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 Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that students who are absent from school have difficulty in keeping up with the class content and conversations with classmates and feel stressed.

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

Means for Solving the Problems

[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 the class or break time. The analysis unit analyzes the data recorded by the recording unit and extracts important information. The transmission unit collectively transmits the information extracted by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment makes it easier for students who have been absent from school to catch up on lesson content and conversations with classmates. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The robot-assisted attendance system according to an embodiment of the present invention is a system that utilizes generative AI to solve the problem of students who are absent feeling stressed because they cannot keep up with the lesson content or conversations with classmates. This system involves placing a small robot on the desk of an absent student, recording what happens during class and breaks, and then sending the compiled data via email. For example, the system places a small robot on the desk of an absent student. This robot is equipped with a camera and microphone and records the teacher's explanations and conversations with classmates during class. Next, the system uses generative AI to analyze the recorded data and extract important points. For example, key points of the lesson or important conversations with classmates. Finally, the system compiles the information extracted by the generative AI and sends it to the student via email. This system allows absent students to understand the lesson content and conversations with classmates, enabling them to smoothly participate in class upon their return. It also reduces the workload of teachers. Furthermore, this system has potential applications in the business field. For example, it can be used by employees who were unable to attend a meeting to understand the meeting's content. In this way, the robot-assisted attendance system allows absent students to understand the lesson content and conversations with classmates, enabling them to smoothly participate in class upon their return.

[0029] The robotic attendance system according to this embodiment comprises a recording unit, an analysis unit, and a transmission unit. The recording unit records the activities of classes and break times. The recording unit is equipped with, for example, a camera and a microphone, and records the teacher's explanations and conversations with classmates during class. For example, the recording unit records the teacher's explanations during class 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 class and the behavior of students in real time. The analysis unit uses a generative 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 generative AI uses a text generation AI (e.g., LLM) to extract important information from the recorded video and audio data. The analysis unit can also use the generative AI to summarize the main points of the class and extract important keywords. For example, the generative AI analyzes the content of the teacher's statements and extracts important keywords. The transmission unit transmits the information extracted by the analysis unit in a compiled form. The transmitting unit, for example, sends the extracted information to students via email. The transmitting unit can compile the extracted information and send it in email format. For example, the transmitting unit can send students an email containing the main points of the lesson and important conversations. This allows the robotic attendance system according to the embodiment to enable absent students to understand the lesson content and conversations with classmates, and to participate in the lesson smoothly upon their return. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can transmit information using an AI model that takes information extracted by a generating AI as input and sends it in email format.

[0030] The recording unit records what happens during classes and breaks. For example, it is equipped with a camera and microphone to record teacher explanations and conversations with classmates during lessons. Specifically, the high-resolution camera in the recording unit clearly records the teacher's blackboard and slide content, capturing the progress of the lesson in detail. The camera also uses a wide-angle lens to record the entire classroom at once. The high-quality microphone clearly records not only the teacher's voice but also classmates' comments and ambient sounds in the classroom. This ensures that discussions and question-and-answer sessions during class are recorded without fail. Furthermore, the recording unit has the ability to stream data in real time and save it to a cloud server. This makes the recorded data immediately available to the analysis and transmission units. The recording unit can also use AI to monitor the progress of the lesson and student behavior in real time. For example, the AI ​​can analyze students' facial expressions and movements from camera footage to evaluate their concentration and comprehension levels. This allows teachers to adjust the pace of the lesson according to the students' understanding. By integrating these functions, the recording unit can meticulously document the overall picture of the lesson, providing information that will be useful when reviewing it later.

[0031] The analysis unit uses generative AI to analyze data recorded by the recording unit and extract key points. Specifically, the generative AI converts video and audio data into text and extracts the main points of the lesson and important conversations with classmates. For example, the generative AI analyzes the teacher's statements using natural language processing technology and extracts important keywords and phrases. This allows for efficient understanding of the lesson's main points. The generative AI can also convert audio data into text using speech recognition technology and extract important information from conversations with classmates. Furthermore, the generative AI can use summarization technology to concisely summarize the lesson content. For example, it can convert a long lesson recording into a summary of a few minutes, allowing students to grasp the main points of the lesson in a short time. The analysis unit can also use the generative AI to analyze the progress of the lesson and student reactions to evaluate the effectiveness of the lesson. For example, it can analyze students' facial expressions and comments to evaluate their understanding and interest in the lesson. This allows teachers to identify areas for improvement in their lessons and incorporate them into future lessons. By integrating these functions, the analysis unit can efficiently extract important information from recorded data and provide it to students and teachers.

[0032] The transmission unit aggregates and transmits the information extracted by the analysis unit. Specifically, the transmission unit sends the extracted information to students in email format. For example, the transmission unit can automatically generate emails containing key points of the lesson and important conversations and send them to students' email addresses. The transmission unit can transmit information using an AI model that takes information extracted by the generation AI as input and sends it in email format. This allows the transmission unit to efficiently aggregate the extracted information and provide it to students. The transmission unit can also transmit information by methods other than email. For example, it can notify students of key points of the lesson and important conversations through a dedicated application. This allows students to check the lesson content anytime, anywhere. Furthermore, the transmission unit can monitor the reception status of transmitted information and confirm whether students have received the information. This allows the transmission unit to guarantee reliable transmission of information. By integrating these functions, the transmission unit can efficiently aggregate the information extracted by the analysis unit and provide it to students. As a result, the robot attendance proxy system according to this embodiment allows absent students to understand the lesson content and conversations with classmates, and to smoothly participate in the lesson upon their return.

