real time and storing it on a cloud server. This makes the recorded data immediately available to the analysis unit and transmission unit. In addition, the recording unit can monitor the progress of the lesson and the behavior of students in real time using AI. For example, AI can analyze the facial expressions and movements of students from the camera footage and evaluate their concentration and comprehension. This allows teachers to adjust the progress of the lesson according to the students' level of understanding. By integrating these functions, the recording section can record the overall picture of a lesson in detail and provide information that will be useful for reviewing it later.

[0031] The analysis unit uses the generation AI to analyze the data recorded by the recording unit and extract important points. Specifically, the generation AI converts the video and audio data into text and extracts the main points of the lesson and important conversations with classmates from it. For example, the generation AI analyzes the teacher's remarks using natural language processing technology and extracts important keywords and phrases. This allows the main points of the lesson to be grasped efficiently. The generation AI can also convert the recorded data into text using voice recognition technology and extract important information from conversations with classmates. Furthermore, the generation AI can summarize the contents of the lesson concisely using summary generation technology. For example, it can convert a long lesson recording into a summary of about a few minutes, allowing students to grasp the main points of the lesson in a short time. The analysis unit can also use the generation AI to analyze the progress of the lesson and the students' reactions and evaluate the effectiveness of the lesson. For example, it can analyze the students' facial expressions and remarks to evaluate their understanding and interest in the lesson. This allows the teacher to understand the areas to improve the lesson and reflect them in the next lesson. By integrating these functions, the analysis unit can efficiently extract important information from the recorded data and provide it to students and teachers.

[0032] The transmission unit compiles and transmits the information extracted by the analysis unit. Specifically, the transmission unit transmits the extracted information to the student in the form of an email. For example, the transmission unit can automatically generate an email including the main points of the lesson and important conversations, and send it to the student's email address. The transmission unit can transmit the information using an AI model that inputs the information extracted by the generation AI and transmits it in the form of an email. This allows the transmission unit to efficiently compile the extracted information and provide it to the student. In addition, the transmission unit can transmit information in a manner other than email. For example, the main points of the lesson and important conversations can be notified through a dedicated application. This allows the student to check the content of the lesson anytime and anywhere. Furthermore, the transmission unit can monitor the reception status of the transmitted information and check whether the student has received the information. This allows the transmission unit to ensure reliable transmission of the information. By integrating these functions, the transmission unit can efficiently compile the information extracted by the analysis unit and provide it to the student. This allows the robot proxy attendance system according to the embodiment to allow an absent student to grasp the content of the lesson and conversations with classmates, and to smoothly participate in the lesson when returning.

[0033] The recording unit is equipped with a camera or microphone and can record the teacher's explanation during the lesson or the conversation with classmates. The camera or microphone includes, but is not limited to, a high-resolution camera and a high-quality microphone. For example, the recording unit records the teacher's explanation during the lesson with a high-resolution camera and records the conversation with classmates with a high-quality microphone. For example, the recording unit uses a 4K resolution camera to clearly record the teacher's explanation. The recording unit can also use a high-sensitivity microphone to clearly record the conversation with classmates. Furthermore, the recording unit can provide an optimal recording and recording environment by adjusting the installation location of the camera and microphone. For example, the recording unit installs a camera in the center of the classroom to record the overall situation. The recording unit can also install a microphone near the teacher to clearly record the teacher's voice. This allows absent students to understand the content of the lesson and the conversation with classmates by recording the teacher's explanation during the lesson and the conversation with classmates. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can use AI to optimize the placement of cameras and microphones, improving the quality of video and audio recording.

[0034] The analysis unit can analyze the recorded data and extract the main points of the lesson and important conversations with classmates. The analysis unit analyzes the recorded data, for example, using a generation AI. For example, the analysis unit inputs a prompt to the generation AI, "Please extract the main points of the lesson," in order to extract the main points of the lesson. The generation AI analyzes the recorded data and extracts the main points of the lesson. In addition, the analysis unit inputs a prompt to the generation AI, "Please extract important conversations," in order to extract important conversations with classmates. The generation AI can analyze the recorded data and extract important conversations. For example, the generation AI analyzes the contents of the teacher's remarks and extracts important keywords. In addition, the generation AI can analyze the contents of the classmates' remarks and extract highly relevant conversations. In this way, by analyzing the recorded data and extracting the main points of the lesson and important conversations with classmates, students who are absent can grasp important information. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can extract information using an AI model that takes the information extracted by the generative AI as input and extracts important information.

[0035] The transmission unit can compile the extracted information and send it to the student by email. The transmission unit, for example, transmits the extracted information in the form of an email. For example, the transmission unit transmits an email including the main points of the lesson and an important conversation to the student. The transmission unit can compile the extracted information and send it in the form of an email. For example, the transmission unit transmits an email including the main points of the lesson and an important conversation to the student. The transmission unit can compile the extracted information and send it in the form of an email. For example, the transmission unit transmits an email including the main points of the lesson and an important conversation to the student. In this way, by compile the extracted information and sending it to the student by email, the absent student can understand the content of the lesson and the conversation with his / her classmates. Part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can transmit information using an AI model that inputs the information extracted by the generation AI and transmits it in the form of an email.

[0036] The recording unit is equipped with a sensor and can monitor the state of a class or a break in real time. The sensor includes, for example, a motion detection sensor and a voice sensor, but is not limited to such examples. The recording unit, for example, uses a motion detection sensor to monitor the movement of students during a class in real time. The recording unit can also use a voice sensor to monitor the voice during a class in real time. For example, the recording unit installs a motion detection sensor in a classroom to monitor the movement of students in real time. The recording unit can also install a voice sensor in a classroom to monitor the voice during a class in real time. As a result, by installing a sensor and monitoring the state of a class or a break in real time, more accurate information can be recorded. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can analyze data acquired from the sensor by AI and monitor it in real time.

[0037] The transmission unit can be used to grasp the contents of a meeting in the business field. The transmission unit, for example, transmits the contents of the meeting in the form of an email. For example, the transmission unit transmits an email including the main points of the meeting and important remarks to employees. The transmission unit can summarize the contents of the meeting and transmit it in the form of an email. For example, the transmission unit transmits an email including the main points of the meeting and important remarks to employees. The transmission unit can summarize the contents of the meeting and transmit it in the form of an email. For example, the transmission unit transmits an email including the main points of the meeting and important remarks to employees. By using the transmission unit to grasp the contents of a meeting in the business field, employees who were unable to attend the meeting can grasp the contents of the meeting. A part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can transmit information using an AI model that receives information extracted by the generation AI as input and transmits the information in the form of an email.

