System

Abstract

An object of the system according to the embodiment is to provide appropriate feedback to a new teacher and improve the quality of a class.SOLUTION: A system includes a monitoring unit, an analysis unit, and a provision unit. The monitoring unit monitors a state of a class. The analysis unit analyzes the data collected by the monitoring unit. The providing unit provides feedback based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not provide sufficient feedback and coaching to new teachers on their lessons, leaving room for improvement.

[0005] The system according to the embodiment aims to provide appropriate feedback to newly appointed teachers and improve the quality of their lessons. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, an analysis unit, and a providing unit. The monitoring unit monitors the state of the class. The analysis unit analyzes the data collected by the monitoring unit. The providing unit provides feedback based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate feedback to new teachers and improve the quality of their lessons. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

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

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A coaching follow-up system according to an embodiment of the present invention is a system that uses a generative AI to build a coaching and follow-up system for newly appointed teachers. The coaching follow-up system monitors the state of a class, and the generative AI analyzes and provides feedback to improve the quality of the teacher's class. The coaching follow-up system also analyzes the classroom atmosphere, grasps students' emotions and level of concentration, and proposes feedback and improvement measures to the teacher. For example, the coaching follow-up system monitors the state of a class. For example, it collects the state of the class in real time using cameras and microphones in the classroom. Next, the coaching follow-up system analyzes the teacher's speaking style and students' reactions based on the collected data. For example, the generative AI analyzes the teacher's speaking style and students' reactions and provides specific feedback. Next, the coaching follow-up system provides specific feedback to the teacher based on the analysis results. For example, the generative AI suggests areas for improvement to the teacher. Furthermore, the coaching follow-up system analyzes students' facial expressions, tone of voice, sitting position, etc. to grasp students' emotions and level of concentration. For example, the generative AI analyzes students' emotions and level of concentration and proposes appropriate feedback and improvement measures to the teacher. This allows the coaching and follow-up system to provide real-time feedback to even newly hired teachers in classroom situations where appropriate responses are required, eliminating subjective judgment. This allows the coaching and follow-up system to support teacher growth and improve the quality of education. For example, even if a teacher misses a student's response during class, the generative AI can detect that response and provide appropriate feedback to the teacher, thereby improving the quality of the lesson. Furthermore, the generative AI can accumulate data on the teacher's lesson progress and student responses and propose long-term improvement measures. For example, it can provide specific advice on improving the teacher's speaking style and how the lesson is conducted based on past lesson data. This allows teachers to continuously improve the quality of their lessons.

[0029] A coaching follow-up system according to an embodiment includes a monitoring unit, an analysis unit, and a providing unit. The monitoring unit monitors the state of a class. Examples of the state of a class include, but are not limited to, the teacher's speaking style, students' reactions, and the classroom environment. The monitoring unit collects information about the state of a class in real time using, for example, a camera or microphone in the classroom. The monitoring unit can also monitor environmental sounds, temperature, and lighting conditions in the classroom. For example, if the temperature in the classroom is too high, the monitoring unit can suggest lowering the air conditioner setting. The analysis unit analyzes the teacher's speaking style and students' reactions based on the collected data. Examples of the analysis include, but are not limited to, the type of data, analysis algorithm, and purpose of the analysis. For example, the analysis unit analyzes the speed and tone of the teacher's speaking style to suggest effective communication methods. The analysis unit can also analyze students' facial expressions, tone of voice, sitting posture, and the like to understand their emotions and level of concentration. For example, the analysis unit analyzes students' facial expressions and calculates an emotion score. The providing unit provides specific feedback to the teacher based on the analysis results. The feedback includes, for example, indications of areas for improvement and specific advice, but is not limited to such examples. For example, the providing unit suggests specific areas for improvement to the teacher based on the analysis results. The providing unit can also compare the content of the feedback with the teacher's past lesson data and suggest long-term improvement measures. For example, the providing unit provides specific advice to improve the teacher's speaking style and how the lesson is conducted based on the past lesson data. This allows the coaching follow-up system according to the embodiment to improve the quality of the teacher's lessons. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the analysis results into a generation AI, which then generates feedback.

[0030] The monitoring unit can collect information about the classroom through cameras or microphones in the classroom. Examples of cameras or microphones in the classroom include, but are not limited to, fixed cameras, PTZ cameras, and directional microphones. The monitoring unit can collect information about the entire classroom using, for example, fixed cameras. The monitoring unit can also track the movements of specific students or teachers using PTZ cameras. For example, the PTZ camera can adjust the camera's direction and zoom using a remote control. The monitoring unit can also collect specific sounds using directional microphones. For example, directional microphones can emphasize and collect sounds from specific directions. This allows accurate data to be obtained by collecting information about the classroom in real time using cameras and microphones in the classroom. Some or all of the above-described processing in the monitoring unit can be performed using, for example, a generation AI. For example, the monitoring unit can input data collected by the cameras or microphones into a generation AI, which then analyzes the data.

[0031] The analysis unit can analyze the teacher's speaking style and the student's responses based on the collected data. The teacher's speaking style includes, but is not limited to, the speaking speed, tone, and choice of words. For example, the analysis unit can analyze the teacher's speaking speed and, if the speaking speed is too fast, suggest adjusting the speaking speed. The analysis unit can also analyze the teacher's tone of voice and, if the tone is monotonous, suggest changing the tone. For example, if the teacher's speaking style is ineffective, the analysis unit can suggest specific improvements. The student's responses include, but are not limited to, facial expressions, remarks, and behavior. For example, the analysis unit can analyze the student's facial expressions and calculate an emotion score. The analysis unit can also analyze the content of the student's remarks to evaluate the student's level of understanding of the lesson. For example, if the student's remarks are in line with the content of the lesson, the analysis unit can evaluate the student's level of understanding of the lesson as high. The analysis unit can also analyze the student's behavior to evaluate the student's level of concentration. For example, the analysis unit analyzes the students' eye-tracking data and evaluates that the students' attention level is high if their gaze is directed toward the teacher. This allows the analysis of the teacher's speaking style and the students' reactions to provide specific feedback to improve the quality of the lesson. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the collected data into a generation AI, which then analyzes the data.

[0032] The providing unit can provide specific feedback to the teacher based on the analysis results. Specific feedback includes, but is not limited to, suggestions for improvement and specific advice. For example, the providing unit can suggest specific areas for improvement to the teacher based on the analysis results. The providing unit can also compare the content of the feedback with the teacher's past lesson data and suggest long-term improvement measures. For example, the providing unit can provide specific advice for improving the teacher's speaking style and how the lesson is conducted based on the past lesson data. The providing unit can also customize the content of the feedback according to the teacher's field of expertise. For example, the providing unit can suggest effective lesson conduct methods based on the teacher's field of expertise. Furthermore, the providing unit can adjust the content of the feedback according to the teacher's teaching style. For example, the providing unit can suggest specific areas for improvement according to the teacher's teaching style. In this way, by providing specific feedback based on the analysis results, the quality of the teacher's lessons can be improved. Some or all of the above-mentioned processing by the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the analysis results into a generation AI, which can generate feedback.

[0033] The analysis unit can analyze students' facial expressions, tone of voice, and sitting posture to understand their emotions and level of concentration. Examples of student facial expressions include, but are not limited to, smiling, serious, and confused expressions. The analysis unit, for example, analyzes students' facial expressions and calculates an emotion score. The analysis unit can also analyze students' tone of voice to estimate their emotions. For example, if a student's voice tone is high, the analysis unit estimates that the student is excited. The analysis unit can also analyze students' sitting posture to evaluate their level of concentration. For example, if a student is sitting leaning forward, the analysis unit evaluates that the student has a high level of concentration. This allows the progress of the class to be appropriately adjusted by understanding students' emotions and level of concentration. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input students' facial expression data into a generation AI, which can then estimate their emotions.