[0033] The recording unit is equipped with a camera or microphone and can record the teacher's explanations or conversations with classmates during class. The camera or microphone may include, but is not limited to, a high-resolution camera or a high-quality microphone. For example, the recording unit could record the teacher's explanations with a high-resolution camera and record conversations with classmates with a high-quality microphone. For instance, the recording unit could use a 4K resolution camera to clearly record the teacher's explanations. It could also use a high-sensitivity microphone to clearly record conversations with classmates. Furthermore, the recording unit can provide an optimal recording environment by adjusting the placement of the camera and microphone. For example, the recording unit could place a camera in the center of the classroom to record the overall scene. It could also place a microphone near the teacher to clearly record the teacher's voice. This allows students who are absent to catch up on the lesson content and conversations with classmates by recording the teacher's explanations and conversations with classmates during class. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not. For example, the recording unit can use AI to optimize the placement of cameras and microphones, thereby improving the quality of video and audio recording.

[0034] The analysis unit can analyze recorded video and audio data to extract key points of the lesson and important conversations with classmates. The analysis unit analyzes the recorded video and audio data, for example, using a generative AI. For example, to extract key points of the lesson, the analysis unit inputs the prompt "Please extract the key points of the lesson" to the generative AI. The generative AI analyzes the recorded video and audio data and extracts the key points of the lesson. The analysis unit also inputs the prompt "Please extract important conversations" to the generative AI to extract important conversations with classmates. The generative AI can analyze the recorded video and audio data and extract important conversations. For example, the generative AI analyzes the teacher's statements and extracts important keywords. The generative AI can also analyze the classmates' statements and extract highly relevant conversations. In this way, by analyzing the recorded video and audio data and extracting key points of the lesson and important conversations with classmates, students who were absent can grasp important information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can take information extracted by the generating AI as input and extract information using an AI model that extracts important information.

[0035] The sending unit can aggregate the extracted information and send it to students via email. The sending unit can, for example, send the extracted information in email format. For example, the sending unit can send students an email containing the key points of the lesson and important conversations. The sending unit can aggregate the extracted information and send it in email format. For example, the sending unit can send students an email containing the key points of the lesson and important conversations. The sending unit can aggregate the extracted information and send it in email format. For example, the sending unit can send students an email containing the key points of the lesson and important conversations. This allows absent students to catch up on the lesson content and conversations with classmates by sending the extracted information to students via email. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can send information using an AI model that takes information extracted by a generative AI as input and sends it in email format.

[0036] The recording unit is equipped with sensors that can monitor the situation during classes or breaks in real time. These sensors include, but are not limited to, motion detection sensors and sound sensors. For example, the recording unit can use motion detection sensors to monitor students' movements during class in real time. It can also use sound sensors to monitor audio during class in real time. For instance, the recording unit can install motion detection sensors in the classroom to monitor students' movements in real time. It can also install sound sensors in the classroom to monitor audio during class in real time. By equipping the unit with sensors and monitoring classes and breaks in real time, more accurate information can be recorded. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not. For example, the recording unit can analyze data acquired from sensors using AI and monitor it in real time.

[0037] The sending unit can be used to grasp the content of meetings in the business field. The sending unit can, for example, send meeting content in email format. For example, the sending unit can send an email to an employee containing the key points and important remarks of the meeting. The sending unit can summarize meeting content and send it in email format. For example, the sending unit can send an email to an employee containing the key points and important remarks of the meeting. The sending unit can summarize meeting content and send it in email format. For example, the sending unit can send an email to an employee containing the key points and important remarks of the meeting. This allows employees who were unable to attend a meeting to grasp the content of the meeting by using it to grasp the content of meetings in the business field. Some or all of the above processing in the sending unit may be performed using AI, for example, or not using AI. For example, the sending unit can send information using an AI model that takes information extracted by a generative AI as input and sends it in email format.

[0038] The recording unit can adjust the resolution and sound quality of video and audio recordings based on the importance of the lesson content. For example, the recording unit can record video and audio in high resolution and high sound quality for important lesson content. It can also record video and audio in standard resolution and standard sound quality for general lesson content. Furthermore, it can record video and audio in low resolution and low sound quality for repeated content or review. This allows important information to be recorded in high quality by adjusting the resolution and sound quality of video and audio recordings based on the importance of the lesson content. Some or all of the above processing in the recording unit may be performed using AI, for example, or not. For example, the recording unit can use AI to evaluate the importance of the lesson content and automatically adjust the resolution and sound quality of video and audio recordings.

[0039] The recording unit can filter out ambient noise during class and record only important audio. For example, the recording unit can use noise cancellation technology to filter out ambient noise during class. For example, the recording unit can prioritize recording the teacher's voice and filter out background noise. The recording unit can also record classmates' comments according to their importance and filter out unnecessary conversations. Furthermore, the recording unit can record important audio during class (e.g., sounds from experiments) and filter out other sounds. This allows for the recording of only important audio by filtering out ambient noise during class. Some or all of the above processing in the recording unit may be performed using AI, for example, or not. For example, the recording unit can optimize noise cancellation technology using AI to record only important audio.

[0040] The recording unit can track the teacher's movements during class and record important actions. For example, the recording unit uses motion detection technology to track the teacher's movements during class. For example, the recording unit can track and record the teacher's actions when writing on the blackboard. The recording unit can also track and record the teacher's actions when conducting experiments. Furthermore, the recording unit can track and record the teacher's actions when explaining to students. This ensures that important actions are recorded by tracking the teacher's movements during class. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can optimize motion detection technology using AI to track the teacher's movements.

[0041] The recording unit can automatically capture the content written on the blackboard during class and integrate it into the video recording data. For example, the recording unit can use character recognition technology to automatically capture the content written on the blackboard during class. For example, the recording unit can automatically capture the content written by the teacher on the blackboard and integrate it into the video recording data. The recording unit can also automatically capture the content written by the teacher on a whiteboard and integrate it into the video recording data. Furthermore, the recording unit can automatically capture the content displayed by the teacher on a projector and integrate it into the video recording data. This allows the content written on the blackboard during class to be automatically captured and integrated into the video recording data. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can optimize character recognition technology using AI to automatically capture the content written on the blackboard.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the lesson content. For example, the analysis unit can perform a detailed analysis for important lesson content. It can also perform a standard analysis for general lesson content. Furthermore, the analysis unit can perform a simplified analysis for repeated or review material. This allows for detailed analysis of important information by adjusting the level of detail of the analysis based on the importance of the lesson content. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to evaluate the importance of the lesson content 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. For example, the analysis unit can apply an analysis algorithm for experimental data in the case of a science lesson. It can also apply an analysis algorithm for mathematical formulas in the case of a mathematics lesson. Furthermore, it can apply a text analysis algorithm in the case of a Japanese language lesson. By applying different analysis algorithms depending on the category of the lesson, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to select and apply different analysis algorithms depending on the category of the lesson.