[0038] The recording unit can adjust the resolution and sound quality of the video and audio recording based on the importance of the lesson content. The recording unit adjusts the resolution and sound quality of the video and audio recording based on the importance of the lesson content, for example. For example, the recording unit records and records with high resolution and high sound quality in the case of important lesson content. In addition, the recording unit can also record and record with standard resolution and standard sound quality in the case of general lesson content. Furthermore, the recording unit can also record and record with low resolution and low sound quality in the case of repetitive content or review. As a result, important information can be recorded with high quality by adjusting the resolution and sound quality of the video and audio recording based on the importance of the lesson content. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can evaluate the importance of the lesson content by AI and automatically adjust the resolution and sound quality of the video and audio recording.

[0039] The recording unit can filter environmental sounds during a lesson and record only important sounds. The recording unit uses, for example, noise canceling technology to filter environmental sounds during a lesson. For example, the recording unit prioritizes recording of the teacher's voice and filters background noise. The recording unit can also record classmates' remarks according to their importance and filter out unnecessary conversations. Furthermore, the recording unit can record important sounds during a lesson (for example, sounds of an experiment, etc.) and filter out other sounds. This makes it possible to record only important sounds by filtering environmental sounds during a lesson. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can optimize noise canceling technology using AI and record only important sounds.

[0040] The recording unit can track the teacher's movements during class and record important actions. The recording unit uses, for example, motion detection technology to track the teacher's movements during class. For example, the recording unit tracks the teacher's actions of writing on the blackboard and records the actions. The recording unit can also track the teacher's actions of conducting an experiment and record the actions. Furthermore, the recording unit can track the teacher's actions of explaining to students and record the actions. In this way, important actions can be reliably recorded by tracking the teacher's movements during class. Some or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can optimize the motion detection technology using AI to track the teacher's movements.

[0041] The recording unit can automatically capture the contents of the blackboard during the lesson and integrate it into the recording data. The recording unit uses character recognition technology, for example, to automatically capture the contents of the blackboard during the lesson. For example, the recording unit automatically captures the contents written by the teacher on the blackboard and integrates it into the recording data. The recording unit can also automatically capture the contents written by the teacher on the whiteboard and integrate it into the recording data. Furthermore, the recording unit can automatically capture the contents displayed by the teacher on the projector and integrate it into the recording data. In this way, the contents of the blackboard during the lesson can be automatically captured and integrated into the recording data. Some or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can optimize character recognition technology by AI to automatically capture the contents of the blackboard.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the lesson content. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the lesson content. For example, the analysis unit performs a detailed analysis in the case of important lesson content. In addition, the analysis unit can also perform a standard analysis in the case of general lesson content. Furthermore, the analysis unit can also perform a simplified analysis in the case of repetitive content or review. As a result, important information can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the lesson content. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can evaluate the importance of the lesson content using AI and automatically adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the lesson. The analysis unit applies different analysis algorithms depending on the category of the lesson, for example. For example, in the case of a science lesson, the analysis unit applies an analysis algorithm of experimental data. In addition, in the case of a mathematics lesson, the analysis unit can also apply an analysis algorithm of a mathematical formula. Furthermore, in the case of a Japanese language lesson, the analysis unit can also apply an analysis algorithm of a sentence. In this way, by applying different analysis algorithms depending on the category of the lesson, more appropriate analysis results can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can select and apply different analysis algorithms using AI depending on the category of the lesson.

[0044] The analysis unit can analyze the contents of the teacher's remarks during the lesson and extract important keywords. The analysis unit, for example, uses a generation AI to analyze the contents of the teacher's remarks during the lesson. For example, the analysis unit inputs a prompt to the generation AI, "Please extract important keywords," in order to extract important keywords from the teacher's remarks. The generation AI analyzes the contents of the teacher's remarks and extracts important keywords. The analysis unit can also input a prompt to the generation AI, "Please extract key points," in order to extract key points from the teacher's explanation. The generation AI analyzes the contents of the teacher's explanation and extracts key points. Furthermore, the analysis unit can also input a prompt to the generation AI, "Please extract important keywords," in order to extract important keywords from the teacher's questions. The generation AI analyzes the contents of the teacher's questions and extracts important keywords. In this way, important keywords can be extracted by analyzing the contents of the teacher's remarks during the lesson. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can extract information using an AI model that takes keywords extracted by the generation AI as input and extracts important keywords.

[0045] The analysis unit can analyze the contents of the remarks made by classmates during class and extract highly relevant conversations. The analysis unit, for example, uses a generation AI to analyze the contents of the remarks made by classmates during class. For example, the analysis unit inputs a prompt to the generation AI, "Please extract highly relevant conversations," in order to extract highly relevant conversations from the remarks made by classmates. The generation AI analyzes the contents of the remarks made by classmates and extracts highly relevant conversations. The analysis unit can also input a prompt to the generation AI, "Please extract important points," in order to extract important points from the discussions made by classmates. The generation AI analyzes the contents of the discussions made by classmates and extracts important points. The analysis unit can also input a prompt to the generation AI, "Please extract highly relevant conversations," in order to extract highly relevant conversations from the questions made by classmates. The generation AI analyzes the contents of the questions made by classmates and extracts highly relevant conversations. In this way, highly relevant conversations can be extracted by analyzing the contents of the remarks made by classmates during class. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can extract information using an AI model that takes conversations extracted by the generation AI as input and extracts highly relevant conversations.

[0046] The transmission unit can adjust the level of detail of the transmission based on the importance of the lesson content at the time of transmission. The transmission unit adjusts the level of detail of the transmission based on, for example, the importance of the lesson content. For example, in the case of important lesson content, the transmission unit transmits an email including detailed information. In addition, in the case of general lesson content, the transmission unit can also transmit an email including standard information. Furthermore, in the case of repetitive content or review, the transmission unit can also transmit an email including simplified information. In this way, important information can be transmitted in detail by adjusting the level of detail of the transmission based on the importance of the lesson content. Part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can evaluate the importance of the lesson content by AI and automatically adjust the level of detail of the transmission.

[0047] The transmission unit can apply different transmission methods depending on the category of the lesson at the time of transmission. The transmission unit applies different transmission methods depending on, for example, the category of the lesson. For example, in the case of a science lesson, the transmission unit transmits an email including experimental data. In addition, in the case of a mathematics lesson, the transmission unit can also transmit an email including a formula. Furthermore, in the case of a Japanese language lesson, the transmission unit can also transmit an email including a sentence. In this way, by applying different transmission methods depending on the category of the lesson, more appropriate information can be provided. Part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can select and apply different transmission methods depending on the category of the lesson by AI.