[0034] The providing unit can propose appropriate feedback and improvement measures to the teacher based on the student's emotions and level of concentration. Appropriate feedback includes, but is not limited to, specific suggestions for improvement and advice. The providing unit can also propose specific improvement measures to the teacher based on the student's emotions and level of concentration. The providing unit can also provide advice for adjusting the way a lesson is conducted based on the student's emotions and level of concentration. For example, the providing unit can suggest changing the progress of the lesson if the student's level of concentration is low. The providing unit can also suggest adjusting the classroom environment based on the student's emotions and level of concentration. For example, the providing unit can suggest adjusting the temperature and lighting conditions in the classroom. This allows the quality of lessons to be improved by proposing appropriate feedback and improvement measures based on the student's emotions and level of concentration. Some or all of the above-described processing by the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input student emotion data into a generation AI, which can then generate feedback.

[0035] The monitoring unit can analyze sounds in the classroom and identify factors affecting the progress of the lesson. Examples of sounds in the classroom include, but are not limited to, noise, disturbances, and private conversations between students. For example, if the noise in the classroom increases, the monitoring unit can identify the cause and suggest measures to the teacher to reduce the noise. Furthermore, if noise from outside the classroom is affecting the lesson, the monitoring unit can provide the teacher with information about the noise and suggest measures such as closing the windows. For example, if there is an increase in private conversations between students, the monitoring unit can detect this situation and provide feedback to the teacher to alert them. In this way, by analyzing the environmental sounds in the classroom, factors affecting the progress of the lesson can be identified and appropriate measures can be suggested. Some or all of the above-mentioned processing by the monitoring unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the monitoring unit can input audio data from the classroom into a generation AI, which can then analyze the audio data.

[0036] The monitoring unit monitors the temperature and lighting conditions in the classroom and can make suggestions to maintain an environment suitable for the progress of lessons. Examples of the temperature in the classroom include, but are not limited to, the temperature setting of the air conditioner and the humidity in the room. For example, if the temperature in the classroom is too high, the monitoring unit can suggest lowering the temperature setting of the air conditioner. Furthermore, if the temperature in the classroom is too low, the monitoring unit can also suggest raising the temperature setting of the air conditioner. For example, if the lighting in the classroom is too dim, the monitoring unit can suggest turning up the lights. In this way, by monitoring the temperature and lighting conditions in the classroom, an environment suitable for the progress of lessons can be maintained. Some or all of the above-described processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input data on the temperature and lighting in the classroom into a generation AI, which can then analyze the data.

[0037] The monitoring unit can analyze the teacher's movements and gestures and provide feedback for effective lesson progress. Examples of the teacher's movements include, but are not limited to, movements within the classroom, hand movements, and body orientation. For example, if the teacher moves little during class, the monitoring unit can suggest that the teacher move more and provide specific actions to attract students' attention. Furthermore, if the teacher uses gestures frequently, the monitoring unit can evaluate their effectiveness and suggest more effective ways to use them. For example, if the teacher tends to stay in a specific place, the monitoring unit can suggest that the teacher move more within the classroom, strengthening a visual approach to all students. Thus, analyzing the teacher's movements and gestures can provide feedback for effective lesson progress. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input data on the teacher's movements and gestures into a generation AI, which can then analyze the data.

[0038] The monitoring unit can monitor seating arrangements in a classroom and identify factors that affect students' concentration. Examples of seating arrangements include, but are not limited to, seating plans and seat types. For example, if students' concentration levels decrease when they sit in a particular seat, the monitoring unit can suggest changing the seating arrangement. The monitoring unit can also suggest increasing the distance between seats if students are too close to each other. For example, if students tend to gather in a particular place, the monitoring unit can identify the cause and suggest reconsidering the seating arrangement. Thus, by monitoring seating arrangements in a classroom, factors that affect students' concentration levels can be identified and appropriate measures can be proposed. Some or all of the above-described processing by the monitoring unit can be performed using, or without, a generation AI. For example, the monitoring unit can input seating arrangement data into a generation AI, which can then analyze the data.

[0039] The monitoring unit can monitor conditions outside the classroom and identify factors that affect the progress of a lesson. Conditions outside the classroom include, but are not limited to, hallway noise and the external environment. For example, if hallway noise is affecting the classroom, the monitoring unit can provide that information to the teacher and suggest countermeasures. Furthermore, if activities outside the classroom are affecting the lesson, the monitoring unit can provide that information to the teacher and suggest adjusting the progress of the lesson. For example, if noise outside the classroom is concentrated during a specific time period, the monitoring unit can suggest adjusting the class schedule based on that information. Thus, by monitoring the environment outside the classroom, factors that affect the progress of the lesson can be identified and appropriate countermeasures can be suggested. Some or all of the above-described processing by the monitoring unit can be performed using, or without, a generation AI. For example, the monitoring unit can input audio data from outside the classroom into a generation AI, which can then analyze the data.

[0040] The monitoring unit monitors the usage of devices in the classroom and makes suggestions for effective lesson progress. Examples of devices in the classroom include, but are not limited to, projectors, whiteboards, tablets, etc. For example, if the projector is used infrequently, the monitoring unit may suggest increasing its use and provide specific ways to utilize it. Furthermore, if the use of the whiteboard is ineffective, the monitoring unit may suggest improvements and provide more effective usage methods. For example, the monitoring unit may monitor device usage and suggest optimal device usage methods in line with the progress of the lesson. In this way, by monitoring the device usage in the classroom, suggestions for effective lesson progress can be made. Some or all of the above-described processing in the monitoring unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the monitoring unit may input device usage data into a generation AI, which may analyze the data.

[0041] The analysis unit can analyze the teacher's speaking speed and tone and suggest effective communication methods. Examples of the teacher's speaking speed include, but are not limited to, fast, slow, and appropriate speeds. For example, if the teacher speaks too fast, the analysis unit can suggest adjusting the speaking speed based on that information. Furthermore, if the teacher's tone of voice is monotonous, the analysis unit can also suggest changing the tone of voice based on that information. For example, if the teacher's speaking style is ineffective, the analysis unit can suggest specific improvements based on that information. In this way, by analyzing the teacher's speaking speed and tone, an effective communication method can be suggested. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input data on the teacher's speaking style into a generation AI, which can then analyze the data.

[0042] The analysis unit can analyze the content of students' utterances and evaluate their level of understanding of the lesson. Examples of student utterances include, but are not limited to, utterances that are in line with the content of the lesson, utterances that are off-topic, and unclear utterances. For example, if the content of a student's utterances is in line with the content of the lesson, the analysis unit can evaluate the student's level of understanding of the lesson based on that information. Furthermore, if the content of a student's utterances is off-topic, the analysis unit can evaluate the student's level of understanding of the lesson based on that information and provide specific feedback to the teacher. In this way, by analyzing the content of a student's utterances, the student's level of understanding of the lesson can be evaluated and appropriate feedback can be provided. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without a generation AI. For example, the analysis unit can input student utterance data into a generation AI, which can then analyze the data.