[0044] The analysis unit can analyze the content of the teacher's remarks during class and extract important keywords. For example, the analysis unit uses a generative AI to analyze the content of the teacher's remarks during class. For example, to extract important keywords from the teacher's remarks, the analysis unit inputs the prompt "Please extract important keywords" to the generative AI. The generative AI analyzes the content of the teacher's remarks and extracts important keywords. The analysis unit can also input the prompt "Please extract the main points" to the generative AI to extract the main points from the teacher's explanation. The generative AI analyzes the content of the teacher's explanation and extracts the main points. Furthermore, the analysis unit can also input the prompt "Please extract important keywords" to the generative AI to extract important keywords from the teacher's questions. The generative AI analyzes the content of the teacher's questions and extracts important keywords. In this way, important keywords can be extracted by analyzing the content of the teacher's remarks during class. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can take keywords extracted by the generating AI as input and extract information using an AI model that extracts important keywords.

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

[0046] The sending unit can adjust the level of detail in its transmissions based on the importance of the lesson content. For example, the sending unit can adjust the level of detail based on the importance of the lesson content. For example, if the lesson content is important, the sending unit can send an email containing detailed information. The sending unit can also send an email containing standard information if the lesson content is general. Furthermore, if the content is repetitive or a review, the sending unit can send an email containing simplified information. This allows important information to be transmitted in detail by adjusting the level of detail based on the importance of the lesson content. Some or all of the above processing in the sending unit may be performed using AI, for example, or not. For example, the sending unit can use AI to evaluate the importance of the lesson content and automatically adjust the level of detail in its transmissions.

[0047] The sending unit can apply different sending methods depending on the category of the lesson. For example, in the case of a science lesson, the sending unit can send an email containing experimental data. In the case of a mathematics lesson, the sending unit can also send an email containing mathematical formulas. Furthermore, in the case of a Japanese language lesson, the sending unit can send an email containing text. By applying different sending methods depending on the category of the lesson, more appropriate information can be provided. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can use AI to select and apply different sending methods depending on the category of the lesson.

[0048] The transmitting unit can adjust the timing of transmission based on the progress of the lesson. For example, the transmitting unit can transmit video recordings immediately after the lesson ends. It can also transmit video recordings when important points in the lesson have been covered. Furthermore, it can transmit video recordings during breaks in the lesson. By adjusting the timing of transmission based on the progress of the lesson, information can be transmitted at the appropriate time. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can use AI to evaluate the progress of the lesson and automatically adjust the timing of transmission.

[0049] The sending unit can automatically attach and send relevant course materials when sending. For example, the sending unit can automatically attach and send course slides. The sending unit can also automatically attach and send handouts. Furthermore, the sending unit can automatically attach and send course references. This allows all necessary information to be provided at once by automatically attaching and sending course-related materials. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can use AI to select course-related materials and automatically attach and send them.

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

[0051] The robotic attendance system can also be equipped with a translation unit. This unit can translate teacher explanations and classmates' conversations in real time, making them understandable to students who speak different languages. For example, it can translate a lesson conducted in English into Japanese and provide it to Japanese-speaking students. It can also translate conversations with classmates in real time, facilitating communication between students who speak different languages. Furthermore, the unit can summarize key points and important conversations in multiple languages ​​and provide them to students who speak different languages. This makes it easier for students who speak different languages ​​to understand the lesson content and conversations with classmates.

[0052] The recording unit can also be equipped with sensors to monitor students' health. For example, it can be fitted with sensors to measure heart rate and body temperature, allowing for real-time monitoring of students' health. The recording unit can issue alerts if heart rate is abnormally high or body temperature rises. The recording unit can also transmit health data to the analysis unit and adjust the lesson progress based on the student's health. For example, if a student is feeling unwell, the lesson can be slowed down or a break can be added. This makes it possible to conduct lessons while taking students' health into consideration.

[0053] The analytics unit can further analyze students' learning styles and provide individually optimized learning plans. For example, it can analyze students' past learning data to identify which learning methods are most effective. It can provide students who prefer visual learning with materials that heavily utilize visual aids, and students who prefer auditory learning with materials that emphasize audio explanations. Furthermore, the analytics unit can adjust learning plans according to students' progress to support effective learning. This allows for the provision of a learning experience optimized for each individual student.

[0054] The transmission unit can also be equipped with a function to report students' learning progress to parents. For example, the transmission unit can periodically send parents reports summarizing the key points of the lesson and the student's level of understanding. The transmission unit can also provide visual reports using graphs and charts to make it easier for parents to understand their child's learning situation. Furthermore, the transmission unit can receive feedback from parents and incorporate it into the lesson content and progress. This allows for collaboration with parents to support students' learning.