[0048] The transmission unit can adjust the timing of transmission based on the progress of the class at the time of transmission. The transmission unit adjusts the timing of transmission based on the progress of the class, for example. For example, the transmission unit transmits the video recording and audio recording data immediately after the class ends. The transmission unit can also transmit the video recording and audio recording data at the timing when an important point of the class is over. Furthermore, the transmission unit can transmit the video recording and audio recording data during a break in the class. In this way, by adjusting the timing of transmission based on the progress of the class, information can be transmitted at an appropriate timing. A part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can evaluate the progress of the class by AI and automatically adjust the timing of transmission.

[0049] The transmission unit can automatically attach and transmit the materials related to the lesson when transmitting. The transmission unit, for example, automatically attaches and transmits the materials related to the lesson. For example, the transmission unit automatically attaches and transmits slide materials for the lesson. The transmission unit can also automatically attach and transmit handouts for the lesson. Furthermore, the transmission unit can automatically attach and transmit reference materials for the lesson. In this way, by automatically attaching and transmitting the materials related to the lesson, it is possible to provide necessary information at once. A part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can select materials related to the lesson by AI, automatically attach them, and transmit them.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The robot proxy attendance system may further include a translation unit. The translation unit may translate the teacher's explanation during the lesson and conversations with classmates in real time, so that students who speak different languages ​​can understand them. For example, the translation unit may translate a lesson given in English into Japanese and provide it to students who speak Japanese. The translation unit may also translate conversations with classmates in real time, so that students who speak different languages ​​can communicate more smoothly. Furthermore, the translation unit may summarize the main points of the lesson and important conversations in multiple languages ​​and provide them to students who speak different languages. This may make it easier for students who speak different languages ​​to understand the lesson content and conversations with classmates.

[0052] The recording unit can further include sensors for monitoring the health of students. For example, sensors for measuring heart rate and body temperature can be installed to monitor the health of students in real time. The recording unit can issue an alert if the heart rate is abnormally high or the body temperature rises. The recording unit can also transmit health condition data to the analysis unit and adjust the progress of lessons based on the health condition. For example, if a student is feeling unwell, the progress of the lesson can be delayed or a break can be inserted. This makes it possible to proceed with lessons taking into account the health condition of students.

[0053] The analysis unit can further analyze the learning style of the student and provide an individually optimized learning plan. For example, the analysis unit can analyze the student's past learning data and identify what learning method is most effective. The analysis unit can provide materials that make heavy use of visual aids to students who prefer visual learning, and materials that emphasize audio commentary to students who prefer auditory learning. The analysis unit can also adjust the learning plan according to the student's progress to support effective learning. This makes it possible to provide an optimized learning experience for each student.

[0054] The transmission unit can further include a function of reporting the learning progress of the students to the parents. For example, the transmission unit can periodically send a report summarizing the main points of the lessons and the students' understanding to the parents. The transmission unit can also provide visual reports using graphs and charts to make it easier for the parents to grasp the learning situation of the students. The transmission unit can also receive feedback from the parents and reflect it in the lesson content and progress. This makes it possible to support the students' learning in cooperation with the parents.

[0055] The recording unit may further include a sensor that collects environmental data during the lesson. For example, environmental data such as temperature, humidity, and illuminance may be collected in real time to identify factors that affect the progress of the lesson. The recording unit may also transmit the environmental data to the analysis unit and make adjustments to provide an optimal teaching environment. For example, if the temperature in the classroom is too high, the recording unit may issue an instruction to adjust the air conditioner settings. Also, if the illuminance is low, the recording unit may issue an instruction to adjust the lighting. This helps maintain a comfortable teaching environment and improves students' concentration.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The recording unit records the situation during lessons and breaks. The recording unit is equipped with, for example, a camera and a microphone, and records the teacher's explanations during the lesson and conversations with classmates. For example, the recording unit records the teacher's explanations during the lesson with a high-resolution camera and records conversations with classmates with a high-quality microphone. The recording unit can also monitor the progress of the lesson and the behavior of students in real time. Step 2: The analysis unit uses the generation AI to analyze the data recorded by the recording unit and extract important points. For example, the analysis unit extracts the main points of the lesson and important conversations with classmates. The generation AI uses a text generation AI (e.g., LLM) to extract important information from the video and audio data. The analysis unit can also use the generation AI to summarize the main points of the lesson and extract important keywords. For example, the generation AI analyzes the content of the teacher's remarks and extracts important keywords. Step 3: The transmission unit compiles and transmits the information extracted by the analysis unit. The transmission unit, for example, transmits the extracted information to the student by email. The transmission unit can compile the extracted information and transmit it in the form of an email. For example, the transmission unit transmits an email including the main points of the lesson and important conversations to the student. In this way, the robot proxy attendance system according to the embodiment allows the absent student to understand the lesson content and conversations with classmates, and smoothly participate in the lesson when returning. Some or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can transmit the information using an AI model that receives the information extracted by the generation AI as input and transmits it in the form of an email.

[0058] (Example 2) The robot proxy attendance system according to the embodiment of the present invention is a system that utilizes generative AI to solve the problem of students who are absent from school being unable to keep up with the content of lessons or conversations with classmates and feeling stressed. This system installs a small robot on the desk of an absent student, records the state of lessons and break times, and finally sends them together by email. For example, the system installs a small robot on the desk of an absent student. This robot is equipped with a camera and a microphone, and records the teacher's explanation during class and conversations with classmates. Next, the system uses generative AI to analyze the recorded and recorded data and extracts important points. For example, the main points of the lesson and important conversations with classmates. Finally, the system compiles the information extracted by the generative AI and sends it to the student by email. This system allows absent students to understand the content of lessons and conversations with classmates, and to participate in classes smoothly when they return. It also reduces the workload of teachers. Furthermore, this system may be used in the business field. For example, it can be used by employees who were unable to attend a meeting to understand the content of the meeting. As a result, the robot proxy attendance system allows absent students to understand the content of lessons and conversations with classmates, and to participate in classes smoothly when they return.