[0043] The analysis unit can analyze the state of group discussions in the classroom and propose effective methods for conducting group activities. Examples of the state of group discussions include, but are not limited to, the frequency of comments, the content of comments, and bias in comments. For example, if the group discussion is not active, the analysis unit can suggest specific improvements based on that information. Furthermore, if the group discussion is biased toward a few students, the analysis unit can also suggest a method for conducting the group discussion that allows all students to participate based on that information. For example, if the group discussion is ineffective, the analysis unit can suggest specific improvements based on that information and provide feedback to the teacher. In this way, by analyzing the state of group discussions in the classroom, effective methods for conducting group activities can be proposed. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input group discussion data into a generation AI, which can analyze the data.

[0044] The analysis unit can analyze the student's written content and evaluate the student's level of understanding of the lesson. Examples of the student's written content include, but are not limited to, writing that is in line with the lesson content, writing that deviates from the lesson content, and unclear writing. For example, if the student's written content is in line with the lesson content, the analysis unit can evaluate the student's level of understanding of the lesson based on that information. Furthermore, if the student's written content deviates from the lesson content, the analysis unit can evaluate the student's level of understanding of the lesson based on that information and provide specific feedback to the teacher. In this way, analyzing the student's written content can evaluate the student's level of understanding of the lesson and provide appropriate feedback. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without a generation AI. For example, the analysis unit can input the student's written data into a generation AI, which can then analyze the data.

[0045] The analysis unit can analyze gaze tracking data in the classroom to evaluate students' concentration levels. The gaze tracking data includes, but is not limited to, gaze fixation time and gaze movement patterns. For example, if a student's gaze is directed toward the teacher, the analysis unit can evaluate the student's concentration level based on that information. Furthermore, if a student's gaze is wandering around the classroom, the analysis unit can evaluate the student's concentration level based on that information and provide specific feedback to the teacher. For example, if a student's gaze is focused on a specific location, the analysis unit can evaluate the student's concentration level based on that information and provide specific feedback to the teacher. In this way, analyzing the gaze tracking data in the classroom can evaluate the student's concentration level and provide appropriate feedback. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI. For example, the analysis unit can input the gaze tracking data into a generation AI, which can then analyze the data.

[0046] The analysis unit can analyze audio data in the classroom and identify factors affecting the progress of the lesson. Audio data includes, but is not limited to, noise, disturbances, and student conversations. For example, if noise in the classroom increases, the analysis unit can identify the cause and suggest measures to the teacher to reduce the noise. Furthermore, if noise from outside the classroom is affecting the lesson, the analysis unit can provide the teacher with information about the noise and suggest measures such as closing the windows. For example, if there is an increase in students talking privately, the analysis unit can detect this situation and provide feedback to the teacher to alert them. In this way, by analyzing the audio data in the classroom, factors affecting the progress of the lesson can be identified and appropriate measures can be suggested. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input audio data into a generation AI, which can then analyze the data.

[0047] The providing unit can compare the content of the feedback with the teacher's past lesson data and propose long-term improvement measures. Long-term improvement measures include, but are not limited to, for example, adjusting the way lessons are conducted and the classroom environment. For example, the providing unit can propose specific improvements based on the teacher's past lesson data to improve the quality of the teacher's lessons. The providing unit can also analyze the teacher's past lesson data and propose long-term improvement measures to support the teacher's growth. For example, the providing unit can compare the teacher's past lesson data and propose effective ways to conduct lessons. By comparing the data with the teacher's past lesson data, long-term improvement measures can be proposed and support the teacher's growth. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the teacher's past lesson data into a generation AI, which can analyze the data and propose improvement measures.

[0048] The providing unit can customize the content of the feedback according to the teacher's field of expertise. Examples of the teacher's field of expertise include, but are not limited to, science, mathematics, and social studies. For example, the providing unit can provide specific feedback according to the teacher's field of expertise to improve the quality of lessons. The providing unit can also suggest effective ways to conduct lessons based on the teacher's field of expertise. For example, the providing unit can suggest specific areas for improvement according to the teacher's field of expertise to support the teacher's growth. This allows the quality of lessons to be improved by customizing the feedback according to the teacher's field of expertise. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input data on the teacher's field of expertise into a generation AI, which can analyze the data and customize the feedback.

[0049] The providing unit can adjust the content of the feedback according to the teacher's teaching style. Teaching styles include, but are not limited to, lecture style and discussion style. For example, the providing unit can provide specific feedback according to the teacher's teaching style to improve the quality of the lesson. The providing unit can also suggest effective ways to proceed with the lesson based on the teacher's teaching style. For example, the providing unit can suggest specific areas for improvement according to the teacher's teaching style to support the teacher's growth. In this way, the quality of the lesson can be improved by adjusting the feedback according to the teacher's teaching style. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input teacher's teaching style data into a generation AI, which can analyze the data and adjust the feedback.

[0050] The providing unit can customize the content of the feedback according to each student's individual learning style. Learning styles include, but are not limited to, visual, auditory, and experiential learning. For example, the providing unit can provide specific feedback according to each student's learning style to improve the quality of lessons. The providing unit can also suggest effective ways to conduct lessons based on each student's learning style. For example, the providing unit can suggest specific areas for improvement according to each student's learning style to support the teacher's growth. This allows the quality of lessons to be improved by customizing feedback according to each student's individual learning style. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input student learning style data into a generation AI, which can analyze the data and customize the feedback.

[0051] The providing unit can compare the content of the feedback with the student's past learning data and propose individual improvement measures. Examples of individual improvement measures include, but are not limited to, changing learning methods and selecting teaching materials. For example, the providing unit can propose specific improvements based on the student's past learning data to improve the quality of lessons. The providing unit can also analyze the student's past learning data and propose individual improvement measures to support the teacher's growth. For example, the providing unit can compare the student's past learning data and propose effective ways to conduct lessons. By comparing the data with the student's past learning data, individual improvement measures can be proposed and the quality of lessons can be improved. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the student's past learning data into a generation AI, which can analyze the data and propose improvement measures.

[0052] The providing unit can adjust the content of the feedback according to the teacher's career stage. Career stages include, but are not limited to, new teachers, mid-career teachers, and veteran teachers. For example, the providing unit can provide specific feedback according to the teacher's career stage to improve the quality of lessons. The providing unit can also suggest effective ways to conduct lessons based on the teacher's career stage. For example, the providing unit can suggest specific areas for improvement according to the teacher's career stage to support the teacher's growth. In this way, the quality of lessons can be improved by adjusting the feedback according to the teacher's career stage. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the teacher's career stage data into a generation AI, which can analyze the data and adjust the feedback.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The coaching follow-up system can further include a learning style analysis unit that analyzes students' learning styles. The learning style analysis unit analyzes whether a student's learning style is visual, auditory, or experiential, and suggests appropriate teaching methods to the teacher. For example, it can suggest that visual students make extensive use of diagrams and graphs, and auditory students make use of audio materials. It can also suggest that experiential students incorporate experiments and practical training. This makes it possible to provide teaching methods that suit students' learning styles and improve learning effectiveness.

[0055] The coaching and follow-up system can further include a preparation support section that supports teachers in preparing for lessons. The preparation support section provides materials on the content and progress of lessons, helping teachers to prepare lessons efficiently. For example, it can provide the latest research papers and teaching materials related to the topic of the lesson. It can also provide specific advice on how to proceed with the lesson. This can make teachers' lesson preparation more efficient and improve the quality of lessons.