[0055] The recording unit can also be equipped with sensors to collect environmental data during lessons. For example, it can collect environmental data such as temperature, humidity, and illuminance in real time to identify factors that affect the progress of the lesson. The recording unit can also transmit the environmental data to the analysis unit, which can then make adjustments to provide an optimal learning environment. For example, if the classroom temperature is too high, it can issue instructions to adjust the air conditioning settings. It can also issue instructions to adjust the lighting if the illuminance is low. This helps maintain a comfortable learning environment and improves students' concentration.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The recording unit records what is happening in class and during breaks. The recording unit is equipped with, for example, a camera and microphone to record the teacher's explanations and conversations with classmates during class. For example, the recording unit can record the teacher's explanations with a high-resolution camera and record conversations with classmates with a high-quality microphone. The recording unit can also monitor the progress of the lesson and student behavior in real time. Step 2: The analysis unit uses a generation AI to analyze the data recorded by the recording unit and extract key 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 teacher's statements and extracts important keywords. Step 3: The transmission unit sends the information extracted by the analysis unit in a single package. The transmission unit sends the extracted information to students via email, for example. The transmission unit can send the extracted information in a single package via email. For example, the transmission unit can send students an email containing the main points of the lesson and important conversations. This allows the robot attendance proxy system according to the embodiment to enable absent students to understand the lesson content and conversations with classmates, and to participate in the lesson smoothly upon their return. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can send information using an AI model that takes information extracted by a generating AI as input and sends it via email.

[0058] (Example of form 2) The robot-assisted attendance system according to an embodiment of the present invention is a system that utilizes generative AI to solve the problem of students who are absent feeling stressed because they cannot keep up with the lesson content or conversations with classmates. This system involves placing a small robot on the desk of an absent student, recording what happens during class and breaks, and then sending the compiled data via email. For example, the system places a small robot on the desk of an absent student. This robot is equipped with a camera and microphone and records the teacher's explanations and conversations with classmates during class. Next, the system uses generative AI to analyze the recorded data and extract important points. For example, key points of the lesson or important conversations with classmates. Finally, the system compiles the information extracted by the generative AI and sends it to the student via email. This system allows absent students to understand the lesson content and conversations with classmates, enabling them to smoothly participate in class upon their return. It also reduces the workload of teachers. Furthermore, this system has potential applications in the business field. For example, it can be used by employees who were unable to attend a meeting to understand the meeting's content. In this way, the robot-assisted attendance system allows absent students to understand the lesson content and conversations with classmates, enabling them to smoothly participate in class upon their return.

[0059] The robotic attendance system according to this embodiment comprises a recording unit, an analysis unit, and a transmission unit. The recording unit records the activities of classes and break times. The recording unit is equipped with, for example, a camera and a microphone, and records the teacher's explanations and conversations with classmates during class. For example, the recording unit records the teacher's explanations during class 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 class and the behavior of students in real time. The analysis unit uses a generative 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 generative AI uses a text generation AI (e.g., LLM) to extract important information from the recorded video and audio data. The analysis unit can also use the generative AI to summarize the main points of the class and extract important keywords. For example, the generative AI analyzes the content of the teacher's statements and extracts important keywords. The transmission unit transmits the information extracted by the analysis unit in a compiled form. The transmitting unit, for example, sends the extracted information to students via email. The transmitting unit can compile the extracted information and send it in email format. For example, the transmitting unit can send students an email containing the main points of the lesson and important conversations. This allows the robotic attendance system according to the embodiment to enable absent students to understand the lesson content and conversations with classmates, and to participate in the lesson smoothly upon their return. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can transmit information using an AI model that takes information extracted by a generating AI as input and sends it in email format.

[0060] The recording unit records what happens during classes and breaks. For example, it is equipped with a camera and microphone to record teacher explanations and conversations with classmates during lessons. Specifically, the high-resolution camera in the recording unit clearly records the teacher's blackboard and slide content, capturing the progress of the lesson in detail. The camera also uses a wide-angle lens to record the entire classroom at once. The high-quality microphone clearly records not only the teacher's voice but also classmates' comments and ambient sounds in the classroom. This ensures that discussions and question-and-answer sessions during class are recorded without fail. Furthermore, the recording unit has the ability to stream data in real time and save it to a cloud server. This makes the recorded data immediately available to the analysis and transmission units. The recording unit can also use AI to monitor the progress of the lesson and student behavior in real time. For example, the AI ​​can analyze students' facial expressions and movements from camera footage to evaluate their concentration and comprehension levels. This allows teachers to adjust the pace of the lesson according to the students' understanding. By integrating these functions, the recording unit can meticulously document the overall picture of the lesson, providing information that will be useful when reviewing it later.

[0061] The analysis unit uses generative AI to analyze data recorded by the recording unit and extract key points. Specifically, the generative AI converts video and audio data into text and extracts the main points of the lesson and important conversations with classmates. For example, the generative AI analyzes the teacher's statements using natural language processing technology and extracts important keywords and phrases. This allows for efficient understanding of the lesson's main points. The generative AI can also convert audio data into text using speech recognition technology and extract important information from conversations with classmates. Furthermore, the generative AI can use summarization technology to concisely summarize the lesson content. For example, it can convert a long lesson recording into a summary of a few minutes, allowing students to grasp the main points of the lesson in a short time. The analysis unit can also use the generative AI to analyze the progress of the lesson and student reactions to evaluate the effectiveness of the lesson. For example, it can analyze students' facial expressions and comments to evaluate their understanding and interest in the lesson. This allows teachers to identify areas for improvement in their lessons and incorporate them into future lessons. By integrating these functions, the analysis unit can efficiently extract important information from recorded data and provide it to students and teachers.

[0062] The transmission unit aggregates and transmits the information extracted by the analysis unit. Specifically, the transmission unit sends the extracted information to students in email format. For example, the transmission unit can automatically generate emails containing key points of the lesson and important conversations and send them to students' email addresses. The transmission unit can transmit information using an AI model that takes information extracted by the generation AI as input and sends it in email format. This allows the transmission unit to efficiently aggregate the extracted information and provide it to students. The transmission unit can also transmit information by methods other than email. For example, it can notify students of key points of the lesson and important conversations through a dedicated application. This allows students to check the lesson content anytime, anywhere. Furthermore, the transmission unit can monitor the reception status of transmitted information and confirm whether students have received the information. This allows the transmission unit to guarantee reliable transmission of information. By integrating these functions, the transmission unit can efficiently aggregate the information extracted by the analysis unit and provide it to students. As a result, the robot attendance proxy system according to this embodiment allows absent students to understand the lesson content and conversations with classmates, and to smoothly participate in the lesson upon their return.