[0059] The robot proxy attendance system according to the embodiment includes a recording unit, an analysis unit, and a transmission unit. The recording unit records the state of classes and break times. The recording unit is equipped with, for example, a camera and a microphone, and records the teacher's explanation during the class and the conversation with classmates. For example, the recording unit records the teacher's explanation during the class with a high-resolution camera and records the conversation with classmates with a high-quality microphone. The recording unit can also monitor the progress of the class and the behavior of students in real time. The analysis unit uses a generation AI to analyze the data recorded by the recording unit and extract important points. For example, the analysis unit extracts the main points of the class and important conversations with classmates. The generation AI uses a text generation AI (for example, LLM) to extract important information from the video and audio data. The analysis unit can also use the generation AI to summarize the main points of the class and extract important keywords. For example, the generation AI analyzes the content of the teacher's remarks and extracts important keywords. The transmission unit transmits the information extracted by the analysis unit together. The transmission unit, for example, transmits the extracted information to the student by email. The transmission unit can compile the extracted information and transmit it in the form of an email. For example, the transmission unit transmits an email including the main points of the lesson and important conversations to the student. In this way, the robot proxy attendance system according to the embodiment allows an absent student to understand the lesson content and conversations with classmates, and smoothly participate in the lesson when returning. Some or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can transmit information using an AI model that receives the information extracted by the generation AI as input and transmits it in the form of an email.

[0060] The recording unit records the state of lessons and breaks. The recording unit is equipped with, for example, a camera and a microphone, and records the teacher's explanations during the lesson and conversations with classmates. Specifically, the high-resolution camera installed in the recording unit can clearly record the contents of the teacher's blackboard notes and slides, and capture the progress of the lesson in detail. In addition, the camera uses a wide-angle lens to record the state of the entire classroom at once. The high-quality microphone can clearly record not only the teacher's voice, but also the remarks of classmates and the environmental sounds in the classroom. This ensures that the contents of discussions and question-and-answer sessions during the lesson are recorded without omission. In addition, the recording unit has the function of streaming data in real time and storing it on a cloud server. This makes the recorded data immediately available to the analysis unit and transmission unit. In addition, the recording unit can monitor the progress of the lesson and the behavior of students in real time using AI. For example, AI can analyze the facial expressions and movements of students from the camera footage and evaluate their concentration and comprehension. This allows teachers to adjust the progress of the lesson according to the students' level of understanding. By integrating these functions, the recording section can record the overall picture of a lesson in detail and provide information that will be useful for reviewing it later.

[0061] The analysis unit uses the generation AI to analyze the data recorded by the recording unit and extract important points. Specifically, the generation AI converts the video and audio data into text and extracts the main points of the lesson and important conversations with classmates from it. For example, the generation AI analyzes the teacher's remarks using natural language processing technology and extracts important keywords and phrases. This allows the main points of the lesson to be grasped efficiently. The generation AI can also convert the recorded data into text using voice recognition technology and extract important information from conversations with classmates. Furthermore, the generation AI can summarize the contents of the lesson concisely using summary generation technology. For example, it can convert a long lesson recording into a summary of about a few minutes, allowing students to grasp the main points of the lesson in a short time. The analysis unit can also use the generation AI to analyze the progress of the lesson and the students' reactions and evaluate the effectiveness of the lesson. For example, it can analyze the students' facial expressions and remarks to evaluate their understanding and interest in the lesson. This allows the teacher to understand the areas to improve the lesson and reflect them in the next lesson. By integrating these functions, the analysis unit can efficiently extract important information from the recorded data and provide it to students and teachers.

[0062] The transmission unit compiles and transmits the information extracted by the analysis unit. Specifically, the transmission unit transmits the extracted information to the student in the form of an email. For example, the transmission unit can automatically generate an email including the main points of the lesson and important conversations, and send it to the student's email address. The transmission unit can transmit the information using an AI model that inputs the information extracted by the generation AI and transmits it in the form of an email. This allows the transmission unit to efficiently compile the extracted information and provide it to the student. In addition, the transmission unit can transmit information in a manner other than email. For example, the main points of the lesson and important conversations can be notified through a dedicated application. This allows the student to check the content of the lesson anytime and anywhere. Furthermore, the transmission unit can monitor the reception status of the transmitted information and check whether the student has received the information. This allows the transmission unit to ensure reliable transmission of the information. By integrating these functions, the transmission unit can efficiently compile the information extracted by the analysis unit and provide it to the student. This allows the robot proxy attendance system according to the embodiment to allow an absent student to grasp the content of the lesson and conversations with classmates, and to smoothly participate in the lesson when returning.

[0063] The recording unit is equipped with a camera or microphone and can record the teacher's explanation during the lesson or the conversation with classmates. The camera or microphone includes, but is not limited to, a high-resolution camera and a high-quality microphone. For example, the recording unit records the teacher's explanation during the lesson with a high-resolution camera and records the conversation with classmates with a high-quality microphone. For example, the recording unit uses a 4K resolution camera to clearly record the teacher's explanation. The recording unit can also use a high-sensitivity microphone to clearly record the conversation with classmates. Furthermore, the recording unit can provide an optimal recording and recording environment by adjusting the installation location of the camera and microphone. For example, the recording unit installs a camera in the center of the classroom to record the overall situation. The recording unit can also install a microphone near the teacher to clearly record the teacher's voice. This allows absent students to understand the content of the lesson and the conversation with classmates by recording the teacher's explanation during the lesson and the conversation with classmates. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can use AI to optimize the placement of cameras and microphones, improving the quality of video and audio recording.

[0064] The analysis unit can analyze the recorded data and extract the main points of the lesson and important conversations with classmates. The analysis unit analyzes the recorded data, for example, using a generation AI. For example, the analysis unit inputs a prompt to the generation AI, "Please extract the main points of the lesson," in order to extract the main points of the lesson. The generation AI analyzes the recorded data and extracts the main points of the lesson. In addition, the analysis unit inputs a prompt to the generation AI, "Please extract important conversations," in order to extract important conversations with classmates. The generation AI can analyze the recorded data and extract important conversations. For example, the generation AI analyzes the contents of the teacher's remarks and extracts important keywords. In addition, the generation AI can analyze the contents of the classmates' remarks and extract highly relevant conversations. In this way, by analyzing the recorded data and extracting the main points of the lesson and important conversations with classmates, students who are absent can grasp important information. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can extract information using an AI model that takes the information extracted by the generative AI as input and extracts important information.

[0065] The transmission unit can compile the extracted information and send it to the student by email. The transmission unit, for example, transmits the extracted information in the form of an email. For example, the transmission unit transmits an email including the main points of the lesson and an important conversation to the student. The transmission unit can compile the extracted information and send it in the form of an email. For example, the transmission unit transmits an email including the main points of the lesson and an important conversation to the student. The transmission unit can compile the extracted information and send it in the form of an email. For example, the transmission unit transmits an email including the main points of the lesson and an important conversation to the student. In this way, by compile the extracted information and sending it to the student by email, the absent student can understand the content of the lesson and the conversation with his / her classmates. Part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can transmit information using an AI model that inputs the information extracted by the generation AI and transmits it in the form of an email.