[0056] The coaching follow-up system can further include a home environment analysis unit that provides feedback that takes into account the student's home environment. The home environment analysis unit collects information about the student's home environment and reflects it in the progress of lessons and feedback. For example, if the home environment is affecting learning, the unit can use that information to suggest appropriate support methods to the teacher. It can also provide more challenging tasks to students with stable home environments. This makes it possible to provide individual support that takes into account the student's home environment and improve learning effectiveness.

[0057] The coaching follow-up system can further include a social skills evaluation unit that evaluates students' social skills. The social skills evaluation unit evaluates students' communication abilities and cooperation and provides appropriate feedback to teachers. For example, if a student actively speaks up in a group activity, the unit can provide specific feedback to the teacher based on that information. Also, if a student cooperates with other students to solve a problem, the unit can evaluate the teacher based on that information. In this way, by evaluating students' social skills and providing appropriate feedback, the quality of lessons can be improved.

[0058] The coaching follow-up system can further include a progress monitoring unit that monitors the student's learning progress in real time. The progress monitoring unit monitors the student's learning progress in real time and provides appropriate feedback to the teacher. For example, if a student finishes an assignment early, it can suggest providing additional assignments based on that information. Also, if a student is struggling with an assignment, it can suggest providing support based on that information. In this way, by monitoring the student's learning progress in real time and providing appropriate feedback, it is possible to improve learning effectiveness.

[0059] The coaching follow-up system can further include a motivation evaluation unit that evaluates students' motivation to learn. The motivation evaluation unit evaluates students' motivation to learn and provides appropriate feedback to teachers. For example, if a student is actively asking questions, the unit can provide specific feedback to the teacher based on that information. Also, if a student shows a positive attitude toward assignments, the unit can evaluate the teacher based on that information. In this way, by evaluating students' motivation to learn and providing appropriate feedback, the quality of lessons can be improved.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The monitoring unit monitors the state of the class. The state of the class includes the teacher's speaking style, students' reactions, and the classroom environment. The monitoring unit collects information about the class in real time through cameras and microphones in the classroom. It can also monitor environmental sounds, temperature, and lighting conditions in the classroom. For example, if the temperature in the classroom is too high, it will suggest lowering the air conditioner setting. Step 2: The analysis unit analyzes the teacher's speaking style and the students' reactions based on the collected data. The analysis includes the type of data, analysis algorithm, and purpose of the analysis. For example, it can analyze the speed and tone of the teacher's speaking style to suggest effective communication methods. It can also analyze students' facial expressions, tone of voice, and sitting posture to understand their emotions and level of concentration. For example, it can analyze students' facial expressions and calculate an emotion score. Step 3: The providing unit provides specific feedback to the teacher based on the analysis results. The feedback may include suggestions for improvement and specific advice. For example, specific improvements may be suggested to the teacher based on the analysis results. The content of the feedback may also be compared with the teacher's past lesson data to suggest long-term improvement measures. For example, specific advice may be provided to improve the teacher's speaking style and how the lesson is conducted based on past lesson data. Some or all of the processing in the providing unit may be performed using or without the generation AI. For example, the analysis results may be input into the generation AI, which then generates the feedback.

[0062] (Example 2) A coaching follow-up system according to an embodiment of the present invention is a system that uses a generative AI to build a coaching and follow-up system for newly appointed teachers. The coaching follow-up system monitors the state of a class, and the generative AI analyzes and provides feedback to improve the quality of the teacher's class. The coaching follow-up system also analyzes the classroom atmosphere, grasps students' emotions and level of concentration, and proposes feedback and improvement measures to the teacher. For example, the coaching follow-up system monitors the state of a class. For example, it collects the state of the class in real time using cameras and microphones in the classroom. Next, the coaching follow-up system analyzes the teacher's speaking style and students' reactions based on the collected data. For example, the generative AI analyzes the teacher's speaking style and students' reactions and provides specific feedback. Next, the coaching follow-up system provides specific feedback to the teacher based on the analysis results. For example, the generative AI suggests areas for improvement to the teacher. Furthermore, the coaching follow-up system analyzes students' facial expressions, tone of voice, sitting position, etc. to grasp students' emotions and level of concentration. For example, the generative AI analyzes students' emotions and level of concentration and proposes appropriate feedback and improvement measures to the teacher. This allows the coaching and follow-up system to provide real-time feedback to even newly hired teachers in classroom situations where appropriate responses are required, eliminating subjective judgment. This allows the coaching and follow-up system to support teacher growth and improve the quality of education. For example, even if a teacher misses a student's response during class, the generative AI can detect that response and provide appropriate feedback to the teacher, thereby improving the quality of the lesson. Furthermore, the generative AI can accumulate data on the teacher's lesson progress and student responses and propose long-term improvement measures. For example, it can provide specific advice on improving the teacher's speaking style and how the lesson is conducted based on past lesson data. This allows teachers to continuously improve the quality of their lessons.

[0063] A coaching follow-up system according to an embodiment includes a monitoring unit, an analysis unit, and a providing unit. The monitoring unit monitors the state of a class. Examples of the state of a class include, but are not limited to, the teacher's speaking style, students' reactions, and the classroom environment. The monitoring unit collects information about the state of a class in real time using, for example, a camera or microphone in the classroom. The monitoring unit can also monitor environmental sounds, temperature, and lighting conditions in the classroom. For example, if the temperature in the classroom is too high, the monitoring unit can suggest lowering the air conditioner setting. The analysis unit analyzes the teacher's speaking style and students' reactions based on the collected data. Examples of the analysis include, but are not limited to, the type of data, analysis algorithm, and purpose of the analysis. For example, the analysis unit analyzes the speed and tone of the teacher's speaking style to suggest effective communication methods. The analysis unit can also analyze students' facial expressions, tone of voice, sitting posture, and the like to understand their emotions and level of concentration. For example, the analysis unit analyzes students' facial expressions and calculates an emotion score. The providing unit provides specific feedback to the teacher based on the analysis results. The feedback includes, for example, indications of areas for improvement and specific advice, but is not limited to such examples. For example, the providing unit suggests specific areas for improvement to the teacher based on the analysis results. The providing unit can also compare the content of the feedback with the teacher's past lesson data and suggest long-term improvement measures. For example, the providing unit provides specific advice to improve the teacher's speaking style and how the lesson is conducted based on the past lesson data. This allows the coaching follow-up system according to the embodiment to improve the quality of the teacher's lessons. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the analysis results into a generation AI, which then generates feedback.

[0064] The monitoring unit can collect information about the classroom through cameras or microphones in the classroom. Examples of cameras or microphones in the classroom include, but are not limited to, fixed cameras, PTZ cameras, and directional microphones. The monitoring unit can collect information about the entire classroom using, for example, fixed cameras. The monitoring unit can also track the movements of specific students or teachers using PTZ cameras. For example, the PTZ camera can adjust the camera's direction and zoom using a remote control. The monitoring unit can also collect specific sounds using directional microphones. For example, directional microphones can emphasize and collect sounds from specific directions. This allows accurate data to be obtained by collecting information about the classroom in real time using cameras and microphones in the classroom. Some or all of the above-described processing in the monitoring unit can be performed using, for example, a generation AI. For example, the monitoring unit can input data collected by the cameras or microphones into a generation AI, which then analyzes the data.