[0063] The recording unit is equipped with a camera or microphone and can record the teacher's explanations or conversations with classmates during class. The camera or microphone may include, but is not limited to, a high-resolution camera or a high-quality microphone. For example, the recording unit could record the teacher's explanations with a high-resolution camera and record conversations with classmates with a high-quality microphone. For instance, the recording unit could use a 4K resolution camera to clearly record the teacher's explanations. It could also use a high-sensitivity microphone to clearly record conversations with classmates. Furthermore, the recording unit can provide an optimal recording environment by adjusting the placement of the camera and microphone. For example, the recording unit could place a camera in the center of the classroom to record the overall scene. It could also place a microphone near the teacher to clearly record the teacher's voice. This allows students who are absent to catch up on the lesson content and conversations with classmates by recording the teacher's explanations and conversations with classmates during class. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not. For example, the recording unit can use AI to optimize the placement of cameras and microphones, thereby improving the quality of video and audio recording.

[0064] The analysis unit can analyze recorded video and audio data to extract key points of the lesson and important conversations with classmates. The analysis unit analyzes the recorded video and audio data, for example, using a generative AI. For example, to extract key points of the lesson, the analysis unit inputs the prompt "Please extract the key points of the lesson" to the generative AI. The generative AI analyzes the recorded video and audio data and extracts the key points of the lesson. The analysis unit also inputs the prompt "Please extract important conversations" to the generative AI to extract important conversations with classmates. The generative AI can analyze the recorded video and audio data and extract important conversations. For example, the generative AI analyzes the teacher's statements and extracts important keywords. The generative AI can also analyze the classmates' statements and extract highly relevant conversations. In this way, by analyzing the recorded video and audio data and extracting key points of the lesson and important conversations with classmates, students who were absent can grasp important information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can take information extracted by the generating AI as input and extract information using an AI model that extracts important information.

[0065] The sending unit can aggregate the extracted information and send it to students via email. The sending unit can, for example, send the extracted information in email format. For example, the sending unit can send students an email containing the key points of the lesson and important conversations. The sending unit can aggregate the extracted information and send it in email format. For example, the sending unit can send students an email containing the key points of the lesson and important conversations. The sending unit can aggregate the extracted information and send it in email format. For example, the sending unit can send students an email containing the key points of the lesson and important conversations. This allows absent students to catch up on the lesson content and conversations with classmates by sending the extracted information to students via email. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can send information using an AI model that takes information extracted by a generative AI as input and sends it in email format.

[0066] The recording unit is equipped with sensors that can monitor the situation during classes or breaks in real time. These sensors include, but are not limited to, motion detection sensors and sound sensors. For example, the recording unit can use motion detection sensors to monitor students' movements during class in real time. It can also use sound sensors to monitor audio during class in real time. For instance, the recording unit can install motion detection sensors in the classroom to monitor students' movements in real time. It can also install sound sensors in the classroom to monitor audio during class in real time. By equipping the unit with sensors and monitoring classes and breaks in real time, more accurate information can be recorded. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not. For example, the recording unit can analyze data acquired from sensors using AI and monitor it in real time.

[0067] The sending unit can be used to grasp the content of meetings in the business field. The sending unit can, for example, send meeting content in email format. For example, the sending unit can send an email to an employee containing the key points and important remarks of the meeting. The sending unit can summarize meeting content and send it in email format. For example, the sending unit can send an email to an employee containing the key points and important remarks of the meeting. The sending unit can summarize meeting content and send it in email format. For example, the sending unit can send an email to an employee containing the key points and important remarks of the meeting. This allows employees who were unable to attend a meeting to grasp the content of the meeting by using it to grasp the content of meetings in the business field. Some or all of the above processing in the sending unit may be performed using AI, for example, or not using AI. For example, the sending unit can send information using an AI model that takes information extracted by a generative AI as input and sends it in email format.

[0068] The recording unit can estimate students' emotions and adjust the timing of video and audio recording based on the estimated emotions. For example, the recording unit may use facial recognition technology to estimate students' emotions. For example, the recording unit may capture students' facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, if a student is excited, the recording unit may increase the frequency of video and audio recording to avoid missing important moments. Conversely, if a student is relaxed, the recording unit may return to the normal frequency of video and audio recording. Furthermore, if a student is focused, the recording unit may adjust the timing of video and audio recording to avoid missing key points in the lesson. This ensures that important moments are recorded without being missed by adjusting the timing of video and audio recording based on students' emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input image data of students captured by a camera into a generating AI, which can then perform the estimation of the students' emotions.

[0069] The recording unit can adjust the resolution and sound quality of video and audio recordings based on the importance of the lesson content. For example, the recording unit can record video and audio in high resolution and high sound quality for important lesson content. It can also record video and audio in standard resolution and standard sound quality for general lesson content. Furthermore, it can record video and audio in low resolution and low sound quality for repeated content or review. This allows important information to be recorded in high quality by adjusting the resolution and sound quality of video and audio recordings based on the importance of the lesson content. Some or all of the above processing in the recording unit may be performed using AI, for example, or not. For example, the recording unit can use AI to evaluate the importance of the lesson content and automatically adjust the resolution and sound quality of video and audio recordings.

[0070] The recording unit can filter out ambient noise during class and record only important audio. For example, the recording unit can use noise cancellation technology to filter out ambient noise during class. For example, the recording unit can prioritize recording the teacher's voice and filter out background noise. The recording unit can also record classmates' comments according to their importance and filter out unnecessary conversations. Furthermore, the recording unit can record important audio during class (e.g., sounds from experiments) and filter out other sounds. This allows for the recording of only important audio by filtering out ambient noise during class. Some or all of the above processing in the recording unit may be performed using AI, for example, or not. For example, the recording unit can optimize noise cancellation technology using AI to record only important audio.