[0066] The recording unit is equipped with a sensor and can monitor the state of a class or a break in real time. The sensor includes, for example, a motion detection sensor and a voice sensor, but is not limited to such examples. The recording unit, for example, uses a motion detection sensor to monitor the movement of students during a class in real time. The recording unit can also use a voice sensor to monitor the voice during a class in real time. For example, the recording unit installs a motion detection sensor in a classroom to monitor the movement of students in real time. The recording unit can also install a voice sensor in a classroom to monitor the voice during a class in real time. As a result, by installing a sensor and monitoring the state of a class or a break in real time, more accurate information can be recorded. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can analyze data acquired from the sensor by AI and monitor it in real time.

[0067] The transmission unit can be used to grasp the contents of a meeting in the business field. The transmission unit, for example, transmits the contents of the meeting in the form of an email. For example, the transmission unit transmits an email including the main points of the meeting and important remarks to employees. The transmission unit can summarize the contents of the meeting and transmit it in the form of an email. For example, the transmission unit transmits an email including the main points of the meeting and important remarks to employees. The transmission unit can summarize the contents of the meeting and transmit it in the form of an email. For example, the transmission unit transmits an email including the main points of the meeting and important remarks to employees. By using the transmission unit to grasp the contents of a meeting in the business field, employees who were unable to attend the meeting can grasp the contents of the meeting. A part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can transmit information using an AI model that receives information extracted by the generation AI as input and transmits the information in the form of an email.

[0068] The recording unit can estimate the student's emotion and adjust the timing of video recording and audio recording based on the estimated student's emotion. The recording unit, for example, uses facial expression recognition technology to estimate the student's emotion. For example, the recording unit captures the student's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the recording unit increases the frequency of video recording and audio recording when the student is excited so as not to miss important moments. In addition, the recording unit returns the frequency of video recording and audio recording to normal when the student is relaxed. Furthermore, the recording unit can also adjust the timing of video recording and audio recording when the student is concentrating so as not to miss the main points of the lesson. As a result, by adjusting the timing of video recording and audio recording based on the student's emotion, it is possible to record important moments without missing them. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recording unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recording unit can input image data of a student taken with a camera into the generation AI and have the generation AI estimate the student's emotions.

[0069] The recording unit can adjust the resolution and sound quality of the video and audio recording based on the importance of the lesson content. The recording unit adjusts the resolution and sound quality of the video and audio recording based on the importance of the lesson content, for example. For example, the recording unit records and records with high resolution and high sound quality in the case of important lesson content. In addition, the recording unit can also record and record with standard resolution and standard sound quality in the case of general lesson content. Furthermore, the recording unit can also record and record with low resolution and low sound quality in the case of repetitive content or review. As a result, important information can be recorded with high quality by adjusting the resolution and sound quality of the video and audio recording based on the importance of the lesson content. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can evaluate the importance of the lesson content by AI and automatically adjust the resolution and sound quality of the video and audio recording.

[0070] The recording unit can filter environmental sounds during a lesson and record only important sounds. The recording unit uses, for example, noise canceling technology to filter environmental sounds during a lesson. For example, the recording unit prioritizes recording of the teacher's voice and filters background noise. The recording unit can also record classmates' remarks according to their importance and filter out unnecessary conversations. Furthermore, the recording unit can record important sounds during a lesson (for example, sounds of an experiment, etc.) and filter out other sounds. This makes it possible to record only important sounds by filtering environmental sounds during a lesson. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can optimize noise canceling technology using AI and record only important sounds.

[0071] The recording unit can estimate the student's emotion and determine the priority of video recording and recording based on the estimated student's emotion. The recording unit, for example, uses facial expression recognition technology to estimate the student's emotion. For example, the recording unit captures the student's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, when the student is excited, the recording unit records and records important moments with priority. In addition, when the student is relaxed, the recording unit records and records normal lesson content with priority. Furthermore, when the student is concentrating, the recording unit can also record and record the main points of the lesson with priority. In this way, important moments can be recorded with priority by determining the priority of video recording and recording based on the student's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recording unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recording unit can input image data of a student taken with a camera into the generation AI and have the generation AI estimate the student's emotions.

[0072] The recording unit can track the teacher's movements during class and record important actions. The recording unit uses, for example, motion detection technology to track the teacher's movements during class. For example, the recording unit tracks the teacher's actions of writing on the blackboard and records the actions. The recording unit can also track the teacher's actions of conducting an experiment and record the actions. Furthermore, the recording unit can track the teacher's actions of explaining to students and record the actions. In this way, important actions can be reliably recorded by tracking the teacher's movements during class. Some or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can optimize the motion detection technology using AI to track the teacher's movements.

[0073] The recording unit can automatically capture the contents of the blackboard during the lesson and integrate it into the recording data. The recording unit uses character recognition technology, for example, to automatically capture the contents of the blackboard during the lesson. For example, the recording unit automatically captures the contents written by the teacher on the blackboard and integrates it into the recording data. The recording unit can also automatically capture the contents written by the teacher on the whiteboard and integrate it into the recording data. Furthermore, the recording unit can automatically capture the contents displayed by the teacher on the projector and integrate it into the recording data. In this way, the contents of the blackboard during the lesson can be automatically captured and integrated into the recording data. Some or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can optimize character recognition technology by AI to automatically capture the contents of the blackboard.

[0074] The analysis unit can estimate the student's emotion and adjust the expression method of the analysis result based on the estimated student's emotion. The analysis unit uses, for example, facial expression recognition technology to estimate the student's emotion. For example, the analysis unit captures the student's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit provides a detailed analysis result when the student is relaxed. In addition, the analysis unit can provide a concise analysis result that covers the main points when the student is in a hurry. Furthermore, the analysis unit can provide an analysis result with a visually stimulating effect when the student is excited. This makes it possible to provide a more appropriate analysis result by adjusting the expression method of the analysis result based on the student's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input image data of a student taken with a camera into the generation AI and have the generation AI estimate the student's emotions.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the lesson content. The analysis unit adjusts the level of detail of the analysis based on, for example, the importance of the lesson content. For example, the analysis unit performs a detailed analysis in the case of important lesson content. In addition, the analysis unit can also perform a standard analysis in the case of general lesson content. Furthermore, the analysis unit can also perform a simplified analysis in the case of repetitive content or review. As a result, important information can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the lesson content. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can evaluate the importance of the lesson content using AI and automatically adjust the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the category of the lesson. The analysis unit applies different analysis algorithms depending on the category of the lesson, for example. For example, in the case of a science lesson, the analysis unit applies an analysis algorithm of experimental data. In addition, in the case of a mathematics lesson, the analysis unit can also apply an analysis algorithm of a mathematical formula. Furthermore, in the case of a Japanese language lesson, the analysis unit can also apply an analysis algorithm of a sentence. In this way, by applying different analysis algorithms depending on the category of the lesson, more appropriate analysis results can be provided. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can select and apply different analysis algorithms using AI depending on the category of the lesson.