[0065] The analysis unit can analyze the teacher's speaking style and the student's responses based on the collected data. The teacher's speaking style includes, but is not limited to, the speaking speed, tone, and choice of words. For example, the analysis unit can analyze the teacher's speaking speed and, if the speaking speed is too fast, suggest adjusting the speaking speed. The analysis unit can also analyze the teacher's tone of voice and, if the tone is monotonous, suggest changing the tone. For example, if the teacher's speaking style is ineffective, the analysis unit can suggest specific improvements. The student's responses include, but are not limited to, facial expressions, remarks, and behavior. For example, the analysis unit can analyze the student's facial expressions and calculate an emotion score. The analysis unit can also analyze the content of the student's remarks to evaluate the student's level of understanding of the lesson. For example, if the student's remarks are in line with the content of the lesson, the analysis unit can evaluate the student's level of understanding of the lesson as high. The analysis unit can also analyze the student's behavior to evaluate the student's level of concentration. For example, the analysis unit analyzes the students' eye-tracking data and evaluates that the students' attention level is high if their gaze is directed toward the teacher. This allows the analysis of the teacher's speaking style and the students' reactions to provide specific feedback to improve the quality of the lesson. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the collected data into a generation AI, which then analyzes the data.

[0066] The providing unit can provide specific feedback to the teacher based on the analysis results. Specific feedback includes, but is not limited to, suggestions for improvement and specific advice. For example, the providing unit can suggest specific areas for improvement to the teacher based on the analysis results. The providing unit can also compare the content of the feedback with the teacher's past lesson data and suggest long-term improvement measures. For example, the providing unit can provide specific advice for improving the teacher's speaking style and how the lesson is conducted based on the past lesson data. The providing unit can also customize the content of the feedback according to the teacher's field of expertise. For example, the providing unit can suggest effective lesson conduct methods based on the teacher's field of expertise. Furthermore, the providing unit can adjust the content of the feedback according to the teacher's teaching style. For example, the providing unit can suggest specific areas for improvement according to the teacher's teaching style. In this way, by providing specific feedback based on the analysis results, the quality of the teacher's lessons can be improved. Some or all of the above-mentioned processing by the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the analysis results into a generation AI, which can generate feedback.

[0067] The analysis unit can analyze students' facial expressions, tone of voice, and sitting posture to understand their emotions and level of concentration. Examples of student facial expressions include, but are not limited to, smiling, serious, and confused expressions. The analysis unit, for example, analyzes students' facial expressions and calculates an emotion score. The analysis unit can also analyze students' tone of voice to estimate their emotions. For example, if a student's voice tone is high, the analysis unit estimates that the student is excited. The analysis unit can also analyze students' sitting posture to evaluate their level of concentration. For example, if a student is sitting leaning forward, the analysis unit evaluates that the student has a high level of concentration. This allows the progress of the class to be appropriately adjusted by understanding students' emotions and level of concentration. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input students' facial expression data into a generation AI, which can then estimate their emotions.

[0068] The providing unit can propose appropriate feedback and improvement measures to the teacher based on the student's emotions and level of concentration. Appropriate feedback includes, but is not limited to, specific suggestions for improvement and advice. The providing unit can also propose specific improvement measures to the teacher based on the student's emotions and level of concentration. The providing unit can also provide advice for adjusting the way a lesson is conducted based on the student's emotions and level of concentration. For example, the providing unit can suggest changing the progress of the lesson if the student's level of concentration is low. The providing unit can also suggest adjusting the classroom environment based on the student's emotions and level of concentration. For example, the providing unit can suggest adjusting the temperature and lighting conditions in the classroom. This allows the quality of lessons to be improved by proposing appropriate feedback and improvement measures based on the student's emotions and level of concentration. Some or all of the above-described processing by the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input student emotion data into a generation AI, which can then generate feedback.

[0069] The monitoring unit can estimate the teacher's emotions and adjust the timing of monitoring based on the estimated teacher's emotions. Examples of teacher emotions include, but are not limited to, tension, relaxation, and fatigue. For example, if the teacher is nervous, the monitoring unit can intensify monitoring immediately after the start of the lesson and provide frequent feedback until the teacher's tension subsides. Alternatively, if the teacher is relaxed, the monitoring unit can monitor at appropriate times in line with the progress of the lesson and provide feedback only when necessary. For example, if the teacher is tired, the monitoring unit can intensify monitoring in the latter half of the lesson to prevent the teacher's fatigue from affecting the quality of the lesson. This allows for more effective feedback by adjusting the timing of monitoring based on the teacher's emotions. Some or all of the above-described processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input the teacher's emotion data into a generation AI, which can then adjust the timing of monitoring.

[0070] The monitoring unit can analyze sounds in the classroom and identify factors affecting the progress of the lesson. Examples of sounds in the classroom include, but are not limited to, noise, disturbances, and private conversations between students. For example, if the noise in the classroom increases, the monitoring unit can identify the cause and suggest measures to the teacher to reduce the noise. Furthermore, if noise from outside the classroom is affecting the lesson, the monitoring unit can provide the teacher with information about the noise and suggest measures such as closing the windows. For example, if there is an increase in private conversations between students, the monitoring unit can detect this situation and provide feedback to the teacher to alert them. In this way, by analyzing the environmental sounds in the classroom, factors affecting the progress of the lesson can be identified and appropriate measures can be suggested. Some or all of the above-mentioned processing by the monitoring unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the monitoring unit can input audio data from the classroom into a generation AI, which can then analyze the audio data.

[0071] The monitoring unit monitors the temperature and lighting conditions in the classroom and can make suggestions to maintain an environment suitable for the progress of lessons. Examples of the temperature in the classroom include, but are not limited to, the temperature setting of the air conditioner and the humidity in the room. For example, if the temperature in the classroom is too high, the monitoring unit can suggest lowering the temperature setting of the air conditioner. Furthermore, if the temperature in the classroom is too low, the monitoring unit can also suggest raising the temperature setting of the air conditioner. For example, if the lighting in the classroom is too dim, the monitoring unit can suggest turning up the lights. In this way, by monitoring the temperature and lighting conditions in the classroom, an environment suitable for the progress of lessons can be maintained. Some or all of the above-described processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input data on the temperature and lighting in the classroom into a generation AI, which can then analyze the data.

[0072] The monitoring unit can analyze the teacher's movements and gestures and provide feedback for effective lesson progress. Examples of the teacher's movements include, but are not limited to, movements within the classroom, hand movements, and body orientation. For example, if the teacher moves little during class, the monitoring unit can suggest that the teacher move more and provide specific actions to attract students' attention. Furthermore, if the teacher uses gestures frequently, the monitoring unit can evaluate their effectiveness and suggest more effective ways to use them. For example, if the teacher tends to stay in a specific place, the monitoring unit can suggest that the teacher move more within the classroom, strengthening a visual approach to all students. Thus, analyzing the teacher's movements and gestures can provide feedback for effective lesson progress. Some or all of the above-described processing in the monitoring unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the monitoring unit can input data on the teacher's movements and gestures into a generation AI, which can then analyze the data.

[0073] The monitoring unit can estimate the teacher's emotions and determine the priority of items to monitor based on the estimated teacher's emotions. Examples of teacher emotions include, but are not limited to, tension, relaxation, and fatigue. For example, if the teacher is nervous, the monitoring unit can prioritize monitoring the teacher's speaking style and tone of voice and provide appropriate feedback. Furthermore, if the teacher is relaxed, the monitoring unit can prioritize monitoring the students' reactions and concentration and provide that information to the teacher. For example, if the teacher is tired, the monitoring unit can prioritize monitoring the progress of the class and suggest that the teacher take a break. Thus, by determining the priority of items to monitor based on the teacher's emotions, more effective feedback can be provided. Some or all of the above-described processing in the monitoring unit may be performed using, or without, a generation AI. For example, the monitoring unit can input the teacher's emotion data into the generation AI and determine the priority of items to be monitored by the generation AI.