[0071] The recording unit can estimate students' emotions and determine recording priorities based on the estimated emotions. For example, the recording unit may use facial recognition technology to estimate students' emotions. For example, the recording unit may capture students' facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, if a student is excited, the recording unit may prioritize recording important moments. If a student is relaxed, the recording unit may prioritize recording regular lesson content. Furthermore, if a student is focused, the recording unit may prioritize recording key points of the lesson. This allows for prioritizing recording of important moments by determining recording priorities based on students' emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input image data of students captured by a camera into a generating AI, which can then perform the estimation of the students' emotions.

[0072] The recording unit can track the teacher's movements during class and record important actions. For example, the recording unit uses motion detection technology to track the teacher's movements during class. For example, the recording unit can track and record the teacher's actions when writing on the blackboard. The recording unit can also track and record the teacher's actions when conducting experiments. Furthermore, the recording unit can track and record the teacher's actions when explaining to students. This ensures that important actions are recorded by tracking the teacher's movements during class. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can optimize motion detection technology using AI to track the teacher's movements.

[0073] The recording unit can automatically capture the content written on the blackboard during class and integrate it into the video recording data. For example, the recording unit can use character recognition technology to automatically capture the content written on the blackboard during class. For example, the recording unit can automatically capture the content written by the teacher on the blackboard and integrate it into the video recording data. The recording unit can also automatically capture the content written by the teacher on a whiteboard and integrate it into the video recording data. Furthermore, the recording unit can automatically capture the content displayed by the teacher on a projector and integrate it into the video recording data. This allows the content written on the blackboard during class to be automatically captured and integrated into the video recording data. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can optimize character recognition technology using AI to automatically capture the content written on the blackboard.

[0074] The analysis unit can estimate a student's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, the analysis unit may use facial recognition technology to estimate a student's emotions. For example, the analysis unit may capture a student's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, if the student is relaxed, the analysis unit may provide detailed analysis results. If the student is in a hurry, the analysis unit may also provide concise analysis results that get straight to the point. Furthermore, if the student is excited, the analysis unit may provide analysis results with visually stimulating effects. By adjusting the presentation of the analysis results based on the student's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data of students captured by a camera into a generating AI, which can then perform the estimation of the students' emotions.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the lesson content. For example, the analysis unit can perform a detailed analysis for important lesson content. It can also perform a standard analysis for general lesson content. Furthermore, the analysis unit can perform a simplified analysis for repeated or review material. This allows for detailed analysis of important information by adjusting the level of detail of the analysis based on the importance of the lesson content. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to evaluate the importance of the lesson content 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. For example, the analysis unit can apply an analysis algorithm for experimental data in the case of a science lesson. It can also apply an analysis algorithm for mathematical formulas in the case of a mathematics lesson. Furthermore, it can apply a text analysis algorithm in the case of a Japanese language lesson. By applying different analysis algorithms depending on the category of the lesson, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to select and apply different analysis algorithms depending on the category of the lesson.

[0077] The analysis unit can estimate students' emotions and determine the priority of analysis results based on the estimated emotions. For example, the analysis unit may use facial recognition technology to estimate students' emotions. For example, the analysis unit may capture students' facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, if a student is excited, the analysis unit may prioritize analyzing important points. The analysis unit may also analyze in the normal order if the student is relaxed. Furthermore, if a student is focused, the analysis unit may prioritize analyzing the key points of the lesson. This allows for prioritizing the analysis of important information by determining the priority of analysis results based on students' emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data of students captured by a camera into a generating AI, which can then perform the estimation of the students' emotions.

[0078] The analysis unit can analyze the content of the teacher's remarks during class and extract important keywords. For example, the analysis unit uses a generative AI to analyze the content of the teacher's remarks during class. For example, to extract important keywords from the teacher's remarks, the analysis unit inputs the prompt "Please extract important keywords" to the generative AI. The generative AI analyzes the content of the teacher's remarks and extracts important keywords. The analysis unit can also input the prompt "Please extract the main points" to the generative AI to extract the main points from the teacher's explanation. The generative AI analyzes the content of the teacher's explanation and extracts the main points. Furthermore, the analysis unit can also input the prompt "Please extract important keywords" to the generative AI to extract important keywords from the teacher's questions. The generative AI analyzes the content of the teacher's questions and extracts important keywords. In this way, important keywords can be extracted by analyzing the content of the teacher's remarks during class. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can take keywords extracted by the generating AI as input and extract information using an AI model that extracts important keywords.

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

[0080] The sending unit can estimate the student's emotions and adjust the way the message is presented based on the estimated emotions. For example, the sending unit may use facial recognition technology to estimate the student's emotions. For example, the sending unit may capture the student's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, if the student is relaxed, the sending unit may send an email containing detailed information. If the student is in a hurry, the sending unit may also send a concise email that gets straight to the point. Furthermore, if the student is excited, the sending unit may send an email with visually stimulating effects. This allows for the provision of more appropriate information by adjusting the way the message is presented based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the transmission unit can input image data of students captured by a camera into a generating AI, which can then perform the estimation of the students' emotions.

[0081] The sending unit can adjust the level of detail in its transmissions based on the importance of the lesson content. For example, the sending unit can adjust the level of detail based on the importance of the lesson content. For example, if the lesson content is important, the sending unit can send an email containing detailed information. The sending unit can also send an email containing standard information if the lesson content is general. Furthermore, if the content is repetitive or a review, the sending unit can send an email containing simplified information. This allows important information to be transmitted in detail by adjusting the level of detail based on the importance of the lesson content. Some or all of the above processing in the sending unit may be performed using AI, for example, or not. For example, the sending unit can use AI to evaluate the importance of the lesson content and automatically adjust the level of detail in its transmissions.

[0082] The sending unit can apply different sending methods depending on the category of the lesson. For example, in the case of a science lesson, the sending unit can send an email containing experimental data. In the case of a mathematics lesson, the sending unit can also send an email containing mathematical formulas. Furthermore, in the case of a Japanese language lesson, the sending unit can send an email containing text. By applying different sending methods depending on the category of the lesson, more appropriate information can be provided. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can use AI to select and apply different sending methods depending on the category of the lesson.