[0077] The analysis unit can estimate the student's emotion and determine the priority of the analysis result based on the estimated student's emotion. The analysis unit uses, for example, facial expression recognition technology to estimate the student's emotion. For example, the analysis unit captures the student's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit prioritizes important points in the analysis when the student is excited. In addition, the analysis unit can also analyze in the normal order when the student is relaxed. Furthermore, the analysis unit can also analyze the main points of the lesson with priority when the student is concentrating. In this way, important information can be prioritized in the analysis by determining the priority of the analysis result based on the student's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input image data of a student taken with a camera into the generation AI and have the generation AI estimate the student's emotions.

[0078] The analysis unit can analyze the contents of the teacher's remarks during the lesson and extract important keywords. The analysis unit, for example, uses a generation AI to analyze the contents of the teacher's remarks during the lesson. For example, the analysis unit inputs a prompt to the generation AI, "Please extract important keywords," in order to extract important keywords from the teacher's remarks. The generation AI analyzes the contents of the teacher's remarks and extracts important keywords. The analysis unit can also input a prompt to the generation AI, "Please extract key points," in order to extract key points from the teacher's explanation. The generation AI analyzes the contents of the teacher's explanation and extracts key points. Furthermore, the analysis unit can also input a prompt to the generation AI, "Please extract important keywords," in order to extract important keywords from the teacher's questions. The generation AI analyzes the contents of the teacher's questions and extracts important keywords. In this way, important keywords can be extracted by analyzing the contents of the teacher's remarks during the lesson. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can extract information using an AI model that takes keywords extracted by the generation AI as input and extracts important keywords.

[0079] The analysis unit can analyze the contents of the remarks made by classmates during class and extract highly relevant conversations. The analysis unit, for example, uses a generation AI to analyze the contents of the remarks made by classmates during class. For example, the analysis unit inputs a prompt to the generation AI, "Please extract highly relevant conversations," in order to extract highly relevant conversations from the remarks made by classmates. The generation AI analyzes the contents of the remarks made by classmates and extracts highly relevant conversations. The analysis unit can also input a prompt to the generation AI, "Please extract important points," in order to extract important points from the discussions made by classmates. The generation AI analyzes the contents of the discussions made by classmates and extracts important points. The analysis unit can also input a prompt to the generation AI, "Please extract highly relevant conversations," in order to extract highly relevant conversations from the questions made by classmates. The generation AI analyzes the contents of the questions made by classmates and extracts highly relevant conversations. In this way, highly relevant conversations can be extracted by analyzing the contents of the remarks made by classmates during class. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can extract information using an AI model that takes conversations extracted by the generation AI as input and extracts highly relevant conversations.

[0080] The transmission unit can estimate the student's emotion and adjust the expression method of the transmission content based on the estimated student's emotion. The transmission unit uses, for example, facial expression recognition technology to estimate the student's emotion. For example, the transmission unit captures the student's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the student is relaxed, the transmission unit transmits an email containing detailed information. Also, if the student is in a hurry, the transmission unit can transmit a concise email with the main points. Furthermore, if the student is excited, the transmission unit can transmit an email with a visually stimulating effect. This allows more appropriate information to be provided by adjusting the expression method of the transmission content based on the student's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the transmission unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmission unit can input image data of a student taken by a camera into the generation AI and have the generation AI estimate the student's emotions.

[0081] The transmission unit can adjust the level of detail of the transmission based on the importance of the lesson content at the time of transmission. The transmission unit adjusts the level of detail of the transmission based on, for example, the importance of the lesson content. For example, in the case of important lesson content, the transmission unit transmits an email including detailed information. In addition, in the case of general lesson content, the transmission unit can also transmit an email including standard information. Furthermore, in the case of repetitive content or review, the transmission unit can also transmit an email including simplified information. In this way, important information can be transmitted in detail by adjusting the level of detail of the transmission based on the importance of the lesson content. Part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can evaluate the importance of the lesson content by AI and automatically adjust the level of detail of the transmission.

[0082] The transmission unit can apply different transmission methods depending on the category of the lesson at the time of transmission. The transmission unit applies different transmission methods depending on, for example, the category of the lesson. For example, in the case of a science lesson, the transmission unit transmits an email including experimental data. In addition, in the case of a mathematics lesson, the transmission unit can also transmit an email including a formula. Furthermore, in the case of a Japanese language lesson, the transmission unit can also transmit an email including a sentence. In this way, by applying different transmission methods depending on the category of the lesson, more appropriate information can be provided. Part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can select and apply different transmission methods depending on the category of the lesson by AI.

[0083] The transmission unit can estimate the emotion of the student and determine the priority of the transmission contents based on the estimated emotion of the student. The transmission unit uses, for example, facial expression recognition technology to estimate the emotion of the student. For example, the transmission unit captures the facial expression of the student with a camera and estimates the emotion using an emotion estimation algorithm. For example, when the student is excited, the transmission unit transmits important information with priority. In addition, when the student is relaxed, the transmission unit can also transmit information in a normal order. Furthermore, when the student is concentrating, the transmission unit can also transmit the main points of the lesson with priority. In this way, by determining the priority of the transmission contents based on the emotion of the student, important information can be transmitted with priority. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the transmission unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transmission unit can input image data of a student taken by a camera into the generation AI and have the generation AI estimate the student's emotions.

[0084] The transmission unit can adjust the timing of transmission based on the progress of the class at the time of transmission. The transmission unit adjusts the timing of transmission based on the progress of the class, for example. For example, the transmission unit transmits the video recording and audio recording data immediately after the class ends. The transmission unit can also transmit the video recording and audio recording data at the timing when an important point of the class is over. Furthermore, the transmission unit can transmit the video recording and audio recording data during a break in the class. In this way, by adjusting the timing of transmission based on the progress of the class, information can be transmitted at an appropriate timing. A part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can evaluate the progress of the class by AI and automatically adjust the timing of transmission.