[0074] The monitoring unit can monitor seating arrangements in a classroom and identify factors that affect students' concentration. Examples of seating arrangements include, but are not limited to, seating plans and seat types. For example, if students' concentration levels decrease when they sit in a particular seat, the monitoring unit can suggest changing the seating arrangement. The monitoring unit can also suggest increasing the distance between seats if students are too close to each other. For example, if students tend to gather in a particular place, the monitoring unit can identify the cause and suggest reconsidering the seating arrangement. Thus, by monitoring seating arrangements in a classroom, factors that affect students' concentration levels can be identified and appropriate measures can be proposed. Some or all of the above-described processing by the monitoring unit can be performed using, or without, a generation AI. For example, the monitoring unit can input seating arrangement data into a generation AI, which can then analyze the data.

[0075] The monitoring unit can monitor conditions outside the classroom and identify factors that affect the progress of a lesson. Conditions outside the classroom include, but are not limited to, hallway noise and the external environment. For example, if hallway noise is affecting the classroom, the monitoring unit can provide that information to the teacher and suggest countermeasures. Furthermore, if activities outside the classroom are affecting the lesson, the monitoring unit can provide that information to the teacher and suggest adjusting the progress of the lesson. For example, if noise outside the classroom is concentrated during a specific time period, the monitoring unit can suggest adjusting the class schedule based on that information. Thus, by monitoring the environment outside the classroom, factors that affect the progress of the lesson can be identified and appropriate countermeasures can be suggested. Some or all of the above-described processing by the monitoring unit can be performed using, or without, a generation AI. For example, the monitoring unit can input audio data from outside the classroom into a generation AI, which can then analyze the data.

[0076] The monitoring unit monitors the usage of devices in the classroom and makes suggestions for effective lesson progress. Examples of devices in the classroom include, but are not limited to, projectors, whiteboards, tablets, etc. For example, if the projector is used infrequently, the monitoring unit may suggest increasing its use and provide specific ways to utilize it. Furthermore, if the use of the whiteboard is ineffective, the monitoring unit may suggest improvements and provide more effective usage methods. For example, the monitoring unit may monitor device usage and suggest optimal device usage methods in line with the progress of the lesson. In this way, by monitoring the device usage in the classroom, suggestions for effective lesson progress can be made. Some or all of the above-described processing in the monitoring unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the monitoring unit may input device usage data into a generation AI, which may analyze the data.

[0077] The analysis unit can estimate a student's emotions and improve the accuracy of the analysis based on the estimated student's emotions. Examples of student emotions include, but are not limited to, concentration, stress, and relaxation. For example, if a student is concentrating, the analysis unit can improve the accuracy of the analysis based on that information and provide specific feedback to the teacher. Furthermore, if a student is feeling stressed, the analysis unit can improve the accuracy of the analysis based on that information and suggest appropriate improvement measures to the teacher. For example, if a student is relaxed, the analysis unit can improve the accuracy of the analysis based on that information and suggest effective ways to conduct the lesson to the teacher. In this way, by improving the accuracy of the analysis based on the student's emotions, more accurate feedback can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input student emotion data into a generation AI, which then analyzes the data.

[0078] The analysis unit can analyze the teacher's speaking speed and tone and suggest effective communication methods. Examples of the teacher's speaking speed include, but are not limited to, fast, slow, and appropriate speeds. For example, if the teacher speaks too fast, the analysis unit can suggest adjusting the speaking speed based on that information. Furthermore, if the teacher's tone of voice is monotonous, the analysis unit can also suggest changing the tone of voice based on that information. For example, if the teacher's speaking style is ineffective, the analysis unit can suggest specific improvements based on that information. In this way, by analyzing the teacher's speaking speed and tone, an effective communication method can be suggested. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input data on the teacher's speaking style into a generation AI, which can then analyze the data.

[0079] The analysis unit can analyze the content of students' utterances and evaluate their level of understanding of the lesson. Examples of student utterances include, but are not limited to, utterances that are in line with the content of the lesson, utterances that are off-topic, and unclear utterances. For example, if the content of a student's utterances is in line with the content of the lesson, the analysis unit can evaluate the student's level of understanding of the lesson based on that information. Furthermore, if the content of a student's utterances is off-topic, the analysis unit can evaluate the student's level of understanding of the lesson based on that information and provide specific feedback to the teacher. In this way, by analyzing the content of a student's utterances, the student's level of understanding of the lesson can be evaluated and appropriate feedback can be provided. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without a generation AI. For example, the analysis unit can input student utterance data into a generation AI, which can then analyze the data.

[0080] The analysis unit can analyze the state of group discussions in the classroom and propose effective methods for conducting group activities. Examples of the state of group discussions include, but are not limited to, the frequency of comments, the content of comments, and bias in comments. For example, if the group discussion is not active, the analysis unit can suggest specific improvements based on that information. Furthermore, if the group discussion is biased toward a few students, the analysis unit can also suggest a method for conducting the group discussion that allows all students to participate based on that information. For example, if the group discussion is ineffective, the analysis unit can suggest specific improvements based on that information and provide feedback to the teacher. In this way, by analyzing the state of group discussions in the classroom, effective methods for conducting group activities can be proposed. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input group discussion data into a generation AI, which can analyze the data.

[0081] The analysis unit can estimate the student's emotions and adjust the display method of the analysis results based on the estimated student's emotions. Examples of student emotions include, but are not limited to, tension, relaxation, and fatigue. For example, if the student is tense, the analysis unit can display the analysis results in a simple manner based on that information to reduce visual stress. Furthermore, if the student is relaxed, the analysis unit can display the analysis results in detail based on that information to provide information for deeper understanding. For example, if the student is tired, the analysis unit can display the analysis results in a concise manner based on that information to reduce visual strain. Adjusting the display method of the analysis results based on the student's emotions can reduce visual stress and deepen understanding. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input student emotion data into a generation AI, which can analyze the data and adjust the display method.

[0082] The analysis unit can analyze the student's written content and evaluate the student's level of understanding of the lesson. Examples of the student's written content include, but are not limited to, writing that is in line with the lesson content, writing that deviates from the lesson content, and unclear writing. For example, if the student's written content is in line with the lesson content, the analysis unit can evaluate the student's level of understanding of the lesson based on that information. Furthermore, if the student's written content deviates from the lesson content, the analysis unit can evaluate the student's level of understanding of the lesson based on that information and provide specific feedback to the teacher. In this way, analyzing the student's written content can evaluate the student's level of understanding of the lesson and provide appropriate feedback. Some or all of the above-described processing in the analysis unit can be performed, for example, using a generation AI, or can be performed without a generation AI. For example, the analysis unit can input the student's written data into a generation AI, which can then analyze the data.

[0083] The analysis unit can analyze gaze tracking data in the classroom to evaluate students' concentration levels. The gaze tracking data includes, but is not limited to, gaze fixation time and gaze movement patterns. For example, if a student's gaze is directed toward the teacher, the analysis unit can evaluate the student's concentration level based on that information. Furthermore, if a student's gaze is wandering around the classroom, the analysis unit can evaluate the student's concentration level based on that information and provide specific feedback to the teacher. For example, if a student's gaze is focused on a specific location, the analysis unit can evaluate the student's concentration level based on that information and provide specific feedback to the teacher. In this way, analyzing the gaze tracking data in the classroom can evaluate the student's concentration level and provide appropriate feedback. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI. For example, the analysis unit can input the gaze tracking data into a generation AI, which can then analyze the data.