[0083] The transmitting unit can estimate the student's emotions and determine the priority of the transmitted content based on the estimated emotions. For example, the transmitting unit may use facial recognition technology to estimate the student's emotions. For example, the transmitting unit may capture the student's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, if the student is excited, the transmitting unit may prioritize transmitting important information. The transmitting unit may also transmit information in the normal order if the student is relaxed. Furthermore, if the student is focused, the transmitting unit may prioritize transmitting the key points of the lesson. In this way, by determining the priority of the transmitted content based on the student's emotions, important information can be transmitted with priority. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmission unit can input image data of students captured by a camera into a generating AI, which can then perform the estimation of the students' emotions.

[0084] The transmitting unit can adjust the timing of transmission based on the progress of the lesson. For example, the transmitting unit can transmit video recordings immediately after the lesson ends. It can also transmit video recordings when important points in the lesson have been covered. Furthermore, it can transmit video recordings during breaks in the lesson. By adjusting the timing of transmission based on the progress of the lesson, information can be transmitted at the appropriate time. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can use AI to evaluate the progress of the lesson and automatically adjust the timing of transmission.

[0085] The sending unit can automatically attach and send relevant course materials when sending. For example, the sending unit can automatically attach and send course slides. The sending unit can also automatically attach and send handouts. Furthermore, the sending unit can automatically attach and send course references. This allows all necessary information to be provided at once by automatically attaching and sending course-related materials. Some or all of the above processing in the sending unit may be performed using AI, for example, or without AI. For example, the sending unit can use AI to select course-related materials and automatically attach and send them.

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

[0087] The robotic attendance system can also be equipped with a translation unit. This unit can translate teacher explanations and classmates' conversations in real time, making them understandable to students who speak different languages. For example, it can translate a lesson conducted in English into Japanese and provide it to Japanese-speaking students. It can also translate conversations with classmates in real time, facilitating communication between students who speak different languages. Furthermore, the unit can summarize key points and important conversations in multiple languages ​​and provide them to students who speak different languages. This makes it easier for students who speak different languages ​​to understand the lesson content and conversations with classmates.

[0088] The recording unit can also be equipped with sensors to monitor students' health. For example, it can be fitted with sensors to measure heart rate and body temperature, allowing for real-time monitoring of students' health. The recording unit can issue alerts if heart rate is abnormally high or body temperature rises. The recording unit can also transmit health data to the analysis unit and adjust the lesson progress based on the student's health. For example, if a student is feeling unwell, the lesson can be slowed down or a break can be added. This makes it possible to conduct lessons while taking students' health into consideration.

[0089] The analytics unit can further analyze students' learning styles and provide individually optimized learning plans. For example, it can analyze students' past learning data to identify which learning methods are most effective. It can provide students who prefer visual learning with materials that heavily utilize visual aids, and students who prefer auditory learning with materials that emphasize audio explanations. Furthermore, the analytics unit can adjust learning plans according to students' progress to support effective learning. This allows for the provision of a learning experience optimized for each individual student.

[0090] The transmission unit can also be equipped with a function to report students' learning progress to parents. For example, the transmission unit can periodically send parents reports summarizing the key points of the lesson and the student's level of understanding. The transmission unit can also provide visual reports using graphs and charts to make it easier for parents to understand their child's learning situation. Furthermore, the transmission unit can receive feedback from parents and incorporate it into the lesson content and progress. This allows for collaboration with parents to support students' learning.

[0091] The recording unit can also be equipped with sensors to collect environmental data during lessons. For example, it can collect environmental data such as temperature, humidity, and illuminance in real time to identify factors that affect the progress of the lesson. The recording unit can also transmit the environmental data to the analysis unit, which can then make adjustments to provide an optimal learning environment. For example, if the classroom temperature is too high, it can issue instructions to adjust the air conditioning settings. It can also issue instructions to adjust the lighting if the illuminance is low. This helps maintain a comfortable learning environment and improves students' concentration.

[0092] The recording unit can estimate students' emotions and adjust the lesson content based on those estimates. For example, if students are excited, the recording unit can speed up the lesson to keep them engaged. If students are tired, the recording unit can slow down the lesson and include breaks. Furthermore, if students are focused, the recording unit can introduce more challenging content. In this way, adjusting the lesson content based on students' emotions can support more effective learning.

[0093] The analysis unit can estimate students' emotions and provide feedback based on those estimates. For example, if a student is feeling anxious, the analysis unit can provide encouraging messages to boost their motivation. If a student is confident, the analysis unit can provide feedback that encourages further challenges. Furthermore, if a student is confused, the analysis unit can provide additional explanations or supplementary materials. This allows for improved learning outcomes by providing appropriate feedback based on students' emotions.

[0094] The transmitter can estimate the student's emotions and adjust the tone of the message based on those emotions. For example, if a student is feeling down, the transmitter can send information in an encouraging tone to boost their motivation. If a student is excited, the transmitter can send information in a calm tone to help them maintain focus. Furthermore, if a student is relaxed, the transmitter can send information in a friendly tone. This allows for more effective communication by adjusting the tone of the message based on the student's emotions.

[0095] The analysis unit can estimate students' emotions and adjust learning progress based on those estimates. For example, if a student is stressed, the analysis unit can slow down learning progress and provide time to relax. Conversely, if a student is excited, the analysis unit can speed up learning progress to keep them engaged. Furthermore, if a student is focused, the analysis unit can provide more challenging tasks. In this way, adjusting learning progress based on students' emotions can support more effective learning.