[0085] The transmission unit can automatically attach and transmit the materials related to the lesson when transmitting. The transmission unit, for example, automatically attaches and transmits the materials related to the lesson. For example, the transmission unit automatically attaches and transmits slide materials for the lesson. The transmission unit can also automatically attach and transmit handouts for the lesson. Furthermore, the transmission unit can automatically attach and transmit reference materials for the lesson. In this way, by automatically attaching and transmitting the materials related to the lesson, it is possible to provide necessary information at once. A part or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can select materials related to the lesson by AI, automatically attach them, and transmit them.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The robot proxy attendance system may further include a translation unit. The translation unit may translate the teacher's explanation during the lesson and conversations with classmates in real time, so that students who speak different languages ​​can understand them. For example, the translation unit may translate a lesson given in English into Japanese and provide it to students who speak Japanese. The translation unit may also translate conversations with classmates in real time, so that students who speak different languages ​​can communicate more smoothly. Furthermore, the translation unit may summarize the main points of the lesson and important conversations in multiple languages ​​and provide them to students who speak different languages. This may make it easier for students who speak different languages ​​to understand the lesson content and conversations with classmates.

[0088] The recording unit can further include sensors for monitoring the health of students. For example, sensors for measuring heart rate and body temperature can be installed to monitor the health of students in real time. The recording unit can issue an alert if the heart rate is abnormally high or the body temperature rises. The recording unit can also transmit health condition data to the analysis unit and adjust the progress of lessons based on the health condition. For example, if a student is feeling unwell, the progress of the lesson can be delayed or a break can be inserted. This makes it possible to proceed with lessons taking into account the health condition of students.

[0089] The analysis unit can further analyze the learning style of the student and provide an individually optimized learning plan. For example, the analysis unit can analyze the student's past learning data and identify what learning method is most effective. The analysis unit can provide materials that make heavy use of visual aids to students who prefer visual learning, and materials that emphasize audio commentary to students who prefer auditory learning. The analysis unit can also adjust the learning plan according to the student's progress to support effective learning. This makes it possible to provide an optimized learning experience for each student.

[0090] The transmission unit can further include a function of reporting the learning progress of the students to the parents. For example, the transmission unit can periodically send a report summarizing the main points of the lessons and the students' understanding to the parents. The transmission unit can also provide visual reports using graphs and charts to make it easier for the parents to grasp the learning situation of the students. The transmission unit can also receive feedback from the parents and reflect it in the lesson content and progress. This makes it possible to support the students' learning in cooperation with the parents.

[0091] The recording unit may further include a sensor that collects environmental data during the lesson. For example, environmental data such as temperature, humidity, and illuminance may be collected in real time to identify factors that affect the progress of the lesson. The recording unit may also transmit the environmental data to the analysis unit and make adjustments to provide an optimal teaching environment. For example, if the temperature in the classroom is too high, the recording unit may issue an instruction to adjust the air conditioner settings. Also, if the illuminance is low, the recording unit may issue an instruction to adjust the lighting. This helps maintain a comfortable teaching environment and improves students' concentration.

[0092] The recorder can estimate the student's emotions and adjust the lesson content based on the estimated student's emotions. For example, if the student is excited, the recorder can speed up the lesson and keep the student interested. If the student is tired, the recorder can slow down the lesson and insert a break. Furthermore, if the student is focused, the recorder can introduce more difficult content. This makes it possible to support more effective learning by adjusting the lesson content based on the student's emotions.

[0093] The analysis unit can estimate the student's emotions and provide feedback based on the estimated student's emotions. For example, if the student feels anxious, the analysis unit can provide an encouraging message to increase motivation. If the student feels confident, the analysis unit can provide feedback to encourage further challenges. Furthermore, if the student is confused, the analysis unit can provide additional explanations or supplementary materials. This makes it possible to improve learning effectiveness by providing appropriate feedback based on the student's emotions.

[0094] The transmitting unit can estimate the emotion of the student and adjust the tone of the transmitted content based on the estimated emotion of the student. For example, if the student is depressed, the transmitting unit can transmit information in an encouraging tone to increase the student's motivation. Also, if the student is excited, the transmitting unit can transmit information in a calm tone to help the student maintain concentration. Furthermore, if the student is relaxed, the transmitting unit can transmit information in a friendly tone. In this way, more effective communication can be achieved by adjusting the tone of the transmitted content based on the student's emotion.

[0095] The analysis unit can estimate the student's emotions and adjust the learning progress based on the estimated student's emotions. For example, if the student is stressed, the analysis unit can slow down the learning progress and provide time to relax. If the student is excited, the analysis unit can also speed up the learning progress and keep the student interested. Furthermore, if the student is focused, the analysis unit can provide a more difficult task. This makes it possible to support more effective learning by adjusting the learning progress based on the student's emotions.

[0096] The transmission unit can estimate the emotions of the students and determine the priority of the transmission contents based on the estimated emotions of the students. For example, when the students are excited, the transmission unit prioritizes transmitting important information. Also, when the students are relaxed, the transmission unit can transmit information in the normal order. Furthermore, when the students are concentrating, the transmission unit can prioritize transmitting the main points of the lesson. In this way, by determining the priority of the transmission contents based on the emotions of the students, important information can be transmitted with priority.

[0097] The process flow of the second embodiment will be briefly described below.

[0098] Step 1: The recording unit records the situation during lessons and breaks. The recording unit is equipped with, for example, a camera and a microphone, and records the teacher's explanations during the lesson and conversations with classmates. For example, the recording unit records the teacher's explanations during the lesson with a high-resolution camera and records conversations with classmates with a high-quality microphone. The recording unit can also monitor the progress of the lesson and the behavior of students in real time. Step 2: The analysis unit uses the generation AI to analyze the data recorded by the recording unit and extract important points. For example, the analysis unit extracts the main points of the lesson and important conversations with classmates. The generation AI uses a text generation AI (e.g., LLM) to extract important information from the video and audio data. The analysis unit can also use the generation AI to summarize the main points of the lesson and extract important keywords. For example, the generation AI analyzes the content of the teacher's remarks and extracts important keywords. Step 3: The transmission unit compiles and transmits the information extracted by the analysis unit. The transmission unit, for example, transmits the extracted information to the student by email. The transmission unit can compile the extracted information and transmit it in the form of an email. For example, the transmission unit transmits an email including the main points of the lesson and important conversations to the student. In this way, the robot proxy attendance system according to the embodiment allows the absent student to understand the lesson content and conversations with classmates, and smoothly participate in the lesson when returning. Some or all of the above-mentioned processing in the transmission unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmission unit can transmit the information using an AI model that receives the information extracted by the generation AI as input and transmits it in the form of an email.