[0084] The analysis unit can analyze audio data in the classroom and identify factors affecting the progress of the lesson. Audio data includes, but is not limited to, noise, disturbances, and student conversations. For example, if noise in the classroom increases, the analysis unit can identify the cause and suggest measures to the teacher to reduce the noise. Furthermore, if noise from outside the classroom is affecting the lesson, the analysis unit can provide the teacher with information about the noise and suggest measures such as closing the windows. For example, if there is an increase in students talking privately, the analysis unit can detect this situation and provide feedback to the teacher to alert them. In this way, by analyzing the audio data in the classroom, factors affecting the progress of the lesson can be identified and appropriate measures can be suggested. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input audio data into a generation AI, which can then analyze the data.

[0085] The providing unit can estimate the teacher's emotions and adjust the way feedback is expressed based on the estimated teacher's emotions. Examples of the teacher's emotions include, but are not limited to, tension, relaxation, and fatigue. For example, if the teacher is tense, the providing unit can provide feedback in gentle language based on that information to relieve the teacher's tension. Furthermore, if the teacher is relaxed, the providing unit can provide detailed feedback based on that information to deepen the teacher's understanding. For example, if the teacher is tired, the providing unit can provide concise and to-the-point feedback based on that information to reduce the teacher's burden. In this way, adjusting the way feedback is expressed based on the teacher's emotions can reduce the teacher's burden and deepen their understanding. Some or all of the above-described processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input the teacher's emotion data into the generation AI, which can then adjust the way feedback is expressed.

[0086] The providing unit can compare the content of the feedback with the teacher's past lesson data and propose long-term improvement measures. Long-term improvement measures include, but are not limited to, for example, adjusting the way lessons are conducted and the classroom environment. For example, the providing unit can propose specific improvements based on the teacher's past lesson data to improve the quality of the teacher's lessons. The providing unit can also analyze the teacher's past lesson data and propose long-term improvement measures to support the teacher's growth. For example, the providing unit can compare the teacher's past lesson data and propose effective ways to conduct lessons. By comparing the data with the teacher's past lesson data, long-term improvement measures can be proposed and support the teacher's growth. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the teacher's past lesson data into a generation AI, which can analyze the data and propose improvement measures.

[0087] The providing unit can customize the content of the feedback according to the teacher's field of expertise. Examples of the teacher's field of expertise include, but are not limited to, science, mathematics, and social studies. For example, the providing unit can provide specific feedback according to the teacher's field of expertise to improve the quality of lessons. The providing unit can also suggest effective ways to conduct lessons based on the teacher's field of expertise. For example, the providing unit can suggest specific areas for improvement according to the teacher's field of expertise to support the teacher's growth. This allows the quality of lessons to be improved by customizing the feedback according to the teacher's field of expertise. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input data on the teacher's field of expertise into a generation AI, which can analyze the data and customize the feedback.

[0088] The providing unit can adjust the content of the feedback according to the teacher's teaching style. Teaching styles include, but are not limited to, lecture style and discussion style. For example, the providing unit can provide specific feedback according to the teacher's teaching style to improve the quality of the lesson. The providing unit can also suggest effective ways to proceed with the lesson based on the teacher's teaching style. For example, the providing unit can suggest specific areas for improvement according to the teacher's teaching style to support the teacher's growth. In this way, the quality of the lesson can be improved by adjusting the feedback according to the teacher's teaching style. Some or all of the above-mentioned processing in the providing unit can be performed using, or without, a generation AI. For example, the providing unit can input teacher's teaching style data into a generation AI, which can analyze the data and adjust the feedback.

[0089] The providing unit can estimate the teacher's emotions and determine the priority of feedback based on the estimated teacher's emotions. Examples of the teacher's emotions include, but are not limited to, tension, relaxation, and fatigue. For example, if the teacher is tense, the providing unit can prioritize important feedback based on that information to relieve the teacher's tension. Furthermore, if the teacher is relaxed, the providing unit can provide detailed feedback based on that information to deepen the teacher's understanding. For example, if the teacher is tired, the providing unit can prioritize brief and to-the-point feedback based on that information to reduce the teacher's burden. Thus, by determining the priority of feedback based on the teacher's emotions, more effective feedback can be provided. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the teacher's emotion data into the generation AI, which can then determine the priority of feedback.

[0090] The providing unit can customize the content of the feedback according to each student's individual learning style. Learning styles include, but are not limited to, visual, auditory, and experiential learning. For example, the providing unit can provide specific feedback according to each student's learning style to improve the quality of lessons. The providing unit can also suggest effective ways to conduct lessons based on each student's learning style. For example, the providing unit can suggest specific areas for improvement according to each student's learning style to support the teacher's growth. This allows the quality of lessons to be improved by customizing feedback according to each student's individual learning style. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input student learning style data into a generation AI, which can analyze the data and customize the feedback.

[0091] The providing unit can compare the content of the feedback with the student's past learning data and propose individual improvement measures. Examples of individual improvement measures include, but are not limited to, changing learning methods and selecting teaching materials. For example, the providing unit can propose specific improvements based on the student's past learning data to improve the quality of lessons. The providing unit can also analyze the student's past learning data and propose individual improvement measures to support the teacher's growth. For example, the providing unit can compare the student's past learning data and propose effective ways to conduct lessons. By comparing the data with the student's past learning data, individual improvement measures can be proposed and the quality of lessons can be improved. Some or all of the above-described processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the student's past learning data into a generation AI, which can analyze the data and propose improvement measures.

[0092] The providing unit can adjust the content of the feedback according to the teacher's career stage. Career stages include, but are not limited to, new teachers, mid-career teachers, and veteran teachers. For example, the providing unit can provide specific feedback according to the teacher's career stage to improve the quality of lessons. The providing unit can also suggest effective ways to conduct lessons based on the teacher's career stage. For example, the providing unit can suggest specific areas for improvement according to the teacher's career stage to support the teacher's growth. In this way, the quality of lessons can be improved by adjusting the feedback according to the teacher's career stage. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the teacher's career stage data into a generation AI, which can analyze the data and adjust the feedback. === Hard Collateral 1-1 === Each of the multiple elements including the monitoring unit, the analysis unit, and the providing unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit can collect the state of the lesson in real time using the camera 42 and the microphone 38B of the smart device 14. For example, the analysis unit can analyze the data collected by the specific processing unit 290 of the data processing device 12 to analyze the teacher's speaking style and the students' reactions. For example, the providing unit can provide specific feedback to the teacher based on the analysis results by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the monitoring unit, the analyzing unit, and the providing unit described above is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit can collect the state of the lesson in real time using the camera 42 and the microphone 238 of the smart glasses 214. For example, the analyzing unit can analyze the data collected by the specific processing unit 290 of the data processing device 12 to analyze the teacher's speaking style and the students' reactions. For example, the providing unit can provide specific feedback to the teacher based on the analysis results by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the monitoring unit, analysis unit, and provision unit described above is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the monitoring unit can collect the state of the lesson in real time using the camera 42 and microphone 238 of the headset type terminal 314. For example, the analysis unit can analyze the data collected by the specific processing unit 290 of the data processing device 12 to analyze the teacher's speaking style and the students' reactions. For example, the provision unit can provide specific feedback to the teacher based on the analysis results by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the monitoring unit, the analysis unit, and the provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit can collect information about the class in real time using the camera 42 and the microphone 238 of the robot 414. For example, the analysis unit can analyze the data collected by the specific processing unit 290 of the data processing device 12 to analyze the teacher's speaking style and the students' reactions. For example, the provision unit can provide specific feedback to the teacher based on the analysis results by the specific processing unit 290 of the data processing device 12.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The coaching follow-up system can further include a health management unit that monitors the health status of teachers. The health management unit monitors the teacher's heart rate and stress level in real time and provides feedback based on the teacher's health status. For example, if the teacher's heart rate is high, it can suggest breathing techniques to help them relax. It can also suggest taking a short break if the teacher's stress level is high. This allows the system to provide feedback that takes the teacher's health status into consideration and improve the quality of lessons.