[0096] The transmitting unit can estimate the student's emotions and prioritize the content to be transmitted based on those emotions. For example, if the student is excited, the transmitting unit will prioritize sending important information. If the student is relaxed, the transmitting unit can also send information in the usual order. Furthermore, if the student is focused, the transmitting unit can prioritize sending the key points of the lesson. In this way, by prioritizing the content to be transmitted based on the student's emotions, important information can be sent first.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The recording unit records what is happening in class and during breaks. The recording unit is equipped with, for example, a camera and microphone to record the teacher's explanations and conversations with classmates during class. For example, the recording unit can record the teacher's explanations with a high-resolution camera and record conversations with classmates with a high-quality microphone. The recording unit can also monitor the progress of the lesson and student behavior in real time. Step 2: The analysis unit uses a generation AI to analyze the data recorded by the recording unit and extract key 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 teacher's statements and extracts important keywords. Step 3: The transmission unit sends the information extracted by the analysis unit in a single package. The transmission unit sends the extracted information to students via email, for example. The transmission unit can send the extracted information in a single package via email. For example, the transmission unit can send students an email containing the main points of the lesson and important conversations. This allows the robot attendance proxy system according to the embodiment to enable absent students to understand the lesson content and conversations with classmates, and to participate in the lesson smoothly upon their return. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can send information using an AI model that takes information extracted by a generating AI as input and sends it via 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 audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the recording unit, analysis unit, and transmission unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recording unit records the class and break times using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the recorded data using generating AI and extracts important points. The transmission unit is implemented in the specific processing unit 290 of the data processing unit 12, which sends the extracted information to students via email. Some or all of the recording unit, analysis unit, and transmission unit may be implemented in the control unit 46A of the smart device 14, for example. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the recording unit, analysis unit, and transmission unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the recording unit uses the camera 42 and microphone 238 of the smart glasses 214 to record what is happening in class or during break time. The analysis unit is implemented in, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the recorded data using generated AI and extracts important points. The transmission unit is implemented in, for example, the identification processing unit 290 of the data processing unit 12, which sends the extracted information to students via email. Some or all of the recording unit, analysis unit, and transmission unit may be implemented in, for example, the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

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

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

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

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

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

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the recording unit, analysis unit, and transmission unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recording unit uses the camera 42 and microphone 238 of the headset terminal 314 to record what is happening during classes and breaks. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the recorded data using generated AI and extract important points. The transmission unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to send the extracted information to students via email. Some or all of the recording unit, analysis unit, and transmission unit may be implemented in the control unit 46A of the headset terminal 314, for example. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the recording unit, analysis unit, and transmission unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the recording unit records the activities of classes and break times using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, which analyzes the recorded data using a generated AI and extracts important points. The transmission unit is implemented in the specific processing unit 290 of the data processing unit 12, which sends the extracted information to students via email. Some or all of the recording unit, analysis unit, and transmission unit may be implemented in the control unit 46A of the robot 414, for example. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0153] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it 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 user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A recording department that records what happens during classes or breaks, An analysis unit analyzes the data recorded by the recording unit and extracts important information, The system includes a transmission unit that transmits the information extracted by the analysis unit in a consolidated manner. A system characterized by the following features. (Note 2) The system according to Appendix 1, characterized in that the recording unit is equipped with a camera or microphone and records the teacher's explanation or conversations with classmates during class. (Note 3) The aforementioned analysis unit, The system analyzes recorded video and audio data to extract key points from lessons and important conversations with classmates. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned transmitting unit The extracted information is compiled and sent to the students via email. The system described in Appendix 1, characterized by the features described herein. (Note 5) The system according to Appendix 1, characterized in that the recording unit is equipped with a sensor and monitors the situation during classes or breaks in real time. (Note 6) The aforementioned transmitting unit It can be used to understand the content of business meetings. The system described in Appendix 1, characterized by the features described herein. (Note 7) The recording unit is, The system estimates the students' emotions and adjusts the timing of video and audio recordings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The recording unit is, The resolution and sound quality of video and audio recordings are adjusted based on the importance of the lesson content. The system described in Appendix 1, characterized by the features described herein. (Note 9) The recording unit is, Filter out ambient noise during class and record only the important audio. The system described in Appendix 1, characterized by the features described herein. (Note 10) The recording unit is, The system estimates students' emotions and prioritizes video and audio recordings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The recording unit is, Track the teacher's movements during class and record important actions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The recording unit is, Automatically captures the content of the whiteboard during class and integrates it into the video recording. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the students' emotions and adjusts the way the analysis results are presented based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Adjust the level of detail in the analysis based on the importance of the lesson content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Apply different analysis algorithms depending on the category of the course. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates students' emotions and prioritizes the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Analyze what the teacher says during class and extract important keywords. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Analyze what classmates say during class and extract highly relevant conversations. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned transmitting unit The system estimates the student's emotions and adjusts the way the message is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned transmitting unit When sending, adjust the level of detail based on the importance of the lesson content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned transmitting unit When sending, apply different sending methods depending on the course category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned transmitting unit The system estimates students' emotions and prioritizes the content to be sent based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned transmitting unit When sending, the timing of the transmission will be adjusted based on the progress of the lesson. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned transmitting unit When sending an email, relevant course materials will be automatically attached. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A device comprising a processor and a memory for storing a program executed by the processor, The processor executes the program, The recording department is responsible for documenting what happens during break time, The data recorded by the recording unit, which includes video and audio recordings showing what happened during the break time, is analyzed using a generating AI, and the analysis unit extracts conversations that are highly relevant from the statements of classmates. A transmission unit that estimates the emotions of students, and adjusts the method of expressing the information extracted by the analysis unit based on the estimated emotions of the students, and transmits it. Functions A system characterized by the following features.

2. The recording unit is, Equipped with a camera or microphone, it records conversations with classmates. The system according to feature 1.

3. The aforementioned transmitting unit The extracted information is compiled and sent to the students via email. The system according to feature 1.

4. The recording unit is, It is equipped with sensors to monitor what is happening during break time in real time. The system according to feature 1.

5. The aforementioned transmitting unit It can be used to understand the content of business meetings. The system according to feature 1.

6. The recording unit is, Adjust the timing of video and audio recordings based on estimated student emotions. The system according to feature 1.

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