[0099] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0100] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0101] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements including the recording unit, the analysis unit, and the transmission 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 recording unit records the state of a class or a break time using the camera 42 and the microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the recorded data using a generation AI and extracts important points. The transmission unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and transmits the extracted information to the students by email. A part or all of the recording unit, the analysis unit, and the transmission unit may be realized, for example, by the control unit 46A of the smart device 14. The correspondence between each unit and the device or the control unit is not limited to the above example, and various changes are possible.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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, the RAM 30, and the 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.

[0106] 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.

[0107] 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 the voice according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0109] 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 performed in a secure state.

[0110] Fig. 4 shows an example of 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.

[0111] 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.

[0112] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0115] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0116] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0117] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements including the recording unit, the analysis unit, and the transmission 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 recording unit records the state of a class or a break using the camera 42 and the microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the recorded data using a generation AI to extract important points. The transmission unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and transmits the extracted information to the student by email. A part or all of the recording unit, the analysis unit, and the transmission unit may be realized, for example, by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or the control unit is not limited to the above example, and various changes are possible.

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

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

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0126] 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.

[0127] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0129] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements including the recording unit, the analysis unit, and the transmission 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 recording unit records the state of the class or the break time using the camera 42 and the microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the recorded data using a generation AI and extracts important points. The transmission unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and transmits the extracted information to the students by email. A part or all of the recording unit, the analysis unit, and the transmission unit may be realized, for example, by the control unit 46A of the headset type terminal 314. The correspondence between each unit and the device or the control unit is not limited to the above example, and various changes are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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, the RAM 30, and the 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.

[0138] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] 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 the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0141] 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 performed in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0143] 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.

[0144] 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.

[0145] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0146] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0147] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0148] 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 a voice indicating a user input for 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.

[0149] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0150] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0151] Each of the multiple elements including the recording unit, the analysis unit, and the transmission unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the recording unit records the state of a class or a break time using the camera 42 and the microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the recorded data using a generation AI and extracts important points. The transmission unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and transmits the extracted information to the student by email. A part or all of the recording unit, the analysis unit, and the transmission unit may be realized, for example, by the control unit 46A of the robot 414. The correspondence between each unit and the device or the control unit is not limited to the above example, and various changes are possible.

[0152] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0153] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0155] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0157] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0159] 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.

[0160] 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 Universal Serial Bus (USB) 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.

[0161] In addition, 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 upon request from the data processing device 12.

[0162] 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.

[0163] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0164] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0165] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0166] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0168] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0170] (Appendix 1) A recording section for recording the situation during lessons or breaks; an analysis unit that analyzes the data recorded by the recording unit and extracts important information; A transmitting unit that collectively transmits the information extracted by the analyzing unit. A system characterized by: (Appendix 2) The system described in Appendix 1, wherein the recording unit is equipped with a camera or a microphone and records a teacher's explanation during class or a conversation with classmates. (Appendix 3) The analysis unit is Analyze video and audio data to extract key points from lessons and important conversations with classmates 2. The system of claim 1, (Appendix 4) The transmission unit is Collect the extracted information and email it to your students 2. The system of claim 1, (Appendix 5) The system described in Appendix 1 is characterized in that the recording unit is equipped with a sensor and monitors the situation during classes or breaks in real time. (Appendix 6) The transmission unit is It can be used to understand the content of business meetings. 2. The system of claim 1, (Appendix 7) The recording unit is Estimate the student's emotions and adjust the timing of video and audio recording based on the estimated student emotions 2. The system of claim 1, (Appendix 8) The recording unit is Adjust the resolution and sound quality of recordings based on the importance of the lesson content 2. The system of claim 1, (Appendix 9) The recording unit is Filter out ambient noise during class and record only the important audio 2. The system of claim 1, (Appendix 10) The recording unit is Estimate student emotions and prioritize recordings based on the estimated student emotions 2. The system of claim 1, (Appendix 11) The recording unit is Track teachers' movements during lessons and record key actions 2. The system of claim 1, (Appendix 12) The recording unit is Automatically captures the contents of the board during lessons and integrates them into the recording data 2. The system of claim 1, (Appendix 13) The analysis unit is Estimate the student's emotions and adjust the way the analysis results are presented based on the estimated student emotions. 2. The system of claim 1, (Appendix 14) The analysis unit is Adjust the level of analysis detail based on the importance of the lesson content 2. The system of claim 1, (Appendix 15) The analysis unit is Apply different analysis algorithms depending on the lesson category 2. The system of claim 1, (Appendix 16) The analysis unit is Estimate the student's emotions and prioritize the analysis results based on the estimated student emotions. 2. The system of claim 1, (Appendix 17) The analysis unit is Analyzing what teachers say in class and extracting important keywords 2. The system of claim 1, (Appendix 18) The analysis unit is Analyzing what classmates say during class and extracting relevant conversations 2. The system of claim 1, (Appendix 19) The transmission unit is Infer student emotions and adjust how content is expressed based on the estimated student emotions 2. The system of claim 1, (Appendix 20) The transmission unit is When sending, adjust the level of detail in your submission based on the importance of the lesson content 2. The system of claim 1, (Appendix 21) The transmission unit is When sending, apply different sending methods depending on the lesson category 2. The system of claim 1, (Appendix 22) The transmission unit is Estimate student emotions and prioritize sending content based on the estimated student emotions 2. The system of claim 1, (Appendix 23) The transmission unit is When sending, adjust the timing of sending based on the progress of the lesson 2. The system of claim 1, (Appendix 24) The transmission unit is Automatically attach related materials for classes when sending 2. The system of claim 1, [Explanation of symbols]

[0171] 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 recording section for recording the situation during lessons or breaks; an analysis unit that analyzes the data recorded by the recording unit and extracts important information; A transmitting unit that collectively transmits the information extracted by the analyzing unit. A system characterized by:

2. The recording unit is Equipped with a camera or microphone, it can record and record teachers' explanations during lessons or conversations with classmates.

2. The system of claim 1 .

3. The analysis unit is Analyze video and audio data to extract key points from lessons and important conversations with classmates 2. The system of claim 1 .

4. The transmission unit is Collect the extracted information and email it to your students 2. The system of claim 1 .

5. The recording unit is Equipped with sensors, it monitors the situation during lessons and breaks in real time.

2. The system of claim 1 .

6. The transmission unit is It can be used to understand the content of business meetings.

2. The system of claim 1 .

7. The recording unit is Estimate the student's emotions and adjust the timing of video and audio recording based on the estimated student emotions 2. The system of claim 1 .

8. The recording unit is Adjust the resolution and sound quality of recordings based on the importance of the lesson content 2. The system of claim 1 .

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