[0095] The coaching follow-up system can further include a learning style analysis unit that analyzes students' learning styles. The learning style analysis unit analyzes whether a student's learning style is visual, auditory, or experiential, and suggests appropriate teaching methods to the teacher. For example, it can suggest that visual students make extensive use of diagrams and graphs, and auditory students make use of audio materials. It can also suggest that experiential students incorporate experiments and practical training. This makes it possible to provide teaching methods that suit students' learning styles and improve learning effectiveness.

[0096] The coaching and follow-up system can further include a preparation support section that supports teachers in preparing for lessons. The preparation support section provides materials on the content and progress of lessons, helping teachers to prepare lessons efficiently. For example, it can provide the latest research papers and teaching materials related to the topic of the lesson. It can also provide specific advice on how to proceed with the lesson. This can make teachers' lesson preparation more efficient and improve the quality of lessons.

[0097] The coaching follow-up system can further include a home environment analysis unit that provides feedback that takes into account the student's home environment. The home environment analysis unit collects information about the student's home environment and reflects it in the progress of lessons and feedback. For example, if the home environment is affecting learning, the unit can use that information to suggest appropriate support methods to the teacher. It can also provide more challenging tasks to students with stable home environments. This makes it possible to provide individual support that takes into account the student's home environment and improve learning effectiveness.

[0098] The coaching follow-up system may further include a lesson progress adjustment unit that estimates the teacher's emotions and adjusts the progress of the lesson based on the estimated emotions. The lesson progress adjustment unit may suggest that the teacher proceed slowly if the teacher is nervous. It may also suggest that the teacher proceed smoothly if the teacher is relaxed. For example, if the teacher is tired, it may suggest that the lesson progress be simplified and that the teacher take a break. This allows the lesson to proceed according to the teacher's emotions, improving the quality of the lesson.

[0099] The coaching follow-up system can further include a social skills evaluation unit that evaluates students' social skills. The social skills evaluation unit evaluates students' communication abilities and cooperation and provides appropriate feedback to teachers. For example, if a student actively speaks up in a group activity, the unit can provide specific feedback to the teacher based on that information. Also, if a student cooperates with other students to solve a problem, the unit can evaluate the teacher based on that information. In this way, by evaluating students' social skills and providing appropriate feedback, the quality of lessons can be improved.

[0100] The coaching follow-up system can further include a feedback adjustment unit that estimates the teacher's emotions and adjusts the content of the feedback based on the estimated emotions. If the teacher is nervous, the feedback adjustment unit provides feedback in gentle language based on that information to relieve the teacher's tension. Also, if the teacher is relaxed, the feedback adjustment unit can provide detailed feedback based on that information to deepen the teacher's understanding. For example, if the teacher is tired, the system provides concise, to-the-point feedback based on that information to reduce the teacher's burden. In this way, by adjusting the content of the feedback based on the teacher's emotions, the teacher's burden can be reduced and their understanding can be deepened.

[0101] The coaching follow-up system can further include a progress monitoring unit that monitors the student's learning progress in real time. The progress monitoring unit monitors the student's learning progress in real time and provides appropriate feedback to the teacher. For example, if a student finishes an assignment early, it can suggest providing additional assignments based on that information. Also, if a student is struggling with an assignment, it can suggest providing support based on that information. In this way, by monitoring the student's learning progress in real time and providing appropriate feedback, it is possible to improve learning effectiveness.

[0102] The coaching follow-up system can further include a content adjustment unit that estimates the teacher's emotions and adjusts the content of the lesson based on the estimated emotions. If the teacher is nervous, the content adjustment unit can simplify the content of the lesson based on that information to reduce the teacher's burden. Also, if the teacher is relaxed, the content adjustment unit can enrich the content of the lesson based on that information to encourage deeper understanding. For example, if the teacher is tired, the content of the lesson can be shortened based on that information and a suggestion to take a break. This makes it possible to provide lesson content that suits the teacher's emotions and improve the quality of lessons.

[0103] The coaching follow-up system can further include a motivation evaluation unit that evaluates students' motivation to learn. The motivation evaluation unit evaluates students' motivation to learn and provides appropriate feedback to teachers. For example, if a student is actively asking questions, the unit can provide specific feedback to the teacher based on that information. Also, if a student shows a positive attitude toward assignments, the unit can evaluate the teacher based on that information. In this way, by evaluating students' motivation to learn and providing appropriate feedback, the quality of lessons can be improved.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The monitoring unit monitors the state of the class. The state of the class includes the teacher's speaking style, students' reactions, and the classroom environment. The monitoring unit collects information about the class in real time through cameras and microphones in the classroom. It can also monitor environmental sounds, temperature, and lighting conditions in the classroom. For example, if the temperature in the classroom is too high, it will suggest lowering the air conditioner setting. Step 2: The analysis unit analyzes the teacher's speaking style and the students' reactions based on the collected data. The analysis includes the type of data, analysis algorithm, and purpose of the analysis. For example, it can analyze the speed and tone of the teacher's speaking style to suggest effective communication methods. It can also analyze students' facial expressions, tone of voice, and sitting posture to understand their emotions and level of concentration. For example, it can analyze students' facial expressions and calculate an emotion score. Step 3: The providing unit provides specific feedback to the teacher based on the analysis results. The feedback may include suggestions for improvement and specific advice. For example, specific improvements may be suggested to the teacher based on the analysis results. The content of the feedback may also be compared with the teacher's past lesson data to suggest long-term improvement measures. For example, specific advice may be provided to improve the teacher's speaking style and how the lesson is conducted based on past lesson data. Some or all of the processing in the providing unit may be performed using or without the generation AI. For example, the analysis results may be input into the generation AI, which then generates the feedback.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A monitoring department that monitors the class, an analysis unit that analyzes the data collected by the monitoring unit; a providing unit that provides feedback based on the analysis result obtained by the analysis unit; Equipped with A system characterized by:

2. The monitoring unit Collecting classroom footage through cameras or microphones in the classroom 2. The system of claim 1.

3. The analysis unit Analyzing teachers' speech patterns and students' responses based on collected data 2. The system of claim 1.

4. The providing unit Providing specific feedback to teachers based on analysis results 2. The system of claim 1.

5. The analysis unit Analyze students' facial expressions, tone of voice, and sitting posture to understand their emotions and concentration levels 2. The system of claim 1.

6. The providing unit Providing appropriate feedback and suggestions for improvement to teachers based on students' emotions and concentration levels 2. The system of claim 1.

7. The monitoring unit Estimate teacher emotions and adjust monitoring timing based on the estimated teacher emotions 2. The system of claim 1.

8. The monitoring unit Analyzing sounds in the classroom and identifying factors that affect lesson progress 2. The system of claim 1.

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